GAUGES – RADAR – SATELLITECOMBINATION

Prof. Eng. Ezio TODINIe-mail : todini@geomin.unibo.it

PROtezione e GEstione Ambientale Sede Operativa: Via Don Bedetti 20 - 40129 Bologna Tel. 051-6389099 Fax 051-6389100 E-mail: segreteria@progea.net

Improvements in Rainfall Estimates are obtained by combining together the different available Rainfall Measurement Sources

RAIN GAUGESReliable but point measures

Improvements in Rainfall Estimates are obtained by combining together the different available Rainfall Measurement Sources

RADAR Spatial but less reliable

Improvements in Rainfall Estimates are obtained by combining together the different available Rainfall Measurement Sources

METEOSATSpatial but toocoarse resolution

The MUSIC Prototype Integrates:Hydrologic & Hydraulic modelsGIS and Advanced Visual User Interface

THE MUSIC INTEGRATED SYSTEM PROTOTYPE

RAINFALL INPUTS CAN BE FROM:Gauges, Radar, Satellite and Meteorological Models Forecasts

Rain-gaugemeasurements

KRIGED measurements from gauges

Radar measurements,A PRIORI estimates

Combination of radar estimates and gaugesmeasurements, A POSTERIORI estimate

BLOCK KRIGING

KALMAN FILTER

SPATIALMEASUREMENTS

over the radarpixels

Eliminating the BIAS and producing MINIMUMVARIANCE 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 theradar 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 FILTERfinding the a posteriori estimates by combining the

a priori estimates provided by the radar with the blockKriged measurements provided by the gauges,

in a Bayesian framework

1

RAIN-GAUGES

Meteorological RADAR

Meteorological SATELLITE

Measurements of the rainfall field at different scales. combine measurements at multiple resolution.

yUx TMODEL:

UP-SCALING:

yUx T ˆˆ

y disaggregated estimate at the RADAR scale (from the Bayesian combination)

yP~ covariance of the estimation errors at the RADAR scale

aggregated RADAR estimate

UPUP yT

x ~~ 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 Scalex

y

DOWNSCALING

RAD Scale

SAT Scalex

y

UPSCALING

BAYESIAN APPROACH at SATELLITE Scale

TRUE BK-RAD SAT BK-RAD-SAT

BAYEISAN APPROACH at RADAR scale

TRUE BK RAD BK-RAD BK-RAD-SAT

RESULTS (1000 time-steps)RESULTS (1000 time-steps)

BIASBIAS VARIANCEVARIANCE

RADARRADAR 5.0449 5.0449 19.415419.4154

BLOCK KRIGINGBLOCK KRIGING -0.0051-0.0051 30.572430.5724

BLOCK KRIGING + RADAR (RADAR scale)BLOCK KRIGING + RADAR (RADAR scale) -0.0129-0.0129 14.541914.5419

SATELLITESATELLITE 999.7988999.7988 34475.636834475.6368

BK+RADAR AGGREGATED BK+RADAR AGGREGATED (SATELLITE scale)(SATELLITE scale) -1.1297-1.1297 27930.314427930.3144

BK+RADAR + SATELLITE (SATELLITE scale)BK+RADAR + SATELLITE (SATELLITE scale) -0.7325-0.7325 16312.851716312.8517

BK+RADAR + SATELLITE (RADAR scale)BK+RADAR + SATELLITE (RADAR scale) -0.0050-0.0050 13.156713.1567

BLOCK KRIGING BLOCK KRIGING RADAR RADAR SATELLITESATELLITE

BK+RADARBK+RADAR BK+RADAR+SATELLITEBK+RADAR+SATELLITEBK+SATELLITEBK+SATELLITE

6.5 mm6.5 mm0.0 mm0.0 mm 2.2 mm2.2 mm 4.4 mm4.4 mm

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