Implications from Subseasonal Prediction Skills of the Prolonged Heavy Snow Event over Southern China in Early 2008

An exceptionally prolonged heavy snow event(PHSE)occurred in southern China from 10 January to 3 February 2008,which caused considerable economic losses and many casualties.To what extent any dynamical model can predict such an extreme event is crucial for disaster prevention and mitigation.Here,we found the three S2S models(ECMWF,CMA1.0 and CMA2.0)can predict the distribution and intensity of precipitation and surface air temperature(SAT)associated with the PHSE at 10-day lead and 10−15-day lead,respectively.The success is attributed to the models’capability in forecasting the evolution of two important low-frequency systems in the tropics and mid-latitudes[the persistent Siberian High and the suppressed phase of the Madden−Julian Oscillation(MJO)],especially in the ECMWF model.However,beyond the 15-day lead,the three models show almost no skill in forecasting this PHSE.The bias in capturing the two critical circulation systems is responsible for the low skill in forecasting the 2008 PHSE beyond the 15-day lead.On one hand,the models cannot reproduce the persistence of the Siberian High,which results in the underestimation of negative SAT anomalies over southern China.On the other hand,the models cannot accurately capture the suppressed convection of the MJO,leading to weak anomalous southerly and moisture transport,and therefore the underestimation of precipitation over southern China.The Singular Value Decomposition(SVD)analyses between the critical circulation systems and SAT/precipitation over southern China shows a robust historical relation,indicating the fidelity of the predictability sources for both regular events and extreme events(e.g.,the 2008 PHSE).

Analysis on the Heavy Snow Weather Process in Benxi Area

[Objective] The aim was to study the formation and development of a heavy snow in Benxi area. [Method] Based on conventional meteorological data, the formation and development of a heavy snow in Benxi area from December 4th to 5th in 2009 were analyzed from the aspects of weather situation evolution and physical quantity field feature. [Result] The heavy snow was caused by upper trough and North China cyclone. In this process, there was upper level divergence and lower level convergence over Benxi area, and it was warm at low attitude and cold at high attitude; southwest jet at low attitude transported water vapor from Bohai Sea to eastern Liaoning, which provided good water vapor condition for snow, but it didn’t reach heavy snow due to inadequate ascending force. The development of Ural Mountains high ridge played an important role in the snow process and the strengthened high ridge moving northward was beneficial to the southward movement of cold air and deepening of upper trough. Analysis on physical quantity field could provide reference for predicting beginning and ending time and strength of heavy snow. [Conclusion] The study could provide basis for the forecast of heavy snow.

Study on Diagnosis Weather Process and Flight Impact of Heavy Snowfall in Northeast China “11/2021”

Based on ERA5 reanalysis data, the characteristics of weather situation, water vapor condition, dynamic uplift condition, energy condition, ice accumulation environment and flight effect of aircraft in the heavy snowfall process in northeast China from November 5 to 12, 2021 are analyzed. The results show that the heavy snowfall process in Northeast China is caused by the combination of Northeast China Cold Vortex, trough, low level frontal cyclone and cold front. Through the analysis of the physical field, it is found that the sufficient water vapor transport is from the south and the southeast, the convergence rising in the lower layer, divergence “pumping” in the upper layer, air flow rising in the vertical plane and a large amount of convection effective potential energy are all contributing to the heavy snowfall. The impact of heavy snowfall on flight mainly includes low visibility and ice accumulation. Water vapor flux, water vapor flux divergence, vertical velocity, potential temperature and convective effective potential energy can all be used as the judging indexes of heavy snowfall forecast.

Ternary Forecast of Heavy Snowfall in the Honam Area, Korea

The objective of this study is to improve the statistical modeling for the ternary forecast of heavy snowfall in the Honam area in Korea. The ternary forecast of heavy snowfall consists of one of three values, 0 for less than 50 mm, 1 for an advisory （50-150 ram）, and 2 for a warning （more than 150 mm）. For our study, the observed daily snow amounts and the numerical model outputs for 45 synoptic factors at 17 stations in the Honam area during 5 years （2001 to 2005） are used as observations and potential predictors respectively. For statistical modeling and validation, the data set is divided into training data and validation data by cluster analysis. A multi-grade logistic regression model and neural networks are separately applied to generate the probabilities of three categories based on the model output statistic （MOS） method. Two models are estimated by the training data and tested by the validation data. Based on the estimated probabilities, three thresholds are chosen to generate ternary forecasts. The results are summarized in 3 × 3 contingency tables and the results of the three-grade logistic regression model are compared to those of the neural networks model. According to the model training and model validation results, the estimated three-grade logistic regression model is recommended as a ternary forecast model for heavy snowfall in the Honam area.

Comparative Analysis of Two Heavy Snow Processes Based on Doppler Radar Radial Velocity

By using the data of synoptic charts and Datong Doppler radar data, two heavy snow processes in Datong during November 9 - 10, 2009 and on March 14, 2010 were analyzed. The results show that surface current, occluded fronts, high-altitude and low-altitude jet stream were main reasons for the heavy snow processes. Zero velocity curves were like ＂S＂ at elevations of 0.5, 1.5 and 2.4~, and there were a pair of＂ bull＇s-eye＂ structure, showing that heavy snow would occur. As for the two heavy snow processes, the qualitative judgment results based on the area of posi- tive and negative velocity zones were consistent with the quantitative analysis results based on the average divergence well, so we can use radar images to judge features of velocity fields rapidly in practice.

A Case Study on the Rapid Rain-to-Snow Transition in Late Spring 2018 over Northern China:Effects of Return Flows and Topography

Phase changes in the precipitation processes of early winter and late spring in midlatitude regions represent challenges 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 recorded 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 important role in this heavy snow process and presented a characteristic"sandwich"structure.The RFs,complex topography,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 initiated 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 Mountains 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 entrained the middle-level cold air downward,causing the melting layer(from surface to the 0℃-isotherm level)to become 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 concurrent mechanisms:the advective cooling of RF,the melting cooling of cloud snow particles,and the high-altitude effect.Compared with that in the plain area with less urbanization the duration of the RRTS in the plain area with significant urbanization was extended by approximately 2 h.

Risk Information Sources for Snow Disaster Risk Preparedness in Scotland

Heavy snow disruptions are common and costly occurrences in the UK,including Scotland.Yet,heavy snow remains an underresearched aspect of disaster risks in Scotland.This study critically examined the 2018 heavy snow event in Scotland referred to as the“Beast from the East”(BfE)in order to explore the different sources of information used by the public in preparation for and response to heavy snow emergencies.Our study also examined the effectiveness of BfE risk communication between authorities and the public and sought to determine if there is a relationship between risk information received and the intention to mitigate risk.Data were collected through a semistructured survey from(n=180)residents of the Annandale and Eskdale region of Dumfries and Galloway,Scotland.Our analysis shows that public authority information sources were the most sought-after information sources,followed by online and web sources.We found statistically significant differences between groups(such as age,gender,and mobility/disability)in terms of using risk information sources.Further analysis shows that the relationship between information received and the intention to mitigate risks is not linear but influenced by intervening variables such as work pressures,financial commitment,and stakeholders’expectations.We argue that where full adherence to official risk advice is required,policymakers should carefully consider issues around these three factors.

Damage index estimation by analysis of meteorological disasters on film plastic greenhouses

This study focuses on the analysis of the effect of meteorological disasters on film plastic greenhouses by typhoons,heavy snow,strong wind,and heavy rain.The data of meteorological disasters from 1998 to 2015 were analyzed on provincial basis to calculate the damage rate depending on the weather cause.The cumulative damage area is 20279 hm2.The damage rates of typhoons,heavy snow,strong wind,and heavy rain are 46.4%,47.4%,2.5%,and 3.8%,respectively.The damage index of the greenhouse,which is the ratio of the greenhouse area to the cumulative damage area,was proposed to estimate the disaster risk for 17 administrative districts.The damage index data and the cumulative damage areas were divided using the Jenks’Natural Breaks method.The average damage index is 0.66,and the damage indices are high in the metropolitan cities.