Comparison of AI-based approaches for statistical downscaling of surface wind fields in the North Atlantic

2021 ◽  
Author(s):  
Vadim Rezvov ◽  
Mikhail Krinitskiy ◽  
Alexander Gavrikov ◽  
Sergey Gulev
Keyword(s):  
Wind Speed ◽  
Statistical Downscaling ◽  
North Atlantic ◽  
Surface Wind ◽  
Surface Winds ◽  
Wind Fields ◽  
The North ◽  
The North Atlantic ◽  
Vector Wind ◽  
Resolution Data

<p>Surface winds — both wind speed and vector wind components — are fields of fundamental climatic importance. The character of surface winds greatly influences (and is influenced by) surface exchanges of momentum, energy, and matter. These wind fields are of interest in their own right, particularly concerning the characterization of wind power density and wind extremes. Surface winds are influenced by small-scale features such as local topography and thermal contrasts. That is why accurate high-resolution prediction of near‐surface wind fields is a topic of central interest in various fields of science and industry. Statistical downscaling is the way for inferring information on physical quantities at a local scale from available low‐resolution data. It is one of the ways to avoid costly high‐resolution simulations. Statistical downscaling connects variability of various scales using statistical prediction models. This approach is fundamentally data-driven and can only be applied in locations where observations have been taken for a sufficiently long time to establish the statistical relationship. Our study considered statistical downscaling of surface winds (both wind speed and vector wind components) in the North Atlantic. Deep learning methods are among the most outstanding examples of state‐of‐the‐art machine learning techniques that allow approximating sophisticated nonlinear functions. In our study, we applied various approaches involving artificial neural networks for statistical downscaling of near‐surface wind vector fields. We used ERA-Interim reanalysis as low-resolution data and RAS-NAAD dynamical downscaling product (14km grid resolution) as a high-resolution target. We compared statistical downscaling results to those obtained with bilinear/bicubic interpolation with respect to downscaling quality. We investigated how network complexity affects downscaling performance. We will demonstrate the preliminary results of the comparison and propose the outlook for further development of our methods.</p><p>This work was undertaken with financial support by the Russian Science Foundation grant № 17-77-20112-P.</p>

scholarly journals Evaluation of Extratropical Cyclone Precipitation in the North Atlantic Basin: An Analysis of ERA-Interim, WRF, and Two CMIP5 Models

2018 ◽  
Vol 31 (6) ◽  
pp. 2345-2360 ◽  
Author(s):  
James F. Booth ◽  
Catherine M. Naud ◽  
Jeff Willison
Keyword(s):  
Wind Speed ◽  
North Atlantic ◽  
Surface Wind ◽  
Wrf Model ◽  
Surface Wind Speed ◽  
Convective Precipitation ◽  
Cmip5 Models ◽  
Convection Scheme ◽  
The North ◽  
The North Atlantic

The representation of extratropical cyclone (ETC) precipitation in general circulation models (GCMs) and the Weather Research and Forecasting (WRF) Model is analyzed. This work considers the link between ETC precipitation and dynamical strength and tests if parameterized convection affects this link for ETCs in the North Atlantic basin. Lagrangian cyclone tracks of ETCs in ERA-Interim (ERAI), GISS and GFDL CMIP5 models, and WRF with two horizontal resolutions are utilized in a compositing analysis. The 20-km-resolution WRF Model generates stronger ETCs based on surface wind speed and cyclone precipitation. The GCMs and ERAI generate similar composite means and distributions for cyclone precipitation rates, but GCMs generate weaker cyclone surface winds than ERAI. The amount of cyclone precipitation generated by the convection scheme differs significantly across the datasets, with the GISS model generating the most, followed by ERAI and then the GFDL model. The models and reanalysis generate relatively more parameterized convective precipitation when the total cyclone-averaged precipitation is smaller. This is partially due to the contribution of parameterized convective precipitation occurring more often late in the ETC’s life cycle. For reanalysis and models, precipitation increases with both cyclone moisture and surface wind speed, and this is true if the contribution from the parameterized convection scheme is larger or not. This work shows that these different models generate similar total ETC precipitation despite large differences in the parameterized convection, and these differences do not cause unexpected behavior in ETC precipitation sensitivity to cyclone moisture or surface wind speed.

Near-surface wind speeds in ERA5: Climatology, decadal variability and long-term trends

2021 ◽  
Author(s):  
Terhi K. Laurila ◽  
Victoria A. Sinclair ◽  
Hilppa Gregow
Keyword(s):  
Iberian Peninsula ◽  
North Atlantic ◽  
Decadal Variability ◽  
Surface Wind ◽  
Northern Europe ◽  
Near Surface ◽  
Wind Speeds ◽  
The North ◽  
The North Atlantic

<p>The knowledge of long-term climate and variability of near-surface wind speeds is essential and widely used among meteorologists, climate scientists and in industries such as wind energy and forestry. The new high-resolution ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) will likely be used as a reference in future climate projections and in many wind-related applications. Hence, it is important to know what is the mean climate and variability of wind speeds in ERA5.</p><p>We present the monthly 10-m wind speed climate and decadal variability in the North Atlantic and Europe during the 40-year period (1979-2018) based on ERA5. In addition, we examine temporal time series and possible trends in three locations: the central North Atlantic, Finland and Iberian Peninsula. Moreover, we investigate what are the physical reasons for the decadal changes in 10-m wind speeds.</p><p>The 40-year mean and the 98th percentile wind speeds show a distinct contrast between land and sea with the strongest winds over the ocean and a seasonal variation with the strongest winds during winter time. The winds have the highest values and variabilities associated with storm tracks and local wind phenomena such as the mistral. To investigate the extremeness of the winds, we defined an extreme find factor (EWF) which is the ratio between the 98th percentile and mean wind speeds. The EWF is higher in southern Europe than in northern Europe during all months. Mostly no statistically significant linear trends of 10-m wind speeds were found in the 40-year period in the three locations and the annual and decadal variability was large.</p><p>The windiest decade in northern Europe was the 1990s and in southern Europe the 1980s and 2010s. The decadal changes in 10-m wind speeds were largely explained by the position of the jet stream and storm tracks and the strength of the north-south pressure gradient over the North Atlantic. In addition, we investigated the correlation between the North Atlantic Oscillation (NAO) and the Atlantic Multi-decadal Oscillation (AMO) in the three locations. The NAO has a positive correlation in the central North Atlantic and Finland and a negative correlation in Iberian Peninsula. The AMO correlates moderately with the winds in the central North Atlantic but no correlation was found in Finland or the Iberian Peninsula. Overall, our study highlights that rather than just using long-term linear trends in wind speeds it is more informative to consider inter-annual or decadal variability.</p>

scholarly journals Observational Assessment of Nonlocal Heat Flux Feedback in the North Atlantic by GEFA

2013 ◽  
Vol 52 (3) ◽  
pp. 645-653 ◽  
Author(s):  
Na Wen ◽  
Zhengyu Liu ◽  
Qinyu Liu
Keyword(s):  
Heat Flux ◽  
North Atlantic ◽  
Surface Wind ◽  
Turbulent Heat ◽  
Observational Assessment ◽  
The North ◽  
The North Atlantic ◽  
Flux Response ◽  
Sst Anomalies ◽  
Heat Flux Feedback

AbstractMost previous studies have proven the local negative heat flux feedback (the surface heat flux response to SST anomalies) in the midlatitude areas. However, it is uncertain whether a nonlocal heat flux feedback can be observed. In this paper, the generalized equilibrium feedback assessment (GEFA) method is employed to examine the full surface turbulent heat flux response to SST in the North Atlantic Ocean using NCEP–NCAR reanalysis data. The results not only confirm the dominant local negative feedback, but also indicate a robust nonlocal positive feedback of the Gulf Stream Extension (GSE) SST to the downstream heat flux in the subpolar region. This nonlocal feedback presents a strong seasonality, with response magnitudes of in winter and in summer. Further study indicates that the nonlocal effect is initiated by the adjustments of the downstream surface wind to the GSE SST anomalies.

what do we not know about the sea-surface wind stress1

1968 ◽  
Vol 49 (3) ◽  
pp. 247-253 ◽  
Author(s):  
E. B. Kraus
Keyword(s):  
Drag Coefficient ◽  
North Atlantic ◽  
Surface Stress ◽  
Surface Wind ◽  
Sea Surface ◽  
The North ◽  
Sea Surface Wind ◽  
The North Atlantic ◽  
Simple Sampling ◽  
Range Of Values

A simple sampling experiment gives a several octave range of values for the zonal surface stress obtainable from synoptic maps over the North Atlantic. Uncertainty about the value of the drag coefficient account for about half the variance. The different methods that have been used to specify this quantity are reviewed and an attempt is made to state explicitly the assumptions involved in each case.

scholarly journals Statistical Downscaling of Gridded Wind Speed Data Using Local Topography

2017 ◽  
Vol 18 (2) ◽  
pp. 335-348 ◽  
Author(s):  
Adam Winstral ◽  
Tobias Jonas ◽  
Nora Helbig
Keyword(s):  
Wind Speed ◽  
Statistical Downscaling ◽  
Surface Wind ◽  
Horizontal Resolution ◽  
Wind Data ◽  
Wind Fields ◽  
Forecast Performance ◽  
Wind Speeds ◽  
Mountain Environments ◽  
High Winds

Abstract Winds, particularly high winds, strongly affect snowmelt and snow redistribution. High winds during rain-on-snow events can lead to catastrophic flooding while strong redistribution events in mountain environments can generate dangerous avalanche conditions. To provide adequate warnings, accurate wind data are required. Yet, mountain wind fields exhibit a high degree of heterogeneity at small spatial lengths that are not resolved by currently available gridded forecast data. Wind data from over 200 stations across Switzerland were used to evaluate two forecast surface wind products (~2- and 7-km horizontal resolution) and develop a statistical downscaling technique to capture these finer-scaled heterogeneities. Wind exposure metrics derived from a 25-m horizontal resolution digital elevation model effectively segregated high, moderate, and low wind speed sites. Forecast performance was markedly compromised and biased low at the exposed sites and biased high at the sheltered, valley sites. It was also found that the variability of predicted wind speeds at these sites did not accurately represent the observed variability. A novel optimization scheme that accounted for local terrain structure while also nudging the forecasted distributions to better match the observed distributions and variability was developed. The resultant statistical downscaling technique notably decreased biases across a range of elevations and exposures and provided a better match to observed wind speed distributions.

scholarly journals Air/sea DMS gas transfer in the North Atlantic: evidence for limited interfacial gas exchange at high wind speed

2013 ◽  
Vol 13 (5) ◽  
pp. 13285-13322 ◽  
Author(s):  
T. G. Bell ◽  
W. De Bruyn ◽  
S. D. Miller ◽  
B. Ward ◽  
K. Christensen ◽  
...  
Keyword(s):  
Wind Speed ◽  
North Atlantic ◽  
Interfacial Stress ◽  
Horizontal Wind ◽  
Gas Transfer ◽  
High Wind ◽  
High Wind Speed ◽  
Wind Speeds ◽  
The North ◽  
The North Atlantic

Abstract. Shipboard measurements of eddy covariance DMS air/sea fluxes and seawater concentration were carried out in the North Atlantic bloom region in June/July 2011. Gas transfer coefficients (k660) show a linear dependence on mean horizontal wind speed at wind speeds up to 11 m s−1. At higher wind speeds the relationship between k660 and wind speed weakens. At high winds, measured DMS fluxes were lower than predicted based on the linear relationship between wind speed and interfacial stress extrapolated from low to intermediate wind speeds. In contrast, the transfer coefficient for sensible heat did not exhibit this effect. The apparent suppression of air/sea gas flux at higher wind speeds appears to be related to sea state, as determined from shipboard wave measurements. These observations are consistent with the idea that long waves suppress near surface water side turbulence, and decrease interfacial gas transfer. This effect may be more easily observed for DMS than for less soluble gases, such as CO2, because the air/sea exchange of DMS is controlled by interfacial rather than bubble-mediated gas transfer under high wind speed conditions.

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