Search Results (231)

High-resolution numerical weather prediction requires accurate cloud microphysical initial conditions to enhance forecasting capabilities for high-impact severe weather events such as convective storms. This study integrated Fengyun-2 (FY-2) geostationary satellite data (equivalent blackbody temperature and total cloud cover) and next-generation 3D weather radar reflectivity from the China Meteorological Administration (CMA) to construct cloud microphysical initial fields and evaluate their impact on the CMA-MESO 3 km regional model. An analysis of the catastrophic rainfall event in Henan on 20 July 2021, and a 92-day continuous experiment (May–July 2024) revealed that assimilating cloud microphysical variables significantly improved precipitation forecasting: the equitable threat scores (ETSs) for 1 h forecasts of light, moderate, and heavy rain increased from 0.083, 0.043, and 0.007 to 0.41, 0.36, and 0.217, respectively, with average hourly ETS improvements of 21–71% for 2–6 h forecasts and increases in ETSs for light, moderate, and heavy rain of 7.5%, 9.8%, and 24.9% at 7–12 h, with limited improvement beyond 12 h. Furthermore, the root mean square error (RMSE) of the 2 m temperature forecasts decreased across all 1–72 h lead times, with a 4.2% reduction during the 1–9 h period, while the geopotential height RMSE reductions reached 5.8%, 3.3%, and 2.0% at 24, 48, and 72 h, respectively. Additionally, synchronized enhancements were observed in 10 m wind prediction accuracy. These findings underscore the critical role of cloud microphysical initialization in advancing mesoscale numerical weather prediction systems. Full article

This study examines a Mei-Yu rainfall event using rain gauges (RG) and OTT Parsivel disdrometers to observe precipitation characteristics and raindrop size distributions (RSD), with comparisons made against Weather Research and Forecasting (WRF) model simulations. Results show that Parsivel-derived rain rates (RR) are slightly underestimated relative to RG measurements. Both observations and simulations identify 1–3 mm raindrops as the dominant precipitation contributors, though the model overestimates small and large drop contributions. At low RR, decreased small-drop and increased large-drop concentrations cause corresponding leftward and rightward RSD shifts with decreasing altitude—a pattern well captured by simulations. However, at elevated rainfall rates, the simulated concentration of large raindrops shows no significant increase, resulting in negligible rightward shifting of RSD in the model outputs. Autoconversion from cloud droplets to raindrops (ATcr), collision and breakup between raindrops (AGrr), ice melting (MLir), and evaporation of raindrops (VDrv) contribute more to the number density of raindrops. At 0.1 < RR < 1 mm·h⁻¹, ATcr dominates, while VDrv peaks in this intensity range before decreasing. At higher intensities (RR > 20 mm·h⁻¹), AGrr contributes most, followed by MLir. When the RR is high enough, the breakup of raindrops plays a more important role than collision, leading to a decrease in the number density of raindrops. The overestimation of raindrop breakup from the numerical parameterization may be one of the reasons why the RSD does not shift significantly to the right toward the surface under the heavy RR grade. The RSD near the surface varies with the RR and characterizes surface precipitation well. Toward the surface, ATcr and VDrv, but not AGrr, become similar when precipitation approaches. Full article

To address the challenge of insufficient spatial resolution in remote sensing precipitation data, this paper proposes a novel Multi-Scale Residual Generative Adversarial Network (MSRGAN) for reconstructing high-resolution precipitation images. The model integrates multi-source meteorological information and topographic priors, and it employs a Deep Multi-Scale Perception Module (DeepInception), a Multi-Scale Feature Modulation Module (MSFM), and a Spatial-Channel Attention Network (SCAN) to achieve high-fidelity restoration of complex precipitation structures. Experiments conducted using Weather Research and Forecasting (WRF) simulation data over the continental United States demonstrate that MSRGAN outperforms traditional interpolation methods and state-of-the-art deep learning models across various metrics, including Critical Success Index (CSI), Heidke Skill Score (HSS), False Alarm Rate (FAR), and Jensen–Shannon divergence. Notably, it exhibits significant advantages in detecting heavy precipitation events. Ablation studies further validate the effectiveness of each module. The results indicate that MSRGAN not only improves the accuracy of precipitation downscaling but also preserves spatial structural consistency and physical plausibility, offering a novel technological approach for urban flood warning, weather forecasting, and regional hydrological modeling. Full article

Outbreaks of locust plagues are becoming increasingly frequent against the backdrop of climate change. Locust outbreaks in the Caucasus and Central Asia, especially in Kazakhstan, pose continuous threats to neighboring countries, including China, Kyrgyzstan, and more. However, locust outbreak forecasts and migration movement are yet to be studied in this area. In our study, we collected water level data in major lakes and water bodies, as well as annual average precipitation in the past 15 years in Kazakhstan, to analyze their contributions to locust outbreaks. Multiple linear regression analysis revealed a significant negative correlation between overall lake water level and the following year’s locust outbreak area in Kazakhstan. Considering that the overall lake water levels in 2023 and 2024 reached a quite low level historically, we predicted heavy locust outbreaks in 2025. Furthermore, through wind field analysis and wind-born trajectory modeling, we identified two migration routes of locusts from Kazakhstan into Xinjiang, China, riding the northwest wind, with lakes near the Sino-Kazakhstan border as the main sources. Overall, our study identified high locust outbreak challenges in Kazakhstan in recent years and determined two wind-supported migration routes of locusts invading China, which are significant for guiding monitoring and prevention efforts in the Sino-Kazakhstan border area. Full article

Between 14 and 16 June 2023, an extratropical cyclone affected the south-southeastern coast of Brazil, causing significant damage and loss of life. In the state of Rio Grande do Sul, Civil Defense authorities reported at least 16 fatalities. Although numerical models can simulate the general characteristics of extratropical cyclones, they often struggle to accurately represent the intensity and timing of strong winds and heavy precipitation. One approach to improving such simulations is the use of convective-permitting models (CPMs), in which convection is explicitly resolved. In this context, the main objective of this study is to assess the performance of the Weather Research and Forecasting (WRF) model in CP mode, nested in the ERA5 reanalysis, in representing both the synoptic and mesoscale structures of the cyclone, as well as its associated strong winds and precipitation. The WRF-CP successfully simulated the cyclone’s track, though with some discrepancies in the cyclone location during the first 12 h. Comparisons with radar-based precipitation estimates indicated that the WRF-CP captured the location of the observed precipitation bands. During the cyclone’s occlusion phase—when precipitation was particularly intense—hourly simulated precipitation and 10 m wind (speed, zonal, and meridional components) were evaluated against observations from meteorological stations. WRF-CP demonstrated strong skill in simulating both the timing and intensity of precipitation, with correlation coefficients exceeding 0.4 and biases below 0.5 mm h⁻¹. Some limitations were observed in the simulation of 10 m wind speed, which tended to be overestimated. However, the model performed well in simulating the wind components, particularly the zonal component, as indicated by predominantly high correlation values (most above 0.4), suggesting a good representation of wind direction, which is a function of the zonal and meridional components. Overall, the simulation highlights the potential of WRF-CP for studying extreme weather events, including the small-scale structures embedded within synoptic-scale cyclones responsible for producing adverse weather. Full article

Climate change requires efficient water resource management, especially in regions where viticulture is developing. This study evaluates the water requirements, precipitation deficits, and irrigation needs of vineyards in two locations in southern Poland. The analysis covers both a reference period (1931–2020) and a forecast period (2030–2100), based on two climate change scenarios: RCP 4.5 and RCP 8.5. Grapevine water requirements were estimated using a crop coefficient tailored to Poland’s agroclimatic conditions, combined with meteorological data on air temperature and precipitation. Monthly crop coefficient values were calculated as the ratio of grapevine potential evapotranspiration, estimated using the Penman–Monteith method, to reference evapotranspiration, calculated using the Treder approach for the period 1981–2010. Precipitation deficits were assessed for normal, medium dry, and very dry years using the Ostromęcki method. Irrigation water demand was estimated for light, medium, and heavy soils using the Pittenger method. The results indicate a significant increase in both water demand and precipitation deficits in the forecast period, regardless of the scenario. In very dry years, irrigation will be necessary for all soil types. In medium dry years, water deficits will primarily affect vineyards on light soils. These findings underscore the urgent need for improvements in irrigation planning, especially in areas with low soil water. They offer practical insights for estimating future water storage needs and implementing precision irrigation adapted to changing climate conditions. Adopting such adaptive strategies is essential for sustaining vineyard productivity and improving water use efficiency. This study also supports the integration of climate projections into regional planning and calls for investment in innovative, water-saving technologies to strengthen the long-term resilience of Poland’s wine industry. Full article

Middle East (ME) countries have arid and semi-arid climates with low annual precipitation and considerable geographical and temporal variability, which contribute to their extremely erratic rainfall. The generation of timely and accurate climatic information for the ME is anticipated to be aided by global reanalysis products and satellite-based precipitation estimations. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Climate Hazards Group Infra-Red Precipitation (CHIRPS) on Google Earth Engine (GEE) were used to study rainfall in eleven chosen ME counties from 2000 to 2023. This study shows that Saudi Arabia (509.64 mm/December–January–February; DJF), Iraq (211.50 mm/September–October–November; SON), Iran (306.35 mm/SON), Jordan (161.28 mm/DJF), Kuwait (44.66 mm), Syria (246.51 mm/DJF), UAE–Qatar–Bahrain (28.62 mm/SON), Oman (64.90 mm/June–July–August; JJA), and Yemen (240.27 mm/SON) were the countries with the highest rainfall. Due to improved ground station integration, CHIRPS also reports larger rainfall anomalies, with a peak of 59.15 mm in DJF, mainly in northern Iran, Iraq, and Syria. PERSIANN understates heavy rainfall, probably because it relies on infrared satellite data, with a maximum anomaly of 4.15 mm. Saudi Arabia saw heavy rain during the JJA months, while others received less. More accurate rainfall forecasts in the ME can lessen the effects of floods and droughts, promoting environmental resilience and regional economic stability. Therefore, a more comprehensive understanding of all the relevant components is necessary to address these difficulties. Both environmental and human impacts must be taken into account for sustainable solutions. Full article

Heavy rainfall events are the main cause of flooding, especially in regions like Kerala, India. Kerala is vulnerable to extreme weather due to its geographical location in the Western Ghats. Accurate forecasting of rainfall events is essential for minimizing the impact of floods on life, infrastructure, and agriculture. For accurate forecasting of heavy rainfall events in this region, region-specific evaluations of NWP model performance are very important. This study evaluated the performance of six Numerical Weather Prediction (NWP) models—NCEP, NCMRWF, ECMWF, CMA, UKMO, and JMA—in forecasting heavy rainfall events in Kerala. A comprehensive assessment of these models was performed using traditional performance metrics, categorical precipitation metrics, and Fractional Skill Scores (FSSs) across different forecast lead times. FSSs were calculated for different rainfall thresholds (100 mm, 50 mm, 5 mm). The results reveal that all models captured rainfall patterns well for the lower threshold of 5 mm, but most of the models struggled to accurately forecast heavy rainfall, especially for longer lead times. JMA performed well overall in most of the metrics except False Alarm Ratio (FAR). It showed high FAR, which revealed that it may predict false rainfall events. ECMWF demonstrated consistent performance. NCEP and UKMO performed moderately well. CMA, and NCMRWF had the lowest accuracy either due to more errors or biases. The findings underscore the trade-offs in model performance, suggesting that model selection should depend on the accuracy required or rainfall event prediction capability. This study recommends the use of Multi-Model Ensembles (MME) to improve forecasting accuracy, integrate the strengths of the best-performing models, and reduce biases. Future research can also focus on expanding observational networks and employing advanced data assimilation techniques for more reliable predictions, particularly in regions with complex terrain such as Kerala. Full article

Nowcasting is a critical technology for disaster prevention and mitigation, and the accuracy of radar echo extrapolation directly impacts forecasting performance. In most deep learning-based models, accurately predicting heavy precipitation remains a challenging task. Focusing on the region of China, this study proposes an improved model based on residual and attention mechanisms—RA-UNet—for precipitation nowcasting with a lead time of 3 h. The model introduces the residual neural network (ResNet) and the convolutional block attention module (CBAM) to integrate multi-scale features into the U-Net encoder–decoder architecture, enhancing its ability to capture the spatiotemporal evolution of precipitation systems. Meanwhile, depthwise separable convolutions are employed to replace conventional convolutions, significantly improving computational efficiency while preserving model performance. To evaluate the model’s performance, experiments were conducted using 6 min resolution radar echo data from China in 2024, with comparisons made against the optical flow (OF) method and the U-Net model. The experimental results show that RA-UNet demonstrates significant advantages in 3 h forecasting: its mean absolute error (MAE) is reduced by approximately 7%, the false alarm rate (FAR) decreases by about 20%, and it outperforms the comparison models in metrics such as the critical success index (CSI) and structural similarity index (SSIM). Notably, RA-UNet effectively mitigates intensity degradation in long-term forecasts, successfully predicting the trend of >40 dBZ strong echo cores in two typical cases and significantly improving the premature dissipation problem of precipitation fields. This study provides a new approach to refined forecasting of complex precipitation systems, and future work will combine multi-source data fusion with physical constraint mechanisms to further enhance precipitation event prediction capabilities. Full article

In the Himalayan regions of complex terrains, such as Himachal Pradesh, the occurrence of extreme rainfall events (EREs) has been increasing, triggering landslides and flash floods. Investigating the dynamics and precipitation characteristics and improving the prediction of such events are crucial and could play a vital role in contributing to sustainable development in the region. This study employs a high-resolution numerical weather prediction framework, the weather research and forecasting (WRF) model, to deeply investigate an ERE which occurred between 8 July and 13 July 2023. This ERE caused catastrophic floods in the Mandi and Kullu districts of Himachal Pradesh. The WRF model was configured with nested domains of 12 km and 4 km horizontal grid resolutions, and the results were compared with global high-resolution precipitation products and the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis dataset. The selected case study was amplified by the synoptic scale features associated with the position and intensity of the monsoon trough, including mesoscale processes like orographic lifting. The presence of a western disturbance and the heavy moisture transported from the Arabian Sea and the Bay of Bengal both intensified this event. The model has effectively captured the spatial distribution and large-scale dynamics of the phenomenon, demonstrating the importance of high-resolution numerical modeling in accurately simulating localized EREs. Statistical evaluation revealed that the WRF model overestimated extreme rainfall intensity, with the root mean square error reaching 17.33 mm, particularly during the convective peak phase. The findings shed light on the value of high-resolution modeling in capturing localized EREs and offer suggestions for enhancing disaster management and flood forecasting. Full article

This study investigates the impacts of climate change on precipitation and snowpack in the interior western United States (IWUS) using two sets of convection-permitting Weather Research and Forecasting model simulations. One simulation represents the ~1990 climate, and another represents an ~2050 climate using a pseudo-global warming approach. Climate perturbations for the future climate are given by the CMIP5 ensemble-mean global climate models under the high-end emission scenario. The study analyzes the projected changes in spatial patterns of seasonal precipitation and snowpack, with particular emphasis on the effects of elevation on orographic precipitation and snowpack changes in four key mountain ranges: the Montana Rockies, Greater Yellowstone area, Wasatch Range, and Colorado Rockies. The IWUS simulations reveal an increase in annual precipitation across the majority of the IWUS in this warmer climate, driven by more frequent heavy to extreme precipitation events. Winter precipitation is projected to increase across the domain, while summer precipitation is expected to decrease, particularly in the High Plains. Snow-to-precipitation ratios and snow water equivalent are expected to decrease, especially at lower elevations, while snowpack melt is projected to occur earlier by up to 26 days in the ~2050 climate, highlighting significant impacts on regional water resources and hydrological management. Full article

Business Finland 6G Visible project’s objective is the development of 6G-era service and architecture solutions utilizing autonomous and semi-autonomous driving, with both physical and logical computational elements and with use cases for real-life verification and validation. Finnish Meteorological Institute is focusing especially on weather- and safety-related services for autonomous vehicles. We are tailoring our road weather services for the special needs of autonomous driving, keeping in mind that autonomous vehicles are more sensitive to the harsh winter weather conditions and benefit from more accurate weather information considering the sensor systems of each vehicle. Employing weather radar-based nowcasting of more accurate short-term precipitation forecasting benefits autonomous traffic, especially in cases of heavy local precipitation by re-routing/route planning and avoiding heaviest precipitation. Evaluation of autonomous vehicles’ sensor systems’ sensitivity to harsh weather conditions allows for weather forecasting based on the real vulnerability of each vehicle. Full article

In this research, we introduce a novel method leveraging the Transformer architecture to generate high-fidelity precipitation model outputs. This technique emulates the statistical characteristics of high-resolution datasets while substantially lowering computational expenses. The core concept involves utilizing a blend of coarse and fine-grained simulated precipitation data, encompassing diverse spatial resolutions and geospatial distributions, to instruct Transformer in the transformation process. We have crafted an innovative ST-Transformer encoder component that dynamically concentrates on various regions, allocating heightened focus to critical spatial zones or sectors. The module is capable of studying dependencies between different locations in the input sequence and modeling at different scales, which allows it to fully capture spatiotemporal correlations in meteorological element data, which is also not available in other downscaling methods. This tailored module is instrumental in enhancing the model’s ability to generate outcomes that are not only more realistic but also more consistent with physical laws. It adeptly mirrors the temporal and spatial distribution in precipitation data and adeptly represents extreme weather events, such as heavy and enduring storms. The efficacy and superiority of our proposed approach are substantiated through a comparative analysis with several cutting-edge forecasting techniques. This evaluation is conducted on two distinct datasets, each derived from simulations run by regional climate models over a period of 4 months. The datasets vary in their spatial resolutions, with one featuring a 50 km resolution and the other a 12 km resolution, both sourced from the Weather Research and Forecasting (WRF) Model. Full article

Due to model errors caused by local variations in cloud precipitation processes, there are still significant uncertainties in current predictions and simulations of short-duration heavy rainfall. To tackle this problem, the effects of warming on cloud-precipitation efficiency was analyzed utilizing a weather research and forecasting (WRF) model. The analysis focused on a rainstorm corridor event that took place in July 2020. Rainstorm events from 4–6 July formed a narrow rain belt with precipitation exceeded 300 mm in the middle and lower reaches of the Yangtze River. Temperature sensitivity tests revealed that warming intensified the potential temperature gradient between north and south, leading to stronger upward motion on the front. It also strengthened the southwest wind, which resulted in more pronounced precipitation peaks. Warming led to a stronger accumulation and release of convective instability energy. Convective available potential energy (CAPE) and convective inhibition (CIN) both increased correspondingly with the temperature. The precipitation efficiency increased sequentially with 2 °C warming to 27.4%, 31.2%, and 33.1%. Warming can affect the cloud precipitation efficiency by both promoting and suppressing convective activity, which may be one of the reasons for the enhancement of extreme precipitation under global warming. The diagnostic relationship between upward moisture flux and lower atmospheric stability during precipitation evolution was also revealed. Full article

The spatial and temporal distribution of heavy rainfall across the Taihang Mountains exhibits significant variation. Due to the region’s unstable geological conditions, frequent heavy rainfall events can lead to secondary disasters such as landslides, debris flows, and floods, thus intensifying both the frequency and severity of extreme events. Understanding the spatiotemporal evolution of heavy rainfall and its response to atmospheric circulation patterns is crucial for effective disaster prevention and mitigation. This study utilized daily precipitation data from 13 meteorological stations in the Taihang Mountains spanning from 1973 to 2022, employing Rotated Empirical Orthogonal Function (REOF), the Mann–Kendall Trend Test, and Continuous Wavelet Transform (CWT) to examine the spatiotemporal characteristics of heavy rainfall and its relationship with large-scale atmospheric circulation patterns. The results reveal that: (1) Heavy rainfall in the Taihang Mountains can be categorized into six distinct regions, each demonstrating significant spatial heterogeneity. Region I, situated in the transition zone between the plains and mountains, experiences increased rainfall due to orographic lifting, while Region IV, located in the southeast, receives the highest rainfall, driven primarily by monsoon lifting. Conversely, Regions III and VI receive comparatively less precipitation, with Region VI, located in the northern hilly area, experiencing the lowest rainfall. (2) Over the past 50 years, all regions have experienced an upward trend in heavy rainfall, with Region II showing a notable increase at a rate of 14.4 mm per decade, a trend closely linked to the intensification of the hydrological cycle driven by global warming. (3) The CWT results reveal significant 2–3-year periodic fluctuations in rainfall across all regions, aligning with the quasi-biennial oscillation (QBO) characteristic of the East Asian summer monsoon, offering valuable insights for future climate predictions. (4) Correlation and wavelet coherence analyses indicate that rainfall in Regions II, III, and IV is positively correlated with the Southern Oscillation Index (SOI) and the Pacific Warm Pool (PWP), while showing a negative correlation with the Pacific Decadal Oscillation (PDO). Rainfall in Region I is negatively correlated with the Indian Ocean Dipole (IOD). These climatic factors exhibit a lag effect on rainfall patterns. Incorporating these climatic factors into future rainfall prediction models is expected to enhance forecast accuracy. This study integrates REOF analysis with large-scale circulation patterns to uncover the complex spatiotemporal relationships between heavy rainfall and climatic drivers, offering new insights into improving heavy rainfall event forecasting in the Taihang Mountains. The complex topography of the Taihang Mountains, combined with unstable geological conditions, leads to uneven spatial distribution of heavy rainfall, which can easily trigger secondary disasters such as landslides, debris flows, and floods. This, in turn, further increases the frequency and severity of extreme events. Full article