scholarly journals Characteristics of LDAPS-Predicted Surface Wind Speed and Temperature at Automated Weather Stations with Different Surrounding Land Cover and Topography in Korea

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1224
Author(s):  
Dong-Ju Kim ◽  
Geon Kang ◽  
Do-Yong Kim ◽  
Jae-Jin Kim
Keyword(s):  
Wind Speed ◽  
Land Cover ◽  
Local Heating ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Mean Bias Error ◽  
Bias Error ◽  
Flat Terrain ◽  
Weather Stations ◽  
Grid Points

We investigated the characteristics of surface wind speeds and temperatures predicted by the local data assimilation and prediction system (LDAPS) operated by the Korean Meteorological Administration. First, we classified automated weather stations (AWSs) into four categories (urban flat (Uf), rural flat (Rf), rural mountainous (Rm), and rural coastal (Rc) terrains) based on the surrounding land cover and topography, and selected 25 AWSs representing each category. Then we calculated the mean bias error of wind speed (WE) and temperature (TE) using AWS observations and LDAPS predictions for the 25 AWSs in each category for a period of 1 year (January–December 2015). We found that LDAPS overestimated wind speed (average WE = 1.26 m s−1) and underestimated temperature (average TE = −0.63 °C) at Uf AWSs located on flat terrain in urban areas because it failed to reflect the drag and local heating caused by buildings. At Rf, located on flat terrain in rural areas, LDAPS showed the best performance in predicting surface wind speed and temperature (average WE = 0.42 m s−1, average TE = 0.12 °C). In mountainous rural terrain (Rm), WE and TE were strongly correlated with differences between LDAPS and actual altitude. LDAPS underestimated (overestimated) wind speed (temperature) for LDAPS altitudes that were lower than actual altitude, and vice versa. In rural coastal terrain (Rc), LDAPS temperature predictions depended on whether the grid was on land or sea, whereas wind speed did not depend on grid location. LDAPS underestimated temperature at grid points on the sea, with smaller TE obtained for grid points on sea than on land.

scholarly journals Characteristics of LDAPS-predicted Surface Wind Speed and Temperature at Automated Weather Stations with Different Surrounding Land Cover and Topography in Korea

2020 ◽  
Author(s):  
Dong-Ju Kim ◽  
Geon Kang ◽  
Do-Yong Kim ◽  
Jae‒Jin Kim
Keyword(s):  
Wind Speed ◽  
Land Cover ◽  
Local Heating ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Mean Bias Error ◽  
Bias Error ◽  
Flat Terrain ◽  
Weather Stations ◽  
Grid Points

We investigated the characteristics of surface wind speeds and temperatures predicted by the local data assimilation and prediction system (LDAPS) operated by the Korean Meteorological Administration. First, we classified automated weather stations (AWSs) into four categories [urban flat (Uf), rural flat (Rf), rural mountainous (Rm), and rural coastal (Rc) terrains] based on the surrounding land cover and topography, and selected 25 AWSs representing each category. Then we calculated the mean bias error of wind speed (WE) and temperature (TE) using AWS observations and LDAPS predictions for the 25 AWSs in each category for a period of 1 year (January–December 2015). We found that LDAPS overestimated wind speed (average WE = 1.26 m s–1) and underestimated temperature (average TE = –0.63°C) at Uf AWSs located on flat terrain in urban areas because it failed to reflect the drag and local heating caused by buildings. At Rf, located on flat terrain in rural areas, LDAPS showed the best performance in predicting surface wind speed and temperature (average WE = 0.42 m s–1, average TE = 0.12°C). In mountainous rural terrain (Rm), WE and TE were strongly correlated with differences between LDAPS and actual altitude. LDAPS underestimated (overestimated) wind speed (temperature) for LDAPS altitudes that were lower than actual altitude, and vice versa. In rural coastal terrain (Rc), LDAPS temperature predictions depended on whether the grid was on land or sea, whereas wind speed did not depend on grid location. LDAPS underestimated temperature at grid points on the sea, with smaller TE obtained for grid points on sea than on land.

scholarly journals Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

scholarly journals Impact of higher-resolved satellite-based land cover classification on near surface wind speed forecasts

2021 ◽  
Author(s):  
Frank Baier ◽  
Annekatrin Metz-Marconcini ◽  
Thomas Esch ◽  
Marion Schroedter-Homscheidt
Keyword(s):  
Wind Speed ◽  
Land Cover ◽  
Surface Wind ◽  
Land Cover Classification ◽  
Surface Wind Speed ◽  
Near Surface

Recurrence Characteristics Analysis of Near-Surface Wind Speed Signal with Different Sampling Frequencies

2014 ◽  
Vol 599-601 ◽  
pp. 1605-1609 ◽  
Author(s):  
Ming Zeng ◽  
Zhan Xie Wu ◽  
Qing Hao Meng ◽  
Jing Hai Li ◽  
Shu Gen Ma
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Recurrence Plot ◽  
Sampling Frequency ◽  
Near Surface ◽  
Gas Leakage ◽  
Time Period ◽  
Characteristics Analysis ◽  
Quantification Analysis

The wind is the main factor to influence the propagation of gas in the atmosphere. Therefore, the wind signal obtained by anemometer will provide us valuable clues for searching gas leakage sources. In this paper, the Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA) are applied to analyze the influence of recurrence characteristics of the wind speed time series under the condition of the same place, the same time period and with the sampling frequency of 1hz, 2hz, 4.2hz, 5hz, 8.3hz, 12.5hz and 16.7hz respectively. Research results show that when the sampling frequency is higher than 5hz, the trends of recurrence nature of different groups are basically unchanged. However, when the sampling frequency is set below 5hz, the original trend of recurrence nature is destroyed, because the recurrence characteristic curves obtained using different sampling frequencies appear cross or overlapping phenomena. The above results indicate that the anemometer will not be able to fully capture the detailed information in wind field when its sampling frequency is lower than 5hz. The recurrence characteristics analysis of the wind speed signals provides an important basis for the optimal selection of anemometer.

Ocean surface wind speed retrieval from C-band quad-polarized SAR measurements at optimal spatial resolution

2020 ◽  
Vol 12 (2) ◽  
pp. 155-164
Author(s):  
He Fang ◽  
William Perrie ◽  
Gaofeng Fan ◽  
Tao Xie ◽  
Jingsong Yang
Keyword(s):  
Wind Speed ◽  
Spatial Resolution ◽  
Surface Wind ◽  
Ocean Surface ◽  
Surface Wind Speed ◽  
Ocean Surface Wind

scholarly journals A Composite View of Surface Signatures and Interior Properties of Nonlinear Internal Waves: Observations and Applications

2008 ◽  
Vol 25 (7) ◽  
pp. 1218-1227 ◽  
Author(s):  
Ming-Huei Chang ◽  
Ren-Chieh Lien ◽  
Yiing Jang Yang ◽  
Tswen Yung Tang ◽  
Joe Wang
Keyword(s):  
Wind Speed ◽  
Surface Waves ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Horizontal Velocity ◽  
Surface Scattering ◽  
Full Width ◽  
Empirical Formulas ◽  
Marine Radar ◽  
Scattering Strength

Abstract Surface signatures and interior properties of large-amplitude nonlinear internal waves (NLIWs) in the South China Sea (SCS) were measured during a period of weak northeast wind (∼2 m s−1) using shipboard marine radar, an acoustic Doppler current profiler (ADCP), a conductivity–temperature–depth (CTD) profiler, and an echo sounder. In the northern SCS, large-amplitude NLIWs propagating principally westward appear at the tidal periodicity, and their magnitudes are modulated at the spring–neap tidal cycle. The surface scattering strength measured by the marine radar is positively correlated with the local wind speed when NLIWs are absent. When NLIWs approach, the surface scattering strength within the convergence zone is enhanced. The sea surface scattering induced by NLIWs is equivalent to that of a ∼6 m s−1 surface wind speed (i.e., 3 times greater than the actual surface wind speed). The horizontal spatial structure of the enhanced sea surface scattering strength predicts the horizontal spatial structure of the NLIW. The observed average half-amplitude full width of NLIWs λη/2 is 1.09 ± 0.2 km; the average half-amplitude full width of the enhanced scattering strength λI/2 is ∼0.57 λη/2. The average half-amplitude full width of the enhanced horizontal velocity convergence of NLIWs λ∂xu/2 is approximately equal to λI/2. The peak of the enhanced surface scattering leads the center of NLIWs by ∼0.46 λη/2. NLIW horizontal velocity convergence is positively correlated with the enhancement of the surface scattering strength. NLIW amplitude is positively correlated with the spatial integration of the enhancement of the surface scattering strength within the convergence zone of NLIWs. Empirical formulas are obtained for estimating the horizontal velocity convergence and the amplitude of NLIWs using radar measurements of surface scattering strength. The enhancement of the scattering strength exhibits strong asymmetry; the scattering strength observed from behind the propagating NLIW is 24% less than that observed ahead, presumably caused by the skewness and the breaking of surface waves induced by NLIWs. Above the center of NLIWs, the surface scattering strength is enhanced slightly, associated with isotropic surface waves presumably induced or modified by NLIWs. This analysis concludes that in low-wind conditions remote sensing measurements may provide useful predictions of horizontal velocity convergences, amplitudes, and spatial structures of NLIWs. Further applications and modification of the presented empirical formulas in different conditions of wind speed, surface waves, and NLIWs or with other remote sensing methods are encouraged.

Sign in / Sign up

Export Citation Format

Share Document