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Deriving large-scale and high-quality precipitation products from satellite remote-sensing spectral data is always challenging in quantitative precipitation estimation (QPE), and limited studies have been conducted even using China's latest Fengyun-4A (FY-4A) geostationary satellite. Taking three rainstorm events over South China as examples, a machine-learning-based regression model was established using the random forest (RF) method to derive QPE from FY-4A observations, in conjunction with cloud parameters and physical quantities. The cross-validation results indicate that both daytime (DQPE) and nighttime (NQPE) RF algorithms performed well in estimating QPE, with the bias score, correlation coefficient and root-mean-square error of DQPE (NQPE) of 2.17 (2.42), 0.79 (0.83) and 1.77 mm h−1 (2.31 mm h−1), respectively. Overall, the algorithm has a high accuracy in estimating precipitation under the heavy-rain level or below. Nevertheless, the positive bias still implies an overestimation of precipitation by the QPE algorithm, in addition to certain misjudgements from non-precipitation pixels to precipitation events. Also, the QPE algorithm tends to underestimate the precipitation at the rainstorm or even above levels. Compared to single-sensor algorithms, the developed QPE algorithm can better capture the spatial distribution of land-surface precipitation, especially the centre of strong precipitation. Marginal difference between the data accuracy over sites in urban and rural areas indicate that the model performs well over space and has no evident dependence on landscape. In general, our proposed FY-4A QPE algorithm has advantages for quantitative estimation of summer precipitation over East Asia.

Satellite-based measurements are of crucial importance for the accurate global-scale estimation of precipitation at fine spatiotemporal resolutions. Integrated Multi-satellitE Retrievals for Global...

Surface ozone (O3) pollution during summer (June–August) over eastern China has become more severe in recent years, resulting in a co-occurrence of surface O3 and PM2.5 (particulate matter with aerodynamic diameters ≤ 2.5 µm in the air) pollution. However, the mechanisms regarding how the synoptic weather pattern (SWP) might influence this compound pollution remain unclear. In this study, we applied the T-mode principal component analysis (T-PCA) method to objectively classify the occurrence of four SWPs over eastern China, based on the geopotential heights at 500 hPa during summer (2015–2018). These four SWPs over eastern China were closely related to the western Pacific subtropical high (WPSH), exhibiting significant intra-seasonal and interannual variations. Based on ground-level air quality observations, remarkable spatial and temporal disparities of surface O3 and PM2.5 pollution were also found under the four SWPs. In particular, there were two SWPs that were sensitive to compound pollution (Type 1 and Type 2). Type 1 was characterized by a stable WPSH ridge with its axis at about 22∘ N and the rain belt located south of the Yangtze River Delta (YRD); Type 2 also exhibited WPSH dominance (ridge axis at ∼ 25∘ N) but with the rain belt (over the YRD) at a higher latitude compared to Type 1. In general, SWPs have played an important role as driving factors of surface O3–PM2.5 compound pollution in a regional context. Our findings demonstrate the important role played by SWPs in driving regional surface O3–PM2.5 compound pollution, in addition to the large quantities of emissions, and may also provide insights into the regional co-occurring high levels of both PM2.5 and O3 via the effects of certain meteorological factors.

Aerosol pollution is a significant threat to public health, and ground-based lidar can effectively monitor air pollution and regional air quality, including providing accurate observations of the vertical distribution of aerosols. Two long-distance trans-boundary aerosol episodes (named P2 and P5; P2 was the heavy pollution period (from 19:00 on January 19 to 15:00 on January 20) and P5 was the moderate pollution period (from 03:00 on January 25 to 21:00 on January 25)) occurred in the western Yangtze River Delta region, China during 17–26 January 2019, causing significant PM2.5 pollution. The ground-based lidar average value of the aerosol extinction coefficient below 0.5 km was highly correlated with surface PM2.5 concentration, and the correlation coefficient was 0.89. The regression relationship between the planetary boundary layer (PBL) height and PM2.5 concentration value was significantly exponential with a correlation coefficient of −0.59. The PBL height during P2 and P5 was below 0.5 km, relative to other periods with PBL height ranging from 0.5 to 2.0 km. Based on the backward trajectory and weather pattern analysis and surface PM2.5 observations, aerosols during P2 and P5 were mainly contributed by transported PM2.5 blown from northern China during the winter monsoon. The depolarization ratios derived from the lidar data show that the persistent pollution episode was mainly caused by strong mixing of transported and local aerosols. Relative to the P2 period, the wind speed in the P5 period was relatively strong, so transmission and diffusion took place relatively quickly and the duration of the pollution process was shorter. Our findings provide scientific evidence that transboundary air pollution were closely associated with different weather conditions and mixed aerosol sources, particularly for various PBL heights.

Significance Reconstructing past extreme weather events, such as tropical cyclones and cold surges, using natural paleoclimate archives can lengthen the instrumental data and help us have a broader understanding of the range of weather variability not evident in instrumental record. However, the time resolution of current paleoarchives, from millennial to monthly at best, is usually too low to explore past weather events. Here we found that the Tridacna shells from South China Sea, western Pacific, have continuous daily growth bands, and several daily to hourly resolution biogeochemical proxy records were developed. Our results demonstrate that these records can record nearby tropical cyclones and cold surges, indicating that Tridacna shells have the potential to be an unprecedented ultra-high-resolution archive for paleoweather reconstructions., Paleoclimate research has built a framework for Earth’s climate changes over the past 65 million years or even longer. However, our knowledge of weather-timescale extreme events (WEEs, also named paleoweather), which usually occur over several days or hours, under different climate regimes is almost blank because current paleoclimatic records rarely provide information with temporal resolution shorter than monthly scale. Here we show that giant clam shells (Tridacna spp.) from the tropical western Pacific have clear daily growth bands, and several 2-y-long (from January 29, 2012 to December 9, 2013) daily to hourly resolution biological and geochemical records, including daily growth rate, hourly elements/Ca ratios, and fluorescence intensity, were obtained. We found that the pulsed changes of these ultra-high-resolution proxy records clearly matched with the typical instrumental WEEs, for example, tropical cyclones during the summer−autumn and cold surges during the winter. When a tropical cyclone passes through or approaches the sampling site, the growth rate of Tridacna shell decreases abruptly due to the bad weather. Meanwhile, enhanced vertical mixing brings nutrient-enriched subsurface water to the surface, resulting in a high Fe/Ca ratio and strong fluorescence intensity (induced by phytoplankton bloom) in the shell. Our results demonstrate that Tridacna shell has the potential to be used as an ultra-high-resolution archive for paleoweather reconstructions. The fossil shells living in different geological times can be built as a Geological Weather Station network to lengthen the modern instrumental data and investigate the WEEs under various climate conditions.

Data gaps in surface air quality measurements significantly impair the data quality and the exploration of these valuable data sources. In this study, a novel yet practical method called diurnal-cycle-constrained empirical orthogonal function (DCCEOF) was developed to fill in data gaps present in data records with evident temporal variability. The hourly PM2.5 concentration data retrieved from the national ambient air quality monitoring network in China were used as a demonstration. The DCCEOF method aims to reconstruct the diurnal cycle of PM2.5 concentration from its discrete neighborhood field in space and time firstly and then predict the missing values by calibrating the reconstructed diurnal cycle to the level of valid PM2.5 concentrations observed at adjacent times. The statistical results indicate a high frequency of data gaps in our retrieved hourly PM2.5 concentration record, with PM2.5 concentration measured on about 40 % of the days suffering from data gaps. Further sensitivity analysis results reveal that data gaps in the hourly PM2.5 concentration record may introduce significant bias to its daily averages, especially during clean episodes at which PM2.5 daily averages are observed to be subject to larger uncertainties compared to the polluted days (even in the presence of the same amount of missingness). The cross-validation results indicate that our suggested DCCEOF method has a good prediction accuracy, particularly in predicting daily peaks and/or minima that cannot be restored by conventional interpolation approaches, thus confirming the effectiveness of the consideration of the local diurnal variation pattern in gap filling. By applying the DCCEOF method to the hourly PM2.5 concentration record measured in China from 2014 to 2019, the data completeness ratio was substantially improved while the frequency of days with gapped PM2.5 records reduced from 42.6 % to 5.7 %. In general, our DCCEOF method provides a practical yet effective approach to handle data gaps in time series of geophysical parameters with significant diurnal variability, and this method is also transferable to other data sets with similar barriers because of its self-consistent capability.

Correct understanding of the land-surface processes and cloud-precipitation processes in the Tibetan Plateau (TP) is an important prerequisite for the study and forecast of the downstream activities of weather systems and one of the key points for understanding the global atmospheric movement. In order to show the achievements that have been made, this paper reviews the progress on the observations for the atmospheric boundary layer, land-surface heat fluxes, cloud-precipitation distributions and vertical structures by using ground- and space-based multiplatform, multisensor instruments and the effect of the cloud system in the TP on the downstream weather. The results show that the form drag related to the topography, land–atmosphere momentum and scalar fluxes is an important part of the parameterization process. The sensible heat flux decreased especially in the central and northern TP caused by the decrease in wind speeds and the differences in the ground-air temperatures. Observations show that the cloud and precipitation over the TP have a strong diurnal variation. Studies also show the compressed-air column in the troposphere by the higher-altitude terrain of the TP makes particles inside clouds vary at a shorter distance in the vertical direction than those in the non-plateau area so that precipitation intensity over the TP is usually small with short duration, and the vertical structure of the convective precipitation over the TP is obviously different from that in other regions. In addition, the influence of the TP on severe weather downstream is preliminarily understood from the mechanism. It is necessary to use model simulations and observation techniques to reveal the difference between cloud precipitation in the TP and non-plateau areas in order to understand the cloud microphysical parameters over the TP and the processes of the land boundary layer affecting cloud, precipitation and weather in the downstream regions., Research progresses of atmospheric boundary layer, land surface heat fluxes, cloud-precipitation distributions and vertical structures in the Tibetan Plateau (TP) by in-situ measurements, remote sensing and numerical modelling, and the effect of TP cloud system on the downstream weather are reviewed.

The change of extreme precipitation with temperature has regional characteristics in the context of global warming. In this study, radiosonde data, co-located rain gauge (RG) observations, and Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) products are used to explore the relationship between extreme precipitation intensity and near-surface temperature in Middle–East China (MEC) and the eastern Tibetan Plateau (TP) during 1998–2012. The results show that extreme precipitation intensity increases with increasing temperature at an approximate Clausius–Clapeyron (C–C) rate (i.e., water vapor increases by 7% as temperature increases by 1°C based on the C–C equation) in MEC and TP, but the rate of increase is larger in TP than in MEC. This is probably because TP (MEC) is featured with deep convective (stratiform) precipitation, which releases more (less) latent heat and strengthens the convection intensity on a shorter (longer) timescale. It is also found that when temperature is higher than 25°C (15°C) in MEC (TP), the extreme precipitation intensity decreases with rise of temperature, suggesting that the precipitation intensity does not always increase with warming. In this case, the limited atmospheric humidity and precipitable water could be the primary factors for the decrease in extreme precipitation intensity at higher temperatures.

A complex and varied terrain has a great impact on the distribution of wind energy resources, resulting in uncertainty in accurately assessing wind energy resources. In this study, three wind speed distributions of kernel, Weibull, and Rayleigh type for estimating average wind power density were first compared by using meteorological tower data from 2018 to 2020 under varied desert steppe terrain contexts in northern China. Then three key parameters of scale factor (c) and shape factor (k) from the Weibull model and surface roughness (z0) were investigated for estimating wind energy resource. The results show that the Weibull distribution is the most suitable wind speed distribution over that terrain. The scale factor (c) in the Weibull distribution model increases with an increase in height, exhibiting an obvious form of power function. While there were two different forms for the relationship between the shape factor (k) and height: i.e., the reciprocal of the quadratic function and the logarithmic function, respectively. The estimated roughness length (z0) varied with the withering period, the growing period, and the lush period, which can be represented by the estimated median value in each period. The maximum and minimum values of surface roughness length over the whole period are 0.15 m and 0.12 m, respectively. The power-law model and the logarithmic model are used to estimate the average power density values at six specific heights, which show greater differences in autumn and winter, and smaller differences in spring and summer. The gradient of the increase in average power density values with height is largest in autumn and winter, and smallest in spring and summer. Our findings suggest that dynamic changes in three key parameters (c, k, and z0) should be accurately considered for estimating wind energy resources under varied desert steppe terrain contexts.

Previous studies show that the environment in the Guangdong, Hong Kong, and Macao region (GHMR) is under the double stress of global warming and urbanization. Here we show that due to the increase of regional greenness, the effect of urbanization warming on surface air temperature (SAT) decreased with time and became statistically insignificant from 2004 to 2018, compared to 1979 onward; while the urbanization itself has significantly warmed land surface temperature (LST), with a warming rate of 0.14±0.04 °C/10a at daytime and 0.02±0.02 °C/10a at nighttime during 2004-2018, respectively. The anthropogenic heat was found to have a limited influence on SAT, but more significant and tangible effects on LST. It is essential to improve the control of additional warming effects caused by urbanization.

Due to rapid urbanization and intense human activities, the urban heat island (UHI) effect has become a more concerning climatic and environmental issue. A high spatial resolution canopy UHI monitoring method would help better understand the urban thermal environment. Taking the city of Nanjing in China as an example, we propose a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a Random Forest (RF) model. Firstly, the observed environmental parameters [e.g., surface albedo, land use/land cover, impervious surface, and anthropogenic heat flux (AHF)] around densely distributed meteorological stations were extracted from satellite images. These parameters were used as independent variables to construct an RF model for predicting air temperature. The correlation coefficient between the predicted and observed air temperature in the test set was 0.73, and the average root-mean-square error was 0.72 °C. Then, the spatial distribution of CUHII was evaluated at 30-m resolution based on the output of the RF model. We found that wind speed was negatively correlated with CUHII, and wind direction was strongly correlated with the CUHII offset direction. The CUHII reduced with the distance to the city center, due to the de-creasing proportion of built-up areas and reduced AHF in the same direction. The RF model framework developed for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII, as well as the spatial pattern of urban thermal environments.

Despite advances in remote sensing–based gross primary productivity (GPP) modeling, the calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (GPPMOD) is less well understood over rice–wheat-rotation cropland. To improve the performance of GPPMOD, a random forest (RF) machine learning model was constructed and employed over the rice–wheat double-cropping fields of eastern China. The RF-derived GPP (GPPRF) agreed well with the eddy covariance (EC)-derived GPP (GPPEC), with a coefficient of determination of 0.99 and a root-mean-square error of 0.42 g C m−2 d−1. Therefore, it was deemed reliable to upscale GPPEC to regional scales through the RF model. The upscaled cumulative seasonal GPPRF was higher for rice (924 g C m−2) than that for wheat (532 g C m−2). By comparing GPPMOD and GPPEC, we found that GPPMOD performed well during the crop rotation periods but underestimated GPP during the rice/wheat active growth seasons. Furthermore, GPPMOD was calibrated by GPPRF, and the error range of GPPMOD (GPPRF minus GPPMOD) was found to be 2.5–3.25 g C m−2 d−1 for rice and 0.75–1.25 g C m−2 d−1 for wheat. Our findings suggest that RF-based GPP products have the potential to be applied in accurately evaluating MODIS-based agroecosystem carbon cycles at regional or even global scales.

Synoptic weather conditions can modulate short-term variations in the marine biophysical environment. However, the impact of large-scale synoptic circulation patterns (LSCPs) on variations in chlorophyll-a (chl-a) and sea surface temperature (SST) in the South China Sea (SCS) remains unclear. Using a T-mode principal component analysis method, four types of LSCP related to the Northwest Pacific subtropical high are objectively identified over the SCS for the summers of 2015–2018. Type 1 exhibits a lower chl-a concentration of &lt, 0.3 mg m−3 offshore of southern Vietnam with respect to the other three types. For Type 2, the high chl-a concentration zone (&gt, 0.3 mg m−3) along the coast of Guangdong exhibits the widest areas of coverage. The offshore chl-a bloom jet (&gt, 0.3 mg m−3) formed in southern Vietnam is the most obvious under Type 3. Under Type 4, the high chl-a concentration zone along the coast of Guangdong is the narrowest, while the chl-a concentration in the middle of the SCS is the lowest (&lt, 0.1 mg m−3). These type differences are mostly caused by the various monsoon circulations, local ocean mesoscale processes and resultant differences in localized precipitation, wind vectors, photosynthetically active radiation and SST. In particular, precipitation over land helps to transport nutrients from the land to the shore, which is conducive to the increase of chl-a. However, precipitation over ocean will dilute the upper seawater and reduce chl-a. Typhoons pump the deeper seawater with nutrients to the surface, and therefore make a positive contribution to chl-a in most offshore areas, however, they also disturb shallower water and hinder the growth of phytoplankton, making a negative contribution near the coast of Guangdong. In general, our findings will provide a better understanding of wind pump impact: the responses of marine biophysical environments to LSCPs.

As one of the aridest regions in the mid-latitudes, arid Northwest China (ANC) has scarce water resources year-round, its annual total precipitation is mainly contributed by summer extreme precipitation events, which may also cause disastrous flash floods. However, the physical mechanism and climatic features of extreme precipitation events in ANC are not fully understood and warrant in-depth investigations. Taking Xinjiang as an example, this study investigates the underlying mechanisms and long-term trend of extreme precipitation events in ANC during 1961‒2019 using observational and reanalysis datasets. Results suggest that summer extreme precipitation events in ANC are characterized by a zonal wave pattern with the deepening of the western Siberian trough, central Asian high, and Mongolian high. Under the effects of the zonal wave pattern, the westerly and the easterly airflows anomalously intensify and converge in ANC, transporting a large amount of water vapor from the Arctic Ocean, the Caspian Sea, the Aral Sea, the Arabian Sea, and eastern China to ANC. Meanwhile, a deep anomalous cyclone center induced by the deep wave pattern appears over ANC, thus providing favorable conditions for the occurrence of extreme precipitation events in ANC. Over the long-term period, total summer precipitation and the frequency of extreme precipitation events in ANC show significant increasing trends, indicating that summer climate in ANC is becoming more humid, and extreme precipitation events are becoming more frequent. These findings provide scientific insights for understanding the formation mechanism of summer extreme precipitation events and mitigating the corresponding disasters in arid regions.

Rapid increases in urban sprawl affect the observational environment around meteorological stations by changing the land use/land cover (LULC) and the anthropogenic heat flux (AHF). Based on remote sensing images and GIS technology, we investigated the impact of changes in both LULC and AHF induced by urbanization on the meteorological observational environment in the Yangtze River Delta (YRD) during 2000–2018. Our results show that the observational environments around meteorological stations were significantly affected by the rapid expansion of built-up areas and the subsequent increase in the AHF, with a clear spatiotemporal variability. A positive correlation was observed between the proportion of built-up areas and the AHF around meteorological stations. The AHF was in the order urban stations > suburban stations > rural stations, but the increases in the AHF were greater around suburban and rural stations than around urban stations. Some meteorological stations need to be relocated to address the adverse effects induced by urbanization. The proportion of built-up areas and AHF around the new stations decreased significantly after relocation, weakening the urban heat island effect on the meteorological observations and substantially improving the observational environment. As a result, the observed daily mean temperature (relative humidity) decreased (increased) around the new stations after relocation. Our study comprehensively shows the impact of rapid urban sprawl on the observational environment around meteorological stations by assessing changes in both LULC and the AHF induced by urbanization. These findings provide scientific insights for the selection and construction of networks of meteorological stations and are therefore helpful in scientifically evaluating and correcting the impact of rapid urban sprawl on meteorological observations.

The ocean surface and subsurface biophysical responses and their feedbacks to super typhoon Nida were comprehensively investigated based on a substantial dataset of multiple-satellite observations, Argo profiles, and reanalysis data. Nida experienced two Category 5 stages: a rapid intensification stage that was fast moving along a straight-line track, and a rapid weakening stage that was slowly moving along a sharp-left sudden-turning track. During the straight-line stage, Nida caused an average sea surface temperature (SST) cooling of 1.44 °C and a chlorophyll-a (chl-a) concentration increase of 0.03 mg m−3. During the sudden-turning stage, cyclonic sudden-turning induced a strong cold cyclonic eddy (SSHA < −60 cm) by strong upwelling, which caused the maximum SST cooling of 6.68 °C and a long-lasting chl-a bloom of 0.6 mg m−3 on the left-hand side of the track, resulting in substantial impacts on the ocean ecological environment. Furthermore, the enhanced ocean cold wake and the longer air–sea interaction in turn decreased the average inner-core SST of 4 °C and the corresponding enthalpy flux of 780 W m−2, which induced a notable negative feedback to the typhoon intensity by weakening it from Category 5 to Category 2. The left bias response and notable negative feedback are special due to sharp-left sudden-turning of typhoon. Comparing with the previously found slow translation speed (~4 m s−1) of significant ocean response, the negative feedback requires even more restriction of translation speed (−1) and sharp sudden-turning could effectively relax restrictions by making equivalent translation speed lower and air-sea interaction time longer. Our findings point out that there are some unique features in ocean–typhoon interactions under sudden-turning and/or lingering tracks comparing with ordinary tracks.

Large-scale modifications to urban underlying surfaces owing to rapid urbanization have led to stronger urban heat island (UHI) effects and more frequent urban heat wave (HW) events. Based on observations of automatic weather stations in Beijing during the summers of 2014–2020, we studied the interaction between HW events and the UHI effect. Results showed that the UHI intensity (UHII) was significantly aggravated (by 0.55°C) during HW periods compared to non-heat wave (NHW) periods. Considering the strong impact of unfavorable weather conditions and altered land use on the urban thermal environment, we evaluated the modulation of HW events and the UHI effect by wind speed and local climatic zones (LCZs). Wind speeds in urban areas were weakened due to the obstruction of dense high-rise buildings, which favored the occurrence of HW events. In detail, 35 HW events occurred over the LCZ1 of a dense high-rise building area under low wind speed conditions, which was much higher than that in other LCZ types and under high wind speed conditions (< 30 HW events). The latent heat flux in rural areas has increased more due to the presence of sufficient water availability and more vegetation, while the increase in heat flux in urban areas is mainly in the form of sensible heat flux, resulting in stronger UHI effect during HW periods. Compared to NHW periods, lower boundary layer and wind speed in the HW events weakened the convective mixing of air, further expanding the temperature gap between urban and rural areas. Note that LCZP type with its high-density vegetation and water bodies in the urban park area generally exhibited, was found to have a mitigating effect on the UHI, whilst at the same time increasing the frequency and duration of HW events during HW periods. Synergies between HWs and the UHI amplify both the spatial and temporal coverage of high-temperature events, which in turn exposes urban residents to additional heat stress and seriously threatens their health. The findings have important implications for HWs and UHII forecasts, as well as for scientific guidance on decision-making to improve the thermal environment and to adjust the energy structure.

Chinese University of Hong Kong ‐ University of Exeter Joint Centre for Environmental Sustainability and Resilience (ENSURE)

The Urban Boundary layer (UBL) structure plays an important role in the accumulation of air pollutants in cities. To understand how the near-surface UBL structure affects air pollutants, we analyzed 40-day vertical observations collected at a 325-m meteorology tower in Beijing from 1 December 2016 to 9 January 2017. The occurrences of heavy pollution episodes (HPEs) in the study period were closely associated with weak wind speed (WS), high temperature, high relative humidity, weak fraction velocity (u∗) and weak turbulence kinetic energy (TKE) in near-surface UBL. In particular, the thickness and intensity of the temperature inversions were enhanced during all HPEs at nighttime. In addition, the PM2.5 concentration at the ground was significantly negative/positive correlation with vertical dynamic factors (e.g., WS, u∗ and TKE)/temperature inversion in the near-surface UBL. Diurnal variations in the vertical WS, potential temperature (θ), u∗ and TKE were less evident on the 23 polluted days than those on the 14 clean days; specifically, there were larger differences in the WS and θ between polluted and clean days at higher levels. Note that the varying quantitative relationships between the observed PM2.5 concentration and UBL dynamic factors during the daytime were much more significant than those at nighttime at all vertical levels. Compared with the WS, u∗ and TKE, the PM2.5 concentration showed a much more sensitive change with u∗ (WS) during the daytime (at nighttime).

Transboundary air pollution (TAP), which is strongly affected by weather and climate, is a primary contributor to air pollution. Both El Nino-Southern Oscillation (ENSO) events and heatwaves exert influences on TAP, thereby affecting air quality, which has yet to be well understood. This study applied an integrated statistical scheme to examine the contributions of local emission sources and TAP to particulate matter (PM), sulfur dioxide, and nitrogen dioxide during four ENSO events and 20 heatwaves from 2002 to 2016. Hong Kong was studied as an example because of its substantial TAP that can provide useful reference for cities with TAP. We found a remarkable and consistent reduction in the TAP contribution to pollutants during two El Nino events because of the associated rainfall increase, decreased frequency of northerly winds below 700 hPa, and overall stronger wind speed. Moreover, the TAP was the predominant contributor to the increase in the ambient PM concentration during the two La Nina events because of the associated decrease in rainfall, higher frequency of northerly winds below 900 hPa, and weaker wind speed, which were favorable to long-range transport and pollutant accumulation. The ten heatwaves associated with tropical cyclones within a distance of 1100 km from Hong Kong were accompanied by an increase in pollutant concentration, which was featured by the lower wind speed and less rainfall. Our findings highlight the long-term seasonal variability of TAP, and increases in TAP during the El Nino events and heatwaves.

Cirrus clouds strongly influence weather and climate processes due to their effects on the radiative balance of the Earth-atmosphere system. In this work, longwave radiative forcing of nighttime cirrus was studied at grid-cell scales covering the regions between 60°S and 60°N. The measurements of CloudSat and CALIPSO were used to obtain the occurrence frequencies of cirrus clouds with different optical thickness, and then the SBDART radiative transfer model was employed to compute the radiative forcing. For the study region, the occurrence frequencies at night time for subvisual (τ ≤ 0.03), thin (0.03 ≤ τ 3) cirrus types are found to be 7.43%, 9.83%, 8.62%, and 1%; the average occurrence frequency can reach ∼26.89%. Moreover, the mean effective radii are ∼22.87, 19.23, 19.82, 27.6 and 39.18 μm corresponding to total, subvisual, thin, opaque and thick cirrus clouds. We found that average longwave (4–50 μm) radiative effect (LWRE) for nighttime cirrus clouds is ∼17.38W.m − 2 at the top of the atmosphere (TOA) and ∼1.14W.m − 2 at the Earth surface. Finally, the LWRF of opaque cirrus is identified to be the largest contribution to the warming effects at nighttime (∼ 7.76W.m − 2at the TOA, 0.68W.m − 2 at the surface), primarily due to their high fractions and optical thickness, appearing simultaneously. These findings provide valuable references to understanding the role of nighttime cirrus in regulating the radiation field.

Monsoon precipitation is the major driver of agricultural productivity in the Myanmar Coast, it is crucial to quantify and understand recent changes in precipitation during the monsoon season over this region. By using multiple precipitation datasets, we demonstrate that total precipitation during monsoon season over the Myanmar Coast has increased slightly but not significantly, but precipitation during the onset and withdrawal phases of monsoon season exhibit a significant increasing trend during 1979–2015, and the contribution of precipitation during the two phases to total monsoon precipitation has increased significantly. The increased precipitation during the onset phase over the Myanmar Coast directly results from the earlier onset of the South Asian Summer Monsoon in recent decades, which is associated with the phase transition of the Inter-decadal Pacific Oscillation in the late 1990s. And the precipitation increase during the withdrawal phase is directly due to the enhances of the ascending motion and convection around this region, which is dynamically correlated to the anomalous cyclone-like circulation around the Bay of Bengal as well as the strengthening of the cross-equatorial flow around the equatorial Indian Ocean.

In this paper, through the Aerosol-lidar observation data located in the western suburbs of Hefei, a continuous aerosol pollution incident was observed from December 13 to 15, 2018. The range corrected signal and extinction coefficient profile continuously observed through fixed-point observation of ground-based lidar can accurately reflect the aerosol content and the characteristics of aerosol changes. The aerosol is mainly distributed below 1.5km. Through depolarization analysis of aerosol particles, the depolarization ratio is below 0.16, and it is concluded that the aerosol particles are spherical particles with a smaller particle size, and combined with the HYSPLIT model analysis, the main pollutants are transmission and industrial emissions of northern and eastern coastal cities in China.

Rwanda has experienced high temperature rising phenomena over the last decades and hence, highly vulnerable to climate change. This paper examined the spatial and temporal variations of daily maximum and minimum surface air temperature (Tmin and Tmax) and diurnal temperature range (DTR). It studied variables at monthly, seasonal and annual time-scales from 1961 to 2014. The study applied various statistical methods such as ordinary least-square fitting, Mann-Kendall, Sen’ slope and Sequential Mann-Kendall statistical test to the new reconstructed ENACTS dataset that cover the period from 1983 to 2014 while pre-1983s recorded data from 24 meteorological stations have been added to complete the lengthiness of ENACTS data. The January to February season did not show a significant trend at seasonal time-scales. The authors decided only to consider March-to-May, June-to-August and October-to-December seasons for further analyses. Topography impacts on temperature classified stations into three regions: region one (R1) (1,000–1,500 m), region two (R2) (1,500–2,000 m) and region three (R3) (≥2,000 m). With high confidence, the results indicate a significant positive trend in both Tmin and Tmax in all three regions during the whole study period. However, the magnitude rate of temperatures change is different in three regions and it varies in seasonal and annual scale. The spatial distributions of Tmax and Tmin represent a siginificant warming trend over the whole country notably since the early 1980s. Surprisingly, Tmin increased at a faster rate than Tmax in R3 (0.27 vs. 0.07°C/decade in March-to-May) and (0.29 vs. 0.04°C/decade in October-to-December), resulting in a significant decrease in the DTR. This is another confirmation of warming in Rwanda. The mutation test application exhibited most of the abrupt changes in the seasonal and annual Tmax and Tmin trends between 1984 and 1990. The present work mainly focus on the spatial and temporal variability of Tmin, Tmax and DTR in Rwanda and their relationship with elevation change, leaving a gap in other potential cause factors explored in the future.

Low-level jet (LLJ) significantly affects the synoptic-scale hydrometeorological conditions in the South China Sea, although the impact of LLJs on the marine ecological environment is still unclear. We used multi-satellite observation data and meteorological reanalysis datasets to study the potential impact of LLJs on the marine biophysical environment over the Beibuwan Gulf (BBG) in summer during 2015–2019. In terms of the summer average, the sea surface wind vectors on LLJ days became stronger in the southwesterly direction relative to those on non-LLJ days, resulting in enhanced Ekman pumping (the maximum upwelling exceeds 10 × 10–6 m s–1) in most areas of the BBG, accompanied by stronger photosynthetically active radiation (increased by about 20 μmol m–2 s–1) and less precipitation (decreased by about 3 mm day–1). These LLJ-induced hydrometeorological changes led to an increase of about 0.3 °C in the nearshore sea surface temperature and an increase of 0.1–0.5 mg m−3 (decrease of 0.1–0.3 mg m−3) in the chlorophyll-a (chl-a) concentrations in nearshore (offshore) regions. Intraseasonal and diurnal changes in the incidence and intensity of LLJs potentially resulted in changes in the biophysical ocean environment in nearshore regions on intraseasonal and semi-diurnal timescales. The semi-diurnal peak and amplitude of chl-a concentrations on LLJ days increased with respect to those on non-LLJ days. Relative to the southern BBG, LLJ events exhibit greater impacts on the northern BBG, causing increases of the semi-diurnal peak and amplitude with 1.5 mg m−3 and 0.7 mg m−3, respectively. This work provides scientific evidence for understanding the potential mechanism of synoptic-scale changes in the marine ecological environment in marginal seas with frequent LLJ days.

In recent years, surface ozone (O3) pollution during summertime (June–August) over eastern China has become more serious, and it is even the case that surface O3 and PM2.5 (particulate matter with aerodynamic diameter &leq; 2.5 μm in the air) pollution can co-occur. However, the synoptic circulation pattern related to this compound pollution remains unclear. In this study, the T-mode principal component analysis method is used to objectively classify four synoptic weather patterns (SWPs) that occur over eastern China, based on the geopotential heights at 500 hPa during summertime from 2015 to 2018. Four SWPs of eastern China are closely related to the western Pacific subtropical high (WPSH), exhibiting, significant intraseasonal and interannual variations. Note that remarkable spatial and temporal disparities of surface O3 and PM2.5 pollution are given under these four different SWPs according to the ground-level air quality and meteorological observations. In areas controlled by the WPSH or the prevailing westerlies, O3 pollution is mainly caused by photochemical reactions of nitrogen oxides and volatile organic compounds under weather conditions of high temperature, moderate humidity and slight precipitation. In particular, the warm moist flow brought by the WPSH can promote hygroscopic growth of fine particulate matter in some local areas, resulting in the increase of PM2.5 concentrations, which may form co-occurring surface O3 and PM2.5 pollution. In addition, the low boundary layer height and frequency of light-wind days are closely related to the transmission and diffusion of pollutants under the different SWPs, modulating the levels of O3–PM2.5 compound pollution. Overall, our findings demonstrate the different roles played by synoptic weather patterns in driving regional surface O3–PM2.5 compound pollution, in addition to the large quantities of emissions, and may also provide insights into the regional co-occurring high PM2.5 and high O3 level via the effects of certain meteorological factors.

High particulate matter (PM) and ozone (O3) concentration in Hong Kong are frequently observed during the summertime typhoon season. Despite the critical effect of a typhoon on air pollution, contributions of vertical wind profile and cloud movement during transboundary air pollution (TAP) on surface PM and O3 concentration have yet to be fully understood. This work is the first study to apply a network of Doppler light detection and ranging (LiDAR) as well as back trajectory analysis to comprehensively analyze the effect of a weak Typhoon (Danas) occurring during 16–19 July 2019 on different variations in PM and O3 concentration. During the typhoon Danas, three types of surface air pollution with five episodes were identified: (1) low PM and high O3 concentration; (2) co-occurring high PM and O3 concentration and (3) high PM and low O3 concentration. Employing our 3D Real-Time Atmospheric Monitoring System (3DREAMs) along with surface observations, we found the important role of TAP in the increases in surface PM and O3 concentration with significant vertical wind shear that transported air pollutants at upper levels, and strong vertical mixing that brought air pollutants to the ground level. Cloud movement related to typhoon periphery, as well as high solar radiation due to sinking motion and remote transport by continental wind, have an impact on local O3 concentration. For the substantial difference in O3 concentration between two air quality measurement sites, the similar vertical aerosol distributions and wind profiles suggest the comparable TAP contributions at the two sites and thus infer the critical role of local O3 photochemical process in the O3 difference. This work comprehensively reveals the influences of a weak typhoon on variations in PM and O3 during the five episodes, providing important references for air quality monitoring and forecast in regions under the influence of typhoon.

Turbulent mixing is critical in affecting urban climate and air pollution. Nevertheless, our understanding of it, especially in a cloud-topped boundary layer (CTBL), remains limited. High-temporal resolution observations provide sufficient information of vertical velocity profiles, which is essential for turbulence studies in the atmospheric boundary layer (ABL). We conducted Doppler Light Detection and Ranging (LiDAR) measurements in 2019 using the 3-Dimensional Real-time Atmospheric Monitoring System (3DREAMS) to reveal the characteristics of typical daytime turbulent mixing processes in CTBL over Hong Kong. We assessed the contribution of cloud radiative cooling on turbulent mixing and determined the altitudinal dependence of the contribution of surface heating and vertical wind shear to turbulent mixing. Our results show that more downdrafts and updrafts in spring and autumn were observed and positively associated with seasonal cloud fraction. These results reveal that cloud radiative cooling was the main source of downdraft, which was also confirmed by our detailed case study of vertical velocity. Compared to winter and autumn, cloud base heights were lower in spring and summer. Cloud radiative cooling contributed ~32% to turbulent mixing even near the surface, although the contribution was relatively weaker compared to surface heating and vertical wind shear. Surface heating and vertical wind shear together contributed to ~45% of turbulent mixing near the surface, but wind shear can affect up to ~1100 m while surface heating can only reach ~450 m. Despite the fact that more research is still needed to further understand the processes, our findings provide useful references for local weather forecast and air quality studies.

Precipitation estimates with finer quality and spatio-temporal resolutions play significant roles in understanding the global and regional cycles of water, carbon and energy. Satellite-based precipitation products are capable of detecting spatial patterns and temporal variations of precipitation at finer resolutions, which is particularly useful over poorly gauged regions. However, satellite-based precipitation product are the indirect estimates of precipitation, inherently containing regional and seasonal systematic biases and random errors. In this study, focusing on the potential drawbacks in generating Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and its recently updated retrospective IMERG in Tropical Rainfall Measuring Mission (TRMM) era (finished in July, 2019), which were only calibrated at monthly scale using ground observations, Global Precipitation Climatology Centre (GPCC, 1.0°/Monthly), we aimed to propose a new calibration algorithm for IMERG at daily scale, and to provide a new AIMERG precipitation dataset (0.1°/half-hourly, 2000–2015, Asia) with better quality, calibrated by Asian Precipitation Highly Resolved Observational Data Integration (APHRODITE, 0.25°/Daily) at daily scale for the Asian applications. And the main conclusions included but not limited to: (1) the proposed daily calibration algorithm (Daily Spatio-Temporal Disaggregation Calibration Algorithm, DSTDCA) was effective in considering the advantages from both satellite-based precipitation estimates and the ground observations; (2) AIMERG performed better than IMERG at different spatio-temporal scales, in terms of both systematic biases and random errors, over the China Main land; and (3) APHRODITE demonstrated significant advantages than GPCC in calibrating the IMERG, especially over the mountainous regions with complex terrain, e.g., the Tibetan Plateau. Additionally, Results of this study suggests that it is a promising and applicable daily calibration algorithm for GPM in generating the future IMERG in either operational scheme or retrospective manner. The AIMERG data record (0.1°/half-hourly, 2000–2015, Asia) is freely available at http://argi-basic.hihanlin.com:8000/d/d925fecf60/. Additionally, the AIMERG data is also freely accessible at https://doi.org/10.5281/zenodo.3609352 (for the period from 2000 to 2008) and https://doi.org/10.5281/zenodo.3609507 (for the period from 2009 to 2015) (Ma et al., 2020).

Vertical wind shear (VWS) is one of the key meteorological factors in modulating ground-level particulate matter with an aerodynamic diameter of 2.5 µm or less (PM2.5). Due to the lack of high-resolution vertical wind measurements, how the VWS affects ground-level PM2.5 remains highly debated. Here we employed the wind profiling observations from the fine-time-resolution radar wind profiler (RWP), together with hourly ground-level PM2.5 measurements, to explore the wind features in the planetary boundary layer (PBL) and their association with aerosols in Beijing for the period from December 1, 2018, to February 28, 2019. Overall, southerly wind anomalies almost dominated throughout the whole PBL or even beyond the PBL under polluted conditions during the course of a day, as totally opposed to the northerly wind anomalies in the PBL under clean conditions. Besides, the ground-level PM2.5 pollution exhibited a strong dependence on the VWS. A much weaker VWS was observed in the lower part of the PBL under polluted conditions, compared with that under clean conditions, which could be due to the strong ground-level PM2.5 accumulation induced by weak vertical mixing in the PBL. Notably, weak northbound transboundary PM2.5 pollution mainly appeared within the PBL, where relatively small VWS dominated. Above the PBL, strong northerlies winds also favored the long-range transport of aerosols, which in turn deteriorated the air quality in Beijing as well. This was well corroborated by the synoptic-scale circulation and backward trajectory analysis. Therefore, we argued here that not only the wind speed in the vertical but the VWS were important for the investigation of aerosol pollution formation mechanism in Beijing. Also, our findings offer wider insights into the role of VWS from RWP in modulating the variation of PM2.5, which deserves explicit consideration in the forecast of air quality in the future.

Accurate estimation of the spatiotemporal variations of solar radiation is crucial for assessing and utilizing solar energy, one of the fastest‐growing and most important clean and renewable resources. Based on observations from 2,379 meteorological stations along with scare solar radiation observations, the random forest (RF) model is employed to construct a high‐density network of daily global solar radiation (DGSR) and its spatiotemporal variations in China. The RF‐estimated DGSR is in good agreement with site observations across China, with an overall correlation coefficient (R) of 0.95, root‐mean‐square error of 2.34 MJ/m2, and mean bias of −0.04 MJ/m2. The geographical distributions of R values, root‐mean‐square error, and mean bias values indicate that the RF model has high predictive performance in estimating DGSR under different climatic and geographic conditions across China. The RF model further reveals that daily sunshine duration, daily maximum land surface temperature, and day of year play dominant roles in determining DGSR across China. In addition, compared with other models, the RF model exhibits a more accurate estimation performance for DGSR. Using the RF model framework at the national scale allows the establishment of a high‐resolution DGSR network, which can not only be used to effectively evaluate the long‐term change in solar radiation but also serve as a potential resource to rationally and continually utilize solar energy.

Studying precipitation diurnal variation characteristics is crucial to better understand their formation mechanism. Moreover, it is fundamental in assessing regional climate variability and to validate the effectiveness of cloud and precipitation parameterization schemes in weather and climate models. Based on observational data from 2008 to 2017, this manuscript objective is to assess the aerosol effects on summer precipitation on an hourly-scale diurnal variation basis in the Beijing metropolitan area. The results put in evidence that the precipitation frequency and duration on polluted days were 25.0% and 14.8% lower with respect to clean days. No significant differences were observed in total daily accumulated rainfall between clear and polluted days. While heavy precipitation mean intensity on polluted days increased by 13.5%, which is potentially linked with aerosol microphysical effects. Note that precipitation occurs earlier on polluted days, with peak time of 1 similar to 2-hour in advance compared with clean days over the urban areas of Beijing, which may be primarily ascribed to the influence of advanced turning local circulation of mountain-valley breezes and urban heat island circulation due to aerosol-radiation effects.

The optical and microphysical features of precipitating clouds are key information for studying the satellite-based precipitation estimation, cloud radiative effects, aerosol–cloud–precipitation interactions, cloud and precipitation parameterization in weather and climate models. In this study, 15-year synchronous spectral and radar observations from the TRMM satellite were used to statistically explore the optical and microphysical features of precipitating clouds (PCs), including cloud effective radius (CER), cloud optical thickness (COT), cloud water path (CWP), thermal infrared brightness temperature at channel 4 (TB4) of cloud top, and storm top height (STH) and their relationships with surface rain rates in summer over Yangtze–Huaihe River Valley (YHRV). Results show that the optical and microphysical features of PCs/stratiform PCs/convective PCs vary with geographical locations in summer over YHRV, due to the different ambient meteorological and topographical conditions. Higher CER/COT/CWP/STH and lower TB4 mainly locate at areas of bigger rain rates. For PCs, their spatial distribution of CER is mainly dominated by stratiform PCs, while their spatial distribution of COT/CWP is mainly dominated by convective PCs. Moreover, stratiform precipitation is the dominant form in summer over YHRV and, thus, most PCs present vertical structures of optical and microphysical features as stratiform PCs. Stratiform PCs are usually thicker and contain more water vapor with bigger cloud particles than convective PCs (including deep and shallow convective PCs). In addition, existing shallow convective PCs are associated with lower storm heights and warmer cloud tops. Finally, surface rain rates of PCs (convective/stratiform PCs) increase gradually with the increment of CER/COT/CWP/STH, especially under 5 (15/5) mm/h. Similar relationship between surface rain rates and COT/CWP for shallow convective PCs is also found under 0.75 mm/h. Surface rain rate of PCs (convective/stratiform PCs) with cold cloud tops (TB4 264 K), surface rain rate usually increases as CER decreases, which suggests that aerosol indirect effects are dominant in lower PCs, because over pollution regions abundant aerosols enter into lower clouds more easily and then suppress the development of shallow convective PCs.

Urbanization has led to a significant urban heat island (UHI) effect in Beijing in recent years. At the same time, air pollution caused by a large number of fine particles significantly influences the atmospheric environment, urban climate, and human health. The distribution of fine particulate matter (PM2.5) concentration and its relationship with the UHI effect in the Beijing area are analyzed based on station-observed hourly data from 2012 to 2016. We conclude that, (1) in the last five years, the surface concentrations of PM2.5 averaged for urban and rural sites in and around Beijing are 63.2 and 40.7 µg m−3, respectively, with significant differences between urban and rural sites (ΔPM2.5) at the seasonal, monthly and daily scales observed; (2) there is a large correlation between ΔPM2.5 and the UHI intensity defined as the differences in the mean (ΔTave), minimum (ΔTmin), and maximum (ΔTmax) temperatures between urban and rural sites. The correlation between ΔPM2.5 and ΔTmin (ΔTmax) is the highest (lowest); (3) a Granger causality analysis further shows that ΔPM2.5 and ΔTmin are most correlated for a lag of 1–2 days, while the correlation between ΔPM2.5 and ΔTave is lower; there is no causal relationship between ΔPM2.5 and ΔTmax; (4) a case analysis shows that downwards shortwave radiation at the surface decreases with an increase in PM2.5 concentration, leading to a weaker UHI intensity during the daytime. During the night, the outgoing longwave radiation from the surface decreases due to the presence of daytime pollutants, the net effect of which is a slower cooling rate during the night in cities than in the suburbs, leading to a larger ΔTmin.

In this paper, taking the Hefei City in China as an example, a population-weighted urban surface thermal environment index (IPWUSTE) is constructed with the MODIS-based land surface temperature (LS...

An extreme drought on record occurred in eastern China in 2019 and caused severe terrestrial water storage (TWS) deficits. It is important to understand this drought process and assess its impacts. We combine the Gravity Recovery and Climate Experiment (GRACE) observations and land surface model (LSM) outputs to thoroughly investigate this drought event. To consolidate the quantitative analysis, we fuse five sets of GRACE-derived TWS data and four sets of LSM-based soil moisture storage (SMS) data, respectively, using the generalized three-cornered hat method and the least-square method, and thereby generate robust TWS and SMS results. Both the GRACE and LSM results suggest that this drought event started in July in northeastern and northern China and then spread south to southern China in August and lasted until December, influencing a vast area of ∼2.0 × 106 km2 in eastern China. The drought center first appeared in North China, then moved southward to the middle and lower reaches of the Yangtze River basin, and finally stayed in South China. TWS results from GRACE show that the largest regional TWS deficit was 94.1 ± 17.7 mm and happened in September in the middle and lower reaches of the Yangtze River basin. SMS results from LSMs exhibit similar spatiotemporal characteristics to the GRACE results but with smaller magnitudes. Meteorological Drought Composite Index from the China Meteorological Administration agrees well with the GRACE-derived TWS deficits and LSM-based SMS deficits in terms of their spatiotemporal distributions. These three independent indicators together identify and assess this severe drought event, providing us a quantitative understanding of it. The water budget analysis demonstrates that the precipitation deficit in the second half of 2019 is the main reason for this drought event. The precipitation deficit is mainly due to the northerly wind anomaly and sinking anomaly over eastern China, which are closely associated with the anomalous cyclonic circulation over the western Pacific.

Evaluation of the cloud seeding effect is a challenge due to lack of directly physical observational evidence. In this study, an approach for directly observing the cloud seeding effect is proposed using a 1548 nm coherent Doppler wind lidar (CDWL). Normalized skewness was employed to identify the components of the reflectivity spectrum. The spectrum detection capability of a CDWL was verified by a 24.23-GHz Micro Rain Radar (MRR) in Hefei, China (117°15′ E, 31°50′ N), and different types of lidar spectra were detected and separated, including aerosol, turbulence, cloud droplet, and precipitation. Spectrum analysis was applied as a field experiment performed in Inner Mongolia, China (112°39′ E, 42°21′ N ) to support the cloud seeding operation for the 70th anniversary of China’s national day. The CDWL can monitor the cloud motion and provide windshear and turbulence information ensuring operation safety. The cloud-precipitation process is detected by the CDWL, microwave radiometer (MWR) and Advanced Geosynchronous Radiation Imager (AGRI) in FY4A satellites. In particular, the spectrum width and skewness of seeded cloud show a two-layer structure, which reflects cloud component changes, and it is possibly related to cloud seeding effects. Multi-component spectra are separated into four clusters, which are well distinguished by spectrum width and vertical velocity. In general, our findings provide new evidence that the reflectivity spectrum of CDWL has potential for assessing cloud seeding effects.

The study investigated the dominant atmospheric circulation patterns associated with abnormal rainfall over Rwanda during the March–May (MAM) rainfall season in 1981–2010. The data sets used in this study include: rainfall, wind, sea surface temperature (SST), and humidity. Correlation and composite analysis and Percent of Normal Index (PNI) were deployed in this study. In the wet years (1987, 1988, and 1998), the country was dominated by moisture convergence, which is in line with wind anomalies that exhibits strong westerly winds from the Atlantic Ocean and southeasterly winds originated from the Indian Ocean. These winds carry moist air mass passing over Congo to the study area, leading to wet events. On the other hand, easterly winds were noted over the study area during the dry years (1984, 2000, 2007, and 2008). The observed wet years coincided with the El Nino events, while the dry years are noted during the La Nina episodes. The dry years exhibited a wide spread of moisture divergence anomaly at the low level and were characterized by the sinking motion as opposed to the wet years with the rising motion. The anomalies of velocity potential/divergence further showed that the wet (dry) years were characterized by convergence (divergence) at the low level. The results also show that there exists a low positive correlation between mean MAM rainfall and SST over the Indian Ocean, which shows minimum influence of the Ocean. On the other hand, it was noted that rainfall amounts is significantly correlated at 95% confidence level with the elevation (altitude) of a given station. This study improves the understanding of the occurrence of wet and dry events in Rwanda, which is helpful in future monitoring of these events.

Taking winter and summer in eastern China as an example application, a grid-cell method of aerosol direct radiative forcing (ADRF) calculation is examined using the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model with inputs from MODIS and AERONET observations and reanalysis data. Results show that there are significant seasonal and regional differences in climatological mean aerosol optical parameters and ADRF. Higher aerosol optical depth (AOD) occurs in summer and two prominent high aerosol loading centers are observed. Higher single scattering albedo (SSA) in summer is likely associated with the weak absorbing secondary aerosols. SSA is higher in North China during summer but higher in South China during winter. Aerosols induce negative forcing at the top of the atmosphere (TOA) and surface during both winter and summer, which may be responsible for the decrease in temperature and the increase in relative humidity. Values of ADRF at the surface are four times stronger than those at the TOA. Both AOD and ADRF present strong interannual variations; however, their amplitudes are larger in summer. Moreover, patterns and trends of ADRF do not always correspond well to those of AOD. Differences in the spatial distributions of ADRF between strong and weak monsoon years are captured effectively. Generally, the present results justify that to calculate grid-cell ADRF at a large scale using the SBDART model with observational aerosol optical properties and reanalysis data is an effective approach.

The presences of anvil clouds significantly affect the tropical mean radiation budget and increase the uncertainty of climate model simulations. In this study, the climatological mean distributions of thick anvil parameters, such as top, bottom, occurrence, cloud effective radius (CER) and cloud optical depth (COD) in the tropics (20°S–20°N) are investigated by Tropical Rainfall Measuring Mission's (TRMM) precipitation radar (PR) and visible and infrared scanner (VIRS) from 1998 to 2007. The thick anvil radiative forcing at shortwave (0.2 ∼ 4 µm) and longwave (4 ∼ 50 µm) length, i.e. Shortwave radiative forcing (SRF) and Longwave radiative forcing (LRF) and their net effects at different altitudes are simulated with Santa Barbara DISORT Atmospheric Radiative Transfer Model (SBDART). The results show that thick anvils present higher top/bottom, smaller CER, and thicker COD over land than those over ocean. At the top of atmosphere (TOA), net radiative effects of thick anvils are positive warming, which means the earth-atmosphere system obtains energy forced by thick anvils. At earth surface, net radiative effects of thick anvils are positive warming at land surface and negative cooling at ocean surface, respectively. In general, anvil SRF, LRF and net effects vary with different geographical locations and also present large land–ocean differences in the tropics, due to different anvil properties forced by the surface heating and topography. All spatial patterns of stronger anvil SRF, LRF and net effects are well matched with the places where exist higher fractions of anvils, such as Asian monsoon zone, the Intertropical Convergence Zone (ITCZ), the South Pacific Convergence Zone (SPCZ), tropical Africa, Mid-America and South America. In addition, the present work provides an evidence that it is an effective approach to calculate quantitatively the grid-cell SRF and LRF of cloud at a large scale by using the SBDART model with inputs from satellite observations.

Precipitation is an important indicator of climate change and a critical process in the hydrological cycle, on both the global and regional scales. Methods of precipitation observation and associated analyses are of strategic importance in global climate change research. As the first space-based radar, the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) has been in operation for almost 17 years and has acquired a huge amount of cloud and precipitation data that provide a distinctive view to help expose the nature of cloud and precipitation in the tropics and subtropics. In this paper we review recent advances in summer East Asian precipitation climatology studies based on long-term TRMM PR measurements in the following three aspects: (1) the three-dimensional structure of precipitation, (2) the diurnal variation of precipitation, and (3) the recent precipitation trend. Additionally, some important prospects regarding satellite remote sensing of precipitation and its application in the near future are discussed.

The vertical profiles and trends of temperature and humidity at the South Pole up to 10 km above mean sea level (amsl) were investigated by using radiosonde data collected from March 2005 to February 2018. During an average year between 2005 and 2018, the highest (lowest) temperature in the lower troposphere was approximately &minus, 25 &deg, C (&minus, 60 &deg, C) in December (July) at a height of about 500 m above the surface (at the surface). A temperature inversion layer above the surface was found during the whole year but was weaker during the summer, while the inversion layers at the tropopause (about 8 km amsl) mostly disappeared during spring and winter. General warming trends were found at all heights and months, but in a few heights and months cooling trends still occurred (e.g., in September below 7 km amsl). Nevertheless, seasonal and yearly averaged temperatures all presented warming trends: 1.1, 1.3, 0.6, 1.5 and 1.1 &deg, C/decade at the surface, and 0.7, 1.0, 0.3, 0.3 and 0.6 &deg, C/decade for the layer average from the surface to 10 km amsl, for spring, summer, autumn, winter, and yearly average, respectively. Most of the water vapor was confined in the lowermost 3 km of the atmosphere with a maximum of 0.35 g kg&minus, 1 in December at a 200 m height above surface, and the specific humidity had the similar characteristic of annual cycle and inversion layers as the temperature. At heights below 5 km amsl, increasing trends of specific humidity larger than 0.02 g kg&minus, 1/decade occurred during summer months, including the late spring and early autumn, and the annual mean showed an increasing trend of about 0.01&ndash, 0.02 g kg&minus, 1/decade. Meanwhile, above 5 km amsl, the trends became small and generally less than 0.02 g kg&minus, 1/decade in all the months, and beyond 7 km amsl the specific humidity remained almost invariant due to its small moisture content as compared with lower levels. From the surface to 10 km amsl, the specific humidity averaged trends of 0.0062, 0.019, 0.0013, 0.002 and 0.007 g kg&minus, 1/decade for spring, summer, autumn, winter and yearly average, respectively.

The accurate simulation of typhoon hydrometeors remains a challenge. This study attempts to evaluate the performances of five microphysics schemes (MPSs) in the Weather Research and Forecasting (WRF) model in simulating the supertyphoon Neoguri in July 2014. The observed microwave brightness temperature, as well as retrieved data from the microwave radiometer imager (MWRI) onboard Chinese FY-3B satellite, are used to test hydrometeor simulations. In particular, two MWRI radiance indices, including the emission index (EI) and scattering index (SI), are used to assess the performance of five MPSs in simulating liquid and frozen hydrometeors, respectively. Overall, the WRF model can well reproduce the overall pattern of typhoon-produced precipitation, albeit with slightly overestimated precipitation in the inner rainband and underestimated precipitation in the stratiform rainband. Moreover, ice water paths (IWPs) from all five MPS simulations are higher than those estimated from MWRI retrieval in most areas, and the spatial pattern and values of IWP for the National Severe Storms Laboratory double-moment MPS (NSSL) are much closer to those for MWRI. The NSSL scheme reproduces a more realistic joint histogram distribution of SI and EI than other MPSs do, relative to the observation. Besides, the nonlinear Lucas–Kanade optical flow approach has been used to reflect the horizontal distribution of hydrometeors in the typhoon. The results show that the simulated EI and SI from the five MPSs show a systematic southwest bias of approximately about 10∼20 km and significant intensity bias in the convection area. Further model sensitivity tests confirm that the NSSL scheme generates more realistic graupel and supercooled water close to the observations among all MPSs. The findings suggest that satellite measurements would be helpful to assess MPSs in numeric weather models, especially for hydrometeor distributions in the whole typhoon system.