Abstract:
keep_len="250">The spatial distribution of precipitation anomalies during flood season and characteristics of drought and flood disasters in China are directly affected by the speed and stagnation of the East Asian summer monsoon (EASM). EASM is significantly affected by external forcing such as sea surface temperature, land surface processes, ice and snow cover, and internal dynamic anomalies of atmospheric circulation. The sea surface temperature (SST) anomaly and its evolution have always been important factors for predicting precipitation during the flood season, considering lead time and the strength of precipitation prediction in flood season.Based on the scientific understanding and application of the mechanism of El Niño-southern oscillation (ENSO) cycle and other Ocean SST on the key factors of EASM, the prediction skill of flood season precipitation is reviewed. According to a prediction evaluation spanning over 40 years of historical records, the prediction accuracy for different types of rainfall pattern, the prediction accuracy of rain types in 1981-1990, 1991-2000, 2001-2010, and 2011-2020 is 50%/30%, 60%/30%, 50%/40%, and 70%/50%, respectively. In other words, the prediction of the primary rainfall patterns during the flood season in China is closer to the observation, and the accuracy of predicting spatial distribution patterns of drought and flood has significantly improved. This improvement can be attributed to the in-depth understanding of the impact of SST on EASM activities and enhancements made to dynamic climate models. In the history of flood season prediction, there have been both successful and unsuccessful cases. The years with low prediction accuracy and significant flooding events are as follows: 1983, 1991, 1999, 2003, and 2014. The primary basis for prediction is analyzed, revealing that the limited understanding of the mechanism of SST affecting the EASM had a great impact on the skill of precipitation predictions during the flood season. Among these factors, the influence of different phases of the ENSO cycle, the asymmetry of ENSO's influence, the change in ENSO spatial patterns, and the influence of other local seas, such as the Indian Ocean SST anomaly, all play important roles.The importance of multi-factor and multi-scale synergy theory and application, as well as the technical support of the objectification method for prediction, are emphasized in summarizing causes for low prediction skill cases. Finally, some suggestions for improving future flood season precipitation predictions are put forward, and it is emphasized that the development of a multi-factor and multi-time scale synergistic theory, an objective climate prediction method, and an integrated system for monitoring, predictions and impact assessment will significantly enhance predictions and provide services for flood season precipitation. The spatial distribution of precipitation anomalies during flood season and characteristics of drought and flood disasters in China are directly affected by the speed and stagnation of the East Asian summer monsoon (EASM). EASM is significantly affected by external forcing such as sea surface temperature, land surface processes, ice and snow cover, and internal dynamic anomalies of atmospheric circulation. The sea surface temperature (SST) anomaly and its evolution have always been important factors for predicting precipitation during the flood season, considering lead time and the strength of precipitation prediction in flood season.Based on the scientific understanding and application of the mechanism of El Niño-southern oscillation (ENSO) cycle and other Ocean SST on the key factors of EASM, the prediction skill of flood season precipitation is reviewed. According to a prediction evaluation spanning over 40 years of historical records, the prediction accuracy for different types of rainfall pattern, the prediction accuracy of rain types in 1981-1990, 1991-2000, 2001-2010, and 2011-2020 is 50%/30%, 60%/30%, 50%/40%, and 70%/50%, respectively. In other words, the prediction of the primary rainfall patterns during the flood season in China is closer to the observation, and the accuracy of predicting spatial distribution patterns of drought and flood has significantly improved. This improvement can be attributed to the in-depth understanding of the impact of SST on EASM activities and enhancements made to dynamic climate models. In the history of flood season prediction, there have been both successful and unsuccessful cases. The years with low prediction accuracy and significant flooding events are as follows: 1983, 1991, 1999, 2003, and 2014. The primary basis for prediction is analyzed, revealing that the limited understanding of the mechanism of SST affecting the EASM had a great impact on the skill of precipitation predictions during the flood season. Among these factors, the influence of different phases of the ENSO cycle, the asymmetry of ENSO's influence, the change in ENSO spatial patterns, and the influence of other local seas, such as the Indian Ocean SST anomaly, all play important roles.The importance of multi-factor and multi-scale synergy theory and application, as well as the technical support of the objectification method for prediction, are emphasized in summarizing causes for low prediction skill cases. Finally, some suggestions for improving future flood season precipitation predictions are put forward, and it is emphasized that the development of a multi-factor and multi-time scale synergistic theory, an objective climate prediction method, and an integrated system for monitoring, predictions and impact assessment will significantly enhance predictions and provide services for flood season precipitation.

Abstract:
keep_len="250">Satellite data assimilation pre-processing plays a crucial role in bridging satellite data pre-processing and numerical weather prediction model assimilation. It involves integrating pre-processed satellite measurements, orbital splicing and sparsification of satellite data, the fusion of boundary condition parameters under satellite pixels, and the assimilation pre-processing quality control of satellite data by using a unified file format and in accordance with the requirements of the data assimilation system. In the variational assimilation of numerical forecasts, assimilation pre-processing filters effective information from satellite pre-processed measurements, supports the positive contribution of satellite data assimilation to numerical forecast operations, and is an important link in determining the efficiency, quality and effectiveness of assimilation of large quantities of satellite data. In response to the complex format of satellite data from multiple channels, CMA has developed a standard format of high time-sensitive satellite data splicing technology in the pre-processing of satellite data assimilation, which effectively reduces the negative impact of the time lag of whole-orbit satellite data on operational numerical prediction. In the assimilation pre-processing of Fengyun satellite data, the assimilation prequality control of multi-spectral data fusion is achieved by front-loading cloud and precipitation detection, data analysis, analysis and other processing into the assimilation pre-processing, which ensures the positive assimilation contribution of Fengyun microwave temperature sounding data and infrared hyperspectral data. In the development of satellite data assimilation pre-processing technology, reprocessing of pre-processed satellite data by using a unified data format, expanding the processing for satellite imagery and active detection data, and front-loading a part of satellite data assimilation quality control function into data assimilation pre-processing are important trends in the development of future assimilation pre-processing technology for Fengyun satellite data. Satellite data assimilation pre-processing plays a crucial role in bridging satellite data pre-processing and numerical weather prediction model assimilation. It involves integrating pre-processed satellite measurements, orbital splicing and sparsification of satellite data, the fusion of boundary condition parameters under satellite pixels, and the assimilation pre-processing quality control of satellite data by using a unified file format and in accordance with the requirements of the data assimilation system. In the variational assimilation of numerical forecasts, assimilation pre-processing filters effective information from satellite pre-processed measurements, supports the positive contribution of satellite data assimilation to numerical forecast operations, and is an important link in determining the efficiency, quality and effectiveness of assimilation of large quantities of satellite data. In response to the complex format of satellite data from multiple channels, CMA has developed a standard format of high time-sensitive satellite data splicing technology in the pre-processing of satellite data assimilation, which effectively reduces the negative impact of the time lag of whole-orbit satellite data on operational numerical prediction. In the assimilation pre-processing of Fengyun satellite data, the assimilation prequality control of multi-spectral data fusion is achieved by front-loading cloud and precipitation detection, data analysis, analysis and other processing into the assimilation pre-processing, which ensures the positive assimilation contribution of Fengyun microwave temperature sounding data and infrared hyperspectral data. In the development of satellite data assimilation pre-processing technology, reprocessing of pre-processed satellite data by using a unified data format, expanding the processing for satellite imagery and active detection data, and front-loading a part of satellite data assimilation quality control function into data assimilation pre-processing are important trends in the development of future assimilation pre-processing technology for Fengyun satellite data.

Abstract:
keep_len="250">The first step in improving a model is to identify deficiencies in the model forecast. With the continuous advancement of numerical prediction technology, the precise assessment and analysis of model prediction errors, particularly the traceable technology of systematic errors, has gradually become a pivotal issue in model evaluation. Atmospheric energy circulation, as a fundamental principle of atmospheric motion, accurately represents the dynamic and physical interaction mechanisms. With a deeper understanding of the atmospheric energy cycle process, its applications have also expanded continuously. Particularly in recent decades, it has been used to assess the performance of numerical models and reanalysis datasets, serving as an essential metric for understanding model forecast capability and identifying systematic errors. Encompassed within the mixed space-time domain energy cycle are the mean circulation, stationary (deviation from the zonal mean), and transient (deviation from the temporal mean) eddies, and their interconversions of the available potential energy and kinetic energy, with each component holding physical significance. Based on the mixed space-time domain energy cycle framework and scale analysis methods, the energy cycle error characteristics and sources in CMA-GFS at planetary scales (zonal wavenumber 1-3) and synoptic and below (greater than zonal wavenumber 3) scales are examined using CMA-GFS global forecast product and ERA5 global reanalysis data in 2022. Results show that CMA-GFS can effectively replicate characteristics of the atmospheric energy cycle. However, its overestimation of baroclinity results in a stronger available potential energy of the mean circulation, which shows an increasing trend with forecast lead time. The stationary and transient eddy energy are dominated by planetary scales and synoptic and below scales, respectively. Errors in the available potential energy of the stationary eddy component and transient eddy component are determined by thermal conditions. CMA-GFS shows higher stationary eddy available potential energy and less transient eddy available potential energy. Systematic underestimations are observed in kinetics of stationary eddy component and transient eddy component, with predominantly negative errors concentrated near centers of subtropical jets and the polar night jet. This is primarily due to stronger barotropic transports, which transfer more energy from eddies to the mean circulation. As the baroclinity gradually increased, the transient eddy also increased after 120 h lead time. CMA-GFS underestimates four eddy energies in the boreal winter and overestimates or slightly underestimates them in the boreal summer, leading to a significant weakening of their seasonal variation. The first step in improving a model is to identify deficiencies in the model forecast. With the continuous advancement of numerical prediction technology, the precise assessment and analysis of model prediction errors, particularly the traceable technology of systematic errors, has gradually become a pivotal issue in model evaluation. Atmospheric energy circulation, as a fundamental principle of atmospheric motion, accurately represents the dynamic and physical interaction mechanisms. With a deeper understanding of the atmospheric energy cycle process, its applications have also expanded continuously. Particularly in recent decades, it has been used to assess the performance of numerical models and reanalysis datasets, serving as an essential metric for understanding model forecast capability and identifying systematic errors. Encompassed within the mixed space-time domain energy cycle are the mean circulation, stationary (deviation from the zonal mean), and transient (deviation from the temporal mean) eddies, and their interconversions of the available potential energy and kinetic energy, with each component holding physical significance. Based on the mixed space-time domain energy cycle framework and scale analysis methods, the energy cycle error characteristics and sources in CMA-GFS at planetary scales (zonal wavenumber 1-3) and synoptic and below (greater than zonal wavenumber 3) scales are examined using CMA-GFS global forecast product and ERA5 global reanalysis data in 2022. Results show that CMA-GFS can effectively replicate characteristics of the atmospheric energy cycle. However, its overestimation of baroclinity results in a stronger available potential energy of the mean circulation, which shows an increasing trend with forecast lead time. The stationary and transient eddy energy are dominated by planetary scales and synoptic and below scales, respectively. Errors in the available potential energy of the stationary eddy component and transient eddy component are determined by thermal conditions. CMA-GFS shows higher stationary eddy available potential energy and less transient eddy available potential energy. Systematic underestimations are observed in kinetics of stationary eddy component and transient eddy component, with predominantly negative errors concentrated near centers of subtropical jets and the polar night jet. This is primarily due to stronger barotropic transports, which transfer more energy from eddies to the mean circulation. As the baroclinity gradually increased, the transient eddy also increased after 120 h lead time. CMA-GFS underestimates four eddy energies in the boreal winter and overestimates or slightly underestimates them in the boreal summer, leading to a significant weakening of their seasonal variation.

Abstract:
keep_len="250">East Asian winter monsoon (EAWM) is one of the most crucial circulation systems in the Northern Hemisphere during winter, significantly influencing the weather and climate of East Asia. Therefore, predicting EAWM variations is considered as a key issue in winter climate prediction. The EAWM intensity index, as defined by Liu Shi (I_SA) has shown a strong and consistent correlation with the interannual and interdecadal variations of winter temperature in Northeast China. However, the precursors influencing the EAWM (I_SA) changed significantly with the decadal shift of the EAWM in the late 1990s. Predictions of EAWM have become less effective, and it is necessary to identify new predictors. Therefore, correlation analysis is conducted to identify the key factors influencing I_SA based on the sea-ice-air system using reanalysis data produced by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR), as well as optimum interpolation SST V2 data from the National Oceanic and Atmospheric Administration (NOAA). EAWM precursor factors are established and their possible interactions are discussed. Factors are used to construct a statistical prediction model using multiple linear regression method, which is evaluated through cross-validation. Results reveal a significant positive correlation between I_SA and the horseshoe-shaped sea surface temperature (SST) pattern over the tropical Pacific autumn, as well as SST over the Gulf Stream and the Eurasian mid-high latitude circulation pattern in stratosphere. I_SA shows a stronger and more consistent negative correlation with the sea ice concentration of the Barents Sea than that of the Kara Sea and Laptev Sea. These precursors influence I_SA through land/sea thermal differences, winter atmospheric circulation patterns such as the East Asian trough, Ural blocking, and the East Asian subtropical westerly jet. The aforementioned prediction model demostrates a good fit and can be utilized to predict EAWM intensity under the current interdecadal background, with a consistency in the anomaly sign rate of 81.8% (9/11) during 11-year hindcast from 2012 to 2022. An analysis of two years of prediction failures reveals that the winter Arctic Oscillation (AO) forecasts, as well as the abrupt transition of the AO from autumn to winter, should be considered in the EAWM prediction process. East Asian winter monsoon (EAWM) is one of the most crucial circulation systems in the Northern Hemisphere during winter, significantly influencing the weather and climate of East Asia. Therefore, predicting EAWM variations is considered as a key issue in winter climate prediction. The EAWM intensity index, as defined by Liu Shi (I_SA) has shown a strong and consistent correlation with the interannual and interdecadal variations of winter temperature in Northeast China. However, the precursors influencing the EAWM (I_SA) changed significantly with the decadal shift of the EAWM in the late 1990s. Predictions of EAWM have become less effective, and it is necessary to identify new predictors. Therefore, correlation analysis is conducted to identify the key factors influencing I_SA based on the sea-ice-air system using reanalysis data produced by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR), as well as optimum interpolation SST V2 data from the National Oceanic and Atmospheric Administration (NOAA). EAWM precursor factors are established and their possible interactions are discussed. Factors are used to construct a statistical prediction model using multiple linear regression method, which is evaluated through cross-validation. Results reveal a significant positive correlation between I_SA and the horseshoe-shaped sea surface temperature (SST) pattern over the tropical Pacific autumn, as well as SST over the Gulf Stream and the Eurasian mid-high latitude circulation pattern in stratosphere. I_SA shows a stronger and more consistent negative correlation with the sea ice concentration of the Barents Sea than that of the Kara Sea and Laptev Sea. These precursors influence I_SA through land/sea thermal differences, winter atmospheric circulation patterns such as the East Asian trough, Ural blocking, and the East Asian subtropical westerly jet. The aforementioned prediction model demostrates a good fit and can be utilized to predict EAWM intensity under the current interdecadal background, with a consistency in the anomaly sign rate of 81.8% (9/11) during 11-year hindcast from 2012 to 2022. An analysis of two years of prediction failures reveals that the winter Arctic Oscillation (AO) forecasts, as well as the abrupt transition of the AO from autumn to winter, should be considered in the EAWM prediction process.

Abstract:
keep_len="250">Synoptic conditions and microphysical formation mechanisms for hail events form the basis for investigating hail suppression technology. There are few relevant studies on hail formation mechanisms in spring in southern China. Most previous theories on hail formation are primarily based on numerical simulations and lack sufficient validation through observations. The atmospheric circulation, stratification, and hail microphysical properties of a typical spring hail event of Honghe in Yunnan on 28 March 2023 are investigated using meteorological and C-band dual-pol radar data. The hail formation mechanisms are compared with those derived from a cloud model with hail-bin microphysics. Results indicate that the synoptic conditions for the hail process are closely associated with the south branch of the westerly winds, which are caused by the blocking effect of the Tibetan Plateau, and the warm moist air carried by the southwesterlies around the western edge of the South Asian tropical high. Due to the relatively weak thermodynamics in spring, small-sized hail below 10 mm is predominant at the surface, with the maximum hail size reaching 20 mm. The microphysical structure of the hail cloud features a warm base and a highly active warm rain process. The dual-polarization radar products of differential reflectivity (Z_DR), specific differential phase (K_DP) and correlation coefficient indicate that during the initial stage of hail formation, the hail formation region consisted of spherical-shaped hail and supercooled raindrops. It suggests that hail embryos are formed through the freezing process of small-sized supercooled raindrops. As the hail embryos descend, the radar reflectivity increased and the particle shape tended to become discoid, indicating that the hail undergoes a growth process through collision with supercooled cloud water during the descent. The shape also changes from spherical to plate-like. it is because during the initial stage of hail formation, raindrops carry to the upper levels by updrafts are relatively small and had spherical shapes, causing their freezing process to form nearly spherical hail embryos. These spherical hail embryos collide with supercooled cloud water and form discoid hailstones during the falling process, which is consistent with shapes of hailstones collected at the surface. Numerical simulations show that hail embryos are primarily formed through homogeneous freezing of supercooled raindrops, and the growth of these embryos depends on accretion with supercooled cloud water, which is well consistent with products by dual-pol radar. Synoptic conditions and microphysical formation mechanisms for hail events form the basis for investigating hail suppression technology. There are few relevant studies on hail formation mechanisms in spring in southern China. Most previous theories on hail formation are primarily based on numerical simulations and lack sufficient validation through observations. The atmospheric circulation, stratification, and hail microphysical properties of a typical spring hail event of Honghe in Yunnan on 28 March 2023 are investigated using meteorological and C-band dual-pol radar data. The hail formation mechanisms are compared with those derived from a cloud model with hail-bin microphysics. Results indicate that the synoptic conditions for the hail process are closely associated with the south branch of the westerly winds, which are caused by the blocking effect of the Tibetan Plateau, and the warm moist air carried by the southwesterlies around the western edge of the South Asian tropical high. Due to the relatively weak thermodynamics in spring, small-sized hail below 10 mm is predominant at the surface, with the maximum hail size reaching 20 mm. The microphysical structure of the hail cloud features a warm base and a highly active warm rain process. The dual-polarization radar products of differential reflectivity (Z_DR), specific differential phase (K_DP) and correlation coefficient indicate that during the initial stage of hail formation, the hail formation region consisted of spherical-shaped hail and supercooled raindrops. It suggests that hail embryos are formed through the freezing process of small-sized supercooled raindrops. As the hail embryos descend, the radar reflectivity increased and the particle shape tended to become discoid, indicating that the hail undergoes a growth process through collision with supercooled cloud water during the descent. The shape also changes from spherical to plate-like. it is because during the initial stage of hail formation, raindrops carry to the upper levels by updrafts are relatively small and had spherical shapes, causing their freezing process to form nearly spherical hail embryos. These spherical hail embryos collide with supercooled cloud water and form discoid hailstones during the falling process, which is consistent with shapes of hailstones collected at the surface. Numerical simulations show that hail embryos are primarily formed through homogeneous freezing of supercooled raindrops, and the growth of these embryos depends on accretion with supercooled cloud water, which is well consistent with products by dual-pol radar.

Abstract:
keep_len="250">The water vapor content is a crucial factor in assessing cloud water resources, and the content and distribution of cloud liquid water are important reference indicators for determining the quantity and location of catalysts in weather modification operations. Based on inversion results of G-band water vapor radiometer, ground-based microwave radiometer and cloud radar, combined with FY-4A measurements, meteorological observations, radar products and reanalysis data, distribution characteristics of water vapor and liquid water in the warm zone of a stratiform cloud is studied in North China on 15 May 2021, in order to provide some reference for the study of macro-micro structure and precipitation mechanism of the warm zone of precipitable stratiform clouds and weather modification operations.The horizontal distribution of the warm zone is not uniform, and there is also clear horizontal inhomogeneity in the distribution of water vapor and liquid water. The integrated water vapor content and liquid water path, detected by G-band water vapor radiometer, fluctuate during the level flight of aircraft, with maximum values of 4.00 cm and 1.87 mm, respectively. As the cloud top height and cloud thickness decrease in the warm zone, the integrated water vapor content and liquid water path also decrease to 0.89 cm and 0.13 mm. The liquid water path detected by G-band water vapor radiometer is primarily derived from low-level clouds in the warm zone and is also influenced by high-level supercooled water clouds or mixed clouds. With the onset of precipitation, the ground-based microwave radiometer detected a surge in integrated water vapor content and liquid water path, reaching peaks of 8.62 cm and 3.85 mm, respectively. The thickness of liquid water content accumulation zone, as well as its maximum value and height in the vertical direction, initially increase and then decrease with precipitation. The temporal and spatial evolution of liquid water is highly significant for understanding the occurrence and development of precipitation, as well as for identifying the timing and location of precipitation enhancement in warm zones. The liquid water content retrieved by the cloud radar also exhibits a jump phenomenon. When the reflectivity factor of the cloud radar is high and the falling velocity and velocity dispersion of particles are high below 1 km, the liquid water content is abundant, leading to significant rainfall on the ground. Particle collision is the primary mechanism of precipitation in the warm zone. The water vapor content is a crucial factor in assessing cloud water resources, and the content and distribution of cloud liquid water are important reference indicators for determining the quantity and location of catalysts in weather modification operations. Based on inversion results of G-band water vapor radiometer, ground-based microwave radiometer and cloud radar, combined with FY-4A measurements, meteorological observations, radar products and reanalysis data, distribution characteristics of water vapor and liquid water in the warm zone of a stratiform cloud is studied in North China on 15 May 2021, in order to provide some reference for the study of macro-micro structure and precipitation mechanism of the warm zone of precipitable stratiform clouds and weather modification operations.The horizontal distribution of the warm zone is not uniform, and there is also clear horizontal inhomogeneity in the distribution of water vapor and liquid water. The integrated water vapor content and liquid water path, detected by G-band water vapor radiometer, fluctuate during the level flight of aircraft, with maximum values of 4.00 cm and 1.87 mm, respectively. As the cloud top height and cloud thickness decrease in the warm zone, the integrated water vapor content and liquid water path also decrease to 0.89 cm and 0.13 mm. The liquid water path detected by G-band water vapor radiometer is primarily derived from low-level clouds in the warm zone and is also influenced by high-level supercooled water clouds or mixed clouds. With the onset of precipitation, the ground-based microwave radiometer detected a surge in integrated water vapor content and liquid water path, reaching peaks of 8.62 cm and 3.85 mm, respectively. The thickness of liquid water content accumulation zone, as well as its maximum value and height in the vertical direction, initially increase and then decrease with precipitation. The temporal and spatial evolution of liquid water is highly significant for understanding the occurrence and development of precipitation, as well as for identifying the timing and location of precipitation enhancement in warm zones. The liquid water content retrieved by the cloud radar also exhibits a jump phenomenon. When the reflectivity factor of the cloud radar is high and the falling velocity and velocity dispersion of particles are high below 1 km, the liquid water content is abundant, leading to significant rainfall on the ground. Particle collision is the primary mechanism of precipitation in the warm zone.

Abstract:
keep_len="250">Based on the MODIS (moderate-resolution imaging spectroradiometer) AOD (aerosol optical depth) and MEIC (multi-resolution emission inventory for China) in Northeast China from 2003 to 2022, the spatial distribution and interannual trend of AOD in Northeast China are analyzed. Effects of meteorological factors and anthropogenic emissions on AOD changes in Northeast China are discussed. Results show that the AOD maximum in central Liaoning is 0.6, followed by an average AOD of 0.4 in western Jilin and 0.3 in Heilongjiang. The average AOD in Northeast China is lower than that in north China, Yangtze River Delta and other frequent pollution areas. High AOD occurs in spring and summer in Northeast China, and it decreases spatially in autumn while increases in winter. The summer AOD in Liaoning is significantly higher than that in other regions, when the average value in central Liaoning and the Bohai Rim increases to 0.6. The highest values of AOD in different seasons occur in Liaoning, followed by Jilin and Heilongjiang. The increase of AOD in summer is mainly related to environmental humidity, and adverse meteorological conditions and local emissions have certain effects on near-surface atmospheric extinction in winter. The annual occurrence frequency of AOD in the range of [0.1, 0.2) and [0.2, 0.3) in Liaoning is up to 50%, the annual occurrence frequency of AOD in the range of [0.1, 0.2) in Jilin and Heilongjiang is up to 25%-30%, and the annual occurrence frequency of extreme clean condition in Heilongjiang is up to 15%. Affected by dust events in spring, the occurrence frequency of AOD [0.2, 0.3) and AOD [0.3, 0.4) in Northeast China is 25%. The regional average value of AOD in Northeast China is higher in 2003 and 2014, which is mainly influenced by boundary layer meteorological elements and anthropogenic emission of SO₂, PM_2.5, organic carbon (OC) and NO₂. In Northeast China, AOD is negatively correlated with boundary layer height and average wind speed, and positively correlated with anthropogenic emissions. The correlation coefficient between AOD and anthropogenic emissions of SO₂, PM_2.5 and OC is the highest in Liaoning. From 2003 to 2022, AOD in Liaoning shows a weak negative growth trend (about-0.1 per decade), while the AOD in Jilin and Heilongjiang shows little change trend. From the perspective of seasonal interannual trend, there is a transition from a negative increasing trend in spring to a positive increasing trend in summer before 2012. After 2013, the summer AOD in Northeast China shows a negative growth trend (-0.3 per decade), which confirms that the contribution of summer aerosol to atmospheric extinction in Northeast China is decreasing in the past 10 years. Based on the MODIS (moderate-resolution imaging spectroradiometer) AOD (aerosol optical depth) and MEIC (multi-resolution emission inventory for China) in Northeast China from 2003 to 2022, the spatial distribution and interannual trend of AOD in Northeast China are analyzed. Effects of meteorological factors and anthropogenic emissions on AOD changes in Northeast China are discussed. Results show that the AOD maximum in central Liaoning is 0.6, followed by an average AOD of 0.4 in western Jilin and 0.3 in Heilongjiang. The average AOD in Northeast China is lower than that in north China, Yangtze River Delta and other frequent pollution areas. High AOD occurs in spring and summer in Northeast China, and it decreases spatially in autumn while increases in winter. The summer AOD in Liaoning is significantly higher than that in other regions, when the average value in central Liaoning and the Bohai Rim increases to 0.6. The highest values of AOD in different seasons occur in Liaoning, followed by Jilin and Heilongjiang. The increase of AOD in summer is mainly related to environmental humidity, and adverse meteorological conditions and local emissions have certain effects on near-surface atmospheric extinction in winter. The annual occurrence frequency of AOD in the range of [0.1, 0.2) and [0.2, 0.3) in Liaoning is up to 50%, the annual occurrence frequency of AOD in the range of [0.1, 0.2) in Jilin and Heilongjiang is up to 25%-30%, and the annual occurrence frequency of extreme clean condition in Heilongjiang is up to 15%. Affected by dust events in spring, the occurrence frequency of AOD [0.2, 0.3) and AOD [0.3, 0.4) in Northeast China is 25%. The regional average value of AOD in Northeast China is higher in 2003 and 2014, which is mainly influenced by boundary layer meteorological elements and anthropogenic emission of SO₂, PM_2.5, organic carbon (OC) and NO₂. In Northeast China, AOD is negatively correlated with boundary layer height and average wind speed, and positively correlated with anthropogenic emissions. The correlation coefficient between AOD and anthropogenic emissions of SO₂, PM_2.5 and OC is the highest in Liaoning. From 2003 to 2022, AOD in Liaoning shows a weak negative growth trend (about-0.1 per decade), while the AOD in Jilin and Heilongjiang shows little change trend. From the perspective of seasonal interannual trend, there is a transition from a negative increasing trend in spring to a positive increasing trend in summer before 2012. After 2013, the summer AOD in Northeast China shows a negative growth trend (-0.3 per decade), which confirms that the contribution of summer aerosol to atmospheric extinction in Northeast China is decreasing in the past 10 years.

Abstract:
keep_len="250">Sea surface wind (SSW) is an essential physical parameter in the ocean and atmosphere, playing an irreplaceable role in hydrological and energy cycles, as well as in global and local climate systems. Polar-orbiting satellite instruments can gather a large amount of SSW information by observing surface roughness or wave height. Although observations from polar-orbiting satellites can cover the globe, there is a significant temporal gap for observing a fixed region. However, a geostationary satellite enables a relatively high observation frequency to accomplish this mission. Due to limitations in resolution, power consumption, and other factors, it is difficult for geostationary satellites to directly retrieve SSW. Nevertheless, it can obtain wind vectors at different altitudes by tracking the movement of clouds or clear-sky water vapor gradients in continuous satellite imagery, which is called atmospheric motion vector (AMV). There is a strong correlation between low-level AMV and SSW, and SSW could be estimated from low-level AMV. Previous studies estimated SSW based on low-level AMV mainly by empirical methods, which is unable to take the variation of AMV with height and latitude into consideration. Therefore, a new method based on a fully-connected neural network (FCNN) is proposed to address this issue. The theory of atmospheric dynamics, which explains how wind varies with altitude and latitude, is referenced, and FCNN is constructed by selecting physical parameters with strong causal relationships from geostationary satellite AMV information. The experiment is performed using GOES-16 advanced baseline imager (ABI) visible band AMV with a resolution of 0.5 km. After completing the wind estimation and comparing it with data from 93 National Data Buoy Center buoys nearshore or offshore North America from 1 January to 31 December in 2021, results show that root mean square error (RMSE) of the estimated wind speed from FCNN is less than 1.5 m·s^-1. This represents a reduction of up to 0.24 m·s^-1 compared to the empirical model. Additionally, the estimated wind direction shows slight improvement compared to AMV. After applying the model to the vicinity of a hurricane, and comparing it with reanalysis information for a total of 13 hours for 3 North Atlantic hurricanes and 3 Eastern Pacific hurricanes in 2022, results show that RMSE of wind speed estimated from FCNN is less than 1.1 m·s^-1, reduced up to 0.04 m·s^-1 compared to the result of the traditional empirical model. Additionally, there is no systematic bias in the low wind speed range. Sea surface wind (SSW) is an essential physical parameter in the ocean and atmosphere, playing an irreplaceable role in hydrological and energy cycles, as well as in global and local climate systems. Polar-orbiting satellite instruments can gather a large amount of SSW information by observing surface roughness or wave height. Although observations from polar-orbiting satellites can cover the globe, there is a significant temporal gap for observing a fixed region. However, a geostationary satellite enables a relatively high observation frequency to accomplish this mission. Due to limitations in resolution, power consumption, and other factors, it is difficult for geostationary satellites to directly retrieve SSW. Nevertheless, it can obtain wind vectors at different altitudes by tracking the movement of clouds or clear-sky water vapor gradients in continuous satellite imagery, which is called atmospheric motion vector (AMV). There is a strong correlation between low-level AMV and SSW, and SSW could be estimated from low-level AMV. Previous studies estimated SSW based on low-level AMV mainly by empirical methods, which is unable to take the variation of AMV with height and latitude into consideration. Therefore, a new method based on a fully-connected neural network (FCNN) is proposed to address this issue. The theory of atmospheric dynamics, which explains how wind varies with altitude and latitude, is referenced, and FCNN is constructed by selecting physical parameters with strong causal relationships from geostationary satellite AMV information. The experiment is performed using GOES-16 advanced baseline imager (ABI) visible band AMV with a resolution of 0.5 km. After completing the wind estimation and comparing it with data from 93 National Data Buoy Center buoys nearshore or offshore North America from 1 January to 31 December in 2021, results show that root mean square error (RMSE) of the estimated wind speed from FCNN is less than 1.5 m·s^-1. This represents a reduction of up to 0.24 m·s^-1 compared to the empirical model. Additionally, the estimated wind direction shows slight improvement compared to AMV. After applying the model to the vicinity of a hurricane, and comparing it with reanalysis information for a total of 13 hours for 3 North Atlantic hurricanes and 3 Eastern Pacific hurricanes in 2022, results show that RMSE of wind speed estimated from FCNN is less than 1.1 m·s^-1, reduced up to 0.04 m·s^-1 compared to the result of the traditional empirical model. Additionally, there is no systematic bias in the low wind speed range.

Abstract:
keep_len="250">During the process of cloud-to-ground lightning connection, the propagation of downward leader to the near-ground area can elevate the electric field at one or several points on the surface of ground tip object to the breakdown threshold of surrounding air, initiating one or more upward leaders, which are known as multiple upward leaders. The emergence of tall buildings has led to an increase in the number of observations of upward lightning strikes on different buildings or the same building. The presence of multiple upward leaders means that multiple parts of the building may be struck. Conducting simulation experiments to study the mechanism of the multiple upward leader phenomenon is of great significance for developing lightning protection. The relative velocity ratio of the downward and upward leaders may be one of the key factors in the lightning connection process. The relative speed ratio of leader propagation in random lightning connection mode cannot accurately describe the relative distance ratio of downward and upward leader propagation. Taking into account the optical observation facts and the electric field environment during thunderstorms, the background electric field module setting is improved on the basis of the existing three-dimensional random mode for multiple upward leaders. It also incorporates a relative propagation speed module for the downward negative and upward positive leaders, establishing the relative propagation speed of leaders according to their propagation distance. Applying the new model to simulate multiple upward leader phenomena triggered by a flat-roofed single building, compared with the previous version, parameters of the new model, such as flash distance and upward leader length, show better consistency with natural lightning. On this basis, the lightning connection process on the high-rise buildings in the Pearl River New Town is simulated, and the improved model can more accurately replicate the lightning occurrence patterns of complex buildings. Characteristic parameters of lightning strikes on urban building clusters are mainly determined by factors such as the shape characteristics, relative position, and relative height of each building. The distance at which lightning strikes buildings is positively correlated with their height. The probability of lightning strikes, the distance of lightning strikes, and other parameters of buildings with similar shapes in the same building group are relatively consistent during ground lightning activities. However, there are still special events that occur when a branch of the downward leader is in close spatial proximity to the building, causing the upward leader to initiate at the top of the building and connect to it. During the process of cloud-to-ground lightning connection, the propagation of downward leader to the near-ground area can elevate the electric field at one or several points on the surface of ground tip object to the breakdown threshold of surrounding air, initiating one or more upward leaders, which are known as multiple upward leaders. The emergence of tall buildings has led to an increase in the number of observations of upward lightning strikes on different buildings or the same building. The presence of multiple upward leaders means that multiple parts of the building may be struck. Conducting simulation experiments to study the mechanism of the multiple upward leader phenomenon is of great significance for developing lightning protection. The relative velocity ratio of the downward and upward leaders may be one of the key factors in the lightning connection process. The relative speed ratio of leader propagation in random lightning connection mode cannot accurately describe the relative distance ratio of downward and upward leader propagation. Taking into account the optical observation facts and the electric field environment during thunderstorms, the background electric field module setting is improved on the basis of the existing three-dimensional random mode for multiple upward leaders. It also incorporates a relative propagation speed module for the downward negative and upward positive leaders, establishing the relative propagation speed of leaders according to their propagation distance. Applying the new model to simulate multiple upward leader phenomena triggered by a flat-roofed single building, compared with the previous version, parameters of the new model, such as flash distance and upward leader length, show better consistency with natural lightning. On this basis, the lightning connection process on the high-rise buildings in the Pearl River New Town is simulated, and the improved model can more accurately replicate the lightning occurrence patterns of complex buildings. Characteristic parameters of lightning strikes on urban building clusters are mainly determined by factors such as the shape characteristics, relative position, and relative height of each building. The distance at which lightning strikes buildings is positively correlated with their height. The probability of lightning strikes, the distance of lightning strikes, and other parameters of buildings with similar shapes in the same building group are relatively consistent during ground lightning activities. However, there are still special events that occur when a branch of the downward leader is in close spatial proximity to the building, causing the upward leader to initiate at the top of the building and connect to it.

Abstract:
keep_len="250">A single map of meteorological basic information is a demonstration of the integration of meteorological data resources and industry data. By establishing a spatiotemporal linkage for meteorological big data applications, it solves the challenge of integrating multi-source meteorological data for application services. The information map enables unified access, processing, storage, and management of multi-source heterogeneous spatiotemporal data through three key technologies: Spatial processing of high-resolution meteorological grid data, retrieval of long sequences of massive grid data within arbitrary spatiotemporal ranges, and fusion services for meteorological data and three-dimensional terrain. It provides users with refined data acquisition services. The information map is created using the meteorological big data cloud platform. The virtual server resources, spatial database resources, and meteorological data resources utilized are all sourced from the meteorological big data cloud platform. The information map offers functions such as spatial analysis of multi-source data, layer management, online mapping, and scene application. It extends the data service mode of the meteorological big data cloud platform. For the first time in the meteorological industry, the Beidou grid location code technology has been utilized to create a map of fundamental meteorological information. Through spatial dimensionality reduction, two-dimensional longitude and latitude values are transformed into one-dimensional Beidou grid location codes. Through algorithms, rapid spatial positioning is achieved, addressing the issue of low efficiency in creating spatial indexes for massive data. The information map integrates five categories of data resources: Basic geography, natural resources, ecological environment, socio-economic, and meteorology. It can dynamically combine and construct thematic services based on various meteorological application scenarios, achieving fast and efficient multi-source data services and applications. A map of meteorological basic information provides business support through layer and geographic spatial retrieval services for the new generation of short-term and imminent forecasting and early warning systems. This promotes the development of business systems and ensures the efficient and stable operation of meteorological services. A single map of meteorological basic information supports spatial query services for public meteorological reality mini-programs and China Meteorological Data Network. It expands channels and methods for the public to access meteorological information through public meteorological services. A single map of meteorological basic information ensures the provision of customized grid data temporal query services for major events, achieving integrated meteorological data query services for historical, current, and forecasted events. Accurate meteorological data play a crucial role in ensuring the success of major national events, such as Beijing Winter Olympic Games and Winter Paralympic Games in 2022, as well as in providing essential weather forecasts for events like "7·20" extremely heavy rainstorm of Zhengzhou in 2021. Additionally, meteorological data is shared for social service purposes. A map of meteorological basic information provides effective support in ensuring "life safety, production development, a prosperous life, and a healthy ecology". A single map of meteorological basic information is a demonstration of the integration of meteorological data resources and industry data. By establishing a spatiotemporal linkage for meteorological big data applications, it solves the challenge of integrating multi-source meteorological data for application services. The information map enables unified access, processing, storage, and management of multi-source heterogeneous spatiotemporal data through three key technologies: Spatial processing of high-resolution meteorological grid data, retrieval of long sequences of massive grid data within arbitrary spatiotemporal ranges, and fusion services for meteorological data and three-dimensional terrain. It provides users with refined data acquisition services. The information map is created using the meteorological big data cloud platform. The virtual server resources, spatial database resources, and meteorological data resources utilized are all sourced from the meteorological big data cloud platform. The information map offers functions such as spatial analysis of multi-source data, layer management, online mapping, and scene application. It extends the data service mode of the meteorological big data cloud platform. For the first time in the meteorological industry, the Beidou grid location code technology has been utilized to create a map of fundamental meteorological information. Through spatial dimensionality reduction, two-dimensional longitude and latitude values are transformed into one-dimensional Beidou grid location codes. Through algorithms, rapid spatial positioning is achieved, addressing the issue of low efficiency in creating spatial indexes for massive data. The information map integrates five categories of data resources: Basic geography, natural resources, ecological environment, socio-economic, and meteorology. It can dynamically combine and construct thematic services based on various meteorological application scenarios, achieving fast and efficient multi-source data services and applications. A map of meteorological basic information provides business support through layer and geographic spatial retrieval services for the new generation of short-term and imminent forecasting and early warning systems. This promotes the development of business systems and ensures the efficient and stable operation of meteorological services. A single map of meteorological basic information supports spatial query services for public meteorological reality mini-programs and China Meteorological Data Network. It expands channels and methods for the public to access meteorological information through public meteorological services. A single map of meteorological basic information ensures the provision of customized grid data temporal query services for major events, achieving integrated meteorological data query services for historical, current, and forecasted events. Accurate meteorological data play a crucial role in ensuring the success of major national events, such as Beijing Winter Olympic Games and Winter Paralympic Games in 2022, as well as in providing essential weather forecasts for events like "7·20" extremely heavy rainstorm of Zhengzhou in 2021. Additionally, meteorological data is shared for social service purposes. A map of meteorological basic information provides effective support in ensuring "life safety, production development, a prosperous life, and a healthy ecology".