Numerical model forecast product objective release method based on space-time local model
A numerical model, space-time local technology, applied in data processing applications, forecasting, weather condition forecasting, etc., to achieve the effect of improving the level of interpretation
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Embodiment 1
[0058] Objectively interpret the fine grid (0.125°×0.125°) 10m wind speed forecast product of Hainan Island for the 3rd hour from 00:00 (UTC) every day by the ECMWF model.
[0059] Step 1: Divide Hainan Island into 18 sub-regions according to administrative divisions;
[0060] Step 2: Obtain the 0.05°×0.05° China Smart Grid Fusion Analysis Product (CLDAS) of Hainan Island and the 10m fine-grid wind speed forecast product of Hainan Island for the 3rd hour reported by the historical ECMWF model at 00:00 (UTC); and Referring to the method of Zeng Xiaoqing (Zeng Xiaoqing, Xue Feng, Yao Li, Zhao Shengrong. Comparison of grid point forecast correction schemes for model wind fields [J]. Acta Applied Meteorology, 2019, 30(01): 49-60), the 10m wind speed (observation data, forecast products, error of starting time) sample set;
[0061] Step 3: Pretreatment and quality control of the samples in step 2;
[0062] Step 4: For each sub-region, the interpretation model adopts 2-variable we...
Embodiment 2
[0067] The global ensemble forecast model for the five centers of the European Center for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the US National Center for Environmental Prediction (NCEP), the China Meteorological Administration (CMA) and the UK Met Office (UKMO) at 12 o'clock every day (Universal time) The 500hPa geopotential height field control forecast product of the northeast hemisphere (0-80°N, 0-160°E) on the 3rd day of the report, for multi-model integration and objective interpretation.
[0068] Step 1: Divide the Northeast Hemisphere into different sub-regions according to the area size of 10°×10°;
[0069] Step 2: Obtain NCEP / NCAR daily 12:00500hPa geopotential height field reanalysis data, and build a sample set (observation data, forecast products) with the historical control forecast products of the global ensemble forecast model of each center.
[0070] Step 3: Pretreatment and quality control of the samples in step 2;
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