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Millimeter-wave cloud radar (MMCR) provides the capability of detecting the features of micro particles inside clouds and describing the internal microphysical structure of the clouds. Therefore, MMCR has been widely applied in cloud observations. However, due to the influence of non-meteorological factors such as insects, the cloud observations are often contaminated by non-meteorological echoes in the clear air, known as clear-air echoes. It is of great significance to automatically identify the clear-air echoes in order to extract effective meteorological information from the complex weather background. The characteristics of clear-air echoes are studied here by combining data from four devices: an MMCR, a laser-ceilometer, an L-band radiosonde, and an all-sky camera. In addition, a new algorithm, which includes feature extraction, feature selection, and classification, is proposed to achieve the automatic identification of clear-air echoes. The results show that the recognition algorithm is fairly satisfied in both simple and complex weather conditions. The recognition accuracy can reach up to 95.86% for the simple cases when cloud echoes and clear-air echoes are separate, and 88.38% for the complicated cases when low cloud echoes and clear-air echoes are mixed.

Frequent emergency room (ER) visits occur for acute symptoms of hypertension or related complications in Beijing, China. Among the meteorological variables, atmospheric pressure directly relates to weather conditions and is hardly affected by artificial factors. In the studies of weather conditions and hypertension-related risks, atmospheric pressure is rarely found in literatures. Based on records of ER visit related to hypertension from three major hospitals in Beijing, from 2008 to 2012, the daily maximum (Pmax) and minimum (Pmin) atmospheric pressures were investigated in this study to identify the influences of air pressure on ER visits. Advanced time series models were utilized in quantifying their associations. In addition, major air pollutants and other meteorological variables including temperature, solar duration, and humidity were incorporated in the multivariate models to reveal the independent association between atmospheric pressure and ER visits. The results indicated the following: 1. The number of ER visits positively correlates with all atmospheric pressure metrics in both bivariate and partial correlation analyses. 2. The estimated relative risk (RR) corresponding to 1 hPa change in Pmax indicated a significant effect (Pmax = 1029 hPa, RR = 1.107, 95% CIs: 1.034-1.185) from high Pmax on short lag terms. RR for males and the elders grew prominently at lag 1 d, whereas females responded to Pmax at lag 4 d. 3. The middle-aged group (45–65 years) was threatened by extremely low Pmin with one day lag. 4. As for young (< 45 years) hypertensive patients, no effect from atmospheric pressure was identified. This study may provide new evidence that extreme atmospheric pressure is an independent inducer of hypertension-related symptoms or complications.

Ambient air temperature is a key index that affects human health, including mortality risks of cardiovascular disease (CVD). This study quantitatively investigated CVD mortality burden with respect to various segments across the climatic temperature range. Daily data on average temperature and CVD deaths in nine Chinese cities during 2010–2016 were collected for the study. The association between temperature and city-specific CVD mortality was investigated with a distributed lag nonlinear model across lag 0–21 days for cold temperature and lag 0–2 days for hot temperature, and then pooled the association results in a multivariate meta-analysis to derive the pooled estimates of temperature on CVD mortality at national level. Attributable fractions of CVD mortality to cold- and heat-related (i.e., at temperatures below and above the minimum mortality temperature [MMT]) were calculated. In addition, temperature was further separated at 1 °C intervals of ambient temperature and the attributable fractions of each range were calculated. The results showed that the MMT varied from the 71th to 79th percentiles of temperature in nine Chinese cities, centering at 76th at national level. In total, 16.88% of CVD mortality was attributable to nonoptimal temperature, ranging from 9.73% in Hefei to 24.48% in Nanjing. Cold temperature was responsible for most CVD death burden, with a fraction of 14.62%, compared with 2.26% CVD mortality for heat at overall level. The results of temperature stratification suggested that the highest CVD deaths due to temperature fall within moderate cold. Specifically, the highest attributable fractions were at 7 °C, 7 °C, 8 °C, 4 °C, 5 °C, 4 °C, 4 °C, 5 °C, and 6 °C for Harbin, Changchun, Shenyang, Beijing, Shijiazhuang, Nanjing, Hefei, Shanghai, and Chengdu, respectively. Furthermore, the highest CVD deaths due to temperature were near at the start and end time of heating for five northern cities (Harbin, Changchun, Shenyang, Beijing, and Shijiazhuang). Hence, moderately cold temperature plays a noticeable role in impact mortality. Although moderate cold had a slightly lower relative risk than extreme cold, it occurred on more days than did extreme cold. Finally, the cumulative total counts of CVD deaths caused by moderate cold were the largest. We should pay more attention to the adverse health effects of moderate cold in the future. Additionally, the government and heating departments should slow down the rate of increase (or decrease) temperature at the start (or end) time of heating during moderate time in northern China. The findings have important implications for health promotion and disease prevention strategies of adverse temperatures.

The bright band, a layer of enhanced radar reflectivity associated with melting ice particles, is a major source of significant overestimation in quantitative precipitation estimation (QPE) based on the Z–R (reflectivity factor–rain rate) relationship. The effects of the bright band on radar-based QPE can be eliminated by vertical profile of reflectivity (VPR) correction. In this study, we applied bright-band correction algorithms to evaluate three different bands (S-, C- and X-band) of dual-polarized radars and to reduce overestimation errors in Z–R relationship–based QPEs. After the reflectivity was corrected by the algorithms using average VPR (AVPR) alone and a combination of average VPR and the vertical profile of the copolar correlation coefficient (AVPR+CC), the QPEs were derived. The bright-band correction and resulting QPEs were evaluated in eight precipitation events by comparing to the uncorrected reflectivity and rain-gauge observations, separately. The overestimation of Z–R relationship–based QPEs associated with the bright band was reduced after correction by the two schemes for which hourly rainfall was less than 5 mm. For the verification metrics of RMSE (root-mean-square error), RMAE (relative mean absolute error) and RMB (relative mean bias) of QPEs, averaged over all eight cases, the AVPR method improved from 2.28, 0.94 and 0.78 to 1.55, 0.60 and 0.40, respectively, while the AVPR+CC method improved to 1.44, 0.55 and 0.30, respectively. The QPEs after AVPR+CC correction had less overestimation than those after AVPR correction, and similar conclusions were drawn for all three different bands of dual-polarized radars.

Due to their particular physiographic, geomorphic, soil cover, and complex surface-subsurface hydrologic conditions, karst regions produce distinct land–atmosphere interactions. It has been found that floods and droughts over karst regions can be more pronounced than those in non-karst regions following a given rainfall event. Five convective weather events are simulated using the Weather Research and Forecasting model to explore the potential impacts of land-surface conditions on weather simulations over karst regions. Since no existing weather or climate model has the ability to represent karst landscapes, simulation experiments in this exploratory study consist of a control (default land-cover/soil types) and three land-surface conditions, including barren ground, forest, and sandy soils over the karst areas, which mimic certain karst characteristics. Results from sensitivity experiments are compared with the control simulation, as well as with the National Centers for Environmental Prediction multi-sensor precipitation analysis Stage-IV data, and near-surface atmospheric observations. Mesoscale features of surface energy partition, surface water and energy exchange, the resulting surface-air temperature and humidity, and low-level instability and convective energy are analyzed to investigate the potential land-surface impact on weather over karst regions. We conclude that: (1) barren ground used over karst regions has a pronounced effect on the overall simulation of precipitation. Barren ground provides the overall lowest root-mean-square errors and bias scores in precipitation over the peak-rain periods. Contingency table-based equitable threat and frequency bias scores suggest that the barren and forest experiments are more successful in simulating light to moderate rainfall. Variables dependent on local surface conditions show stronger contrasts between karst and non-karst regions than variables dominated by large-scale synoptic systems; (2) significant sensitivity responses are found over the karst regions, including pronounced warming and cooling effects on the near-surface atmosphere from barren and forested land cover, respectively; (3) the barren ground in the karst regions provides conditions favourable for convective development under certain conditions. Therefore, it is suggested that karst and non-karst landscapes should be distinguished, and their physical processes should be considered for future model development.

Annual precipitation anomalies over eastern China are characterized by a north–south dipole pattern, referred to as the “southern flooding and northern drought” pattern (SF/ND), fluctuating on decadal time scales. Previous research has suggested possible links with oceanic forcing, but the underlying physical mechanisms by which sea surface temperature (SST) variability impacts the dipole pattern remains unclear. Idealized atmospheric general circulation model experiments conducted by the U.S. CLIVAR Drought Working Group are used to investigate the role of historical SST anomalies associated with Pacific El Niño–Southern Oscillation (ENSO)-like and the Atlantic multidecadal oscillation (AMO) patterns in this dipole pattern. The results show that the Pacific SST pattern plays a dominant role in driving the decadal variability of this dipole pattern and the associated atmospheric circulation anomalies, whereas the Atlantic SST pattern contributes to a much lesser degree. The direct atmospheric response to the Pacific SST pattern is a large-scale cyclonic or anticyclonic circulation anomaly in the lower troposphere occupying the entire northern North Pacific. During the warm phase of the Pacific SST pattern, it is cyclonic with northwesterly wind anomalies over northern China pushing the monsoon front to the south and consequently SF/ND. During the cold phase of the Pacific SST pattern, the circulation anomaly reverses with southeasterly winds over northern China allowing the monsoon front and the associated rainband to migrate northward, resulting in southern drought and northern flooding. The Atlantic SST pattern plays a supplementary role, enhancing the dipole pattern when the Pacific SST and Atlantic SST patterns are in opposite phases and weakening it when the phases are the same.

Worldwide epidemiological studies have demonstrated that short-term associations of particulate matter (PM), might be further complicated by ambient temperatures, and increase human mortality. In China, among various PM indices, the health effects of black carbon (BC) are less understood due to the lack of data availability. Additionally, it is unclear how temperature modifies simultaneous and adverse effects of BC, and fine (PM size 28 °C. Harmful effects were estimated for increases in non-accidental, respiratory, and cardiovascular mortality per interquartile increments in BC (4.11 μg/m3), PM2.5 (62.37 μg/m3), and PM2.5-10 (46.71 μg/m3) concentrations from the moving average of current and previous day (lag01) under high temperature (>28 °C). Mortality increases in non-accidental, respiratory, and cardiovascular categories were 5.12% (95% confidence interval [CI]: 4.79, 5.45), 7.41% (95%CI: 6.42, 8.40), and 6.36% (95%CI: 2.45, 10.3) for BC; 3.85% (95%CI: 3.27, 4.43), 6.64% (95%CI: 5.63, 7.65), and 4.54% (95%CI: 3.31, 5.78) for PM2.5; and 2.56% (95%CI: 1.45, 3.67), 3.41% (95%CI: 1.53, 5.29), and 3.19% (95%CI: 2.44, 3.94), for PM2.5-10, respectively. These findings suggest that controlling the emission of ambient particles, especially BC, and improving air quality during summer would substantially benefit population health. Furthermore, BC should be considered as a crucial air quality indicator to reflect PM health risk.

This study presents a temperature indicator for heavy air pollution risks (TIP). This TIP indicator is a temperature threshold, at which the concentrations of air pollutants and air pollution indices easily reach their maxima. A connotation discussion ascertains that TIP is intrinsically associated with the atmospheric structure and static stability in the planetary boundary layer, especially during stagnant weather and moderately cold temperatures. Thus, TIP offers a simple measure of meteorological conditions that reflect the boundary layer static characteristics. It therefore provides a simple measure of air pollution potential, and can be used to quantify the potential air pollution severity for a given pollution emission level. From the data collected in China, TIP increases gradually from north to south and decreases slightly from east to west, which essentially indicates a function of latitude and altitude. A simple regressive function is built for TIP, and the resulting TIP index further confirms that it is indicative of the potential heavy air pollution events across the country. Being an easy-to-obtain and simple-to-use meteorological parameter, TIP can be used for air pollution potential assessment and prediction, as well as pollution prevention and control applications.

The risks of emergency room (ER) visits for cerebral infarction (CI) and intracerebral hemorrhage (ICH) is found to differ in different age groups under different climatic thermal environments. Based on CI and ICH related ER-visit records from three major hospitals in Beijing, China, from 2008 to 2012, the advanced Universal Thermal Climate Index (UTCI), was adopted in this study to assess the climatic thermal environment. Particularly, daily mean UTCI was used as a predictor for the risk of ER visits for CI and ICH. A generalized quasi-Poisson additive model combined with a distributed lag non-linear model was performed to quantify their association. The results indicated that (i) the highest growth rate of ER visits for ICH occurred in age 38 to 48, whereas an increasing ER admissions for CI maintained at age 38 to 78. (ii) The frequency distribution of UTCI in Beijing peaked at −8 and 30 °C, corresponding to moderate cold stress and moderate heat stress, respectively. (iii) Correlation analysis indicated that ICH morbidity was negatively correlated with UTCI, whereas occurrence of CI showed no significant association with UTCI. (iv) The estimated relative risk of ER visits corresponding to 1 °C change in UTCI, which was then stratified by age and gender, indicated that all sub-groups of ICH patients responded similarly to thermal stress. Namely, there is an immediate ICH risk (UTCI = −13 °C, RR = 1.35, 95% CIs: 1.11–1.63) from cold stress on the onset day, but non-significant impact from heat stress. As for CI occurrences, no effect from cold stress was identified, except for only those aged 45 to 65 were threatened by heat stress (UTCI = 38 °C, RR = 1.64, 95% CIs: 1.10–2.44) on lag 0–2 d.

Particulate matter (PM) has been proven to cause health risks and may result in hospital emergency room visits (ERVs), which might be complicated by extreme temperature events. However, it remains unclear how temperature modulates the effect of different-sized particles on ERVs. This study used three separate time series analyses (2009–2011) to explore such temperature modulation effect in Beijing, China. The analytical approaches included a bivariate response surface model, a non-stratification parametric model, and a stratification parametric model. Results showed that the average daily concentrations of PM10 and PM2.5 in Beijing were 110.16 and 67.89 μg/m3, respectively, during the study period, which were higher than in most Western countries. Our findings indicated that the temperature modulation effects of PM2.5 were more evident than that of PM10. The effects of PM on morbidity depend on temperature. The effects were estimated for the increases in total, respiratory, and cardiovascular ERVs per 10 μg/m3 increase in PM2.5 and PM10 concentrations at high temperature level (> 28 °C). The estimated increases in the three types of ERVs for PM2.5 were 0.15, 0.35, and 0.34%, respectively. For PM10, the increases were 0.12, 0.08, and 0.14%, respectively. In addition, the results showed that the elderly (age ≥ 65) and women are more vulnerable to PM at high temperatures. These findings may have implications for the health impact associated with both air pollution and global climate change.

The Global Precipitation Measurement (GPM) mission has generated global precipitation products of improved accuracy and coverage that are promising for advanced hydrological and meteorological studies. This study evaluates three Integrated Multi-satellitE Retrievals for GPM (IMERG) Hourly products, including the Early-, Late-, and Final-run products (IMERG-HE, IMERG-HL, and IMERG-HF, respectively), over Sichuan Basin of China. This highly complex terrain of the steep mountainous region offers further scrutiny on the quality and applicability of the data. The China Meteorological Precipitation Analysis (CMPA) data from January 2016 to December 2018 are used as the reference for the evaluation. Results show that: (1) At grid scale, IMERG-HL and IMERG-HF outperform IMERG-HE in terms of correlation coefficient (CC) and root-mean-square error (RMSE), but IMERG-HL has smaller relative bias (RB) than that of the IMERG-HF (by 21.16%). IMERG-HF presents the highest probability of detection (POD = 0.52) and critical success index (CSI = 0.32), except for high false alarm ratio (FAR) for light precipitation. (2) At regional scale, IMERG-HF outperforms IMERG-HE and IMERG-HL in annual evaluation in all the metrics except for the serious overestimation as shown in RB (20.18%, 3.84%, and 4.97%, respectively). Its accumulative precipitation deviation mainly comes from moderate precipitation events (1&ndash, 10 mm/h), while better detection capability is seen in light precipitation (&lt, 1 mm/h). Seasonally, IMERG-HF performs the best in winter, while IMERG-HL performs the best in the other seasons. (3) IMERG-HF captures the peak precipitation more accurately in all seasons. In reproducing the diurnal cycle, IMERG-HF performs better in winter, while IMERG-HL performs better in summer and autumn, and IMERG-HE in spring. However, all three products overestimate the early morning precipitation (01:00&ndash, 08:00 local standard time) of the diurnal cycle in spring, summer, and autumn.

Emergency room (ER) visits for accidental casualties, according to the International Classification of Deceases 10th Revision Chapters 19 and 20, include injury, poisoning, and external causes (IPEC). Annual distribution of 187,008 ER visits that took place between 2009 and 2011 in Beijing, China displayed regularity rather than random characteristics. The annual cycle from the Fourier series fitting of the number of ER visits was found to explain 63.2% of its total variance. In this study, the possible effect and regulation of meteorological conditions on these ER visits are investigated through the use of correlation analysis, as well as statistical modeling by using the Distributed Lag Non-linear Model and Generalized Additive Model. Correlation analysis indicated that meteorological variables that positively correlated with temperature have a positive relationship with the number of ER visits, and vice versa. The temperature metrics of maximum, minimum, and mean temperatures were found to have similar overall impacts, including both the direct impact on human mental/physical conditions and indirect impact on human behavior. The lag analysis indicated that the overall impacts of temperatures higher than the 50th percentile on ER visits occur immediately, whereas low temperatures show protective effects in the first few days. Accidental casualties happen more frequently on warm days when the mean temperature is higher than 14 °C than on cold days. Mean temperatures of around 26 °C result in the greatest possibility of ER visits for accidental casualties. In addition, males were found to face a higher risk of accidental casualties than females at high temperatures. Therefore, the IPEC-classified ER visits are not pure accidents; instead, they are associated closely with meteorological conditions, especially temperature.

Tornado hazards have caused frequent and severe damages to built environment and lives. The severity of the hazards directly relates to the strength of the tornadic events in terms of meteorological parameters, as well as the resistance of buildings to the strong tornadic impacts. The tornado hazards are reviewed from both meteorological and engineering perspectives in this study. For each of the perspectives, current status of tornado hazard studies including the observation, monitoring, analysis, numerical and laboratory simulations, and limitations and future demands of development are discussed. While the two disciplines are fast advancing individually, their interaction and interdependence are deemed prominent and imperative in the future interdisciplinary studies.