Filling Gaps in Hourly Air Temperature Data Using Debiased ERA5 Data
<p>Altitude (m a.s.l.) and position of the Kikinda (Serbia) automated weather station (AWS).</p> "> Figure 2
<p>Altitude (m a.s.l.) and position of the Gumpenstein (Austria) automated weather station (AWS).</p> "> Figure 3
<p>Altitude (m a.s.l.) and position of the el-Bahariya oasis (Egypt) automated weather station (AWS).</p> "> Figure 4
<p>Altitude (m a.s.l.) and position of the Montecristo Island (Italy) automated weather station (AWS).</p> "> Figure 5
<p>Altitude (m a.s.l.) and position of the Pianosa Island (Italy) automated weather station (AWS).</p> "> Figure 6
<p>(<b>a</b>) Time series with a gap in temperature observations. The blue line represents observations, the dashed blue line represents hidden observations, the red line represents ERA5 values for the nearest grid point, and the green line represents the result of the debiasing process; (<b>b</b>) Linear regression of learning data for one-time step in the gap following the standard equation: <math display="inline"><semantics> <mrow> <mi>O</mi> <mi>B</mi> <mi>S</mi> <mo>=</mo> <mi>k</mi> <mo> </mo> <mo>×</mo> <mi>E</mi> <mi>R</mi> <msub> <mi>A</mi> <mn>5</mn> </msub> <mo>+</mo> <mi>n</mi> </mrow> </semantics></math>.</p> "> Figure 7
<p>RMSE<sub>DEB</sub> (<b>left panel</b>) and (RMSE<sub>DEB</sub>-RMSE<sub>ERA5</sub>) difference (<b>right panel</b>) for DOY 60–273 in 2014 in Kikinda (Serbia).</p> "> Figure 8
<p>Standard deviation of the observed data which are used for bias correction for DOY 60–273 in 2014 in Kikinda (Serbia).</p> "> Figure 9
<p>RMSE<sub>DEB</sub> (<b>left panel</b>) and (RMSE<sub>DEB</sub>-RMSE<sub>ERA5</sub>) difference (<b>right panel</b>) for DOY 121–334 in 2017 in Gumpenstein (Austria).</p> "> Figure 10
<p>Standard deviation of observed data which were used for bias correction for DOY 121–334 in 2017 in Gumpenstein (Austria).</p> "> Figure 11
<p>RMSE<sub>DEB</sub> (<b>left panel</b>) and (RMSE<sub>DEB</sub>-RMSE<sub>ERA5</sub>) difference (<b>right panel</b>) for DOY 121–334 in 2017 in Bahariya (Egypt).</p> "> Figure 12
<p>Standard deviation of observed data which are used for bias correction for DOY 121–334 in 2017 in Bahariya (Egypt).</p> "> Figure 13
<p>Daily variation in air temperature for DOY 215–226 inBahariya (Egypt).</p> "> Figure 14
<p>Standard deviation of the observed data which were used for bias correction for the islands of Montecristo (<b>left panel</b>) and Pianosa (<b>right panel</b>) for the DOY 122–320 in 2016.</p> "> Figure 15
<p>RMSE<sub>DEB</sub> (<b>left panel</b>) and (RMSE<sub>DEB</sub>-RMSE<sub>ERA5</sub>) difference (<b>right panel</b>) for DOY 122–320in 2016 in Montecristo Island (Italy).</p> "> Figure 16
<p>RMSE<sub>DEB</sub> (<b>left panel</b>) and (RMSE<sub>DEB</sub>-RMSE<sub>ERA5</sub>) difference (<b>right panel</b>) for DOY 122–320 in 2016 in Pianosa Island (Italy).</p> "> Figure 17
<p>RMSE<sub>DEB</sub> for gap width = 1 and the correlation coefficient between the standard deviation of the observed data used for linear regression and RMSE<sub>DEB</sub> for Kikinda (Serbia; <b>top left</b>), Bahariya (Egypt; <b>top right</b>), Gumpenstein (Austria; <b>middle</b>), Montecristo (Italy; <b>bottom left</b>) and Pianosa (Italy; <b>bottom right</b>).</p> "> Figure 17 Cont.
<p>RMSE<sub>DEB</sub> for gap width = 1 and the correlation coefficient between the standard deviation of the observed data used for linear regression and RMSE<sub>DEB</sub> for Kikinda (Serbia; <b>top left</b>), Bahariya (Egypt; <b>top right</b>), Gumpenstein (Austria; <b>middle</b>), Montecristo (Italy; <b>bottom left</b>) and Pianosa (Italy; <b>bottom right</b>).</p> "> Figure A1
<p>B<sub>DEB</sub> (<b>top</b>, <b>left panel</b>), B<sub>ERA5</sub> (<b>top</b>, <b>right panel</b>), U(B)<sub>DEB</sub> (<b>bottom</b>, <b>left panel</b>), and U(B)<sub>ERA5</sub>(<b>bottom</b>, <b>right panel</b>) calculated using data for DOY 60-273 2014 in Kikinda.</p> "> Figure A2
<p>B<sub>DEB</sub> (<b>top</b>, <b>left panel</b>), B<sub>ERA5</sub> (<b>top</b>, <b>right panel</b>), U(B)<sub>DEB</sub> (<b>bottom</b>, <b>left panel</b>), and U(B)<sub>ERA5</sub> (<b>bottom</b>, <b>right panel</b>) calculated using data for DOY 121–334 2017 in Gumpenstein.</p> "> Figure A3
<p>B<sub>DEB</sub> (<b>top</b>, <b>left panel</b>), B<sub>ERA5</sub> (<b>top</b>, <b>right panel</b>), U(B)<sub>DEB</sub> (<b>bottom</b>, <b>left panel</b>), and U(B)<sub>ERA5</sub> (<b>bottom</b>, <b>right panel</b>) calculated using data for DOY 121–334 2017 in Bahariya.</p> "> Figure A4
<p>B<sub>DEB</sub> (<b>top</b>, <b>left panel</b>), B<sub>ERA5</sub> (<b>top</b>, <b>right panel</b>), U(B)<sub>DEB</sub> (<b>bottom</b>, <b>left panel</b>), and U(B)<sub>ERA5</sub> (<b>bottom</b>, <b>right panel</b>) calculated using data for DOY 122–320 2016 in Montecristo.</p> "> Figure A4 Cont.
<p>B<sub>DEB</sub> (<b>top</b>, <b>left panel</b>), B<sub>ERA5</sub> (<b>top</b>, <b>right panel</b>), U(B)<sub>DEB</sub> (<b>bottom</b>, <b>left panel</b>), and U(B)<sub>ERA5</sub> (<b>bottom</b>, <b>right panel</b>) calculated using data for DOY 122–320 2016 in Montecristo.</p> "> Figure A5
<p>B<sub>DEB</sub> (<b>top</b>, <b>left panel</b>), B<sub>ERA5</sub> (<b>top</b>, <b>right panel</b>), U(B)<sub>DEB</sub> (<b>bottom</b>, <b>left panel</b>), and U(B)<sub>ERA5</sub> (<b>bottom</b>, <b>right panel</b>) calculated using data for DOY 122–320 2016 in Pianosa.</p> "> Figure A6
<p>RMSE<sub>DEB</sub> <b>(left panel</b>) and (RMSE<sub>DEB</sub>-RMSE<sub>ERA5</sub>) difference (<b>right panel</b>) calculated using daily data for DOY 60-273 2014 in Kikinda.</p> "> Figure A7
<p>RMSE<sub>DEB</sub> <b>(left panel</b>) and (RMSE<sub>DEB</sub>-RMSE<sub>ERA5</sub>) difference (<b>right panel</b>) calculated using daily data for DOY 121–334 2017 in Gumpenstein.</p> "> Figure A8
<p>RMSE<sub>DEB</sub> (<b>left panel</b>) and (RMSE<sub>DEB</sub>-RMSE<sub>ERA5</sub>) difference (<b>right panel</b>) calculated using daily data calculated using daily data for DOY 121–334 2017 in Bahariya.</p> "> Figure A9
<p>RMSE<sub>DEB</sub> (<b>left panel</b>) and (RMSE<sub>DEB</sub>-RMSE<sub>ERA5</sub>) difference (<b>right panel</b>) calculated using daily data for DOY 122-320 2016 in Montecristo.</p> "> Figure A10
<p>RMSE<sub>DEB</sub> (<b>left panel</b>) and (RMSE<sub>DEB</sub>-RMSE<sub>ERA5</sub>) difference (<b>right panel</b>) calculated using daily data for DOY 122-320 2016 in Pianosa.</p> ">
Abstract
:1. Introduction
2. Experiments
2.1. Data Set Description
2.1.1. ERA5 Reanalysis Data
2.1.2. Measured Weather Data
- Canopy measurements due to the impact of plants on micrometeorological conditions that cannot be identified by ERA5 due to the resolution of the land use map and the model itself;
- Mountain regions due to difficulties resulting from the distribution of orography over grid elements and the representation of mountains in the model used to produce ERA5 data;
- Islands due to the coupling of sea surface processes and atmospheric processes and, in the case of small islands, their “visibility” on a 30 km resolution grid, which is highly questionable; and
- Desert oases, due to the position of these oases in tropical and subtropical areas where the meteorological measurement network is sparse.
2.2. Gap Filling Method Description
- In the case of temperature data, due to the high autocorrelation of the time series, important information is contained within the time series before and after the gap [27]. Therefore, a portion of the time series that is not missing is used for the bias correction of ERA5 data;
- It is important to limit the amount of data used for the gap filling method (learning period) to avoid seasonal changes in temperature data; however, it is also important to use enough data to be able to exploit the high autocorrelation in the time series. The determination coefficient R2 was used to validate that enough data was present in the fitting process;
- Diurnal temperature biases [23] are recognized as a possible problem and new technique for filtering the learning data was applied to improve this methodology.
- To efficiently fill gaps in large datasets, a simple, fast, and reliable approach of linear regression was selected.
3. Results
3.1. Lowland Data Sets
3.2. Mountain Data Sets
3.3. Desert Data Sets
3.4. Island Data Sets
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Pearson r | p Value | |||||||
---|---|---|---|---|---|---|---|---|
Min | Mean | Median | Max | Min | Mean | Median | Max | |
Kikinda | 0.896 | 0.950 | 0.955 | 0.979 | 9.18 × 10−77 | 2.24 × 10−40 | 1.31 × 10−55 | 2.85 × 10−38 |
Bahariya | 0.730 | 0.904 | 0.914 | 0.966 | 7.02 × 10−63 | 1.52 × 10−12 | 1.72 × 10−38 | 1.83 × 10−10 |
Gumpenstein | 0.762 | 0.889 | 0.894 | 0.957 | 4.01 × 10−36 | 7.66 × 10−10 | 8.25 × 10−25 | 1.07 × 10−7 |
Montecristo | 0.707 | 0.834 | 0.846 | 0.912 | 9.93 × 10−28 | 5.80 × 10−10 | 3.75 × 10−18 | 3.49 × 10−8 |
Pianosa | 0.545 | 0.760 | 0.747 | 0.939 | 5.69 × 10−36 | 0.002 | 1.83 × 10−9 | 0.029 |
Input Quantity | Description | Location | Input Estimate | Standard Uncertainty | Pearson r | Measurement Unit | Type of Evaluation |
---|---|---|---|---|---|---|---|
M | Measured two-meter temperature | Kikinda | 17.2 | 5.2 | °C | A | |
Gumpenstein | 12.8 | 4.4 | °C | A | |||
Bahariya | 27.2 | 5.5 | °C | A | |||
Montecristo | 21.3 | 1.9 | °C | A | |||
Pianosa | 22.0 | 2.4 | °C | A | |||
Predicted two-meter temperature (ERA5 reanalysis) | Kikinda | 17.6 | 4.4 | 0.966 | °C | A | |
Gumpenstein | 8.4 | 4.3 | 0.849 | °C | A | ||
Bahariya | 26.4 | 5.1 | 0.940 | °C | A | ||
Montecristo | 21.3 | 1.1 | 0.753 | °C | A | ||
Pianosa | 21.3 | 1.1 | 0.562 | °C | A | ||
Predicted two-meter temperature (DEBIAS) | Kikinda | 17.2 | 5.1 | 0.969 | °C | A | |
Gumpenstein | 13.3 | 4.2 | 0.905 | °C | A | ||
Bahariya | 27.1 | 5.4 | 0.961 | °C | A | ||
Montecristo | 21.6 | 1.6 | 0.854 | °C | A | ||
Pianosa | 22.3 | 2.1 | 0.862 | °C | A |
Appendix B
Appendix C
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Location | Time Series | Landscape | Position | ERA5 | ||||
---|---|---|---|---|---|---|---|---|
Latitude (°) | Longitude (°) | Altitude (m) | Latitude (°) | Longitude (°) | Altitude (m) | |||
Kikinda | 2014–2017 | Lowland | 45.87 | 20.46 | 82 | 45.9 | 20.4 | 75 |
Gumpenstein | 2014–2017 | Mountains | 47.49 | 14.09 | 700 | 47.4 | 14.1 | 1080 |
Bahariya | 2017 | Desert | 28.41 | 28.93 | 99 | 28.4 | 28.9 | 97 |
Montecristo | 2016 | Island | 42.34 | 10.31 | 645 | 42.3 | 10.2 | * |
Pianosa | 2016 | Island | 42.58 | 10.08 | 29 | 42.6 | 10.2 | * |
Mean B | Mean U(B) | Mean RMSE | ||||
---|---|---|---|---|---|---|
ERA5 | DEB | ERA5 | DEB | ERA5 | DEB | |
Kikinda | 0.387 | −0.027 | 6.56 | 5.61 | 1.563 | 1.321 |
Bahariya | −0.723 | −0.033 | 8.39 | 6.67 | 2.046 | 1.536 |
Gumpenstein | −4.385 | 0.556 | 10.18 | 7.37 | 4.969 | 1.877 |
Montecristo | −0.009 | 0.284 | 5.58 | 4.27 | 1.367 | 1.079 |
Pianosa | −0.769 | 0.222 | 8.69 | 5.32 | 2.158 | 1.260 |
Location | Bahariya (°C) | Gumpenstein (°C) | Kikinda (°C) | Montecristo (°C) | Pianosa (°C) |
---|---|---|---|---|---|
max(|RMSEDEB-RMSEERA5|) for hourly data | 1.81 | 6.34 | 0.87 | 0.95 | 2.12 |
max(|RMSEDEB-RMSEERA5|) for daily average | 1.31 | 7.32 | 1.03 | 1.08 | 1.63 |
max(|RMSEDEB|) for hourly data | 2.54 | 4.19 | 2.21 | 2.18 | 2.31 |
max(|RMSEDEB|) for daily average | 1.82 | 4.02 | 1.69 | 2.06 | 1.98 |
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Lompar, M.; Lalić, B.; Dekić, L.; Petrić, M. Filling Gaps in Hourly Air Temperature Data Using Debiased ERA5 Data. Atmosphere 2019, 10, 13. https://doi.org/10.3390/atmos10010013
Lompar M, Lalić B, Dekić L, Petrić M. Filling Gaps in Hourly Air Temperature Data Using Debiased ERA5 Data. Atmosphere. 2019; 10(1):13. https://doi.org/10.3390/atmos10010013
Chicago/Turabian StyleLompar, Miloš, Branislava Lalić, Ljiljana Dekić, and Mina Petrić. 2019. "Filling Gaps in Hourly Air Temperature Data Using Debiased ERA5 Data" Atmosphere 10, no. 1: 13. https://doi.org/10.3390/atmos10010013