Judit Kerényi. OMSZ-Hungarian Meteorological Service P.O.Box 38, H-1525, Budapest Hungary Abstract

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1 Comparison of the precipitation products of Hydrology SAF with the Convective Rainfall Rate of Nowcasting-SAF and the Multisensor Precipitation Estimate of EUMETSAT Judit Kerényi OMSZ-Hungarian Meteorological Service P.O.Box 38, H-525, Budapest Hungary Abstract In the framework of the Hydrology SAF different precipitation products are derived operationally. The Hungarian Meteorological Service is consortium member of the Hydrology SAF. We participate in the validation of the precipitation products. The precipitation values are derived from meteorological satellite radiances sensed by microwave (on-board low-orbiting satellites) and infrared (on-board geostationary satellites) sensors. At the validation the precipitation products are compared to radar observation. In this study our aim was to compare and investigate these products not only with radar data but with other precipitation products. The Multi-sensor Precipitation Estimate (MPE) was developed by EUMETSAT. At the calculation of it the rain rates are derived from microwave and infrared measurement. The Nowcasting SAF derives operationally Convective Rainfall Rate (CRR) from infrared, visible and water-vapour channels. In some case studies, at intensive convective clouds they are compared to the Hydrology SAF products. INTRODUCTION The estimation of precipitation parameters from satellite data has quite a long history. The major problem of all methods is the very in-direct relation between the precipitation on the ground and the measured satellite signal. The EUMETSAT established several Satellite Application Facilities (SAF), such as the Nowcasting SAF and Hydrology SAF to develop products or softwares to derive meteorological products. Six precipitation products (H, H2, H3, H4, H5, H6) have been developed until now by the H-SAF developers. All products are derived operationally and they can be downloaded from the webpage ( or from the H-SAF s server.the EUMETSAT derives also meteorological products independently these SAFs. Our aim was to compare the different precipitation products derived by EUMETSAT and the two SAFs. These products based on two different methods: - using infared (IR) or IR and visible (VIS) channels - using infrared and microwave measurement. Measurements in the microwave spectral region use the absorption of microwave radiation by liquid water or on the scattering by ice particles. The use of infrared satellite data is an even more in-direct approach. The cloud top temperature is only for convective systems directly related to the surface rainfall and even in these cases the amount of precipitation depends significantly on the stage in the life cycle of the convective system. The combination of both kinds of data, microwave (MW) data from polar orbiting satellites and IR data from geostationary systems is an obvious approach to overcome some of the shortcomings in the estimation of precipitation. The Convective Rainfall Rate (CRR) of the Nowcasting SAF is based on the method using IR channel, while the Multi-sensor Precipitation Estimate (MPE) derived by EUMETSAT and H3 product of Hydrology SAF are derived using IR and MW measurements.

2 In this study we investigated these products in different weather situation (convective, frontal cases). The investigation has two parts: visual interpretation, statistical comparison Case studies for the different H-SAF products (H-H5) were presented earlier by Kerényi (2), when we compared the different products with radar data at different weather situations. PRECIPITATION PRODUCTS MPE The Multi-sensor Precipitation Estimate (MPE) is based on monotonic functions relating the measured IR brightness temperature to the rain rate. The coldest temperatures are associated to the highest rain rates. For temperatures above a certain threshold no precipitation is estimated. The form of this function depends on the current weather situation. Therefore the MPE algorithm adjusts it geographically and temporarily using derived rain-rates from the passive MW measurements as calibration values. Due to the poor spatial coverage of MW measurements the adjustment cannot take place for each individual image but must be based on accumulated data over a certain time period. In practice we co-locate IR images and MW data over up to 2 hours in geographical 5 x5 boxes and derive a rain rate function for each of the geographical boxes. The matching between MW rain rates and IR brightness temperatures is done with a direct histogram matching technique starting from the coldest (rainiest) data points. The derived functions are stored in the form of lookup tables (LUT) and can be applied to IR images taken in similar weather situations as the images which were used to derive the LUTs. Usually images taken within or shortly after the accumulation period are used. CRR Convective Rainfall Rate (CRR) product is a Nowcasting tool that provides information on convective, and stratiform associated to convection, instantaneous rain rates and hourly accumulations. In the processing of the product, CRR uses some calibration matrices that have been calibrated taking as truth the radar data. There are two types of calibration matrices: Two dimensional matrices that have as axis.8ir and (.8IR - 6.2WV) SEVIRI channels Three dimensional matrices that have as axis.8ir, (.8IR - 6.2WV) and.6vis SEVIRI channels. For each type of calibration there are two regional matrices. The basic CRR mm/h value for each pixel is obtained from the calibration matrices. Then a filtering process is performed in order to eliminate stratiform rain data which are not associated to convective clouds. H3 Product The relationship linking IR brightness temperature and precipitation is very much indirect, since IR is only sensitive to the cloud top structure. Measurements are qualitative and mostly applicable to convective precipitation. After an initial start-up phase of NHOURS (NHOURS is a tunable parameter set to 24 h), needed to build meaningful statistical relationships over the entire study area, every time that a MW overpass is available, the corresponding IR image is "calibrated" against the precipitation measurement from SSM/I. The "calibration" is thereafter propagated to follow-on IR images, till the next MW image is available. The "Rapid-update" method is being used operationally in NOAA and experimentally in Europe. More details about the H-SAF precipitation products can be found in the H- SAF report (H-SAF Project Team 22). REFERENCE DATA In the validation activity the aim is to compare the precipitation products with ground measurement. In Hungary we use radar data for the comparison. The Hungarian radar network consists of 3 Doppler radars. Every 5 minutes a composite image is derived from the 3 radar measurements. The resolution of the image is 2 km x 2 km. The radar intensity is calculated from the reflectivity (log Z) using the Marshall- Palmer formula. Since January 22 the data are available in every 5 minutes, so the comparison will be much more exact.

3 VALIDATION Figure. The different precipitation products at 5. April 22 between 5: - 23:45 UTC. The upper left panel: H3, upper right panel: radar, botton left panel: CRR, bottom right panel: MPE

4 The software written for the Hungarian Advanced Weather workstation (HAWK) is used by forecasters to visualize and handle all available meteorological information together with the satellite images and products (Rajnai et al., 25). This was one of the tools we used for the qualitative comparison. Using this tool we can easily investigate the different precipitation products together the radar images. We calculate several statistical values (standard deviation, root mean square error etc) and also multicategorical scores such as Probability of Detection (), False Alarm Rate (), Critical Success Index (SCI) and Symmetric Extremal Dependency Index () also. At the comparison of the different (H3, MPE, CRR) precipitation products the first step was to adjust the different precipitation products for the radar projection. Figure shows the 3 precipitation products with the radar data at 5 th April 22 between 5-23:45 UTC. During this day several thunderstorms developed over Hungary. We can see a good correlation, both in the precipitation values and the form and geographical position of precipitation patches. Following the changes of the convective systems we can observe the H3 product can detect best the convective precipitation considering the position of the rain, only it overestimated the size of the precipitation areas. Investigating the other products we can see, for example the MPE product did not give precipitation at the north-east part of Hungary. If we look at the intensity the MPE we can observe it was little bit overestimated. It should be mentioned, CRR product can detect best the extension of the convective precipitation. H :3 UTC MPE :3 UTC CRR :3 UTC Figure 2. The distribution of the pixels at 9:3 UTC for the three precipitation products. The x axis is the radar precipitation intensity, the y axis is the calculated precipitation values. The figure 2 shows the distribution of the pixels at the 3 precipitation products at 9:3 UTC (6 th image at figure ). The x axis is the radar precipitation intensity, the y axis is the calculated precipitation values. At the CRR because the precipitation value is determined into intervals we get this kind of distribution. At H3 and MPE the distribution of the pixels is very similar, except that part when the MPE is higher than 4 mm while radar values are lower than 5 mm. At the multi-categorical scores the Symmetric Extremal Dependence Index () is also calculated. (Ferro & Stephenson 2). This measure was chosen as it has many desirable properties, such as asymptotically equitable, not trivial to hedge. Many verification measures tend to meaningless values for rare events but is independent of the frequency of occurrence of an event and therefore can be used for both rare and common events. The value could be between - and. Value of indicates perfect forecast skill. is given by ln F lnh + ln ( H) ln ( F) = ln F + lnh + ln ( H) + ln ( F) where H is the hit rate (H = a/(a+ c)) and F is the false alarm rate (F = b/(b + d)). Multi-categorical scores show better correlation between the H3 and radar, than MPE. The of H3 is higher than MPE s during the whole period. If we look at the trend of and we can see that they change together at MPE, while at H3 after a short agreement the became lower than, the increasing trend cannot be seen in it as in. Comparing the values we can see that it is lower during the whole period at MPE, it means the false alarm was less at MPE than at H3. The reason is H3 overestimated the precipitation clouds as we have seen at the visual interpretation.

5 ,8,6,4,2 H3,8,6,4,2 MPE Figure 3 Multi-categorical scores, such as Probability of Detection (), False Alarm Rate (), and Critical Success Index (SCI) and Symmetric Extremal Dependence Index () for H3 and MPE product between 5:-23:45 UTC at 5 April 22. Figure 4. shows the different precipitation products at 25 th June 22 between 8:25-2: UTC. Figure 4. The different precipitation products at 25. June 22 between 8:25 2: UTC. The upper left panel: H3, upper right panel: radar, botton left panel: CRR, bottom right panel: MPE

6 8,25 9,5 3,25 4,5 5,75 8,25 9,5 2,75 8,25 9,5 3,25 4,5 5,75 8,25 9,5 2,75 A cold front brought precipitation to Hungary. At the beginning of the period we can see good agreement between the different products and the radar data, but later only H3 derived well the position of the rain, while MPE and CRR did not detect lot of rainy area. As a short summary of this case we can say that for the retrieval of frontal precipitation H3 is better than MPE or CRR. If we look at the multi-categorical scores of H3 and MPE (fig. 5) we can see the H3 much better than MPE. The is higher than.6 during the whole period, while at MPE is less than.4, at the end of the period it is almost. The and decreases during the period in the same trend.,8,6,4,2,8,6,4,2 -,2 Figure 5. Multi-categorical scores, such as Probability of Detection (), False Alarm Rate (), and Critical Success Index (SCI) and Symmetric Extremal Dependence Index () for H3 and MPE product between8:25-2: UTC at 25 June 22. CONCLUSION In this study we compared the different precipitation products at convective and frontal cases. Based on these validations it became obvious, that during convective weather situation the H3 product can reproduce mostly well the precipitation. MPE and CRR do not detect the convective precipitations in some cases. At H3 product the biggest problem is the determination of the precipitation area extension, in most cases the H3 overestimates it. At frontal cases the correlation is not so obvious between the radar and the different products. As we have shown in the case study we got good agreement between the different products and the radar data, but at the end of the investigated period only H3 derived well the position of the precipitation, while MPE and CRR did not detect lot of precipitation patterns. As a short summary of these cases we can say that for the retrieval of precipitation H3 is better than MPE or CRR. At the statistical investigation we have got the same results. At frontal cases the was large than.6 at H3, but it was less than.4 at MPE. We investigated the multi-categorical score in these case studies. In the H-SAF project these case study validations continuously give information/help for the developers to improve the precipitation products and the planned precipitation products in the future. KNOWLEDGEMENTS The author would like to thank EUMETSAT for funding this project. Also we would like to say thanks for the cooperation of the Precipitation Product s validation Working Group. REFERENCES Ferro, C. A. T., Stephenson, D. B. 2: Extremal Dependence Indices: Improved Verification Measures for Deterministic Forecasts of Rare Binary Events. Weather Forecast. 26, H-SAF Project Team, 22: EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF), Product requirement Document, Doc No: SAF/HSAF/PRD/.2 Issue version.2 date: //22 Kerényi, J. 2: Satellite derived precipitation estimations developed by the Hydrology SAF project Case studies for the investigation of their accuracy and features in Hungary, EUMETSAT Meteorological satellite conference, 2-7 september 22, Sopot, Poland, EUMETSAT P.6 Rajnai, M., Kertész, S., Szabó, L., Vörös, M. 25: Recent developments at HMS,