A scheme of assimilating radar-retrieved water vapor is adopted to improve the quality of NWP initial field for improvement of the accuracy of short-range precipitation prediction. To reveal the impact of the assimila...A scheme of assimilating radar-retrieved water vapor is adopted to improve the quality of NWP initial field for improvement of the accuracy of short-range precipitation prediction. To reveal the impact of the assimilation of radar-retrieved water vapor on short-term precipitation forecast, three parallel experiments, cold start, hot start and hot start plus the assimilation of radar-retrieved water vapor, are designed to simulate the 31 days of May, 2013 with a fine numerical model for South China. Furthermore, a case of heavy rain that occurred from 8-9 May 2013 over the region from the southwest of Guangdong province to Pearl River Delta is analyzed in detail. Results show that the cold start experiment is not conducive to precipitation 12 hours ahead; the hot start experiment is able to reproduce well the first6 hours of precipitation, but badly for subsequent prediction; the experiment of assimilating radar-retrieved water vapor is not only able to simulate well the precipitation 6 hours ahead, but also able to correctly predict the evolution of rain bands from 6 to 12 hours in advance.展开更多
Calculation by means of the previous indices of the seismic activity can have the matter element analysis possess the forecast function. Readjusting repeatedly the grade limit value of every index can maximize the his...Calculation by means of the previous indices of the seismic activity can have the matter element analysis possess the forecast function. Readjusting repeatedly the grade limit value of every index can maximize the historical fitting ratio of the calculated and actual grade of the annual maximum magnitude, whose result is relatively ideal.展开更多
为了解决传统水文时间序列预测模型预测精度不高、未考虑实际噪声影响等问题,将小波消噪(Wavelet De-noise,WD)与秩次集对分析(Rank Set Pair Analysis,RSPA)方法耦合,建立基于小波消噪的秩次集对分析水文预测模型(WD-RSPA),并应用于马...为了解决传统水文时间序列预测模型预测精度不高、未考虑实际噪声影响等问题,将小波消噪(Wavelet De-noise,WD)与秩次集对分析(Rank Set Pair Analysis,RSPA)方法耦合,建立基于小波消噪的秩次集对分析水文预测模型(WD-RSPA),并应用于马口站年总径流量以及深圳市年总降雨量预测。结果表明:当集合维数T=4时,coif3-RSPA模型预测马口站径流量的预测误差|e|=11.97%;T=6时,db5-RSPA模型预测深圳市降雨量的预测误差|e|=17.73%。相较于传统AR(1)模型和单一的RSPA模型,WD-RSPA模型更接近真实值,是一种切实可行的水文时间序列预测方法。展开更多
基金National Natural Science Foundation of China(41075040,41475102)"973"project for typhoon(2015CB452802)+1 种基金CMA Special Welfare Research Fund(GYHY201406009)Public Welfare(Meteorological Sector)Research Fund(GYHY201406003)
文摘A scheme of assimilating radar-retrieved water vapor is adopted to improve the quality of NWP initial field for improvement of the accuracy of short-range precipitation prediction. To reveal the impact of the assimilation of radar-retrieved water vapor on short-term precipitation forecast, three parallel experiments, cold start, hot start and hot start plus the assimilation of radar-retrieved water vapor, are designed to simulate the 31 days of May, 2013 with a fine numerical model for South China. Furthermore, a case of heavy rain that occurred from 8-9 May 2013 over the region from the southwest of Guangdong province to Pearl River Delta is analyzed in detail. Results show that the cold start experiment is not conducive to precipitation 12 hours ahead; the hot start experiment is able to reproduce well the first6 hours of precipitation, but badly for subsequent prediction; the experiment of assimilating radar-retrieved water vapor is not only able to simulate well the precipitation 6 hours ahead, but also able to correctly predict the evolution of rain bands from 6 to 12 hours in advance.
文摘Calculation by means of the previous indices of the seismic activity can have the matter element analysis possess the forecast function. Readjusting repeatedly the grade limit value of every index can maximize the historical fitting ratio of the calculated and actual grade of the annual maximum magnitude, whose result is relatively ideal.
文摘为了解决传统水文时间序列预测模型预测精度不高、未考虑实际噪声影响等问题,将小波消噪(Wavelet De-noise,WD)与秩次集对分析(Rank Set Pair Analysis,RSPA)方法耦合,建立基于小波消噪的秩次集对分析水文预测模型(WD-RSPA),并应用于马口站年总径流量以及深圳市年总降雨量预测。结果表明:当集合维数T=4时,coif3-RSPA模型预测马口站径流量的预测误差|e|=11.97%;T=6时,db5-RSPA模型预测深圳市降雨量的预测误差|e|=17.73%。相较于传统AR(1)模型和单一的RSPA模型,WD-RSPA模型更接近真实值,是一种切实可行的水文时间序列预测方法。
