An Investigation of the Stability Dependence of SST-Induced Vertical Mixing over the Ocean in the Operational Met Office Model

2017 ◽  
Vol 30 (1) ◽  
pp. 91-107 ◽  
Author(s):  
Qingtao Song ◽  
Dudley B. Chelton ◽  
Steven K. Esbensen ◽  
Andrew R. Brown
Keyword(s):  
Wind Speed ◽  
Weather Prediction ◽  
Surface Wind ◽  
Vertical Mixing ◽  
Surface Wind Speed ◽  
Satellite Observations ◽  
Sea Surface ◽  
Stable Conditions ◽  
Wide Range ◽  
The Impact

This study presents an assessment of the impact of a March 2006 change in the Met Office operational global numerical weather prediction model through the introduction of a nonlocal momentum mixing scheme. From comparisons with satellite observations of surface wind speed and sea surface temperature (SST), it is concluded that the new parameterization had a relatively minor impact on SST-induced changes in sea surface wind speed in the Met Office model in the September and October 2007 monthly averages over the Agulhas Return Current region considered here. The performance of the new parameterization of vertical mixing was evaluated near the surface layer and further through comparisons with results obtained using a wide range of sensitivity of mixing parameterization to stability in the Weather Research and Forecasting (WRF) Model, which is easily adapted to such sensitivity studies. While the new parameterization of vertical mixing improves the Met Office model response to SST in highly unstable (convective) conditions, it is concluded that significantly enhanced vertical mixing in the neutral to moderately unstable conditions (nondimensional stability [Formula: see text] between 0 and −2) typically found over the ocean is required in order for the model surface wind response to SST to match the satellite observations. Likewise, the reduced mixing in stable conditions in the new parameterization is also relatively small; for the range of the gradient Richardson number typically found over the ocean, the mixing was reduced by a maximum of only 10%, which is too small by more than an order of magnitude to be consistent with the satellite observations.

scholarly journals Impact Assessment of Assimilating NASA’s RapidScat Surface Wind Retrievals in the NOAA Global Data Assimilation System

2018 ◽  
Vol 146 (4) ◽  
pp. 929-942
Author(s):  
Ling Liu ◽  
Kevin Garrett ◽  
Eric S. Maddy ◽  
Sid-Ahmed Boukabara
Keyword(s):  
Wind Speed ◽  
Weather Prediction ◽  
Surface Wind ◽  
Space Station ◽  
Surface Wind Speed ◽  
Background Field ◽  
Wind Vector ◽  
Forecast Models ◽  
Wind Retrievals ◽  
The Impact

The National Aeronautics and Space Administration (NASA) RapidScat scatterometer on board the International Space Station (ISS) provides observations of surface winds that can be assimilated into numerical weather prediction (NWP) forecast models. In this study, the authors assess the data quality of the RapidScat Level 2B surface wind vector retrievals and the impact of those observations on the National Oceanic and Atmospheric Administration (NOAA) Global Forecast System (GFS). The RapidScat is found to provide quality measurements of surface wind speed and direction in nonprecipitating conditions and to provide observations that add both information and robustness to the global satellite observing system used in NWP models. The authors find that with an assumed uncertainty in wind speed of around 2 m s−1, the RapidScat has neutral impact on the short-range forecast of surface wind vectors in the tropics but improves both the analysis and background field of surface wind vectors. However, the deployment of RapidScat on the ISS presents some challenges for use of these wind vector observations in operational NWP, including frequent maneuvers of the spacecraft that could alter instrument performance.

scholarly journals Nowcasting of surface wind speed using probabilistic, explainable Deep Learning

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>

scholarly journals A statistical model for sea surface diurnal warming driven by numerical weather prediction fluxes and winds

2010 ◽  
Vol 7 (4) ◽  
pp. 1497-1532 ◽  
Author(s):  
M. J. Filipiak ◽  
C. J. Merchant ◽  
H. Kettle ◽  
P. Le Borgne
Keyword(s):  
Heat Flux ◽  
Wind Speed ◽  
Statistical Model ◽  
Weather Prediction ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Surface Heat ◽  
Sea Surface ◽  
Diurnal Warming ◽  
Nwp Model

Abstract. A statistical model is derived relating the diurnal variation of sea surface temperature (SST) to the net surface heat flux and surface wind speed from a numerical weather prediction (NWP) model. The model is derived using fluxes and winds from the European Centre for Medium-Range Weather Forecasting (ECMWF) NWP model and SSTs from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). In the model, diurnal warming has a linear dependence on the net surface heat flux integrated since (approximately) dawn and an inverse quadratic dependence on the maximum of the surface wind speed in the same period. The model coefficients are found by matching, for a given integrated heat flux, the frequency distributions of the maximum wind speed deceedance and the observed warming exceedance. Diurnal cooling, where it occurs, is modelled as proportional to the integrated heat efflux divided by the heat capacity of the seasonal mixed layer. The model reproduces the statistics (mean, standard deviation, and 95-percentile) of the diurnal variation of SST seen by SEVIRI and reproduces the geographical pattern of mean warming seen by the Advanced Microwave Scanning Radiometer (AMSR-E). We use the functional dependencies in the statistical model to test the behaviour of two physical model of diurnal warming that display contrasting systematic errors.

scholarly journals The Impact of Atmosphere–Ocean–Wave Coupling on the Near-Surface Wind Speed in Forecasts of Extratropical Cyclones

2021 ◽  
Author(s):  
Emanuele S. Gentile ◽  
Suzanne L. Gray ◽  
Janet F. Barlow ◽  
Huw W. Lewis ◽  
John M. Edwards
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
Model Systems ◽  
Sea State ◽  
Near Surface ◽  
Extreme Surface ◽  
Wind Speeds ◽  
The Impact

AbstractAccurate modelling of air–sea surface exchanges is crucial for reliable extreme surface wind-speed forecasts. While atmosphere-only weather forecast models represent ocean and wave effects through sea-state independent parametrizations, coupled multi-model systems capture sea-state dynamics by integrating feedbacks between the atmosphere, ocean and wave model components. Here, we investigate the sensitivity of extreme surface wind speeds to air–sea exchanges at the kilometre scale using coupled and uncoupled configurations of the Met Office’s UK Regional Coupled Environmental Prediction system. The case period includes the passage of extra-tropical cyclones Helen, Ali, and Bronagh, which brought maximum gusts of 36 m s$$^{-1}$$ - 1 over the UK. Compared with the atmosphere-only results, coupling to the ocean decreases the domain-average sea-surface temperature by up to 0.5 K. Inclusion of coupling to waves reduce the 98th percentile 10-m wind speed by up to 2 m s$$^{-1}$$ - 1 as young, growing wind waves reduce the wind speed by increasing the sea-surface aerodynamic roughness. Impacts on gusts are more modest, with local reductions of up to 1 m s$$^{-1}$$ - 1 , due to enhanced boundary-layer turbulence which partially offsets air–sea momentum transfer. Using a new drag parametrization based on the Coupled Ocean–Atmosphere Response Experiment 4.0 parametrization, with a cap on the neutral drag coefficient and reduction for wind speeds exceeding 27 m s$$^{-1}$$ - 1 , the atmosphere-only model achieves equivalent impacts on 10-m wind speeds and gusts as from coupling to waves. Overall, the new drag parametrization achieves the same 20% improvement in forecast 10-m wind-speed skill as coupling to waves, with the advantage of saving the computational cost of the ocean and wave models.

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