Weather Risk Research Articles

Offshore wind project installations: an advanced approach for weather risk assessments

Abstract This paper presents the development of a sequence-based simulation tool for the weather risk assessment of offshore work processes. The approach developed at the Fraunhofer Institute for Wind Energy Systems is the Advanced Weather Time Series Scheduling (Ad-WATSS) method. For statistical significance, this method combines hindcast weather time series of many years with the weather restrictions associated with offshore activities. The method is incorporated into the software tool COAST, where a project plan schedule is simulated by placing activities according to their operational limits, constraints and relationships, and minimum-required time for each operation. A scenario of an offshore wind installation project in the North Sea is used as reference test case for a weather risk assessment using the developed tool and ERA5 weather data. The assessment is then compared with the weather window statistics approach, based on the same weather data sets. The results from both approaches are analysed and compared on a project and task level detail. The results from the sequence-based simulations show that the method is able to capture the long-term variability and extreme events in weather conditions, making it a reliable tool for the simplification of the weather data-based planning, validation, and assessment of offshore projects.

Risk Management of Green Bean Farming Production in Gluranploso Village, Gresik District

Green beans are the most important food crop in Indonesia and production continues to increase to meet domestic demand. However, production is still insufficient due to several risks faced by farmers. This research analyzes the risk management of green bean production in Gluranploso Village using the Coefficient of Variation (KV) and Failure Mode and Effect Analysis (FMEA) methods. The results showed that yield variation was relatively low with a KV value of 17%, indicating good yield stability. The FMEA analysis identified four main sources of risk affecting production: seed aspect with an RPN value of 5.808, weather with an RPN value of 7.04, pests and diseases with an RPN value of 12.936, and technical aspects with an RPN value of 47.88, particularly delays in pest and disease control spraying. In addition, the market aspect is also a source of risk with the largest value in the increase in production input prices at 12.288. These sources require special attention in risk management. The study concludes that the implementation of appropriate risk management strategies, focusing on the identified key risk sources, can help farmers improve yield stability and farm profitability. These strategies include improving seed quality, mitigating weather risks, controlling pests and diseases, and improving technical procedures in cultivation and market risks. Thus, effective risk management can be key in increasing the productivity and sustainability of green bean farming in Gluranploso village.

The impact of agricultural insurance on farmers’ mental health: what we can learn from the literature

AbstractThis systematic review aimed to examine evidence in the extant literature on the nexus between agricultural insurance and the mental health of farmers. The key hypothesis was that increasing access to agricultural insurance will enhance the mental and emotional well-being of farmers globally and will consequently preserve the future of agriculture, particularly as climate change exacerbates weather risk. A systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. We searched several databases, including EBSCOHost Megafile, Scopus, Web of Science, PubMed (Medline) and Google Scholar, based on predetermined criteria in July 2024. We conducted a full-text review of twelve potential articles. None of the articles met the inclusion criteria and reported a clear understanding of the relationship between access to agricultural insurance and the mental health nexus. We concluded that there is no evidence to support the hypothesis that access to agricultural insurance has a positive impact on farmer’s mental health. Our hypothesis is premised on the fact that while agricultural insurance smoothens farmers’ income, this should lead to some forms of mental health advantage, but we do not have any evidence in extant literature. The lack of literature is perhaps due to the complexity of the agricultural insurance product design. We recommend studies that will provide reliably conclusive evidence on this critical issue because agriculture requires risk management tools to help farmers cope with multidimensional risks, including exacerbated weather events due to climate change.

Advancing Subseasonal Surface Air Temperature and Heat Wave Prediction Skill in China by Incorporating Scale Interaction in a Deep Learning Model

AbstractAccurate subseasonal predictions of high surface air temperature (SAT) and heat wave events 10–30 days in advance are crucial for mitigating the risks of extreme weather; however, they pose a challenge for current operational models. In this study, we trained a convolutional neural network (CNN)‐based deep learning model to exploit the modulations in China's SAT by precursor signals across different timescales to improve predictions of future SAT and heat wave events. This CNN model demonstrated superior capability in capturing the evolution of SAT anomalies and the occurrence of heat wave events with forecast lead times beyond 20 days, compared with that of the operational models of the China Meteorological Administration and European Centre for Medium‐Range Weather Forecasts. Explainability analysis highlighted that subseasonal SAT predictability in China is driven primarily by large‐scale intraseasonal perturbations from both lower‐ and higher‐latitude regions of Eurasia and interannual variability.

More than Magnitude: Towards a multidimensional understanding of unprecedented weather to better support disaster management

The 1900 Galveston Texas Hurricane, the 2021 Pacific Northwest heatwave, and the 2023 Tropical Cyclone Freddy were all events that were unprecedented in diverse ways and had severe humanitarian impacts. Understanding past and future risk of unprecedented weather is an emerging question across climate science disciplines but use of this research by the humanitarian sector has been limited. This cross-disciplinary paper is an effort by climate scientists and humanitarian practitioners to address this gap. For it, we combined narrative and scoping literature reviews with structured practitioner engagement to develop a working definition and typology of unprecedented weather through a disaster management lens. We qualitatively coded over 400 peer-reviewed articles to highlight the current state of research on unprecedented weather, and then discussed these findings in a workshop with 48 humanitarian practitioners. Our results show that, while analyses of past and future unprecedented weather often focus on the magnitude of such events, extreme weather can be unprecedented in many other dimensions, all which have significant implications for early warning, anticipatory action, and disaster response planning. We conclude with a call for more imagination and diversity in research on extreme weather risks, and for closer collaboration between climate scientists and disaster managers to design and answer questions that matter for humanitarian outcomes.

The role of rural circular migration in shaping weather risk management for smallholder farmers in India, Nepal, and Bangladesh

Circular migration, defined as migration where migrants return to their original home area, has become an increasingly important component of rural livelihoods and can significantly impact the risk management strategies of smallholder agricultural households in the face of climate change. To unpack the associations between climate change, migration decisions, and agricultural outcomes for smallholder agricultural households, we use an embedded mixed methods approach that uses quantitative data from a structured household survey from over 2,000 rural households in Nepal, India, and Bangladesh, along with qualitative data from interviews and focus group discussions. We use these data to identify the influence of socio-economic, climate, and weather factors on long (≥12 months) and short-term (<12 months) migration decisions and the impacts of migration on risk management strategies in agriculture. Our research shows that the drivers and effects of migration differ based on migration characteristics, including the length of time a family member migrates and whether the destination is domestic or international. We find that households with limited resources, such as constrained irrigation access, use short-term migration to cope with weather variability, whereas long-term migration is generally undertaken by wealthier households motivated to improve long-term economic outcomes. Considering the impacts of migration on risk management, we find that short-term migration of household members results in increased investment in agriculture, such as increasing inputs and adopting new varieties. In contrast, long-term and international migration is associated with disinvestments in agriculture, such as reduced cropped area and inputs. Our results highlight the importance of migration in shaping agricultural management practices amidst the challenges of climate change.

Weather, Climate, and Society: A Retrospective between 2009 and 2022 Using Bibliometric and Content Analysis

Abstract This paper aims to analyze the bibliometrics of articles published in Weather, Climate, and Society (WCAS) over the last 14 years (2009–22). We conducted an analysis using the Scopus database and cross-checked the WCAS online journal and obtained 577 document results (article) and then analyzed and visualized using VOSviewer, Sankey diagrams, altmetric, and content analysis. This research was conducted to show publication trends, citation rates, productivity levels, and developments in research topics at WCAS. The study results show that the 577 articles published in Weather, Climate, and Society have become references for 7106 other articles and are spread across 160 publication sources. Based on the most cited articles, five cluster topics are references for many papers, namely, climate change and environmental management policy, weather risk assessment, climate hazard assessment, anthropogenic climate change, and decision support systems. Climatic Change, International Journal of Disaster Risk Reduction, and Sustainability Switzerland are the three publications with the most citations of articles published by WCAS. Research topics related to climate change, decision-making, risk assessment, the United States, societal impacts, social science, communications/decision-making, and vulnerability are the eight most commonly discussed topics in WCAS articles. Significance Statement From 2009 to 2022, 1773 authors published WCAS articles, either individually or collaboratively, across 1350 institutional affiliations in 70 countries. Using Scopus metadata, this study employs bibliometric and content analysis to explore temporal trends, article impact metrics, citation characteristics, and topic shifts in WCAS publications over this period. The findings indicate that analyzing citations, topics, and authors aids in understanding a journal’s interdisciplinary accessibility. Notably, highly cited papers in WCAS do not necessarily score high on altmetrics, suggesting that future impact measures might integrate academic and social media metrics. Prolific authors often collaborate rather than publish solo. Dominant research topics include climate change, decision-making, risk assessment, social impacts, and U.S. issues.

Multi-Objective Route Planning Model for Ocean-Going Ships Based on Bidirectional A-Star Algorithm Considering Meteorological Risk and IMO Guidelines

In this study, a new route planning model is proposed to help ocean-going ships avoid dangerous weather conditions and ensure safe ship navigation. First, we integrate ocean-going ship vulnerability into the study of the influence of meteorological and oceanic factors on navigational risk. A multi-layer fuzzy comprehensive evaluation model for weather risk assessment is established. A multi-objective nonlinear route planning model is then constructed by comprehensively considering the challenges of fuel consumption, risk, and time during ship navigation. The International Maritime Organization (IMO) guidelines are highlighted as constraints in the calculations, and wind, wave, and calm water resistance to ships in the latest ITTC method is added to the fuel consumption and sailing time in the objective function. Finally, considering the large amount of data required for ocean voyages, the bidirectional A* algorithm is applied to solve the model and reduce the planning time. Furthermore, our model is applied to the case of an accident reported in the Singapore Maritime Investigation Report, and the results show that the model-planned route is very close to the original planned route using the Towing Manual, with an average fit of 98.22%, and the overall meteorological risk of the model-planned route is 11.19% smaller than the original route; our model can therefore be used to plan a safer route for the vessel. In addition, the importance of risk assessments and the IMO guidelines as well as the efficiency of the bidirectional A* algorithm were analyzed and discussed. The results show that the model effectively lowers the meteorological risk, is more efficient than the traditional route planning algorithm, and is 86.82% faster than the Dijkstra algorithm and 49.16% faster than the A* algorithm.

A Statistical Analysis of Drought and Fire Weather Indicators in the Context of Climate Change: The Case of the Attica Region, Greece

As warmer and drier conditions associated with global warming are projected to increase in southern Europe, the Mediterranean countries are currently the most prone to wildfire danger. In the present study, we investigated the statistical relationship between drought and fire weather risks in the context of climate change using drought index and fire weather-related indicators. We focused on the vulnerable and long-suffering area of the Attica region using high-resolution gridded climate datasets. Concerning fire weather components and fire hazard days, the majority of Attica consistently produced values that were moderately to highly anti-correlated (−0.5 to −0.9). This suggests that drier circumstances raise the risk of fires. Additionally, it was shown that the spatial dependence of each variable on the 6-months scale Standardized Precipitation Evapotranspiration Index (SPEI6), varied based on the period and climate scenario. Under both scenarios, an increasing rate of change between the drought index and fire indicators was calculated over future periods versus the historical period. In the case of mean and 95th percentiles of FWI with SPEI6, abrupt changes in linear regression slope values were observed, shifting from lower in the past to higher values in the future periods. Finally, the fire indicators’ future projections demonstrated a tendency towards an increasing fire weather risk for the region’s non-urban (forested and agricultural) areas. This increase was evident from the probability distributions shifting to higher mean and even more extreme values in future periods and scenarios. The study demonstrated the region’s growing vulnerability to future fire incidents in the context of climate change.

A case study into the safety management systems for the effects of potential space weather risks, for operators of very high altitude ‘near space’ flights from Mojave Air & Space Port, California, and the associated regulatory challenges

Mojave Air & Space Port is located in Mojave, California, United States. It is at an elevation of 2,801 feet (854 m), is nearly 3000 acres in size and has three runways. It is licensed by the FAA for horizontal launches of reusable spacecraft and is already a major hub for aerospace research and space enterprises, which includes potential space flights and very high altitude subsonic, supersonic, and hypersonic flights.During potential very high-altitude ‘near space’ space flights, the effects of cosmic radiation exposure, especially during sudden changes in space weather, such as ground level enhancement (GLE) or solar particle events (SPEs), could have significant health implications for crew and passengers. This case study examines the intricate landscape of radiation risks, regulatory challenges, licensing complexities, and approaches to risk management for very high-altitude ‘near space’ space flights from Mojave Air and Space Port carrying one or more paying "space flight participant" (being an individual, who is not crew, carried aboard a launch vehicle or re-entry vehicle).The study explores the specific challenges of risk assessment of very high-altitude flights, looking in detail at the risk posed by the space weather radiation environment in flight planning and execution, for both Space Port and flight operator. The study covers the ‘end to end’ licensing process and the regulatory considerations of space weather required for both Space Port and flight operator. Further, we look at the integration of Safety Management Systems (SMS), namely, we explore how SMS frameworks proactively identify, assess, and mitigate risks throughout the ‘near space’ space flight process. Further, the study presents a template for addressing the regulatory framework for flights, risk assessment, pre-flight briefings, and the flight licensing procedure.This case study offers valuable insights for Space Port and flight operators, regulators, and policymakers, contributing to the development of comprehensive safety strategies, which are crucial for safe very high-altitude ‘near space’ space exploration.Plain Language Summary: A case study of how the potential space weather risks can be successfully managed for very high altitude ‘near space’ subsonic, supersonic, and hypersonic flights from Mojave Air and Space Port, California, USA carrying one or more space flight participant(s).

EEMD-ARIMA Model for annual precipitation forecasting to aid weather insurance decision-making research

Climate risk poses significant threats to human life and property. Climate insurance can effectively mitigate and disperse these risks. This paper addresses the weakness in weather risk prediction for climate insurance formulation by combining Ensemble Empirical Mode Decomposition (EEMD) and Autoregressive Integrated Moving Average (ARIMA) models. Four models, namely EMD, EEMD, ARIMA, and EMD-ARIMA, were established for modeling and forecasting Chinas annual precipitation data. The results show that the EEMD-ARIMA model can suppress the modal aliasing problem in time series and has the best fit compared to other models. This model can more accurately describe the variation in annual precipitation in forecasting applications, providing significant predictive value for insurance companies and government decisions regarding insurance and climate risk management.

Nitrogen facilitation was maintained in sown Mediterranean forage mixtures despite drought stress conditions with concurrent general benefits upon plant aboveground water status and yield

There is an urge to optimise the sustainability and resilience of grasslands, especially in light of the extreme weather risks posed by climate change. Plant sown diversity could contribute to mitigating these risks by improving resource use efficiency as well as reducing reliance on fertilisation. We established a field experiment to determine forage swards’ performance in terms of water use, nitrogen (N) use, and dry matter (DM) yield in a semi-arid Mediterranean area which faced a severe drought period. Most variables were tested over three growing seasons, whilst DM yield had an extra year of sampling in which two levels of N fertilisation were tested. Following the Generalised Diversity-Interactions modelling approach, we manipulated the composition and evenness of forage swards including a grass (Festuca arundinacea), a legume (Medicago sativa), and a non-legume forb (Cichorium intybus). Communities were grown as monocultures or mixtures of three species according to a simplex design, which allowed us to estimate separately the two components of the diversity effect: the individual species effects and that due to species interactions. Models were tested both with sown proportions (Pi0) and realised proportions (PiR). The Pi0 models indicated a major beneficial effect of mixtures in contrast to the average monoculture performance for all studied variables. In contrast, PiR models revealed that some of the effects in Pi0 models are indeed explained by the growing proportion of M. sativa over time. However, beneficial interaction effects are still found in PiR models, especially concerning N2 fixation (with δ15N as proxy), which was maintained regardless of the drought stress level experienced by the swards. All things considered, mixed swards merit attention as a valuable nature-based solution that can optimise N management (by boosting N2 input through fixation) as well as water use. This would result in well-nurtured swards with a better ability to cope with drought stress and maintain productivity.

Assessing Extreme Weather Risks for Insurance Underwriting with ARIMA and Entropy Weight Method: Insights from Brazil and India

Assessing Extreme Weather Risks for Insurance Underwriting with ARIMA and Entropy Weight Method: Insights from Brazil and India

Water and development in the Asian tropics, 1900–1939

AbstractEnvironmental conditions significantly affected development in the Asian tropics. This paper investigates the relationship between weather risk and agriculture in four regions with distinct climatological features. Using new data, we estimate the scale of crop output sensitivity to rainfall shocks across ecological zones. Output was sensitive to shocks in regions with low levels, concentrated spells and high volatility of rainfall. Canal irrigation protected some districts while unirrigated regions remained vulnerable. Regions with high rainfall levels and long seasons remained protected. Regions with large interruptions deterred investment and were underdeveloped while regions with small interruptions invited investment‐led growth.

Bringing Statistics to Storylines: Rare Event Sampling for Sudden, Transient Extreme Events

AbstractA leading goal for climate science and weather risk management is to accurately model both the physics and statistics of extreme events. These two goals are fundamentally at odds: the higher a computational model's resolution, the more expensive are the ensembles needed to capture accurate statistics in the tail of the distribution. Here, we focus on events that are localized in space and time, such as heavy precipitation events, which can start suddenly and decay rapidly. We advance a method for sampling such events more efficiently than straightforward climate model simulation. Our method combines elements of two existing approaches: adaptive multilevel splitting (AMS), a rare event algorithm that generates rigorous statistics but fails to enhance the sampling of sudden, transient extremes; and “ensemble boosting,” which generates physically plausible storylines of these events but not their statistics. We modify AMS by splitting trajectories well in advance of the event's onset, following the approach of ensemble boosting. Early splitting requires a rejection step that reduces efficiency, but it is critical for amplifying and diversifying simulated events in tests with the Lorenz‐96 model, for which we demonstrate improved sampling of extreme local energy fluctuations by approximately a factor of 10 relative to direct sampling. Our method is related to previous algorithms, including subset simulation and anticipated AMS, but is distinctly tailored to handle bursting events caused by chaotic traveling waves. Our work makes progress toward the goal of efficiently sampling such transient local extremes in atmospheric models.

Heatwave attribution based on reliable operational weather forecasts

The 2021 Pacific Northwest heatwave was so extreme as to challenge conventional statistical and climate-model-based approaches to extreme weather attribution. However, state-of-the-art operational weather prediction systems are demonstrably able to simulate the detailed physics of the heatwave. Here, we leverage these systems to show that human influence on the climate made this event at least 8 [2–50] times more likely. At the current rate of global warming, the likelihood of such an event is doubling every 20 [10–50] years. Given the multi-decade lower-bound return-time implied by the length of the historical record, this rate of change in likelihood is highly relevant for decision makers. Further, forecast-based attribution can synthesise the conditional event-specific storyline and unconditional event-class probabilistic approaches to attribution. If developed as a routine service in forecasting centres, it could provide reliable estimates of human influence on extreme weather risk, which is critical to supporting effective adaptation planning.

Extreme Weather Risk Assessment and Insurance Decision Making Based on ARIMA Model and Visual Analysis

Extreme Weather Risk Assessment and Insurance Decision Making Based on ARIMA Model and Visual Analysis

Heterogeneous impact of crop diversification on farm net returns and risk exposure: Empirical evidence from Ghana

AbstractIncreasing frequency of extreme weather events threatens the livelihoods of low‐income farm households due to the heavy dependence on rain‐fed agriculture coupled with the under‐developed formal markets for risk management products. Thus, crop diversification is one of the widely used ex ante adaptation strategies to hedge against weather risk exposure. In this study, we use survey data from the northern Savanna zone of Ghana merged with historical weather data to shed light on the heterogeneous impact of crop diversification on farm net returns and risk exposure. We employ the dose response function and instrumental variable techniques to address potential endogeneity concerns. Overall, our findings show that crop diversification is a welfare‐enhancing strategy that significantly increases farm net returns, lowers the probability of crop failure, and thus decreases downside risk exposure. Notably, our dose‐response function analysis demonstrates that the positive benefits of crop diversification are particularly pronounced at lower intensities, reaching an optimal threshold. Beyond this point, the incremental advantages tend to diminish, suggesting the importance of carefully considering the optimal level of diversification for maximum benefits. The results further underscore the significant impact of both access to agricultural extension services and fertilizer usage on the adoption of crop diversification.

Weather rebate sharing contract for enhancing supply chain performance

Weather risk in a supply chain has become a potential issue. We propose a new class of contract, a weather rebate sharing contract, that enhances the supply chain performance under weather risk. To design the contract parameters, the risk aversion of supply chain members is taken into account. The Conditional Value at Risk (CVaR) function is used to measure a firm’s objective under risk. The optimal hedging and ordering decisions are obtained by maximizing the CVaR of the supply chain’s expected profit. The weather rebate sharing contract parameters have been designed so that both parties obtain a Pareto-improving solution. We have investigated the performance of the traditional revenue sharing contract and our designed contract under weather risk by using industry demand data and corresponding temperature data. Irrespective of the risk aversion of the supply chain members, we conclude that our designed contract coordinates effectively under all weather conditions and performs better than a traditional revenue sharing contract. Moreover, our results show that the designed contract coordinates under different weather conditions and provides a better coordination framework than the previously designed contracts under weather risk. We recommend that practitioners use this class of contract for administering the supply chain under weather-related uncertainties.

Implementation of a UAV-aided calibration method for a mobile dual-polarization weather radar

Weather radar calibration is a crucial factor to be considered for quantitative applications, such as QPE (Quantitative Precipitation Estimation), which is used as input for weather risks management. The present work proposes a novel approach to the end-to-end radar calibration method through the characterization of the radar weighting functions. These are Gaussian functions that model an additional attenuation factor to the radar received power. This approach, based on the inclusion these parameters, allow the obtainment of a calibrated equivalent reflectivity factor expression for a Doppler dual-polarization weather radar that operates in the X band. To calculate these parameters, a UAS (Unmanned Aircraft System) was implemented for suspending the calibration target with a well-defined cross-section and for measuring its inclination due to wind using an IMU (Inertial Measurement Unit). From its measurements, the position of the target can be estimated, which is essential to the characterization of the weighting functions. Their inclusion within the radar equation, alongside the implementation of the angular measurement system highlight the innovation to the traditional radar calibration methodology that does not contemplate them from the explored state-of-the-art. The reflectivity was compared with the measurements from a disdrometer for a moderate rain event. An average reflectivity difference of 0.75 dBZ and a percent bias of 3.3 % were obtained between the expected and estimated measurements when including these functions compared to the 1.51 dBZ and –62.7 % obtained when disregarding them. These experimental results point out that the proposed method can deliver superior accuracy in the reflectivity estimation.