Variation Characteristics of Heat Resources in Liaoning Province, China in Recent 60 Years and Their Impact on Meteorological Services

To study the temporal and spatial changes of heat resources to serve agricultural production and economic development. The climate data from 1958 to 2017 were selected, and the temporal and spatial changes of heat resources in Liaoning, China were analyzed by using the method of climate diagnosis and analysis. The results show that: the southern coastal area of Liaoning has the most heat resources, the northwestern hilly area and the eastern mountainous area the least, and the central plain area in the middle distribution pattern;compared with 1958-1980, 1981-2017 ≥0°C, ≥10°C accumulated temperature, etc. The value line is advanced 100-160km northward. The accumulated temperature of ≥0°C and ≥10°C in Liaoning experienced abrupt changes in climate in 1993 and 1994, and the climate abrupt change in the southern coastal area was earlier than that in the inland. , the increase in the northwestern hilly area and the eastern mountainous area is less than that in the central plain area and the southern coastal area. The study on the impact on meteorological services found that the increase or decrease of heat resources has a significant impact on agro-meteorological services, which can prolong the growing season of crops, relatively reduce the damage of frost, improve comprehensive utilization of land, and increase crop yields;at the same time, with the increase of heat resources and the northward expansion of the isoline of accumulated temperature, the area affected by pests and diseases has expanded, which has aggravated the harm to agriculture economic burden.

Classification of Northeast China Cold Vortex Activity Paths in Early Summer Based on K-means Clustering and Their Climate Impact

The classification of the Northeast China Cold Vortex(NCCV)activity paths is an important way to analyze its characteristics in detail.Based on the daily precipitation data of the northeastern China(NEC)region,and the atmospheric circulation field and temperature field data of ERA-Interim for every six hours,the NCCV processes during the early summer(June)seasons from 1979 to 2018 were objectively identified.Then,the NCCV processes were classified using a machine learning method(k-means)according to the characteristic parameters of the activity path information.The rationality of the classification results was verified from two aspects,as follows:(1)the atmospheric circulation configuration of the NCCV on various paths;and(2)its influences on the climate conditions in the NEC.The obtained results showed that the activity paths of the NCCV could be divided into four types according to such characteristics as the generation origin,movement direction,and movement velocity of the NCCV.These included the generation-eastward movement type in the east of the Mongolia Plateau(eastward movement type or type A);generation-southeast longdistance movement type in the upstream of the Lena River(southeast long-distance movement type or type B);generationeastward less-movement type near Lake Baikal(eastward less-movement type or type C);and the generation-southward less-movement type in eastern Siberia(southward less-movement type or type D).There were obvious differences observed in the atmospheric circulation configuration and the climate impact of the NCCV on the four above-mentioned types of paths,which indicated that the classification results were reasonable.