Statistical downscaling forecasting of precipitation and 2 m air temperature was obtained based on ECMWF model forecast data from March to November 2018. The interpolated precipitation was corrected using the frequency matching method firstly and then the threshold method, the interpolated temperature was corrected by using the Kalman filter-type decreasing average statistical downscaling technique, finally the hourly precipitation and temperature prediction were obtained. The results are as follows: (1) For the accuracy of rain probability forecast, it was obviously improved by using the frequency matching method and the threshold method for most forecasting time, and the maximum improvement range was more than 20% for the former. For the relative error, the threshold method had reduced the occurrence of false alarms considerably. For the short-term heavy rainfall with 1 h rainfall greater than or equal to 20 mm, the TS score was also improved significantly after using the frequency matching method. For the Typhoon “Amby” event in 2018, in addition to the above improvement effects, the frequency matching method improved the prediction capacity of the model about the amount and patterns of rainfalls, and the threshold method corrected false-alarm station completely. (2) For the test of temperature forecast of ECMWF model, the absolute error was the largest in March for almost forecast time. After using the Kalman filter-type decreasing average statistical downscaling technique, the absolute error of temperature in different months decreased to varying degrees. In general, the absolute error curve after correction still had the periodic fluctuation with the extension of forecast period, and the position of wave peak and trough was basically the same as those before correction, and the greater the absolute error, the greater the correction range was. For the temperature case, the accuracy of the spatial distribution of temperature prediction was retained, and the absolute error decreased significantly after correction.
Based on the data of winter wheat sowing test under different sowing dates and combined with the actual data of winter wheat frost damage survey in natural field, the cause of winter wheat frost damage and the effect on yield of winter wheat were analyzed in northern North China during 2017/2018. The results show that the sowing date of winter wheat was delayed to adapt to climate change. However, the sowing date should not be later than October 21 in northern part of North China. If the sowing date was postponed or the straw was returned to the field, the seeding rate should be increased to ensure the emergence rate and basic seedlings. When the varieties were promoted and produced, the winter and semi-winter varieties should be planted together to prevent the potential risk of winter wheat freezing caused by “cold winter”. The dead seedling rate during overwintering increased by 1%, and the yield decreased by about 1 kg·hm-2. The sowing period of winter wheat was affected by rainfall and more precipitation. Late sowing and poor sowing quality, differences between winter and spring characteristics of winter wheat varieties, improper use of herbicides and pesticides were the main reasons for increasing the death rate of winter frozen seedlings.
According to the date of thunderstorm-day from1968 to 2007 and lightning disaster statistics data from 1998 to 2007 in Langfang, combined with its economic status and population density, vulnerability analysis, evaluation and vulnerability division of lightning disaster aremade. Four indices are selected to evaluate the vulnerability of lightning disaster in Langfang, including annual mean thunderstorm days (M), lightning disaster frequency (R), economic vulnerabilitymodule(D), economic lossmodulu(D), vital vulnerabilitymodule(L) and life and injurymodulus (L’). Based on them, the vulnerability assessmentstructure of thunderstorm disasters in each counties ofLangfang are given. Then, the vulnerability assessment indices of thunderstorm disasters are classified as five gradeswith given values. The comprehensives vulnerability assessment indices of these nine counties are also graded as corresponding five degrees. Finally, vulnerability division of the lighting disaster is obtained based on the vulnerability degree values of the nine counties in Langfang. The result shows thatKaifaqu and Guangyang districtbelong to themaximal damageable area, Xianghe county, Bazhou city, Sanhe city, AncidisrictandGu’an county belong to the high damageable area.The restcounties belong to themedium dam- ageable area. Scientific basismightbe provided for the planning of regionalprevention and reduction of lightning disaster in Langfang.
