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The complexity of the human-environment interface predicates the need for tools and techniques that can enable the effective translation of weather and climate products into decision-relevant information. Indices are a category of such tools that may be used to simplify multi-faceted climate information for economic and other decision-making. Climate indices for tourism have been popularized in the literature over the past three decades, but despite their prevalence, these indices have a number of limitations, including coarse temporal resolution, subjective rating and weighting schemes, and lack of empirical validation. This paper critically assesses the design of the tourism climate index, the holiday climate index-beach, and a new, mathematically optimized index developed for the unique contextual realities of Great Lakes beach tourism. This new methodology combines the use of expert knowledge, stated visitor preferences, and mathematical optimization to develop an index that assigns daily weather scores based on four weather sub-indices (thermal comfort, wind speed, precipitation, and cloud cover). These daily scores are then averaged to the monthly level and correlated to visitation data at two beach parks in Ontario (Canada). This optimized index demonstrates a strong fit (R2 = 0.734, 0.657) with observed visitation at Pinery Provincial Park and Sandbanks Provincial Park, outperforming both the tourism climate index (R2 = 0.474, 0.018) and the holiday climate index-beach (R2 = 0.668, 0.427). This study advances our understanding of the magnitude and seasonality of weather impact on beach tourist visitation and can inform decision-making of tourism marketers and destination managers.

Global warming is expected to increase droughts and heatwaves, and consequently fire danger in southern Europe in the forthcoming decades. However, an assessment of the uncertainties associated with this general trend at regional scales, relevant to decision-making, is still missing. This study aims at assessing potential climate change impacts on fire danger over France through the projection of the widely used Fire Weather Index (FWI) and at quantifying the different sources of climate-driven uncertainty associated with these projections. We used daily climate experiments covering the 1995–2098 period under two scenarios (RCP4.5 and RCP8.5) provided by the EURO-CORDEX initiative. Our results show an overall increase in FWI throughout the century, with the largest absolute increases in the Mediterranean area. Model uncertainty was very high in western France, previously identified as a potential fire-prone region under future climate. In contrast, large increases in FWI in the Mediterranean area showed low uncertainty across models. Besides, analyzing the natural variability of FWI revealed that extreme years under present-day climate could become much more frequent by the end of the century. The FWI is projected to emerge from the background of natural variability by mid-twenty-first century with a summer elevated fire danger three times more likely when summer temperature anomaly exceeds + 2 °C.

Yellow stem borer (YSB) is a major pest responsible for substantial rice yield losses which can be significantly reduced through accurate forecasting, enabling timely interventions. This study aimed to develop a forewarning model for YSB using weather parameters and remotely sensed vegetation indices based on 19 years (2000–2018) of data from Raipur, Chhattisgarh. Weather variables and satellite derived vegetation indices were used as predictors, with pest population as the response variable. The model developed for the 39th Standard Meteorological Week (SMW) indicated that lag-time period of four week i.e., advance prediction of peak YSB population by 35th SMW  achieved with high coefficient of determination (R² = 0.77), low root mean square error (RMSE = 0.34) and low mean absolute percentage error (MAPE = 15%). Key predictors included the interaction of land surface wetness index and enhanced vegetation index, evening relative humidity and maximum temperature. A risk zoning map generated using the model indicated that most of Raipur falls under a low pest risk zone. Overall, this study highlights the potential of integrating satellite-based variables into pest forewarning systems, providing a foundation for more accurate agromet-advisory services in India.

Forest fires are of critical importance in the Mediterranean region. Fire weather indices are meteorological indices that produce information about the impact as well as the characteristics of a fire event in an ecosystem and have been developed for that reason. This study explores the spatiotemporal patterns of the FWI system within a study area defined by the boundaries of the Greek state. The FWI has been calculated and studied for current and future periods using data from the CFSR reanalysis model from the National Centers for Environmental Protection (NCEP) as well as data from NASA satellite programs and the European Commission for Medium-Range Weather Forecasts (ECWMF) in the form of netCDF files. The calculation and processing of the results were conducted in the Python programming language, and additional drought- and fire-related indices were calculated, such as the standardized precipitation index (SPI), number of consecutive 50-day dry periods (Dry50), the Fosberg fire weather index (FFWI), the days where the FWI exceeds values of 40 and 50 days (FWI > 40) and (days FWI > 50). Similar patterns can easily be noted for all indices that seem to have their higher values concentrated in the southeast of the country owing to the higher temperatures and more frequent drought events that affect the indices’ behavior in both the current and future periods.

The Niger River basin is drought-prone, and farmers are often exposed to the vagaries of severe weather and extreme climate events of the region. Spatiotemporal characteristics of drought are important for its mitigation. With 52 years of gauged-based monthly rainfall, the study investigates the potentials of Standardized Precipitation Index (SPI) as standard measure for meteorological drought, its characterization, early warning systems and use in weather index-based insurance. Gamma probability distribution type 2, which best fits the rainfall frequency distribution of the region, was used for the transformation of the skewed rainfall data to derive the SPI. Results showed 9, 5, 5 and 6 drought events of severe to extreme intensities occurred in the headwaters of the basin, inner delta, middle Niger, and lower Niger sub-watersheds, respectively. Their magnitudes were in the range 1–5, 2–6, 2–8 and 2–7, respectively. Spatially, results further showed that the 1970s and 1980s drought events were dominantly of moderate (SPI values −1 to −1.49) and severe (SPI values −1.5 to −1.99) intensities, respectively, with sporadic cases of severe to extreme drought intensities occurring in 1970s and extreme to exceptional intensities in the 1980s. Further investigations show that 3-month SPI indicated 85% of variance in the standardized cereal crop yield, which suites well as weather index insurance variable. The study therefore proposes SPI weather index-based insurance as a pathway forward to ameliorate the negative impacts on insured farmers in this region in terms of indemnity payouts whenever drought disaster occurs.

The Total Electron Content (TEC) is an important parameter that describes the morphology and structure of the ionosphere. Deep learning is an important and effective tool for forecasting TEC, but the role of different solar activity indices and geomagnetic indices in TEC prediction remains unclear. The Long Short-Term Memory (LSTM) network has special structure design and good generalization ability, which is capable of learning the features of long-term sequence data and has been widely applied in the research of ionosphere prediction. Therefore, in this study, the LSTM network is used to achieve short-term forecasting of low, middle, and high latitudes TEC during geomagnetic storms that occurred in 2016. At the same time, the effects of four different index combinations, F10.7, Kp, Dst, and AE indices, on the prediction results at different latitudes were analyzed. The results show that the appropriate combination of index inputs effectively improves the prediction performance of the model. At low latitudes, the model incorporating Kp, Dst and F10.7 indices performed best, with a 51.3% average decrease in RMSE compared to the model without any additional indices. The best model is one that uses Kp and F10.7 indices at middle latitudes, compared to model without any indices, its average RMSE decreased by 57.0%. At high latitudes, the model using Kp, Dst, and AE indices performed best, with a 43.2% average decrease in RMSE compared to the model without any indices. However, more indices do not necessarily improve prediction accuracy.

Past research has extensively analyzed the impact of individual weather factors on wheat yields, leading to the use of discriminant function and principal component analysis to harness weekly weather data to develop robust forecast models (Agrawal et al., 2012; Sisodia et al., 2014; Kumar et al., 2019). Model 1 used five un-weighted weather indices Model 2 used five weighted weather indices Model 3 used 30 indices (both weighted and un-weighted, including interaction indices) Model 4 used five weighted and five un-weighted weather indices Model 5 used five un-weighted and 10 un-weighted interaction weather indices and Model 6 used five weighted and 10 weighted weather indices The weather data of five weather parameters (maximum and minimum temperatures, rainfall, wind velocity, and sunshine hours), from the 44th standard meteorological week (SMW) to the 7th SMW of the following year, for 15 years (2000-01 to 2014-15) were used for developing models. Authors contribution: P. K. Singh: Conceptualized, data analysis, and drafted the manuscript; P. Singh: Data collection and interpretation of the analysis; V. D. Chaturvedi: Reviewing, editing and final manuscript.

This study assesses future wildfire risk in Greece using the Fire Weather Index (FWI), based on data from the Copernicus Climate Change Service. Historical conditions (1971–2000) and future projections (2069–2098) under RCP4.5 and RCP8.5 scenarios were analyzed, with a primary focus on the core fire season (May–October) and consideration of April and November to evaluate potential seasonal extension. The results show a significant shift toward higher fire risk classes, with the “very high” category increasing from 24% historically to 31% under RCP4.5 and 37% under RCP8.5, and the “extreme” class rising from 4% to 11% and 16%, respectively. Southern Greece, especially Crete, and the Dodecanese, is projected to experience the most severe increases. These changes, driven by rising temperatures and intensified drought conditions, indicate an increased likelihood of extreme fire events, posing increased risks to ecosystems, infrastructure, and regional economies. The findings highlight the need for targeted adaptation and fire management strategies.

Rice is a major staple food grain for more than half of the world’s population, and China is the largest rice producer and consumer in the world. In a climate-warming context, the frequency, duration and intensity of heat waves tend to increase, and rice production will be exposed to higher heat damage risks. Understanding the negative impacts of climate change on the rice supply is a critical issue. In this study, a new perspective on agricultural weather index insurance is proposed to investigate the impact of extreme high-temperature events on rice production in South China in the context of climate change. Based on data from meteorological stations in Anhui Province in China from 1961 to 2018 and the projected data from five Global Climate Models under three representative concentration pathway (RCP) scenarios from 2021 to 2099, the spatial–temporal characteristics of heat stress and its influence on rice production were analyzed by employing a weather index insurance model. The interdecadal breakpoints in the trends of the heat stress weather insurance index (HSWI) and the payout from 1961 to 2018 in 1987 were both determined, which are consistent with the more significant global warming since the 1980s. The largest increase after 1987 was found in the southeastern part of the study area. The projected HSWI and the payout increased significantly from 2021 to 2099, and their growth was faster with higher radiative forcing levels. The HSWI values were on average 1.4 times, 3.3 times and 6.1 times higher and the payouts were on average 3.9 times, 9.8 times and 15.0 times higher than the reference values for the near future, mid-future and far future, respectively. The results suggest that a more severe influence of heat damage on rice production will probably happen in the future, and it is vital to develop relevant adaptation strategies for the effects of a warmer climate and heat stress on rice production. This paper provides an alternative way to transform the evaluation of the extreme climate event index into the quantitative estimation of disaster impacts on crop production.

Power systems and their primary components, mostly the transmission and distribution of overhead lines, substations, and other power facilities, are distributed in space and are exposed to various atmospheric and meteorological conditions. These conditions carry a certain level of risk for reliable electrical power delivery. Various atmospheric hazards endanger the operation of power systems, where the most significant are thunderstorms, wildfire events, and floods which can cause various ranges of disturbances, faults, and damages to the power grid, or even negatively affect the quality of life. By utilizing a weather monitoring and early warning system, it is possible to ensure a faster reaction against different weather-caused fault detections and elimination, to ensure a faster and more adequate preparation for fighting extreme weather events, while maintaining overhead line protection and fault elimination. Moreso, it is possible to bypass overhead lines that have the highest risk of unfavorable meteorological events and hazards, and reroute the energy, thus providing electricity to endangered areas in times of need while minimizing blackouts, and consequently, improving the quality of human life. This paper will present an analysis of the various risks of atmospheric phenomena, in the meteorological and climate context, and discuss various power system components, the power system control, operations, planning, and power quality. A concept with the main functionalities and data sources needed for the establishment of an early warning weather hazard system will be proposed. The proposed solution can be used as a utility function in power system control to mitigate risks to the power system due to atmospheric influences and ongoing climate change.