Itamar M Lensky

On 15 September 2015, a convective storm yielded heavy rainfalls that caused the strongest flash flood in the last 50 years in the South Negev Desert (Israel). None of the operational forecast models predicted the event, and thus, no warning was provided. We analyzed this event using satellite, radar, and numerical weather prediction model data. We generated cloud-free climatological values on a pixel basis using Temporal Fourier Analysis on a time series of MSG geostationary satellite data. The discrepancy between the measured and climatological values was used to detect “cloud-contaminated” pixels. This simple, robust, fast, and accurate method is valuable for the early detection of convection. The first clouds were detected 30 min before they were detected by the official MSG cloud mask, 4.5 h before the radar, and 10 h before the flood reached the main road. We used the “severe storms” RGB composite and the satellite-retrieved vertical profiles of cloud top temperature–particle’...

ABSTRACT Land Surface Temperature (LST) controls most physical and biological processes on Earth. Knowledge of the LST at high spatial resolution enables representation of different climate regimes. The main factors controlling LST are the seasonal and diurnal cycles, land cover, cloud cover, and atmospheric processes at several scales. Lensky and Dayan analyzed atmospheric processes at the topoclimatic scale, and the mesoscale (Lensky and Dayan 2011, 2012). Here we will demonstrate an analysis of the spatial distribution of LST anomaly as affected by typical synoptic circulation patterns over the Eastern Mediterranean (EM). LST anomaly is defined as the difference between daily and climatological LST. Using LST anomaly reduces the effects of land cover and the seasonal and diurnal cycles, enabling a better detection of surface temperature patterns induced by synoptic circulation. In this study we used all available 2000-2012 NASA daily MODIS LST data over the EM, together with NCEP/NCAR Reanalysis data of SLP, surface winds and Omega (at 700hPa). We will present two frequent synoptic circulation patterns as classified by Levy and Dayan (2008) to demonstrate the LST patterns induced by synoptic circulation over the EM. The first is the "Red Sea Trough" (RST) with eastern axis, which is an extension of a low surface pressure from a tropical depression toward the Red Sea, penetrating up north as far as Turkey. It migrates from south to north and mostly frequent during the autumn. The axis of the RST separates distinctively between regions of positive (warm) anomalies over Turkey and regions of negative anomalies (cold) over Egypt induced by the wind flow from both sides of the axis. The second synoptic circulation pattern is "shallow Cyprus low to the north", which is a disturbance of the polar front extending southward. This synoptic system some times migrates over the Mediterranean eastward toward the EM during the winter season. The strong northwesterly flow featuring the cold (western) sector of this low is responsible for strong negative anomaly over most of the domain. The upper air support for this cyclone is confirmed by maximal Omega values over the region. This study gives the opportunity to quantify the effect of air mass advection on the spatial distribution of LST anomaly. Lensky, I. M., and U. Dayan, 2011: Detection of fine-scale climatic features from satellites and possible implications Bull. Amer. Meteor. Soc., 92, 1131-1136. doi:10.1175/2011BAMS3160.1 Lensky, I. M., and U. Dayan, 2012: Continuous detection and characterization of the sea breeze in clear sky conditions using Meteosat Second Generation. Atmos. Chem. Phys., 12, 6505-6513. doi:10.5194/acp-12-6505-2012 Levy I., U. Dayan and Y. Mahrer, 2008: A five-year study of coastal recirculation and its effects on air pollutants over the East Mediterranean region. J. Geophy. Res., 113, D16121, doi:10.1029/2007JD009529

Evaporation from water bodies strongly depends on surface water salinity. Spatial variation of surface salinity of saline water bodies commonly occurs across diluted buoyant plumes fed by freshwater inflows. Although mainly studied at the pan evaporation scale, the effect of surface water salinity on evaporation has not yet been investigated by means of direct measurement at the scale of natural water bodies. The Dead Sea, a large hypersaline lake, is fed by onshore freshwater springs that form local diluted buoyant plumes, offering a unique opportunity to explore this effect. Surface heat fluxes, micrometeorological variables, and water temperature and salinity profiles were measured simultaneously and directly over the salty lake and over a region of diluted buoyant plume. Relatively close meteorological conditions prevailed in the two regions; however, surface water salinity was significantly different. Evaporation rate from the diluted plume was occasionally 3 times larger than ...

Partitioning between the relative effects of the radiative and aerodynamic components of the atmospheric forcing on evaporation is challenging since diurnal distributions of wind speed and solar radiation typically overlap. The Dead Sea is located about a 100 km off the Eastern Mediterranean coast, where and the Mediterranean Sea breeze front reaches it after sunset. Therefore, in the Dead Sea the peaks of solar radiation and wind speed diurnal cycles in the Dead Sea are distinctly separated in time, offering a unique opportunity to distinguish between their relative impacts on evaporation. We present mid‐summer eddy covariance and meteorological measurements of evaporation rate and surface energy fluxes over the Dead Sea. The evaporation rate is characterized by a clear diurnal cycle with a daytime peak, few hours after solar radiation peak, and a nighttime peak coincident with wind speed peak. Evaporation rate is minimum during sunrise and sunset. Measurements of evaporation rate ...

Several recent studies have shown that global models are not capable to predict accurately the evolution of climate changes and variability on the regional scale. Misinterpretation of the anticipated climate change impacts on plants and animals are caused by failure of models to capture ...

A methodology for representing much of the physical information content of the METEOSAT Second Generation (MSG) geostationary satellite using red-green-blue (RGB) composites of the computed physical values of the picture elements is presented. The physical values are the solar reflectance in the solar channels and brightness temperature in the thermal channels. The main RGB compositions are (1) "Day Natural Colors", presenting vegetation in green, bare surface in brown, sea surface in black, water clouds as white, ice as magenta; (2) "Day Microphysical", presenting cloud microstructure using the solar reflectance component of the 3.9 μm, visible and thermal IR channels; (3) "Night Microphysical", also presenting clouds microstructure using the brightness temperature differences between 10.8 and 3.9 μm; (4) "Day and Night", using only thermal channels for presenting surface and cloud properties, desert dust and volcanic emissions; (5) "Air Mass", presenting mid and upper tropospheric features using thermal water vapor and ozone channels. The scientific basis for these rendering schemes is provided, with examples for the applications. The expanding use of these rendering schemes requires their proper documentation and setting as standards, which is the main objective of this publication.

Herbivorous insects play important roles in agriculture as pests or as weed biological control agents. Predicting the timing of herbivore insect population development can thus be of paramount importance for agricultural planning and sustainable land management. Numerical simulation models driven by temperature are often used to predict insect pest population build-up in agriculture. Such simulation models intend to use station-derived temperatures to drive the development of the target insect, although this temperature may differ substantially from that experienced by the insect on the plant. To improve the estimations, it has been suggested to replace air temperature in the model by land surface temperature (LST) data. Here, we use a numerical simulation model of insect population dynamics driven by either air temperature (combined with atmospheric temperature sound- ings) or land surface temperature derived from satellites to predict the population trends of the leaf beetle Ophraella communa, a potential biological control agent of Ambrosia artemisiifolia in Europe. For this, we conducted an extensive field experiment that included caged O. communa populations at five sites along an altitudinal gradient (125–1250 m a.s.l.) in Northern Italy during 2015 and 2016. We compared our model predictions using air or land surface temperature with observed beetle population build-up. Model predictions with both air and land surface temperatures predicted a similar phenology to observed populations but overestimated the abundance of the observed populations. When taking into consideration the error of the two measurement methods, the predictions of the model were in overlapping timeframes. Therefore, the current model driven by LST can be used as a proxy for herbivore impact, which is a novel tool for weed biocontrol.

Despite of the economic importance of the olive fly (Bactrocera oleae) and the large amount of biological and ecological studies on the insect, the factors driving its population dynamics (i.e., population persistence and regulation) had not been analytically investigated until the present study. Specifically, our study investigated the autoregressive process of the olive fly populations, and the joint role of intrinsic and extrinsic factors molding the population dy-namics of the insect. Accounting for endogenous dynamics and the influences of exoge-nous factors such as olive grove temperature, the North Atlantic Oscillation and the presence of potential host fruit, we modeled olive fly populations in five locations in the East-ern Mediterranean region. Our models indicate that the rate of population change is mainly shaped by first and higher order non-monotonic, endogenous dynamics (i.e., density-de-pendent population feedback). The olive grove temperature was the main exogenous ...

Abstract In recent years, roof greening technologies have been developed and implemented worldwide, as green roofs are an effective nature-based solution for alleviating outdoor heat and reducing building energy costs. While most observational green roof studies have investigated the cooling effect of model or test plots, few studies have conducted full-scale measurements; however, to guide green roof implementation, the thermal performance of full-scale roofs is crucial. This experimental study explored the ability of extensive green roofs to reduce outdoor temperatures on multiple time scales. The outdoor cooling effect of green roofs and the main factors that drive its performance were analyzed using long-term observational data collected from May 2016 to April 2019 in Nanjing, China. The results suggest that the cooling effect of green roofs exhibits significant diurnal, seasonal, annual, and vertical trends, and that the cooling performance across different time scales corresponds to weather and soil characteristics. The best cooling effect occurred at a height of 60 cm. The green roof displayed a temporary diurnal warming effect but had a good cooling effect on a seasonal time scale, with the largest amount of cooling occurring during the summer (average of 0.28 °C). The findings of this study can support the development, management, and maintenance of green roofs in subtropical areas.

Background Natural environments may have beneficial impacts on pregnancy outcomes. However, longitudinal evidence is limited and the associations with variance in surrounding greenness is unknown. Our objective was to evaluate these associations among 73 221 live births in Tel Aviv, Israel. Methods Longitudinal exposure to mean of greenness during pregnancy and trimesters were calculated using satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) Normalised Difference Vegetation Index (NDVI) data. In addition, exposure to mean and variation of NDVI from high-resolution satellite and percentage of tree cover [Vegetation Continuous Fields (VCF)] at 300-m buffer were evaluated in a cross-sectional approach. Generalized linear models were used to estimate the crude and adjusted associations. We explore the possible mediating role of ambient exposures and distance to ‘outdoor gyms’ located in parks. Results Crude beneficial associations between exposure to higher mean NDV...

We present a simple model to retrieve actual evapotranspiration (ET) solely from satellites (PaVI-E). The model is based on empirical relationships between vegetation indices (NDVI and EVI from MODIS) and total annual ET (ET<sub>Annual</sub>) from 16 FLUXNET sites representing a wide range of plant functional types and ET<sub>Annual</sub>. The model was applied separately for (a) annual vegetation systems (i.e., croplands and grasslands) and (b) systems with combined annual and perennial vegetation (i.e., woodlands, forests, savannah and shrublands). It explained most of the variance in ET<sub>Annual</sub> in those systems (71% for annuals, and 88% for combined annuals and perennials systems) while multiple regression and modified Temperature and Greenness models using also land surface temperature did not improve its performance (<i>p</i> > 0.1). PaVI-E was used to retrieve ET<sub>Annual</sub> at 250 m spatial resolutio...

Temporal Fourier analysis (TFA) on eight years of MODIS data produced a compact data set of the mean as well as the amplitude and phase of the annual, biannual and tri-annual harmonics of the following products: day and night land surface temperature (LST), 11 and 12 micron emissivity, 0.6, 0.8 and 2.1 micron solar reflectance, enhanced vegetation index and normalized

Wildfire simulations depend on fuel representation. Present fuel models are mainly based on the density and properties of different vegetation types. This study aims to improve the accuracy of WRF-Fire wildfire simulations, by using synthetic-aperture radar (SAR) data to estimate the fuel load and the trend of vegetation index to estimate the dryness of woody vegetation. We updated the chaparral and timber standard woody fuel classes in the WRF-Fire fuel settings. We used the ESA global above-ground biomass (AGB) based on SAR data to estimate the fuel load, and the Landsat normalized difference vegetation index (NDVI) trends of woody vegetation to estimate the fuel moisture content. These fuel sub-parameters represent the dynamic changes and spatial variability of woody fuel. We simulated two wildfires in Israel while using three different fuel models: the original 13 Anderson Fire Behavior fuel model, and two modified fuel models introducing AGB alone, and AGB and dryness. The upda...

Despite of the economic importance of the olive fly (Bactrocera oleae) and the large amount of biological and ecological studies on the insect, the factors driving its population dynamics (i.e., population persistence and regulation) had not been analytically investigated until the present study. Specifically, our study investigated the autoregressive process of the olive fly populations, and the joint role of intrinsic and extrinsic factors molding the population dynamics of the insect. Accounting for endogenous dynamics and the influences of exogenous factors such as olive grove temperature, the North Atlantic Oscillation and the presence of potential host fruit, we modeled olive fly populations in five locations in the Eastern Mediterranean region. Our models indicate that the rate of population change is mainly shaped by first and higher order non-monotonic, endogenous dynamics (i.e., density-dependent population feedback). The olive grove temperature was the main exogenous driv...

We present a phenology-based approach for optimizing the number and timing of unmanned aerial vehicle imagery acquisition, based on a priori near-surface observations. A ground-placed camera was used for generating annual time series of spectral indices in four different East Mediterranean sites. The time series dataset represented 1852 individuals of 12 common vegetation species. Feature selection was used for identifying the optimal dates for species classification. A UAV was flown for acquiring five overhead multiband orthomosaics, based on the five optimal dates identified in the feature selection of the near-surface time series of the previous year. An object-based classification was used for species classification, and resulted in an average overall accuracy of 85% and an average Kappa coefficient of 0.82. This cost-effective approach has high potential for detailed vegetation mapping, regarding the accessibility of UAV-produced time series, compared to hyper-spectral imagery ...

Discriminating between woody plant species using a single image is not straightforward due to similarity in their spectral signatures, and limitations in the spatial resolution of many sensors. Seasonal changes in vegetation indices can potentially improve vegetation mapping; however, for mapping at the individual species level, very high spatial resolution is needed. In this study we examined the ability of the Israel/French satellite of VENμS and other sensors with higher spatial resolutions, for identifying woody Mediterranean species, based on the seasonal patterns of vegetation indices (VIs). For the study area, we chose a site with natural and highly heterogeneous vegetation in the Judean Mountains (Israel), which well represents the Mediterranean maquis vegetation of the region. We used three sensors from which the indices were derived: a consumer-grade ground-based camera (weekly images at VIS-NIR; six VIs; 547 individual plants), UAV imagery (11 images, five bands, seven VI...

Satellite precipitation estimation from space is normally done using passive microwave data and the estimation methods, with only a few exceptions, make very little use of microphysical information from visible/infrared sensors that are widely available on geostationary and polar orbits. These two classes of sensors have their own well known strengths and weaknesses in identifying and estimate precipitation areas and

Abstract Assessing crops water use is essential for agricultural water management and planning, particularly in water-limited regions. Here, we present a biophysical model to estimate crop actual evapotranspiration and root-zone soil water content using proximal sensing and meteorological data (Crop RS-Met). The model, which is based on the dual FAO56 formulation, uses a water deficit factor calculated from rainfall and atmospheric demand information to constrain actual evapotranspiration and soil water content in crops growing under dry conditions. We tested the Crop RS-Met model in a dryland experimental field comprising a variety of wheat (Triticum aestivum L. and T. durum) cultivars with diverse phenology. Crop RS-Met was shown to accurately capture seasonal changes in wheat water use during the growing season. The average R2 of modeled vs. observed soil water content for all cultivars (N = 11) was 0.92 ± 0.02 with average relative RMSE and bias of 9.29 ± 1.30% and 0.13 ± 0.03%, respectively. We found that changing the integration time period of the water deficit factor in Crop RS-Met affects the accuracy of the model implying that this factor has a vital role in modeling crop water use under dry conditions. Currently, Crop RS-Met has a simple representation of surface runoff and does not take into consideration heterogeneity in the soil profile. Thus, efforts to combine numerical models that simulate soil water dynamics with a Crop RS-Met model driven by high-resolution remote sensing data may be needed for a spatially continuous assessment of crop water use in fields with more complex edaphic characteristics.

Environmental and economic constraints are forcing farmers to be more precise in the rates and timing of nitrogen (N) fertilizer application to wheat. In practice, N is frequently applied without knowledge of the precise amount needed or the likelihood of significant protein enhancement. The objective of this study was to help farmers optimize top dress N application by adopting the use of within-field reference N strips. We developed an assisting app on the Google Earth Engine (GEE) platform to map the spatial variability of four different vegetation indices (VIs) in each field by calculating the mean VI, masking extreme values (three standard deviations, 3σ) of each field, and presenting the anomaly as a deviation of ±σ and ±2σ or deviation of percentage. VIs based on red-edge bands (REIP, NDRE, ICCI) were very useful for the detection of wheat above ground N uptake and in-field anomalies. VENµS high temporal and spatial resolutions provide advantages over Sentinel-2 in monitoring...

Climatic conditions during the grain-filling period are a major factor affecting wheat grain yield and quality. Wheat in many semi-arid and arid areas faces high-temperature stress during this period. Remote sensing can be used to monitor both crops and environmental temperature. The objective of this study was to develop a tool to optimize field management (cultivar and sowing time). Analysis of 155 cultivar experiments (from 10 growth seasons) representing different environmental conditions revealed the required degree-days for each Israeli spring wheat cultivar to reach heading (from emergence). We developed a Google Earth Engine (GEE) app to analyze time series of gap-filled 1 km MODIS land surface temperature (LSTcont). By changing the cultivar and/or emergence date in the GEE app, the farmer can “expose” each wheat field to different climatic conditions during the grain-filling period, thereafter enabling him to choose the best cultivar to be sown in the field with the right t...

Satellite land surface temperature (LST) is vital for climatological and environmental studies. However, LST datasets are not continuous in time and space mainly due to cloud cover. Here we combine LST with Climate Forecast System Version 2 (CFSv2) modeled temperatures to derive a continuous gap filled global LST dataset at a spatial resolution of 1 km. Temporal Fourier analysis is used to derive the seasonality (climatology) on a pixel-by-pixel basis, for LST and CFSv2 temperatures. Gaps are filled by adding the CFSv2 temperature anomaly to climatological LST. The accuracy is evaluated in nine regions across the globe using cloud-free LST (mean values: R2 = 0.93, Root Mean Square Error (RMSE) = 2.7 °C, Mean Absolute Error (MAE) = 2.1 °C). The provided dataset contains day, night, and daily mean LST for the Eastern Mediterranean. We provide a Google Earth Engine code and a web app that generates gap filled LST in any part of the world, alongside a pixel-based evaluation of the data ...

<p><strong>.</strong> The study deals with an intense rainstorm that hit the Middle–East between 24 and 27 April 2018. The storm reached its peak over Israel in April 26, when it produced a heavy flash flood that took the lives of ten people. The rainfall observed in the southern Negev was comparable to the long-term annual rainfall there. The timing of the storm is also unique, at the end of the rainy season when rain is relatively rare and spotty. The study analyzes the dynamic and thermodynamic conditions that made this rainstorm one of the latest spring severe events in the region during the last 3 decades.</p> <p>The synoptic background was an upper-level cut-off low that entered the region from west, along 30° N latitude, which is rather exceptional for such systems in the late spring. While approaching the Levant, it slowed its movement from ~ 10 to < 5 ms<sup>−1</sup>. On the day of maximum intensity, the radius of the cyclone shrank to 275 km. The effect of the small radius was estimated by the measure of its curvature vorticity (MCV), which was the largest among the spring rainstorms during the latest 33 years.</p> <p>The lower-levels were dominated by a northwesterly wind that advected moist air from the Mediterranean inland. During the approach of the storm, the atmosphere over Israel became unstable, with instability indices reaching values favorable for thunderstorms (CAPE = 909 J Kg<sup>−1</sup>, LI = −4.9 K, SI = −2.7 K and MKI = 30 K), and the precipitable water increased from 17 to 30 mm. The latter is explained here by a combined effect of the lower-level moisture advection and a mid-level band of tropical moisture that entered Israel above it.</p> <p>Three major rain centers were active during April 26, two of them were non-orographic, which is unusual for this type of system. This is explained by the dominance of sub-synoptic features, found in the 0.25 resolution data of ERA5 that were used to derive Omega and MKI maps. The buildup of static instability is explained by a −5 K temperature anomaly over the region, caused by a northerly flow east of the blocking high over North Europe that transported cold air over the Mediterranean water.</p> <p>The unique intensity of this storm is attributed to an amplification of a mid-latitude disturbance, which produced a cut-off low, with its implied high relative vorticity, low upper-level temperatures, and slow progression. All these, combined with the contribution of several moisture sources, led to extreme dynamic and thermodynamic conditions favorable for this exceptionally severe rain-storm.</p>

This study conducts a 30-year climatological analysis of Tropical Plumes (TPs) observed over the Middle East (ME). These moisture bursts, conveying water vapour from tropical Africa to the arid ME at mid to upper tropospheric levels, were identified and analysed using multiple data sources and empirical tools, including satellite images, reanalysis data, backward trajectories, and calculation of moisture profiles, water vapour transport and moisture flux convergence. The analysis of the 140 days in which TPs were identified focused on three main elements: (i) TPs seasonal distribution and contribution to rainfall regime, (ii) TPs moisture pathways, and (iii) the mechanisms leading to TP-induced precipitation. TPs over the ME are found to be most frequent in the winter season, with the second highest frequency observed during the spring. The estimation of TPs contribution to the rainfall regime over the ME, the first of its kind, shows that such contribution is limited. However, extreme events may have a significant effect on the overall annual precipitation amount. Two moisture pathways are identified, exhibiting very limited mixing, if any, with the dry air mass at shallow tropospheric levels. The first, originating in tropical West Africa, is associated with the penetration of an intensified subtropical jet stream towards lower latitudes. The second, emanating from East to Central African sources, is closely associated with an anomalous anticyclonic flow over Southern Arabia. Moisture conveyed by the latter pathway is supplied from sources that are closer to the target area, transported at lower atmospheric levels, and exhibits more pronounced vertical dispersion. Compared to TPs that did not lead to precipitation, precipitative TPs feature enhanced moisture transport and stronger convergence of moisture flux. Similarly, the belt of moisture flux convergence stretching from the tropics to the ME is interrupted when non-precipitative TPs occur but uninterrupted during precipitative events.