Meteorological Applications最新文献

Drought, a significant natural hazard, continues to pose considerable threats to agriculture, particularly in arid and semi-arid regions. Timely and accurate monitoring of drought conditions is essential for effective mitigation and adaptation strategies. This study evaluates the efficacy of three remote-sensing-based drought indices: VCI, TCI, and VHI in detecting and monitoring agricultural drought in the Saurashtra region of Gujarat. The research employs MODIS (moderate resolution imaging spectroradiometer)-derived NDVI (normalized difference vegetation index), and LST (land surface temperature) data to compute the indices. To validate these remotely sensed indices, their values were correlated with the standardized precipitation index (SPI) calculated for 3-, 6-, and 12-month reference periods using precipitation data from the India Meteorological Department (IMD). Furthermore, the spatial distributions and index values were compared between 2002, identified as a drought year by IMD, and 2023, considered a normal reference year. The results indicate that VHI shows the strongest correlation with SPI-6 (r = 0.67), followed by SPI-3 (r = 0.49) and SPI-12 (r = 0.40). This finding aligns with the Standardized Precipitation Index User Guide (WMO-No. 1090, World Meteorological Organization), which recommends using SPI-6 for agricultural drought assessment. Both VCI and TCI exhibit a moderate correlation with SPI-6 (r = 0.62 and 0.56, respectively) but weaker correlations with SPI-12 (r = 0.39 and 0.37). The spatial comparison of VCI, TCI, and VHI between 2002 and 2023 demonstrates that VHI effectively captures the intensity and extent of drought, as it integrates vegetation and thermal stress. Overall, the study highlights the potential of VHI as a reliable, remote-sensing-based drought indicator that provides timely information on drought severity and spatial extent, particularly in arid and semi-arid regions. Integrating VHI with soil-moisture data could yield an even more robust composite drought index for policymakers and agricultural stakeholders to support strategies that mitigate the adverse impacts of drought on crop production and livelihoods.

The Indian subcontinent shows significant spatial and temporal variability of precipitation. A small change in precipitation frequency and its distribution may affect agriculture and water resources and can lead to extreme events such as floods and droughts. In the present study changing precipitation characteristics over different meteorological Indian sub-regions are presented. Indian Meteorological Department (IMD) gridded precipitation and ECMWF Reanalysis 5th Generation (ERA5) reanalysis data during 1970–2020 are considered. Furthermore, the Theil–Sen slope test and Pettit's test are used for calculating the magnitude of trend and change point respectively for the number of precipitating days and associated precipitation over India and its sub-regions. Early arrival of the wettest day (day with maximum precipitation) is observed over northeast India and northern central northeast India, while the increase in the duration of the rainy season over northwest India is observed. Extension of higher precipitation to July–August–September–October is distinct over India except for the central northeast. Change point detection shows these changes occurred mostly after 1996. The decreasing precipitation trend across northeast and central northeast, while the increasing trend over northwest India reflects a westward strengthening of the monsoon precipitation. Additionally, greater moisture transport from the Arabian Sea and Bay of Bengal is detected in the recent period (1997–2020), which may be the reason for higher precipitation over northwest India. Overall, the results will aid in understanding how climate change affects the Indian summer monsoon, which will support policy making and adapting water management techniques.

The emergence of high-resolution numerical weather prediction (NWP) systems over recent decades has brought new verification challenges, namely accounting for the “double penalty” effect. While spatial verification methods have been developed to mitigate this issue, they generally provide domain-wide performance assessments, potentially obscuring spatial heterogeneity in the NWP performances. This study introduces a novel methodology for evaluating the NWP performances at the local scale within a neighborhood-based framework. Local contingency tables are constructed for each cell of the grid, populated with events occurring within a defined neighborhood window, allowing for the compensation of spatial location errors. These local contingency tables are then temporally aggregated across a set of forecasts to produce a temporal local contingency table at each grid point, thereby enabling localized performance assessment. The methodology was applied to a large region centered in Southern Quebec using forecasts from six NWP systems (GDPS, RDPS, HRDPS, GFS, NAM, and RAP) over a 2-year period (2022–2023). Analyses were conducted across four precipitation intensity thresholds (0.1, 5, 10, and 25 mm/6 h) and three forecast lead-time categories (Days 1–2, 3–4, and 5–7 combined, depending on data availability). Four metrics were employed in the evaluation: Bias, false alarm ratio (FAR), probability of detection (POD), and equitable threat score (ETS). The performance is primarily governed by the precipitation intensity threshold, with forecast skill deteriorating as the threshold increases, particularly, for intense and extreme events. Although forecast lead-time has a secondary yet nonnegligible influence, spatial variability of metric values becomes increasingly pronounced at higher intensity thresholds, despite certain limitations in evaluating extreme precipitation events. Notably, the evaluation at the local scale and the delineation of homogeneous regions proved particularly valuable at the 5 mm/6 h threshold, underscoring the relevance of localized verification approaches for moderate precipitation events.

This study proposes a pattern-referencing model for hourly temperature forecasting in coastal regions, specifically designed for scenarios with missing data. The Chiayi–Tainan coastal plain in Taiwan exhibits pronounced spatiotemporal temperature variations driven by sea–land breezes, topography, and solar radiation, impacting real-time decision-making in industries such as aquaculture, agriculture, and tourism. The proposed model directly utilizes all available input data without requiring prior imputation or specialized pretraining. In a multistation study involving 14 weather stations, the model employs a weighted K-nearest neighbors (WKNN) approach, using a masked Euclidean distance and the Dudani weighting scheme. The optimal configuration (look-back length = 1, number of neighbors = 18) achieved mean absolute errors of 0.35°C–0.59°C and root-mean-square errors of 0.45°C–0.86°C across diverse weather scenarios, outperforming both persistence forecasts and an autoregressive integrated moving average (ARIMA) model. The model performs best under low-temperature conditions but shows a slight tendency to underestimate at high temperatures; nighttime forecasts are the most stable, while daytime errors are larger. Even with missing station data, the model maintains its predictive capability, offering decision-makers more reliable hourly forecasts in resource-limited networks with unstable data availability, and enabling policymakers to build early-warning systems that help coastal communities and industries respond to extreme temperature events.

This study employed high-resolution (1 × 1 km) multisource fusion data (HCLDAS) and observational data from 190 automatic weather stations to analyze summer temperature variations across 12 altitude levels in the Hotan area from June to August 2023. Statistical methods, including root mean square error (RMSE) and temperature accuracy rates (TT1, TT2), were applied to validate data reliability and investigate spatiotemporal patterns. Key findings include: (1) Data Validation: HCLDAS demonstrated high accuracy, with a mean RMSE of 0.42°C and temperature accuracies of 98.15% (≤ 1°C) and 99.08% (≤ 2°C), confirming its suitability for complex terrains. (2) Altitude-Dependent Trends: High elevations (≥ 4500 m): Continuous warming from July to August (+0.37°C to +0.96°C), driven by glacier-albedo feedback (e.g., Muztagh Ata retreat) and weakened westerlies enhancing thermal forcing, elevating the 0°C isotherm. Mid-elevations (2000–4500 m): Sharp vertical cooling (−18.21°C total) but significant June–July warming (+1.24°C to +2.96°C). Low elevations: July–August cooling (−0.07°C to −1.05°C) due to cold air drainage and oasis effects (evaporation/dust reflection). (3) Diurnal Variability: Maximum daily temperature range (12.6°C) occurred at 1300–1500 m (arid landscapes), while the minimum (6.08°C) was observed at 4000–4500 m (rocky terrain). (4) Threshold Analysis: ≤ 0°C grids (38.51% of total) concentrated above 2500 m, while ≥ 35°C grids (55.59%) dominated below 3000 m, with cumulative hours increasing at lower altitudes. The results provide a scientific basis for high-temperature monitoring, snowmelt flood warnings, and optimized meteorological infrastructure in arid, high-altitude regions.

The Little Dry Season (LDS), a distinct feature of the West African Monsoon system, separates the major and minor rainfall seasons in the Guinea Coast's bimodal rainfall regime. Despite its significant socio-economic implications, the LDS is poorly understood in terms of its historical patterns, key drivers, and future projections. In this study, we analyze the historical and future patterns, variability and drivers of the LDS, pinpointing August as the month when its characteristics are most prominent. The historical period comprised data from 1990 to 2020, and the projection data was split into three climate regimes namely near-future (2011–2040), mid-future (2041–2070) and far-future (2071–2100). We identify Sea Surface Temperature (SST) and Top of the Atmosphere Outgoing Longwave Radiation as critical surface drivers for detecting and characterizing the LDS. Subsequently, we validate CMIP6 climate models against CHIRPS observational data, applying bias correction to enhance their accuracy in simulating LDS rainfall. Future LDS patterns are projected under three Shared Socio-economic Pathways (SSPs), revealing increased light rainfall events, significant spatial variability, and strong scenario dependence, particularly under SSP5-8.5. These findings underscore the need for integrated climate adaptation strategies and highlight the critical importance of global mitigation efforts in shaping future climate risks in this sensitive region. Understanding and preparing for shifts in LDS patterns is crucial for sustainable development and resilience in West Africa.

Summer precipitation over the northern part of the Korean Peninsula (SP-NPKP) is critical for water resources, agriculture, and disaster prevention. This study aims to detect suitable atmospheric circulation indices for annual prediction of SP-NPKP and to evaluate their predictive skill. We used 77 years of data from 1948 to 2024, including NCEP/NCAR reanalysis variables and observed summer precipitation from 37 stations. The study is based on the finding that 1-year lag correlations between selected indices and SP-NPKP generally exceed concurrent correlations. We analyzed linear trends of SP-NPKP, sea level pressure over Asia, 700-hPa vorticity anomalies, and Arctic Oscillation indices. Using the ‘area shift’ experiment, we identified optimal domains for sea level pressure anomalies over Asia and the North Pacific, yielding effective predictors: the SLP anomaly index over central Eurasia (SLPAI-1), that over the Okhotsk Sea (SLPAI-2), the 700-hPa vorticity anomaly index (VORAI-700), the 500-hPa temperature anomaly index (TAI-500), and the leading SLP principal component (SLP-PC1). Annual predictions were performed using principal component regression (PCR) and backpropagation neural network (BPNN) models. Based on 5-fold cross-validation, PCR showed limited skill with R² = 0.1628, RMSE = 151.57 mm, and MAE = 124.22 mm, while BPNN demonstrated significantly superior performance with R² = 0.4031, RMSE = 119.81 mm, and MAE = 103.15 mm. This confirms that neural networks better capture the nonlinear dynamics of regional precipitation. Our study provides a novel, data-driven framework for identifying region-specific predictors, offering valuable insights for improving operational seasonal prediction systems in East Asia.

This study addresses the critical issue of missing marine wave observation data, including significant wave height, mean wave period, and mean wave direction, which are essential for oceanographic analyses and marine operations. An imputation model based on the Weighted K-Nearest Neighbors (WKNN) algorithm is proposed, using the square of wave height as the primary input feature. This height-squared formulation, physically motivated by wave energy density being proportional to the square of wave height, has been shown to improve imputation accuracy for missing wave data, particularly when combined with standardization preprocessing. It outperforms the more common but less effective practice of using unsquared wave height values. The model is evaluated using real-world data from four buoys deployed in the northeastern waters of Taiwan. This improvement raises overall data completeness from 63.1% to 98.9%. The model yields physically plausible estimates, demonstrating strong performance in smooth to moderate WMO sea states. In rough-and-above regimes, however, the imputation results can be slightly conservative, including during typhoons. Notably, the proposed approach remains effective even when data from up to half of the buoy stations are unavailable. By generating high-quality imputed data, the model directly enhances the reliability of real-time marine monitoring and provides robust support for wave climate analysis and marine energy assessments. The results highlight the computational efficiency, robustness, and practical applicability of the WKNN algorithm in operational oceanographic systems.

Extreme weather far beyond what has been experienced in recent memory can be especially dangerous and costly. Proactively identifying locations at high risk of experiencing unprecedented weather can assist with disaster preparedness. Such locations can be referred to as having soft records, meaning the most extreme event in observational records is not particularly severe compared to what is possible. In previous studies, the systematic identification of soft records over a large spatial domain involves applying extreme value analysis to gridded observational or reanalysis data. A limitation of these studies is the small sample size, which we propose can be addressed by adapting the UNprecedented Simulated Extremes using ENsembles (UNSEEN) approach that is commonly used to estimate event likelihood in the aftermath of isolated unprecedented events. The UNSEEN approach makes use of seasonal or decadal forecast/hindcast ensembles, which provide a large sample of plausible events over recent decades. To demonstrate the utility of applying the UNSEEN approach to a large spatial grid, we assessed record daily maximum temperatures across Australia using gridded observations and data from 10 different decadal forecasting systems. The observation-based results highlighted broad areas of soft records in the south-east of mainland Australia, extending north into south-west and western Queensland. The UNSEEN-based analysis also identified soft records in western Queensland, but not in the south-east where the underlying positive trends in extreme temperature were far less severe in the models than in observations. We suggest that the use of large model ensembles (i.e., an UNSEEN-based approach) can complement an observation-based approach to identifying soft records over large gridded spatial domains.

We apply a tropical cyclone (TC) track classification method to the subseasonal TC predictions issued by the European ensemble prediction model (EPS) on the Southwest Indian Ocean. This track typology is based on a subdivision of the basin into three areas. Each simulated storm track is assigned a three-digit code, where each digit represents the area of genesis, the westernmost position, and the easternmost position, respectively. To account for the intensity bias in the simulated TCs, we conduct sensitivity tests that result in lowering the tropical storm wind threshold to 29kt and filtering out systems with a lifetime maximum intensity lower than 34kt. The model skill is evaluated against the performance of a 1-month moving climatology, to validate its ability to capture intra-seasonal variations in TC activity and favoured track typology. Results show an overestimation of occurrence probabilities for all track types, with the central part of the basin and the Mozambique Channel being the regions most affected. These limitations confine the raw model's skill to the second week (W2, i.e., +7 to +14 days) of forecast only. However, when evaluating the model on its capacity to assign a track type to TC genesis known to be valid at the basin scale, the EPS exhibits a 20% performance gain for W2 and a 5% gain at W3 and W4, compared to the moving climatology. These results demonstrate that when TC forecasters consider an EPS genesis prediction to be reliable, they can leverage the corresponding track type predictions to characterise TC risk more precisely, even a month in advance. Furthermore, aggregating track types based on their likelihood of impacting inhabited areas within the basin further enhances predictive skill. An impact-based forecasting product is derived from this work and will be evaluated in operations by the TC forecasters in La Reunion.