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Abstract
A statistical model is presented for the determination of hourly global solar radiation from the National Oceanic and Atmospheric Administration advanced very high resolution radiometer (NOAA AVHRR) satellite data, which provide wide coverage together with adequate spatial resolution (around 1.1 km at the nadir). The process is divided into three steps. The first step consists of a cloud detection procedure. The second step determines the cloud index for each point on the satellite image, which is then used for the third step, which is the application of the global solar radiation statistical model. The coefficients for the model are determined by regression from the data obtained from 11 global surface solar radiation measurement stations (pyranometers). Once the coefficients have been determined, a surface interpolation is performed to obtain the entire coefficient field for the area under study, with the objective of applying the model. The estimates obtained from the model were compared with data from another 10 ground radiation measurement stations in Catalonia, Spain. This model was tested for the 11 consecutive months beginning in February of 1998, with an excellent correlation being obtained between the estimate provided by the model and the data from the measurement stations, which resulted in a coefficient of determination of greater than 0.98 in all cases, together with an rmse of between 9.6% and 15.8% and a bias that varied from 9.5% to 1.3%. In southern Catalonia, satellite-estimated and surface-interpolated hourly global solar radiation were of equal quality (rmse of about 3%–15%). In northern Catalonia, where the stations are more sparse, the satellite-estimated values were more accurate (rmse of 7%) than those obtained from interpolation of surface station data (rmse of 11%–16%).
Abstract
A statistical model is presented for the determination of hourly global solar radiation from the National Oceanic and Atmospheric Administration advanced very high resolution radiometer (NOAA AVHRR) satellite data, which provide wide coverage together with adequate spatial resolution (around 1.1 km at the nadir). The process is divided into three steps. The first step consists of a cloud detection procedure. The second step determines the cloud index for each point on the satellite image, which is then used for the third step, which is the application of the global solar radiation statistical model. The coefficients for the model are determined by regression from the data obtained from 11 global surface solar radiation measurement stations (pyranometers). Once the coefficients have been determined, a surface interpolation is performed to obtain the entire coefficient field for the area under study, with the objective of applying the model. The estimates obtained from the model were compared with data from another 10 ground radiation measurement stations in Catalonia, Spain. This model was tested for the 11 consecutive months beginning in February of 1998, with an excellent correlation being obtained between the estimate provided by the model and the data from the measurement stations, which resulted in a coefficient of determination of greater than 0.98 in all cases, together with an rmse of between 9.6% and 15.8% and a bias that varied from 9.5% to 1.3%. In southern Catalonia, satellite-estimated and surface-interpolated hourly global solar radiation were of equal quality (rmse of about 3%–15%). In northern Catalonia, where the stations are more sparse, the satellite-estimated values were more accurate (rmse of 7%) than those obtained from interpolation of surface station data (rmse of 11%–16%).
Abstract
Accurate representation of wind forcing and mean sea level pressure is important for modeling waves and surges. This is especially important for complex coastal zone areas. The Weather Research and Forecasting (WRF) model has been run at 12-, 4-, and 1.33-km resolution for a storm event over the Irish Sea. The outputs were used to force the coupled hydrodynamic and the Proudman Oceanographic Laboratory Coastal Ocean Modeling System (POLCOMS)–Wave Model (WAM) and the effect on storm surge and waves has been assessed. An improvement was observed in the WRF model pressure and wind speed when moving from 12- to 4-km resolution with errors in wind speed decreasing more than 10% on average. When moving from 4 to 1.33 km no further significant improvement was observed. The atmospheric model results at 12 and 4 km were then applied to the ocean model. Wave direction was seen to improve with increased ocean model resolution, and higher-resolution forcing was found to generally increase the wave height over the Irish Sea by up to 40 cm in places. Improved clustering of wave direction was observed when 4-km meteorological forcing was used. Large differences were seen in the coastal zone because of the improved representation of the coastline and, in turn, the atmospheric boundary layer. The combination of high-resolution atmospheric forcing and a coupled wave–surge model gave the best result.
Abstract
Accurate representation of wind forcing and mean sea level pressure is important for modeling waves and surges. This is especially important for complex coastal zone areas. The Weather Research and Forecasting (WRF) model has been run at 12-, 4-, and 1.33-km resolution for a storm event over the Irish Sea. The outputs were used to force the coupled hydrodynamic and the Proudman Oceanographic Laboratory Coastal Ocean Modeling System (POLCOMS)–Wave Model (WAM) and the effect on storm surge and waves has been assessed. An improvement was observed in the WRF model pressure and wind speed when moving from 12- to 4-km resolution with errors in wind speed decreasing more than 10% on average. When moving from 4 to 1.33 km no further significant improvement was observed. The atmospheric model results at 12 and 4 km were then applied to the ocean model. Wave direction was seen to improve with increased ocean model resolution, and higher-resolution forcing was found to generally increase the wave height over the Irish Sea by up to 40 cm in places. Improved clustering of wave direction was observed when 4-km meteorological forcing was used. Large differences were seen in the coastal zone because of the improved representation of the coastline and, in turn, the atmospheric boundary layer. The combination of high-resolution atmospheric forcing and a coupled wave–surge model gave the best result.