Search Results (21)

Accurate simulation of extreme precipitation events is crucial for managing climate-vulnerable sectors in Southern Africa, as such events directly impact agriculture, water resources, and disaster preparedness. However, global climate models frequently struggle to capture these phenomena, which limits their practical applicability. This study investigates the effectiveness of three bias correction techniques—scaled distribution mapping (SDM), quantile distribution mapping (QDM), and QDM with a focus on precipitation above and below the 95th percentile (QDM95)—and the daily precipitation outputs from 11 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset was served as a reference. The bias-corrected and native models were evaluated against three observational datasets—the CHIRPS, Multi-Source Weighted Ensemble Precipitation (MSWEP), and Global Precipitation Climatology Center (GPCC) datasets—for the period of 1982–2014, focusing on the December-January-February season. The ability of the models to generate eight extreme precipitation indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI) was evaluated. The results show that the native and bias-corrected models captured similar spatial patterns of extreme precipitation, but there were significant changes in the amount of extreme precipitation episodes. While bias correction generally improved the spatial representation of extreme precipitation, its effectiveness varied depending on the reference dataset used, particularly for the maximum one-day precipitation (Rx1day), consecutive wet days (CWD), consecutive dry days (CDD), extremely wet days (R95p), and simple daily intensity index (SDII). In contrast, the total rain days (RR1), heavy precipitation days (R10mm), and extremely heavy precipitation days (R20mm) showed consistent improvement across all observations. All three bias correction techniques enhanced the accuracy of the models across all extreme indices, as demonstrated by higher pattern correlation coefficients, improved Taylor skill scores (TSSs), reduced root mean square errors, and fewer biases. The ranking of models using the comprehensive rating index (CRI) indicates that no single model consistently outperformed the others across all bias-corrected techniques relative to the CHIRPS, GPCC, and MSWEP datasets. Among the three bias correction methods, SDM and QDM95 outperformed QDM for a variety of criteria. Among the bias-corrected strategies, the best-performing models were EC-Earth3-Veg, EC-Earth3, MRI-ESM2, and the multi-model ensemble (MME). These findings demonstrate the efficiency of bias correction in improving the modeling of precipitation extremes in Southern Africa, ultimately boosting climate impact assessments. Full article

Precipitation nowcasting is pivotal in monitoring extreme weather events and issuing early warnings for meteorological disasters. However, the inherent complexity of precipitation systems, coupled with their nonlinear spatiotemporal evolution, poses significant challenges for traditional numerical weather prediction methods in capturing multi-scale details effectively. Existing deep learning models similarly struggle to simultaneously capture local multi-scale features and global long-term spatiotemporal dependencies. To tackle this challenge, we propose SwinNowcast, a deep learning model based on the Swin Transformer architecture. Through the novel design of a multi-scale feature balancing module (M-FBM), the model dynamically integrates local-scale features with global spatiotemporal dependencies. Specifically, the multi-scale convolutional block attention module (MSCBAM) captures local multi-scale features, while the gated attention feature fusion unit (GAFFU) adaptively regulates the fusion intensity, thereby enhancing spatial structure and temporal continuity in a synergistic manner. Experiments were performed on the precipitation dataset from the Royal Netherlands Meteorological Institute (KNMI) under thresholds of 0.5 mm, 5 mm, and 10 mm. The results indicate that SwinNowcast surpasses six state-of-the-art approaches regarding the critical success index (CSI) and the Heidke skill score (HSS), while markedly reducing the false alarm rate (FAR). The proposed model holds substantial practical value in applications such as short-term heavy rainfall monitoring and urban flood early warning, offering effective technological support for meteorological disaster mitigation. 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

This research evaluates the quality of high-impact rainfall forecasts across Vietnam and its sub-climate regions. The 3-day rainfall forecast products evaluated include the European Centre for Medium-Range Weather Forecasts (ECMWF) High-Resolution Integrated Forecasting System (IFS) and its downscaled outputs using the Weather Research and Forecasting (WRF) model with the Advanced Research WRF core (WRF-ARW): direct downscaling and downscaling with data assimilation. A full 5-year validation period from 2019 to 2025 was processed. The validation focused on basic rainfall thresholds and also considered the distribution of skill scores for intense events and extreme events. The validations revealed systematic errors (bias) in the models at low rainfall thresholds. The forecast skill was the lowest for northern regions, while the central regions exhibited the highest. For regions strongly affected by terrain, high-resolution downscaling with local observation data assimilation is necessary to improve the detectability of extreme events. Full article

Accurate wind speed and direction data are vital for coastal engineering, renewable energy, and climate resilience, particularly in regions with sparse observational datasets. This study evaluates the ERA5 reanalysis model’s performance in predicting wind speeds and directions at ten coastal and offshore stations in Kuwait from 2010 to 2017. This analysis reveals that ERA5 effectively captures general wind speed patterns, with offshore stations demonstrating stronger correlations (up to 0.85) and higher Perkins Skill Score (PSS) values (up to 0.94). However, the model consistently underestimates wind variability and extreme wind events, especially at coastal stations, where correlation coefficients dropped to 0.35. Wind direction analysis highlighted ERA5’s ability to replicate dominant northwest wind patterns. However, it reveals notable biases and underrepresented variability during transitional seasons. Taylor diagrams and error metrics further emphasize ERA5’s challenges in capturing localized dynamics influenced by land-sea interactions. Enhancements such as localized calibration using high-resolution datasets, hybrid models incorporating machine learning techniques, and long-term monitoring networks are recommended to improve accuracy. By addressing these limitations, ERA5 can more effectively support engineering applications, including coastal infrastructure design and renewable energy development, while advancing Kuwait’s sustainable development goals. This study provides valuable insights into refining reanalysis model performance in complex coastal environments. Full article

Urban environments experience amplified thermal stress due to the climate change, leading to increased health risks during extreme temperature events. Existing numerical weather prediction systems often lack the spatial resolution required to capture this phenomenon. This study assesses the efficacy of a coupled modeling system, the numerical weather prediction AROME model and the land-surface model SURFace EXternalisée in a stand alone mode (SURFEX-SA), in forecasting air temperatures at high resolutions ($2.5$km to $100$m) across four Austrian cities (Vienna, Linz, Klagenfurt and Innsbruck). The system is updated with the, according to the author’s knowledge, most accurate land use and land cover input to evaluate the added value of incorporating detailed urban environmental representations. The analysis focuses on the years 2019, 2023, and 2024, examining both summer and winter seasons. SURFEX-SA demonstrates improved performance in specific scenarios, particularly during nighttime in rural and suburban areas during the warmer season. By comprehensively analyzing this prediction system with operational and citizen weather stations in a deterministic and probabilistic mode across several time periods and various skill scores, the findings of this study will enable readers to determine whether high-resolution forecasts are necessary in specific use cases. Full article

This study advances the precision and efficiency of precipitation nowcasting, particularly under extreme weather conditions. Traditional forecasting methods struggle with precision, spatial feature generalization, and recognizing long-range spatial correlations, challenges that intensify during extreme weather events. The Enhanced Temporal Correlation Jump Prediction Network (ETCJ-PredNet) introduces a novel attention mechanism that optimally leverages spatiotemporal data correlations. This model scrutinizes and encodes information from previous frames, enhancing predictions of high-intensity radar echoes. Additionally, ETCJ-PredNet addresses the issue of gradient vanishing through an innovative jump connection strategy. Comparative experiments on the Moving Modified National Institute of Standards and Technology (Moving-MNIST) and Hong Kong Observatory Dataset Number 7 (HKO-7) validate that ETCJ-PredNet outperforms existing models, particularly under extreme precipitation conditions. Detailed evaluations using Critical Success Index (CSI), Heidke Skill Score (HSS), Probability of Detection (POD), and False Alarm Ratio (FAR) across various rainfall intensities further underscore its superior predictive capabilities, especially as rainfall intensity exceeds 30 dbz,40 dbz, and 50 dbz. These results confirm ETCJ-PredNet’s robustness and utility in real-time extreme weather forecasting. Full article

Variational data assimilation theoretically assumes Gaussian-distributed observational errors, yet actual data often deviate from this assumption. Traditional quality control methods have limitations when dealing with nonlinear and non-Gaussian-distributed data. To address this issue, our study innovatively applies two advanced machine learning (ML)-based quality control (QC) methods, Minimum Covariance Determinant (MCD) and Isolation Forest, to process precipitable water (PW) data derived from satellite FengYun-2E (FY2E). We assimilated the ML QC-processed TPW data using the Gridpoint Statistical Interpolation (GSI) system and evaluated its impact on heavy precipitation forecasts with the Weather Research and Forecasting (WRF) v4.2 model. Both methods notably enhanced data quality, leading to more Gaussian-like distributions and marked improvements in the model’s simulation of precipitation intensity, spatial distribution, and large-scale circulation structures. During key precipitation phases, the Fraction Skill Score (FSS) for moderate to heavy rainfall generally increased to above 0.4. Quantitative analysis showed that both methods substantially reduced Root Mean Square Error (RMSE) and bias in precipitation forecasting, with the MCD method achieving RMSE reductions of up to 58% in early forecast hours. Notably, the MCD method improved forecasts of heavy and extremely heavy rainfall, whereas the Isolation Forest method demonstrated a superior performance in predicting moderate to heavy rainfall intensities. This research not only provides a basis for method selection in forecasting various precipitation intensities but also offers an innovative solution for enhancing the accuracy of extreme weather event predictions. Full article

This work assesses the ability of nine Coupled Model Intercomparison Project phase 6 (CMIP6) High-Resolution Model Intercomparison Project (HighResMIP) models and their ensemble mean to reproduce precipitation extremes over East Africa for the period 1995–2014. The model datasets are assessed against two observation datasets: CHIRPS and GPCC. The precipitation indices considered are CDD, CWD, R1mm, R10mm, R20mm, SDII, R95p, PRCPTOT, and Rx1day. The overall results show that HighResMIP models reproduce annual variability fairly well; however, certain consistent biases are found across HighResMIP models, which tend to overestimate CWD and R1mm and underestimate CDD and SDII. The HighResMIP models are ranked using the Taylor diagram and Taylor Skill Score. The results show that the models reasonably simulate indices, such as PRCPTOT, R1mm, R10mm, R95p, and CDD; however, the simulation of SDII CWD, SDII, and R20mm is generally poor. They are CMCC-CM2-VHR4, HadGEM31-MM, HadGEM3-GC31-HM, and GFDL-CM4. Conversely, MPI-ESM1-2-XR and MPI-ESM1-2-HR show remarkable performance in simulating the OND season while underestimating the MAM season. A comparative analysis demonstrates that the MME has better accuracy than the individual models in the simulation of the various indices. The findings of the present study are important to establish the ability of HighResMIP data to reproduce extreme precipitation events over East Africa and, thus, help in decision making. However, caution should be exercised in the interpretation of the findings based on individual CMIP6 models over East Africa given the overall weakness observed in reproducing mean precipitation. Full article

Solar flares are characterized by sudden bursts of electromagnetic radiation from the Sun’s surface, and are caused by the changes in magnetic field states in active solar regions. Earth and its surrounding space environment can suffer from various negative impacts caused by solar flares, ranging from electronic communication disruption to radiation exposure-based health risks to astronauts. In this paper, we address the solar flare prediction problem from magnetic field parameter-based multivariate time series (MVTS) data using multiple state-of-the-art machine learning classifiers that include MINImally RandOm Convolutional KErnel Transform (MiniRocket), Support Vector Machine (SVM), Canonical Interval Forest (CIF), Multiple Representations Sequence Learner (Mr-SEQL), and a Long Short-Term Memory (LSTM)-based deep learning model. Our experiment is conducted on the Space Weather Analytics for Solar Flares (SWAN-SF) benchmark data set, which is a partitioned collection of MVTS data of active region magnetic field parameters spanning over nine years of operation of the Solar Dynamics Observatory (SDO). The MVTS instances of the SWAN-SF dataset are labeled by GOES X-ray flux-based flare class labels, and attributed to extreme class imbalance because of the rarity of the major flaring events (e.g., X and M). As a performance validation metric in this class-imbalanced dataset, we used the True Skill Statistic ($TSS$) score. Finally, we demonstrate the advantages of the MVTS learning algorithm MiniRocket, which outperformed the aforementioned classifiers without the need for essential data preprocessing steps such as normalization, statistical summarization, and class imbalance handling heuristics. Full article

Fire danger poses a pressing threat to ecosystems and societies worldwide. Adequate preparation and forewarning can help reduce these threats, but these rely on accurate prediction of extreme fire danger. With the knowledge that climatic conditions contribute heavily to overall fire danger, this study evaluates the skill with which episodes of extreme fire danger in Australia can be predicted from the activity of large-scale climate driver patterns. An extremal dependence index for extreme events is used to depict the historical predictive skill of the Australian Bureau of Meteorology’s subseasonal climate prediction system in replicating known relationships between the probability of top-decile fire danger and climate driver states at a lead time of 2–3 weeks. Results demonstrate that the El Niño Southern Oscillation, Southern Annular Mode, persistent modes of atmospheric blocking, Indian Ocean Dipole and Madden-Julian Oscillation are all key for contributing to predictability of fire danger forecasts in different regions during critical fire danger periods. Northwest Australia is found to be particularly predictable, with the highest mean index differences (>0.50) when certain climate drivers are active, compared with the climatological index mean. This integrated approach offers a valuable resource for decision-making in fire-prone regions, providing greater confidence to users relying on fire danger outlooks for key management decisions, such as those involved in the sectors of national park and forest estate management, agriculture, emergency services, health and energy. Furthermore, the results highlight strengths and weaknesses in both the Australian Fire Danger Rating System and the operational climate model, contributing additional information for improving and refining future iterations of these systems. Full article

The probabilistic prediction skill of the weekly forecasts of extreme cold events (ECE) is illustrated and measured in the form of the Brier Skill Score (BSS) and the area under Relative Operating Characteristics (ROC) curves based on the subseasonal-to-seasonal (S2S) prediction project database. The ROC scores show that six S2S models have the good potential predictability skill required for use in ECE probabilistic forecasts, and they were more useful than climatologic probabilistic models in creating forecasts of about 3–4 weeks in length. However, the BSS results show that the actual prediction skill of six models used in ECE probabilistic forecasts are different. The ECMWF model has a good performance, and its actual probabilistic prediction skill of ECE for forecasts of about 3–4 weeks in length was higher than those of climatology, which operates close to its potential predictability. The actual probabilistic prediction skill of the NCEP model for ECE was only about 2 weeks over the extra-tropics, and no skill was recorded over the tropics given its bad reliability, especially over the tropics. BoM, JMA, and CNRM models only have a 1-week actual prediction skill over the Northern Hemisphere extra-tropics, and they have no skill over the rest of the world’s land area. The CNR-ISAC model has a 1-week actual prediction skill over the extra-tropics and about 4 weeks over the tropics. There is still much room for improvement in the prediction ability of models used for ECE. MJO in tropical regions has an important influence on the probabilistic prediction skill of ECE required at middle and high latitudes. When there is an MJO in the initial conditions, the potential predictability and actual prediction skill of ECE probabilistic forecasts over North America in the 3rd week and over Europe in the 3rd–4th weeks are higher than those without MJO. Full article

Accurate and continuous rainfall monitoring is essential for effective water resources management, especially in arid and semi-arid regions such as the United Arab Emirates (UAE). Significant spatio-temporal precipitation variation in the UAE necessitates the use of the latest techniques to measure rainfall intensity accurately. This study investigates the consistency and applicability of four satellite precipitation products, namely PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now, over the UAE. Daily time series data from 2011 to 2020 were analyzed using various statistical measures and climate indices to develop the belief in the products and for their inter-comparison. The analysis revealed that the average probability of detection (POD) for PDIR and CDR was the highest, with values ranging from 0.7–0.9 and 0.6–0.9, respectively. Similarly, CDR has a better Heidke Skill Score (HSS) with an average value of 0.26. CDR outperformed its counterparts with an average correlation coefficient value of 0.70 vs. 0.65, 0.40, and 0.34 for PDIR, CCS, and PERSIANN, respectively. Precipitation indices analysis revealed that all the products overestimated the number of consecutive wet days by 15–20%, while underestimating consecutive dry days by 5–10%. The quantitative estimations indicate that all the products were matching with the gauge values during the wet months (January–April), while they showed significant overestimation during the dry months. CDR and PDIR were in close agreement with the gauge data in terms of maximum daily rainfall with an error of less than 10% for both products. As compared to others, PERSIANN-CDR provided better estimates, particularly in terms of capturing extreme rainfall events and spatial distribution of rainfall. This study provides the first comprehensive evaluation of four PERSIANN family products based on recent daily rainfall data of UAE. The findings can provide future insights into the applicability and improvement of PERSIANN products in arid and semi-arid regions. Full article

Lake Tana, the largest inland water body in Ethiopia, has witnessed significant changes due to ongoing urbanization and socioeconomic activities in recent times. In this study, the two-decade recordings of moderate resolution imaging spectroradiometer (MODIS) were used to derive Forel–Ule index (FUI). The FUI, which ranges from 1 (dark-blue pristine water) to 21 (yellowish-brown polluted water), is important to fully understand the quality and trophic state of the lake in the last two decades. The analysis of FUI over a period of 22 years (2000–2021) indicates that Lake Tana is in a eutrophic state as confirmed by FUI values ranging from 11 to 17. This is in agreement with the trophic state index (TSI) estimated from MERIS diversity-II chlorophyll a (Chl$\_a$) measurements for the overlapping 2003-2011 period. The categorical skill scores show that FUI-based lake water trophic state classification relative to MERIS-based TSI has a high performance. FUI has a positive correlation with TSI, (Chl$\_a$), turbidity, and total suspended matter (TSM) and negative relations with Chl$\_a$ and TSM (at the lake shoreline) and colored dissolved organic matter. The annual, interannual and seasonal spatial distribution of FUI over the lake show a marked variation. The hydro-meteorological, land-use–land-cover (LULC) related processes are found to modulate the spatiotemporal variability of water quality within the range of lower and upper extremes of the eutrophic state as revealed from the FUI composite analysis. The FUI composites were obtained for the terciles and extreme percentiles of variables representing hydro-meteorological and LULC processes. High FUI composite (poor water quality) is associated with above-normal and extremely high (85 percentile) lake bottom layer temperature, wind speed, precipitation, surface runoff, and hydrometeorological drought as captured by high negative standardized precipitation-evapotranspiration index (SPEI). In contrast, a high FUI composite is observed during below-normal and extremely low (15 percentile) lake skin temperature and evaporation. Conversely good water quality (i.e., low FUI) was observed during times of below-normal and above-normal values of the above two sets of drivers respectively. Moreover, FUI varies in response to seasonal NDVI/EVI variabilities. The relationship between water quality and its drivers is consistent with the expected physical processes under different ranges of the drivers. High wind speed, for instance, displaces algae blooms to the shoreline whereas intense precipitation and increased runoff lead to high sediment loads. Increasing lake skin temperature increases evaporation, thereby decreasing water volume and increasing insoluble nutrients, while the increasing lake bottom layer temperature increases microbial activity, thereby enhancing the phosphorus load. Moreover, during drought events, the low inflow and high temperature allow algal bloom, Chl$\_a$, and suspended particles to increase, whereas high vegetation leads to an increase in the non-point sources of total phosphorus and nitrogen. Full article

In order to focus on pentad-scale precipitation forecasts, we investigated the coupling relationship between 500 hPa geopotential height (Z500) anomalies and precipitation anomalies using the China Meteorological Administration Global Land Surface ReAnalysis Interim (CRA40/Land) gridded precipitation dataset from 1999 to 2018 and the National Centers for Environmental Prediction 1 reanalysis dataset for Z500. We obtained a dynamical–statistical downscaling model (DSDM) on the pentad scale and used the daily Z500 forecast product for sub-seasonal to seasonal forecasts (15–60 days) of the FGOALS-f2 model as the predictor. Our results showed that pentad-scale prediction of precipitation is the key to bridging the current deficiencies in sub-seasonal forecasts. Compared with the FGOALS-f2 model, the pentad DSDM had a higher skill for prediction of precipitation in China at lead times longer than four pentads throughout the year and of two pentads in the summer months. FGOALS-f2 had excellent precipitation predictability at lead times less than three pentads (15 days), so the proposed pentad DSDM could not perform better than FGOALS-f2 in this period. However, at lead times greater than four pentads, the precipitation prediction scores (such as the anomaly correlation coefficient (ACC), the temporal correlation coefficient (TCC) and the mean square skill score (MSSS)) of the pentad DSDM for the whole of China were higher than those of the FGOALS-f2 model. With the rate of increase ranging from 76% to 520%, the mean ACC scores of pentad DSDM were basically greater than 0.04 after a lead time of five pentads, whereas those of the FGOALS-f2 were less than 0.04. An analysis of the Zhengzhou “720” super heavy rainstorm event showed that the pentad DSDM also had better predictability for the distribution of precipitation at lead times of three pentads than the FGOALS-f2 model for the extreme precipitation event. Full article