Explore the vast range of titles available.

Heat waves (HWs) can have disastrous impacts on human activities and natural systems, and are one of the current foci of scientific research, particularly in the context of global warming. However, there is no standard definition of a HW, which makes assessment of temporal trends a challenge. In this study, based on daily mean, maximum and minimum temperature, and relative humidity datasets from China Meteorological Administration, the patterns, trends and variations of HW in China during 1961–2014 are investigated. Sixteen previously published HW indices (HIs) are calculated, which are divided into two types using relative and absolute threshold temperatures, respectively. During 1961–2014, both relative and absolute threshold HIs show the highest number of HW in Jianghua and South China, geographically consistent with the climate characteristics of China. The majority of HIs shows negative/positive trends of HW days before/after 1990 over the whole of China, but especially in Jianghua and South China, which reflects rapid warming since 1990. There are significant correlations among different HIs in the same type (both absolute and relative), but correlations are weak between relative and absolute threshold HIs. Because relative and absolute HIs show contrasting trends, the choice of HI is therefore critical for future analysis

Heat waves are becoming more intense and extreme as a consequence of global warming. Epidemiological evidence reveals the health impacts of heat waves in mortality and morbidity outcomes, however, few studies have been conducted in tropical regions, which are characterized by high population density, low income and low health resources, and susceptible to the impacts of extreme heat on health. The aim of this paper is to estimate the effects of heat waves on cardiovascular and respiratory mortality in the city of Rio de Janeiro, Brazil, according to sex, age, and heat wave intensity. We carried out a time-stratified case-crossover study stratified by sex, age (0-64 and 65 or above), and by sex for the older group. Our analyses were restricted to the hot season. We included 42,926 participants, 29,442 of whom died from cardiovascular and 13,484 from respiratory disease, between 2012 and 2017. The death data were obtained from Rio de Janeiro's Municipal Health Department. We estimated individual-level exposure using the inverse distance weighted (IDW) method, with temperature and humidity data from 13 and 12 stations, respectively. We used five definitions of heat waves, based on temperature thresholds (90th, 92.5th, 95th, 97.5th, and 99th of individual daily mean temperature in the hot season over the study period) and a duration of two or more days. Conditional logistic regression combined with distributed lag non-linear models (DLNM) were used to estimate the short-term and delayed effects of heat waves on mortality over a lag period (5 days for cardiovascular and 10 for respiratory mortality). The models were controlled for daily mean absolute humidity and public holidays. The odds ratios (OR) increase as heat waves intensify, although some effect estimates are not statistically significant at 95% level when we applied the most stringent heat wave criteria. Although not statistically different, our central estimates suggest that the effects were greater for respiratory than cardiovascular mortality. Results stratified by sex and age were also not statistically different, but suggest that older people and women were more vulnerable to the effects of heat waves, although for some heat wave definitions, the OR for respiratory mortality were higher among the younger group. The results also indicate that older women are the most vulnerable to heat wave-related cardiovascular mortality. Our results show an increase in the risk of cardiovascular and respiratory mortality on heat wave days compared to non-heat wave ones. These effects increase with heat wave intensity, and evidence suggests that they were greater for respiratory mortality than cardiovascular mortality. Furthermore, the results also suggest that women and the elderly constitute the groups most vulnerable to heat waves.

The near‐surface specific humidity is critical for accurately estimating the enthalpy flux from the ocean, which plays an important role in tropical cyclone intensification. However, under the severe oceanic and atmospheric conditions of these storms, even spaceborne microwave radiometers struggle to retrieve reliable humidity data. This limitation stems primarily from heavy precipitation, which masks the water‐vapor signal in the observed brightness temperatures. To overcome these challenges, this study introduces a two‐pronged approach: (a) a neural network‐based screening of brightness temperatures to filter out precipitation‐contaminated measurements, and (b) the first use of low‐frequency microwave channel for humidity estimation in tropical cyclones. Comparisons with independent in situ observations demonstrate that our method provides more accurate near‐surface humidity estimates than previous techniques that do not use low‐frequency channels. These improvements will enhance the estimation of ocean‐to‐atmosphere enthalpy fluxes during tropical cyclones and may support better understanding of air‐sea interaction processes under extreme weather conditions.

Cirrus clouds and their potential formation regions, so-called ice supersaturated regions (ISSRs), with values of relative humidity with respect to ice exceeding 100 %, occur frequently in the tropopause region. It is assumed that ISSRs and cirrus clouds can change the tropopause structure by diabatic processes, driven by latent heating due to phase transition and interaction with radiation. For many research questions, a three-dimensional picture including a sufficient temporal resolution of the water vapour fields in the tropopause region is required. This requirement is fulfilled nowadays by reanalysis products such as the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis. However, for a meaningful investigation of water vapour in the tropopause region, a comparison of the reanalysis data with measurement is advisable, since it is difficult to measure water vapour and to assimilate meaningful measurements into reanalysis products. Here, we present an intercomparison of high-resolution in situ measurements aboard passenger aircraft within the European Research Infrastructure IAGOS (In-service Aircraft for a Global Observing System; http://www.iagos.org, last access: 15 January 2020) with ERA-Interim. Temperature and humidity data over the North Atlantic from 2000 to 2009 are compared relative to the dynamical tropopause. The comparison of the temperature shows good agreement between the measurement and ERA-Interim. While ERA-Interim also shows the main features of the water vapour measurements of IAGOS, the variability of the data is clearly smaller in the reanalysis data set. The combination of temperature and water vapour leads to the relative humidity with respect to ice (RHi). Here, ERA-Interim deviates from the measurements concerning values larger than RHi=100 %, both in number and strength of supersaturation. Also, pathlengths of ISSRs along flight tracks are investigated, representing macrophysical properties as linked to atmospheric flows. The comparison of ISSR pathlengths shows distinct differences, which can be traced back to the spatial resolution of both data sets. Also, the seasonal cycle and height dependence of pathlengths changes for the different data sets due to their spatial resolution. IAGOS shows a significantly greater amount of smaller ISSRs compared to ERA-Interim. Good agreement begins only at pathlengths on the order of the ERA-Interim spatial resolution and larger.

Global positioning system (GPS) data from over 260 ground-based permanent stations in China covering the period from 1 March 1999 to 30 April 2015 were used to estimate precipitable water (PW) above each site with an accuracy of about 0.75 mm. Four types of radiosondes (referred to as GZZ2, GTS1, GTS1-1, and GTS1-2) were used in China during this period. Instrumentation type changes in radiosonde records were identified by comparing PW calculated from GPS and radiosonde data. Systematic errors in different radiosonde types introduced significant biases to the estimated PW trends at stations where more than one radiosonde type was used. Estimating PW trends from reanalysis products (ERA-Interim), which assimilate the unadjusted radiosonde humidity data, resulted in an artificial downward PW trend at almost all stations in China. The statistically significant GPS PW trends are predominantly positive, consistent in sign with the increase in moisture expected from the Clausius–Clapeyron relation due to a global temperature increase. The standard deviations of the differences between ERA-Interim and GPS PW in the summer were 3 times larger than the observational error of GPS PW, suggesting that potentially significant improvements to the reanalysis could be achieved by assimilating denser GPS PW observations over China. This work, based on an entirely independent GPS PW dataset, confirms previously reported significant differences in radiosonde PW trends when using corrected data. Furthermore, the dense geographical coverage of the all-weather GPS PW observations, especially in remote areas in western China, provides a valuable resource for calibrating regional trends in reanalysis products.

This study presents a novel approach for conducting all‐day retrieval of cloud macro‐physical properties (single‐layer cloud phase, cloud top height, and cloud base height for optical thickness less than 10) using the Advanced Geostationary Radiation Imager (AGRI) and the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the geostationary meteorological satellite Fengyun‐4A based on machine learning methods. Model accuracy was compared after integrating ECMWF Reanalysis v5 (ERA‐5) data, atmospheric temperature and moisture profiles, and GIIRS clear‐column radiance. Results demonstrate that integrating GIIRS clear‐column radiances can enhance the precision of cloud phase classification and the retrieval of cloud macro‐physical properties. This effectively replaces the role of atmospheric temperature and humidity profiles, which are typically required for thermal infrared remote sensing retrieval. Moreover, the issue of delayed acquisition of ERA‐5 atmospheric temperature and humidity profiles is mitigated, enabling near real‐time and all‐day retrieval of cloud macro‐physical properties. Plain Language Summary This study introduces a new method for accurately determining the macro‐physical properties of clouds using satellite data and machine learning techniques. By combining observations from the Advanced Geostationary Radiation Imager (AGRI) and the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the geostationary meteorological satellite Fengyun‐4A, we improve the accuracy of cloud phase classification and retrieve information regarding cloud height. This new approach reduces reliance on traditional methods that require atmospheric temperature and humidity data, making it easier to obtain real‐time cloud information throughout the day. These findings have the potential to significantly enhance our ability to monitor and understand cloud behavior using satellite technology. This is the first time to combine the observations from both geostationary satellite imager (AGRI) and sounder (GIIRS) to retrieve the macro‐physical properties of clouds. Key Points Combined use of AGRI and GIIRS observations significantly enhances the accuracy of all‐day retrieval of thin cloud macro‐physical properties GIIRS clear‐column radiance can replace ERA reanalysis data in thermal infrared retrieval algorithms, achieving similar or higher accuracy Using GIIRS observations can avoid the issue of lagging atmospheric profile generation, thereby enabling near real‐time cloud retrieval

Increasing climate variability as a result of climate change will be one of the public health challenges to control infectious diseases in the future, particularly in sub-Saharan Africa including Ethiopia. To investigate the effect of climate variability on childhood diarrhea (CDD) and identify high risk periods of diarrheal diseases. The study was conducted in all districts located in three Zones (Awi, West and East Gojjam) of Amhara Region in northwestern parts of Ethiopia. Monthly CDD cases for 24 months (from July 2013 to June 2015) reported to each district health office from the routine surveillance system were used for the study. Temperature, rainfall and humidity data for each district were extracted from satellite precipitation estimates and global atmospheric reanalysis. The space-time permutation scan statistic was used to identify high risk periods of CDD. A negative binomial regression was used to investigate the relationship between cases of CDD and climate variables. Statistical analyses were conducted using SaTScan program and StataSE v. 12. The monthly average incidence rate of CDD was 11.4 per 1000 (95%CI 10.8-12.0) with significant variation between males [12.5 per 1000 (95%CI 11.9 to 13.2)] and females [10.2 per 1000 (95%CI 9.6 to 10.8)]. The space-time permutation scan statistic identified the most likely high risk period of CDD between March and June 2014 located in Huletej Enese district of East Gojjam Zone. Monthly average temperature and monthly average rainfall were positively associated with the rate of CDD, whereas the relative humidity was negatively associated with the rate of CDD. This study found that the most likely high risk period is in the beginning of the dry season. Climatic factors have an association with the occurrence of CDD. Therefore, CDD prevention and control strategy should consider local weather variations to improve programs on CDD.

Accurate measurement of water vapor in the climate-sensitive region near the tropopause is very challenging. Unexplained systematic discrepancies between measurements at low water vapor mixing ratios made by different instruments on airborne platforms have limited our ability to adequately address a number of relevant scientific questions on the humidity distribution, cloud formation and climate impact in that region. Therefore, during the past decade, the scientific community has undertaken substantial efforts to understand these discrepancies and improve the quality of water vapor measurements. This study presents a comprehensive intercomparison of airborne state-of-the-art in situ hygrometers deployed on board the DLR (German Aerospace Center) research aircraft HALO (High Altitude and LOng Range Research Aircraft) during the Midlatitude CIRRUS (ML-CIRRUS) campaign conducted in 2014 over central Europe. The instrument intercomparison shows that the hygrometer measurements agree within their combined accuracy (±10 % to 15 %, depending on the humidity regime); total mean values agree within 2.5 %. However, systematic differences on the order of 10 % and up to a maximum of 15 % are found for mixing ratios below 10 parts per million (ppm) H2O. A comparison of relative humidity within cirrus clouds does not indicate a systematic instrument bias in either water vapor or temperature measurements in the upper troposphere. Furthermore, in situ measurements are compared to model data from the European Centre for Medium-Range Weather Forecasts (ECMWF) which are interpolated along the ML-CIRRUS flight tracks. We find a mean agreement within ±10 % throughout the troposphere and a significant wet bias in the model on the order of 100 % to 150 % in the stratosphere close to the tropopause. Consistent with previous studies, this analysis indicates that the model deficit is mainly caused by too weak of a humidity gradient at the tropopause.

The evapotranspiration-based scheduling method is the most common method for irrigation programming in agriculture. There is no doubt that the estimation of the reference evapotranspiration (ETo) is a key factor in irrigated agriculture. However, the high cost and maintenance of agrometeorological stations and high number of sensors required to estimate it make it non-plausible, especially in rural areas. For this reason, the estimation of ETo using air temperature, in places where wind speed, solar radiation and air humidity data are not readily available, is particularly attractive. A daily data record of 49 stations distributed over Duero basin (Spain), for the period 2000–2018, was used for estimation of ETo based on seven models against Penman–Monteith (PM) FAO 56 (FAO – Food and Agricultural Organization of the United Nations) from a temporal (annual or seasonal) and spatial perspective. Two Hargreaves–Samani (HS) models, with and without calibration, and five Penman–Monteith temperature (PMT) models were used in this study. The results show that the models' performance changes considerably, depending on whether the scale is annual or seasonal. The performance of the seven models was acceptable from an annual perspective (R2>0.91, NSE > 0.88, MAE < 0.52 and RMSE < 0.69 mm d−1; NSE – Nash–Sutcliffe model efficiency; MAE – mean absolute error; RMSE – root-mean-square error). For winter, no model showed good performance. In the rest of the seasons, the models with the best performance were the following three models: PMTCUH (Penman–Monteith temperature with calibration of Hargreaves empirical coefficient – kRS, average monthly value of wind speed, and average monthly value of maximum and minimum relative humidity), HSC (Hargreaves–Samani with calibration of kRS) and PMTOUH (Penman–Monteith temperature without calibration of kRS, average monthly value of wind speed and average monthly value of maximum and minimum relative humidity). The HSC model presents a calibration of the Hargreaves empirical coefficient (kRS). In the PMTCUH model, kRS was calibrated and average monthly values were used for wind speed and maximum and minimum relative humidity. Finally, the PMTOUH model is like the PMTCUH model except that kRS was not calibrated. These results are very useful for adopting appropriate measures for efficient water management, especially in the intensive agriculture in semi-arid zones, under the limitation of agrometeorological data.

Particle bulk optical property parameters play an essential role in evaluating air quality, which, however, can be substantially enhanced under humid atmospheric conditions via hygroscopic growth. Here we use 532 nm polarization lidar observations, ERA5 humidity data, and Hänel hygroscopic-growth parameters from our previous study (Jing et al., 2026) during 2010–2024 to retrieve vertical profiles of ambient and dry aerosol backscatter and extinction coefficients of anthropogenic pollution over central China. Particle hygroscopic growth led to enhanced particle backscatter coefficient by 11 %–46 % below 2 km on an annual basis for the considered time frame. Anthropogenic Aerosol Optical Depth (AOD) was ∼30 % higher under ambient atmospheric conditions. We found values of AODamb=0.404 and AODdry=0.315. In Jing et al. (2025), we reported a significant decline of −0.068 yr−1 for AODamb during China's rapid air-cleaning period of 2010–2017. Here, we find a 28 % lower decline rate for AODdry (−0.049 yr−1), which more accurately reflects the reduction in anthropogenic aerosol emission. Hygroscopic-growth-induced net AOD (ΔAODRH) dropped sharply in 2011–2014, most likely as a result of emission mitigation and drier atmospheric conditions, then rebounded in 2014–2019 as rising humidity conditions and the presence of hydrophilic aerosols. Since 2020, ΔAODRH has remained high attributed to rising humidity conditions but weakening hygroscopicity. AODamb suggests peak pollution in summer, while AODdry identifies winter as the true air-pollution maximum. These results highlight the significant impact of aerosol water uptake on its optical properties; therefore, it must be accounted for to ensure accurate air quality assessments.