A Case Study on the Rapid Rain-to-Snow Transition in Late Spring 2018 over Northern China: Effects o

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Phase changes in the precipitation processes of early winter and late spring in midlatitude regions represent chal- lenges when forecasting the timing and magnitude of snowfall. On 4 April 2018, a heavy snow process occurred in Beijing and northwestern Hebei Province, becoming the most delayed occurrence of heavy spring snow ever recor- ded over Beijing in the last 30 years. This paper uses observational and numerical simulation data to investigate the causes for the rapid rain-to-snow (RRTS) phase transition during this process. The following results are obtained. (1) Return flows (RFs), an interesting type of easterly wind, including those at 1000, 925, and 800 hPa, played an im- portant role in this heavy snow process and presented a characteristic “sandwich” structure. The RFs, complex topo- graphy, and snow particles that dominated the clouds, were the three key factors for the RRTS transition. (2) The RRTS transition in the plains was directly related to the RF at 925 hPa, which brought about advective cooling initi- ated approximately 4–6 h before the onset of precipitation. Then, the RF played a role of diabatic cooling when snow particles began to fall at the onset of precipitation. (3) The RRTS transition in the northern part of the Taihang Moun- tains was closely related to the relatively high altitude that led to a lower surface temperature owing to the vertical temperature lapse rate. Both immediately before and after the onset of precipitation, the snow particles in clouds en- trained the middle-level cold air downward, causing the melting layer (from surface to the 0℃-isotherm level) to be- come very thin; and thus the snow particles did not have adequate time to melt before falling to the ground. (4) The rapid RRTS over the Yanqing mountainous area in the northwest of Beijing could have involved all the three concur- rent mechanisms: the advective cooling of RF, the melting cooling of cloud snow particles, and the high-altitude ef- fect. Compared with that in the plain area with less urbanization, the duration of the RRTS in the plain area with sig- nificant urbanization was extended by approximately 2 h.
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刚刚过去的2021年,全球气象界的一个热点,是以“机器学习”(machine learning,ML)和“神经网络”(neural network,NN)等为代表的AI技术,从几年前有“炒作”嫌疑和爆炸式的登场,到开始入驻气象研究和业务各领域并产生效果和积极影响的过渡年.伴随这样的过渡,“trustworthy”一词被很多学者加在AI之前,组成“可信的AI”.强调AI技术可信,既与AI技术本身的神奇有关,还是现代气象科学本身“物理求真”的传统使然.
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