Accurate and fine-scale short-term precipitation forecasting is crucial for disaster prevention,mitigation,and socioeconomic development.Currently,the direct precipitation forecasts of numerical weather prediction oft...Accurate and fine-scale short-term precipitation forecasting is crucial for disaster prevention,mitigation,and socioeconomic development.Currently,the direct precipitation forecasts of numerical weather prediction often face great challenges and correction methods are still needed to further improve the forecast accuracy.By utilizing the 500-m resolution fusion precipitation data from the Rapid-refresh Integrated Seamless Ensemble(RISE)system in the Beijing-Tianjin-Hebei(BTH)region,this study proposes a new Segmented Classification and Regression machine learning model based on the extreme gradient boosting(XGBoost)algorithm,termed SCR-XGBoost,which can be applied to correct hourly precipitation forecasts in areas with a dense network of weather stations at lead times of 4-6 h.The performance of the model is evaluated according to six metrics:the accuracy(AC),mean absolute error(MAE),root mean square error(RMSE),correlation coefficient(CC),threat score(TS),and bias score(BS).The results indicate that,although the XGBoost algorithm is almost ineffective for directly forecasting precipitation,the SCR-XGBoost model can significantly improve the forecast performance compared with the original RISE forecast,and the segmented correction method for torrential rainfall(≥20 mm h^(-1))outperforms other precipitation grades,which can effectively alleviate the problem of false alarms in the RISE system for heavy rainfall and above(≥10 mm h^(-1)).The optimization rates after applying the SCR-XGBoost model correction in precipitation forecasts can be improved by 6.49%-23.21%in terms of RMSE and MAE reduction,and the CC and AC can be greatly improved by 35.38%-84.39%.Therefore,the SCR-XGBoost algorithm,which introduces precipitation grade classification and multi-layer piecewise machine learning corrections,can significantly improve the 4-6-h precipitation forecast skill,especially for heavy rainfall.The results of this study not only provide new insights for machine learning-based precipitation forecasting,but also help improve rainfall forecasts and the level of disaster prevention and reduction in the BTH region.展开更多
Using a high-density automatic weather stations(AWS)dataset of hourly rainfall observations,the present study investigates the relationship between rainfall and elevation in the Beijing area,and further proposes a rai...Using a high-density automatic weather stations(AWS)dataset of hourly rainfall observations,the present study investigates the relationship between rainfall and elevation in the Beijing area,and further proposes a rainfall amount dependent parameterized algorithm considering the elevation effect on rainfall on hourly timescale.The parameterization equation is defined as a segmented nonlinear model,which calculates the mountain rainfall as a function of valley rainfall amount.Results show that there exists an evident enhancement of rainfall amount by elevation effect in the Beijing area.In particular,larger rainfall amount is generally found in higher mountains,especially for slight rain and moderate rain.Furthermore,six representative station pairs located in valleys and on mountains respectively are selected to estimate the values of optimal parameters in the parameterization equation.The parameterization algorithm of elevation dependence can produce a reduction in the root-mean-square error and obtain a much closer mountain rainfall total to the observations compared with those using no elevation dependence.Furthermore,the spatial distribution of rainfall is more realistic and accurate in mountainous terrain when elevation dependence is considered.This study helps to understand the variability of rainfall with complex terrain in the Beijing area,and gives a possible way to parameterize rainfall–elevation relationship on hourly timescale.展开更多
基金Supported by the National Natural Science Foundation of China(42275012)National Key Research and Development Program of China(2022YFC3004103)+1 种基金Beijing Municipal Science and Technology Project(Z221100005222012)Key Innovation Team Fund of China Meteorological Administration(CMA2022ZD07).
文摘Accurate and fine-scale short-term precipitation forecasting is crucial for disaster prevention,mitigation,and socioeconomic development.Currently,the direct precipitation forecasts of numerical weather prediction often face great challenges and correction methods are still needed to further improve the forecast accuracy.By utilizing the 500-m resolution fusion precipitation data from the Rapid-refresh Integrated Seamless Ensemble(RISE)system in the Beijing-Tianjin-Hebei(BTH)region,this study proposes a new Segmented Classification and Regression machine learning model based on the extreme gradient boosting(XGBoost)algorithm,termed SCR-XGBoost,which can be applied to correct hourly precipitation forecasts in areas with a dense network of weather stations at lead times of 4-6 h.The performance of the model is evaluated according to six metrics:the accuracy(AC),mean absolute error(MAE),root mean square error(RMSE),correlation coefficient(CC),threat score(TS),and bias score(BS).The results indicate that,although the XGBoost algorithm is almost ineffective for directly forecasting precipitation,the SCR-XGBoost model can significantly improve the forecast performance compared with the original RISE forecast,and the segmented correction method for torrential rainfall(≥20 mm h^(-1))outperforms other precipitation grades,which can effectively alleviate the problem of false alarms in the RISE system for heavy rainfall and above(≥10 mm h^(-1)).The optimization rates after applying the SCR-XGBoost model correction in precipitation forecasts can be improved by 6.49%-23.21%in terms of RMSE and MAE reduction,and the CC and AC can be greatly improved by 35.38%-84.39%.Therefore,the SCR-XGBoost algorithm,which introduces precipitation grade classification and multi-layer piecewise machine learning corrections,can significantly improve the 4-6-h precipitation forecast skill,especially for heavy rainfall.The results of this study not only provide new insights for machine learning-based precipitation forecasting,but also help improve rainfall forecasts and the level of disaster prevention and reduction in the BTH region.
基金Supported by the National Natural Science Foundation of China(41605031)National Key Research and Development Program of China(2018YFF0300102 and 2018YFC1507504)Beijing Municipal Science and Technology Plan(Z151100002115012).
文摘Using a high-density automatic weather stations(AWS)dataset of hourly rainfall observations,the present study investigates the relationship between rainfall and elevation in the Beijing area,and further proposes a rainfall amount dependent parameterized algorithm considering the elevation effect on rainfall on hourly timescale.The parameterization equation is defined as a segmented nonlinear model,which calculates the mountain rainfall as a function of valley rainfall amount.Results show that there exists an evident enhancement of rainfall amount by elevation effect in the Beijing area.In particular,larger rainfall amount is generally found in higher mountains,especially for slight rain and moderate rain.Furthermore,six representative station pairs located in valleys and on mountains respectively are selected to estimate the values of optimal parameters in the parameterization equation.The parameterization algorithm of elevation dependence can produce a reduction in the root-mean-square error and obtain a much closer mountain rainfall total to the observations compared with those using no elevation dependence.Furthermore,the spatial distribution of rainfall is more realistic and accurate in mountainous terrain when elevation dependence is considered.This study helps to understand the variability of rainfall with complex terrain in the Beijing area,and gives a possible way to parameterize rainfall–elevation relationship on hourly timescale.
