Assimilating QuikSCAT Ocean Surface Winds with the Weather Research and Forecasting Model for Surface Wind-Field Simulation over the Chukchi/Beaufort Seas

2013 ◽  
Vol 148 (1) ◽  
pp. 207-226 ◽  
Author(s):  
Xingang Fan ◽  
Jeremy R. Krieger ◽  
Jing Zhang ◽  
Xiangdong Zhang
Keyword(s):  
Wind Field ◽  
Surface Wind ◽  
Ocean Surface ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Surface Winds ◽  
Wind Field Simulation ◽  
Field Simulation ◽  
Ocean Surface Winds ◽  
Surface Wind Field

The Effects of Vertical Stratification and Land Use Data on Numerical Simulation of Wind Energy Resources

2014 ◽  
Vol 535 ◽  
pp. 135-140
Author(s):  
Yuan Chang Deng ◽  
Zhen Cao Zou
Keyword(s):  
Numerical Simulation ◽  
Land Use ◽  
Wind Field ◽  
Wind Profile ◽  
Surface Wind ◽  
Wrf Model ◽  
Vertical Stratification ◽  
Near Surface ◽  
Wind Field Simulation ◽  
Surface Wind Field

By adjusting the distribution of vertical layers and increasing its number in WRF model, this paper mainly studies the effects of vertical stratification on the near surface wind field and vertical profile simulation. The test outcomes show that moderately increasing vertical layers can effectively improve the near surface wind field simulation results, while it has little influence on the numeral and changing trend of high vertical wind profile. Considering both accuracy and efficiency, it is recommended to set 10~15 layers below 300m. On the basis of this research, instead of USGS data by using the MODIS_30S data, the data underlying surface land in Shenzhen and HK area are updated. Comparative results between the two schemes, due to the roughness and drag coefficient of difference types of surface are not identical; the surface data has a significant impact on wind field and wind profile simulation. Using the MODIS land use data which is more consistent with the actual situation can improve the accuracy of numerical simulation.

Influence of Assimilating CYGNSS Ocean Surface Wind Data on Tropical Cyclone Analyses and Predictions

2020 ◽  
Author(s):  
Bachir Annane ◽  
Mark Leidner ◽  
Ross Hoffman ◽  
Feixiong Huang ◽  
James Garrisson
Keyword(s):  
Tropical Cyclones ◽  
Surface Wind ◽  
Ocean Surface ◽  
Convective Precipitation ◽  
Weather Research And Forecasting ◽  
Wind Speeds ◽  
Ocean Surface Winds ◽  
Ocean Surface Wind ◽  
The Impact ◽  
Hwrf Model

<div> <div><em>For the analysis and forecasting of tropical cyclones, the main benefits of data from the CYGNSS constellation of satellites are the increased revisit frequency compared with polar-orbiting satellites and the ability to provide ocean surface wind observations through convective precipitation. Consequently, CYGNSS delivers an improved capability to observe the structure and evolution of ocean surface winds in and around tropical cyclones. This study quantifies the impact of assimilating CYGNSS delay-Doppler maps, CYGNSS retrieved wind speeds and derived CYGNSS wind vectors on 6-hourly analyses and 5-day forecasts of developing tropical cyclones, using the 2019 version of NOAA's operational Hurricane Weather Research and Forecasting (HWRF) model.</em></div> </div>

scholarly journals Preliminary step towards comparing large scale features in surface wind field between ERA-Interim re-analysis and a higher resolution simulation

2013 ◽  
Vol 10 (1) ◽  
pp. 113-117
Author(s):  
A. Boilley ◽  
T. Ranchin ◽  
A. Ghennioui
Keyword(s):  
Large Scale ◽  
Surface Wind ◽  
Weather Research And Forecasting ◽  
Small Scale ◽  
Forecasting Model ◽  
Preliminary Step ◽  
The Mean ◽  
Surface Wind Field ◽  
Resolution Dataset ◽  
Resolution Simulation

Abstract. The weather research and forecasting model (WRF) is initialized with ERA-Interim re-analysis to produce a higher resolution dataset over a one month period. WRF is supposed to introduce small scale information and maintain the mean large scale features. A detailed spectral analysis is performed to verify this statement. It reveals that consistency in large-scale features is not achieved for wavelengths greater than or equal to 6° even with the activation of spectral nudging.

Scaling Feature of the Ocean Surface Wind Field

2021 ◽  
Author(s):  
Yang Gao ◽  
Francois G Schmitt ◽  
Jianyu Hu ◽  
Yongxiang Huang
Keyword(s):  
Seasonal Variations ◽  
Wind Field ◽  
Surface Wind ◽  
Ocean Surface ◽  
Scaling Exponent ◽  
Scaling Exponents ◽  
Multiscale Dynamics ◽  
Ocean Surface Wind ◽  
Scaling Features ◽  
Surface Wind Field

<p>The ocean surface wind plays a crucial role in the air-sea exchanges of momentum, heat, and mass, consequently is vital to the controlling of weather and climate. Due to the extremely large range of scales of the motion of the wind field, e.g., flow structures from millimeters to thousands of kilometers, the multiscale dynamics are known to be relevant. In this work, with the help of a Wiener-Khinchine theorem-based Fourier power spectrum estimator, the scaling features of the wind field provided by several satellites, i.e., QuikSCAT, Metop-A, -B, and -C, Haiyang-2B, and China France Oceanography SATellite (CFOSAT), is examined. Power-law scaling behavior is evident in the ranges of 100 to 3000 km with a scaling exponent β varying from 5/3 to 3. The global distributions and seasonal variations of the scaling exponent β have also been considered. The results show that due to the energetic convective activities in the low-latitude zones, the scaling exponents β in these regions are closer to the value of 5/3. As for the mid-latitudes, the values of β are close to 2 and independent of the variation of longitude. Concerning the seasonal variations, for most regions, the scaling exponents measured in winter are larger than those in summer. Furthermore, the seasonal variations of β in low-latitudes are stronger than those in the mid-latitudes. Our preliminary results indicate that all satellites provide a consistent scaling feature of the ocean surface wind field.</p>

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