Mitigation of ECMWF–scatterometer wind biases in view of storm surge applications in the Adriatic Sea

Zecchetto, S.; Valle, Antonio della; Biasio, Francesco De

doi:10.1016/j.asr.2014.12.011

Cited by 15 publications

(13 citation statements)

References 22 publications

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“…Kara et al (2009) compared, on a monthly basis, the QuikSCAT 25 km data with "analysis quality" NWP winds (A. Wallcraft, personal communication, 2014) coming from the NOGAPS dataset at 0.5°of resolution, in the Mediterranean Sea. Zecchetto et al (2015), proposed a new methodology for exploiting satellite-borne scatterometer wind observations to reduce the bias of numerical model prediction wind fields with respect to satellite-detected winds.…”

Section: Differences Between Model and Scatterometer Winds In The Adr...mentioning

confidence: 99%

“…De Biasio et al (2016, Bajo et al (2017) toke advantage of the methodology described in Zecchetto et al (2015), adapting a combined strategy, based on satellite scatterometry and satellite altimetry, in order to improve the prediction skills of a hydrodynamic model used for strom surge forecast in the Adriatic Sea and Venice, with positive results. In their study, the surface wind field was provided by the ECMWF numerical atmospheric model, scatterometer winds were derived from the SeaWinds scatterometer mounted on the American NASA QuikSCAT satellite, and from the ASCAT instruments onboard the European MetOP satellites.…”

Section: Differences Between Model and Scatterometer Winds In The Adr...mentioning

confidence: 99%

“…As an isolated topic, distinguished from the specific context of storm surge forecasting, the WBM procedure was first introduced by Zecchetto et al (2015), who assessed the performance of WBM in a dataset consisting of thirty-three cases of high water in Venice in the period 2004-2014. These cases were selected as occurring at the same time of significant storm surges observed in the Gulf of Venice.…”

Section: Introductionmentioning

confidence: 99%

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Model Wind Optimization with Satellite Wind Observations for Operational Sea Level Prediction

Biasio¹,

Zecchetto²

2022

Preprint

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Sea surface wind forecasts in the Adriatic Sea often suffer for unadequate modelling, especially for the wind speed. This has detrimental effects on the accuracy of sea level and storm surge predictions. We present a numerical method to reduce the bias between the sea surface wind observed by the scatterometers and that supplied by the European Centre for Medium-Range Weather Forecasts (ECMWF) global atmospheric model, for storm surge forecasting applications. The method, called “wind bias mitigation”, relies on scatterometer observations to determine a multiplicative factor ∆ws which modulates the standard model wind in order to decrease the bias between scatterometer and model. We compare four different mathematical approaches to this method, for a total of eight different formulations of the multiplicative factor ∆ws. Four datasets are used for the assessment of the eight different bias mitigation methods: a collection of 29 Storm Surge Events (SEVs) cases in the years 2004-2014, a collection of 48 SEVs in the years 2013-2016, a collection of 364 cases of random sea level conditions in the same period, and a collection of the seven SEVs in 2012-2016 that were worst predicted by the Centro Previsioni e Segnalazioni Maree, Comune di Venezia (Tide Forecast and Early Warning Centre of the Venice Municipality - CPSM). The statistical analysis shows that the bias mitigation procedures supplies a mean wind speed more accurate than the standard forecast, when compared with scatterometer observations, in more than 70% of the analyzed cases.

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Section: Differences Between Model and Scatterometer Winds In The Adr...mentioning

confidence: 99%

Section: Differences Between Model and Scatterometer Winds In The Adr...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model Wind Optimization with Satellite Wind Observations for Operational Sea Level Prediction

Biasio¹,

Zecchetto²

2022

Preprint

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“…The mitigation of the two bias in the model fields uses the bias registered in the preceding few days between scatterometer observations and model analyses, introducing them as counteracting terms in the definition of the mitigated model wind. This methodology has been first proposed and investigated in Zecchetto et al, 2015). The approach for the wind speed relies on the normalized bias Δw N (i, j) (Equation 3) as counteracting term, while that for the wind direction simply relies on Δθ(i,j) (Eq.…”

Section: Bias Mitigation Of the Atmospheric Model Wind Forcingmentioning

confidence: 99%

“…This goal was achieved in the framework of the eSurge-Venice projecta project funded by the Data User Element programme of the European Space Agency (ESA)by using satellite scatterometer wind and radar altimetry Total Water Level Envelope (TWLE: a quantity easily derived from the sea level anomaly) observations to increase the reliability and accuracy of the storm surge prediction in the Gulf of Venice. Satellite-borne scatterometer winds were used to mitigate the biases between the model and the measured winds with a methodology named wind-bias mitigation (Zecchetto, Della Valle, & De Biasio, 2015). Satellite altimetry brought instead a significant improvement of the sea level background field across the basin, through the assimilation of TWLE retrievals into the storm surge model (SSM) SHYFEM (online: http://www.ismar.cnr.it/shyfem), with a dual 4D-Var assimilation algorithm.…”

Section: Introductionmentioning

confidence: 99%

Improvements of storm surge forecasting in the Gulf of Venice exploiting the potential of satellite data: the ESA DUE eSurge-Venice project

Biasio

Bajo

Vignudelli

et al. 2017

European Journal of Remote Sensing

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The northern Adriatic Sea is affected by storm surges, which often cause the flooding in Venice and the surrounding areas. We present the results of the eSurge-Venice project, funded by the European Space Agency (ESA) in the framework of its Data User Element programme: the project was aimed to demonstrate the potential of satellite data in improving storm surge forecasting, with focus on the Gulf of Venice. The satellite data used were scatterometer wind and altimeter sea level height. Hindcast experiments were conducted to assess the sensitivity of a storm surge model to a model wind forcing modified with scatterometer data and to altimeter retrievals assimilated with a dual 4D-Var system. The modified model wind forcing alone was responsible for a reduction of the mean difference between modelled and observed maximum surge peaks from −15.1 to −8.2 cm, while combining together scatterometer and altimeter data the mean difference further reduced to −6.0 cm. In terms of percent, the improvements in the reduction on the mean differences between modelled and observed surge peaks reaches 46% using only the scatterometer data, and 60% using both scatterometer and altimeter data.

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Sea State

Boicourt¹,

Ličer²,

Li³

et al. 2020

Geophysical Monograph Series

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Mitigation of ECMWF–scatterometer wind biases in view of storm surge applications in the Adriatic Sea

Cited by 15 publications

References 22 publications

Model Wind Optimization with Satellite Wind Observations for Operational Sea Level Prediction

Model Wind Optimization with Satellite Wind Observations for Operational Sea Level Prediction

Improvements of storm surge forecasting in the Gulf of Venice exploiting the potential of satellite data: the ESA DUE eSurge-Venice project

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