Improved near real time surface wind resolution over the Mediterranean Sea

Abstract. Several scientific programs, including the Mediterranean Forecasting System Toward Environmental Predictions (MFSTEP project), request high space and time resolutions of surface wind speed and direction. The purpose of this paper is to focus on surface wind improvements over the global Mediterranean Sea, based on the blending near real time remotely sensed wind observations and ECMWF wind analysis. Ocean surface wind observations are retrieved from QuikSCAT scatterometer and from SSM/I radiometers available at near real time at Météo-France. Using synchronous satellite data, the number of remotely sensed data available for each analysis epoch (00:00 h; 06:00 h; 12:00 h; 18:00 h) is not uniformly distributed as a function of space and time. On average two satellite wind observations are available for each analysis time period. The analysis is performed by optimum interpolation (OI) based on the kriging approach. The needed covariance matrixes are estimated from the satellite wind speed, zonal and meridional component observations. The quality of the 6-hourly resulting blended wind fields on 0.25° grid are investigated trough comparisons with the remotely sensed observations as well as with moored buoy wind averaged wind estimates. The blended wind data and remotely wind observations, occurring within 3 h and 0.25° from the analysis estimates, compare well over the global basin as well as over the sub-basins. The correlation coefficients exceed 0.95 while the rms difference values are less than 0.30 m/s. Using measurements from moored buoys, the high-resolution wind fields are found to have similar accuracy as satellite wind retrievals. Blended wind estimates exhibit better comparisons with buoy moored in open sea than near shore.

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The synoptic-scale surface wind climate regimes of the Mediterranean Sea according to the cluster analysis of ERA-40 wind fields

Theoretical and Applied Climatology ◽

10.1007/s00704-008-0033-5 ◽

2008 ◽

Vol 96 (1-2) ◽

pp. 69-83 ◽

Cited By ~ 29

Author(s):

M. Burlando

Keyword(s):

Cluster Analysis ◽

Mediterranean Sea ◽

Surface Wind ◽

Synoptic Scale ◽

Wind Fields ◽

Wind Climate ◽

The Mediterranean ◽

Scale Surface

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A regression-based statistical correction of mesoscale simulations for near-surface wind speed using remotely sensed surface observations

Asia-Pacific Journal of Atmospheric Sciences ◽

10.1007/s13143-012-0040-4 ◽

2012 ◽

Vol 48 (4) ◽

pp. 449-456 ◽

Cited By ~ 1

Author(s):

Do-Yong Kim ◽

Jin-Young Kim ◽

Jae-Jin Kim

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Surface Wind Speed ◽

Remotely Sensed ◽

Near Surface ◽

Statistical Correction ◽

Mesoscale Simulations ◽

Surface Observations

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Tropical Cyclone Destructive Potential by Integrated Kinetic Energy

Bulletin of the American Meteorological Society ◽

10.1175/bams-88-4-513 ◽

2007 ◽

Vol 88 (4) ◽

pp. 513-526 ◽

Cited By ~ 156

Author(s):

Mark D. Powell ◽

Timothy A. Reinhold

Keyword(s):

Wind Speed ◽

Tropical Cyclone ◽

Kinetic Energy ◽

Surface Wind ◽

Surface Wind Speed ◽

Wind Loading ◽

Wind Fields ◽

Damage Potential ◽

Spatial Coverage ◽

Destructive Potential

Tropical cyclone damage potential, as currently defined by the Saffir-Simpson scale and the maximum sustained surface wind speed in the storm, fails to consider the area impact of winds likely to force surge and waves or cause particular levels of damage. Integrated kinetic energy represents a framework that captures the physical process of ocean surface stress forcing waves and surge while also taking into account structural wind loading and the spatial coverage of the wind. Integrated kinetic energy was computed from gridded, objectively analyzed surface wind fields of 23 hurricanes representing large and small storms. A wind destructive potential rating was constructed by weighting wind speed threshold contributions to the integrated kinetic energy, based on observed damage in Hurricanes Andrew, Hugo, and Opal. A combined storm surge and wave destructive potential rating was assigned according to the integrated kinetic energy contributed by winds greater than tropical storm force. The ratings are based on the familiar 1–5 range, with continuous fits to allow for storms as weak as 0.1 or as strong as 5.99.

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Nowcasting of surface wind speed using probabilistic, explainable Deep Learning

10.5194/ems2021-331 ◽

2021 ◽

Author(s):

Francesco Zanetta ◽

Daniele Nerini

Keyword(s):

Deep Learning ◽

Wind Speed ◽

Real Time ◽

Weather Prediction ◽

Surface Wind ◽

Similarity Index ◽

Surface Wind Speed ◽

Second Phase ◽

Post Processing ◽

Wide Range

<div> <div> <div> <p>Surface wind is an extremely difficult parameter to predict, particularly in the complex topography of the Alps. Due to several important processes happening at sub-kilometer scale, even high resolution Numerical Weather Prediction models such as COSMO-1 still present substantial biases. To address this, a wide range of statistical post-processing methods are used. Recently, methods based on Deep Learning have emerged as a new solution and are now actively developed at many weather services, including MeteoSwiss. At the same time, efforts are made to obtain accurate representations of surface wind speed up to a few hours ahead by integrating all available information in real-time, an approach known as nowcasting.</p> <p>With the aim of seamlessly combining nowcasting and post-processing approaches for surface wind speed predictions, we developed a Deep Learning probabilistic post-processing model that is also able to integrate real time observations, and developed a new metric, the Similarity Index, for this purpose. The Similarity Index is a way to estimate the correlation of surface wind speed between two locations, based on their position and geomorphological setting, and can be used to choose the best available observation to be used at any point in space at any given time, and weigh that observation in a way that mimics geostatistical interpolation methods. The proposed methodology yields improved forecasts of wind speed where both systematic and random errors are reduced, thanks to the post-processing and nowcasting components respectively. In a second phase, we implemented a state- of-the-art explainability framework for machine learning, SHAP, and presented how it can be used to get insights into the model and build trust in the results.</p> </div> </div> </div>

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Large and small scale wave forecast in the Mediterranean Sea

Natural Hazards and Earth System Science ◽

10.5194/nhess-9-779-2009 ◽

2009 ◽

Vol 9 (3) ◽

pp. 779-788 ◽

Cited By ~ 12

Author(s):

L. Bertotti ◽

L. Cavaleri

Keyword(s):

Mediterranean Sea ◽

Surface Wind ◽

Surface Wind Speed ◽

Scale Model ◽

Small Scale ◽

Limited Area ◽

Wave Forecast ◽

Forecasting System ◽

The Mediterranean ◽

Wave Forecasting

Abstract. We describe the implementation of an operational high resolution wind and wave forecasting system in the Mediterranean Sea, and then on a limited area centred on the south-east part of Italy, covering parts of the Adriatic and the Ionian seas. We analyse the performance at the two different resolutions during the first four months of operation, using the wind and wave data provided by the QuikSCAT scatterometer, and the Jason and Envisat altimeters. Useful accurate forecasts are found up to 72 h range, the maximum operational one. As expected, we find that the limited area models outperform both the wind and wave global or larger scale model results. However, we still find an appreciable underestimate by the models for surface wind speed and hence wave height, often concentrated on specific events.

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Estimation of Ocean Surface Wind Speed and Direction From Polarimetric Radiometry Data

10.21236/ada533831 ◽

2008 ◽

Author(s):

David R. Lyzenga

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Ocean Surface ◽

Surface Wind Speed ◽

Ocean Surface Wind

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Challenges in developing a high-quality surface wind-speed data-set for Australia

Australian Meteorological and Oceanographic Journal ◽

10.22499/2.6004.001 ◽

2010 ◽

Vol 60 (04) ◽

pp. 227-236 ◽

Cited By ~ 13

Author(s):

D Jakob

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Surface Wind Speed ◽

High Quality ◽

Data Set ◽

Wind Speed Data ◽

Quality Surface ◽

High Quality Surface

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Recurrence Characteristics Analysis of Near-Surface Wind Speed Signal with Different Sampling Frequencies

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.599-601.1605 ◽

2014 ◽

Vol 599-601 ◽

pp. 1605-1609 ◽

Cited By ~ 1

Author(s):

Ming Zeng ◽

Zhan Xie Wu ◽

Qing Hao Meng ◽

Jing Hai Li ◽

Shu Gen Ma

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Surface Wind Speed ◽

Recurrence Plot ◽

Sampling Frequency ◽

Near Surface ◽

Gas Leakage ◽

Time Period ◽

Characteristics Analysis ◽

Quantification Analysis

The wind is the main factor to influence the propagation of gas in the atmosphere. Therefore, the wind signal obtained by anemometer will provide us valuable clues for searching gas leakage sources. In this paper, the Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA) are applied to analyze the influence of recurrence characteristics of the wind speed time series under the condition of the same place, the same time period and with the sampling frequency of 1hz, 2hz, 4.2hz, 5hz, 8.3hz, 12.5hz and 16.7hz respectively. Research results show that when the sampling frequency is higher than 5hz, the trends of recurrence nature of different groups are basically unchanged. However, when the sampling frequency is set below 5hz, the original trend of recurrence nature is destroyed, because the recurrence characteristic curves obtained using different sampling frequencies appear cross or overlapping phenomena. The above results indicate that the anemometer will not be able to fully capture the detailed information in wind field when its sampling frequency is lower than 5hz. The recurrence characteristics analysis of the wind speed signals provides an important basis for the optimal selection of anemometer.

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