1. GAUGES – RADAR – SATELLITE COMBINATION Prof. Eng. Ezio TODINI e-mail : todini@geomin.unibo.it

2. Improvements in Rainfall Estimates are obtained by combining together the different available Rainfall Measurement Sources RAIN GAUGES Reliable but point measures

3. Improvements in Rainfall Estimates are obtained by combining together the different available Rainfall Measurement Sources RADAR Spatial but less reliable

4. Improvements in Rainfall Estimates are obtained by combining together the different available Rainfall Measurement Sources METEOSAT Spatial but too coarse resolution

5. The MUSIC Prototype Integrates: Hydrologic & Hydraulic models GIS and Advanced Visual User Interface THE MUSIC INTEGRATED SYSTEM PROTOTYPE RAINFALL INPUTS CAN BE FROM: Gauges, Radar, Satellite and Meteorological Models Forecasts

6. Rain-gauge measurements KRIGED measurements from gauges Radar measurements, A PRIORI estimates Combination of radar estimates and gauges measurements, A POSTERIORI estimate BLOCK KRIGING KALMAN FILTER SPATIAL MEASUREMENTS over the radar pixels Eliminating the BIAS and producing MINIMUM VARIANCE precipitation estimates on pixels ORIGINAL TECHNIQUE TO COMBINE, IN A BAYESIAN SENSE, AREAL PRECIPITATION FIELDS (RADAR) TO POINT MEASUREMENTS OF PRECIPITATION (RAIN-GAUGES) GROUND BASED TELE- METERING RAIN- GAUGE MEASUREMENTS - accurate in a point - spatial significance decays with the distance and with the area BLOCK KRIGING estimating the average field over the radar pixels and its Variance from the point rain-gauge measurements SPATIAL MEASUREMENTS POINT MEASUREMENTS RADAR MEASUREMENTS - good spatial representation - poor quantitative estimates - biased measurements SPATIAL MEASUREMENTS KALMAN FILTER finding the a posteriori estimates by combining the a priori estimates provided by the radar with the block Kriged measurements provided by the gauges, in a Bayesian framework 1

7. RAIN-GAUGES Meteorological RADAR Meteorological SATELLITE Measurements of the rainfall field at different scales. combine measurements at multiple resolution. MODEL: UP-SCALING: disaggregated estimate at the RADAR scale (from the Bayesian combination) covariance of the estimation errors at the RADAR scale aggregated RADAR estimate variance of the estimation errors of the aggregated RADAR estimate RAIN-GAUGES, RADAR AND SATELLITE COMBINATION The true rainfall at the upper scale can be obtained simply by summing the true rainfall at the lower scale RAD Scale SAT Scale x y DOWNSCALING RAD Scale SAT Scale x y UPSCALING

8. BAYESIAN APPROACH at SATELLITE Scale TRUEBK-RADSATBK-RAD- SAT

9. BAYEISAN APPROACH at RADAR scale TRUEBKRADBK-RADBK-RAD-SAT

10. RESULTS (1000 time-steps) BIASVARIANCE RADAR RADAR5.044919.4154 BLOCK KRIGING BLOCK KRIGING-0.005130.5724 BLOCK KRIGING + RADAR (RADAR scale) BLOCK KRIGING + RADAR (RADAR scale)-0.012914.5419 SATELLITE SATELLITE999.798834475.6368 BK+RADAR AGGREGATED BK+RADAR AGGREGATED (SATELLITE scale) (SATELLITE scale)-1.129727930.3144 BK+RADAR + SATELLITE (SATELLITE scale) BK+RADAR + SATELLITE (SATELLITE scale)-0.732516312.8517 BK+RADAR + SATELLITE (RADAR scale) BK+RADAR + SATELLITE (RADAR scale)-0.005013.1567

11. BLOCK KRIGING RADARSATELLITE BK+RADARBK+RADAR+SATELLITEBK+SATELLITE 6.5 mm 0.0 mm 2.2 mm 4.4 mm

12. The present status: - Block-Kriging software package Completed(*) - Raingauge – Radar combination Completed - Raingauge – Satellite combination Completed - Raingauge-Radar-Satellite comb. Completed - Coupling with TOPKAPICompleted (*) Under revision