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18 pages, 17888 KiB  
Article
Morphological Features of Severe Ionospheric Weather Associated with Typhoon Doksuri in 2023
by Wang Li, Fangsong Yang, Jiayi Yang, Renzhong Zhang, Juan Lin, Dongsheng Zhao and Craig M. Hancock
Remote Sens. 2024, 16(18), 3375; https://doi.org/10.3390/rs16183375 - 11 Sep 2024
Viewed by 177
Abstract
The atmospheric gravity waves (AGWs) generated by severe typhoons can facilitate the transfer of energy from the troposphere to the ionosphere, resulting in medium-scale traveling ionospheric disturbances (MSTIDs). However, the complex three-dimensional nature of MSTIDs over oceanic regions presents challenges for detection using [...] Read more.
The atmospheric gravity waves (AGWs) generated by severe typhoons can facilitate the transfer of energy from the troposphere to the ionosphere, resulting in medium-scale traveling ionospheric disturbances (MSTIDs). However, the complex three-dimensional nature of MSTIDs over oceanic regions presents challenges for detection using ground-based Global Navigation Satellite System (GNSS) networks. This study employs a hybrid approach combining space-based and ground-based techniques to investigate the spatiotemporal characteristics of ionospheric perturbations during Typhoon Doksuri. Plane maps depict significant plasma fluctuations extending outward from the typhoon’s gale wind zone on 24 July, reaching distances of up to 1800 km from the typhoon’s center, while space weather conditions remained relatively calm. These ionospheric perturbations propagated at velocities between 173 m/s and 337 m/s, consistent with AGW features and associated propagation speeds. Vertical mapping reveals that energy originating from Typhoon Doksuri propagated upward through a 500 km layer, resulting in substantial enhancements of plasma density and temperature in the topside ionosphere. Notably, the topside horizontal density gradient was 1.5 to 2 times greater than that observed in the bottom-side ionosphere. Both modeling and observational data convincingly demonstrate that the weak background winds favored the generation of AGWs associated with Typhoon Doksuri, influencing the development of distinct MSTIDs. Full article
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Figure 1
<p>The path of Typhoon Doksuri from July 20 to 28, 2023 (<b>a</b>), along with variations in central pressure (<b>b</b>) and average speed (<b>c</b>). The triangle denotes the location of GNSS receivers.</p>
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<p>Fluctuations in proton density (<b>a</b>), temperature (<b>b</b>), and speed (<b>c</b>) of solar wind, as well as Dst (<b>d</b>) and Kp (<b>e</b>), throughout the progression of Typhoon Doksuri.</p>
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<p>Plasma irregularities over the stations PTAG, CHEN, and HKCL in the period of DOY 203–208.</p>
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<p>Plasma irregularities calculated by GPS data from CHEN on DOY 205.</p>
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<p>Temporal-distance profile of STEC fluctuations within a 1800 km radius from the typhoon’s center on DOY 205.</p>
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<p>Spatial dynamic maps of plasma irregularities on DOY 205.</p>
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<p>Changes in electron density and electron temperature, derived from Swarm-A on DOY 200–206, 2023. The dashed circles signify distances of 700 km and 2000 km away from the typhoon’s eye, and the stars signify the typhoon’s eye at 09 UT on DOY 202–206.</p>
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<p>Ground tracks of plasma profiles from COSMIC-2 on DOY 205 and the corresponding changes of plasma density gradient.</p>
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<p>Topography along the trajectory of Typhoon Doksuri on DOY 205, where the red region with a radius of 700 km indicates the influence of gale-force winds.</p>
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<p>Time-latitudinal variations of (<b>a</b>) TEC and (<b>e</b>) thermospheric O/N<sub>2</sub> ratio during DOY 202–207 simulated by the Thermosphere-Ionosphere-Electrodynamics General Circulation Model, along with observational data, including (<b>b</b>) Global Navigation Satellite System-TEC, (<b>c</b>,<b>d</b>) Zonal and Meridional winds (121°E, 23°N) simulated by the Horizontal Wind Model 2014 empirical model on DOY 205, and (<b>f</b>) temporal variation of the equatorial electrojet estimated by the difference between DLH and PHU. Additionally, (<b>g</b>–<b>l</b>) showcase changes in the thermospheric O/N<sub>2</sub> ratio within a longitudinal range of 110–140°E, as derived from the Global Ultraviolet Imager (GUVI) on TIMED satellite.</p>
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18 pages, 20146 KiB  
Article
Changed Relationship between the Spring North Atlantic Tripole Sea Surface Temperature Anomalies and the Summer Meridional Shift of the Asian Westerly Jet
by Lin Chen, Gen Li and Jiaqi Duan
Atmosphere 2024, 15(8), 922; https://doi.org/10.3390/atmos15080922 - 1 Aug 2024
Viewed by 397
Abstract
The summer Asian westerly jet (AWJ)’s shifting in latitudes is one important characteristic of its variability and has great impact on the East Asian summer climate. Based on the observed and reanalyzed datasets from the Hadley Center Sea Ice and Sea Surface Temperature [...] Read more.
The summer Asian westerly jet (AWJ)’s shifting in latitudes is one important characteristic of its variability and has great impact on the East Asian summer climate. Based on the observed and reanalyzed datasets from the Hadley Center Sea Ice and Sea Surface Temperature dataset (HadISST), the Japanese 55-year reanalysis (JRA-55), and the fifth generation of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5), this study investigates the relationship between the spring tripole North Atlantic SST (TNAT) anomalies and the summer meridional shift of the AWJ (MSJ) for the period of 1958–2020. Through the method of correlation analysis and regression analysis, we show that the ‘+ - +’ TNAT anomalies in spring could induce a northward shift of the AWJ in the following summer. However, such a climatic effect of the spring TNAT anomalies on the MSJ is unstable, exhibiting an evident interdecadal strengthening since the early 1990s. Further analysis reveals that this is related to a strengthened intensity of the spring TNAT anomalies in the most recent three decades. Compared to the early epoch (1958–1993), the stronger spring TNAT anomalies in the post epoch (1994–2020) could cause a stronger pan-tropical climate response until the following summer through a series of ocean–atmosphere interactions. Through Gill responses, the resultant more prominent cooling in the central Pacific in response to the ‘+ - +’ TNAT anomalies induces a pan-tropical cooling in the upper troposphere, which weakens the poleward gradient of the tropospheric temperature over subtropical Asia. As a result, the AWJ shifts northward via a thermal wind effect. By contrast, in the early epoch, the spring TNAT anomalies are relatively weaker, inducing weaker pan-tropical ocean–atmosphere interactions and thus less change in the meridional shit of the summer AWJ. Our results highlight a strengthened lagged effect of the spring TNAT anomalies on the following summer MSJ and have important implications for the seasonal climate predictability over Asia. Full article
(This article belongs to the Section Climatology)
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<p>(<b>a</b>) Climatology and (<b>b</b>) standard deviations of the 200 hPa zonal winds (m s<sup>−1</sup>) during boreal summer (June–July–August) for the period of 1958–2020. The solid black lines in (<b>a</b>,<b>b</b>) denote the axis of the Asian westerly jet (AWJ).</p>
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<p>(<b>a</b>) The leading mode of the empirical orthogonal function (EOF) analysis of the 200 hPa zonal wind anomalies over the region of 15° N–65° N, 30° E–180° E for the period of 1958–2020. (<b>b</b>) Time series of the first principal component (PC1) of the EOF analysis on the 200 hPa zonal wind anomalies over the region of 15° N–65° N, 30° E–180° E.</p>
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<p>Correlations of the summer meridional shift of the AWJ (MSJ) index with the spring sea surface temperature (SST) anomalies for the period of 1958–2020. The dots indicate the correlations at a significance level of <span class="html-italic">p</span> &lt; 0.1.</p>
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<p>Normalized time series of the spring tripole North Atlantic SST (TNAT) index (black solid line) and the summer MSJ index (black dashed line) for the period of 1958–2020. The solid blue line denotes their 21-year sliding correlation coefficients, with the dashed blue line denoting a significance level of <span class="html-italic">p</span> &lt; 0.05. The solid red line denotes the 15-year sliding correlation coefficients, with the dashed red line denoting a significance level of <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>(<b>a</b>) Correlations of the summer MSJ index with the spring SST anomalies for the period of 1958–1993. (<b>b</b>) Same as (<b>a</b>), but for the period of 1994–2020. The dots indicate the correlations at a significance level of <span class="html-italic">p</span> &lt; 0.1.</p>
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<p>The 21-year sliding standard deviation of the spring TNAT index.</p>
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<p>Regressions of (<b>a</b>) spring and (<b>c</b>) summer SST (shaded; °C), 500 hPa omega (contours; pa s<sup>−1</sup>; positive upward; the interval is 3 × 10<sup>−4</sup> pa s<sup>−1</sup> with the green/purple contours denoting negative/positive values) and 850 hPa wind anomalies (arrows; m s<sup>−1</sup>) onto the spring TNAT index for the period of 1958–1993. (<b>b</b>,<b>d</b>) Same as (<b>a</b>,<b>c</b>), but for the period of 1994–2020. The dots indicate the regressed SST anomalies at a significance level of <span class="html-italic">p</span> &lt; 0.1. The black arrows indicate the zonal or meridional components of the wind anomalies at a significance level of <span class="html-italic">p</span> &lt; 0.1. Wind speeds less than 0.25 m s<sup>−1</sup> are not shown. The absolute values of the omega anomalies less than 1.5 × 10<sup>−4</sup> pa s<sup>−1</sup> are not shown.</p>
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<p>Regressions of the summer 200 hPa velocity potential (shaded; 10<sup>5</sup> m<sup>2</sup> s<sup>−1</sup>), divergent wind (arrows; m s<sup>−1</sup>), and precipitation (contours; mm month<sup>−1</sup>; the interval is 7.5 mm month<sup>−1</sup> with the green/purple contours denoting negative/positive values) anomalies onto the spring TNAT index for the period of (<b>a</b>) 1958–1993 and (<b>b</b>) 1994–2020. Wind speeds less than 0.1 m s<sup>−1</sup> are not shown.</p>
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<p>(<b>a</b>) Regressions of the summer 200 hPa air temperature (shaded; K) and wind anomalies (arrows; m s<sup>−1</sup>) onto the spring TNAT index for the period of 1958–1993. (<b>b</b>) Same as (<b>a</b>), but for the period of 1994–2020. The dots indicate the regressed air temperature anomalies at a significance level of <span class="html-italic">p</span> &lt; 0.1. The black arrows indicate the zonal or meridional components of the wind anomalies at a significance level of <span class="html-italic">p</span> &lt; 0.1. Wind speeds less than 0.25 m s<sup>−1</sup> are not shown.</p>
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<p>The height–latitude cross-section of the summer climatological tropospheric temperature (contours; K) and its meridional gradient (shaded; 10<sup>−5</sup> K m<sup>−1</sup>) averaged between (<b>a</b>) 40° E–90° E, (<b>d</b>) 90° E–140° E. (<b>b</b>,<b>e</b>) same as (<b>a</b>,<b>d</b>), but for the summer anomalous tropospheric temperature (contours; K; the interval is 0.05 K) and its meridional gradient (shaded; 10<sup>−6</sup> K m<sup>−1</sup>) regressed onto the spring TNAT index for the period of 1958–1993. (<b>c</b>,<b>f</b>) same as (<b>b</b>,<b>e</b>), but for the period of 1994–2020. The absolute values of the tropospheric temperature anomalies less than 0.025 K are not shown.</p>
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<p>Regressed height–latitude cross-section of the summer zonal wind anomalies (shaded; m s<sup>−1</sup>) averaged between 40° E and 90° E onto the spring TNAT index for the period of (<b>a</b>) 1958–1993, (<b>b</b>) 1994–2020. The dots indicate the regressed zonal wind anomalies at a significance level of <span class="html-italic">p</span> &lt; 0.1. The black solid lines are the zonal averaged (40° E–90° E) climatological zonal winds equal to or larger than 15 m s<sup>−1</sup> with the interval of 5 m s<sup>−1</sup>, denoting the westerly jet. (<b>c</b>,<b>d</b>) same as (<b>a</b>,<b>b</b>), but for those averaged between 90° E and 140° E.</p>
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<p>(<b>a</b>) Regressions of the summer 200 hPa zonal wind anomalies (m s<sup>−1</sup>) onto the spring TNAT index for the period of 1958–1993. (<b>b</b>) Same as (<b>a</b>), but for the period of 1994–2020. The dots indicate the regressed zonal wind anomalies at a significance level of <span class="html-italic">p</span> &lt; 0.1. The black line denotes the AWJ axis.</p>
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<p>Spring TNAT index, summer pan-tropical SST index, summer poleward gradient of the 200 hPa tropospheric temperature (TT) index, and the summer MSJ index regressed onto the spring TNAT index for the periods of 1958–1993 (blue bars) and 1994–2020 (red bars). The solid bars indicate the regressed indices at a significance level of <span class="html-italic">p</span> &lt; 0.1. All data are standardized.</p>
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19 pages, 8155 KiB  
Article
Comparison of the Water Vapor Budget Evolution of Developing and Non-Developing Disturbances over the Western North Pacific
by Zhihong Sun, Si Gao and Maoqiu Jian
Remote Sens. 2024, 16(13), 2396; https://doi.org/10.3390/rs16132396 - 29 Jun 2024
Viewed by 549
Abstract
Tropical cyclone (TC) genesis prediction remains a major operational challenge. Using multiple satellite datasets and a state-of-the-art reanalysis dataset, this study identifies developing and non-developing tropical disturbances over the western North Pacific from June to November of 2000–2019 and conducts composite analyses of [...] Read more.
Tropical cyclone (TC) genesis prediction remains a major operational challenge. Using multiple satellite datasets and a state-of-the-art reanalysis dataset, this study identifies developing and non-developing tropical disturbances over the western North Pacific from June to November of 2000–2019 and conducts composite analyses of their water vapor budget components and relevant dynamic–thermodynamic parameters in the Lagrangian framework following three-day disturbance tracks. Both groups of disturbances have a similar initial 850 hPa synoptic-scale relative vorticity, while the water vapor budget of developing disturbances exhibits distinct stage-wise evolution characteristics from non-developing cases. Three days prior to TC genesis, developing cases are already associated with significantly higher total precipitable water (TPW), vertically integrated moisture flux convergence (VIMFC), and precipitation, of which TPW is the most important parameter to differentiate two groups of disturbances. One day later, all the water vapor budget components (i.e., TPW, VIMFC, precipitation, and evaporation) strengthened, linked with the enhancement of the mid-to lower-tropospheric vortices. A negative radial gradient of evaporation occurs, suggesting the beginning of the wind−evaporation feedback. On the day prior to TC genesis, the water vapor budget components, as well as the mid-to lower-tropospheric vortices, continue to intensify, eventually leading to TC genesis. By contrast, non-developing disturbances are associated with a drier environment and weaker VIMFC, precipitation, and evaporation during the three-day evolution. All these factors are not favorable for the intensification of the mid-to lower-tropospheric vortices; thus, the disturbances fail to upgrade to TCs. The results may shed light on TC genesis prediction. Full article
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<p>Track density (number of occurrences per year per 1° × 1° latitude–longitude grid box) of (<b>a</b>) the developing and (<b>b</b>) non-developing tropical disturbances, as well as climatological mean 850 hPa streamline, during June–November of 2000–2019 over the WNP.</p>
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<p>Composite 3–8-day-filtered 850 hPa relative vorticity (shading, 10<sup>−5</sup> s<sup>−1</sup>) and wind (vector, m s<sup>−1</sup>) for (<b>a</b>–<b>d</b>) the developing, (<b>e</b>–<b>h</b>) non-developing disturbances, and (<b>i</b>–<b>l</b>) their differences from day −3 to 0. Bold black dots indicate the disturbance centers. The shading in (<b>i</b>–<b>l</b>) denotes significantly different relative vorticity at the 95% confidence level based on a <span class="html-italic">t</span> test.</p>
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<p>Time series of area-averaged (<b>a</b>) 3–8-day-filtered 850 hPa relative vorticity (10<sup>−5</sup> s<sup>−1</sup>) within 2° of the disturbance centers and (<b>b</b>) 20-day low-pass filtered 200–850 hPa vertical wind shear (m s<sup>–1</sup>) within 2°–8° of the disturbance center for the developing (red) and non-developing (blue) disturbances. The bars denote standard deviations. The filled markers represent significant differences between two groups of disturbances at the 95% confidence level based on a <span class="html-italic">t</span> test.</p>
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<p>Composite TPW (mm) for (<b>a</b>–<b>d</b>) the developing and (<b>e</b>–<b>h</b>) non-developing disturbances from day −3 to 0, as well as the corresponding radius–time Hovmöller plots for (<b>i</b>) developing and (<b>j</b>) non-developing disturbances and (<b>k</b>) their differences. In (<b>a</b>–<b>h</b>), bold black dots indicate the disturbance centers and concentric circles represent different radii at 1° intervals centered at the disturbance centers. The shading in (<b>k</b>) denotes significant differences at the 95% confidence level based on a <span class="html-italic">t</span> test.</p>
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<p>Time–height cross-sections of specific humidity anomalies (g kg<sup>−1</sup>), which are computed with respect to the Dunion (2011) moist tropical sounding, in a 10° × 10° box centered at (<b>a</b>) the developing disturbances, (<b>b</b>) non-developing disturbances, and (<b>c</b>) their differences. The shading in (<b>c</b>) denotes significant differences at the 95% confidence level based on a <span class="html-italic">t</span> test.</p>
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<p>Same as <a href="#remotesensing-16-02396-f004" class="html-fig">Figure 4</a>, but for evaporation (mm h<sup>−1</sup>).</p>
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<p>Composite SST (°C) for (<b>a</b>–<b>d</b>) the developing and (<b>e</b>–<b>h</b>) non-developing disturbances from day −3 to 0. Bold black dots indicate the disturbance centers, and concentric circles represent different radii at 1° intervals centered at the disturbance centers.</p>
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<p>Same as <a href="#remotesensing-16-02396-f004" class="html-fig">Figure 4</a>, but for surface wind speed (m s<sup>−1</sup>).</p>
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<p>Same as <a href="#remotesensing-16-02396-f005" class="html-fig">Figure 5</a>, but for relative vorticity (10<sup>−5</sup> s<sup>−1</sup>).</p>
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<p>Same as <a href="#remotesensing-16-02396-f004" class="html-fig">Figure 4</a>, but for VIMFC (mm h<sup>−1</sup>).</p>
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<p>Same as <a href="#remotesensing-16-02396-f005" class="html-fig">Figure 5</a>, but for moisture flux convergence (10<sup>−6</sup> kg m<sup>−2</sup> s<sup>−1</sup> hPa<sup>−1</sup>).</p>
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<p>Same as <a href="#remotesensing-16-02396-f005" class="html-fig">Figure 5</a>, but for divergence (10<sup>−5</sup> s<sup>−1</sup>).</p>
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<p>Same as <a href="#remotesensing-16-02396-f004" class="html-fig">Figure 4</a>, but for precipitation (mm h<sup>−1</sup>).</p>
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<p>Composite radial profiles of temperature anomalies (°C), which are computed with respect to Dunion (2011) moist tropical sounding, for (<b>a</b>–<b>d</b>) the developing, (<b>e</b>–<b>h</b>) non-developing disturbances, and (<b>i</b>–<b>l</b>) their differences from day −3 to 0. The shading in (<b>i</b>–<b>l</b>) denotes significantly different relative vorticity at the 95% confidence level based on a <span class="html-italic">t</span> test.</p>
Full article ">Figure 15
<p>BDI of water vapor budget components averaged in 10° × 10° boxes on days −3 (blue), −2 (green), and −1 (red). E and P represent evaporation and precipitation, respectively. The asterisks in corresponding colors beside each parameter denote significant differences between the developing and non-developing disturbances at the 95% confidence level based on a <span class="html-italic">t</span> test.</p>
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15 pages, 6006 KiB  
Technical Note
Satellite-Based Estimation of Near-Surface NO2 Concentration in Cloudy and Rainy Areas
by Fuliang Deng, Yijian Chen, Wenfeng Liu, Lanhui Li, Xiaojuan Chen, Pravash Tiwari and Kai Qin
Remote Sens. 2024, 16(10), 1785; https://doi.org/10.3390/rs16101785 - 17 May 2024
Viewed by 716
Abstract
Satellite-based remote sensing enables the quantification of tropospheric NO2 concentrations, offering insights into their environmental and health impacts. However, remote sensing measurements are often impeded by extensive cloud cover and precipitation. The scarcity of valid NO2 observations in such meteorological conditions [...] Read more.
Satellite-based remote sensing enables the quantification of tropospheric NO2 concentrations, offering insights into their environmental and health impacts. However, remote sensing measurements are often impeded by extensive cloud cover and precipitation. The scarcity of valid NO2 observations in such meteorological conditions increases data gaps and thus hinders accurate characterization and variability of concentration across geographical regions. This study utilizes the Empirical Orthogonal Function interpolation in conjunction with the Extreme Gradient Boosting (XGBoost) algorithm and dense urban atmospheric observed station data to reconstruct continuous daily tropospheric NO2 column concentration data in cloudy and rainy areas and thereby improve the accuracy of NO2 concentration mapping in meteorologically obscured regions. Using Chengdu City as a case study, multiple datasets from satellite observations (TROPOspheric Monitoring Instrument, TROPOMI), near-surface NO2 measurements, meteorology, and ancillary data are leveraged to train models. The results showed that the integration of reconstructed satellite observations with provincial and municipal control surface measurements enables the XGBoost model to achieve heightened predictive accuracy (R2 = 0.87) and precision (RMSE = 5.36 μg/m3). Spatially, this approach effectively mitigates the problem of missing values in estimation results due to absent satellite data while simultaneously ensuring increased consistency with ground monitoring station data, yielding images with more continuous and refined details. These results underscore the potential for reconstructing satellite remote sensing information and combining it with dense ground observations to greatly improve NO2 mapping in cloudy and rainy areas. Full article
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<p>Spatial distribution of ground-based air quality monitoring stations in Chengdu City.</p>
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<p>Flowchart of the methodology.</p>
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<p>Comparison of the spatial pattern of annual coverage of available data before (<b>a</b>) and after (<b>b</b>) the reconstruction of tropospheric NO<sub>2</sub> column concentration in TROPOMI in 2021. (<b>c</b>) denotes the difference in the coverage of available data between before and after the reconstruction.</p>
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<p>The performance of the XGBoost model for each group of experiments. (<b>a</b>–<b>c</b>) denote the results of Group A, Group B, and Group C.</p>
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<p>Comparison of near-ground NO<sub>2</sub> concentration estimation on 21–23 March for each group as well as the ChinaHighNO<sub>2</sub> produced by Wei et al. [<a href="#B3-remotesensing-16-01785" class="html-bibr">3</a>]. (<b>a</b>–<b>c</b>) belong to Group A; (<b>d</b>–<b>f</b>) belong to Group B; (<b>g</b>–<b>i</b>) belong to Group C; (<b>j</b>–<b>l</b>) belong to the ChinaHighNO<sub>2</sub> data; and (<b>m</b>–<b>o</b>) belong to the observed value of ground station. (<b>a</b>,<b>d</b>,<b>g</b>,<b>j</b>,<b>m</b>), (<b>b</b>,<b>e</b>,<b>h</b>,<b>k</b>,<b>n</b>), and (<b>c</b>,<b>f</b>,<b>i</b>,<b>l</b>,<b>o</b>) denote NO<sub>2</sub> concentration estimation on 21–23 March, respectively.</p>
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<p>Comparison of near-ground NO<sub>2</sub> concentration estimation during the Spring Festival holiday period in 2021 (10–13 February) for each group as well as the ChinaHighNO<sub>2</sub> produced by Wei et al. [<a href="#B3-remotesensing-16-01785" class="html-bibr">3</a>]. (<b>a</b>–<b>d</b>) belong to Group A; (<b>e</b>–<b>h</b>) belong to Group B; (<b>i</b>–<b>l</b>) belong to Group C; (<b>m</b>–<b>p</b>) belong to the ChinaHighNO<sub>2</sub> data; and (<b>q</b>–<b>t</b>) belong to the observed value of ground station. (<b>a</b>,<b>e</b>,<b>i</b>,<b>m</b>,<b>q</b>), (<b>b</b>,<b>f</b>,<b>j</b>,<b>n</b>,<b>r</b>), (<b>c</b>,<b>g</b>,<b>k</b>,<b>o</b>,<b>s</b>), and (<b>d</b>,<b>h</b>,<b>l</b>,<b>p</b>,<b>t</b>) denote near-ground NO<sub>2</sub> concentration estimation on 10–13 February, respectively.</p>
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15 pages, 9249 KiB  
Article
Understanding the Inter-Model Spread of PDO’s Impact on Tropical Cyclone Frequency over the Western North Pacific in CMIP6 Models
by Jiawei Feng, Jian Cao, Boyang Wang and Kai Zhao
Atmosphere 2024, 15(3), 276; https://doi.org/10.3390/atmos15030276 - 25 Feb 2024
Cited by 1 | Viewed by 1365
Abstract
This work investigates the inter-model diversity of the Pacific Decadal Oscillation’s (PDO) impact on tropical cyclone frequency (TCF) over the Western North Pacific (WNP) from the historical simulation of twenty-two Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The impact of the PDO [...] Read more.
This work investigates the inter-model diversity of the Pacific Decadal Oscillation’s (PDO) impact on tropical cyclone frequency (TCF) over the Western North Pacific (WNP) from the historical simulation of twenty-two Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The impact of the PDO is expressed as the TCF difference between the positive and negative PDO phases. The comparison between the models with high PDO skill and low PDO skill shows that the PDO-related sea surface temperature (SST) gradient between the western and central tropical Pacific plays an important role in changing the large-scale atmospheric dynamic fields for TC genesis and, thus, the TCF over the WNP. This SST gradient also significantly contributes to the inter-model spread of PDO’s impact on TCF across the 22 CMIP6 models. We, therefore, stress that the PDO-related eastward SST gradient between the western and central tropical Pacific triggers the lower troposphere westerly and eastward extending of the monsoon trough over the WNP. The moistening of the atmosphere and enhancing ascending motion in the mid-troposphere promote convection, leading to the easterly wind anomaly over the upper troposphere, which reduces the vertical wind shear. Those favorable dynamic conditions consistently promote the TC formation over the southeastern part of the Western North Pacific. Our results highlight that PDO could impact the WNP TCF through its associated tropical SST gradient. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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Figure 1
<p>Evaluation of the 22 CMIP6 historical experiment simulated TCF against the observation. (<b>a</b>) spatial distribution of observed (red) and simulated (black) TC genesis locations in JJASO over the box of 5° × 5° (events per year for one box). (<b>b</b>) TCF annual cycle from observation (red) and the ensemble mean of 22 CMIP6 models (black), with the shading indicating the uncertainty range of one standard deviation. (<b>c</b>) The decadal variation of TCF in observation (thick red line) and individual model (colored lines).</p>
Full article ">Figure 2
<p>Evaluation of PDO skill in CMIP6 models. Spatial distribution of (<b>a</b>) observed PDO−related SST and (<b>b</b>) ensemble mean of the 22 CMIP6 model simulated PDO−related SST. (<b>c</b>) Taylor diagram of the PDO performance of the 22 CMIP6 models. In (<b>c</b>), the red and blue dots show the selected five models with the highest Taylor skill scores and the five models with the lowest scores, respectively.</p>
Full article ">Figure 3
<p>Impacts of PDO on TCF and large−scale environmental fields. Differences in (<b>a</b>) TCF, (<b>b</b>) DGPI, (<b>c</b>) SST and 850 hPa circulation (m s<sup>−1</sup>), (<b>d</b>) relative vorticity (10<sup>−6</sup> s<sup>−1</sup>) and circulation (m s<sup>−1</sup>) at 850 hPa, (<b>e</b>) omega (10<sup>−2</sup> Pa s<sup>−1</sup>) at 500 hPa, and (<b>f</b>) vertical wind shear (m s<sup>−1</sup>) and 200 hPa circulation (m s<sup>−1</sup>) between the positive PDO and negative PDO phases. The brown boxes indicate the MDR (5° N–20° N, 140° E–180° E).</p>
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<p>Impacts of PDO on WNP TCF in CMIP6 models. Differences in (<b>a</b>) simulated TCF (MME), (<b>b</b>) observed TCF between the positive PDO and negative PDO phases. (<b>c</b>) is the inter-model relationship between PDO performances and TCF responses in the 22 CMIP6 model. The black boxes in a and b indicate the MDR (5° N–20° N, 140° E–180° E). The inter-model correlation coefficients (R) are displayed in the top left corner of the figure, with the <span class="html-italic">p</span> values in parentheses.</p>
Full article ">Figure 5
<p>Impacts of PDO on WNP TCF and DGPI in good models (Good5) and poor models (Poor5). Differences in (<b>a</b>) TCF from Good5, (<b>b</b>) TCF from Poor5, (<b>c</b>) DGPI from Good5, (<b>d</b>) DGPI from Poor5 between the positive PDO and negative PDO phases. (<b>e</b>,<b>f</b>) are the area-averaged TCF/DGPI changes; the brown boxes indicate the MDR (5° N–20° N, 140° E–180° E).</p>
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<p>Impacts of PDO on large−scale environment factors in good models (Good5, left panel) and poor models (Poor5, middle panel) and the difference between Good5 and Poor5 (right panel). (<b>a</b>) SST (K) and 850 hPa circulation (m s<sup>−1</sup>), (<b>b</b>) 850 hPa circulation (m s<sup>−1</sup>) and relative vorticity (10<sup>−6</sup> s<sup>−1</sup>), (<b>c</b>) omega (10<sup>−2</sup> Pa s<sup>−1</sup>) at 500 hPa, and (<b>d</b>) vertical wind shear (m s<sup>−1</sup>) and 200 hPa circulation (m s<sup>−1</sup>) from five good models. (<b>e</b>–<b>h</b>) as (<b>a</b>–<b>d</b>), but for Poor5. (<b>i</b>–<b>l</b>) show the differences between Good5 and Poor5. The “/” in (<b>i</b>–<b>l</b>) indicates that the difference is significant at a 99% confidence level.</p>
Full article ">Figure 7
<p>Relationship chain of the atmospheric response from the simulated TPG index to TCF change. Scatterplots of the (<b>a</b>) TPG index and TCF, (<b>b</b>) TS2 of PDO and TPG index, (<b>c</b>) TPG index and 850 hPa vorticity (Vor), (<b>d</b>) TPG index and 500 hPa omega, and (<b>e</b>) TPG index and vertical wind shear (VWS). Black stars indicate observations. The inter-model correlation coefficients (R) are displayed in the top left corner of the figure, with the <span class="html-italic">p</span> values in parentheses.</p>
Full article ">Figure 8
<p>Inter-model relation from TPG index to TCF via DGPI. Scatter plot of (<b>a</b>) TPG index and DGPI and (<b>b</b>) DGPI and TCF. Black stars indicate the observations. The inter-model correlation coefficients (R) are displayed in the top left corner of the figure, with the <span class="html-italic">p</span> values in parentheses.</p>
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18 pages, 9663 KiB  
Article
Precipitation and Moisture Transport of the 2021 Shimokita Heavy Precipitation: A Transformed Extratropical Cyclone from Typhoon#9
by Akiyo Yatagai and Shogo Saruta
Atmosphere 2024, 15(1), 94; https://doi.org/10.3390/atmos15010094 - 11 Jan 2024
Cited by 1 | Viewed by 1100
Abstract
This study examines the heavy rainfall event that occurred in the Shimokita Peninsula, Japan, on 9–10 August 2021, resulting from an extra-tropical cyclone that developed from Typhoon#9 (EC9). The objective of this study is to elucidate the relationship between moisture transport and heavy [...] Read more.
This study examines the heavy rainfall event that occurred in the Shimokita Peninsula, Japan, on 9–10 August 2021, resulting from an extra-tropical cyclone that developed from Typhoon#9 (EC9). The objective of this study is to elucidate the relationship between moisture transport and heavy rainfall and to verify the role of EC9. The authors created intensive hourly precipitation data over the Aomori Prefecture and analyzed them together with moisture fields. In most locations where the landslide disaster occurred, there were two precipitation peaks: at 9 UTC and 18 UTC on 9 August. The wind shear was strong from the lower to the upper troposphere with easterly winds in the lower troposphere and warm moist air from south for the first peak. A strong horizontal gradient of equivalent potential temperature, a northerly in lower troposphere, and moisture convergence over Shimokita Peninsula indicate the existence of the stationary front for the latter peak (18 UTC). The heavy precipitation and moisture convergence that caused the Shimokita event were identified by the stationary front of EC9 around the latter peak (15 UTC of 9th–06 UTC of 10 August). The precipitation distribution, which has a precipitation peak northeast of the EC center, is a typical typhoon-turned extratropical cyclone (EC) precipitation distribution. Full article
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Figure 1

Figure 1
<p>(<b>a</b>) Geography of the target area. Color indicates elevation (m), and color scale is the same as that in <a href="#atmosphere-15-00094-f001" class="html-fig">Figure 1</a>b. Two blue arrows indicate the range of cross sections shown later figure. (<b>b</b>) Elevation and rain gauge stations over Aomori Prefecture and (<b>c</b>) Shimokita Peninsula. Blue dots indicate Aomori Prefecture’s rain gauge; yellow dots indicate the Ministry of Land’s rain gauge; and red dots represent that of JMA’s. Blue boxes in <a href="#atmosphere-15-00094-f001" class="html-fig">Figure 1</a>c indicate the Akagawa River basin and Yagen where landslide damage was severe.</p>
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<p>(<b>a</b>) Path of Typhoon#9 in 2021. Open circles indicate the TC/EC center location at 9 AM JST (=00 UTC), and closed circles indicate that of 9 PM JST (=12 UTC). The numbers with small subscripts indicate the date (e.g., “10” indicates 10 August). The best track map is obtained from JMA Tropical Cyclone Tracks (<a href="https://www.data.jma.go.jp/yoho/typhoon/route_map/bstv2021.html" target="_blank">https://www.data.jma.go.jp/yoho/typhoon/route_map/bstv2021.html</a> (accessed on 31 March 2023). (<b>b</b>) Weather chart at 9 JST (00 UTC) on 9 August when the TC9 transformed to an extratropical cyclone (EC9). Processed from JWA tenki.jp “Past weather charts, August 2021” (<a href="https://tenki.jp/past/2021/08/chart/" target="_blank">https://tenki.jp/past/2021/08/chart/</a>) (accessed on 31 March 2023). (<b>c</b>) Same with (<b>b</b>) but at 9 UTC. (<b>d</b>) Same with (<b>b</b>) but at 18 UTC.</p>
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<p>(<b>a</b>) The 48 h precipitation (APHRO_RA) distribution for the target area (mm/48 h). (<b>b</b>) Time series of the average precipitation over a white box designated in (<b>a</b>). Two peaks (9 UTC, 18 UTC) and two terms are marked. (<b>c</b>) Total precipitation distribution for Term 1 (mm/h) over the Shimokita Peninsula. (<b>d</b>) Same as (<b>c</b>), but for Term 2 (mm/h).</p>
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<p>Composited precipitation distribution (mm/h) for (<b>a</b>) TD, (<b>b</b>) TY, and (<b>c</b>) EC. Hourly precipitation patterns are composited according to the angle from the TC center. Labels at X and Y axes indicate angle (degree). Blue and red circles in (<b>c</b>) indicate relative location of Shimokita from the EC center at peak -1 and peak -2, respectively. The number of samples (N) and areal mean precipitation are noted in the upper-right corner of each panel.</p>
Full article ">Figure 5
<p>Composited precipitation distribution (mm/h) for (<b>a</b>) Term 1 (0–15 UTC of 9th) and (<b>b</b>) Term 2 (from 15 UTC of 9th to 6 UTC of 10th). The same scales are used for (<b>a</b>,<b>b</b>). While open circles indicate the relative locations of Shimokita Peninsula at peak 1 and peak 2.</p>
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<p>Vertically integrated specific humidity (kg/m<sup>2</sup>) (shade)and moisture flux (kg/m·s) (vector) in the middle troposphere (400–650 hPa) at (<b>a</b>) 9 UTC (peak 1) and (<b>b</b>) 18 UTC (peak 2) on 9 August. (<b>c</b>) Same as (<b>a</b>) but for a lower troposphere (700 hPa—surface). (<b>d</b>) Same as (<b>b</b>) but for a lower troposphere (700 hPa—surface). (<b>e</b>) Moisture divergence (blue means convergence) at 950 hPa at 9 UTC (unit: 1/s). (<b>f</b>) Same as (<b>e</b>) but for 18 UTC. Black open circles in (<b>e</b>,<b>f</b>) designate the location center of EC9. (<b>g</b>) Vertically integrated moisture flux (kg/M·s) (vector) and its divergence (kg/m<sup>2</sup>·h) in the middle troposphere (400–650 hPa). Only convergence (negative divergence) areas are shaded. (<b>h</b>) Same as (<b>g</b>) but for 18 UTC (peak 2). (<b>i</b>) Same as (<b>g</b>) but for a lower troposphere (700 hPa—surface). (<b>j</b>) Same as (<b>i</b>) but for 18 UTC (peak 2).</p>
Full article ">Figure 6 Cont.
<p>Vertically integrated specific humidity (kg/m<sup>2</sup>) (shade)and moisture flux (kg/m·s) (vector) in the middle troposphere (400–650 hPa) at (<b>a</b>) 9 UTC (peak 1) and (<b>b</b>) 18 UTC (peak 2) on 9 August. (<b>c</b>) Same as (<b>a</b>) but for a lower troposphere (700 hPa—surface). (<b>d</b>) Same as (<b>b</b>) but for a lower troposphere (700 hPa—surface). (<b>e</b>) Moisture divergence (blue means convergence) at 950 hPa at 9 UTC (unit: 1/s). (<b>f</b>) Same as (<b>e</b>) but for 18 UTC. Black open circles in (<b>e</b>,<b>f</b>) designate the location center of EC9. (<b>g</b>) Vertically integrated moisture flux (kg/M·s) (vector) and its divergence (kg/m<sup>2</sup>·h) in the middle troposphere (400–650 hPa). Only convergence (negative divergence) areas are shaded. (<b>h</b>) Same as (<b>g</b>) but for 18 UTC (peak 2). (<b>i</b>) Same as (<b>g</b>) but for a lower troposphere (700 hPa—surface). (<b>j</b>) Same as (<b>i</b>) but for 18 UTC (peak 2).</p>
Full article ">Figure 7
<p>Equivalent potential temperature (θe, unit: K) and horizontal wind vectors calculated from the MSM. White dashed contours indicate convergence (contour interval is 10<sup>−4</sup> s<sup>−1</sup>). (<b>a</b>) The 500 hPa at 9 UTC 9 August 2021. (<b>b</b>) Same as (<b>a</b>) but for 700 hPa. (<b>c</b>) Same as (<b>a</b>) but for 950 hPa. Gray area in (<b>c</b>) indicates land areas. The color scale for equivalent potential temperatures and wind vectors are common for all 9 panels (<b>a</b>–<b>i</b>). (<b>d</b>) Same as (<b>a</b>) but for 15 UTC on 9 August 2021. (<b>e</b>) Same as (<b>d</b>) but for 700 hPa. (<b>f</b>) Same as (<b>d</b>) but for 950 hPa. Gray area in (<b>f</b>) indicates land areas. (<b>g</b>) Same as (<b>a</b>) but for 18 UTC on 9 August 2021. (<b>h</b>) Same as (<b>d</b>) but for 700 hPa. (<b>i</b>) Same as (<b>d</b>) but for 950 hPa. Gray area in (<b>i</b>) indicates land areas.</p>
Full article ">Figure 7 Cont.
<p>Equivalent potential temperature (θe, unit: K) and horizontal wind vectors calculated from the MSM. White dashed contours indicate convergence (contour interval is 10<sup>−4</sup> s<sup>−1</sup>). (<b>a</b>) The 500 hPa at 9 UTC 9 August 2021. (<b>b</b>) Same as (<b>a</b>) but for 700 hPa. (<b>c</b>) Same as (<b>a</b>) but for 950 hPa. Gray area in (<b>c</b>) indicates land areas. The color scale for equivalent potential temperatures and wind vectors are common for all 9 panels (<b>a</b>–<b>i</b>). (<b>d</b>) Same as (<b>a</b>) but for 15 UTC on 9 August 2021. (<b>e</b>) Same as (<b>d</b>) but for 700 hPa. (<b>f</b>) Same as (<b>d</b>) but for 950 hPa. Gray area in (<b>f</b>) indicates land areas. (<b>g</b>) Same as (<b>a</b>) but for 18 UTC on 9 August 2021. (<b>h</b>) Same as (<b>d</b>) but for 700 hPa. (<b>i</b>) Same as (<b>d</b>) but for 950 hPa. Gray area in (<b>i</b>) indicates land areas.</p>
Full article ">Figure 7 Cont.
<p>Equivalent potential temperature (θe, unit: K) and horizontal wind vectors calculated from the MSM. White dashed contours indicate convergence (contour interval is 10<sup>−4</sup> s<sup>−1</sup>). (<b>a</b>) The 500 hPa at 9 UTC 9 August 2021. (<b>b</b>) Same as (<b>a</b>) but for 700 hPa. (<b>c</b>) Same as (<b>a</b>) but for 950 hPa. Gray area in (<b>c</b>) indicates land areas. The color scale for equivalent potential temperatures and wind vectors are common for all 9 panels (<b>a</b>–<b>i</b>). (<b>d</b>) Same as (<b>a</b>) but for 15 UTC on 9 August 2021. (<b>e</b>) Same as (<b>d</b>) but for 700 hPa. (<b>f</b>) Same as (<b>d</b>) but for 950 hPa. Gray area in (<b>f</b>) indicates land areas. (<b>g</b>) Same as (<b>a</b>) but for 18 UTC on 9 August 2021. (<b>h</b>) Same as (<b>d</b>) but for 700 hPa. (<b>i</b>) Same as (<b>d</b>) but for 950 hPa. Gray area in (<b>i</b>) indicates land areas.</p>
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<p>(<b>a</b>) Vertical cross section of eastward moisture flux (m/s) averaged over 140.70–141.5° E at 9 UTC 9 August. Red (blue) color indicates westerly (easterly). Contours indicate moisture convergence (×10<sup>−8</sup>/s). (<b>b</b>) Cross section of precipitation (mm/2 h) averaged over 140.70–141.5° E during 8–10 UTC 9 August. (<b>c</b>) Vertical cross section of northward moisture flux (m/s) averaged over 41–42° N at 9 UTC 9 August. Red (blue) color indicates southerly (northerly). Contours indicate moisture convergence (×10<sup>−8</sup>/s). (<b>d</b>) Cross section of precipitation (mm/2 h) averaged over 41–42° E at 9 UTC 9 August. (<b>e</b>) Same as (<b>a</b>) but for 18 UTC. (<b>f</b>) Same as (<b>b</b>) but for 17–19 UTC. (<b>g</b>) Same as (<b>c</b>) but for 18 UTC. (<b>h</b>) Same as (<b>d</b>) but for 17–19 UTC.</p>
Full article ">Figure 8 Cont.
<p>(<b>a</b>) Vertical cross section of eastward moisture flux (m/s) averaged over 140.70–141.5° E at 9 UTC 9 August. Red (blue) color indicates westerly (easterly). Contours indicate moisture convergence (×10<sup>−8</sup>/s). (<b>b</b>) Cross section of precipitation (mm/2 h) averaged over 140.70–141.5° E during 8–10 UTC 9 August. (<b>c</b>) Vertical cross section of northward moisture flux (m/s) averaged over 41–42° N at 9 UTC 9 August. Red (blue) color indicates southerly (northerly). Contours indicate moisture convergence (×10<sup>−8</sup>/s). (<b>d</b>) Cross section of precipitation (mm/2 h) averaged over 41–42° E at 9 UTC 9 August. (<b>e</b>) Same as (<b>a</b>) but for 18 UTC. (<b>f</b>) Same as (<b>b</b>) but for 17–19 UTC. (<b>g</b>) Same as (<b>c</b>) but for 18 UTC. (<b>h</b>) Same as (<b>d</b>) but for 17–19 UTC.</p>
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14 pages, 5259 KiB  
Article
Impacts of a Recent Interdecadal Shift in the Summer Arctic Dipole on the Variability in Atmospheric Circulation over Eurasia
by Xuanwen Zhang, Xueqi Pang, Xiang Zhang and Bingyi Wu
Atmosphere 2024, 15(1), 71; https://doi.org/10.3390/atmos15010071 - 7 Jan 2024
Cited by 1 | Viewed by 1066
Abstract
This study investigated the relationship between the summer Arctic Dipole (AD) anomaly and the climatic variability in Eurasia during the period 1979–2021. It was found that the summer AD anomaly experienced a phase shift from frequent negative phases before 2006 to positive phases [...] Read more.
This study investigated the relationship between the summer Arctic Dipole (AD) anomaly and the climatic variability in Eurasia during the period 1979–2021. It was found that the summer AD anomaly experienced a phase shift from frequent negative phases before 2006 to positive phases after 2007, as manifested by the shift of the center of the positive (negative) AD anomaly to Greenland (in the Laptev Sea and East Siberian Seas) in the more recent period (2007–2021) from the vicinity of the Kara Sea and Laptev Sea (the Canadian archipelago) in the earlier period (1979–2006). Before the mid-2000s, a wave train was shown in the middle troposphere of Eurasia, and this teleconnection pattern of atmospheric circulation could have resulted in local warm and wet (cool and dry) anomalies over northern Russia and East Asia (Western Europe and the Far east). Since the mid-2000s, the wave train has experienced a notable adjustment that was conducive to East Asian and Arctic cooling, displaying anticyclonic anomalies around northern Eurasia and two cyclonic anomalies centered near the Arctic and East Asia. The presence of a cold Arctic anomaly was found to enhance westerly winds at high latitudes by modulating the meridional temperature gradient (MTG) and impeding the southward propagation of cold Arctic air. Additionally, the warmth of northern Eurasia may have also resulted in a reduction in the MTG between northern Eurasia and the mid-lower latitudes, favoring a weakening of zonal winds over the central region of Eurasia. The increased upper-level westerly winds over southern East Asia implied a weakened East Asian Summer Monsoon, which inhibited precipitation in northeast China. Full article
(This article belongs to the Special Issue Arctic Atmosphere–Sea Ice Interaction and Impacts)
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Figure 1
<p>Mean (June–August) SLP anomalies in the summer (<b>a</b>,<b>c</b>) (shading, unit: hPa) regressed upon the normalized principal component time series corresponding to (<b>a</b>) EOF1 and (<b>b</b>) EOF2 for the interannual component of the Arctic (north of 70° N) summer SLP anomalies during 1979–2021. Stippling is used to denote statistically significant regions at the 90% confidence level according to Student’s <span class="html-italic">t</span>-test. (<b>c</b>,<b>d</b>) The normalized PC time series corresponding to EOF1 and EOF2, respectively. The first two patterns, respectively, account for 60.5% and 14.1% of the total variance. The map begins at 70° N, with dotted lines indicating latitudes separated by 5 degrees.</p>
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<p>Mean summer SLP anomalies (relative to the summer mean averaged over 1979–2021) for individual years since 2007. The map begins at 60° N, with dotted lines indicating latitudes separated by 10 degrees.</p>
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<p>Differences in mean summer (<b>a</b>) SLP and (<b>b</b>) 500 hPa geopotential heights between the period of 1979–2006 and the period of 2007–2021 (the latter minus the former); stippling is used to denote statistically significant regions at the 90% confidence level according to Student’s <span class="html-italic">t</span>-test. The map begins at 20° N, with dotted lines indicating latitudes separated by 10 degrees.</p>
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<p>Summer SLP anomalies derived from a linear regression on the normalized PC2 time series over different domains north of (<b>a</b>) 70°, (<b>b</b>) 60°, (<b>c</b>) 50°, (<b>d</b>) 40°, (<b>e</b>) 30°, and (<b>f</b>) 20° N. Stippling is used to denote statistically significant regions at the 90% confidence level according to Student’s <span class="html-italic">t</span>-test. The map begins at 20° N, with dotted lines indicating latitudes separated by 10 degrees.</p>
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<p>Regression of SLP (shading; unit: hPa) anomalies in summer on the normalized AD− time series during (<b>a</b>) 1979–2006 and on the normalized AD+ time series in (<b>b</b>) 2007–2021. (<b>c</b>,<b>d</b>) The same as in (<b>a</b>) and (<b>b</b>), but for the 500 hPa geopotential heights (shading; unit: gpm) and the corresponding horizontal wave activity flux (purple vector; unit: m s<sup>−1</sup>) anomalies. Stippling is used to denote statistically significant regions at the 90% confidence level according to Student’s <span class="html-italic">t</span>-test. The map begins at 20° N, with dotted lines indicating latitudes separated by 10 degrees.</p>
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<p>Regression of SAT (unit: °C) anomalies in summer on the normalized AD− time series during (<b>a</b>) 1979–2006 and the normalized AD+ time series in (<b>b</b>) 2007–2021. Stippling is used to denote statistically significant regions at the 90% confidence level according to Student’s <span class="html-italic">t</span>-test.</p>
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<p>Regression of summer precipitation (unit: mm) anomalies on the normalized AD− time series during (<b>a</b>) 1979–2006 and on the normalized AD+ time series in (<b>b</b>) 2007–2021. Stippling is used to denote statistically significant regions at the 90% confidence level according to Student’s <span class="html-italic">t</span>-test.</p>
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<p>Regression of 500 hPa zonal wind (shading, unit: m/s) anomalies in summer on the normalized AD− time series during (<b>a</b>) 1979–2006 and on the normalized AD+ time series in (<b>b</b>) 2007–2021. Purple contours indicate the climatological mean zonal wind at 500 hPa during 1979–2021. Stippling is used to denote statistically significant regions at the 90% confidence level according to Student’s <span class="html-italic">t</span>-test. The map begins at 20° N, with dotted lines indicating latitudes separated by 10 degrees.</p>
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<p>Regression of 1000 hPa meridional temperature gradient (MTG, unit: °C/°lat) anomalies in summer on the normalized AD− time series during (<b>a</b>) 1979–2006 and on the normalized AD+ time series in (<b>b</b>) 2007–2021. Stippling is used to denote statistically significant regions at the 90% confidence level according to Student’s <span class="html-italic">t</span>-test.</p>
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<p>Regression of 500 hPa geopotential height (shading, unit: gpm) anomalies in summer on the normalized AD− time series in (<b>a</b>) 1979–2006 and on the normalized AD+ time series in (<b>b</b>) 2007–2021 after removing the NAO variability. Stippling is used to denote statistically significant regions at the 90% confidence level according to Student’s <span class="html-italic">t</span>-test. The map begins at 20° N, with dotted lines indicating latitudes separated by 10 degrees.</p>
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14 pages, 6384 KiB  
Article
Cold Air Outbreaks in Winter over the Continental United States and Its Possible Linkage with Arctic Sea Ice Loss
by Yanshuo Wang, Yuxing Yang and Fei Huang
Atmosphere 2024, 15(1), 63; https://doi.org/10.3390/atmos15010063 - 3 Jan 2024
Viewed by 1203
Abstract
The mechanism for the paradox of global warming and successive cold winters in mid-latitudes remains controversial. In this study, the connection between Arctic sea ice (ASI) loss and frequent cold air outbreaks in eastern Continental United States (CONUS) is explored. Two distinct periods [...] Read more.
The mechanism for the paradox of global warming and successive cold winters in mid-latitudes remains controversial. In this study, the connection between Arctic sea ice (ASI) loss and frequent cold air outbreaks in eastern Continental United States (CONUS) is explored. Two distinct periods of high and low ASI (hereafter high- and low-ice phases) are identified for comparative study. It is demonstrated that cold air outbreaks occur more frequently during the low-ice phase compared to that during the high-ice phase. The polar vortex is weakened and shifted southward during the low-ice phase. Correspondingly, the spatial pattern of 500 hPa geopotential height (GPH), which represents the mid-tropospheric circulation, shows a clear negative Arctic Oscillation-like pattern in the low-ice phase. Specifically, positive GPH anomalies in the Arctic region with two centers, respectively located over Greenland and the Barents Sea, significantly weaken the low-pressure system centered around the Baffin Island, and enhance Ural blocking in the low-ice phase. Meanwhile, the high ridge extending from Alaska to the west coast of North America further intensifies, while the low trough over eastern CONUS deepens. As a result, the atmospheric circulation in North America becomes more conductive to frigid Arctic air outbreaks. It is concluded that the ASI loss contributes to more cold air outbreaks in winter in eastern CONUS through the polar vortex weakening with southward displacement of the polar vortex edge, which lead to the weakening of the meridional potential vorticity gradient between the Arctic and mid-latitude and thus are conducive to the strengthening and long-term maintenance of the blocking high. Full article
(This article belongs to the Special Issue Arctic Atmosphere–Sea Ice Interaction and Impacts)
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Figure 1
<p>500 hPa geopotential height (12 UTC 31 January 2011). (The blue box denotes the study area over (35° N–45° N, 100° W–75° W)).</p>
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<p>Accumulative precipitation (mm) during the period 00 UTC 1–00 UTC 3 February 2011.</p>
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<p>Upper level (200 hPa) synoptic patterns at 00 UTC 1 February (<b>left</b>) and 00 UTC 3 February (<b>right</b>) 2011 (the contours represent the geopotential height and the arrows represent winds).</p>
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<p>Mean sea level pressure 00 UTC 1 February (<b>left</b>) and 00 UTC 3 February 2011 (<b>right</b>).</p>
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<p>Time series (red line) and trends (bule line) of Arctic sea ice concentration and air temperature in September during 1980–2018 (the * represents multiple sign).</p>
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<p>Arctic sea ice concentration distribution in September averaged over high- and low-ice phases and their difference (low-phase minus high-phase).</p>
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<p>Daily variation of coldness index for December (<b>a</b>), January (<b>b</b>), and February (<b>c</b>) over 1990 −2017. The two dashed lines denote years of 1999 and 2007, respectively.</p>
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<p>PV at 330 K isentropic surface (~300 hPa) averaged during high- (<b>a</b>,<b>d</b>,<b>g</b>) and low-ice (<b>b</b>,<b>e</b>,<b>h</b>) phases and their difference between low phase and high phase (<b>c</b>,<b>f</b>,<b>i</b>) in December (<b>a</b>–<b>c</b>), January (<b>d</b>–<b>f</b>) and February (<b>g</b>–<b>i</b>).</p>
Full article ">Figure 9
<p>500 hPa geopotential height averaged during high- (<b>a</b>,<b>d</b>,<b>g</b>) and low-ice (<b>b</b>,<b>e</b>,<b>h</b>) phases and their difference between low phase and high phase (<b>c</b>,<b>f</b>,<b>i</b>) in December (<b>a</b>–<b>c</b>), January (<b>d</b>–<b>f</b>) and February (<b>g</b>–<b>i</b>).</p>
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<p>Daily variation of coldness index (shaded) for December (<b>a</b>), January (<b>b</b>), and February (<b>c</b>) over 1979–2020. Contours denote coldness index less than −1 or more than 1 standard deviation, an interval of 0.5. The four dashed lines denote years of 1990, 1999, 2007, and 2017, respectively.</p>
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<p>Arctic sea ice extent (SIE) anomaly in September (bar) and December to February (lines). The four dashed lines denote years of 1990, 1999, 2007, and 2016, respectively.</p>
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17 pages, 18258 KiB  
Article
Refractivity Observations from Radar Phase Measurements: The 22 May 2002 Dryline Case during IHOP Project
by Rubén Nocelo López, Verónica Santalla del Rio and Brais Sánchez-Rama
Atmosphere 2024, 15(1), 33; https://doi.org/10.3390/atmos15010033 - 27 Dec 2023
Viewed by 935
Abstract
The dryline, often associated with the development of severe storms in the Southern Great Plains of the United States of America, is a boundary layer phenomenon that occurs when a warm and moist air mass from the Gulf of Mexico meets a hot [...] Read more.
The dryline, often associated with the development of severe storms in the Southern Great Plains of the United States of America, is a boundary layer phenomenon that occurs when a warm and moist air mass from the Gulf of Mexico meets a hot and dry air mass from the southwest desert area. An accurate knowledge of the water vapor spatio-temporal variability in the lower part of the atmosphere is crucial for a better understanding of the evolution of the dryline. The tropospheric refractivity, directly related to water vapor content, is a proxy for the water vapor content of the troposphere. It has already been demonstrated that the refractivity and the refractivity vertical gradient can be jointly estimated from radar phase measurements. In fact, it has been shown that using kriging interpolation techniques, accurate refractivity maps within the coverage area of the radar can be obtained with high temporal resolution. In this paper, a detailed analysis of the time series of radar-based refractivity maps obtained during a dryline that occurred on the afternoon of 22 May 2002 during the International H2O Project (IHOP_2002) is presented. Comparisons between the time series of radar refractivity maps, obtained with the NCAR S-Pol radar, and the refractivity measurements derived from automatic ground-based weather stations and the AERI instrument, placed at different locations within the coverage area of the NCAR S-Pol radar, demonstrate the accuracy of radar refractivity estimates even for highly variable conditions, both in time and space, in the troposphere. Correlation coefficients higher than 0.95 are obtained in all weather station locations. Regarding the RMSE, errors less than 6 N-units are obtained for all cases, being even as low as 2.92 N-units at some locations. Full article
(This article belongs to the Section Meteorology)
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Figure 1

Figure 1
<p>S-Pol radar coverage domain in the Southern Great Plains of United States (Oklahoma, United States): (<b>a</b>) Google Earth<math display="inline"><semantics> <msup> <mrow/> <mi>TM</mi> </msup> </semantics></math> image centered in the S-Pol radar; (<b>b</b>) Topographic map of the terrain (National Elevation Dataset of the U. S. Geological Survey (USGS)).</p>
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<p>The data processing flowchart to obtain radar refractivity measurements.</p>
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<p>Mean refractivity estimates from NCAR S-Pol Radar phase measurements compared to refractivity estimates from WS measurements at the radar height. Blue dots represent the scatterplot of the radar refractivity estimates and the refractivity derived from the weather stations.</p>
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<p>Clusters of stationary target pairs defined with a k-means clustering algorithm within the coverage area of the NCAR S-Pol radar.</p>
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<p>Time series (<b>left</b>) and scatterplot (<b>right</b>) of kriging interpolated radar refractivity compared with weather station refractivity. ERA5 data are used to obtain the phase difference function.</p>
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<p>Refractivity maps showing the evolution of the dryline on 22 May 2002.</p>
Full article ">Figure 6 Cont.
<p>Refractivity maps showing the evolution of the dryline on 22 May 2002.</p>
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<p>Radar refractivity and weather station data derived refractivity at the weather station locations from 16:00 UTC 22 May to 16:00 UTC 23 May 2002.</p>
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<p>Raised-relief refractivity map from NCAR S-Pol Radar phase measurements at the surface height.</p>
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<p>Time series (<b>left</b>) and scatterplot (<b>right</b>) of kriging interpolated radar refractivity compared with weather stations refractivity. Weather station data are used to obtain the phase difference function.</p>
Full article ">Figure 9 Cont.
<p>Time series (<b>left</b>) and scatterplot (<b>right</b>) of kriging interpolated radar refractivity compared with weather stations refractivity. Weather station data are used to obtain the phase difference function.</p>
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35 pages, 9464 KiB  
Article
A Data-Driven Study of the Drivers of Stratospheric Circulation via Reduced Order Modeling and Data Assimilation
by Julie Sherman, Christian Sampson, Emmanuel Fleurantin, Zhimin Wu and Christopher K. R. T. Jones
Meteorology 2024, 3(1), 1-35; https://doi.org/10.3390/meteorology3010001 - 19 Dec 2023
Viewed by 1245
Abstract
Stratospheric dynamics are strongly affected by the absorption/emission of radiation in the Earth’s atmosphere and Rossby waves that propagate upward from the troposphere, perturbing the zonal flow. Reduced order models of stratospheric wave–zonal interactions, which parameterize these effects, have been used to study [...] Read more.
Stratospheric dynamics are strongly affected by the absorption/emission of radiation in the Earth’s atmosphere and Rossby waves that propagate upward from the troposphere, perturbing the zonal flow. Reduced order models of stratospheric wave–zonal interactions, which parameterize these effects, have been used to study interannual variability in stratospheric zonal winds and sudden stratospheric warming (SSW) events. These models are most sensitive to two main parameters: Λ, forcing the mean radiative zonal wind gradient, and h, a perturbation parameter representing the effect of Rossby waves. We take one such reduced order model with 20 years of ECMWF atmospheric reanalysis data and estimate Λ and h using both a particle filter and an ensemble smoother to investigate if the highly-simplified model can accurately reproduce the averaged reanalysis data and which parameter properties may be required to do so. We find that by allowing additional complexity via an unparameterized Λ(t), the model output can closely match the reanalysis data while maintaining behavior consistent with the dynamical properties of the reduced-order model. Furthermore, our analysis shows physical signatures in the parameter estimates around known SSW events. This work provides a data-driven examination of these important parameters representing fundamental stratospheric processes through the lens and tractability of a reduced order model, shown to be physically representative of the relevant atmospheric dynamics. Full article
(This article belongs to the Special Issue Early Career Scientists' (ECS) Contributions to Meteorology (2023))
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Figure 1
<p>Averaged ECMWF ERA−Interim data.</p>
Full article ">Figure 1 Cont.
<p>Averaged ECMWF ERA−Interim data.</p>
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<p>(<b>a</b>–<b>d</b>) RMSEs for combinations of assimilation period and observation error when the state vector and control parameters <span class="html-italic">h</span> and <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> are estimated simultaneously using synthetic data from the Ruzmaikin model. (<b>e</b>) Bimodality as measured by the percentage of timepoints with sufficiently large Sarle statistic. (<b>f</b>) An example of a bimodal ensemble distribution with a Sarle statistic value of <math display="inline"><semantics> <mrow> <mi>BC</mi> <mo>=</mo> <mn>0.728</mn> </mrow> </semantics></math>, with <math display="inline"><semantics> <mrow> <msubsup> <mo mathvariant="italic">σ</mo> <mi>obs</mi> <mn>2</mn> </msubsup> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> and assimilation period of 3 weeks.</p>
Full article ">Figure 2 Cont.
<p>(<b>a</b>–<b>d</b>) RMSEs for combinations of assimilation period and observation error when the state vector and control parameters <span class="html-italic">h</span> and <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> are estimated simultaneously using synthetic data from the Ruzmaikin model. (<b>e</b>) Bimodality as measured by the percentage of timepoints with sufficiently large Sarle statistic. (<b>f</b>) An example of a bimodal ensemble distribution with a Sarle statistic value of <math display="inline"><semantics> <mrow> <mi>BC</mi> <mo>=</mo> <mn>0.728</mn> </mrow> </semantics></math>, with <math display="inline"><semantics> <mrow> <msubsup> <mo mathvariant="italic">σ</mo> <mi>obs</mi> <mn>2</mn> </msubsup> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> and assimilation period of 3 weeks.</p>
Full article ">Figure 3
<p>(<b>a</b>) RMSE for mean zonal wind using the particle filter with ECMWF reanalysis data. (<b>b</b>–<b>d</b>) Estimates for <span class="html-italic">h</span> and the coefficients of <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> obtained by time-averaging the ensemble mean over the last year of analysis.</p>
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<p>Summary statistics of ESMDA identical twin experiments for varying decorrelation lengths.</p>
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<p>Example model outputs from the ESMDA identical twin experiments, each with <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mi>h</mi> </msub> <mo>=</mo> <mn>91</mn> </mrow> </semantics></math>. (<b>a</b>–<b>c</b>) show the model analysis <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> (red) versus the known, true <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> (black), while (<b>d</b>–<b>f</b>) show these curves for mean zonal wind speed <math display="inline"><semantics> <mrow> <mi>U</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>g</b>–<b>i</b>) Model output (blue) translated into <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>−</mo> <mi>U</mi> </mrow> </semantics></math> phase space superimposed on the bifurcation diagram for <math display="inline"><semantics> <mo>Λ</mo> </semantics></math>. Note that the bifurcation diagrams only showcases the stable equilibrium branches (orange), and this is repeated in subsequent figures. Decorrelation lengths for <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> are set at (<b>a</b>,<b>d</b>,<b>g</b>): <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> <mo>=</mo> <mn>547</mn> </mrow> </semantics></math>, (<b>b</b>,<b>e</b>,<b>h</b>): <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> <mo>=</mo> <mn>182</mn> </mrow> </semantics></math>, and (<b>c</b>,<b>f</b>,<b>i</b>): <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 6
<p>Results from ESMDA fraternal twin experiments for <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> parameterized by estimated <math display="inline"><semantics> <msub> <mo>Λ</mo> <mn>0</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mo>Λ</mo> <mi>a</mi> </msub> </semantics></math>, and <math display="inline"><semantics> <mo mathvariant="italic">ϵ</mo> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> free. (<b>a</b>) RMSE (blue) and variance (orange) of model analysis <math display="inline"><semantics> <mrow> <mi>U</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>, while (<b>b</b>) shows variance (blue) and mean (orange) of <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>, each for varying values of <math display="inline"><semantics> <msub> <mo mathvariant="italic">τ</mo> <mi>h</mi> </msub> </semantics></math>. (<b>c</b>) Model output for normalized variables <math display="inline"><semantics> <mrow> <mi>U</mi> <mo>,</mo> </mrow> </semantics></math><math display="inline"><semantics> <mrow> <mi>h</mi> <mo>,</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mo>Λ</mo> </semantics></math>. (<b>d</b>) Comparison of estimated wind speeds (red) and observed wind speeds (black). (<b>e</b>) <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>−</mo> <mi>U</mi> </mrow> </semantics></math> phase space of model output (blue) superimposed on the bifurcation diagram for <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> (orange). (<b>c</b>–<b>e</b>) Model output is from the lowest RMSE experiment (<math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mi>h</mi> </msub> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>).</p>
Full article ">Figure 7
<p>Results from ESMDA fraternal twin experiments for <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> free and <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> constant. (<b>a</b>) RMSE (blue) and variance (orange) of model analysis <math display="inline"><semantics> <mrow> <mi>U</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>, while (<b>b</b>) shows the variance (blue) and mean (orange) of <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>, each for varying values of <math display="inline"><semantics> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> </semantics></math>.</p>
Full article ">Figure 8
<p>Example model results for <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> free and <span class="html-italic">h</span> constant. (<b>a</b>–<b>c</b>) Model output for normalized variables <span class="html-italic">U</span> (red) and <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> (black). (<b>d</b>–<b>f</b>) Comparison of model estimated wind speeds (red) with the observed wind speeds (black). (<b>g</b>–<b>i</b>) <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>−</mo> <mi>U</mi> </mrow> </semantics></math> phase space of model output (blue) superimposed on the bifurcation diagram for <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> (orange). Decorrelation lengths for <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> are set at (<b>a</b>,<b>d</b>,<b>g</b>): <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> <mo>=</mo> <mn>547</mn> </mrow> </semantics></math>, (<b>b</b>,<b>e</b>,<b>h</b>): <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> <mo>=</mo> <mn>182</mn> </mrow> </semantics></math>, and (<b>c</b>,<b>f</b>,<b>i</b>): <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 9
<p>Summary statistics for ESMDA fraternal twin experiments with free <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> and varying decorrelation lengths.</p>
Full article ">Figure 10
<p>Example model results with both <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> free and <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mi>h</mi> </msub> <mo>=</mo> <mn>365</mn> </mrow> </semantics></math>. (<b>a</b>–<b>c</b>) Model output for normalized variables <span class="html-italic">U</span> (red), <span class="html-italic">h</span> (blue), and <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> (black). (<b>d</b>–<b>f</b>) Comparison of model estimated wind speeds (red) with the observed wind speeds (black). (<b>g</b>–<b>i</b>) <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>−</mo> <mi>U</mi> </mrow> </semantics></math> phase space of model output superimposed on the bifurcation diagram for <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>.</mo> </mrow> </semantics></math> Decorrelation lengths for <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> are set at (<b>a</b>,<b>d</b>,<b>g</b>): <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> <mo>=</mo> <mn>547</mn> </mrow> </semantics></math>, (<b>b</b>,<b>e</b>,<b>h</b>): <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> <mo>=</mo> <mn>182</mn> </mrow> </semantics></math>, and (<b>c</b>,<b>f</b>,<b>i</b>): <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 11
<p>Examples of model output around known SSW events for <span class="html-italic">U</span> (orange), <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> (blue), and <span class="html-italic">h</span> (red), each with <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mi>h</mi> </msub> <mo>=</mo> <mn>365</mn> </mrow> </semantics></math>. Decorrelation lengths for <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> are set at (<b>a</b>–<b>c</b>): <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> <mo>=</mo> <mn>547</mn> </mrow> </semantics></math>, (<b>d</b>–<b>f</b>): <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> <mo>=</mo> <mn>182</mn> </mrow> </semantics></math>, and (<b>g</b>–<b>i</b>): <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 12
<p>Examples of model output around known SSW events for <span class="html-italic">U</span> (orange), <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> (blue), and <span class="html-italic">h</span> (red), each with <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mi>h</mi> </msub> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>. Decorrelation lengths for <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> are set at (<b>a</b>–<b>c</b>): <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> <mo>=</mo> <mn>547</mn> </mrow> </semantics></math>, (<b>d</b>–<b>f</b>): <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> <mo>=</mo> <mn>182</mn> </mrow> </semantics></math>, and (<b>g</b>–<b>i</b>): <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 13
<p>Mean (blue) and variance (orange) of (<b>a</b>–<b>c</b>): <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> and (<b>d</b>–<b>f</b>): <math display="inline"><semantics> <mrow> <mi>U</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> over several different moving windows, 1 year, 2 years, and 5 years.</p>
Full article ">Figure A1
<p>Results from ESMDA fraternal twin experiments for <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> parameterized by <math display="inline"><semantics> <mrow> <msub> <mo>Λ</mo> <mn>0</mn> </msub> <mo>=</mo> <mn>0.75</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mo>Λ</mo> <mi>a</mi> </msub> <mo>=</mo> <mn>2.25</mn> </mrow> </semantics></math> m/s/km, and <math display="inline"><semantics> <mrow> <mo mathvariant="italic">ϵ</mo> <mo>=</mo> <mn>0.3</mn> </mrow> </semantics></math> fixed and <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> free. (<b>a</b>) RMSE (blue) and variance (orange) of model analysis <math display="inline"><semantics> <mrow> <mi>U</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> while (<b>b</b>) shows the variance (blue) and mean (orange) of <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>, each for varying values of <math display="inline"><semantics> <msub> <mo mathvariant="italic">τ</mo> <mi>h</mi> </msub> </semantics></math>. (<b>c</b>) Model output for variables normalized <span class="html-italic">U</span> (red), <span class="html-italic">h</span> (blue), and <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> (black). (<b>d</b>) Comparison of model estimated wind speeds (red) with the observed wind speeds (black). (<b>e</b>) <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>−</mo> <mi>U</mi> </mrow> </semantics></math> phase space of model output (blue) superimposed on the bifurcation diagram for <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> (orange). (<b>c</b>–<b>e</b>) Model output is from the lowest RMSE experiment (<math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mi>h</mi> </msub> <mo>=</mo> <mn>91</mn> </mrow> </semantics></math>).</p>
Full article ">Figure A2
<p>Results from ESMDA fraternal twin experiments with <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> parameterized by <math display="inline"><semantics> <msub> <mo>Λ</mo> <mn>0</mn> </msub> </semantics></math> and <math display="inline"><semantics> <mo mathvariant="italic">ϵ</mo> </semantics></math> free, <math display="inline"><semantics> <mrow> <msub> <mo>Λ</mo> <mi>a</mi> </msub> <mo>≥</mo> <mn>2.25</mn> </mrow> </semantics></math> m/s/km, and <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> free. (<b>a</b>) RMSE (blue) and variance (orange) of model analysis <math display="inline"><semantics> <mrow> <mi>U</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> while (<b>b</b>) shows the variance (blue) and mean (orange) of <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>, each for varying values of <math display="inline"><semantics> <msub> <mo mathvariant="italic">τ</mo> <mi>h</mi> </msub> </semantics></math>. (<b>c</b>) Model output for variables normalized <span class="html-italic">U</span> (red), <span class="html-italic">h</span> (blue), and <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> (black). (<b>d</b>) Comparison of model estimated wind speeds (red) with the observed wind speeds (black). (<b>e</b>) <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>−</mo> <mi>U</mi> </mrow> </semantics></math> phase space of model output (blue) superimposed on the bifurcation diagram for <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> (orange). (<b>c</b>–<b>e</b>) Model output is from the lowest RMSE experiment (<math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mi>h</mi> </msub> <mo>=</mo> <mn>547</mn> </mrow> </semantics></math>).</p>
Full article ">Figure A3
<p>(<b>a</b>–<b>n</b>) <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> (blue), <span class="html-italic">U</span> (orange), and <span class="html-italic">h</span> (red) around known SSW events with <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> <mo>=</mo> <mn>547</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mi>h</mi> </msub> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>–<b>n</b>) <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> (blue), <span class="html-italic">U</span> (orange), and <span class="html-italic">h</span> (red) around known SSW events with <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> <mo>=</mo> <mn>182</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mi>h</mi> </msub> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>.</p>
Full article ">Figure A5
<p>(<b>a</b>–<b>n</b>) <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> (blue), <span class="html-italic">U</span> (orange), and <span class="html-italic">h</span> (red) around known SSW events with <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mo mathvariant="italic">λ</mo> </msub> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mo mathvariant="italic">τ</mo> <mi>h</mi> </msub> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>.</p>
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15 pages, 8720 KiB  
Technical Note
Fast Observation Operator for Global Navigation Satellite System Tropospheric Gradients
by Florian Zus, Rohith Thundathil, Galina Dick and Jens Wickert
Remote Sens. 2023, 15(21), 5114; https://doi.org/10.3390/rs15215114 - 26 Oct 2023
Cited by 1 | Viewed by 1257
Abstract
From the raw measurements at a single Global Navigation Satellite System (GNSS) ground-based station, the Zenith Total Delay (ZTD) and the tropospheric gradient can be estimated. In order to assimilate such data into Numerical Weather Prediction (NWP) models, the observation operator must be [...] Read more.
From the raw measurements at a single Global Navigation Satellite System (GNSS) ground-based station, the Zenith Total Delay (ZTD) and the tropospheric gradient can be estimated. In order to assimilate such data into Numerical Weather Prediction (NWP) models, the observation operator must be developed. Our previously developed tropospheric gradient operator is based on a linear combination of tropospheric delays and, therefore, is difficult to implement into NWP Data Assimilation (DA) systems. In this technical note, we develop a fast observation operator. This observation operator is based on an integral expression which contains the north–south and east–west horizontal gradients of refractivity. We run a numerical weather model (the horizontal resolution is 10 km) and show that for stations located in central Europe and in the warm season, the root-mean-square deviation between the tropospheric gradients calculated by the fast and original approach is about 0.15 mm. This deviation is regarded acceptable for assimilation since the typical root-mean-square deviation between observed and forward modelled tropospheric gradients is about 0.5 mm. We then implement the developed operator in our experimental DA system and test the proposed approach. In particular, we analyze the impact of the assimilation on the refractivity field. The developed tropospheric gradient operator, together with its tangent linear and adjoint version, is freely available (Fortran code) and ready to be implemented into NWP DA systems. Full article
(This article belongs to the Special Issue GNSS in Meteorology and Climatology)
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Figure 1

Figure 1
<p>On 27 May 2013, 12 UTC large tropospheric gradients were present in central Europe. The <b>left</b> (<b>middle</b>) panel shows the NWM (GNSS) tropospheric gradient map. The tropospheric gradient maps are combined with radar precipitation data provided by the Deutscher Wetter Dienst (DWD). The radar precipitation data correspond to the instantaneous rain and is measured in mm/h. The color scale is yellow-green-blue-purple-red. The darker tone of a specific color means a higher rainfall intensity. The GNSS Integrated Water Vapor (IWV) map is shown in the <b>right</b> panel. Courtesy of Michael Kačmařík.</p>
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<p>Map of tropospheric gradient components valid at 7.6.2021, 12 UTC. The original tropospheric gradient operator was utilized to derive the north and the east gradient component. The <b>left</b> (<b>right</b>) panel shows the <b>east</b> (<b>north</b>) gradient component.</p>
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<p>Same as <a href="#remotesensing-15-05114-f002" class="html-fig">Figure 2</a> but the fast tropospheric gradient operator was utilized to derive the north and the east gradient component.</p>
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<p>Zoom into the time series of the north and the east gradient components at the station POTS (Potsdam, Germany). The <b>upper</b> panel shows the tropospheric gradient components derived with the original (fast) observation operator in black (red) as a function of the time. The <b>lower</b> panel shows the differences between the tropospheric gradient components as a function of the time.</p>
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<p>We compare GNSS and NWM ZTDs for a two month period (May and June 2013). The 250 stations cover Germany, the Czech Republic and parts of Austria and Poland. The <b>upper</b> panel shows the data availability in percent per station. The <b>middle</b> panel shows the station specific mean deviation. The <b>lower</b> panel shows the station specific standard deviation. The number (yellow background) equals the average value of the respective parameter.</p>
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<p>We compare GNSS and NWM tropospheric gradient components for a two month period (May and June 2013). The 250 stations cover central Europe. The fast tropospheric gradient operator is utilized for this comparison. The <b>left</b> (<b>right</b>) panel shows the statistic for the <b>east</b> (<b>north</b>) gradient components. The <b>upper</b> panel shows the station specific mean deviation. The <b>lower</b> panel shows the station specific standard deviation. The number (yellow background) equals the average value of the respective parameter.</p>
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<p>Same as <a href="#remotesensing-15-05114-f006" class="html-fig">Figure 6</a> but the original tropospheric gradient operator is utilized for this comparison.</p>
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<p>The root-mean-square error of the refractivity in percent as a function of the pressure. The black line corresponds to the background, the blue line corresponds to the case when ZTDs are assimilated and the red line corresponds to the case where both ZTDs and tropospheric gradients are assimilated. GNSS ZTDs and tropospheric gradients for the single station Potsdam are assimilated four times per day (0, 6, 12, 18 UTC) for a period of four months (May, June, July and August 2021). The original tropospheric gradient operator is utilized in the assimilation.</p>
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<p>Same as <a href="#remotesensing-15-05114-f008" class="html-fig">Figure 8</a> but the fast tropospheric gradient operator is utilized in the assimilation.</p>
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8 pages, 2574 KiB  
Proceeding Paper
A Break of the Etesian Winds Regime Early in July 2022
by Nicholas G. Prezerakos
Environ. Sci. Proc. 2023, 26(1), 172; https://doi.org/10.3390/environsciproc2023026172 - 5 Sep 2023
Viewed by 560
Abstract
The predominant climatological wind regime in summer over the Greek Seas and especially over the Aegean Sea is undoubtedly the Etesian winds system, very well known since the time of the ancient Greeks, who first identified and described its main characteristics. The Etesian [...] Read more.
The predominant climatological wind regime in summer over the Greek Seas and especially over the Aegean Sea is undoubtedly the Etesian winds system, very well known since the time of the ancient Greeks, who first identified and described its main characteristics. The Etesian winds have been under continuous and intensive research since early 1900s, and numerous papers have been published, which have revealed all the secrets associated with the physical mechanisms responsible for their creation and maintenance. The ordinary synoptic situation, which is closely associated with the appearance of spells of Etesian winds outbreaks over the Aegean Sea, refers to cases in which the Subtropical Jet Stream (SJS) is situated over the Greek mainland. Then the frontal surfaces associated with the Polar Jet Stream (PJS), or a polar jet streak, passing through Greece from the north can cause severe weather in northern Greece as far south as Larisa (39.39° N, 22.26° E). Precipitation does not usually occur south of Larisa, because the SJS constitutes a barrier to the southward extension of the upper half of the frontal surface. In this case, cold advection occurs in the lower troposphere, resulting in a drop in temperatures even in southern Greece, due to the establishment of an Etesian winds outbreak. Thereafter, these north-easterly winds persist for a long time, weakening gradually, following the variation of the pressure gradient due to the combination of the mobile dynamic anticyclone positioned over the Balkans, after the passage of the cold front and the permanent Cyprus surface low. The main goal of this article is to investigate how much the case of a time period around 9 July 2022 differs from the conceptual model mentioned above, as deep convection occurred over all of Greece over three successive days, breaking the Etesian winds regime and defeating the low tropospheric stability usually accompanying this wind regime. Full article
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Figure 1
<p>500 hPa gpm colored contours per 40 gpm and MSL, white isobars per 5 hPa at 00:00 UTC on 6–9 July 2022.</p>
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<p>6 h cumulative total precipitation in mm during the period from 8 to 10 July 2022 in the vicinity of Greece. Red dots indicate convective precipitation.</p>
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<p>(<b>a</b>) Surface chart at 12:00 UTC on 9 July 2022. Black isobars per 4 hPa, Black bold line segments, trough lines, and bold bullet center of low at 500 hPa. The wind and the fronts are represented with WMO conventional symbols. (<b>b</b>) Infrared satellite chart: with 500 hPa temperature (blue contours), 300 hPa stream lines (thin red lines), upper level cold front (black broken line) and CAPE (yellow/red contours) per 200 j/kg bellow/above 1000 j/kg.</p>
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<p>Cross-section along 23° meridian from 20° N to 60° N on 6 July 2022/12:00 UTC. Labelled dotted lines: potential temperature (θ) with a contour interval of 5 K; increasing shading enclosed by solid isotachs: normal (u) component of the wind speed ≥ 5 m/s, with a contour interval 5 m/s; Red arrows: air flow parallel to the section (v, ω). Colored contours indicate synoptic scale vertical motion in Pa/s. The bold black line is the dynamical tropopause, labelled 2 PVU. The vertical dashed black line indicates the location of Athens.</p>
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<p>As <a href="#environsciproc-26-00172-f004" class="html-fig">Figure 4</a>, but for (<b>a</b>) 7, (<b>b</b>) 8 and (<b>c</b>) 9 July 2022/12:00 UTC.</p>
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<p>Wind speed at 300 hPa. The axes of PJS and PJS are depicted clearly.</p>
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6 pages, 1563 KiB  
Proceeding Paper
Investigation of the Influence of Stratospheric Shear on Baroclinic Instability
by Christos Gkoulekas and Nikolaos A. Bakas
Environ. Sci. Proc. 2023, 26(1), 73; https://doi.org/10.3390/environsciproc2023026073 - 25 Aug 2023
Viewed by 737
Abstract
Baroclinic instability is one of the main mechanisms for the formation of synoptic scale systems. Previous studies examined the exponential growth of small perturbations for a stably stratified troposphere in the case of a constant meridional temperature gradient ignoring the stratosphere (Eady’s model). [...] Read more.
Baroclinic instability is one of the main mechanisms for the formation of synoptic scale systems. Previous studies examined the exponential growth of small perturbations for a stably stratified troposphere in the case of a constant meridional temperature gradient ignoring the stratosphere (Eady’s model). However, since stratospheric flow also affects to some extent the motions in the troposphere, in this work we investigate the effect of stratospheric wind shear on baroclinic instability using the tools of Generalized Stability Theory (GST). GST is a linear stability theory that addresses both the exponential growth of perturbations that is pertinent in the large time asymptotic limit and the transient growth of perturbations at finite time. The optimal initial perturbations leading to the largest growth over a specified time interval are calculated for three main cases of stratospheric shear: positive, zero and negative shear over the stratosphere. It is found that the inclusion of stratospheric shear in all three cases decreases perturbation growth and influences the scale of the structures that will dominate the flow. For optimizing times of the order of a week, the development of systems with larger spatial scale compared to the prediction of the Eady model is expected, while for optimizing times of the order of a day, smaller scale systems are found to develop. Full article
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Figure 1
<p>Vertical profile of the zonal wind speed <math display="inline"><semantics> <mrow> <mi>U</mi> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> for the cases of (<b>a</b>) positive, (<b>b</b>) zero and (<b>c</b>) negative stratospheric shear. (<b>d</b>) The vertical profile of the Brunt–Väisälä frequency.</p>
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<p>Maximum exponential growth rate of perturbations as a function of wavenumber <math display="inline"><semantics> <mi>K</mi> </semantics></math> for perturbations with <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> in the three cases of stratospheric flow and in the case of the Eady model.</p>
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<p>Optimal energy growth <math display="inline"><semantics> <mi>G</mi> </semantics></math> as a function of the wavenumber <math display="inline"><semantics> <mi>K</mi> </semantics></math> and the orientation <math display="inline"><semantics> <mi>θ</mi> </semantics></math> of the perturbations for <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> day. Shown is the growth for (<b>a</b>) the Eady model and for (<b>b</b>) positive, (<b>c</b>) zero and (<b>d</b>) negative stratospheric shear.</p>
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<p>Optimal energy growth <math display="inline"><semantics> <mi>G</mi> </semantics></math> as a function of the wavenumber <math display="inline"><semantics> <mi>K</mi> </semantics></math> and the orientation <math display="inline"><semantics> <mi>θ</mi> </semantics></math> of the perturbations for <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> days. Shown is the growth for (<b>a</b>) the Eady model and for (<b>b</b>) positive, (<b>c</b>) zero and (<b>d</b>) negative stratospheric shear.</p>
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16 pages, 7488 KiB  
Article
Characteristics of Advection Fog at Qingdao Liuting International Airport
by Zhiwei Zhang, Yunying Li, Laurent Li, Chao Zhang and Guorong Sun
Atmosphere 2023, 14(8), 1310; https://doi.org/10.3390/atmos14081310 - 19 Aug 2023
Viewed by 1142
Abstract
The advection fog characteristics at Qingdao Liuting International Airport during 2000–2022 are studied based on surface observation, sounding and reanalysis data. Surface observation data show that there were two types of fog: evaporation fog (EF) dominated by northwesterly wind in winter and cooling [...] Read more.
The advection fog characteristics at Qingdao Liuting International Airport during 2000–2022 are studied based on surface observation, sounding and reanalysis data. Surface observation data show that there were two types of fog: evaporation fog (EF) dominated by northwesterly wind in winter and cooling fog (CF) dominated by southeasterly wind in spring and summer. CF is thicker than EF due to different planetary boundary layer (PBL) structures. For EF, the middle and low troposphere are affected by dry and cold air, while CF is affected by warm and moist air below 850 hPa. When EF formed, downdrafts and a positive vertical gradient of the pseudo-equivalent potential temperature indicate stable PBL, surface heat flux is upward from sea to atmosphere and surface wind diverges near the air–sea interface. When CF formed, these characteristics are reversed. Fog is significantly affected by sea–land–atmosphere interactions. The moisture source is mainly from surface fluxes released by the Yellow Sea in the case of EF, while it is from moist air at low latitudes and local land transpiration in the case of CF. The difference in temperature between the sea surface and surface air changes from the range of 0–8 K for EF but from −4–0 K for CF. Full article
(This article belongs to the Section Meteorology)
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Figure 1
<p>Topography (m) and the location of the airport (black triangle) used in this study.</p>
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<p>Wind rose at the time of fog formation.</p>
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<p>(<b>a</b>) Annual and (<b>b</b>) seasonal variations in fog frequency. (<b>c</b>) Boxplot of EF and CF visibility (m) and (<b>d</b>) diurnal variation in fog frequency at formation time. Black lines represent the total fog frequency, and blue and red lines represent the fog frequency under northwesterly wind and southeasterly wind, respectively.</p>
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<p>(<b>a</b>) Visibility (m), (<b>b</b>) SLP (hPa), (<b>c</b>) wind speed (m s<sup>−1</sup>), (<b>d</b>) wind direction (°) and (<b>e</b>) temperature and dew point (K) time series on 10–11 December 2021 and 8–9 November 2022. Blue and red lines represent EF and CF variables, respectively. The solid and dashed lines represent the temperature and dew point in (e), respectively.</p>
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<p>(<b>a</b>) Temperature and dew point (K), (<b>b</b>) RH (%), and (<b>c</b>) wind speed (m s<sup>−1</sup>) profiles at 12 UTC on 10 December 2021 (black lines), at 00 UTC on 11 December 2021 (red lines) and at 12 UTC on 11 December 2021 (blue lines). (<b>d</b>–<b>f</b>) Same as (<b>a</b>–<b>c</b>), but variables at 12 UTC on 8 November 2022, at 00 UTC on 9 November 2022 and at 12 UTC on 9 November 2022. Vertical solid and dashed lines represent temperature and dew point profiles in (a,d), respectively. Horizontal solid lines represent TI top and base heights.</p>
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<p>(<b>a</b>) Geopotential height (isolines, dagpm), wind (vectors, m s<sup>−1</sup>) and RH (shaded, %) at 500 hPa of EF. (<b>b</b>) Same as (<b>a</b>), but with CF. (<b>c</b>,<b>d</b>) Same as (<b>a</b>,<b>b</b>) but at 850 hPa. (<b>e</b>,<b>f</b>) Same as (<b>a</b>,<b>b</b>) but at 925 hPa. SLP (isolines, hPa), 10 m wind (vectors, m s<sup>−1</sup>) and 2 m RH (shaded, %) of (<b>g</b>) EF and (<b>h</b>) CF. The black triangle represents the airport location.</p>
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<p>(<b>a</b>) Vertical cross section of zonal-vertical circulation (vectors, zonal wind in m s<sup>−1</sup> and −ω in 10<sup>−2</sup> Pa s<sup>−1</sup>), <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>s</mi> <mi>e</mi> </mrow> </msub> </mrow> </semantics></math> (isolines, K) and its vertical gradience (shaded, K) along 120.5° E at the formation stage of EF. (<b>b</b>) Same as (<b>a</b>), but at the formation stage of CF. (<b>c</b>) The 10 m wind divergence (shaded, 10<sup>−5</sup> s<sup>−1</sup>) and EIS (isolines, K) of EF. (<b>d</b>) Same as (<b>c</b>), but with CF. The black triangle represents the airport.</p>
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<p>(<b>a</b>) Water vapor flux (vectors and shaded, g/(cm·hPa·s)) of EF. (<b>b</b>) Same as (<b>a</b>), but with CF. (<b>c</b>) Temperature advection based on SAT and 10 m wind (shaded, 10<sup>−5</sup> K s<sup>−1</sup>) and SST−SAT (isolines, K) of EF. (<b>d</b>) Same as (<b>c</b>), but with CF. (<b>e</b>) SHF (shaded, W m<sup>−2</sup>) and LHF (isolines, W m<sup>−2</sup>) of EF. (<b>f</b>) Same as (<b>e</b>), but with CF. Triangles represent the airport, black solid lines represent positive SST−SAT, and the opposite is true for dashed lines.</p>
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<p>Density scatter plot of SST−SAT (K) and wind speed (m s<sup>−1</sup>). Blue and red dots represent EF and CF, respectively.</p>
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<p>Anomaly of SLP (shaded, hPa) and 10 m wind (vectors, m s<sup>−1</sup>) at (<b>a</b>) 06 and (<b>b</b>) 18 UTC. Anomaly of LHF (shaded, W m<sup>−2</sup>) and SHF (isolines, W m<sup>−2</sup>) at (<b>c</b>) 06 and (<b>d</b>) 18 UTC. Black solid lines represent positive SHF anomalies, while the opposite is true for black dashed lines.</p>
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<p>(<b>a</b>) Vertical cross section of meridional-vertical circulation (vectors, meridional wind in m s<sup>−1</sup> and −ω in 10<sup>−2</sup> Pa s<sup>−1</sup>) and −ω (shaded) anomalies along 120.5° E at 06 UTC. (<b>b</b>) Same as (<b>a</b>), but at 18 UTC. (<b>c</b>) Vertical cross section of RH (shaded, %) and temperature (isolines, K) anomalies along 120.5° E at 06 UTC. (<b>d</b>) Same as (<b>c</b>), but at 18 UTC. Black solid lines represent positive temperature anomalies, while the opposite is true for black dashed lines.</p>
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<p>Seasonal variation of TI frequency (%) at 0800 BST (<b>a</b>) and 2000 BST (<b>b</b>). (<b>c</b>) boxplot of estimated EF and CF thickness (m). Red pentagram (*) and (+) represents average thickness and outlier, respectively.</p>
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21 pages, 7463 KiB  
Article
Characteristics of Air Pollutant Distribution and Sources in the East China Sea and the Yellow Sea in Spring Based on Multiple Observation Methods
by Yucheng Wang, Guojie Xu, Liqi Chen and Kui Chen
Remote Sens. 2023, 15(13), 3262; https://doi.org/10.3390/rs15133262 - 25 Jun 2023
Cited by 4 | Viewed by 1741
Abstract
The composition of marine aerosol is quite complex, and its sources are diverse. Across the East China Sea (ECS) and the Yellow Sea (YS), multi-dimensional analysis of marine aerosols was conducted. The characteristics of carbonaceous aerosols and gaseous pollutants were explored through in [...] Read more.
The composition of marine aerosol is quite complex, and its sources are diverse. Across the East China Sea (ECS) and the Yellow Sea (YS), multi-dimensional analysis of marine aerosols was conducted. The characteristics of carbonaceous aerosols and gaseous pollutants were explored through in situ ship-based observation, MERRA-2 reanalysis datasets and TROPOMI data from Sentinel-5P satellite. Black carbon (BC)’s average concentration is 1.35 ± 0.78 μg/m3, with high-value BC observed during the cruise. Through HYSPLIT trajectory analysis, sources of BC were from the northern Eurasian continent, the Shandong Peninsula, the ECS and Northwest Pacific Ocean (NWPO). The transport of marine sources like ship emissions cannot be ignored. According to the absorption Angstrom exponent (AAE), BC originates from biomass burning (BB) in the shortwave band (~370 nm) and from fossil fuel combustion in the longwave band (~660 nm). Organic carbon (OC), sulfate (SO42−) and BC report higher Angstrom exponent (AE) while dust and sea salt reveal lower AE, which can be utilized to classify the aerosols as being fine- or coarse-mode, respectively. OC has the highest AE (ECS: 1.98, YS: 2.01), indicating that anthropogenic activities could be a significant source. The process of biomass burning aerosol (BBA) mixed with sea salt could contribute to the decline in BBA’s AE. Ship emissions may affect the distribution of tropospheric nitrogen dioxide (NO2) in the ECS, especially during the COVID-19 pandemic. Tropospheric NO2 over the YS has the highest value (up to 12 × 1015 molec/cm2). Stratospheric NO2 has a ladder-like distribution from north to south, and the variation gradient was lower than that in the troposphere. Carbon monoxide (CO) accumulates in the south and east of the ECS and the east of the YS, while the variation over the eastern YS is relatively frequent. Seas near the Korean Peninsula have extremely high CO concentration (up to 1.35 × 1017 molec/cm2). Full article
(This article belongs to the Special Issue Air Quality Mapping via Satellite Remote Sensing)
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Figure 1

Figure 1
<p>(<b>a</b>) The route of the cruise. Solid lines in black, red and yellow, respectively, represent departing routes 1, 2 and 3, while dotted black line represents return route. The contour plot represents monthly mean BC surface mass concentration (μg/m<sup>3</sup>) based on remote sensing. The arrows represent BC horizontal mass flux (combining u and v wind); (<b>b</b>) BC variation in hourly concentration along the route based on in situ observation. Black circles are selected points (Nos. 1–5) to analyze the backward trajectory.</p>
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<p>Variations in BC concentrations during the cruise (average: 1.41 ± 0.924 μg/m<sup>3</sup> (uncorrected), 1.35 ± 0.78 μg/m<sup>3</sup> (corrected)). Black diamonds represent selected points to analyze the backward trajectory.</p>
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<p>Comparison of BC concentrations during this cruise and land-based and other oceanic observations. Squares represent average values. Bars at both ends represent the maximum and minimum values.</p>
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<p>(<b>a</b>) Variation in BB contribution (%) during the cruise, observed from AE33. Purple dots represent BB contribution less than 20%. (<b>b</b>) The correlation between BC concentration and BB contribution (%). Blue dots and red crosses represent samples over the YS and the ECS, respectively. Blue and red lines represent fit results over the YS and the ECS, respectively.</p>
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<p>Correlation heatmap between BC and meteorological parameters (pressure, temperature, RH and wind speed) over the YS (<b>a</b>) and the ECS (<b>b</b>). “**” and “*” markers mean <span class="html-italic">p</span> &lt; 0.01 and <span class="html-italic">p</span> &lt; 0.05, respectively.</p>
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<p>(<b>a</b>–<b>e</b>) Cluster analysis of backward trajectory clustering analysis results ((<b>a</b>–<b>e</b>) represent Point Nos. 1–5, respectively).</p>
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<p>Angstrom exponent (AE) distribution of 6 variables ((<b>a</b>): black carbon (BC), (<b>b</b>): dust, (<b>c</b>): organic carbon (OC), (<b>d</b>): sea salt, (<b>e</b>): SO4 (sulfate (SO<sub>4</sub><sup>2−</sup>)) and (<b>f</b>): total aerosol) from MERRA-2. The upper and lower boxes represent the selected YS and ECS areas, respectively.</p>
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<p>(<b>a</b>) Average of NO<sub>2</sub> vertical column density (VCD) in the troposphere. (<b>b</b>–<b>e</b>) The tropospheric NO<sub>2</sub>-VCD anomaly in each time period (9, 14, 19, 24 April).</p>
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<p>(<b>a</b>) Average of NO<sub>2</sub> vertical column density (VCD) in the stratosphere. (<b>b</b>–<b>e</b>) The stratospheric NO<sub>2</sub>-VCD anomaly in each time period (9, 14, 19, 24 April).</p>
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<p>(<b>a</b>) Average of CO vertical column density (VCD). (<b>b</b>–<b>e</b>) CO-VCD anomaly in each time period (9, 14, 19, 24 April).</p>
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