Institute of Meteorology and Water Management, POLAND, Krakow EUMETSAT H-SAF – Satellite Application Facility in Support to Operational Hydrology and Water Management Validation of satellite precipitation products with use of hydrological models – EUMETSAT H-SAF activities Jerzy Niedbała Institute of Meteorology and Water Management Hydrological Forecasting Office, Kraków Bożena Łapeta, Piotr Struzik Satellite Remote Sensing Centre, Kraków Whole H-SAF Team contributed

Presentation outline: EUMETSAT H-SAF activities (very shortly). H-SAF validation programme IMWM Poland – H-SAF validation studies of precipitation products: - Conventional validation, - Hydrological impact studies. Conclusions

The main objectives of H-SAF : Institute of Meteorology and Water Management, Kraków, Poland Hydrological Forecasting Office Satellite Remote Sensing Centre EUMETSAT Satellite Application Facility in Support to Operational Hydrology and Water Management (H-SAF) H-SAF activities officially started (15 Sept.2005) – development phase 2005-2010, 12 European countries involved. Poland coordinates Hydrological Validation and implementation cluster. The main objectives of H-SAF : to provide new satellite-derived products from existing and future satellites with sufficient time and space resolution to satisfy the needs of operational hydrology; identified products: precipitation soil moisture snow to perform independent validation of the usefulness of the new products for fighting against floods, landslides, avalanches, and evaluating water resources.

EUMETSAT Satellite Application Facility in Support to Operational Hydrology and Water Management (H-SAF) H-SAF bottom-up aproach Requirements driven by operational hydrology needs. Creation of operational satellite products for: Better spatialisation of conventional measurements, To complement ground observations on the areas with sparse ground networks and/or not covered by radars, Merging satellite products with other data sources, Redundancy of information - useful in case of disaster situation (damage of measuring posts or data links) Final assessment of satellite products to be done by Hydrological Impact Studies. Demonstration and training on satellite products use, in real operational environment of State Hydrological Services

Hydrological cycle vs. satellite products Impact studies

Precipitation Products Products from H-SAF (quality figures refer to the Operational Phase (2010-2015) Product Resolution (Europe) Accuracy Cycle (Europe) Timeliness PR-OBS-1 SSMI/SSMIS Precipitation rate at ground by MW conical scanners 10 km (with CMIS) 15 km (with other GPM) 10-20 % (rate > 10 mm/h), 20-40 % (rate 1 to 10 mm/h), 40-80 % (rate < 1 mm/h) 6 h (with CMIS only) 3 h (with full GPM) 15 min PR-OBS-2 (AMSU Data NOAA) Precipitation rate at ground by MW cross-track scanners Two subproducts 2.1, 2.2 10 km Ranging from MW performance to degraded one to an amount to be assessed 6h 5 min PR-OBS-3 Precipitation rate at ground by GEO/IR supported by LEO/MW 8Km 40-80% (rate > 10 mm/h) 15min 5min PR-OBS-5 3, 6, 12 and 24 h cumulated rain (from merged MW + IR) Depending on integration interval. Tentative: 10 % over 24 h, 30 % over 3 h 3 h CMIS = Conical Scanner Microwave Imager - DMSP GPM = Global Precipitation Measurement mission

Soil Moisture Products Soil moisture in the surface layer 25 km (from ASCAT) 40 km (from CMIS) 0.05 m3 m-3 (depending On vegetation) 36 h (from ASCAT) 6 h (from CMIS) 2 h the roots region To be assessed (model-dependent). Tentative: 0.05 m3 m-3 From METOP

correct classification basin size and complexity) Snow Products Snow recognition 5 km (in MW) 2 km (in VIS/SWIR/TIR) 95 % probability of correct classification 6 h 2 h Snow effective coverage 10 km (in MW) 5 km (in VIS/SWIR/TIR) 15 % (depending on basin size and complexity) Snow status (wet or dry) 5 km 80 % probability of Snow waterEquivalent 10 km To be assessed. Tentative: 20 mm SWIR = Short Wave Infrared TIR = Thermal Infrared

Logic of the incremental development scheme End-user feedback Augmented databases Advanced algorithms New instruments Initial Baseline Current Cal/val programme Version-1 Version-2 Final Version Prototyping Operational End-users and Hydrological validation programme 2007 Inter Consortium beta product delivering

H-SAF activities on satellite products validation Cluster 1 Precipitation products Cluster 2 Soil Moisture products Cluster 3 Snow products Finland Germany Poland Turkey Romania Belgium Germany Hungary Italy Poland Slovakia Turkey Classical Validation. Comparison to ground measurements. Austria France ECMWF Cluster4 Hydrological Validation programme Belgium, France, Germany, Italy, Poland, Slovakia, Turkey At least 24 catchments, 14 operational models

Lightning detection SAFIR METEOSAT (7,8,9) NOAA (all) FengYun 1D Ready for Metop 60 Synop 152 Climate 978 Raingauges 196 Snow obs. posts NWP: LM-COSMO, ALADIN IMWM Poland Fig. 28 - Composite image from all Polish radars. 8 radars 989 telemetric posts

Variety of climatological conditions Variety of terrain conditions Variety of land cover Different hydrological regimes Catchment size: 242 – 102000 km2 902 raingauges, 21 radars

Classical validation Case studies Three days: 22 May, 04 June and 05 June 2007, during which convective precipitation occurred, very heavy, in places. 22 May 2007, Zielona Gora, South-Western Poland NOAA/AVHRR, RGB channels 1, 2 and 4

RG Data 10 minute precipitation sums form Polish automatic rain gauges network. RG data quality control – analysis of the measurements performed by each gauge (the posts are equipped with two gauges) in order to exclude the wrong data. The time slots closest to satellite overpass were taken into consideration.

Validation method For each satellite AMSU / NOAA pixels, the automatic rain gauges situated within that pixel were found. Different pixel’s size was taken into account depending both on the direction (along and across track) and the pixel position in scanning line. The pixel shape was assumed to be the rectangular. If more than one rain gauge were found within one satellite pixel, the ground rain rate value was calculated as a mean of all rain gauges measurements.

H01, 2007-05-22 15.20 UTC H02 v.1.1, 2007-06-04 14.37 UTC H02 v.2.1, 2007-06-04 14:37 UTC H03 v.1.0, 2007-06-04 14.27 UTC H05 v.1.0, 2007-06-04 15.00 UTC

Preliminary results Three days with typical convective precipitation 05.2007 i 04-05.06.2007. All available precipitation products were analysed:

Results of the categorical (dichotomous) statistics obtained for H-SAF precipitation products H01 v1.0 H02 v1.1 H02 v2.1 H03 v1.0 H05 v1.0 Hit rate * 0.28 0.26 0.96 0.33 False-alarm rate 0.69 0.62 0.92 0.89 0.68 Odds ratio 13.37 11.13 7.73 4.92 4.00 Accuracy 0.94 0.30 0.91 0.81 Frequency Bias 0.7 12.49 2.4 1.06 * Strongly dependant on the rain/no rain class numbering

Results of continuous statistics for H-SAF precipitation products obtained with the use of rain gauges data (in [mm/h] for H01-H03 and in [mm] for H05) H01 v1.0 [mm/h] H02 v1.1 H02 v2.1 H03 v1.0 H05 v1.0 [mm] Mean error -2.938 1.10 -0.82 -1.86 0.01 Mean absolute error 4.14 2.69 1.25 4.35 1.35 RMSE 7.12 4.12 2.93 11.69 9.08 Multiplicative Bias 0.4 1.43 0.48 0.57 1.00 Standard Deviation of diff. 6.62 4.00 2.82 11.55 9.09

August 2006 – August 2007

First conclusions: The obtained results showed that both, detection and estimation of intensity of convective rainfall is very difficult task. All rain rate products significantly underestimate high precipitation and overestimate low precipitation. For all products, except for H02 v21, the similar values of the hit rate, false alarm rate and accuracy were obtained. However, high value of odds ratio for H01 suggests that this product recognises the precipitation better than other. Both, dichotomous and continuous statistics, showed that rain rate products H02 v21 was too ‘rainy’, especially for the low precipitation categories (form 0 to 1 mm/h) The quality of H02 products depends on the pixel number: the closer to the ends of AMSU scan line, the lower quality. H03 products quality seems to be lower, with reasonably higher RMSE and lower correlation coefficient. This product has been preliminary validated with the use of hydrological model

General hydrological validation algorithm (1) Preparation of tool verification - the hydrological model simulated mode - calibration and the verification of hydrological model input data for hydrological models from manual and automatic ground stations and experimental resarch hydrological model output from hydrological model (simulated hydrograph) comparing simulated and observed hydrographs average square error average square relative error maximum relative error time relative error

General hydrological validation algorithm (2) input data for hydrological models from radar system (now-casting) and meteorological model (forecasting) General hydrological validation algorithm (2) Hydrological model in operating mode operating mode - starting hydrological model in operating mode input data for hydrological models from manul and automatic ground stations and experimental resarch hydrological model output from hydrological model (forecasted hydrograph) comparing forecasting and observed hydrographs in non-operating time average square error average square relative error maximum relative error time relative error

General hydrological validation algorithm (3) input data for hydrological models from radar system (now-casting) and meteorological model (forecasting) General hydrological validation algorithm (3) satellite data Hydrological model in operating mode using satellite data satellite data rainfall and snow operating mode - starting hydrological model in operating mode output from hydrological model (standard forecasted hydrograph and forecasted hydrograph computed using satellite data) comparing two forecasted hydrographs (computed on base standard or satellite data) with observed hydrograph in non-operating time input data for hydrological models from manul and automatic ground stations and experimental resarch hydrological model average square error average square relative error maximum relative error time relative error soil moisture temperature, rainfall and snow

average square relative error maximum relative error General hydrological validation algorithm (4) Hydrological validation plan criteria of choice use of the satellite data increases the quality of hydrological forecasting comparing two forecasting hydrographs (computed on base of standard or satellite data) with observed hydrograph in non-operating time statistical analyses NEITHER „YES” NOR „NO” YES NO average square error average square relative error maximum relative error time relative error we recommend satellite data as an input to hydrological forecasting model we recommend standard data as an input to hydrological forecasting model Further research must be done: when, where and why use of satellite data gives negative results. Satellite data could be used in case when other data are not available Feedback to Clusters 1,2,3

Test site Soła rainfall event: September 2007 calibration: based on manual ground stations based on telemetric stations simulation: imput data from telemetric stations were exchanged by satellite data during 5-6.09.2007

Modelling results input data from telemetric stations were exchanged by satellite data during 5-6.09.2007 manual ground stations telemetric stations

Error estimation (preliminary) CONCLUSION: calibration basis on the standard net basis on the telemetric net simulation basis on the satellite data correlation coeficient (model quality(*)) 0,969 (very good) 0,992 (excellent) 0,966 peak error 0,483 0,081 0,341 volume error 0,295 0,159 0,251 peak time error 0,125 -0,042 -0,153 root mean square error 0,926 0,983 0,913 water balance 0,0 12,8 (*) - on basis Sarma P.B.S., Delleur J.W., Rao A.R., 1973, Comparison of rainfall - runoff models for urban areas, Journal of Hydrology 18(3/4), 329-347 (preliminary) CONCLUSION: exchange of ground observations by satellite data don’t relapse results of hydrological model

Conclusions: H-SAF is preparing operational structure for hydrology. Without acceptation of products and their quality (at least among EUMETSAT Member and Cooperationg States), this activity will be useless. First H-SAF products already available (inter SAF distribution). Preliminary validation is promising – further studies required. Hydrological impact studies – way forward to avoid problem with comparison of completely different data.