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 Characteristics and performance of vertical winds as observed by the radar wind profiler network of China

2020 ◽  
Author(s):  
Boming Liu ◽  
Jianping Guo ◽  
Wei Gong ◽  
Lijuan Shi ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Vertical Wind ◽  
Horizontal Wind ◽  
Wind Profiler ◽  
Numerical Weather ◽  
And Performance ◽  
Wind Profiles ◽  
Vertical Winds

Abstract. Vertical wind profiles are the foundation for numerical weather prediction systems research. Large-scale vertical wind data have been previously documented from network observations in several countries, but the nationwide vertical wind observations are poorly understood in China. In this study, the salient characteristics and performance of vertical winds as observed by the radar wind profiler network of China was investigated, which consists of more than 100 stations instrumented with 1290-MHz Doppler radar designed primarily for measuring vertical-resolved winds. This network has good spatial coverage, with denser sites in coastal areas. The vertical 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 analysis with reanalysis data indicated that the observation data at 89 stations are recommended, and 17 stations are unrecommended. The vertical wind profiles can serve as important input dataset assimilated into numerical weather prediction system at both regional and global scales.

scholarly journals Maximum Instantaneous Wind Speed Forecasting and Performance Evaluation by Using Numerical Weather Prediction and On-Site Measurement

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 316
Author(s):  
Atsushi Yamaguchi ◽  
Takeshi Ishihara
Keyword(s):  
Wind Speed ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Area Under The Curve ◽  
Horizontal Resolution ◽  
Model Parameters ◽  
Numerical Weather ◽  
And Performance ◽  
Model Output Statistics ◽  
Accuracy Of Prediction

A maximum instantaneous wind speed forecast methodology based on the autoregressive with exogenous inputs (ARX) model is proposed, in which numerical weather prediction and on-site measurement are used as inputs and the model parameters are estimated using non-parametric regression with forgetting factors. The accuracy of prediction using a proposed dynamic model is then evaluated and compared to the conventional static model output statistics (MOS) model. It is found that the prediction accuracy is improved by utilizing numerical weather prediction with a higher horizontal resolution. Finally, the predictability of the maximum instantaneous wind speed higher than 15m/s is evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The optimal quantile level of the maximum instantaneous wind speed is derived using a cost function.

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 On the Impact of Unmanned Aerial System Observations on Numerical Weather Prediction in the Coastal Zone

2018 ◽  
Vol 146 (2) ◽  
pp. 599-622 ◽  
Author(s):  
David D. Flagg ◽  
James D. Doyle ◽  
Teddy R. Holt ◽  
Daniel P. Tyndall ◽  
Clark M. Amerault ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Research Laboratory ◽  
Weather Prediction ◽  
Ocean Model ◽  
Naval Research ◽  
Unmanned Aerial System ◽  
Numerical Weather ◽  
The Impact

Abstract The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.

Simulating boundary-layer rolls with a numerical weather prediction model

2015 ◽  
Vol 142 (694) ◽  
pp. 211-223 ◽  
Author(s):  
William Thurston ◽  
Robert J. B. Fawcett ◽  
Kevin J. Tory ◽  
Jeffrey D. Kepert
Keyword(s):  
Boundary Layer ◽  
Prediction Model ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Numerical Weather

Evaluation of the Rapid Refresh Numerical Weather Prediction Model Over Arctic Alaska

2021 ◽  
Author(s):  
Matthew T. Bray ◽  
David D. Turner ◽  
Gijs de Boer
Keyword(s):  
Boundary Layer ◽  
Sea Ice ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Numerical Weather ◽  
Arctic Alaska ◽  
Short Term Forecasting ◽  
Northern Alaska ◽  
Mixed Phase Clouds

AbstractDespite a need for accurate weather forecasts for societal and economic interests in the U.S. Arctic, thorough evaluations of operational numerical weather prediction in the region have been limited. In particular, the Rapid Refresh Model (RAP), which plays a key role in short-term forecasting and decision making, has seen very limited assessment in northern Alaska, with most evaluation efforts focused on lower latitudes. In the present study, we verify forecasts from version 4 of the RAP against radiosonde, surface meteorological, and radiative flux observations from two Arctic sites on the northern Alaskan coastline, with a focus on boundary-layer thermodynamic and dynamic biases, model representation of surface inversions, and cloud characteristics. We find persistent seasonal thermodynamic biases near the surface that vary with wind direction, and may be related to the RAP’s handling of sea ice and ocean interactions. These biases seem to have diminished in the latest version of the RAP (version 5), which includes refined handling of sea ice, among other improvements. In addition, we find that despite capturing boundary-layer temperature profiles well overall, the RAP struggles to consistently represent strong, shallow surface inversions. Further, while the RAP seems to forecast the presence of clouds accurately in most cases, there are errors in the simulated characteristics of these clouds, which we hypothesize may be related to the RAP’s treatment of mixed-phase clouds.

scholarly journals Evaluation of wind profiles from the NERC MST Radar, Aberystwyth, UK

2014 ◽  
Vol 7 (5) ◽  
pp. 4589-4621
Author(s):  
C. F. Lee ◽  
G. Vaughan ◽  
D. A. Hooper
Keyword(s):  
Wind Speed ◽  
Standard Error ◽  
Random Error ◽  
Sampling Error ◽  
Weather Prediction ◽  
Horizontal Wind ◽  
Wind Speeds ◽  
Mst Radar ◽  
Wind Profiles ◽  
Aspect Sensitivity

Abstract. This study quantifies the uncertainties in winds measured by the Aberystwyth Mesosphere-Stratosphere-Troposphere (MST) radar (52.4° N, 4.0° W), before and after its renovation in March 2011. 127 radiosondes provide an independent measure of winds. Differences between radiosonde and radar-measured horizontal winds are correlated with long-term averages of vertical velocities, suggesting an influence from local mountain waves. These local influences are an important consideration when using radar winds as a measure of regional conditions, particularly for numerical weather prediction. In those applications, local effects represent a source of sampling error additional to the inherent uncertainties in the measurements themselves. The radar renovation improved the SNR of measurements, with correspondingly improved altitude coverage. It also corrected an under-estimate of horizontal wind speeds attributed to beam formation problems, due to component failure pre-renovation. The standard error in radar-measured winds averaged over half-an-hour increases with wind speed and altitude, and is 0.6–2.5 m s−1 (5–20% of wind speed) for post-renovation horizontal winds. Pre-renovation values are typically 0.4 m s−1 (0.03 m s−1) larger. The standard error in radial velocities is < 0.04 m s−1. Eight weeks of special radar operation are used to investigate the effects of echo power aspect sensitivity. Corrections for echo power aspect sensitivity remove an underestimate of horizontal wind speeds, however aspect sensitivity is azimuthally anisotropic at the scale of routine observations (≈ 1 h). This anisotropy introduces additional random error into wind profiles. For winds averaged over half-an-hour, the random error is around 3.5% above 8 km, but as large as 4.5% in the mid-troposphere.

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