The prediction based on dynamic downscaling prediction technology of the climate extension of weather research and forecasting (CWRF) model to summer precipitation has a certain deviation, so it is difficult to achieve accurate prediction. This paper analyzed the correlated meteorological elements with summer precipitation based on the climatic characteristics of summer precipitation in the main land of China. And on this basis, the reforecasts of summer precipitation by CWRF model in China during 1996-2019 were corrected by using the combined method of dendritic network (DD) and artificial neural network (ANN). Finally, the correction effect was tested by mean square error (MSE), anomaly correlation coefficient (ACC) and temporal correlation coefficient (TCC), etc. The results show that the correction effect to summer precipitation based on the artificial dendritic neural network (ADNN) algorithm model was better than the historical reforecasts of CWRF model in China. The ACC and TCC both increased by about 0.10, MSE dropped by about 26%, and the overall trend anomaly test scores improved by 6.55, which indicated that the ADNN machine learning method could achieve correction to summer precipitation forecasts of CWRF model to a certain extent, thus it could improve the accuracy of precipitation forecasts of CWRF model.
Based on data such as first frost date in Ningxia, geopotential height, sea surface temperature (SST), snow cover area, and sea ice area from 1981 to 2019, the influence of external forcing factors including sea surface temperature, sea ice area, and snow cover area on the abnormally early and late first frost in Ningxia was studied. On the basis of above, a physical conceptual model and an objective prediction model for predicting first frost date were established. The results are as follows: (1) In the early years of first frost, the SST in the equatorial central and eastern Pacific continued to be significantly warmer in the early period, and the SST anomaly presented an obvious ENSO model. When the SST of the equatorial central and eastern Pacific was warmer in the early period, the east Asian trough was stronger and the subtropical high was weaker, which was conducive to cold air activity. So, first frost date was early, otherwise it was late. (2) The snow cover area in the northern hemisphere from May to August in the early period and the sea ice area of Greenland from January to July had a continuously and significantly negative correlation with the date of first frost. When the snow cover in the northern hemisphere decreased or the sea ice in Greenland decreased, the east Asian trough was weaker and the western Pacific subtropical high was relatively stronger, which was not conducive to active cold air, causing first frost to be late, and vice versa. (3) The main factors affecting the date of first frost in Ningxia were the intensity of the east Asian trough, the SST anomaly in the NINO3.4 area, the SST anomaly in the tropical south Atlantic, the snow area in the northern hemisphere, the intensity of the western Pacific subtropical high, and the area of Greenland sea ice. The objective prediction model established by using the multiple regression equation had a good prediction effect.
Based on monthly temperature in winter from 20 meteorological stations in Ningxia, monthly sea ice concentration in autumn from the Hadley Centre of UK and monthly atmospheric reanalysis in winter from the NCEP/NCAR from 1961 to 2016, temperature anomaly and its causes in winter of 2016 in Ningxia were studied. The results show that the temperature in winter of 2016 was the highest in the same period since 1961 in Ningxia. In 2016, the 500 hPa zonal circulation was obvious over the middle and high latitudes of Eurasia, and the Ural mountains blocking high was unusually weak, and geopotential height over the mainland China was unusually higher, and position of polar vortex skewed Europe and North America. East Asian winter monsoon index was 1.3 m·s-1, which was the fifth low value since 1961, and Siberia high intensity anomaly was 1.5 hPa, which was the second low value since 2000. The sea ice concentration of Greenland sea in autumn had a significant influence on temperature in winter in Ningxia. When the sea ice concentration was low, the East Asian winter wind was weaker,the wave pattern labeling “-+-” on 500 hPa geopotential height in Arctic, Eurasia and Aleutian region enhanced the height difference between the Arctic and Eurasia in the middle and high latitudes, and the westerly airflow over the middle and high latitudes was stronger, and the zonal activity was strengthened. At the same time the weaker Siberian high in the sea level pressure field was not conducive to the Arctic cold air intruding into low latitude region. All above reasons resulted in higher temperature anomaly in 2016 in Ningxia.
The TelatlOnShlp hetWeen near SLII}aCP LI1tTaVlOlet TadlatlOn and meteOTOlOglCal COnd1t10nS 1S analyZed by LISlng OhSPTVatlOnal data of ultraviolet radiation, daily cloud cover, visibility and humidity in Rizhao meteorological station from March 2003 to Febrnaly 2004. The eXpeTlmental fOTPCaStlng fOTmLIlaS fOT near SLII}aCe LI1tTaVlOlet TadlatlOn lntenSlty 1S g1Ven and the fnreeaSt by It In MaTCh and April in 2004 shows the results well.
