GEP  Vol.7 No.3 , March 2019
Short-Term Precipitation Forecasting Rolling Update Correction Technology Based on Optimal Fusion Correction
Abstract: In order to improve the availability of regional model precipitation forecast, this project intends to use the measured heavy rainfall data of dense automatic stations to carry out historical precipitation in the high resolution: the Severe Weather Automatic Nowcast System (SWAN) quantitative precipitation forecast and the High-Resolution Rapid Refresh (HRRR) regional numerical model precipitation forecast in short-term nowcasting aging. Based on the error analysis, the grid fusion technology is used to establish the measured rainfall, HRRR regional model precipitation forecast, and optical flow radar quantitative precipitation forecast (QPF) three-source fusion correction scheme, comprehensively integrate the revised forecasting effect, adjust the fusion correction parameters, establish an optimal correction plan, generate a frozen rolling update revised product based on measured dense data and short-term forecast, and put it into business operation, and perform real-time effect rolling test evaluation on the forecast product.
Cite this paper: Huang, M. , Lin, Q. , Pan, N. , Fan, N. , Jiang, T. , He, Q. and Huang, L. (2019) Short-Term Precipitation Forecasting Rolling Update Correction Technology Based on Optimal Fusion Correction. Journal of Geoscience and Environment Protection, 7, 145-159. doi: 10.4236/gep.2019.73008.

[1]   Awadallah, A., & Awadallah, N. (2013). A Novel Approach for the Joint Use of Rainfall Monthly and Daily Ground Station Data with TRMM Data to Generate IDF Estimates in a Poorly Gauged Arid Region. Open Journal of Modern Hydrology, 3, 1-7.

[2]   Chen, M., Yu, X., Tan, X. et al. (2004). Development and Research Progress of Convective Weather Nowcasting Technology. Journal of Applied Meteorology, 15, 754-766.

[3]   Dong, Q. (2018). Calibration and Quantitative Forecast of Extreme Daily Precipitation Using the Extreme Forecast Index (EFI). Journal of Geoscience and Environment Protection, 6, 143-164.

[4]   Goerss, J. S., & Jeffries, R. A. (1994). Assimilation of Synthetic Tropical Cyclone Observations into the Navy Operational Global Atmospheric Prediction System. Weather & Forecasting, 9, 557-576.<0557:AOSTCO>2.0.CO;2

[5]   Hu, S., Sun, G., Zheng, Y. et al. (2011). The Characteristics of the Nowcasting System (SWAN) and Its Application in the Strong Convection Process in Guangzhou on May 7, 2010. Guangdong Meteorology, 33, 11-15.

[6]   Huang, W., Zhang, X., & Wei, X. (2011). An Improved Contract Net Protocol with Multi-Agent for Reservoir Flood Control Dispatch. Journal of Water Resource and Protection, 3, 735-746.

[7]   Kissi, A., Abbey, G., Agboka, K., & Egbendewe, A. (2015). Quantitative Assessment of Vulnerability to Flood Hazards in Downstream Area of Mono Basin, South-Eastern Togo: Yoto District. Journal of Geographic Information System, 7, 607-619.

[8]   Long, Q., Liu, H., Gu, J. et al. (2014). Research on Fusion Method of Radar Data and Mesoscale Numerical Prediction and Its Application in Nowcasting. Meteorology, 40, 1248-1258.

[9]   Sakijege, T., Sartohadi, J., Marfai, M., Kassenga, G., & Kasala, S. (2014). Government and Community Involvement in Environmental Protection and Flood Risk Management: Lessons from Keko Machungwa, Dar es Salaam, Tanzania. Journal of Environmental Protection, 5, 760-771.

[10]   Tanessong, R., Vondou, D., Igri, P., & Kamga, F. (2017). Bayesian Processor of Output for Probabilistic Quantitative Precipitation Forecast over Central and West Africa. Atmospheric and Climate Sciences, 7, 263-286.

[11]   Yang, D., Shen, S., Shao, L. et al. (2010). Radar Data and Numerical Model Product Fusion Technology Research. Meteorology, 36, 53-60.

[12]   Zhang, X., Liu, J., Gao, Y., & Yang, X. (2017). Study on Precipitation Forecast and Testing Methods of Numerical Forecast in Fuxin Area. Journal of Geoscience and Environment Protection, 5, 32-38.