scholarly journals Improved Gridded Wind Speed Forecasts by Statistical Postprocessing of Numerical Models with Block Regression

2016 ◽  
Vol 31 (6) ◽  
pp. 1929-1945 ◽  
Author(s):  
Michaël Zamo ◽  
Liliane Bel ◽  
Olivier Mestre ◽  
Joël Stein
Keyword(s):  
Wind Speed ◽  
Random Forests ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Numerical Models ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Near Surface ◽  
Numerical Weather ◽  
Spatial Coverage

Abstract Numerical weather forecast errors are routinely corrected through statistical postprocessing by several national weather services. These statistical postprocessing methods build a regression function called model output statistics (MOS) between observations and forecasts that is based on an archive of past forecasts and associated observations. Because of limited spatial coverage of most near-surface parameter measurements, MOS have been historically produced only at meteorological station locations. Nevertheless, forecasters and forecast users increasingly ask for improved gridded forecasts. The present work aims at building improved hourly wind speed forecasts over the grid of a numerical weather prediction model. First, a new observational analysis, which performs better in terms of statistical scores than those operationally used at Météo-France, is described as gridded pseudo-observations. This analysis, which is obtained by using an interpolation strategy that was selected among other alternative strategies after an intercomparison study conducted internally at Météo-France, is very parsimonious since it requires only two additive components, and it requires little computational resources. Then, several scalar regression methods are built and compared, using the new analysis as the observation. The most efficient MOS is based on random forests trained on blocks of nearby grid points. This method greatly improves forecasts compared with raw output of numerical weather prediction models. Furthermore, building each random forest on blocks and limiting those forests to shallow trees does not impair performance compared with unpruned and pointwise random forests. This alleviates the storage burden of the objects and speeds up operations.

scholarly journals Weakly Coupled Ocean–Atmosphere Data Assimilation in the ECMWF NWP System

2019 ◽  
Vol 11 (3) ◽  
pp. 234 ◽  
Author(s):  
Philip Browne ◽  
Patricia de Rosnay ◽  
Hao Zuo ◽  
Andrew Bennett ◽  
Andrew Dawson
Keyword(s):  
Sea Ice ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Coupled Analysis ◽  
Forecast Errors ◽  
Numerical Weather ◽  
Weakly Coupled ◽  
Spatial Coverage ◽  
Ocean Atmosphere

Numerical weather prediction models are including an increasing number of components of the Earth system. In particular, every forecast now issued by the European Centre for Medium-Range Weather Forecasts (ECMWF) runs with a 3D ocean model and a sea ice model below the atmosphere. Initialisation of different components using different methods and on different timescales can lead to inconsistencies when they are combined in the full system. Historically, the methods for initialising the ocean and the atmosphere have been typically developed separately. This paper describes an approach for combining the existing ocean and atmospheric analyses into what we categorise as a weakly coupled assimilation scheme. Here, we show the performance improvements achieved for the atmosphere by having a weakly coupled ocean–atmosphere assimilation system compared with an uncoupled system. Using numerical weather prediction diagnostics, we show that forecast errors are decreased compared with forecasts initialised from an uncoupled analysis. Further, a detailed investigation into spatial coverage of sea ice concentration in the Baltic Sea shows that a much more realistic structure is obtained by the weakly coupled analysis. By introducing the weakly coupled ocean–atmosphere analysis, the ocean analysis becomes a critical part of the numerical weather prediction system and provides a platform from which to build ever stronger forms of analysis coupling.

scholarly journals Parametrizing Horizontally-Averaged Wind and Temperature Profiles in the Urban Roughness Sublayer

2019 ◽  
Vol 173 (3) ◽  
pp. 321-348 ◽  
Author(s):  
Natalie E. Theeuwes ◽  
Reinder J. Ronda ◽  
Ian N. Harman ◽  
Andreas Christen ◽  
C. Sue B. Grimmond
Keyword(s):  
Wind Speed ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Roughness Sublayer ◽  
Vertical Profiles ◽  
Flux Boundary Condition ◽  
Numerical Weather ◽  
Urban Roughness Sublayer ◽  
Urban Roughness

Abstract Tower-based measurements from within and above the urban canopy in two cities are used to evaluate several existing approaches that parametrize the vertical profiles of wind speed and temperature within the urban roughness sublayer (RSL). It is shown that current use of Monin–Obukhov similarity theory (MOST) in numerical weather prediction models can be improved upon by using RSL corrections when modelling the vertical profiles of wind speed and friction velocity in the urban RSL using MOST. Using anisotropic building morphological information improves the agreement between observed and parametrized profiles of wind speed and momentum fluxes for selected methods. The largest improvement is found when using dynamically-varying aerodynamic roughness length and displacement height. Adding a RSL correction to MOST, however, does not improve the parametrization of the vertical profiles of temperature and heat fluxes. This is expected since sources and sinks of heat are assumed uniformly distributed through a simple flux boundary condition in all RSL formulations, yet are highly patchy and anisotropic in a real urban context. Our results can be used to inform the choice of surface-layer representations for air quality, dispersion, and numerical weather prediction applications in the urban environment.

scholarly journals Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja

2016 ◽  
Author(s):  
N. S. Wagenbrenner ◽  
J. M. Forthofer ◽  
B. K. Lamb ◽  
K. S. Shannon ◽  
B. W. Butler
Keyword(s):  
Numerical Weather Prediction ◽  
Complex Terrain ◽  
Prediction Models ◽  
Wildland Fire ◽  
Weather Prediction ◽  
Surface Wind ◽  
Atmospheric Stability ◽  
Wind Model ◽  
Near Surface ◽  
Numerical Weather

Abstract. Wind predictions in complex terrain are important for a number of applications. Dynamic downscaling of numerical weather prediction (NWP) model winds with a high resolution wind model is one way to obtain a wind forecast that accounts for local terrain effects, such as wind speed-up over ridges, flow channeling in valleys, flow separation around terrain obstacles, and flows induced by local surface heating and cooling. In this paper we investigate the ability of a mass-consistent wind model for downscaling near-surface wind predictions from four NWP models in complex terrain. Model predictions are compared with surface observations from a tall, isolated mountain. Downscaling improved near-surface wind forecasts under high-wind (near-neutral atmospheric stability) conditions. Results were mixed during upslope and downslope (non-neutral atmospheric stability) flow periods, although wind direction predictions generally improved with downscaling. This work constitutes evaluation of a diagnostic wind model at unprecedented high spatial resolution in terrain with topographical ruggedness approaching that of typical landscapes in the western US susceptible to wildland fire.

scholarly journals Can Reanalysis Products Outperform Mesoscale Numerical Weather Prediction Models in Modeling the Wind Resource in Simple Terrain?

2021 ◽  
Author(s):  
Vincent Pronk ◽  
Nicola Bodini ◽  
Mike Optis ◽  
Julie K. Lundquist ◽  
Patrick Moriarty ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Atmospheric Stability ◽  
Negative Bias ◽  
Numerical Weather ◽  
Wind Resource ◽  
Mesoscale Numerical Weather Prediction

Abstract. Mesoscale numerical weather prediction (NWP) models are generally considered more accurate than reanalysis products in characterizing the wind resource at heights of interest for wind energy, given their finer spatial resolution and more comprehensive physics. However, advancements in the latest ERA-5 reanalysis product motivate an assessment on whether ERA-5 can model wind speeds as well as a state-of-the-art NWP model – the Weather Research and Forecasting (WRF) model. We consider this research question for both simple terrain and offshore applications. Specifically, we compare wind profiles from ERA-5 and the preliminary WRF runs of the Wind Integration National Dataset (WIND) Toolkit Long-term Ensemble Dataset (WTK-LED) to those observed by lidars at site in Oklahoma, United States, and in a U.S. Atlantic offshore wind energy area. We find that ERA-5 shows a significant negative bias (~ −1 m s−1 ) at both locations, with a larger bias at the land-based site. WTK-LED-predicted wind speed profiles show a slight negative bias (~ −0.5 m s−1 ) offshore and a slight positive bias (~ +0.5 m s−1) at the land-based site. Surprisingly, we find that ERA-5 outperforms WTK-LED in terms of the centered root-mean-square error (cRMSE) and correlation coefficient, for both the land-based and offshore cases, in all atmospheric stability conditions. We find that WTK-LED’s higher cRMSE is caused by its tendency to overpredict the amplitude of the wind speed diurnal cycle both onshore and offshore.

Impact of assimilation of radiosonde and UAV observations on numerical weather prediction analyses and forecasts in the Arctic and Antarctic

2020 ◽  
Author(s):  
Timo Vihma ◽  
Tuomas Naakka ◽  
Qizhen Sun ◽  
Tiina Nygård ◽  
Michael Tjernström ◽  
...  
Keyword(s):  
Wind Speed ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
The Arctic ◽  
Observation Site ◽  
Near Surface ◽  
Numerical Weather ◽  
Background Fields ◽  
The Impact

<p>Weather forecasting in the Arctic and Antarctic is a challenge above all due to rarity of observations to be assimilated in numerical weather prediction (NWP) models. As observations are expensive and logistically challenging, it is important to evaluate the benefit that additional observations could bring to NWP.</p><p>Considering the Arctic, in this study the effects of the spatial coverage of the network on numerical weather prediction were evaluated by comparing radiosonde observations from land station taken from Integrated Global Radiosonde Archive (IGRA) and radiosonde observations from expeditions in the Arctic Ocean with operational analyses and background fields (12‐hr forecasts) of the European Centre for Medium Range Weather Forecasts (ECMWF). The focus was on 850 hPa level temperature for the period January 2016 – September 2018. Comparison of the analyses and background fields showed that radiosoundings had a remarkable impact on improving operational analyses but the impact had a large geographical variation. In particular, radiosonde observations from islands (Jan Mayen and Bear Island) in the northern North Atlantic and from Arctic expeditions substantially improved analyses suggesting that those observations were critical for the quality of analyses and forecasts. Comparison of two cases with and without assimilation of radiosonde sounding data from expeditions of Icebreaker Oden in 2016 and 2018 in the central Artic Ocean showed that satellite observations were not able to compensate for the large spatial gap in the radiosounding network. In the areas where the network is reasonably dense, the density of the sounding network was not the most critical factor for the quality of background fields. Instead, the quality of background field was more related to how radiosonde observations were utilized in the assimilation and to the quality of those observations.</p><p>Considering the Antarctic, we applied radiosonde sounding and Unmanned Aerial Vehicles (UAV) observations from an RV Polarstern cruise in the ice-covered Weddell Sea in austral winter 2013 to evaluate the impact of their assimilation in the Polar version of the Weather Research and Forecasting (Polar WRF) model. Our experiments revealed small or moderate impacts of radiosonde and UAV data assimilation. In any case, the assimilation of sounding data from both radiosondes and UAVs improved the analyses of air temperature, wind speed, and humidity at the observation site for most of the time. Further, the impact on the results of 5-day long Polar WRF experiments was often felt over distances of at least 300 km from the observation site. All experiments succeeded in capturing the main features of the evolution of near-surface variables, but the effects of data assimilation varied between different cases. Due to the limited vertical extent of the UAV observations, the impact of their assimilation was limited to the lowermost 1-2 km layer, and assimilation of radiosonde data was more beneficial for modelled sea level pressure and near-surface wind speed. Considering the perspectives for technological advance, atmospheric soundings applying UAV have a large potential to supplement conventional radiosonde sounding observations.</p><p>The differences in the results obtained for the Arctic and Antarctic are discussed.</p>

scholarly journals Manual and Semiautomated Wind Direction Editing for Use in the Generation of Synthetic Aperture Radar Wind Speed Imagery

2007 ◽  
Vol 46 (6) ◽  
pp. 776-790 ◽  
Author(s):  
George S. Young ◽  
Todd D. Sikora ◽  
Nathaniel S. Winstead
Keyword(s):  
Wind Speed ◽  
Synthetic Aperture Radar ◽  
Numerical Weather Prediction ◽  
Wind Direction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Synthetic Aperture ◽  
Numerical Weather ◽  
Sar Imagery ◽  
Aperture Radar

Abstract Previous studies have demonstrated that satellite synthetic aperture radar (SAR) can be used as an accurate scatterometer, yielding wind speed fields with subkilometer resolution. This wind speed generation is only possible, however, if a corresponding accurate wind direction field is available. The potential sources of this wind direction information include satellite scatterometers, numerical weather prediction models, and SAR itself through analysis of the spatial patterns caused by boundary layer wind structures. Each of these wind direction sources has shortcomings that can lead to wind speed errors in the SAR-derived field. Manual and semiautomated methods are presented for identifying and correcting numerical weather prediction model wind direction errors. The utility of this approach is demonstrated for a set of cases in which the first-guess wind direction data did not adequately portray the features seen in the SAR imagery. These situations include poorly resolved mesoscale phenomena and misplaced synoptic-scale fronts and cyclones.

scholarly journals Computational fluid dynamic (CFD) simulation of snowdrift in alpine environments, including a local weather model, for operational avalanche warning

2008 ◽  
Vol 48 ◽  
pp. 150-158 ◽  
Author(s):  
Simon Schneiderbauer ◽  
Thomas Tschachler ◽  
Johann Fischbacher ◽  
Walter Hinterberger ◽  
Peter Fischer
Keyword(s):  
Wind Speed ◽  
Numerical Weather Prediction ◽  
Wind Field ◽  
Snow Depth ◽  
Prediction Models ◽  
Fluid Dynamic ◽  
Weather Prediction ◽  
Time Varying ◽  
Numerical Weather ◽  
Avalanche Warning

AbstractA new continuum approach to snowdrift modelling is introduced. In addition, numerical studies are carried out to identify the influence of time-varying wind conditions on snowdrift simulations. We compare the snowdrift patterns at Grimming mountain, Austria, derived using a time-averaged wind field and a time-varying wind field obtained from the numerical weather prediction model INCA. The results show significant differences in the deposition patterns and snow depth even after a 6 hour drift period. Using time-averaged boundary conditions leads to an underprediction of the resulting snow depth caused by averaging the wind speed, which lets gusts of wind disappear while snow transport is a non-linear function of the wind speed. Using numerical weather prediction models for snowdrift simulation therefore provides enhanced knowledge of the snow depth for local avalanche warning services.

scholarly journals Characteristics and performance of wind profiles as observed by the radar wind profiler network of China

2020 ◽  
Vol 13 (8) ◽  
pp. 4589-4600 ◽  
Author(s):  
Boming Liu ◽  
Jianping Guo ◽  
Wei Gong ◽  
Lijuan Shi ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Horizontal Wind ◽  
Wind Profiler ◽  
Numerical Weather ◽  
Spatial Coverage ◽  
And Performance ◽  
Wind Profiles

Abstract. Wind profiles are fundamental to the research and applications in boundary layer meteorology, air quality and numerical weather prediction. Large-scale wind profile data have been previously documented from network observations in several countries, such as Japan, the USA, various European countries and Australia, but nationwide wind profiles observations are poorly understood in China. In this study, the salient characteristics and performance of wind profiles as observed by the radar wind profiler network of China are investigated. This network consists of more than 100 stations instrumented with 1290 MHz Doppler radar designed primarily for measuring vertically resolved winds at various altitudes but mainly in the boundary layer. It has good spatial coverage, with much denser sites in eastern China. The wind profiles observed by this network can provide the horizontal wind direction, horizontal wind speed and vertical wind speed for every 120 m interval within the height of 0 to 3 km. The availability of the radar wind profiler network has been investigated in terms of effective detection height, data acquisition rate, data confidence and data accuracy. Further comparison analyses with reanalysis data indicate that the observation data at 89 stations are recommended and 17 stations are not recommended. The boundary layer wind profiles from China can provide useful input to numerical weather prediction systems at regional scales.

scholarly journals Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja

2016 ◽  
Vol 16 (8) ◽  
pp. 5229-5241 ◽  
Author(s):  
Natalie S. Wagenbrenner ◽  
Jason M. Forthofer ◽  
Brian K. Lamb ◽  
Kyle S. Shannon ◽  
Bret W. Butler
Keyword(s):  
Numerical Weather Prediction ◽  
Complex Terrain ◽  
Prediction Models ◽  
Wildland Fire ◽  
Weather Prediction ◽  
Surface Wind ◽  
Atmospheric Stability ◽  
Wind Model ◽  
Near Surface ◽  
Numerical Weather

Abstract. Wind predictions in complex terrain are important for a number of applications. Dynamic downscaling of numerical weather prediction (NWP) model winds with a high-resolution wind model is one way to obtain a wind forecast that accounts for local terrain effects, such as wind speed-up over ridges, flow channeling in valleys, flow separation around terrain obstacles, and flows induced by local surface heating and cooling. In this paper we investigate the ability of a mass-consistent wind model for downscaling near-surface wind predictions from four NWP models in complex terrain. Model predictions are compared with surface observations from a tall, isolated mountain. Downscaling improved near-surface wind forecasts under high-wind (near-neutral atmospheric stability) conditions. Results were mixed during upslope and downslope (non-neutral atmospheric stability) flow periods, although wind direction predictions generally improved with downscaling. This work constitutes evaluation of a diagnostic wind model at unprecedented high spatial resolution in terrain with topographical ruggedness approaching that of typical landscapes in the western US susceptible to wildland fire.

Sign in / Sign up

Export Citation Format

Share Document