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]


9 - 13 September 2013 | Reading | UK 2

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



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



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)



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



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


9 - 13 September 2013 | Reading | UK

4.0 –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




November 18, 2008QUIK ECMWF ALADIN LAMI WRF03:50 03:00 03:00 03:00 03:00



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



5.2 – SCAT/MODEL local statistics: wind direction difference

mean of wind direction difference

<WDSCAT–WDECMWF> <WDSCAT–WDALADIN> <WDSCAT–WDLAMI>



5.3 – SCAT/MODEL local statistics: wind speed correlation

wind speed correlation

corrcoef(WSSCAT,WSECMWF) corrcoef(WSSCAT,WSALADIN) corrcoef(WSSCAT,WSLAMI)



5.4 – SCAT/MODEL local statistics: wind direction correlation

wind direction correlation

corrcoef(WDSCAT,WDECMWF) corrcoef(WDSCAT,WDALADIN) corrcoef(WDSCAT,WDLAMI)



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)



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)



6.1 – SCAT/MODEL global statistics. Wind speed and direction distributions.



6.2 – SCAT/MODEL global statistics. TAYLOR diag. of wind speed



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



EFFECTIVE RESOLUTION OF A LAM-> NEED OF HIGHER RESOLUTION



INTERPRETATION OF GLOBAL STATISTICS



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Thank you for your attention

model and scatterometer sea surface winds for storm surge applications in the adriatic sea

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