Model and scatterometer sea surface winds for storm surge applications in the Adriatic Sea
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Transcript of Model and scatterometer sea surface winds for storm surge applications in the Adriatic Sea
13th EMS & 11th ECAM
Model and scatterometer sea surface winds for storm surge applications in the Adriatic Sea
Francesco De Biasio*, Stefano Zecchetto, Mario Marcello MigliettaIstituto di Scienze dell'Atmosfera e del Clima (ISAC)
Consiglio Nazionale delle Ricerche (CNR)
Corso Stati Uniti 4, 35127 Padova, Italy
* email: [email protected]
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Motivations for the study
Some operational NWP models available in the Adriatic Sea
EO surface wind data available in the Adriatic Sea
Do NWP models and EO wind data look similar?
A case study
Local statistics
Global statistics
Presentation outline
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This study was performed in the framework of the eSurge-Venice project, a project funded by the European Space Agency (ESA). It aims to:
Compare the performances of the Atmospheric Global Circulation Models (GCM) and Limited Area Models (LAM) wind fields to be used as forcing into the storm surge models in the Adriatic Sea.
1 -Motivations
Satellite-borne scatterometers surface winds are exploited as the reference fields to be used in the assessment of the quality of GCM and LAM wind forecasts in the Adriatic Sea
NWP limits: initial conditions, parameterization schemes, …
EO limits: data availability, revisitation time, distance to coast.
Period: August 2008 – July 2011
http://www.esurge-venice.eu
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GLOBAL MODELS:
IFS – operational at ECMWF•From 01/02/2006 to 26/01/2010: TL799 (25 km), from 26/01/2010 to present: TL1279 (16 km)•Grid spacing used (after interpolation): 12,0 km
LOCAL AREA MODELS (THE FIRST TWO PROVIDED TO “MUNICIPAL STORM SURGE FORECASTING OFFICE IN VENICE”:
ALADIN – operational at DHZ (Croatian Meteorological and Hydrological Service)•initialized with ARPEGE (Meteo-France), two nested grids (only the first, larger domain is used here)•Grid spacing: 8 km
•COSMO-LAMI – operational at ARPA Emilia-Romagna•initialized with ECMWF, one grid•Grid spacing: 7 km
WRF – Weather Research and Forecasting Model•initialized with ECMWF, one grid•Grid spacing: 10 km
2 - Atmospheric numerical weather prediction models available in the Adriatic Sea (surface wind)
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3.1 - EO surface wind fields available in the Adriatic Sea
SENSOR: SeaWinds on QuikSCAT ASCAT on MetOp
TYPE: Ku-band scatterometer C-band scatterometer
AVAILABILITY: August 2008-November 2009 March 2009-July 2011
GRID SPACING: 12.5km OWV 12.5km OWV
SWATH: One swath 1800 km wide Two swaths 550 km wide
AVERAGE HITS: ~30/mo in the Adriatic Sea ~20/mo in the Adriatic Sea
Mean number of hits/month
Actually 3 additional sensors are
available in the area
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3.2 – Main wind regimes in the Adriatic Sea
Bora: a violent, cold, northeasterly winter wind on the Adriatic Sea
Sirocco: a warm, moist, southeast wind blowing on the northern Mediterranean coast
BOTH WINDS CAN CAUSE SEVERE STORM SURGE IN THE NORTHERN ADRIATIC SEA AND IN THE VENICE LAGOON
Mediterranean Sea
Adriatic Sea
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4.0 –Wind fields in the Adriatic Sea: Bora event November 17, 2008
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4.1 –Wind fields in the Adriatic Sea: Bora event November 17, 2008
November 17, 2008QUIK ECMWF ALADIN LAMI WRF18:08 18:00 18:00 18:00 18:00
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4.2 –Wind fields in the Adriatic Sea: Bora event November 17, 2008
November 18, 2008QUIK ECMWF ALADIN LAMI WRF03:50 03:00 03:00 03:00 03:00
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4.3 –Wind fields in the Adriatic Sea: Bora event November 17, 2008
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5.1 – SCAT/MODEL local statistics: wind speed bias
wind speed bias
<WSSCAT–WSECMWF> <WSSCAT–WSALADIN> <WSSCAT–WSLAMI>
The forecast range +12-+36 h is used for sake of comparison
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5.2 – SCAT/MODEL local statistics: wind direction difference
mean of wind direction difference
<WDSCAT–WDECMWF> <WDSCAT–WDALADIN> <WDSCAT–WDLAMI>
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5.3 – SCAT/MODEL local statistics: wind speed correlation
wind speed correlation
corrcoef(WSSCAT,WSECMWF) corrcoef(WSSCAT,WSALADIN) corrcoef(WSSCAT,WSLAMI)
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5.4 – SCAT/MODEL local statistics: wind direction correlation
wind direction correlation
corrcoef(WDSCAT,WDECMWF) corrcoef(WDSCAT,WDALADIN) corrcoef(WDSCAT,WDLAMI)
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5.5 – SCAT/MODEL local statistics: wind speed centered RMSD
wind speed centered Root Mean Square Difference
cRMSD(WSSCAT,WSECMWF) cRMSD(WSSCAT,WSALADIN) cRMSD(WSSCAT,WSLAMI)
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5.6 – SCAT/MODEL local statistics: wind direction difference std
standard dev. of wind direction difference
std_dev(WDSCAT-WDECMWF) std_dev(WDSCAT-WDALADIN) std_dev(WDSCAT-WDLAMI)
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6.1 – SCAT/MODEL global statistics. Wind speed and direction distributions.
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6.2 – SCAT/MODEL global statistics. TAYLOR diag. of wind speed
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7 – Conclusions
• LAMs apparently perform similarly as ECMWF in the simulation of winds over the Adriatic sea
• The performance is better in open sea and worse near the coastlines
• ALADIN performs better in terms of correlation and standard deviation, LAMI has a lower bias (LAMI has a smaller domain extension which may affect the results)
• The errors in speed/direction are due to misplacing of wind patterns and inaccurate time evolution
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EFFECTIVE RESOLUTION OF A LAM-> NEED OF HIGHER RESOLUTION
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INTERPRETATION OF GLOBAL STATISTICS
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Thank you for your attention