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GAUGES – RADAR – SATELLITECOMBINATION
Prof. Eng. Ezio TODINIe-mail : [email protected]
PROtezione e GEstione Ambientale Sede Operativa: Via Don Bedetti 20 - 40129 Bologna Tel. 051-6389099 Fax 051-6389100 E-mail: [email protected]
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Improvements in Rainfall Estimates are obtained by combining together the different available Rainfall Measurement Sources
RAIN GAUGESReliable but point measures
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Improvements in Rainfall Estimates are obtained by combining together the different available Rainfall Measurement Sources
RADAR Spatial but less reliable
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Improvements in Rainfall Estimates are obtained by combining together the different available Rainfall Measurement Sources
METEOSATSpatial but toocoarse resolution
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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
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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
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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
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BAYESIAN APPROACH at SATELLITE Scale
TRUE BK-RAD SAT BK-RAD-SAT
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BAYEISAN APPROACH at RADAR scale
TRUE BK RAD BK-RAD BK-RAD-SAT
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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
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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
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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