Clouds are crucial components of the climate system and exert profound influences on climate processes. They play a key role in global and regional climate change, energy balance, and water cycle, and have a critical impact on precipitation and radiative transfer processes. Based on daily total cloud cover (TCC) observations from 9 stations over the Tianshan Mountains, China (TM) and 17 stations over the mountain foothills (MF) from 1970 to 2009, this study systematically assesses the spatiotemporal variability of TCC, with a focus on its long-term trends, seasonal characteristics, and statistical associations with surface temperature (T) and relative humidity (RH). Station observations indicate that both TM and MF exhibit clear spatial and seasonal differences in TCC; however, based on the Trend-Free Prewhitening Mann–Kendall test, neither TM nor MF exhibited a statistically significant long-term trend in annual mean TCC during 1970–2009 (TM: 0.04% per decade, p=0.81 MF: 0.24% per decade, p=0.32). After removing the 8 stations with Standard Normal Homogeneity Test-detected breakpoints within this period, the trends remained nonsignificant. The TCC based on ERA5 also showed no significant trend over TM during 1970–2009 (–0.10% per decade, p=0.44), supporting the station-based conclusion. The apparent post-2010 increase should be interpreted cautiously because of possible observing-system inhomogeneity. In terms of spatial distribution, station observations indicate that TCC at western TM stations is higher than at eastern TM stations, and that TCC at northern MF stations is higher than at southern MF stations. In terms of seasonal characteristics, TCC over TM exceeds that over MF in spring and summer, while the opposite occurs in winter; both TM and MF exhibit a seasonal cycle with a spring maximum and an autumn minimum. After detrending and controlling for T, TCC was significantly positively partially correlated with RH in all seasons, especially in spring, autumn, and winter (R > 0.6, p <0.001). This study provides an observational basis for understanding the spatiotemporal variability of TCC over the Tianshan region, while highlighting the inhomogeneity-related uncertainties in detecting long-term climate trends from ground-based cloud observations, as well as the potential risks of extrapolating the short-term post-2010 increase as a long-term climate signal.
Understanding the combined influence of land use/land cover (LULC) and climate change on watershed hydrology is essential for sustainable water resource management in rapidly changing environments. This study evaluated the isolated and combined impacts of LULC and climate change on streamflow and basin water balance in the Upper Awash subbasin (UASB), Ethiopia, using geographic information system (GIS), remote sensing techniques, and the soil and water assessment tool (SWAT) model. LULC maps for 1990, 2005, and 2020 were prepared using ERDAS Imagine 2015 through supervised classification with a maximum likelihood algorithm. The results revealed substantial expansion of agricultural land from 64.16% in 1990 to 82% in 2020 and urban/settlement areas from 0.93% to 4.52%, while forest, shrubland, and grassland declined significantly. The SWAT model was calibrated (2002–2006) and validated (2007–2010) at the Hombole gauging station, yielding reliable prediction with R2 values of 0.83 and 0.77, Nash–Sutcliffe efficiency (NSE) values of 0.78 and 0.73, and percent bias (PBIAS) values of 11.35 and 8.9, respectively. To assess the effects of LULC and climate change, three periods of climate data (1989–1999, 2000–2010, and 2011–2019) and corresponding LULC maps were analyzed. Scenario analysis showed that LULC change alone increased average monthly streamflow by 4.8% due to increased surface runoff associated with agricultural and settlement expansion. In contrast, climate change reduced streamflow by 1.26% as a result of declining rainfall (12.1%) and increasing potential evapotranspiration (PET, 4.1%). Under the combined LULC and climate-change scenario, streamflow increased by 4.6%, indicating that the hydrological influence of LULC change outweighed climate effects during the study period. However, combined impacts reduced total basin water yield by 35.9%, highlighting increasing hydrological stress and declining water availability. These insights are useful to mitigate hazards, such as floods and droughts in a region facing rapid population growth and climate variability, ensuring the basin’s resilience and sustainable development.
This study focuses on the hourly precipitation forecasts based on the CMA-GD(R3) model. By combining with the actual precipitation dataset of meteorological stations in Guangdong, different training durations are adopted, and the frequency-matching method (FMM) is utilized to correct the hourly precipitation. Moreover, through case analysis and statistical tests, a comparative analysis of the forecasting effects before and after the correction is carried out. From the perspective of case analysis, the hourly frequency-matching correction method has a certain corrective effect on both the precipitation area and intensity. The model shows a negative bias in predicting light rain during the flood season in 2024. As the precipitation threshold and the number of lead forecast hours increase, the model gradually exhibits the characteristic of “overestimating light rain and underestimating heavy rain.” Based on the analysis of the correction coefficients (CCs), the model has poor forecasting ability for precipitation that has not occurred upstream, resulting in underestimated precipitation forecasts. For precipitation that has already occurred, the model has an overestimated forecasting error. However, as the precipitation system becomes more distinct, the model’s grasp of the situation gradually improves. When comparing the threat score (TS) before and after the correction, it is found that when using the FMM to correct the hourly precipitation forecasts of the CMA-GD(R3) model in the Guangdong region, it is not always the case that a longer training duration leads to a better correction effect. On average during the flood season, the correction effect is optimal when the training duration is set to 6 h, and from the perspective of horizontal distribution, the correction effect is the most stable. For the correction of short-term precipitation forecasts in the northern part of Guangdong and the western part of western Guangdong, the correction effect is the best when a training duration of 24 h is adopted.
Coastal areas are among the most popular tourist destinations globally but are increasingly vulnerable to the impacts of climate change. Previous studies have assessed climatic suitability for tourism by quantifying ideal and unacceptable conditions based on beach user behavior and survey responses. Tourism contributes ~17% to the Fiji Islands’ GDP, highlighting its strong economic dependence on the sector, particularly coastal tourism. However, there is limited research assessing the impacts of climate change on tourism climatic comfort. To address this gap, the present study evaluates the holiday climate index (HCI) for urban and beach destinations across the Fiji Islands. The analysis incorporates both historical years (2014–2024) and projected climatic scenarios (2025–2050) and (2071–2100) under representative concentration pathways (RCPs) (RCP 4.5 and RCP 8.5). Bias-corrected outputs from global circulation models (GCMs) downscaled through regional climate models (RCMs) were used to derive monthly temperature, humidity, cloud cover, precipitation, and wind speed. HCI scores were computed for both winter and summer seasons for urban and beach tourism. Results indicate that most coastal and urban locations in the Fiji Islands currently experience favorable climatic conditions for tourism, with winter HCI scores ranging from 78 to 92 (“Very Good” to “Excellent”) and summer scores ranging from 70 to 85 (“Good” to “Very Good”). Under future climate scenarios, average HCI scores are projected to decline by 3%–8% across several regions during 2025–2050, particularly during the summer season. However, projections indicate variability in future climatic suitability, with some regions showing reduced suitability during 2025–2050, while some improvements in HCI scores are projected in 2071–2100, particularly under RCP 8.5. These findings provide a scientific basis for climate-informed tourism planning in the Fiji Islands and highlight the importance of incorporating climate projections into long-term tourism strategies.
The concept of elasticity, which can be defined as the sensitivity of long-term streamflow to changes in climate, is particularly useful as an initial estimate of the impact of climate change on land and water resources projects. This study examines this assumption using long historical annual temperature (T), precipitation (P), potential evapotranspiration (PET), and streamflow data from the Ile-Balkhash basin. Furthermore, in order to evaluate the impact of climatic variation on streamflow, the trends of streamflow were explored using the Mann–Kendall method. Based on the annual average climate variables, the climate elasticity in the Ile-Balkhash river basin was estimated as follows: precipitation elasticity (εP) ≈ 2.5, temperature elasticity (εT) ≈ −1.5, PET elasticity (εPET) ≈ −1.5, and total elasticity (εtot) ≈ 1.6. The results demonstrated that the mean sensitivity coefficients of streamflow to precipitation were approximately ≈2.5 indicating that 10% increase in precipitation would result in a 25% increase or decrease in streamflow, respectively. Consequently, it was determined that the annual streamflow was susceptible to annual precipitation for all the basin under examination. Furthermore, the results of the partial correlation analysis show that temperature and precipitation affect some river basins in the region. In the Zhetysu Alatau basin, these factors have a negative effect, while in the Ile Alatau basin, they may have a positive effect, largely due to glacier melting. The findings from this study can serve as a guide for the enhancement and regulation of using local water resources.
Rapid urbanization combined with climate variability has intensified extreme heat exposure in tropical Vietnam. Using multi-decadal ground observations (1981–2022), ERA5 reanalysis, and Landsat-derived land surface temperature (LST), this study investigates long-term trends of heatwaves and surface urban heat island (SUHI) intensity in Hanoi and Ho Chi Minh City—two major metropolitan areas representing the northern and southern climate regimes. Results show statistically significant increases in heatwave frequency and duration, with the heat index (HI) rising by ~0.4–0.5°C per decade. SUHI hotspots expanded substantially between 2010 and 2020, by ~40% in Hanoi and ~10% in Ho Chi Minh City, consistent with ongoing urban expansion and land-surface modification. Large-scale circulation diagnostics reveal distinct regional controls on heatwave behavior, with ENSO exerting differentiated influences across northern and southern Viet Nam. Overall, the findings demonstrate rapidly intensifying and spatially differentiated urban heat risks shaped by the interaction between background climate warming, urban land-surface transformation, and large-scale atmospheric variability. By integrating long-term observations, satellite-based thermal analysis, and synoptic diagnostics, this study advances a coupled urban-climate perspective on heat intensification in tropical Southeast Asian megacities.
Surface Radiative Fluxes (SRFs) have far-reaching impacts in many areas, such as climate, energy and health, which pose huge challenges for various operational applications. In this study, we assess the variability of SRF over West Africa by using multi-year observational data from seven sites located in the Sudanian climate of Benin (Bellefoungou and Nalohou), the Sahelian climate in Niger (Wankama North and South) and Mali (Agoufou, Bamba and Kobou). Using wavelet transform (WT) and principal component analysis (PCA), we found at all sites a characteristic distribution of radiation flux components with two distinct periods of variability (4–8 and 8–16 months). Sites located within the Sahelian climate showed higher amounts in all components of SRF and air temperature. Furthermore, the PCA results reveal that these variabilities stem from the land–atmosphere changes due to the West African monsoon (WAM). The first principal component (PC1) accounts for the largest proportion of total variance, ranging from 22.37% at Nalohou to 57.27% at Bamba, reflecting surface thermal characteristics across all sites during the wet season. Similarly, the second PC explains the behaviour of SRF due to both albedo and cloud-free nature of the atmosphere during the dry season except for the Bellefoungou site. The wavelet coherence further confirmed the agreement between air temperature and incoming shortwave and outgoing longwave radiation. These findings may advance our understanding of land–atmosphere interactions and will contribute to improving further the reliability of weather forecasts.
This study used a multi-index approach to identify the hotspots of agricultural drought within the White Volta Basin (WVB) in Ghana. Ground-based rainfall and temperature data were utilised together with satellite and reanalysis data to calculate multiple drought indices: Standardised Precipitation Index (SPI), Standardised Precipitation Evapotranspiration Index (SPEI), and Soil Moisture Condition Index (SMCI). Various drought characteristics like count, duration, intensity and severity were computed for each grid within the basin to create spatial hotspot maps of drought as well as hotspots at the community level with 30 communities selected across the basin. The analysis was conducted for the full year, the growing season (May to October) and the dry season (November to April) for the period 1991–2020. The Mann-Kendall test and Sen’s slope estimator were used to identify the trends in the drought characteristics. The results indicate that at the basin scale, drought dominated from 2013 to 2020 with significant events seen in 2002 during the dry season and 2015 during the growing season. The southwestern portion of the basin was identified as the primary hotspot of drought using the combined index. The greatest vulnerability was seen within the savannah region during the growing season while the upper east dominated during the dry season. The research provides crucial information for targeted drought management and adaptation strategies within the basin.
Local-scale climate studies make it possible to carry out appropriate adaptation strategies. It supports effective climate change research and impact assessment. Thus, this study analyzed the spatiotemporal development of hydroclimate and its impacts in the Dabus subbasin between 1981 and 2020. The study employed several statistical techniques, including modified Mann-Kendall (MMK) trend tests, the Pettit test, and the precipitation concentration index (PCI). Accordingly, the results show that 57.7% of the annual rainfall falls in the summertime. The standard anomaly analysis depicted that 1999 and 2000 were the wettest years during the study period. However 1982, 1983, 1984, 1986, and 2015 were the driest years. Over the last 40 years, the rainfall patterns of the basin have been very erratic. The years 1987, 1991, 2002, 2003, 2006, and 2011 showed the peak PCI, pointing to a highly uneven rainfall distribution. The average annual rainfall and maximum temperature (T-max) both rose significantly (p < 0.05), but the annual minimum temperature (T-min), river flow, and river runoff all decreased. Although Dabus saw an unexpected rise in yearly rainfall recorded in 1996, the maximum temperature (T-max) was observed in 1993 and 1997. The abrupt change aligned with a decrease in river discharge, river runoff, and T-min seen in 1987, 1998, and 1999, respectively. The results show a significant rise in temperature, erratic rainfall patterns, and a drop in river flow and runoff, which caused climatic change in the basin. The result is very important for adapting approaches in Dabus to reduce the vulnerability from more frequent hydrological events.
Groundwater drought poses a critical threat to regional water security and ecological integrity. However, its spatiotemporal evolution and response mechanisms to meteorological drought remain poorly elucidated. This study examines the spatiotemporal dynamics of groundwater drought and its propagation mechanisms in response to meteorological drought across the Yangtze River Basin (YRB) by integrating datasets from the Gravity Recovery and Climate Experiment (GRACE), the Global Land Data Assimilation System (GLDAS), and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5). We construct a Groundwater Drought Severity Index (DSI) based on groundwater storage anomalies (GWSA) and employ the Mann-Kendall (MK) test, Sen's slope estimator, and seasonal cross-correlation analysis to quantify drought characteristics. The results reveal a distinct "wet-dry-wet" regime shift across the basin, with groundwater drought events concentrated between 2006 and 2015. These events were predominantly mild to moderate in severity. Groundwater drought characteristics exhibit strong spatial heterogeneity: the downstream experiences frequent, prolonged, and severe droughts, whereas the midstream features high-frequency but short-duration and mild droughts. Groundwater drought exhibits pronounced seasonal heterogeneity in response to meteorological drought: rapid response (3-4 months) and strong correlation in summer, moderate lag (6-9 months) in autumn, and long lag (11-15 months) with weakened correlation in winter and spring. Summer and autumn responses are primarily driven by temperature and precipitation, whereas winter and spring dynamics shift toward human-driven dominance due to low temperatures and intensive water withdrawals. These findings reveal the complex spatiotemporal heterogeneity of drought propagation in the YRB, providing a foundation for season-specific water resource management.
At midnight on September 30, 2021, a severe hailstorm swept over northeastern China, producing egg-sized hail over Dalian Airport, causing damages to more than 40 aircrafts and imposing tremendous pressure on operational capacity of the airport. The explicit hail prediction skills of the Weather Research and Forecasting (WRF) model are investigated using different multi-moment microphysics schemes, that is, Milbrandt-Yau (MY) and National Severe Storms Laboratory (NSSL) two-moment, and NSSL three-moment schemes. Simulated variables, including the radar reflectivity, maximum estimated size of hail (MESH), and cloud-top temperature (CTT) are verified against radar, satellite, and available reports. Results indicate that the general evolution of the hailstorm system is well-reproduced by the simulations. Additionally, substantial differences are present for the explicit hail prediction across the schemes. Specifically, the MESH values predicted by MY and NSSL two-moment scheme are significantly overestimated, reaching approximately 60 mm. In contrast, surface hail size distribution by NSSL three-moment scheme aligns most closely with the actual observations. Furthermore, total mass for cloud, rain, and hail within simulated hailstorms produced by two-moment schemes are all substantially larger than that by three-moment scheme. This indicates that the fixed shape parameters for hydrometeor in two-moment schemes can lead to excessive hail growth microphysical processes and size sorting of particles. The findings establish valuable references for operational forecasting of severe hail events, providing a scientific basis for aviation safety early warning.
The microphysical characteristics of two typhoons, Rumbia and In-Fa, were analyzed using the data of raindrop size distribution (DSD) measured by the particle measurement system in East China. The results reveal significant disparities in the terminal velocity-particle size fitting relationships for precipitation particles from different typhoons. The peripheral wind speed of the typhoons shows a particular impact on the terminal velocity of raindrop particles. The microphysical parameters of raindrop spectra exhibit variation across different regions and typhoons, with the most pronounced distinctions observed between convective and stratiform types. Notably, while the concentration of small raindrops predominates in typhoon-induced precipitation, it is the medium-sized raindrops that primarily contribute to the intensity of the rainfall. As the rainfall intensity increases, the lgNw - Dm distribution tends to be concentrated, and the average values of Dm and lgNw are 1.13 mm and 4.02, respectively. The typhoons in East China demonstrate maritime convective precipitation characteristics. Furthermore, Typhoon Rumbia, under the influence of cold air during its meandering phases, has DSDs that are more similar to the characteristics of continental precipitation, which shows a very different DSD from that normally observed in landfalling typhoons. The shape-slope and reflectivity-rain rate relationships are expressed as Lambda = 0.013 mu 2 + 1.111 mu + 0.996, Z = 136.1R1.51. This indicates that the microphysical processes of typhoons in East China are different from those observed in other regions. Additionally, the relationships based on dual-polarization parameters exhibit better performance in quantitative precipitation estimation (QPE). These newly derived relations would certainly improve the accuracy of rainfall DSD retrieval and QPE for typhoon types.
Extreme value theory (EVT) is the standard framework for modeling rare events, but most applications remain static and do not explicitly represent temporal dependence. This study develops a synthesis-based stochastic differential equation (SDE) whose stationary distribution is based on the generalized extreme value (GEV) law. The main methodological contribution is the extension of synthesis-based diffusion modeling to the full GEV family, including the Gumbel limit, through explicit derivation of drift and diffusion functions from the GEV density. The resulting diffusion process preserves the marginal extreme-value structure while embedding mean-reverting short-memory dynamics through an interpretable correlation-time parameter. The framework is validated using annual maximum rainfall from four regions of Tanzania: Rukwa, Iringa, Mbeya, and Ruvuma. Region-specific GEV parameters are estimated by maximum likelihood and used to construct corresponding SDEs. Model performance is assessed through histogram comparisons, log-log tail diagnostics, Q-Q envelopes, autocorrelation analysis, return-level evaluation, and sequential in-sample/out-of-sample forecasting. Results show that the synthesized SDE reproduces the fitted marginal distribution and captures the weak persistence structure of the observed extremes. Predictive interval coverage remains close to nominal levels, and forecast error measures indicate stable performance across climatically heterogeneous regions. These findings show that the proposed GEV-based SDE provides a practically interpretable bridge between static EVT inference and dynamic stochastic modeling for hydrological risk assessment. More broadly, the synthesis framework offers a useful basis for simulation, forecasting, and uncertainty quantification, while simulations targeting genuine long-range dependence remain a topic for future research.
The northeastern region of Bangladesh, particularly Sylhet, experiences considerable climatic problems characterized by higher rainfall, humidity, and temperature variations. Accurate forecasting of these meteorological variables is crucial for disaster resilience, public health, and socioeconomic stability. Conventional time-series forecasting models, such as Seasonal Autoregressive Integrated Moving Average (SARIMA), could frequently fail to identify intricate, nonlinear trends. Artificial neural networks (ANNs) could also show lower forecasting accuracy when it comes to long-term data. Hybrid models that integrate SARIMA with ANN provide improved forecasting accuracy and a more profound comprehension of climate trends. This study analyzed SARIMA, ANN, and hybrid SARIMA-ANN models using univariate monthly average time-series data of rainfall, temperature, and humidity for Sylhet. The dataset was obtained from the Sylhet Station of the Bangladesh Meteorological Department (BMD) and covered the period 1974-2022. Modeling accuracy was assessed using root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination (R2), and Willmott's index of agreement. The hybrid SARIMA-ANN model consistently outperformed the SARIMA and ANN across all climatic variables. It achieved lower error measures and higher agreement indices. Projections for the period 2023-2032 indicated persistent climatic variability, demonstrating the model's effectiveness in predicting climatic trends. The hybrid SARIMA-ANN model provided a reliable framework for climate forecasting in Sylhet. Policymakers and agricultural planners are urged to implement this approach to reduce the socioeconomic effects of climatic unpredictability.
Radar echo extrapolation is a key method for weather nowcasting. Deep learning has become a hotspot of radar echo extrapolation, but it suffers from problems such as prediction fuzziness and low prediction metrics, mainly due to two reasons: firstly, existing deep learning methods still have insufficient capabilities in learning spatial-temporal features and focus on critical regions. Second, atmospheric motion is chaotic and uncertain, and only the confidence interval of the future atmospheric state can be predicted. Existing pixel-level loss functions such as MSE loss require the predicted values to be completely consistent with the gold standard values numerically, resulting in the model being unable to converge to the optimal solution. To address these issues, we propose the Spatial-Temporal-Attention Dual-path Extrapolation Network (STADEN) model and a novel multithreshold loss function (MTL). STADEN introduced a dual-branch network based on the inception and metaformer to enhance the ability to learn spatial-temporal information. MTL allows predicted values to fall within a ground-truth interval to achieve the minimum loss value, enhancing model robustness. In the 2-h extrapolation experiment on the Sichuan dataset, STADEN achieved critical success index (CSI) metrics of 0.488, 0.388, and 0.211 at thresholds of 15, 25, and 35, respectively, outperforming all comparative methods. MTL were applied to all comparative methods directly, and improve CSI metrics by 0.078, 0.058, and 0.024. Robustness experiments on the HKO-7 dataset further demonstrate that both STADEN and MTL achieve optimal performance. These results highlight the potential of STADEN and MTL for practical applications in radar echo prediction.
Zhoushan, located along the southeastern coast of China, frequently experiences severe convective winds (SCWs). Employing the advanced time-of-arrival and direction system (ADTD) and surface observation data and the T-mode principal component analysis (PCA-T) method, this paper investigates the multiscale spatiotemporal characteristics of SCWs in the Zhoushan region. The results reveal pronounced spatial and temporal heterogeneity of SCWs, with significantly higher SCW frequencies observed at island stations in northern and southeastern Zhoushan. Both the SCW monthly and diurnal variations exhibit distinct bimodal patterns: SCWs occur most in August, with a secondary peak in May, and two diurnal peaks appear at 1800 and 2300 BJT. Further analysis indicates that SCW events are closely linked to synoptic circulation and the associated thermodynamic and dynamic conditions. Four major circulation types of SCWs are identified: (1) upper-level jet type (ULJ, 32.5%) mainly occurs in spring (March-May), with northwesterly and southerly winds prevailing at the surface, the largest 0-6 km vertical wind shear, the largest LI, but the lowest convective available potential energy (CAPE). (2) Subtropical high margin (SHM) type (30%) primarily emerges in July, dominated by southwesterly winds. (3) Northern low-level vortex (NLV) type (27.5%) occurs most frequently in August, featuring northwesterly winds and the greatest CAPE. (4) Weak forcing (WF) type (10%) appears from July to August, characterized by northeasterly winds, minimal 0-6 km vertical wind shear, and the lowest CCL with low-level moisture. These findings could enhance understanding of SCW characteristics and provide a scientific basis for SCWs' forecasting and early warning in coastal islands such as Zhoushan.
Indonesia's agriculture critically depends on seasonal climate prediction skill, particularly for anticipating rainfall onset and dry-season duration. The ECMWF SEAS5 model offers high-resolution coupled forecasts and representation of large-scale drivers such as ENSO, IOD, and MJO. This study evaluates SEAS5 performance over Indonesia for the 1991-2020 period by benchmarking simulated precipitation and temperature against MSWEP and ERA5 using deterministic, probabilistic, and composite-based approaches. Results show that SEAS5 successfully reproduces the main spatial and seasonal rainfall cycle, with annual-cycle correlations above 0.8 in most regions. Monthly mean temperature correlations range from 0.7 to 0.9. Probabilistic evaluation reveals a positive median continuous ranked probability skill score (CRPSS) of similar to 0.25, indicating that the model generally outperforms climatology. The model more accurately captures the driest and coldest months (61.3% and 62.1% match, respectively) than the wettest or hottest months (50.4% and 47.1%). Furthermore, composite analysis highlights that predictive skill for precipitation is notably higher and more spatially consistent during El Nino phases compared to La Ni & ntilde;a phases. Combined correlation-hit rate classification identifies four regencies (Rote Ndao, Kupang, Kepulauan Tanimbar, and Maros) as top-performing areas with very strong (>0.8) agreement. These areas are recommended as pilot regions for SEAS5-based agricultural climate services, where forecast outputs can be further refined through bias correction and operational post-processing procedures.
Urban atmospheric pollution, particularly particulate matter (PM), has surged due to rapid industrialization and urbanization in recent years. This study analyzed 197 daily PM1 samples from March 12 to December 26, 2018, focusing on their physical and chemical characteristics, including water-soluble ions, inorganic elements, and carbon species. The annual average PM1 concentration was 51 mu g/m(3), ranging from 4 to 346 mu g/m(3). Water-soluble ions made up 23% of PM1, with NH4NO3, (NH4)(2)SO4, and NH4HSO4 as key secondary species. Sulfate was attributed to long-distance transport and local sulfur dioxide (SO2) photochemical oxidation. PM1 morphology indicated sources like coal fly ash, fuel burning, and vehicle emissions. Ratios of SO42-/NO3-(0.37) and Cl-/Na+ (7.61) highlighted road traffic and coal combustion contributions to PM1, supported by high enrichment factors of elements like Pb, As, Cd, Sb, Zn, V, Ni, and Co. Increased organic carbon (OC) was also linked to coal combustion and road traffic. This work emphasizes the significance of managing road traffic and coal combustion in local environmental policies.
This study assesses the performance of the 14-member multi-model ensemble (MME) from the COordinated Regional climate Downscaling EXperiment (CORDEX)-Africa initiative in reproducing precipitation and associated extremes indices over Cote d'Ivoire during the period 1983-2005. The analysis focuses on the three principal phases of the West African Monsoon (WAM): April-June (AMJ, pre-monsoon); July-September (JAS, mature monsoon); and October-December (OND, post-monsoon). Model performance is assessed by comparing the spatial variability of seasonal precipitation and extremes indices with respect to the gridded observation products (CPC and ARC2). The results indicate that CORDEX-Africa MME is able to reproduce the spatial variability of the precipitation and associated extremes, including consecutive dry days (CDDs), consecutive wet days (CWDs), the simple daily intensity index (SDII), and total precipitation above the 95th percentile (R95PTOT) against two gridded observational datasets (CPC and ARC2). The results indicate that CORDEX-Africa MME satisfactorily reproduces the spatial patterns of seasonal precipitation and associated extremes across Cote d'Ivoire, although systematic biases persist, partly reflecting uncertainties between the observational reference datasets. Overall, seasonal precipitation is overestimated during most WAM phases, except during AMJ, when an underestimation of similar to 20% is observed in the coastal (littoral) climate zone. Regarding precipitation extremes, the ensemble generally underestimates rainfall intensity indices (SDII and R95PTOT) across all phases. However, R95PTOT is overestimated in the northern climatic zone during AMJ and OND, with positive biases of similar to 24% and 10%, respectively. Both dry spells (CDDs) and wet spells (CWDs) are predominantly overestimated throughout the monsoon cycle. An exception occurs during JAS, when CDD is underestimated by about 15 days in southern Cote d'Ivoire. These findings emphasize the importance of regional-scale evaluation of climate simulations prior to their application in future climate projections. Such localized assessments are essential to ensure robust interpretation of projected changes and to provide reliable scientific guidance for national adaptation and climate risk management strategies.
A regional extreme cold wave gale process occurred in Shandong and its coastal areas from January 13th to 15th, 2023. In the forecast of this process, there existed an obvious weaker bias between the forecast and the actual. In order to summarize the forecast deviation of this process and accumulate experience for future forecasts, based on conventional observation and numerical forecast data in the same period, the multimodel forecast performance is comprehensively evaluated, using threat score (TS), mean error (ME), mean absolute error (MAE), and root mean square error (RMSE). By comparing the weather situation and forecasting information of this process with those of a previous gale case, the main reasons for the weak bias between the forecast and actual have been identified. Additionally, two conceptual models for extreme cold wave winds along the Shandong Coast are also proposed. In the later forecasting businesses, we should pay attention to not only the intensity and pressure gradient of cold air but also the path of cold air and the situation of the underlying surface, which will play a key role in the generation of gales. When cold air that affects eastern China with the same intensity moves southward through the northerly path, it is more likely to cause extreme windy weather than those that take the other paths.
