The impact of NEFODINA convective clouds identification in the rain rate 

retrieval of H‐SAF

6th IPWG Workshop15‐19 October 2012, Sao Jose dos Campos, Brazil

Francesco Zauli, Daniele Biron, Davide Melfi, Antonio Vocino, Massimiliano Sist

Centro Nazionale di Meteorologia e Climatologia Aeronautica

Outline

H‐Saf overview

NEFODINA software

H15 algorithm

Case study: 1st October 2009

Outlooks: RELASE/H50 algorithm

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H‐Saf overview/1

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The "EUMETSAT Satellite Application Facility on Support to OperationalHydrology and Water Management (H-SAF)" was established by theEUMETSAT Council on 3 July 2005, and kicked-off on 16 September 2005

to provide satellite-derived products from existing and future satellites withsufficient time and space resolution to satisfy the needs of operationalhydrology;

to perform independent validation of the usefulness of the new products forfighting against floods, landslides, avalanches, and evaluating waterresources.

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H‐Saf overview/2

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Precipitation from MicrowaveConical scan satellite (SSMI/S)

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H‐Saf overview/3

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Precipitation from MicrowaveCross scan satellite (AMSU/MHS)

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Multi-platform algorithm: BLENDING TechniqueThe PR-OBS3 algorithm is basedon a collection of time and spaceoverlapping SEVIRI IR images andLow Earth Orbit (LEO) MWradiometers. As a new MW swathis available, the MW-derived pixelsare paired with the time and spacecoincident geostationary (GEO) TBat 10.8 mm. Coincident data aresubsequently located in ageographical latitude-longitude grid(2.5° x 2.5°), and for each grid boxthe histogram of the IR TBs andthat of the corresponding MW rainrates is built.

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H‐Saf overview/4

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Propagation vector matrices areproduced by computing spatiallag correlations over successiveimages of GEO/IR and then usedto propagate the MW-derivedprecipitation estimates in timeand space when updated MWdata are unavailable.

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Multi-platform algorithm: MORPHING Technique

H‐Saf overview/5

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H‐Saf overview/6

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Accumulated Precipitation

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H‐Saf overview/7

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Comparison between precipitation retrieval by microwave sensor on polar satellite (AMSU) and radar.

5th June 2011

Intrinsic Underestimation

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NEFODINA software/1

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• With red shades are indicated the cloud top of the detected convective cell ingrowing phase

• With pink shades are indicated the cloud top of the detected convective cell indecreasing phase.

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Rain redistribution based on convective cell’s area

NEFODINA software/25th June 2011

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H15 algorithm

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HSAF PR-OBS6: BLENDING Technique + NEFODINA

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Case study: 1st October 2009

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The Department of Civil Protection measured 190/200 mm in 5h.

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Case study: 1st October 2009

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The Department of Civil Protection measured 190/200 mm in 5h

Heavy rain triggered normal instability along the sides that can turn into disasters and catastrophes when affecting areas deforested and inhabited

The mud flows evolved along the slopehave reached speeds of several tens ofkilometers per hour and large volumes(estimated at between 60.000 and 80.000cubic meters)

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Case study: 1st October 2009

The mud has incorporated thevegetation, dry stone walls andboulders of the substrateacquiring a considerabledestructive power evidenced bythe effects on this area

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Accumulated precipitation in the previous 3 hours: 20091001 2100

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HSAF H02 precipitation retrieval by AMSUmicrowave sensor on polar satellite (cyanelliptical footprint) and radar (with colour scale).

H02 ellipsis as left and occurred lightnings in15 minutes centred in microwave observationtimestamp.

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Outlooks

The position of convective precipitation is almost entirely seen by means of lightning rate and locations.

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Blue: 0.5 ≤ SRI < 12 mm/hGreen: 12 ≤ SRI < 25 mm/hYellow: 25 ≤ SRI < 50 mm/hRed: SRI > 50 mm/h

07 Sept 2005 02:30 UTC

RADAR

Lightning

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Outlooks ‐ RELASE

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Rainfall Estimation from Lightning And Seviri data

A rainfall retrieval technique that usesgeostationary satellite Infrared (IR)observations and lightning information retrievedfrom LAMPINET (lightning network of the ItalianAir Force Meteorological Service)

A quantitative relationship for rainfall estimationusing lightning and Seviri data has beendeveloped using a bivariate linear regressionfor the cluster's rain volume :

RR = (b0 + b1S/N + b2T)N

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Outlooks ‐ RELASE

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Accumulated precipitation fromRADAR observations

Accumulated precipitation fromLightning observations

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Outlooks ‐ RELASE

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Outlooks ‐ RELASE

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Lightnings

Future LightningSpace-based obs.MTG-LI

Reconstructed rainfall rate (mm/h)

RADAR

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Outlooks – H50

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Thank you for your attention!

us_hsaf@meteoam.it

http://hsaf.meteoam.it