Meteorological Applications最新文献

The efficacy of global and regional datasets on the prediction of extremely severe cyclonic storms over the Bay of Bengal (BoB) was evaluated using the Weather Research and Forecasting (WRF) model in a double-nested domain with a 4 km finer resolution on three different datasets, namely FNL, ERA-Interim, and Indian Monsoon Data Assimilation and Analysis (IMDAA). The initial cyclonic vortex, the vertical profile of horizontal wind speed, and relative humidity from different datasets were assessed to evaluate the initial structure and validated with IMD best-fit track data. The model results highlight that simulations with FNL data predict tracks and intensities more accurately for the majority of cyclonic storms compared with the IMDAA and ERA-Interim datasets. Simulations with FNL data exhibit the least mean track errors of 70, 126, 121, and 204 km for days 1–4, respectively. Additionally, the mean wind error of five extremely severe cyclonic storms (ESCSs) using FNL data is approximately 9.3, 4.6, 7.7, and 10.9 m/s, respectively, from day 1 to day 4. It is observed that the regional reanalysis of IMDAA datasets outperformed the forecast of several parameters such as maximum surface wind speed, central sea level pressure, and rainfall for the ESCSs Fani and Sidr. The FNL dataset overpredicted the amount of 24-h accumulated rainfall compared with the ERA-Interim and IMDAA datasets, whereas the IMDAA dataset performed better with lower values of root mean square error (148 mm/day), standard deviation (124 mm/day), and higher correlation (0.68) with the TRMM dataset. Model predictions highlight that the regional dataset IMDAA performs better in predicting rainfall magnitude compared with the global dataset due to the added assimilation of numerous local observations. The regional dataset could be improved by exploring large-scale circulation features and their significant role in predicting the track, intensity, and landfall location of the tropical cyclones.

Understanding the aberrant weather and farmers' behavior under those is crucial for achieving climate resilience. Among weather parameters, rainfall significantly affects crop production, from pre-sowing decisions to harvesting. However, the existing indices often overlook farmers' decision-making. To address this gap, a new deficient rainfall impact parameter (DRIP) index was utilized to evaluate rainfall variability's effects on principal rainfed crops in Karnataka, India's second-largest dryland agriculture state. Datasets from 2011 to 2022 on area, production, and productivity of major crops of Karnataka were analyzed. Notably, the state's highest DRIP score was recorded in Kharif sorghum during 2016 and 2019 (12.8 and 8.6), indicating an impact of deficient rainfall on its production. Similarly, a higher reduction in the area under rabi sorghum was observed in 2016 with higher DRIP scores (10.9). Conversely, a meager decrease in the area under rainfed rice was observed in 2018 (1.6) and 2016 (1.2) even though there was a deficit of rainfall. In contrast, maize evaded drought impact during 2015–18 with negative DRIP scores, indicating crop shifts. However, finger millet suffered moisture stress in 2016 and 2018. Rabi wheat showed higher DRIP scores in 2016, 2017, and 2018 (12.2, 2.2, and 19.0) due to rainfall deficits. Similarly, the positive DRIP scores for pigeonpea in 2016–2018 signified decreased cultivation due to rainfall deficits. Chickpea, mainly cultivated in vertisols, showed marginal impact from rainfall deficits, except in 2016 and 2021. Groundnut had positive DRIP scores in 2017–2018 (1.1 and 0.5) due to deficit rainfall and in 2020–2021 (1.7 and 0.5) due to crop replacement with onion. Castor, on the other hand, exhibited positive DRIP scores in most years, except 2019, 2020, and 2022. This study underscores the importance of understanding rainfall variability and its implications for agricultural practices, thereby contributing to informed decision-making and strategic planning to ensure regional and national food security.

The COVID-19 pandemic has generated significant global impacts on health and society, imposing a comprehensive analysis of its influencing factors, including weather variables. This study investigates the interaction between meteorological conditions and the spread of COVID-19 in three Italian regions: Lombardia, Emilia-Romagna, and Puglia. Effects of weather variables, such as air temperature, relative humidity, dew point, solar radiation, wind speed, and barometric pressure, are explored in the incidence of disease. Observed meteorological and health data are taken from various sources, such as the citizen-science Meteonetwork Association and the National Department of Civil Protection, respectively, and they are analyzed with statistical methods and machine learning algorithms. The study emphasizes the necessity of carefully considering key meteorological quantities as primary drivers in illness diffusion and prevention strategies, offering valuable insights to address challenges to the pandemic and ensure the safety of global communities. The results reveal a significant correlation between specific atmospheric variables and the spread of COVID-19, with dew point temperature as the most influential parameter at low air temperature values.

A typical question posed following an extreme precipitation event is: How does this compare to past events? This question is being asked more frequently and is of importance to climate monitoring services, such as the Copernicus Climate Change Service (C3S). Currently, the statistics extensively used for this purpose are not generally understandable to the wider public, or they are not tailored towards presenting extremes. To mitigate this situation, this article uses a modified version of the Extreme Rain Multiplier (ERM), which was developed for tropical cyclones, and applies it to precipitation events globally. For daily precipitation considered herein, the ERM is calculated by dividing the daily precipitation accumulation during an event by the mean historical annual maxima of daily precipitation (RX1day), which is computed over 1991–2020. Using the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis, the calculation of the ERM is illustrated for six extreme events around the world; these included convective systems, atmospheric rivers and tropical cyclones. A maximum ERM of 4 was found during Storm Daniel, in Greece, and in Tropical Cyclone Jasper in Australia, implying that four times the mean RX1day precipitation occurred. The ERM will be useful in C3S reporting activities because it can objectively identify extreme precipitation events. Furthermore, after extracting the number of precipitation events per year at each grid point that had an ERM exceeding 1, a trend analysis was undertaken to ascertain if the frequency of extreme events had changed with time. Results showed that the most widespread increasing trends in the ERM were in the tropics, but these trends are thought to be questionable in ERA5. There were few clear trends in other regions. In conclusion, the ERM can communicate the level of extreme precipitation in a clear manner and can be used in climate monitoring activities.

Elementary diagonal score including neighborhood is presented as a new spatial verification tool for ensemble forecasts. It allows a spatial tolerance to be taken into account in the calculation of elementary diagonal scores by considering regional quantiles calculated from cumulative density functions computed on points in a spatial neighborhood. A climatology of the observed regional quantiles is required to define these diagonal scores. As in the case of the elementary diagonal scores without neighborhood, the relationship between error penalty rates and the level of the predicted regional quantile is fixed in order to have a proper score. In addition, this penalty rate is related to the climatological frequency of the event, to ensure an equitable score. The comparison of observations and ensemble forecasts is then summarized in a contingency table for this elementary diagonal score. An integral diagonal score including neighborhood can be calculated by averaging the elementary diagonal scores including neighborhood over a relevant sample of thresholds, as for the integral diagonal score without neighborhood. The properties of these diagonal scores have been illustrated on idealized cases including realistically spatially correlated fields.

When multiple weather-driven hazards such as heatwaves, droughts, storms or floods occur simultaneously or consecutively, their impacts on society and the environment can compound. Despite recent advances in compound event research, risk assessments by practitioners and policymakers remain predominantly single-hazard focused. This is largely due to traditional siloed approaches that assess and manage natural hazards. Hence, there is a need to adopt a more ‘multi-hazard approach’ to managing compound events in practice. This paper summarizes discussions from a 2-day workshop, held in Glasgow in January 2023, which brought together scientists, practitioners and policymakers to: (1) exchange a shared understanding of the concepts of compound and multi-hazard events; (2) learn from examples of science–policy–practice integration from both the single hazard and multi-hazard domains; and (3) explore how success stories could be used to improve the management of compound events and multi-hazard risks. Key themes discussed during the workshop included developing a common language, promoting knowledge co-production, fostering science–policy–practice integration, addressing complexity, utilising case studies for improved communication and centralising information for informed research, tools and frameworks. By bringing together experts from science, policy and practice, this workshop has highlighted ways to quantify compound and multi-hazard risks and synergistically incorporate them into policy and practice to enhance risk management.

The Hengduan Mountains, which comprise numerous north–south-oriented mountains, exhibit unique precipitation characteristics and obvious regional differences. Based on the Global Precipitation Measurement (GPM) dataset, hourly rainfall features in the Hengduan Mountains during the warm season (May–September) from 2001 to 2021 were investigated. A key region with relatively large rainfall amounts and unique morning peaks was found at the western edge of the Hengduan Mountains (WEHM). The diurnal rainfall peaks showed an eastward delay from northern Myanmar to the WEHM. Less frequent long-duration events (longer than 6 h) contributed more than 58% to the cumulative precipitation amount at the WEHM. Moreover, long-duration rainfall exhibited similar eastward propagation features, which were further verified by the hourly variations in the rainfall amount and black-body temperature on long-duration rainfall days. Short-duration rainfall events accounted for below 20% of the cumulative precipitation and presented late-afternoon diurnal peaks at the WEHM. ERA5 data were employed to explain the rainfall propagation signal. The results indicated that the upstream low-level wind field significantly influences the diurnal variation of rainfall at the WEHM, and wind anomaly rotation from night to early morning contributed to the eastward delay in the onset of long-duration rainfall. In general, this work could contribute to a deeper comprehension of the precipitation characteristics and formation of morning rainfall over the WEHM.

Accurate high-resolution temperature forecasting is of great significance for the economic and social development of humanity. Due to the chaotic nature of the atmosphere and the limitations of computational resources, model forecasts often lack sufficient resolution and exhibit systematic biases. Therefore, downscaling methods with smaller computational demands have become a good alternative. This study designed a super resolution generative adversarial network (SRGAN) for temperature downscaling, applying it to the 2 m temperature forecasts for the Southwest region of China from the Global Ensemble Forecasting System (GEFS), with forecast lead times of 1 to 7 days. Meanwhile, linear regression (LR), along with two advanced deep learning downscaling methods, U-Net and super resolution deep residual networks (SRDRNs), were also used as benchmarks. The study shows that both deep learning methods, SRGAN and SRDRNs, can effectively address the issue of blurred temperature fields that may occur when using U-Net. By comparing the Nash-Sutcliffe Efficiency coefficient (NSE), pattern correlation coefficient (PCC), root mean square error (RMSE), and peak signal-to-noise ratio (PSNR), we found that SRGAN demonstrated the best performance among the four methods. In this work, a suitable loss function was set using the VGG network to help SRGAN better capture small-scale details. Additionally, a mean square error decomposition method was used to further diagnose the sources of errors in different models, revealing their ability to calibrate various error sources. The results show that SRGAN, SRDRNs, and LR perform best in correcting the square of the bias (Bias²), while U-Net is most effective in correcting the sequence errors.

This article pioneers a unique approach to examining generic dry spells, shifting focus from traditional rain-free period analysis to a crop-centric perspective that integrates an anticipatory lens inspired by Impact-based Forecasting (IbF). Moving beyond traditional analyses of rain-free periods, the article evaluates these impactful within-season large-scale agrometeorologically relevant dry spells (LARDS) not by the number of days with minimal or no rainfall but by their impact—specifically, the adequacy of root-zone soil moisture to meet the optimal requirements of maize crops, as quantified through the Water Requirement Satisfaction Index (WRSI). LARDS were identified in maize-intensive growing regions of Zimbabwe under two maize planting date scenarios: meteorology-guided and uninformed. The research characterizes impactful within-season LARDS occurring at sub-seasonal to seasonal timescales over 36 years (1983–2018). Findings show that meteorological guidance improves yields while neglecting it results in lower yields. During LARDS, a distinct northwest-to-southeast suppressed rainfall pattern emerges over Zimbabwe, extending into neighbouring countries. This pattern is associated with a southwestward or northeastward displacement of Tropical Temperate Troughs (the regional primary rainfall system) relative to the country's location. Furthermore, LARDS exhibit overarching anticyclonic conditions impeding vertical cloud development with notable changes in the key local large-scale mean climatic features influencing Southern Africa's weather. Specifically, the Mozambique Channel Trough, Angola Tropical Low, Saint Helena High and Mascarene High weaken anomalously, while the Botswana High strengthens during LARDS. Additionally, we demonstrate that LARDS have a northeastward propagation and have atmospheric signatures indicative of being triggered by upstream Rossby waves originating from the south coast of South America.