Quality-Based Combination of Multi-Source Precipitation Data
"> Figure 1
<p>Location of telemetric rain gauges in Poland.</p> "> Figure 2
<p>Coverage by weather radar network POLRAD with 100-, 200-, and 250-km range.</p> "> Figure 3
<p>Example of precipitation accumulation estimated from Convective Rainfall Rate from Cloud Physical Properties (CRR-PH) and Precipitating Clouds from Cloud Physical Properties (PC-PH) products employing the daytime algorithm (17 May 2014, 08:00 UTC): (<b>a</b>) CRR-PH transformed into 10-min accumulation (in mm), (<b>b</b>) PC-PH (precipitation probability in %), and (<b>c</b>) 10-min precipitation accumulation <span class="html-italic">S<sub>day</sub></span> (in mm).</p> "> Figure 4
<p>Example of precipitation accumulation estimated from Convective Rainfall Rate (CRR) and Precipitating Clouds (PC) products employing the nighttime algorithm (16 May 2014, 00:00 UTC): (<b>a</b>) CRR transformed into 10-min accumulation (in mm), (<b>b</b>) PC (precipitation probability in %), and (<b>c</b>) 10-min precipitation accumulation <span class="html-italic">S<sub>night</sub></span> (in mm).</p> "> Figure 5
<p>Precipitation fields (as 10-min accumulations) for the area of Poland, 22 May 2019, 14:20 UTC: (<b>a</b>) precipitation fields: rain gauge (<span class="html-italic">G<sub>int</sub></span>), (<b>b</b>) weather radar (<span class="html-italic">R</span>), and (<b>c</b>) Meteosat satellite (<span class="html-italic">S</span>).</p> "> Figure 6
<p>Quality fields assigned to the precipitation data from <a href="#remotesensing-12-01709-f005" class="html-fig">Figure 5</a>, 22 May 2019, 14:20 UTC: (<b>a</b>) <span class="html-italic">QI</span> fields for precipitation data: rain gauge (<span class="html-italic">QI<sub>G</sub></span>), (<b>b</b>) weather radar (<span class="html-italic">QI<sub>R</sub></span>), and (<b>c</b>) Meteosat satellite (<span class="html-italic">QI<sub>S</sub></span>).</p> "> Figure 7
<p>Result of RainGRS combination for the area of Poland, 22 May 2019, 14:20 UTC: (<b>a</b>) combined precipitation field <span class="html-italic">GRS</span>, (<b>b</b>) relevant quality field <span class="html-italic">QI<sub>GRS</sub></span>.</p> "> Figure 8
<p>Networks of climatological and telemetric rain gauges in Poland.</p> "> Figure 9
<p>Monthly values of quality metrics: (<b>a</b>) correlation coefficient (<span class="html-italic">CC</span>), (<b>b</b>) root relative square error (<span class="html-italic">RRSE</span>), for particular precipitation fields: interpolated rain gauges (<span class="html-italic">G<sub>int</sub></span>), raw radar (<span class="html-italic">R<sub>raw</sub></span>), unbiased radar (<span class="html-italic">R</span>), satellite (<span class="html-italic">S</span>), and merged (<span class="html-italic">GRS</span>). The values are obtained for three months from different seasons: winter (December 2018), mixed (April 2019), and summer (July 2019).</p> "> Figure 10
<p>Impact of rain gauge and radar data qualities on reliability of merged <span class="html-italic">GRS</span> precipitation field: (<b>a</b>) correlation coefficient (<span class="html-italic">CC</span>), (<b>b</b>) root relative square error (<span class="html-italic">RRSE</span>). Data from three months: December 2018, April and July 2019.</p> "> Figure 11
<p>Diagrams of monthly values of correlation coefficient (<span class="html-italic">CC</span>) for particular precipitation fields: interpolated rain gauges (<span class="html-italic">G<sub>int</sub></span>), raw radar (<span class="html-italic">R<sub>raw</sub></span>), unbiased radar (<span class="html-italic">R</span>), and merged (<span class="html-italic">GRS</span>) depending on gauge data quality (<span class="html-italic">QI<sub>G</sub></span>) at the top and radar data quality (<span class="html-italic">QI<sub>R</sub></span>) at the bottom. The diagrams are obtained for winter (December 2018), mixed (April 2019), and summer (July 2019).</p> "> Figure 12
<p>Diagrams of monthly values of root relative square error (<span class="html-italic">RRSE</span>) for particular precipitation fields: interpolated rain gauges (<span class="html-italic">G<sub>int</sub></span>), raw radar (<span class="html-italic">R<sub>raw</sub></span>), unbiased radar (<span class="html-italic">R</span>), and merged (<span class="html-italic">GRS</span>), depending on gauge data quality (<span class="html-italic">QI<sub>G</sub></span>) at the top and radar data quality (<span class="html-italic">QI<sub>R</sub></span>) at the bottom. The diagrams are obtained for winter (December 2018), mixed (April 2019), and summer (July 2019).</p> ">
Abstract
:1. Introduction
2. Precipitation Data Measurement and Processing
2.1. Rain Gauge Network
2.1.1. Measurements
2.1.2. Quality Control
2.1.3. Spatial Interpolation
2.1.4. Quality Field
2.2. Weather Radar Network
2.2.1. Measurements (POLRAD Radar Network)
2.2.2. Quality Control
2.2.3. Precipitation Estimation
2.3. Meteorological Satellite
2.3.1. Measurements (Meteosat) and NWC SAF Processing
2.3.2. Precipitation Estimation
2.3.3. Quality Field
3. Conditional Combination Technique Based on Quality Information
3.1. Algorithm Description
3.2. Example
4. Results
4.1. Validation Criteria
4.2. Dependence on Season
4.3. Dependence on Input Data Qualities
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Measurement Technique | Spatial Resolution | Temporal Resolution | The Most Important Properties for Combination |
---|---|---|---|
Telemetric rain gauge network (G) | Point measurements (spatial interpolation is required) | 10 min | Measurement considered relatively high-quality at gauge locations |
Weather radar network (R) | 1 km × 1 km | 10 min | Numerous measurement errors. Good high-resolution reproduction of spatial distribution of precipitation field. |
Meteorological satellite Meteosat (VIS and IR channels) (S) | About 5–6 km for Poland | 5 min in rapid scan mode; 15 min in standard mode | Low spatial resolution and approximate measurements. Good reproduction of location of clouds and convective phenomena. High data availability. |
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Jurczyk, A.; Szturc, J.; Otop, I.; Ośródka, K.; Struzik, P. Quality-Based Combination of Multi-Source Precipitation Data. Remote Sens. 2020, 12, 1709. https://doi.org/10.3390/rs12111709
Jurczyk A, Szturc J, Otop I, Ośródka K, Struzik P. Quality-Based Combination of Multi-Source Precipitation Data. Remote Sensing. 2020; 12(11):1709. https://doi.org/10.3390/rs12111709
Chicago/Turabian StyleJurczyk, Anna, Jan Szturc, Irena Otop, Katarzyna Ośródka, and Piotr Struzik. 2020. "Quality-Based Combination of Multi-Source Precipitation Data" Remote Sensing 12, no. 11: 1709. https://doi.org/10.3390/rs12111709