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GAUGES – RADAR – SATELLITE COMBINATION

GAUGES – RADAR – SATELLITE COMBINATION. Prof. Eng. Ezio TODINI e-mail : todini@geomin.unibo.it. RAIN GAUGES Reliable but point measures. Improvements in Rainfall Estimates are obtained by combining together the different available Rainfall Measurement Sources.

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GAUGES – RADAR – SATELLITE COMBINATION

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

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

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

  4. Improvements in Rainfall Estimates are obtained by combining togetherthe 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 RAINFALL INPUTS CAN BE FROM: Gauges, Radar, Satellite and Meteorological Models Forecasts THE MUSIC INTEGRATED SYSTEM PROTOTYPE

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

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

  8. TRUE BK-RAD SAT BK-RAD-SAT BAYESIAN APPROACH at SATELLITE Scale

  9. TRUE BK RAD BK-RAD BK-RAD-SAT BAYEISAN APPROACH at RADAR scale

  10. RESULTS (1000 time-steps)

  11. BLOCK KRIGING RADAR SATELLITE BK+RADAR BK+SATELLITE BK+RADAR+SATELLITE 2.2 mm 0.0 mm 4.4 mm 6.5 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 TOPKAPI Completed (*) Under revision

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