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The extreme heavy rainfall event in Henan Province during 17-23 July 2021 with 1-hour rainfall intensity breaking historical record in the inland of China, ranks second among the top 10 weather and climate events in China in 2021. Previous studies have investigated the rain gauge observations collected by the meteorological ground stations to analyze the rainfall situation and count the extreme value of the "21·7" process. Considering the rainfall is uneven in space and time, the observations from a single source has great uncertainty which may miss the actual rainfall extreme value. By comparing the rainfall observations between meteorological and hydrological rain gauge stations, the objectivity and accuracy of the rainfall records from two business systems are analyzed for the "21·7" extreme heavy rainfall event. It is found that the observations exhibit good agreement in the accumulated rainfall distributions of various levels, and the temporal evolution of daily and hourly rainfall. However, the positions and values of accumulated rainfall and hourly rainfall intensity extremum are different in detail according to these two systems. The systematic deviation between the meteorological and hydrological observations is less than 1% in the heavy rainfall area (6-day accumulated rainfall more than 600 mm). These differences are related to the distinctness in the number, location, density of stations, and the accuracy of observation instruments. In addition, the inhomogeneous features of the rainfall in time and space also lead to the deviation of rainfall records between meteorological and hydrological observations.On the other hand, the meteorological and hydrological rainfall data in top 3 accumulated rainfall cities (Zhengzhou, Hebi and Xinxiang) are merged. The results show that merged rainfall data can present more detailed rainfall distributions and more objective rainfall evolution characteristics compared to single source data. Based on the merged rainfall data, the rainfall features in these three cities are summarized. The strongest rainfall period in Hebi and Xinxiang are about 26 hours and 28 hours later than that at Zhengzhou, respectively, while the rainfall events happened in these three cities are characterized by large accumulated amount, extremely strong hourly intensity, concentrated location and sudden increase of rainfall intensity.

Persistent air pollution in the Beijing-Tianjin-Hebei region (BTH) has become an extremely complex challenge due to the combined effects of industrial structure, regional characteristics, weather and climate, and development. Although China’s air pollution levels have reduced significantly since the Airborne Pollution Prevention and Control Action Plan and the Blue Sky Protection Campaign were implemented, the BTH remains a sensitive and vulnerable area. Such large decrease in primary pollution was mainly attributed to the substantial reductions in economic activities and associated emissions during the 2019 novel coronavirus (COVID-19) lockdown, i.e., around the Chinese New Year of 2020. Yet two consecutive severely polluting weather processes occurred in the BTH around the Chinese New Year of 2020, which have seeded doubt among the Chinese public and policymakers regarding the current scientific understanding of the mechanisms of haze pollution. The causes of formation and maintenance of pollution processes can differ significantly. The formation and maintenance of heavy pollution weather is caused by various factors, which is a complex process. Thus, it is crucial to distinguish the contribution of emissions and meteorological conditions on polluting weather, as well as distinguish the contribution of various meteorological factors on the formation and maintenance of polluting weather, for conducting effective attribution diagnostic analysis in actually environmental and meteorological impact assessment operation systems, especially in areas that are sensitive or vulnerable to air pollution. Identifying the specific meteorological conditions formed by polluting weather and establishing a comprehensive model for discriminating atmospheric dynamics and thermodynamics can provide a scientific basis for improving numerical models for air pollution potential forecasting in the future. Therefore, in this study, we focused on two consecutive severely polluting weather processes in BTH around the 2020 Chinese New Year (January to February 2020), as an ideal and unique field experiment for the prevention and control of current severe air pollution. We explored the reasons for the formation and maintenance of continuous severely polluting weather in the context of “continuous emissions reduction” and “relatively low social activity levels” from the perspective of the abnormal structure of the high–low atmospheric circulation system. Based on comprehensive diagnostic analyses, we quantified the relative contribution of each key meteorological factor to the continuous severely polluting weather in BTH by using the standardized multiple linear regression method. The results indicated that stable maintenance of low-level coastal high-pressure systems led to higher relative humidity at ground level compared with normal years and blocking systems, which are two key meteorological factors that induced persistent polluting weather in BTH. The abnormally stable blocking situation provided a special circulation background for the occurrence and maintenance of persistent heavy air pollution in BTH. The continuous and stable easterly and southerly water vapor transportation structure provided the BTH with more moisture than normal years, and it was conducive to increased moisture absorption by aerosols, especially under blocking. The “subsidence warming” effect of the high-level blocking high-pressure system facilitated the production of a “warm cover” structure in the middle of the troposphere. The presence of the anomalous warm cover structure in the troposphere facilitated the establishment of stable and high humidity weather, which was conducive to the accumulation of pollutants and continued air pollution. Dynamic systems (blocking systems) and water vapor transportation factors directly explained 46.8% of the meteorological causes of persistent heavy air polluting weather events around the 2020 Chinese New Year in BTH.

We carried out the first Mobile Field Observation Campaign of Atmospheric Profiles (MFOCAP) in the southeast Tibet and the Three‐River Source Region (TRSR) of the Tibetan Plateau (TP) by adopting two vehicle‐mounted integrated mobile observations (MO) system from July 18 to 30, 2021. Reliable MO data sets of air temperature (Ta), water vapor density (WVD) and relative humidity (RH) with high spatio‐temporal resolution over the TP were obtained and assimilated to improve precipitation forecast using the four‐dimensional variational (4DVAR) data assimilation (DA) method. The results show that Ta, WVD and RH profile data retrieved with the mobile microwave radiometer (MR) are credible over the TP. The atmospheric vertical structure measured by the mobile MR can reproduce the spatio‐temporal evolution characteristics of water vapor transport, temperature stratification and cloud structure. The distribution pattern of 24‐hr accumulated rainfall prediction with Ta profile DA was closer to measurements, and 6–12 hr forecasts for low to moderate rainfall in the central and western regions of Qinghai province were improved significantly. Data assimilation with air temperature retrievals from mobile MR observations were found beneficial for accurate simulation of water vapor transport, convergence and divergence of wind field, and upward motion associated with precipitation events. The finding of this study highlights the value of MR remote sensing observations in improving the rainfall monitoring and forecasts over the TP and downstream regions. Plain Language Summary: The dynamic and thermal effects generated by Tibetan Plateau (TP) have an important impact on weather and climate. Due to sparse and low time‐frequency of conventional radiosonde observation (Raob) over the TP, understanding of the atmospheric dynamic and thermal structure is insufficient, and forecasting errors of precipitation are larger than other regions. To improve the precipitation forecast over the TP, we implemented the first Mobile Field Observation Campaign of Atmospheric Profiles in the southeast Tibet and the Three‐River Source Region using two MO systems and assimilated retrieved Ta and RH profiles data into the Global Forecast System of China Meteorological Administration (CMA_GFS) model, and evaluated the impacts on rainfall prediction over the TP. Mobile MR observations can be used to retrieve reliable products of Ta, water vapor density and RH profiles over the TP. DA with retrieved Ta profile from mobile MR can effectively improve short‐term forecasts of low to moderate precipitation in the central and western regions of Qinghai province, and is beneficial for accurate simulation of water vapor transport, convergence and divergence of wind field, and upward motion associated with precipitation events. This study provides an effective pathway to investigate the formation mechanism and influence of small‐scale and meso‐scale weather system such as TP vortex and shear line. Key Points: Mobile MR observation can be used to retrieve the creditable air temperature, water vapor density and relative humidity profiles over the TPData assimilation with air temperate profiles from Mobile MR can effectively improve the model forecasts of low to moderate precipitationMore accurate representations of water vapor and wind fields with data assimilation of MR retrievals can improve precipitation forecast [ABSTRACT FROM AUTHOR]

Snow cover days (SCD) have increased significantly in winter on the Western Kunlun Mountains and Eastern Pamir Plateau (hereafter referred to as KMPP for short), however the causes have not been well understood so far. Here, we use remote sensing data to analyze the abnormal increase in SCD on the KMPP and explore its causes from the perspective of the local factors and water vapor transport caused by sea surface temperatures (SST) warming. We discover that the winter SCD on the KMPP increased significantly at a rate of 4.75 days/decade (significant at the 0.01 level) during 1989–2020, while there has been a significant decrease on the Tibetan Plateau (TP), with a rate of −1.50 days/decade (significant at the 0.1 level). Based on ERA5, GPCP, GHCN, and station data, we find that, in contrast to the significant warming observed on the TP, temperature changes on the KMPP are negligible, while precipitation is increasing, differing from the decreasing precipitation trend observed on the TP. The differences in local temperature and precipitation changes cause different variations in SCD between the KMPP and the TP. The increase in SCD on the KMPP is primarily driven by increased precipitation (over 97% contribution), with minimal impact from the more or less unchanged temperature. In contrast, the decline in SCD on the TP results from decreased precipitation and significantly increased temperature. Furthermore, we found that changes in SCD on the KMPP are significantly correlated with SST in the northern North Atlantic Ocean. Based on the correlation vector, the anomaly field in the high/low SCD years of water vapor transport, and the FLEXPART model, we show that the northern North Atlantic Ocean is one of the major water vapor sources affecting the SCD on the KMPP. The warming SST in the northern North Atlantic Ocean enhances water vapor transport to the KMPP in winter, leading to an abnormal increase in the SCD that differs from the overall trend on the TP. The findings are conductive to further understand the peculiarity of winter precipitation and SCD on the KMPP, and the "Western Kunlun Mountains Oddity" in mountain glacial change. [ABSTRACT FROM AUTHOR]

Passive microwave radiometers onboard satellites rely on the received upwelling radiation to retrieve precipitation, which is a mixed signal from the surface, atmosphere and precipitation hydrometeors. Liquid precipitation droplets increase the upwelling radiation from the surface at lower frequencies, while ice particles cause a decrease in upwelling radiation at higher frequencies. The task of the retrieval algorithm is to identify the precipitation phase and to isolate the signal of precipitation from that of the surface. This study develops a machine learning method to retrieve rainfall and snowfall rates based on observations from the Microwave Hydrometer Sounder and Microwave Temperature Sounder onboard FY‐3E. Self‐organized mapping (SOM) is selected to classify the precipitation and underlying surface types, and an artificial neural network (ANN) is subsequently used to relate the brightness temperature to the precipitation rate for the clusters derived from the SOM. The half‐hour product of the Integrated Multi‐Satellite Retrieval for Global Precipitation Measurement (IMERG) is used to train the ANN. To address the issue that number of heavy precipitation samples are not enough for training, the simulation of radiative transfer for TOVS is used as a supplement to heavy rain samples. The SOM‐ANN algorithm outperforms the IMERG and Goddard profiling algorithm (GPROF) retrieval products in both rainfall and snowfall retrieval. Compared with the hourly observations at ∼4,400 stations during a 2‐year period, the root mean square errors of SOM‐ANN proposed here are 1.06 and 0.34 mm/hr for the rainfall and snowfall rates, which are better than those of IMERG (1.23 and 0.42 mm/hr) and GPROF (1.22 and 0.44 mm/hr). Plain Language Summary: FY‐3E is the first dawn‐dusk orbit meteorological satellite for civil use, and the development of a retrieval algorithm supplements the lack of passive microwave observations in current global precipitation constellation during this period. The Microwave Hydrometer/Temperature Sounder (MWHS/MWTS) onboard FY‐3E can capture radiation information about water vapor, raindrops, and ice crystals, which can be used to retrieve rainfall/snowfall. In particular, the 166 and 183 GHz channels are proved to have advantages in snowfall retrieval. Self‐organized mapping (SOM) is selected to classify the precipitation and underlying surface types, and an artificial neural network (ANN) is subsequently used to relate the brightness temperature to the precipitation rate for the clusters derived from the SOM. The evaluation against gauge observations at ∼4,400 stations over the north hemisphere land during a two year period indicates that the SOM‐ANN method improves the accuracy of rainfall/snowfall retrieval. Key Points: A machine learning method is proposed to retrieve rainfall and snowfall rates from passive microwave observations from the FY‐3E satelliteThe addition of radiative transfer model enhances the retrieval performance in precipitation intensity distributionThe method better handles the impacts of the underlying surface and precipitation phases, improving the accuracy of the retrieval [ABSTRACT FROM AUTHOR]