Abstract:In recent years, artificial intelligence (AI) has made great achievements in big data analysis in many fields.Consequently, many researchers have attempted to combine geoscience studies with AI, which has made new progress and can promote the development of Earth science.Climate prediction is closely related to human life and disaster prevention and mitigation, thus its prediction accuracy is highly important.This study briefly summarizes the recent progresses on the application of AI to climate prediction, including data assimilation, model parameterization, partial differential equation solution, prediction models, and numerical model output improvement.The results of this study demonstrate the possibility and applicability of using AI to improve climate prediction, which can significantly reduce computational costs and time.However, there are also many challenges involved in the application of AI, such as the construction of input data sets, the applicability of AI models, and their physical interpretability.Exploring and solving these difficult problems can help geoscience that involves many multi-source data to better utilize AI and thus improve climate prediction.

Abstract:The resolution of weather data greatly affects the judgment of meteorological service.Statistical downscaling is one of the effective methods to solve the conversion from low-resolution data by meteorological models to high-resolution data.Traditional statistical downscaling based on interpolation, reconstruction and example learning are some ways to achieve acceptable results.However, since the first application of convolutional neural network (CNN) to the statistical downscaling field, the performance of statistical downscaling has been significantly improved.However, few methods consider multi-layer images.Weather variables tend to be three-dimensional (3D), meaning that maps of the same region have altitudes, and there is a correlation among different dimensions.In this study, aiming at the improvement of spatial resolution of 3D meteorological elements, combined with the mechanisms of interaction of multi meteorological elements, multi-scale action and weather system configuration of multi barosphere meteorological elements, this paper proposes a multi-scale residual Laplacian pyramid network (MSRLapN) to perform 3D spatial downscaling of various meteorological elements.Specifically, a multi-scale resolution block (MSRB) is constructed to automatically extract prediction features from various meteorological elements in 3D space.Next, the multi-scale pyramid technology from the field of machine learning is introduced to describe the multi-scale interaction of meteorological elements.Next, the cycle iteration method of super-resolution reconstruction is used to learn and correct the error of downscaling prediction based on samples of historical data.In addition, seven cutting-edge deep learning super-resolution methods are used to perform spatial downscaling of the 3D spatial meteorological element.In the East China climate region, data for the two meteorological elements of relative humidity and wind speed are tested.The results indicate the following:(1) The MSRB module is more advanced than the linearly connected structure.(2) Considering three dimensions simultaneously enhances the effect in comparison to 2D images.(3) MSRLapN is superior to several state-of-the-art methods in terms of both quantitative assessment and visual quality.

Abstract:A storm surge is the anomalous rising of the sea surface induced by intense atmospheric disturbances.Storm surges caused by tropical cyclones often cause great socio-economic, human activity and life and property hazards to coastal areas.Therefore, realizing accurate and timely storm surge floodplain prediction is critical.Numerical models are currently the primary method used to predict storm surges, and high-resolution floodplain models always need a significant investment in both research funds and processing time.The machine learning approach, which depends on the robust nonlinear mapping capability driven by data, has an edge over the conventional numerical model prediction in terms of research time and computational resource consumption.This paper uses the convolutional long-short term memory network (ConvLSTM) machine learning algorithm to predict storm surge floodplain in the Pearl River Estuary in Guangdong Province.Using the numerical model products driven by reanalysis data, the historical typhoon floodplain data set is constructed for machine learning model training, verification and testing.The paper studies two prediction techniques including the autoregressive prediction based on the sea surface height field and the prediction based on the predicted wind field and initial sea surface height field, which may realize the storm surge floodplain forecast based on data-driven scheme.Among them, the autoregressive prediction model performs better.By testing the previous model, it concludes that ConvLSTM can predict floodplains with a general error of less than 0.2 m based on the sea surface height field a few hours ago, even if the boundary conditions, topography, surface runoff and atmospheric signals are unknown.Under such conditions, the larger errors mostly occur at the coast and on both sides of the river.By analyzing the errors of the two models, it finds that adding wind field input to ConvLSTM does not significantly improve the prediction skills of the model.Further studies are required to determine the better way to train the data-driven prediction model by adding more features.

Abstract:Located in the hinterland of the Tibetan Plateau in China, the Three-River-Source Region is known as "Chinese Water Tower".The distributions and variations of precipitation and cloud water resources over this region are fundamental to the fresh water resources in China, and also affect the ecosystems of the Three-River-Source Region and the downstream area.This paper reviews and summarizes the research results on the spatial distribution and temporal evolution characteristics of precipitation and cloud water resources in recent decades, the impact mechanism of precipitation, and the prediction of future precipitation trends over the Three-River-Source Region, and gives the prospects for further research.

Abstract:The interdecadal reduction of precipitation in flood season in North China has always been one of the important topics in the field of climatology.This paper briefly reviews the latest representative results of the research on drought and flood in flood season in North China, mainly including the objective identification of the beginning and end of flood season in North China, the multi-time-scale variation characteristics of precipitation in flood season in North China, the relationship between precipitation changes in flood season in North China and atmospheric teleconnection patterns, and the stagnation of increasing trend of precipitation in flood season in North China.On this basis, this paper summarizes issues that need to be further studied in this field, such as the unity of initial and final dates of flood season in North China, the physical reason of the attenuation of the interannual oscillation components in the attribution analysis of interdecadal decrease of precipitation in flood season in North China, the reason for the stagnation of interdecadal variation trend of precipitation in flood season in North China, and the time of precipitation recovery and increase in flood season in North China.

Abstract:Typhoon activities and the relevant severe weather lead to disastrous loss of life and property in coastal cities of China.Numerical prediction of typhoons is the key to typhoon disaster mitigation and prevention.Ensemble prediction is a feasible method to quantify and reduce the uncertainty of numerical weather forecasts.This paper summarizes the research progress of typhoon ensemble forecasting in recent years, including the initial ensemble perturbation and the model perturbation schemes and statistical ensemble forecast post-processing techniques.Next, the developments of the main global and regional ensemble forecast systems worldwide for typhoons, including the advancements in China, are reviewed.Finally, based on the review, the remaining problems and possible research directions for typhoon ensemble forecasting are proposed and discussed.

Abstract:The influence of long-distance transport on atmospheric background ozone and the variations in ozone and its regional representativeness at atmospheric background stations in China are reviewed.Studies results on the impact of long-distance transport of ozone from various pollution sources on ozone in China are quite different, and the source and recipient regions with the most significant contribution are also controversial.In adddtion, few studies have considered the ozone contribution from the stratosphere to the troposphere, which is of significant importance in the background regions with few precursor emissions.Each atmospheric background observation station in China has the same maximum monthly distribution of tropospheric ozone column concentration with the surrounding areas in a certain range.However, the analysis of the surface ozone region characteristics significantly related to human health has not been carried out.In view of the limitations of the research methods, the complex causes of the regional ozone variations have not been deeply revealed.Based on the current research progress and deficiency, the scientific problems to be solved include making use of the source-tagging results of global atmospheric chemistry-circulation model to quantitatively determine the contributions of ozone originating from various regions of the world on the atmospheric background ozone, then evaluating the seasonal variations in surface ozone its regional representativeness at the six atmospheric background stations in China.

Abstract:Based on TropFlux, ERA5 and HadlSST datasets from 1979 to 2017, this paper investigates the relationship between the North Atlantic Oscillation (NAO) and the sea-air heat flux in the tropical Indian Ocean in winter.Results show that the NAO index is negatively correlated with the net air-sea heat flux in the tropical Indian Ocean as a whole, which means that when NAO is in a positive phase, the ocean transfers heat to the atmosphere, and the significant region is mainly located in the tropical western Indian Ocean (10°S-10°N, 50°-70°E).The net air-sea heat flux anomalies are mainly contributed by the latent heat flux and shortwave radiation anomalies.The contribution rates of latent heat flux and shortwave radiation are 72.96% and 61.48% (71.72% and 57.06%) during the NAO positive (negative) phase events, respectively.NAO can affect the local atmospheric circulation in the Indian Ocean through Rossby wave train, and then affect the air-sea heat fluxes.When NAO is in a positive phase, the wave train propagates to the Indian Ocean along the mid- and low-latitude path, triggering an anomalous anticyclone circulation in the upper troposphere over the northern Arabian Sea.The anomalous anticyclone circulation enhances the Arabian high, which strengthens the northerly winds in the northern Indian Ocean and the cross-equatorial flow.With the increase of wind speed, the sea surface evaporation is enhanced.At the same time, the enhanced cross-equatorial flow leads to the stronger tropical convergence zone.The strengthening of deep convection leads to the increase of tropospheric water vapor and cloud cover, which further causes the decrease of downward shortware radiation.

Abstract:Based on the sea surface temperature (SST) data of Hadley Center, NCEP/NCAR reanalysis data and the temperature data of 160 stations in China from 1951 to 2016, the association and possible mechanism between the North Atlantic SST anomaly (SSTA) tripole and interannual temperature variation in China in summer are investigated.Results show that there is a significant negative correlation between the North Atlantic SSTA tripole and the interannual temperature variation in Southwest China in summer.The Eurasian teleconnection wave train in the middle and high latitudes of the Northern Hemisphere excited by the North Atlantic SSTA "-+-" tripole in summer causes the geopotential height of Lake Baikal to rise, resulting in an anticyclonic circulation anomaly, and causes the geopotential height of East Asia to decrease, resulting in a cyclonic circulation anomaly.Southwest China is located in the east of the anomalous anticyclonic circulation in Lake Baikal and the west of the anomalous cyclonic circulation in East Asia in the middle and lower troposphere.Under the combined action of the two, affected by the anomalous northerly flow, it is conducive to the southward movement and accumulation of cold air.At the same time, the East Asian subtropical westerly jet in the upper troposphere is located in the south, and Southwest China is located in the anomalous updraft on the south side of the jet stream.This circulation configuration leads to the decrease of temperature in Southwest China, and vice versa.

Abstract:In this study, we explored the relative contribution of ocean modes to the changes of JJA and DJF land precipitation worldwide.By performing SVD analysis upon the tropical land precipitation and sea surface temperature (SST), we identified three dominant ocean modes:global warming (GW), Atlantic Multidecadal Oscillation (AMO) and Interdecadal Pacific Oscillation (IPO), which effect the trend/interdecadal variation of land precipitation.Using the linear multi regression method, we quantitatively assess the relative contribution of GW, AMO and IPO to the land precipitation variation in different regions.Our results show that GW contributes most to global rainfall changes in both the JJA and DJF seasons.AMO (IPO) is the second greatest contributor to the interdecadal/decadal changes of global land precipitation in JJA (DJF).The contribution of GW is relatively large north of 10°N and its contribution is relatively small in south hemisphere in JJA.GW play a dominant role north of 40°N in DJF; AMO is a major contributor in JJA in the latitude bands:10°-40°S and 50°-60°S. IPO play a major role in DJF in mid-latitude of north hemisphere.GW contributes most to the changes of land precipitation over many regions, such as northeastern North America and Asia in JJA, and Europe in DJF.AMO has the greatest impact over the Sahel, Siberia, and South America in JJA, while IPO impacts most greatly over the southwestern United States.In addition, IPO in DJF also affect northeastern North America and northwestern South America, southern Africa, East Australia, the South Asia monsoon region and North China.The information flow method was applied to investigate the causal relationship between land rainfall and SST variation, and to further verify the above conclusions.

Abstract:Based on the daily temperature and precipitation data of 753 stations in China and NCEP/NCAR daily reanalysis data from 1950 to 2018, the strong long-lasting freezing rain and snow events in southern China in winter from 1951 to 2017 are determined, and their temporal and spatial characteristics, divisions and circulation characteristics on the event outbreak day are analyzed.Results show that:1) The long-lasting freezing rain and snow events in southern China have a significant 2-3 a cycle change, and an abrupt change occurred around 1985.Although their intensity has decreased significantly in recent years, there are still many strong long-lasting freezing rain and snow events.2) The long-lasting freezing rain and snow events occur frequently and last for a long time in the central and western parts of southern China, while the intensity is greater in the middle and eastern parts of southern China.3) The 37 strong long-lasting freezing rain and snow events in southern China can be divided into three types:Central China type, South China type and Southwest China type.4) On the outbreak day of three types of long-lasting freezing rain and snow events, 500 hPa geopotential height anomalies in Eurasia are high in the north and low in the south, the Mongolian high is significantly stronger with the center moving southward, and this configuration is conducive to the southward transportation of cold air in the north;In addition, the South Branch trough is significantly deepened, and the water vapor is actively transported to the north.The difference among the three types is that there are differences in the intensity and influence range of Mongolian high, among which Southwest China type is the strongest with the largest range and significant south extension, followed by South China type.The water vapor transport of Central China type and South China type is jointly affected by the South Branch trough and subtropical high, while the water vapor transport of Southwest China type is only controlled by the South Branch trough.

Abstract:In this study, based on the daily minimum temperature in China and NCEP/NCAR daily reanalysis data from the period of 1980 to 2016, the influence and its difference of atmospheric low-frequency oscillation on two extremely cold regional events in the winter of 2015 in northern China are investigated.This is done by means of Morlet wavelet analysis, Butterworth band-pass filtering, and time-delay correlation methods.The results of this paper reveal some key low-frequency circulation systems in the upper and lower troposphere, cold air source and path affecting the extremely cold events.This study also extracts the reference signals for the extended-range prediction of the extremely cold events in northern China.The results reveal the following:1) The two regional extremely cooling processes in winter of 2015 correspond to the changes of the 10-20 d low-frequency component of the minimum temperature from peak to valley.2) In the first (second) process, the Lake Balkhash-Lake Baikal high pressure is located to the west (northeast), the Siberian High pressure is to the south (north), the East Asian trough runs northeast to southwest (south to north), the Aleutian Low is westward and deeper, and there is no negative Arctic Oscillation anomaly.The source of cold air in the upper and lower layers is located near 60°N in the northern part of Lake Baikal (above the north of Lake Balkhash and northern China, respectively), and the cooling rate is slower (faster) in the first (second) regional extremely cooling process.The continuous transmission of low-frequency disturbance energy in the Mediterranean Sea is one of the causes of the difference in the duration of the two regional extremely cooling processes.3) The low-frequency negative (positive) anomaly in western Europe at the 500 hPa height field in the-18 d (-12 d) is an important reference signal for predicting the variation of low-frequency East Asian trough (Lake Balkhash-Lake Baikal high-pressure ridge or Lake Baikal high-pressure ridge) and the regional extremely cold events in northern China.

Abstract:In this study, based on risk matrix theory, the meteorological risk assessment method and process of major activities were established.Taking one major celebration event held in Beijing as an example, the meteorological risk sources during the activities were determined by multiple departments.Using the meteorological data of long-term series and observation data of short-term wind radar, probability analysis of the meteorological risk during the activities was performed.Considering the serious consequences for the activities caused by the meteorological risks, the meteorological risk assessment and risk control research were carried out using the risk matrix.The results show that the main meteorological risks during the major activity were precipitation or cloudy day, daytime high wind, fog and haze, night high wind, high temperature, thunder and lightning, and low temperature.Precipitation or cloudy day, daytime gale, fog and haze are high risks, while night gale is moderate risk, and the remainder are low risks.Finally, the risk control principles and detailed measures were proposed according to the evaluation results, and were successfully applied during the celebration of the major activity.Compared with the weather risk assessment in the existing meteorological business services, this study achieved the transformation from considering the occurrence probability of high impact weather to risk assessment based on impact to major activities.