Machine Learning−based Weather Support for the 2022 Winter Olympics

1.A key support for the 2022 Winter Olympics The XXIV Olympic Winter Games are scheduled to take place from 4 to 22 February 2022,followed by the Paralympic Games from 4 to 13 March,in Beijing and towns in the neighboring Hebei Province,China.Weather plays an extremely important role in the outcome of the games(Chen et al.,2018).It can not only cause a difference between a medal or not,but affect the safety of athletes.Success of the Winter Olympics will greatly depend on weather conditions at the outdoor competition venues,dealing with many weather elements including the snow surface temperature,apparent temperature,gust wind speed,snow,visibility,etc.To ensure that the scheduled games go smoothly,it is imperative to have hourly or even every 10-minutely forecasts as well as updated weather-related risk assessments at the venues for the next 240 hours.So far,the Beijing/Hebei Meteorological Observatory has already started intelligent weather forecasting at 3-km resolution based on the results of numerical weather prediction(NWP)models.However,these experiments have suggested that the current forecasting techniques are incapable of capturing the complex mountain weather variations around some venues.The forecasting capability of NWP is constrained partly by limited knowledge of the local weather mechanisms.

Roles of Wind Stress and Subsurface Cold Water in the Second-Year Cooling of the 2017/18 La Nina Event

After the strong 2015/16 El Nino event,cold conditions prevailed in the tropical Pacific with the second-year cooling of the 2017/18 La Ni?a event.Many coupled models failed to predict the cold SST anomalies(SSTAs)in 2017.By using the ERA5 and GODAS(Global Ocean Data Assimilation System)products,atmospheric and oceanic factors were examined that could have been responsible for the second-year cooling,including surface wind and the subsurface thermal state.A time sequence is described to demonstrate how the cold SSTAs were produced in the central-eastern equatorial Pacific in late 2017.Since July 2017,easterly anomalies strengthened in the central Pacific;in the meantime,wind stress divergence anomalies emerged in the far eastern region,which strengthened during the following months and propagated westward,contributing to the development of the second-year cooling in 2017.At the subsurface,weak negative temperature anomalies were accompanied by upwelling in the eastern equatorial Pacific,which provided the cold water source for the sea surface.Thereafter,both the cold anomalies and upwelling were enhanced and extended westward in the centraleastern equatorial Pacific.These changes were associated with the seasonally weakened EUC(the Equatorial Undercurrent)and strengthened SEC(the South Equatorial Current),which favored more cold waters being accumulated in the central-equatorial Pacific.Then,the subsurface cold waters stretched upward with the convergence of the horizontal currents and eventually outcropped to the surface.The subsurface-induced SSTAs acted to induce local coupled air–sea interactions,which generated atmospheric–oceanic anomalies developing and evolving into the second-year cooling in the fall of 2017.

On the Second-Year Warming in Late 2019 over the Tropical Pacific and Its Attribution to an Indian Ocean Dipole Event

After its maturity,El Niño usually decays rapidly in the following summer and evolves into a La Niña pattern.However,this was not the case for the 2018/19 El Niño event.Based on multiple reanalysis data sets,the space-time evolution and triggering mechanism for the unusual second-year warming in late 2019,after the 2018/19 El Niño event,are investigated in the tropical Pacific.After a short decaying period associated with the 2018/19 El Niño condition,positive sea surface temperature anomalies(SSTAs)re-intensified in the eastern equatorial Pacific in late 2019.Compared with the composite pattern of El Niño in the following year,two key differences are evident in the evolution of SSTAs in 2019.First,is the persistence of the surface warming over the central equatorial Pacific in May,and second,is the re-intensification of the positive SSTAs over the eastern equatorial Pacific in September.Observational results suggest that the re-intensification of anomalous westerly winds over the western and central Pacific,induced remotely by an extreme Indian Ocean Dipole(IOD)event,acted as a triggering mechanism for the second-year warming in late 2019.That is,the IOD-related cold SSTAs in the eastern Indian Ocean established and sustained anomalous surface westerly winds over the western equatorial Pacific,which induced downwelling Kelvin waves propagating eastward along the equator.At the same time,the subsurface ocean provided plenty of warm water in the western and central equatorial Pacific.Mixed-layer heat budget analyses further confirm that positive zonal advection,induced by the anomalous westerly winds,and thermocline feedback played important roles in leading to the second-year warming in late 2019.This study provides new insights into the processes responsible for the diversity of El Niño evolution,which is important for improving the physical understanding and seasonal prediction of El Niño events.

A Deep Spatio-Temporal Forecasting Model for Multi-Site Weather Prediction Post-Processing

In this paper, we propose a deep spatio-temporal forecasting model (DeepSTF) for multi-site weather prediction post-processing by using both temporal andspatial information. In our proposed framework, the spatio-temporal information ismodeled by a CNN (convolutional neural network) module and an encoder-decoderstructure with the attention mechanism. The novelty of our work lies in that our modeltakes full account of temporal and spatial characteristics and obtain forecasts of multiple meteorological stations simultaneously by using the same framework. We applythe DeepSTF model to short-term weather prediction at 226 meteorological stations inBeijing. It significantly improves the short-term forecasts compared to other widelyused benchmark models including the Model Output Statistics method. In order toevaluate the uncertainty of the model parameters, we estimate the confidence intervals by bootstrapping. The results show that the prediction accuracy of the DeepSTFmodel has strong stability. Finally, we evaluate the impact of seasonal changes and topographical differences on the accuracy of the model predictions. The results indicatethat our proposed model has high prediction accuracy.

THE ANALYSIS OF THE RELATIONSHIP BETWEEN THE SUBSYSTEMS OF THE ASIAN SUMMER MONSOON

In this study,the relationship between the subsystems of Asian summer monsoon is analyzed using U.S.National Centers for Environmental Protection/National Center for Atmospheric Research reanalysis and Climate Prediction Center Merged Analysis of Precipitation monthly mean precipitation data.The results showed that there is significant correlation between the subsystems of Asian summer monsoon.The changes of intensity over the same period show that weak large-scale Asian monsoon,Southeast Asia monsoon and South Asian monsoon are associated with strong East Asian monsoon and decreasing rainfall in related areas.And when the large-scale Asian monsoon is strong,Southeast Asia and South Asia monsoons will be strong and precipitation will increase.While the Southeast Asia monsoon is strong,the South Asia monsoon is weak and the rainfall of South Asia is decreasing,and vice versa.The various subsystems are significantly correlated for all periods of intensity changes.

A Review of Research on Warm-Sector Heavy Rainfall in China

Warm-sector heavy rainfall (WSHR) events in China have been investigated for many years. Studies have investigated the synoptic weather conditions during WSHR formation, the categories and general features, the triggering mechanism, and structural features of mesoscale convective systems during these rainfall events. The main results of WSHR studies in recent years are summarized in this paper. However, WSHR caused by micro- to mesoscale systems often occurs abruptly and locally, making both numerical model predictions and objective forecasts difficult. Further research is needed in three areas:(1) The mechanisms controlling WSHR events need to be understood to clarify the specific effects of various factors and indicate the influences of these factors under different synoptic background circulations. This would enable an understanding of the mechanisms of formation, maintenance, and organization of the convections in WSHR events.(2) In addition to South China, WSHR events also occur during the concentrated summer precipitation in the Yangtze River-Huaihe River Valley and North China. A high spatial and temporal resolution dataset should be used to analyze the distribution and environmental conditions, and to further compare the differences and similarities of the triggering and maintenance mechanisms of WSHR events in different regions.(3) More studies of the mechanisms are required, as well as improvements to the model initial conditions and physical processes based on multi-source observations, especially the description of the triggering process and the microphysical parameterization. This will improve the numerical prediction of WSHR events.

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.

Variation in Sunshine Duration and Related Aerosol Influences at Shangdianzi GAW Station, China: 1958–2021

Sunshine duration(SD) is adopted widely to study global dimming/brightening. However, long-term simultaneous measurements of SD and closely related impact factors require further analysis to elucidate how and why SD has varied during the past decades. In this study, a long-term(1958–2021) SD data series obtained from the Shangdianzi Global Atmosphere Watch(GAW) station in China was analyzed to detect linear trends, climatic jumps, and climatic periods in SD using linear fitting, the Mann–Kendall trend test, and the continuous wavelet transform method. Annual SD exhibited steady dimming(-67.3 h decade-1) before 2010, followed by a period of brightening(189.9 h decade-1)during 2011–2020. An abrupt jump in annual SD occurred in 1995, and the annual SD anomaly exhibited significant oscillation with ~3-yr periodicity during 1960–1978. Partial least squares analysis revealed that annual SD anomaly was associated with variations in relative humidity, gale days, cloud cover, and black carbon(BC). Further analysis of the clear-sky daily sunshine percentage(DSP) and simultaneous measurements of aerosol properties, including aerosol optical depth, aerosol extinction coefficient, single scattering albedo(SSA), BC, and total suspended particulates, suggested that variation in DSP was affected primarily by aerosol scattering and absorption. Furthermore, the hourly clear-sky SD at high aerosol loading was approximately 60% and 56% of that at middle and low aerosol loadings, respectively. The pattern of diurnal variation in clear-sky hourly SD, as well as the actual values, can be affected by the fine particulate concentration, aerosol extinction coefficient, and SSA.