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

Abstract

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.

Performance Evaluation of Planetary Boundary Layer Schemes in Simulating Structures of Wintertime Lower Troposphere in Seoul Using One-Hour Interval Radiosonde Observation

“…The magnitude of the 10-m wind speed increase from 1000 LST on 18 January was larger in the four simulations than in the observations. The wind speed decrease in urban areas due to buildings was not well-represented in the numerical simulations, similar to other numerical simulations [28][29][30]. At 925 and 850 hPa, the general tendencies of the simulated air temperatures in the four simulations reproduced those in the radiosonde observation.…”

Real-Time Retrieval of Daily Soil Moisture Using IMERG and GK2A Satellite Images with NWP and Topographic Data: A Machine Learning Approach for South Korea

“…Therefore, VIIRS NDVI and LDAPS DSSF data were used before 2020, and GK2A NDVI and DSR data were used after 2020. LDAPS is a local forecasting model that predicts weather in the Korean Peninsula and provides meteorological and surface data every 3 h (8 times per day) at a resolution of 1.5 km (Table 2) [45][46][47]. DSSF, air temperature (Tair), land surface temperature (LST), soil temperature (Tsoil), relative humidity (RH), and latent heat flux (LE) data from LDAPS were used to perform SM modeling.…”

Spatial Gap-Filling of GK2A Daily Sea Surface Temperature (SST) around the Korean Peninsula Using Meteorological Data and Regression Residual Kriging (RRK)

“…LDAPS produces a local-scale forecast and reanalysis around the Korean Peninsula and helps overcome the spatial resolution and time-scale limitations of the global and regional models. The data is provided eight times (00, 03, 06, 09, 12, 15, 18, and 21 UTC) per day on a 1.5 km grid for the 70 vertical levels up to 40 km [30] based on a three-dimensional variational (3DVAR) data assimilation technique [31] (Figure 2). Table 3 shows the candidates for the explanatory variables from LDAPS for our SST gap-filling: specific humidity, air temperature, skin temperature, relative humidity, maximum wind speed, and precipitation.…”