scholarly journals Improving the Near-Surface Wind Forecast around the Turpan Basin of the Northwest China by Using the WRF_TopoWind Model

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1624
Author(s):  
Hui Ma ◽  
Xiaolei Ma ◽  
Shengwei Mei ◽  
Fei Wang ◽  
Yanwei Jing
Keyword(s):  
Wind Energy ◽  
Complex Terrain ◽  
Wind Farm ◽  
Surface Wind ◽  
Wrf Model ◽  
Forecast Accuracy ◽  
Clean Energy ◽  
Northwest China ◽  
Surface Drag ◽  
Near Surface

Wind energy is a type of renewable and clean energy which has attracted more and more attention all over the world. The Northwest China is a region with the most abundant wind energy not only in China, but also in the whole world. To achieve the goal of carbon neutralization, there is an urgent need to make full use of wind energy in Northwest China and to improve the efficiency of wind power generation systems in this region. As forecast accuracy of the near-surface wind is crucial to wind-generated electricity efficiency, improving the near-surface wind forecast is of great importance. This study conducted the first test to incorporate the subgrid surface drag into the near-surface wind forecast under the complex terrain conditions over Northwest China by using two TopoWind models added by newer versions of the Weather Research and Forecasting (WRF) model. Based on three groups (each group had 28 runs) of forecasts (i.e., Control run, Test 01 and Test 02) started at 12:00 UTC of each day (ran for 48 h) during the period of 1–28 October 2020, it was shown that, overall, both TopoWind models could improve the near-surface wind speed forecasts under the complex terrain conditions over Northwest China, particularly for reducing the errors associated with the forecast of the wind-speed’s magnitude. In addition to wind forecast, the forecasts of sea level pressure and 2-m temperature were also improved. Different geographical features (wind-farm stations located south of the mountain tended to have more accurate forecast) and weather systems were found to be crucial to forecast accuracy. Good forecasts tended to appear when the simulation domain was mainly controlled by the high-pressure systems with the upper-level jet far from it.

scholarly journals Evaluation of the Forecast Accuracy of Near-Surface Temperature and Wind in Northwest China Based on the WRF Model

2018 ◽  
Vol 32 (3) ◽  
pp. 469-490 ◽  
Author(s):  
Haixia Duan ◽  
Yaohui Li ◽  
Tiejun Zhang ◽  
Zhaoxia Pu ◽  
Cailing Zhao ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Wrf Model ◽  
Forecast Accuracy ◽  
Northwest China ◽  
Near Surface

On the Analysis of the Performance of WRF and NICAM in a Hyperarid Environment

2020 ◽  
Vol 35 (3) ◽  
pp. 891-919 ◽  
Author(s):  
Ricardo Fonseca ◽  
Marouane Temimi ◽  
Mohan Satyanarayana Thota ◽  
Narendra Reddy Nelli ◽  
Michael John Weston ◽  
...  
Keyword(s):  
Wind Speed ◽  
Eddy Covariance ◽  
United Arab Emirates ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Wrf Model ◽  
Cloud Microphysics ◽  
Shortwave Radiation ◽  
Surface Drag ◽  
Near Surface

Abstract The Weather Research and Forecasting (WRF) Model and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) are forced with the Global Forecast System (GFS) data and run over the United Arab Emirates (UAE) for two 4-day periods: one in the cold season (16–18 December 2017) and another in the warm season (13–15 April 2018). The models’ performance is evaluated against four observational datasets: weather station observations, eddy-covariance flux measurements at Al Ain, microwave radiometer–derived temperature profile, and twice-daily radiosonde measurements at Abu Dhabi. An overestimation of the daily mean air temperature by 1°–3°C is noticed for both models and periods. This warm bias is attributed to the reduced cloud cover and resulting increased surface downward shortwave radiation flux. A comparison with the eddy-covariance data suggested that both models also underestimate the observed albedo. However, when the models predict heavier amounts of precipitation, they tend to be colder than observations, typically by 2°–3°C. NICAM and WRF overpredict the strength of the near-surface wind speed at all weather stations by roughly 1–3 m s−1, which has been attributed to a poor representation of its subgrid-scale fluctuations and surface drag parameterization. WRF tends to be wetter and NICAM drier than the station observations, possibly because of differences in the cloud microphysics schemes. While the performance of both models for the near-surface fields is comparable, NICAM outperforms WRF in the simulation of vertical profiles of temperature, relative humidity, and wind speed, being able to partially correct some of the biases in the GFS data.

Examination of Errors in Near-Surface Temperature and Wind from WRF Numerical Simulations in Regions of Complex Terrain

2013 ◽  
Vol 28 (3) ◽  
pp. 893-914 ◽  
Author(s):  
Hailing Zhang ◽  
Zhaoxia Pu ◽  
Xuebo Zhang
Keyword(s):  
Complex Terrain ◽  
Forecast Error ◽  
Wrf Model ◽  
Atmospheric Temperature ◽  
Lower Atmosphere ◽  
Forecast Errors ◽  
Atmospheric Conditions ◽  
Near Surface ◽  
System A ◽  
Synoptic Forcing

Abstract The performance of an advanced research version of the Weather Research and Forecasting Model (WRF) in predicting near-surface atmospheric temperature and wind conditions under various terrain and weather regimes is examined. Verification of 2-m temperature and 10-m wind speed and direction against surface Mesonet observations is conducted. Three individual events under strong synoptic forcings (i.e., a frontal system, a low-level jet, and a persistent inversion) are first evaluated. It is found that the WRF model is able to reproduce these weather phenomena reasonably well. Forecasts of near-surface variables in flat terrain generally agree well with observations, but errors also occur, depending on the predictability of the lower-atmospheric boundary layer. In complex terrain, forecasts not only suffer from the model's inability to reproduce accurate atmospheric conditions in the lower atmosphere but also struggle with representative issues due to mismatches between the model and the actual terrain. In addition, surface forecasts at finer resolutions do not always outperform those at coarser resolutions. Increasing the vertical resolution may not help predict the near-surface variables, although it does improve the forecasts of the structure of mesoscale weather phenomena. A statistical analysis is also performed for 120 forecasts during a 1-month period to further investigate forecast error characteristics in complex terrain. Results illustrate that forecast errors in near-surface variables depend strongly on the diurnal variation in surface conditions, especially when synoptic forcing is weak. Under strong synoptic forcing, the diurnal patterns in the errors break down, while the flow-dependent errors are clearly shown.

scholarly journals High-resolution numerical modeling of mesoscale island wakes and sensitivity to static topographic relief data

2015 ◽  
Vol 8 (8) ◽  
pp. 2645-2653 ◽  
Author(s):  
C. G. Nunalee ◽  
Á. Horváth ◽  
S. Basu
Keyword(s):  
High Resolution ◽  
Vortex Shedding ◽  
Weather Prediction ◽  
Surface Wind ◽  
Wrf Model ◽  
Data Sets ◽  
Data Set ◽  
Near Surface ◽  
Nwp Model ◽  
Height Data

Abstract. Recent decades have witnessed a drastic increase in the fidelity of numerical weather prediction (NWP) modeling. Currently, both research-grade and operational NWP models regularly perform simulations with horizontal grid spacings as fine as 1 km. This migration towards higher resolution potentially improves NWP model solutions by increasing the resolvability of mesoscale processes and reducing dependency on empirical physics parameterizations. However, at the same time, the accuracy of high-resolution simulations, particularly in the atmospheric boundary layer (ABL), is also sensitive to orographic forcing which can have significant variability on the same spatial scale as, or smaller than, NWP model grids. Despite this sensitivity, many high-resolution atmospheric simulations do not consider uncertainty with respect to selection of static terrain height data set. In this paper, we use the Weather Research and Forecasting (WRF) model to simulate realistic cases of lower tropospheric flow over and downstream of mountainous islands using the default global 30 s United States Geographic Survey terrain height data set (GTOPO30), the Shuttle Radar Topography Mission (SRTM), and the Global Multi-resolution Terrain Elevation Data set (GMTED2010) terrain height data sets. While the differences between the SRTM-based and GMTED2010-based simulations are extremely small, the GTOPO30-based simulations differ significantly. Our results demonstrate cases where the differences between the source terrain data sets are significant enough to produce entirely different orographic wake mechanics, such as vortex shedding vs. no vortex shedding. These results are also compared to MODIS visible satellite imagery and ASCAT near-surface wind retrievals. Collectively, these results highlight the importance of utilizing accurate static orographic boundary conditions when running high-resolution mesoscale models.

A Simple Three-Dimensional Terrain Modeling Method for Complex Terrain Wind Environment Simulation

2013 ◽  
Vol 397-400 ◽  
pp. 2420-2425
Author(s):  
Shi Ling Chen ◽  
Jun Lu ◽  
Wei Wei Yu ◽  
Shao Liang Zhang
Keyword(s):  
Complex Terrain ◽  
Cfd Simulation ◽  
Surface Wind ◽  
Three Dimensional ◽  
Modeling Method ◽  
Terrain Modeling ◽  
Wind Environment ◽  
Near Surface ◽  
Digital Modeling ◽  
Environment Simulation

In order to solve the problems in complex terrain modeling by computational fluid dynamics(CFD) simulation at prophase, such as difficulty in collecting data, tedious modeling process, wasting times and so on. In this paper, combined various commonly digital technology，and the transformation between the network terrain file and CFD (PHOENICS) solid model is realized by using a new set of outdoor complex terrain rapid digital modeling method. Take mountain city -Chongqing as an object to analyses the near-surface wind environment. The method is directly generated by the network terrain data without any screening or simplified. The virtual model can be matched the actual terrain with the extreme. By using the simulation cycle for complex terrain, time will be greatly shortened for urban planning.

scholarly journals One-Way Coupling of the WRF–QUIC Urban Dispersion Modeling System

2015 ◽  
Vol 54 (10) ◽  
pp. 2119-2139 ◽  
Author(s):  
A. K. Kochanski ◽  
E. R. Pardyjak ◽  
R. Stoll ◽  
A. Gowardhan ◽  
M. J. Brown ◽  
...  
Keyword(s):  
Urban Areas ◽  
Surface Wind ◽  
Wrf Model ◽  
Conclusive Evidence ◽  
Dispersion Modeling ◽  
Industrial Complex ◽  
Urban Dispersion ◽  
Canopy Layer ◽  
Near Surface ◽  
Wind Profiles

AbstractSimulations of local weather and air quality in urban areas must account for processes spanning from meso- to microscales, including turbulence and transport within the urban canopy layer. Here, the authors investigate the performance of the building-resolving Quick Urban Industrial Complex (QUIC) Dispersion Modeling System driven with mean wind profiles from the mesoscale Weather Research and Forecasting (WRF) Model. Dispersion simulations are performed for intensive observation periods 2 and 8 of the Joint Urban 2003 field experiment conducted in Oklahoma City, Oklahoma, using an ensemble of expert-derived wind profiles from observational data as well as profiles derived from WRF runs. The results suggest that WRF can be used successfully as a source of inflow boundary conditions for urban simulations, without the collection and processing of intensive field observations needed to produce expert-derived wind profiles. Detailed statistical analysis of tracer concentration fields suggests that, for the purpose of the urban dispersion, WRF simulations provide wind forcing as good as individual or ensemble expert-derived profiles. Despite problems capturing the strength and the elevation of the Great Plains low-level jet, the WRF-simulated near-surface wind speed and direction were close to observations, thus assuring realistic forcing for urban dispersion estimates. Tests performed with multilayer and bulk urban parameterizations embedded in WRF did not provide any conclusive evidence of the superiority of one scheme over the other, although the dispersion simulations driven by the latter showed slightly better results.

scholarly journals Surface Wind Regionalization over Complex Terrain: Evaluation and Analysis of a High-Resolution WRF Simulation

2010 ◽  
Vol 49 (2) ◽  
pp. 268-287 ◽  
Author(s):  
Pedro A. Jiménez ◽  
J. Fidel González-Rouco ◽  
Elena García-Bustamante ◽  
Jorge Navarro ◽  
Juan P. Montávez ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Dynamical Downscaling ◽  
Surface Wind ◽  
Wrf Model ◽  
Spatial Dimension ◽  
Weather Research And Forecasting ◽  
Observational Period ◽  
Spatiotemporal Resolution ◽  
Wind Variability ◽  
Spatial Coverage

Abstract This study analyzes the daily-mean surface wind variability over an area characterized by complex topography through comparing observations and a 2-km-spatial-resolution simulation performed with the Weather Research and Forecasting (WRF) model for the period 1992–2005. The evaluation focuses on the performance of the simulation to reproduce the wind variability within subregions identified from observations over the 1999–2002 period in a previous study. By comparing with wind observations, the model results show the ability of the WRF dynamical downscaling over a region of complex terrain. The higher spatiotemporal resolution of the WRF simulation is used to evaluate the extent to which the length of the observational period and the limited spatial coverage of observations condition one’s understanding of the wind variability over the area. The subregions identified with the simulation during the 1992–2005 period are similar to those identified with observations (1999–2002). In addition, the reduced number of stations reasonably represents the spatial wind variability over the area. However, the analysis of the full spatial dimension simulated by the model suggests that observational coverage could be improved in some subregions. The approach adopted here can have a direct application to the design of observational networks.

Near-Surface Wind Observation Impact on Forecasts: Temporal Propagation of the Analysis Increment

2017 ◽  
Vol 145 (4) ◽  
pp. 1549-1564 ◽  
Author(s):  
Joël Bédard ◽  
Stéphane Laroche ◽  
Pierre Gauthier
Keyword(s):  
Lead Time ◽  
Surface Wind ◽  
Forecast Accuracy ◽  
Error Statistics ◽  
Short Term ◽  
Background Error ◽  
Near Surface ◽  
Geophysical Model ◽  
Model Output Statistics ◽  
Mass Field

Abstract This study examines the assimilation of near-surface wind observations over land to improve wind nowcasting and short-term tropospheric forecasts. A new geostatistical operator based on geophysical model output statistics (GMOS) is compared with a bilinear interpolation scheme (Bilin). The multivariate impact on forecasts and the temporal evolution of the analysis increments produced are examined as well as the influence of background error covariances on different components of the prediction system. Results show that Bilin significantly degrades surface and upper-air fields when assimilating only wind data from 4942 SYNOP stations. GMOS on the other hand produces smaller increments that are in better agreement with the model state. It leads to better short-term near-surface wind forecasts and does not deteriorate the upper-air forecasts. The information persists longer in the system with GMOS, although the local improvements do not propagate beyond 6-h lead time. Initial model tendencies indicate that the mass field is not significantly altered when using static error covariances and the boundary layer parameterizations damp the poorly balanced increment locally. Conversely, most of the analysis increment is propagated when using flow-dependent error statistics. It results in better balanced wind and mass fields and provides a more persistent impact on the forecasts. Forecast accuracy results from observing system experiments (assimilating SYNOP winds with all observations used operationally) are generally neutral. Nevertheless, forecasts and analyses from GMOS are more self-consistent than those from both Bilin and a control experiment (not assimilating near-surface winds over land) and the information from the observations persists up to 12-h lead time.

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