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Differential reflectivity (${Z}_{\mathrm{D}\mathrm{R}}$) arc is known as an arc-shaped region of high differential reflectivity along the inflow edge of the forward flank, while a down shear elongated ${K}_{\mathrm{D}\mathrm{P}}$ maximum near the echo centerline of the storm is known as ${K}_{\mathrm{D}\mathrm{P}}$ foot. The ${Z}_{\mathrm{D}\mathrm{R}}$ arc and the clear horizontal separation between the areas of ${Z}_{\mathrm{D}\mathrm{R}}$ arc and ${K}_{\mathrm{D}\mathrm{P}}$ foot have been confirmed to be the signatures of hydrometeor size sorting within their forward flank regions in supercell storms. Recent studies have indicated that ${Z}_{\mathrm{D}\mathrm{R}}$ arc and ${Z}_{\mathrm{D}\mathrm{R}}$ arc-${K}_{\mathrm{D}\mathrm{P}}$ foot separation signatures insupercell storms may be related to environmental storm-relative helicity and low-level shear. Based on the conception model and machine learning, the recognition algorithm for ${K}_{\mathrm{D}\mathrm{P}}$ foot and ${Z}_{\mathrm{D}\mathrm{R}}$ arc is designed, and the separation and angle of ${Z}_{\mathrm{D}\mathrm{R}}$ arc and ${K}_{\mathrm{D}\mathrm{P}}$ foot are then calculated. The recognition effect and quantitative calculation are examined using S band polarimetric radar and auto weather station observations of four supercell storms occurred in East China. The results show that the recognition method introduced in this study can identity ${Z}_{\mathrm{D}\mathrm{R}}$ arc and ${K}_{\mathrm{D}\mathrm{P}}$ foot correctly, the variation of ${Z}_{\mathrm{D}\mathrm{R}}$ arc-${K}_{\mathrm{D}\mathrm{P}}$ foot centroid distance and separation angle can indicate the occurrence of extreme gust.
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Using 82892 volume scanning data at 0.5° elevation angle from the S-band dual polarization radar deployed in Guangzhou (CINRAD/SAD) and 538560 1-minute rainfall data from 1,109 stations within the radar's 100 km detection range from 2018 to 2020, three deep learning networks（Z-Rnet, KDP-Rnet and Pol-Rnet）are designed for radar quantitative precipitation estimation (QPE) based on s ingle and three radar moments, respectively. Furthermore, based on the three networks and with KDP = 0.5 °/km as the threshold to divide the training dataset as heavy, light, and all rain data, a total of 9 QPE models are built. On the basis of using the common mean square error as the loss function, a self-defined loss function is proposed by adjusting the weight for different precipitation intensity. Several indexes including ratio deviation, relative deviation, mean square error (MSE), mean absolute error (MAE) and mean relative error (MRE) are then used to evaluate the performance of the models. Finally, three precipitation processes that are respectively dominated by cumulus-stratiform mixed, convective and stratiform clouds are used to test the effect of QPE. The results suggest that the models fitted by deep learning have better QPE results, and the QPE accuracy for the data that are divided into heavy and light rain is better than that for the data that includes all types of rainfall. The MSE, MAE and MRE with the self-defined loss function are improved by 8.62%, 12.52%, 16.34% than that with the traditional mean square error loss function. Among them, the QPE with Pol-Rnet, i.e., ZH, ZDR, and KDP are used as input factors, is the best, and the above indexes are respectively increased by 6.82%, 8.43%, 7.22% than that with Z-Rnet, and by 12.33%, 17.61%, 17.26% than that with KDP-Rnet.
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The influences of active days of the Madden-Julian oscillation (MJO) over the Indian Ocean on summer precipitation days over the middle and lower reaches of the Yangtze River were investigated. Daily precipitation data collected at 753 stations, monthly SST data from the Hadley Centre, daily mean reanalysis data from the National Centers for the Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), and all-season real-time multivariate Madden-Julien Oscillation (RMM) index during 1980—2020 were used. The results show that the active days of MJO over the Indian Ocean have a statistically significant relationship with precipitation days over the middle and lower reaches of the Yangtze River, especially with the heavy precipitation days, since the MJO related circulation anomalies can continuously transport water vapor to eastern China. Further research indicates that the relationship between active days of MJO over the Indian Ocean and the precipitation days over the middle and lower reaches of the Yangtze River has experienced a decadal change with their relationship being significant since the 2000s. The decadal change of this relationship might be attributed to decreased variability in sea surface temperature (SST) in the Indian Ocean. This decreased interannual variability of SST suggests weakened modulation effects on precipitation over the middle and lower reaches of the Yangtze River by the tropical Indian Ocean. In contrast, the MJO effects on the precipitation days over the middle and lower reaches of the Yangtze River turn to be significant after the 2000s due to less disturbances from the Indian Ocean SST.
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2022year No.3
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This study predicts the heat-related excess deaths in the short-term, mid-term and long-term in China under climate change and provides a scientific basis for preventing the heat-related health risk in the future. Based on present-day gridded daily average temperature dataset in China, future daily average temperature datasets under 3 emission scenarios, historical population data, future population data under 3 fertility scenarios and cause of death data, the heat-related exposure-response relationship is simulated and the number of heat-related deaths per day is calculated. The results show that: (1) The average temperature in China will continue to rise in the future, and the increase in northern China will be larger. (2) The total number of heat-related non-accidental deaths in China from 1986 to 2005 is approximately 71 (95%CI: 57—85) thousands. (3) The total number of heat-related non-accidental deaths in China in the future under the scenarios of RCP2.6 and RCP4.5 will increase first and then decrease. At the end of 21 century, the total number of heat-related non-accidental deaths under different scenarios is higher than in the baseline years. (4) The total number of heat-related non-accidental deaths in China under different scenarios in the future will show an upward trend in the Huanghuaihai and Chengdu-Chongqing regions. Under the RCP2.6 and RCP4.5 scenarios, the total number of heat-related non-accidental deaths in northern China will show a downward trend. At the same time, the number in the southeastern coastal area will show a downward trend after the 2030s. Overall, in the context of climate warming, the heat-related risk in China will increase in the future, and it can be effectively suppressed under the RCP2.6 scenario.
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With the increasing impact of climate change on public health, there is an urgent need to evaluate the detrimental effect of non-optimal ambient temperature on health and quantify the temperature-related mortality and corresponding economic losses. Based on the national database of weather conditions and mortality records in 272 main cities in China from 1 January 2013 to 31 December 2015, time-series analyses are conducted to estimate the exposure-response association between temperature and mortality. Besides, meteorological, socioeconomic, and demographic data for cities across China are collected to quantify the attributable deaths and corresponding economic losses due to low and high temperatures in 31 provinces, autonomous regions and municipalities of China. The exposure-response curve for the association between ambient temperature and mortality is J-shaped, with increased mortality risks for both low and high temperatures. As estimated, 842.4 (95%CI: 659.3—1022.0) thousand and 235.8 (95%CI: 146.9—321.7) thousand deaths are attributable to low and high temperatures in 2020 in China, respectively. The corresponding economic losses are 1701.11 (95%CI: 1335.35—2059.77) billion and 509.74 (95%CI: 317.97—694.59) billion Chinese yuan, respectively. The proportion of the overall economic loss to the gross domestic product (GDP) is 2.18%. Non-optimal ambient temperature exposure has led to substantial mortality and economic loss in China. It is necessary to strengthen actions to deal with the health threats of climate change and non-optimal ambient temperature, and local adaptation measures should be taken to protect public health in the future.
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This study explores acute effects of short-term exposure to ambient fine particulate matter (PM2.5) and ozone (O3) on hospital visits in the Beijing-Tianjin-Hebei region and surrounding areas and provides epidemiological evidence for the coordinated management of regional air pollution. Daily outpatient visits at 100 hospitals in 14 cities across the Beijing-Tianjin-Hebei region and surrounding areas and daily mean concentrations of PM2.5 and O3 as well as meteorological factors for the period from 1 January 2013 to 31 December 2018 are collected. Based on time series studies, a two-stage statistical analysis strategy (generalized additive model combined with meta analysis) is adopted to construct a dual-pollutant model by adjusting confounding factors (such as meteorological factors and time trends) to analyze effects of short-term exposure to ambient PM2.5 and O3 on hospital visits. During the study period, the average daily concentrations of ambient PM2.5 and O3 are 72.2±56.8 μg/m3 and 58.2±36.9 μg/m3, respectively, and the number of outpatient visits is 62.57 million. The results of the dual-pollutant model show that per 10 μg/m3 increases in 2 d moving average PM2.5 and O3 concentrations are associated with excess risks of 0.25% (95%CI: 0.20%—0.29%) and 0.15% (95%CI: 0.07%—0.22%) for daily outpatient visits with 0 to 1 d lag, respectively. Fitting the model of seasonal stratification, the acute effect of PM2.5 exposure on outpatient visits is strong in cold season, while the O3-related effect shows a strong effect in warm season. It is found that short-term exposure to ambient PM2.5 and O3 in the Beijing-Tianjin-Hebei region and surrounding areas both can increase the risk of outpatient visits. It is recommended to take active measures to coordinately control the combined pollution of PM2.5 and O3, and pay attention to different risk characteristics of pollutants between the cold and warm seasons.
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Based on the number of influenza cases amongst preschool children and meteorological observations over Beijing-Tianjin-Hebei area from 2014 to 2016, the relationships between the incidence of preschool children influenza and individual meteorological factor as well as their combined conditions are investigated. The results indicate significant linear correlations between the number of influenza infection amongst preschool children and temperature, relative humidity, atmospheric pressure and a defined comprehensive indicator Body Perception Weather Index (BPWI). The exposure-consequence relationship based on the BPWI is more stable, i.e., while the BPWI values equal or smaller than −11 or within the range of 0—10 correspond to higher risk of influenza. Local atmospheric pressure is another key factor. When the station pressure is higher than 905 hPa, higher pressure causes more infection, and the infection peak corresponds to 1007 hPa. On the basis of these understanding, a machine learning method is used to perform prediction experiment, and it is found that the BPWI with a 3 d lead time contributes the most to influenza incidence. The hindcast evaluation reports a fairly good performance of the prediction model, and this provides valuable evidences and scientific clues for pre-intervention.
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Launched in 1925， bimonthly

Supervisor: China Meterological Administration

Sponsor: China Meteorological Society

ISSN0577-6619

CN11-2006/P

Editor In Chief: Yihui DING