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Heavy air pollution is strongly influenced by weather conditions and is thus sensitive to climate change. Especially, for the areas with complex topography such as the Sichuan Basin (SB), one of the most polluted areas of China, the synergistic effects of synoptic weather patterns and topography on air quality are unclear and warrant investigation. This study examined the typical synoptic patterns of SB in winter days of 2013–2017 and revealed their synergistic effects with topography on air quality. Three categories of synoptic patterns including dry low-trough, high-pressure, and wet low-vortex patterns accompanying heavy, medium, and slight air pollution, respectively, were identified. In particular, the dry low-trough patterns occur most frequently, accounting for around 62% of the total days. In the case of this pattern, westerly wind prevails over the SB and the aloft atmosphere is warmer than the Tibetan Plateau (TP) at the same height, which induces the cold air over TP moving eastward to the SB. Under the synergistic effects of the cold air eastward movement and TP, a strong descending motion (known as foehn) is observed on the leeward slope of the towering TP. This foehn warming causes a stable layer above the planetary boundary layer (PBL), which suppresses secondary circulation and PBL. These features restrict atmospheric pollutant dispersion, resulting in poor air quality. In contrast, for the high-pressure and wet low-vortex patterns, cold air masses from the north invade southward and cover the northwest SB. This invasion remarkably decreases the atmospheric stability of the lower troposphere, deepens the PBL, and enhances the height of secondary circulation, thereby facilitating air pollutant dispersion. Moreover, the wet low-vortex pattern is accompanied by frequent precipitation events (with 80% rainy days), further bringing down air pollution levels. These findings provide an insight for improving air pollution forecast in the complex terrain areas under global warming.

Extreme summer heat can have severe socioeconomic impacts and has occurred frequently in North China in recent years, most notably in June–July 2023, when North China experienced the most widespread, persistent, and high-intensity extreme heat on record. Here, typical weather patterns covering North China and its surrounding areas were classified into seven types based on the Cost733class package, and the weather pattern type 4 (T4), characterized by the strengthened ridge and anticyclone anomaly in northeastern China, was found as the most favorable for the occurrence of extreme summer heat in North China (NCSH). Diagnostic and wave activity flux analyses indicate that the Eurasian teleconnection (EAT) pattern from the atmosphere and the Victoria mode (VM) from the ocean are the top two dominant climate drivers of the T4 weather pattern. The empirical models constructed based on the EAT and the VM can effectively simulate the number of days of the T4 weather pattern and the NCSH, respectively. Our results suggest that, with the help of the seasonal forecast from climate models, the EAT and the VM can be used to predict the number of days of the T4 weather pattern and the NCSH for the coming summer, enabling us to protect human health and reduce its socioeconomic impacts through proactive measures in advance.

Ramp phenomena caused by abrupt changes in wind speed may confound the stable operation of correlated electrical power supply systems, yet accurate numerical predictions are challenging, as the wind is affected by complex interactions between large‐scale weather patterns and local geographical conditions. Further, optimal numerical weather prediction (NWP) methods and physics schemes vary as a function of weather patterns. The present study proposed a new real‐time wind power ramp forecast framework based on the flexible selection of optimal NWP models, which were derived via principal component analysis (PCA). The novelty of this analysis lies in that statistical methods were employed for NWP optimization, compared with their more conventional use during an NWP postprocessing. Here, a weather pattern was classified by PCA using outcomes from the global‐scale prediction models, and the optimum regional NWP system settings were acquired according to the weather patterns for further wind field dynamical downscaling. The performance of the developed prediction system was verified with wind power at wind turbine hub‐heights for three areas in eastern Japan, and the Critical Success Index (CSI) indicated an improvement of prediction accuracy over benchmark predictions by ≤0.184 for ramp‐up events and ≤0.127 for ramp‐down events (both observed in Tohoku area). Higher CSI values were consistently seen in three wind farm areas, indicative of the improvement in detection probability for actual ramp events compared with benchmark.

Accurate forecast precipitation is crucial for hydropower generation, drought and flood warning, and hydrological forecasting. However, raw forecast precipitation often suffers from systematic errors due to inaccurate initial conditions in numerical weather prediction (NWP) models. In this study, we develop a deep-learning-based post-processing method to correct forecast precipitation. Our method leverages convolutional neural networks (CNN) to analyze spatial features and long short-term memory networks (LSTM) to capture temporal dynamics, effectively modeling the local spatiotemporal characteristics (e.g., mean sea level pressure and elevation) of precipitation. Crucially, we also consider the impact of large-scale weather patterns (e.g., high-latitude blockings, the Meiyu trough) on precipitation by extracting relevant features through a CNN model and integrating this information with the local spatiotemporal data to improve forecast accuracy. Results indicate that the proposed CNN-CNN-LSTM method outperforms the three baselines (i.e., CNN-LSTM, CNN, LSTM) for all seasons and lead times (15 days) in the Huaihe River basin of China. Specifically, for the summer precipitation with a one-day lead time, the CNN-CNN-LSTM model achieves a 4.7% reduction in root mean square error and a 30.5% reduction in relative bias compared to CNN-LSTM alone. Furthermore, the relative importance of large-scale predictors is constantly increasing with the extension of lead times. By effectively integrating large-scale weather information and local-scale spatiotemporal information, the proposed CNN-CNN-LSTM method offers a novel approach to enhance the correction effect, providing significant valuable for hydrometeorological applications.

We evaluated surface and 500-hPa synoptic weather patterns, and fire weather indices from the Canadian Forest Fire Danger Rating System for 80 large wildfires during 1990–2019 in Alberta that started in May and grew to over 1000 ha. Spread days were identified during the first 4 days of wildfire activity. We observed two distinct synoptic weather patterns on these days. Pre-frontal and frontal passage activity was the predominant feature associated with 48% of the calendar spread days. Strong south–south-east winds from a surface high centred east of Alberta (west of Hudson Bay) and supported by an upper ridge, and a surface low located south-west of the ridge occurred on 26% of the calendar spread days. Surface analysis indicates the spring wildfire season in Alberta is driven by very high to extreme Initial Spread Index, a rating of the expected wildfire rate of spread based on Fine Fuel Moisture Code and wind. Very high to extreme values of Buildup Index, a rating of the amount of fuel available for consumption, are not a prerequisite for large wildfires in May. For Alberta, this means large wildfires in May can occur after only a few days of dry, windy weather.

Extreme precipitation is one of the most frequent and catastrophic extreme weather events in China. In the last few decades, the Yangtze-Huai Region (YHR) has experienced a number of regional extreme precipitation events (REPE) that cause serious damage to human society and natural environment. In order to better understand these events, this study identified the weather patterns responsible for REPE using spectral clustering method considering the multi-level and multi-scale characteristics of weather systems, and further investigated the circulation configurations, main formation conditions and water vapor process of REPE based on the daily precipitation data and reanalysis data from 1979 to 2018 in the YHR. The results showed that there were four weather patterns of REPE, namely low vortex shear weather pattern (Pattern 1), extratropical cyclone weather pattern (Pattern 2), surface cold front weather pattern (Pattern 3) and landfalling typhoon weather pattern (Pattern 4). The four patterns showed different features for the intensity, location and coverage of the western Pacific subtropical high and the South Asian high. For the four patterns, vertical upward motion extended from near the surface to 200 hPa, and three types of moist potential vorticity configurations were favorable for the occurrences of REPE over the YHR. Further analysis of water vapor processes showed that the water vapor flowed in via the west and south boundaries in Pattern 1–3 and the east boundary in Pattern 4. And transient water vapor transport played an important role during REPE in comparison to stationary components. Despite differences between these patterns, the precipitation conversion efficiency was significantly higher near the shear line and coastal areas, and the areas of high-value precipitation conversion efficiency did not correspond to the areas of high-value water vapor content, which needs further investigation in the future.

Dengue is a vector-borne disease transmitted to humans by vectors of genus Aedes causing a global threat to health, social, and economic sectors in many of the tropical countries including Sri Lanka. In Sri Lanka, the tropical climate, marked by seasonal weather primarily influenced by monsoons, fosters optimal conditions for the virus to spread efficiently. This heightened transmission results in increased per-capita vector density. In this work, we investigate the dynamic influence of environmental conditions on dengue emergence in Colombo district − the geographical region with the highest recorded dengue threat in Sri Lanka. An iterative approach is employed to dynamically estimate dengue cases leveraging the Markov chain Monte Carlo simulations, utilizing the dynamics of four seasons per year influenced by monsoon weather patterns governing in the region. The developed algorithm allows to estimate the risk of dengue outbreaks in 2017 and 2019 with high precision, facilitating accurate forecasts of upcoming disease emergence patterns for better preparedness. The uncertainty quantification not only validated the accuracy of outbreak estimates but also showcased the model’s capacity to capture extreme cases and revealed undisclosed external factors such as human mobility and environmental pollution that might affect dengue transmission in the Colombo district of Sri Lanka.

We develop a new methodology to solve the problem of identifying and selecting mesoscale weather patterns (MWPs) from high-dimensional spatio-temporal climate datasets. This problem is important and topical and has many implications for decision-makers across multiple sectors, such as urban design, urban climate, agriculture, transportation, energy, and disaster management. This problem involves selecting a small subset of data (specific days) from the original large dataset (decades long), such that it captures the essential information and characteristics of the large dataset, while minimizing redundancy. This is useful as it makes the dataset more manageable for processing, analysis, and insight gathering without degrading the overall quality of the information content. We develop a novel algorithm that is based on advanced machine learning and optimization techniques and consists of two stages: (1) spatial dimensionality reduction (SDR) to reduce the number of spatial cells analyzed while preserving as much relevant information as possible and (2) representative subset selection (RSS) to find a small subset of days in the dataset that captures the essential patterns, relationships, and information present in the full dataset—these are the mesoscale weather patterns. We demonstrate our methodology by applying it to a spatio-temporal dataset of atmospheric observations, the ERA5 dataset, in Singapore. The MWPs offer valuable insights into the region’s diverse weather conditions and help researchers, climatologists, and policymakers comprehend the complex interactions between atmospheric elements.

The global community is growing increasingly concerned about the impact of climate change, particularly the expected increase in droughts and associated depletion of water resources in the coming years. However, specific future projections using high-resolution climate simulations focusing on the frequency and intensity of hydrological droughts in Japan are currently lacking. In this study, we examined the effects of climate change on hydrological droughts in central Japan through hydrological model simulations utilizing climate projections from a high-resolution ensemble dataset downscaled to a 5-km scale. The results indicated a decrease in streamflow during summer as climate change progressed, corresponding to increased drought events. In addition, there was a considerable increase in the number of consecutive hydrological drought days, reaching an unprecedented level. Moreover, the application of self-organizing maps (SOMs) to atmospheric data allowed for the examination of the relationships between summer river discharge and climate/weather patterns under future and present climate simulations. The SOM analysis indicated that the impact of climate change on river discharge varies by climate/weather patterns. Hydrological drought events tend to be stronger in certain future patterns. In particular, future projections indicate an increase in monthly-scale hydrological droughts in climatic backgrounds characterized by southerly and easterly airflows as precipitation decreases and evapotranspiration increases. The results of this study provide valuable insights for considering adaptation strategies concerning dry-season water use in future climate scenarios.

Seasonal climate prediction in Bangladesh remains challenging due to the nonlinear nature of weather and climate interactions. This study investigates the correlation between nine El Niño–Southern Oscillation (ENSO) indices and seasonal temperature and rainfall patterns across Bangladesh, using monthly data from 29 meteorological stations (1977–2022). Six supervised machine-learning models, such as Random Forest (RF), XGBoost (XGB), Decision Tree (DT), Linear Regression (LR), K-Nearest Neighbors (KNN), and K-Fold Cross-Validation (KFCV) were evaluated using R 2 , MAE, and RMSE. XGB achieved the highest accuracy for temperature prediction (R 2  = 0.8824 for Tmax, 0.9706 for Tmin, and 0.9559 for Tavg), with RF and KFCV performing comparably. Rainfall prediction accuracy was lower, with RF achieving the highest R 2 (0.6273). Overall, the results confirm that multiple ENSO indices significantly influence Bangladesh’s seasonal climate and that advanced ML models, particularly XGB and RF, offer strong potential for improved prediction.