Search

The Mediterranean region is a unique area characterized by a large spectrum of atmospheric phenomena, some of which have a high impact on many aspects of human activities, safety and wellbeing. The area is long considered as a hot spot of such atmospheric phenomena deserving multidisciplinary scientific attention. The scientific research that has been carried out on these high impact atmospheric processes that occur in the Mediterranean area is indeed widespread and the available international literature is very extensive. The paper touches initially the temperature and precipitation regimes, followed by a discussion of floods and droughts. The exciting cyclogenetic patterns of explosive cyclones and medicanes are presented in separate sections. The lightning activity and the presence of dust and other pollutants are also presented herein. The atmospheric chemistry of the region which is increasingly becoming of utmost importance for the area under study is distinctly discussed. Attempts to modify the weather (the precipitation, in particular) are outlined too. The effects of climatic change on various atmospheric processes are considered throughout this paper, in addition to a dedicated section on temperature and precipitation.

A hybrid downscaling model for operational prediction of summer precipitation at 160 stations over China is proposed in this paper. The prediction model is conducted in February, and six predictors extending from the tropics to high latitudes with specific mechanisms are involved. Simultaneous sea level pressure over pan-East Asia and spring sea surface temperature (SST) over the tropical Atlantic Ocean are from the Climate Forecast System version 2 (CFSv2). The others are observational predictors including winter SST over the tropical Indian Ocean, sea ice concentration (SIC) over the Hudson Bay–Baffin Bay–Davis Strait in winter and the Barents–Kara Sea in autumn, and winter outgoing longwave radiation over the tropics. Different predictors impact the summer precipitation of different regions in China. Validations for schemes based on individual predictors (IP-schemes) and all predictors (HD-scheme) indicate that downscaling schemes outperform CFSv2 in predicting summer precipitation over China, especially the HD-scheme. The temporal and spatial anomaly correlation coefficient, root-mean-square errors, and ratio of the same sign (RSS) are estimated. For RSS (Anomalous RSS), the results are improved from 27.8% (23.8%) for CFSv2 to 77.8% (61.9%) for HD-scheme. Notably, the RSSs of IP-schemes based on SIC are equivalent to that of HD-scheme. This partly emphasizes the importance of preceding Arctic sea ice to summer rainfall in China. Moreover, two cases from hindcasts and two from the operational application are chosen for further validation of the prediction skill of HD-scheme. The case studies confirm that the hybrid downscaling model proposed in this paper not only outperforms CFSv2 but can also be applied well to real-time forecasting of summer precipitation over China.

This paper investigates the environmental parameters favoring significant tornadoes in Bangladesh through a simulation of ten high-impact events. A climatological perspective is first presented on classifying significant tornadoes in Bangladesh, noting the challenges since reports of tornadoes are not documented in a formal manner. The statistical relationship between United States and Bangladesh tornado-related deaths suggests that significant tornadoes do occur in Bangladesh so this paper identifies the most significant tornadic events and analyzes the environmental conditions associated with these events. Given the scarcity of observational data to assess the near-storm environment in this region, high-resolution (3-km horizontal grid spacing) numerical weather prediction simulations are performed for events identified to be associated with a significant tornado. In comparison to similar events over the United States, significant tornado environments in Bangladesh are characterized by relatively high convective available potential energy, sufficient deep-layer vertical shear, and a propensity for deviant (i.e., well to the right of the mean flow) storm motion along a low-level convergence boundary.

An intercomparison of eight different microphysics parameterization schemes available in the Weather Research and Forecasting (WRF) model and an analysis of the sensitivity of predicted precipitation to horizontal resolution are presented in this paper. Three different case studies, corresponding to severe rainfall events occurred over the Liguria region (Italy) between October 2010 and November 2011, have been considered. In all the selected cases, the formation of a quasi-stationary mesoscale convective system over the Ligurian Sea interacting with local dynamical effects (orographically-induced low-level wind and temperature gradients) played a crucial role in the generation of severe precipitations. The data set used to evaluate model performances has been extracted from the official regional network, composed of about 150 professional WMO-compliant stations. Two different strategies have been exploited to assess the model skill in forecasting precipitation: a traditional approach, where forecasts and observations are matched on a point-by-point basis, and an object-based method where model success is based on the correct localization and intensity of precipitation patterns. This last method overcomes the known fictitious models performance degradation for increasing spatial resolution. As remarkable results of this analysis, a clear role of horizontal resolution on the model performances accompanied by the identification of a set of best-performing parameterization schemes emerge. The outcomes presented here offer important suggestions for operational weather prediction systems under potentially dangerous heavy precipitations triggered by the mechanisms discussed throughout the paper.

Climate classifications based on temperature and precipitation measurements are increasingly being used for environmental and climate change studies. Using three classification methods (Koppen, Extended Koppen, and Holdridge) and one observational dataset for present climate (CRU, Climate Research Unit), we show that GCMs have bridged the gap that led to the emergence of RCMs thirty years ago, as GCMs can now provide global climate classifications whose accuracy and precision are comparable to those of regional outputs of the RCMs. Projections of high-resolution GCMs for future climates under the assumptions of three Representative Concentration Pathways (RCP26, RCP45 and RCP85) can therefore be used as a primary source for climate change and global warming studies at high resolution. This paper provides comprehensive, model-derived climate classifications for the entire planet, using RCMs and two GCMs for present and future climate-change scenarios, and discusses how well the models actually represent the climates of the world when compared with reference, ground validation data. It turns out that both GCMs and RCMs appear still limited to provide practical estimates of the world climates even for present climate conditions. The modeling of precipitation remains the Achilles' heel of models and thus of multidimensional indices, which are very sensitive to this variable. The conclusion is that model outputs at regional scale need to be taken with extreme caution without venturing into informing policies presenting potentially large societal impacts. Nonetheless, the role of models as privileged tools to advance our scientific knowledge of the Earth's system remains undisputed.

This paper investigates the forecast potential of tropical cyclone (TC) landfall probabilities over the North Indian Ocean (NIO) rim countries using ocean-climate predictor variables in a new statistical seasonal forecast model. A Poisson regression model was used to predict the landfall probabilities for a period of 35 years of TC observations (1979–2013) from the Joint Typhoon Warning Center and the predictor variables include sea surface temperature, Southern Oscillation Index and ocean heat content. Poisson regression was found skilful in hindcasting of tropical cyclone landfall frequency for the NIO region with a correlation coefficient in the forecasted hindcast time series of 0.65 and 31% improvement above climatology. In the present study, genesis was modelled by kernel density estimation, tracks were fitted using a generalised additive model (GAM) approach with a Euler integration step, and landfall location was estimated using a country mask. This GAM model that is previously demonstrated very skilful to simulate TC landfall across the NIO rim countries by Wahiduzzaman et al., 2017, Wahiduzzaman et al., 2019 deems skilful for this study.

In this paper, 34 western track typhoons making landfall over China during the period of 2006–2016 from Jun. to Oct. are taken as research objects. They are divided into strong and weak monsoon influenced categories according to the East Asian summer monsoon index. Using precipitation product that was derived from hourly precipitation observed at automatic weather stations (AWS) in China and retrievals from CMORPH [CPC (Climate Prediction Center) morphing technique] satellite data, Japanese 55-year Reanalysis products (JRA-55) and Blackbody brightness temperature (TBB) data from FengYun 2E (FY-2E) satellite, precipitation intensity and distribution characteristics of strong monsoon influenced typhoons. In addition, the weak monsoon influenced typhoons was compared through composite analysis. Furthermore, to explore the water vapor conditions, dynamic factors, thermal structure, development conditions of mesoscale convective systems, and long-term typhoon maintenance mechanisms of typhoons under both conditions. This research is attempted to obtain the regularity of influences exerted on typhoon precipitation intensity and distribution by summer monsoon, and the possible influencing mechanisms of summer monsoon on typhoon precipitation. Results show that the stronger (weaker) the monsoon flow is, the stronger (weaker) the precipitation intensity of post-landfall western typhoon. Monsoon provides a sufficient amount of water vapor through direct moisture channel. The stronger monsoon flow is, the more obvious the coupling of low-level convergence and upper-level divergence, and thus provides a stronger lifting for typhoon precipitation. A strong monsoon condition is more conducive to the genesis and development of mesoscale convection system. A strong monsoon condition is also favorable for the maintenance of post-landfall typhoon's warm core structure, thereby prolonging the precipitation through delaying the decay of typhoons. All these factors lead up to the influences on precipitation of typhoons by summer monsoon directly or indirectly.

Sand/dust occurrences are one of major disasters in arid and semi-arid areas. Understanding its time evolution as well as the impact factors is crucial for policy makers to protect human from health and economic losses. In this paper, we investigate the features of spring sand/dust frequency during 1961–2016 based on 201 surface meteorological stations in northern China, which are divided into four regions as Xinjiang, Northwest China, North China, and Northeast China according to the spatial distribution of sand/dust frequency. Although the regional mean sand/dust frequency exhibits a general declining trend in the last 56 years, it shows fluctuations with the maximum occurrence in the 1970s and the minimum in the 2010s, and abrupt changes can be found in the late 1980s by the Lepage test. We choose 1961–1988 as the active sand/dust period and 1989–2016 as the inactive sand/dust period. Apparent differences are found between these two epochs in the large-scale atmospheric circulations. The geopotential height over Mongolia and Inner Mongolia of China at mid-and lower troposphere became much higher, and the Asian polar vortex weakened and shrank noticeably in the inactive sand/dust period. For 1961–1988, a meridional dipole pattern of significant correlation in heights at mid-and lower levels was dominant over the northeastern Eurasian continent, while for 1989–2016, sand/dust frequency seems to have much closer relationship with local circulation and a zonal dipole structure at lower troposphere dominated over China. The relationship between the 850 hPa vorticity over Mongolia and sand/dust frequency was strengthened after the late 1980s for the western areas, but that was weakened for the eastern areas of northern China. Besides, the intensity of Asian polar vortex at 500 hPa had higher correlation with sand/dust frequency during 1989–2016 particularly for Northwest China. All these findings are important to refine reliable models for short-term climate prediction of sand/dust occurrence in East Asia.

Precipitation complexity measurement is often overlooked in precipitation time series research. Entropy, as a measure of system complexity, can be used to diagnose the complexity of precipitation. However, it is difficult to judge the applicability of different theoretical entropy models for solving precipitation uncertainty problems. This paper introduces the distinction degree theory and the serial number sum theory to screen the optimal entropy model for precipitation complexity measurement. The optimal entropy model was used to analyze the spatiotemporal differences of monthly precipitation complexity in Heilongjiang Province, China. Possible influencing factors of precipitation complexity were also examined. The results indicated that in the complexity measurement of precipitation based on entropy theory, the stability and reliability of sample entropy was higher than those of fuzzy entropy, wavelet entropy and permutation entropy. The complexity of monthly precipitation in the selected study area significantly increased with time. The average complexity of monthly maximum precipitation, monthly average precipitation and monthly minimum precipitation were 0.665, 0.622 and 0.545, respectively, and their tendency change rates were 0.070/decade, 0.055/decade and 0.038/decade, respectively. The areas with high monthly precipitation complexity were concentrated in the central, eastern and northwest parts of the study area, and the precipitation was less predictable. Monthly precipitation in the southwest was less complex and more predictable. The highest monthly precipitation complexity was 1.012, at Hulin station, and the lowest was 0.510, at Mingshui station. The increasing complexity of monthly precipitation in the province was strongly related to local industrial and agricultural production. The superposition effects of altitude, topographic relief, change in grassland area and agricultural production formed the basic pattern of spatial differences in monthly precipitation complexity. The results may provide a scientific guidance for regional precipitation predictability measurement, effective assessment of droughts and floods, and water resources management.

Attica, which is the region with the largest population in Greece, suffers from numerous high rainfall intensity events inducing flash floods and, consequently, significant damages in the urbanized areas. High rainfall intensities are frequently connected with convection, a quiet often phenomenon during either the transitional seasons or the late summer thunderstorms. The objective of this paper is to implement and evaluate a stratiform-convective classification technique for the rainfall events occurred in Attica during the period 2005–2016. As this methodology requires only the precipitation records, the satisfactory evaluation of this classification approach allows further applications, such as, a climatological analysis regarding convective and stratiform trends in the study area and generally in Greece. Particularly, the rain gauge stations' records used correspond to 10-minute time series from 32 rainfall stations for the same period, and these data are analyzed and processed in order to fit a suitable exponential curve describing the stratiform component. A critical annual threshold of rainfall intensity in mm/6h is then calculated, separating the prevailing convective and stratiform regime above and below this value, respectively. The analysis is performed in three individual subareas of Attica region demarcated initially according to their morphological characteristics and subsequently by checking the cross-correlation for each pair of stations. This critical intensity threshold is determined for each year separately, in order to illustrate the uncertainty and the difficulty in identifying a standard threshold, as it is related to the annual behavior of precipitation. Using this threshold, a total of about 180 six-hour case events is detected as purely convective cases, however this number ranges for each subarea. For these cases, a further analysis is performed with the aim of verifying their convective character. Verification includes time-accurate lightning data, surface synoptic observations from the Hellenic National Meteorological Service and six-hour surface analysis charts from UK MetOffice. Findings, which are presented via indicative verification indices, show in general encouraging performance, considering the highly variable nature of convective precipitation which poses considerable problems in its identification with quite simple techniques.

In this paper, we analyzed the effect of SST on the precipitation of coastal areas of the eastern China. The relationship between the precipitation and SST of different seasons based on CMDC's monthly precipitation data and NOAA's SST data from 1979 to 2016 was calculated. Analysis on the links between precipitation and SST has shown a significant correlation in winter and spring, with three positive strong oceanic signals and two negatively correlated sea areas. Meanwhile, the precipitation and SST of highly correlated regions have increased during the past 38 years. Considering the seasonal variation, precipitation presents upward trend in summer and winter with appreciable seasonal variations. For one of the highly related areas, such as the East China Sea (ECS) and the north of Philippine Sea (PS), the SST there has been increased in all four seasons. Spectral analysis also shows that the precipitation and the MEI have the same cycles about 3–5 years. Moreover, lead-lag analysis between precipitation time-series and three climate indexes (Nino 1 + 2 SST index, Nino 3 SST index and SIOD SST index) were applied. The results show that when the SSTs lead 2 to 4 months, they are highly related to the precipitation in the coastal areas of the eastern China.

Extreme precipitation index (EPI) is used to assess the extreme precipitation events, to further monitor and detect climate change impacts. The primary objective of the paper is to assess the extreme precipitation events and their spatiotemporal variability of 14 EPIs, and further investigate the linear and nonlinear relationships between typical EPIs and global climate anomalies El Nino Southern Oscillation (ENSO), using Nino 3.4 and SOI as climate indices, for the period of 1969–2016 in the Wei River Basin (WRB) of China. Observed daily precipitation data from 18 meteorological stations in the WRB were selected to calculate 14 EPIs derived or modified from core indices of the CCI/WCRP Expert Team for Climate Change Detection Monitoring and Indices (ETCCDMI). The trends of EPIs were estimated using Mann-Kendall non-parametric test. The teleconnections of ENSO to EPIs were revealed using principal component analysis (PCA), Pearson's correlation and wavelet analysis. Results indicate that: (1) The change patterns of extreme precipitation events varied for different EPIs and different meteorological stations for the period of 1969–2016 in the WRB. Four EPIs were negative trends dominated, and six EPIs were positive trends dominated, indicating that the extreme precipitation events had mainly increased for the past 48 years. (2) The spatial distributions of seven categories of trends were scattered and irregular for different EPIs and different areas of WRB. However, some of the EPIs had similar spatial distributions of trends. For example, the stations that had increasing trends mainly located at the northern part of WRB, especially in the Jing River Basin (JRB), for six precipitation intensity EPIs except R150, which was stationary trend dominated. (3) The nonlinear teleconnection relationships between typical EPIs and two climate anomalies revealed using wavelet analysis were consistent with linear relationships using correlation analysis. The Nino 3.4 and SOI had similar wavelet coherence but opposite phase difference with PC1 of typical EPIs. The phase differences between Nino 3.4 and PC1 of EPIs were anti-phase dominated, and in-phase dominated between SOI and PC1 of EPIs, indicating that the Nino 3.4 exerted negative climate impacts on the extreme precipitation events in the WRB and vice-versa for SOI. (4) The results should be beneficial to assess the extreme precipitation events, which provide references for the water resources planning and management in the WRB. It is possible to estimate and predict the future extreme precipitation events using climate models and global climate anomalies as potential predictors, which should be further studied in our future work.

The behavior of the surface solar radiation (SSR) during the first half of 20th century is an open issue due to the scarcity of SSR data during this period. This paper focuses on the re-evaluation of the pyrheliometer measurements in Madrid between September 1910 and January 1929. These records were formerly analyzed by Anton et al. (2014), reporting no statistically significant trends in the atmospheric column transparency. In the present work, metadata regarding the state of the solar disk during each pyrheliometer record have been used in order to remove measurements affected by thin clouds. The re-analysis of the atmospheric column transparency from the pyrheliometer data showed two periods with opposite sign in the trends of this variable: a decrease of (−2.9 ± 0.2)% per decade between 1910 and 1925, followed by a strong increase of (+15.0 ± 2.5)% per decade up to 1929. Both trends were statistically significant at the 95% confidence level. These results suggest a decrease of direct SSR in Madrid during the second decade of the 20th century, in agreement with the final stage of the “early dimming” which was observed at several European sites from the end of the 19th century. Additionally, the marked positive trend of the atmospheric transparency found in this work in the second half of the 1920s is in accordance with the beginning of the SSR recovery in Europe which was observed up to late 1940s (“early brightening”). Therefore, from the results of this study, the use of metadata is strongly required for an accurate assessment of the trends in early pyrheliometer measurements.

The present paper reports a thorough statistical elucidation of the time series of surface temperature in monthly as well as seasonal scale over eastern India during pre-monsoon (March–May) and summer monsoon (June–September) in India. Climate risk is a challenge to millions of people whose livelihood is dependent on agriculture. Considering data for the period of 1901–2007, the autocorrelation functions for both the seasons have been investigated for persistence and an absence of long-term memory has been detected in all the cases. The intrinsic uncertainty has been quantified through Shannon-entropy by framing an appropriate probability distribution and the entropy has been computed for monthly as well as seasonal scales. Finally, using rescaled range analysis, the Hurst exponents have been computed and finally the surface temperature time series has been observed to be anti-persistent for pre-monsoon and Brownian for summer-monsoon months.

Recent studies found a long-term drought and resulting declines in vegetation greenness and canopy water content over the Congo Basin, the second largest rainforest in the world after the Amazon. Since most precipitation in tropical latitudes stems from convection, this paper analyzed 35 years of high-resolution (8 km spatial resolution and 3 h temporal resolution) satellite data to document the long-term trends in the number, size and intensity of thunderstorms activity over the Congo Basin during April, May, and June (AMJ) for the period 1982–2016. Changes in the magnitude and area of cold cloud top brightness temperatures (Tb) at different thresholds were used as a proxy to quantify the number and size of individual thunderstorms at different intensities. We found that the areal extent and intensity of thunderstorms increased over the past 35 years, particularly over Northern Congo Basin, and these changes are consistent with other satellite datasets. Combined with a reanalysis dataset, our work suggests that thunderstorms over the Congo Basin are becoming taller and wider, and likely resulting in a moister (drier) upper (lower) troposphere.

The paper is motivated by the enormous variability of short-term rainfall time structures which need to be discretized into several typical variants and explained from the viewpoint of rainfall types producing them. We present six variants of the time structure of 6-h rainfalls in Czechia, distinguished by a novel methodology for designing synthetic hyetographs. Reference 6-h rainfall episodes were extracted from radar-derived precipitation time series with a time resolution of 10 min, adjusted by daily data from rain gauges. The variants were distinguished by three indexes that quantify the precipitation concentration within time steps from one to six hours. The episodes with steady precipitation intensity during the entire episode are mainly stratiform or possibly mixed and frequently take much longer than six hours because of circulation patterns producing them; central and northeastern cyclonic types are most represented. All other variants of episodes frequently occur when a trough is situated above Central Europe. Episodes with steady intensity lasting about three hours are still mainly stratiform or mixed while two variants represented by “two-humped” hyetographs are usually mixed or convective. Two variants of most concentrated episodes are mainly convective or possibly mixed; they are characterized by enhanced frequency of southwestern and eastern cyclonic types. Future research on cluster frequencies among maximum precipitation episodes in various regions will enable the improvement of design hydrographs of small streams where runoff is basically influenced by the rainfall time structure.

Drylines are a frequent feature of the atmospheric circulation in Argentina. Recent work described their climatological characteristics and synoptic-scale processes associated with their formation. However, little is known about the mesoscale processes that are relevant for their life cycle. In this paper, we investigate the influence of these mechanisms on the formation and movement using a case-study approach. Also, the relationship between these mechanisms and convective initiation near the dryline is investigated. A typical northern Patagonia dryline is simulated using the Weather Research and Forecasting (WRF) with an horizontal resolution of 3 km. The FLEXible PARTicle dispersion-WRF is also used to study some of the processes from a Lagrangian perspective. During the quiescent period corresponding to dryline formation and intensification, the dryline strengthens along the axis of a thermally-induced surface trough that promotes differential moisture advection. Also, convergence along the dryline is enhanced during night-time by the inertial oscillation ahead of the dryline and by the development of a thermally-driven circulation between the elevated terrain in northern Patagonia and the adjacent plains. These two mechanisms contribute to enhancing the intensity of the moisture gradient and to trigger deep moist convection. Under a period of stronger synoptic forcing, the dryline intensifies in response to the confluence induced by the deepening surface trough and moves away from the terrain elevations. Its movement during this period is driven by horizontal dry advection and the vertical mixing enhanced by the entrainment into the boundary layer of extremely dry air, whose origin can be traced back to the mid-levels over the Pacific Ocean. When the magnitude of the low-level westerly winds increase, moister air from lower levels cross the barrier, contributing to weakening the moisture gradient at the dryline.

This paper studies a damaging hail storm that occurred on 6 June 2015 in the complex topography of Switzerland. The storm persisted for several hours and produced large hail resulting in significant damage. Storms of comparable severity occur on average only three times per year within the entire Swiss radar domain, but are rare events at this exact location, according to a set of over 400,000 automatically identified storms. A multitude of datasets, partly novel for central Europe, is now available to study the storm in great detail capturing its impacts, severity and development. The data we use include radar-based hail products, crowd-sourced hail reports, and insurance loss data. These independent datasets permitted a verification of both hail occurrence and hail size estimations by radar. The crowd-sourced reports agree well with radar-based hail observations and insurance data. Model data (ERA-Interim reanalysis, regional COSMO-2 analysis and WRF simulations) and radio-sounding data showed, that conditions were favourable for thunderstorm development due to an unstable and moist atmosphere over Switzerland, brought about by an interplay of large-scale pattern and local processes. Advection ahead of a cold front west of Switzerland and local evapotranspiration lead to high lower-tropospheric moisture. The large-scale flow and topographically induced Alpine pumping resulted in strong directional wind shear, and contributed to the longevity and severity of this storm. The cold front was not relevant for the vertical lifting. Using model simulations with very high resolution, we identified mountain wind systems and cold-air outflow as possible triggering and propagation mechanisms of this hail storm.

Concern about climate change has amplified the need for accurate information about variations in statistical characteristics of extreme rainfall events in both smaller temporal and spatial scale. For this reason, in this paper the hourly precipitation measurements obtained at the Department of Meteorology and Climatology of the Aristotle University of Thessaloniki meteorological station, during the period 1947–2003, are used. A new threshold determining method is proposed, in order to define the extreme rainfall event in Thessaloniki, based upon the polynomial trends of the cumulative distribution functions, retrieved from hourly precipitation measurements. Through this approach, three statistical goodness-of-fit tests were carried out, in order to come up with the best fitting probability distributions, for annual and seasonal extreme precipitation conditions. Based upon this fitting distribution procedure, return periods for different precipitation extreme values were calculated. Results indicate that the value of 6.5 mm/h is the hourly threshold of extreme precipitation in Thessaloniki over the studied period. Furthermore, the Generalized Pareto, and the Johnson SB, the log-Gamma and the Lognormal are the best fitted probability models for the description of annual and seasonal extreme rainfall events, respectively. Last but not least, the contribution of hourly data in the accuracy of the results is discussed.

Regional precipitation patterns may change in a warmer climate, thereby increasing flood and drought risks. In this paper, annual, annual maximum, intense, heavy, moderate, light, and trace precipitation are employed as indicators to assess changes in precipitation patterns under two scenarios in which the global mean temperature increases by 1.5 °C and 2.0 °C relative to pre-industrial levels using the regional climate model COSMO-CLM (CCLM). The results show that annual precipitation in China will be approximately 2.5% higher under 1.5 °C warming relative to the present-day baseline (1980–2009), although it will decrease by approximately 4.0% under an additional 0.5 °C increase in global mean temperature. This trend is spatially consistent for regions with annual precipitation of 400–800 mm, which has experienced a drying trend during the past half century; thus, limiting global warming to 1.5 °C may mitigate these drying conditions. The annual maximum precipitation continues to increase from present day levels to the 2.0 °C warming scenario. Relative to the baseline period, the frequency of trace and light precipitation days exhibits a negative trend, while that of moderate, heavy, and intense precipitation days has a positive trend under the 1.5 °C warming scenario. For the 2.0 °C warming world, the frequency of days is projected to decrease for all precipitation categories, although the intensity of intense precipitation increases. Spatially, a decrease in the number of precipitation days is expected to continue in central and northern China, where a drying trend has persisted over the past half century. Southeastern China, which already suffers greatly from flooding, is expected to face more heavy and intense precipitation with an additional 0.5 °C increase in global mean temperature. Meanwhile, the intensity of intense precipitation is expected to increase in northern China, and the contribution of light and moderate precipitation to the annual precipitation is expected to decrease in southeastern China. Therefore, flood risk in northern China and drought risk in southern China should draw more attention for a global air temperature increase from 1.5 °C to 2.0 °C.

This paper explored the projected changes in precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) over the Yangtze River Basin (YRB) based on 23 Global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). The Empirical Quantile Mapping (EQM) approach was firstly applied to correct the biases in the GCMs. Next, the future changes were investigated by analyzing the multi-model ensemble (MME) of the bias-corrected dataset during 2025–2044 (near-term), 2045–2064 (mid-term), and 2081–2100 (long-term) periods, with reference to the baseline period 1995–2014, under three integrated scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) of the Shared Socioeconomic Pathways (SSPs) and the Representative Concentration Pathways (RCPs). Results show that: (1) the biases in CMIP6 GCMs can be effectively corrected by EQM, and the MME performs better than individual models for each climatic variable. (2) Precipitation over the YRB from 2025 to 2100 is projected to increase at the rate of 9.66 mm/decade, 13.45 mm/decade, and 21.01 mm/decade under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. In the long-term, the annual precipitation is projected to increase by 10.41%, 10.66% and 15.80%, under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5, respectively. (3) Tmax (Tmin) over the YRB is projected to increase by 0.09 (0.07) °C/decade, 0.29 (0.27) °C/decade, and 0.66 (0.64) °C/decade under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. In the long-term, Tmax (Tmin) averaged over the YRB is projected to increase by 1.75 (1.50) °C, 2.72 (2.54) °C and 5.04 (4.85) °C under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5, respectively. (4) Uncertainties in the projected precipitation and temperature over the YRB were reduced based on MME. But further researches, such as selecting the superior models with respect to the regional climate of the YRB from the CMIP6 and using the ensemble methods that assign weight based on the performance of each model, are still needed to provide more reliable climate projections.

The characteristics of precipitation microphysics during various stages of the tropical cyclone (TC) Nisarga (2020) as observed over an orographic station of Western Ghats, India has been highlighted in this paper. The study examines the evolution of convective rain along with the features of surface raindrop size distribution (DSD) within the rainbands and eyewall of the cyclone “Nisarga” based on the observational data of Doppler weather radar and disdrometer during June 02–03, 2020. The cyclone Nisarga, which formed in the Arabian sea, had passed over the Western Ghats after its landfall in the south-western coast of Indian sub-continent. The thermodynamical and microphysical features of the prevailing cloud exhibits strong intensity over the oceanic region than that of over the orographic one along the track of the cyclone. While looking into the impact of rainbands of cyclone as observed by Doppler weather radar over the orographic region, a continuous rise in precipitation is visible when the flow of the cyclone is oriented along the upslope of the terrain. The precipitation intensity and the percentage of occurrences of convective rainfall increased as the primary eyewall of the cyclone reached the orographic station. These are basically due to the presence of the deep convective clouds surrounding the eye of the cyclone, which produces heavy precipitation over the orographic station. Moreover, the orographically generated cloud water along with the shallow convection also plays an important role for the modification of precipitation microphysics over this region of Western Ghats. Thus, these heterogeneous features of rainfall occurrences over the orographic region during the propagation of TC exhibits different radar reflectivity-rain rate (Z-R) relationship which suggests the necessity of a variable relationship for quantitative precipitation estimation (QPE) in different sectors of the TC.

Mesoscale Convective Systems (MCSs) over the southern Arabian Peninsula are more common during the spring months of March and April. In this paper, the processes behind such severe convective events and their local-scale impacts are investigated using reanalysis data, satellite-derived and observational products. The analysis of 95 MCS events from 2000 to 2020 revealed that they are triggered by low-level wind convergence and moisture advection from the Arabian Sea, Arabian Gulf and/or Red Sea. An equatorward displacement and strengthening of the subtropical jet also precondition the environment, as does the presence of a mid-level trough. The latter is generally part of a large-scale pattern of anomalies that are equivalent barotropic in nature, and therefore likely a response to tropical or subtropical forcing. At more local-scale, a drying of the mid-troposphere between 850 and 250 hPa typically by 50%, a reduction of the upper-level winds by about 5 m s−1, and an increase in the upper-tropospheric temperature on average by 2–3 K, are typically observed during MCS events. Over the 21-year period, a statistically significant increase in the MCSs' duration over southeast Arabian Peninsula has been found, suggesting that such extreme events may be even more impactful in a warming world.

Over the past decades, Sicily has undergone an increasing sequence of extreme weather events that have produced, besides huge damages to both environment and territory, the death of hundreds of people together with the evacuation of thousands of residents, which have permanently lost their properties. In this framework, with this paper we have investigated the impact of different grid spacing and geographic data on the performance of forecasts over complex orographic areas. In order to test the validity of this approach we have analyzed and discussed, as case study, the heavy rainfall occurred in Sicily during the night of October 10, 2015. In just 9 h, a Mediterranean depression, centered on the Tunisian coastline, produced a violent mesoscale storm localized on the Peloritani Mountains with a maximum rain accumulation of about 200 mm. The results of these simulations were obtained using the Weather Research and Forecasting (WRF-ARW) Model, version 3.7.1, at different grid spacing values and the Two Way Nesting procedure with a sub-domain centered on the area of interest. The results highlighted that providing correct and timely forecasts of extreme weather events is a challenge that could have been efficiently and effectively countered using proper employment of high spatial resolution models.

Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007–2015. The comparison reveals that rainstorm events are predictable up to 5 days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24 hr forecast to 48 hr forecast in all the three models.

In recent years, climate extreme events have caused increasing direct economic and social losses in the arid region of northwestern China. Based on daily temperature and precipitation data from 1960 to 2010, this paper discussed the linear trend and abrupt changes of climate indices. The general evolution was obtained by the empirical orthogonal function (EOF), the Mann-Kendall test, and the distribution-free cumulative sum chart (CUSUM) test. The results are as follows: (1) climate showed a warming trend at annual and seasonal scale, with all temperature indices exhibiting statistically significant changes. The warm indices have increased, with 1.37%days/decade of warm days (TX90p), 0.17 °C/decade of warmest days (TXx) and 1.97 days/decade of warm spell duration indicator (WSDI), respectively. The cold indices have decreased, with − 1.89%days/decade, 0.65 °C/decade and − 0.66 days/decade for cold nights (TN10p), coldest nights (TNn) and cold spell duration indicator (CSDI), respectively. The precipitation indices have also increased significantly, coupled with the changes of magnitude (max 1-day precipitation amount (RX1day)), frequency (rain day (R0.1)), and duration (consecutive dry days (CDD)). (2) Abrupt changes of the annual regional precipitation indices and the minimum temperature indices were observed around 1986, and that of the maximum temperature indices were observed in 1996. (3) The EOF1 indicated the overall coherent distribution for the whole study area, and its principal component (PC1) was also observed, showing a significant linear trend with an abrupt change, which were in accordance with the regional observation results. EOF2 and EOF3 show contrasts between the southern and northern study areas, and between the eastern and western study areas, respectively, whereas no significant tendency was observed for their PCs. Hence, the climate indices have changed significantly, with linear trends and abrupt changes noted for all climate indices, which will have serious impacts on water resources and ecological environment in the arid region of China.

In midsummer (July-August) of 2017, the high temperature (HT) frequency in China breaks the record since 1951. For each HT threshold from 35 °C to 40 °C, the frequency is beyond the normal by 2.58–3.46 standard deviations. Specifically, the HT events in southern China make major contributions to the new record, and they demonstrate obvious temporal variations. The strongest episode in southern China appears in mid-to-late July and lasts for nearly 20 days. The westward extension and intensification of the western Pacific subtropical high (WPSH) is usually considered as the leading factor of the HT in southern China. However, the diagnostic analysis in this paper shows that the 2017 HT event does not correspond to the intensification of the whole WPSH but to its western part, and there is a cyclonic circulation anomaly over the western Pacific warm pool, which is closely related to the southward extension of the middle-latitude trough over the east side of Japan. This circulation pattern is favorable for the southward retreat of the WPSH northern boundary over the warm pool, which results in the shape change and even the split of the high. Then the west part of the high is incapable of withdrawing eastward, and it dominates over the southern China steadily, leading to the persistent HT events. This conclusion is further confirmed by the statistical analysis both of the multi-year circulations and the typical HT extreme case in 2013, which could provide reference for the sub-seasonal prediction of HT in southern China.

Understanding the mesoscale spatial and temporal variability of precipitation (of the order of 1 to 100 km, and of minutes to hours, respectively) is essential for larger-scale studies, especially in highly heterogeneous areas, such as coastal regions, mountains and urban areas. The adequate capturing of the precipitation patterns in both diagnostic and prognostic terms remains an open challenge. On the one hand, commonly used ground measuring devices, such as rain gauges, disdrometers and other point instruments, exhibit an ineffective sampling for the whole range of scales and areas covered with precipitation. On the other hand, weather radars provide a much greater spatial and temporal resolution which, however, must be exploited with care in rain rate estimations, due to complicated and non-unique transfer functions used in the processing procedures. This study aims at bridging, to at least some extent, this scale gap and to demonstrate the role of fine-scale precipitation estimates derived from weather radar data, and to illustrate their impacts on larger scale weather patterns. The focus is on the precipitable water content and its changes during a Mediterranean cyclone evolution. In this paper, the first results with the regional radar signal processing chain that provides the radar data assimilation (RDA) in the Harmonie convection permitting numerical model are described. The numerical experiments exhibit effective simulation of the precipitable water at different stages of the frontal depression which affected Cyprus in the Eastern Mediterranean, during 31 December 2018–2 January 2019. In particular, it was found that the major volume of precipitable water was related to the cyclone frontal zone (which is in turn related to heavier precipitation), followed by a weaker field of precipitable water behind the frontal zone (and weaker precipitation). During the whole severe weather period, the precipitable water minimum was maintained over the island's central mountain range, reflecting the importance of interactions and feedbacks between the air flow and orography. The radar data assimilation influences the results of the simulations, depending on the particular area and cyclone stage. The largest innovations due to RDA occur with the approach of the cyclone's frontal zone. Enhancement of precipitable water due to RDA is observed over most of the whole area, but this is more pronounced in weaker precipitation locations. The impact is associated with a shift of the precipitable water maximum towards higher values in the precipitable water distribution, which is accompanied by enlarged sizes of areas with greater precipitable water. During the cyclone's mature stage and at the rear zone, the integrated RDA impact over the whole domain is almost negligible but takes the form of mesoscale cells of opposite signs; at this stage, radar measurements yield an alteration of both the positions and rates of spatial precipitable water fields, which result in intermittent mesoscale changes.

Compared with other arid areas, the convective rainstorm occurs more easily in the east side of the Helan Mountain, which is characterized by the short duration, large intensity, high precipitation efficiency and significant local distribution. In this paper, the 5-min and hourly precipitation at 512 regional automatic weather stations and other high-resolution observation data are used to analyze the spatio-temporal distribution and evolution characteristics of the rainstorm in the east side of the Helan Mountain during 2006 and 2019. Then, based on the daily precipitation data at 12 national observation stations, the rainstorm characteristics in this region during 1951 and 2005 are analyzed. On this basis, the rainstorm characteristics in the two periods are compared and studied. Finally, the possible causes of the distribution characteristics of rainstorm are initially discussed. The results show that the rainstorm with large magnitude and high intensity is mainly located in the mountainous region, and it mainly occurs in July with a high frequency period from afternoon to the first half of the night. Compared with 1951–2005, in 2006–2019 the rainstorm frequency, intensity and extremity are all increased, and the nocturnal and topographic features are more significant. The spatio-temporal distribution of rainstorm in the east side of the Helan Mountain is closely related to the nighttime enhancement of the low-level southeasterly jet and the interaction between the low-level jet and the mountainous topography. In the windward slope of the Helan Mountain, the interaction between the low-level jet and the mountainous topography could trigger or enhance the meso- and small-scale rainstorm systems, which would favor the occurrence of rainstorm. Overall, the conclusions in this study can deep the understanding of the rainstorms in the Helan Mountain, which could provide a reference for the monitoring and warning of rainstorms in this region.

Precipitation outputs from 30 Global Circulation Models (GCMs) of the Coupled Model Inter-comparison Project Phase 6 (CMIP6) were evaluated from 1951 to 2014 over six climate zones in arid Central Asia (ACA) using the Climate Research Unit TS 4.04 (CRU) precipitation datasets as reference. An evaluation framework was constructed taking into account metrics of annual precipitation patterns, annual cycle precipitation statistics, categorical validation and long-term precipitation trend. The performance of GCMs vary from region to region. Compared to CRU, the 30 selected GCMs present considerable wet bias in the Southern Xinjiang (SX) and Hexi Corridor (HC) regions which can be larger than 100%. The simulated precipitation from most GCMs shows a consistent annual cycle shape and closer with that of observations in the western ACA, but fails to capture precipitation peak in NK and NX regions. Compared to NK, CD, TM and NX, all models and the full model ensemble have limited ability in reproducing the Probability Density Function (PDF) of observations over SX and HC. Most GCMs show limited competence to reproduce the long-term precipitation trend with high wet bias over the Hexi Corridor. Based on the comprehensive performance ranking, it is discovered that CESM2, CESM2-FV2, CESM2-WACCM and CESM2-WACCM-FV2 from NCAR, ACCESS-CM2 from CSIRO and CanESM5 from Canadian Centre for Climate Modeling demonstrate better performances across ACA. The ensemble of 30 selected GCMs has limited ability to accurately simulate precipitation according to the above four types of metrics. The results of this paper may provide scientific guidance to CMIP6 end-users when selecting the most suitable GCMs for their specific applications over ACA.

The aim of this study is to test how effective and physically correct are the mathematical approaches of operational indices used by relevant National Agencies across the globe. To do so, the following indices were analysed Standardized Precipitation Index (SPI) -1, 3, 6, 12 and 24, Standardized Precipitation – Evapotranspiration Index (SPEI) – 1, 3, 6, 12 and 24, Effective Drought Index (EDI) and Index of Drying Efficiency of Air (IDEA). To make regions more comparable to each other and follow the spatial development of drought SPI index was advised by World Meteorological Organisation to be used widely by official meteorological services. The SPI and SPEI are used for Drought Early Warning in the USA, National Drought Mitigation Center and NASA, and in the EU by the European Drought Centre (EDC) and in the Balkan Region by National Meteorological Agencies. The EDI Index has wide application in Asia. In this paper four different issues were investigated: 1) how the mathematical method used in a drought indicator's computation influence drought indices' (DI) comparative analyses; 2) the sensitivity of the DIs on any change of the length of observational period; 3) similarities between the DIs time series; 4) and how accurate DIs are when compared to historical drought records. Results suggest that it is necessary to apply a few crucial changes in the Drought Monitoring and Early Warning Systems: 1) reconsider use of SPI and SPEI family indices as a measure of quality of other indices; and for Drought Early Recognition Programs 2) switch to DIs with a solid physical background, such as EDI; 3) Adopt solid physics for modelling drought processes and define the physical measure of drought, e.g. EDI and IDEA indices; 4) investigate further the IDEA index, which, supported by our study as well, is valuable for simulation of a drought process.

Despite an important role the aerosols play in all stages of cloud lifecycle, their representation in numerical weather prediction models is often rather crude. This paper investigates the effects the explicit versus implicit inclusion of aerosols in a microphysics parameterization scheme in Weather Research and Forecasting (WRF) – Advanced Research WRF (WRF-ARW) model has on cloud dynamics and microphysics. The testbed selected for this study is a severe mesoscale convective system with supercells that struck west and central parts of Serbia in the afternoon of July 21, 2014. Numerical products of two model runs, i.e. one with aerosols explicitly (WRF-AE) included and another with aerosols implicitly (WRF-AI) assumed, are compared against precipitation measurements from surface network of rain gauges, as well as against radar and satellite observations. The WRF-AE model accurately captured the transportation of dust from the north Africa over the Mediterranean and to the Balkan region. On smaller scales, both models displaced the locations of clouds situated above west and central Serbia towards southeast and under-predicted the maximum values of composite radar reflectivity. Similar to satellite images, WRF-AE shows the mesoscale convective system as a merged cluster of cumulonimbus clouds. Both models over-predicted the precipitation amounts; WRF-AE over-predictions are particularly pronounced in the zones of light rain, while WRF-AI gave larger outliers. Unlike WRF-AI, the WRF-AE approach enables the modelling of time evolution and influx of aerosols into the cloud which could be of practical importance in weather forecasting and weather modification. Several likely causes for discrepancies between models and observations are discussed and prospects for further research in this field are outlined.

In the first part of this paper, monthly precipitation data from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) and Tropical Rainfall Measuring Mission 3B42 algorithm Version 7 (TRMM-3B42V7) are evaluated over Iran using the Generalized Three-Cornered Hat (GTCH) method which is self-sufficient of reference data as input. Climate Data Unit (CRU) is added to the GTCH evaluations as an independent gauge-based dataset thus, the minimum requirement of three datasets for the model is satisfied. To ensure consistency of all datasets, the two satellite products were aggregated to 0.5° spatial resolution, which is the minimum resolution of CRU. The results show that the PERSIANN-CDR has higher Signal to Noise Ratio (SNR) than TRMM-3B42V7 for the monthly rainfall estimation, especially in the northern half of the country. All datasets showed low SNR in the mountainous area of southwestern Iran, as well as the arid parts in the southeast region of the country. Additionally, in order to evaluate the efficacy of PERSIANN-CDR and TRMM-3B42V7 in capturing extreme daily-precipitation amounts, an in-situ rain-gauge dataset collected by the Islamic Republic of the Iran Meteorological Organization (IRIMO) was employed. Given the sparsity of the rain gauges, only 0.25° pixels containing three or more gauges were used for this evaluation. There were 228 such pixels where daily and extreme rainfall from PERSIANN-CDR and TRMM-3B42V7 could be compared. However, TRMM-3B42V7 overestimates most of the intensity indices (correlation coefficients; R between 0.7648–0.8311, Root Mean Square Error; RMSE between 3.29mm/day-21.2mm/5day); PERSIANN-CDR underestimates these extremes (R between 0.6349–0.7791 and RMSE between 3.59mm/day-30.56mm/5day). Both satellite products show higher correlation coefficients and lower RMSEs for the annual mean of consecutive dry spells than wet spells. The results show that TRMM-3B42V7 can capture the annual mean of the absolute indices (the number of wet days in which daily precipitation > 10 mm, 20 mm) better than PERSIANN-CDR. The results of daily evaluations show that the similarity of Empirical Cumulative Density Function (ECDF) of satellite products and IRIMO gauges daily precipitation, as well as dry spells with different thresholds in some selected pixels (include at least five gauges), are significant. The results also indicate that ECDFs become more significant when threshold increases. In terms of regional analyses, the higher SNR of the products on monthly (based on the GTCH method) and daily evaluations (significant ECDFs) is mostly consistent.