Overview of extreme weather events, impacts and forecasting techniques

doi:10.1016/b978-0-12-820124-4.00009-8

Cited by 2 publications

(2 citation statements)

References 245 publications

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“…High temperatures, coupled with low precipitation [71] and increased wind speed, can lead to the advection of dry air, resulting in decreased humidity levels [75]. This aligns with the findings in Figure 5i,j, where days/zones with fires consistently show significantly lower relative humidity throughout the year (90% of wildfires occur in conditions below the median of the entire zone), especially during DJF and MAM.…”

Section: Wildfire Atmospheric Soil and Vegetation Characteristicssupporting

confidence: 85%

Wildfire Scenarios for Assessing Risk of Cover Loss in a Megadiverse Zone within the Colombian Caribbean

Cabrera,

Ferro,

Casallas

et al. 2024

Sustainability

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Rising wildfire incidents in South America, potentially exacerbated by climate change, require an exploration of sustainable approaches for fire risk reduction. This study investigates wildfire-prone meteorological conditions and assesses the susceptibility in Colombia’s megadiverse northern region. Utilizing this knowledge, we apply a machine learning model and the Monte Carlo approach to evaluate sustainability strategies for mitigating fire risk. The findings indicate that a substantial number of fires occur in the southern region, especially in the first two seasons of the year, and in the northeast in the last two seasons. Both are characterized by high temperatures, minimal precipitation, strong winds, and dry conditions. The developed model demonstrates significant predictive accuracy with the HIT, FAR, and POC of 87.9%, 28.3%, and 95.7%, respectively, providing insights into the probabilistic aspects of fire development. Various scenarios showed that a decrease in soil temperature reduces the risk mostly in lower altitudes and leaf skin reservoir content in the highest altitudes, as well as in the north region. Sustainability strategies, such as tree belts, agroforestry mosaics, and forest corridors emerge as crucial measures. The results underscore the importance of proactive measures in mitigating wildfire impact, offering actionable insights for crafting effective sustainability strategies amid escalating fire risks.

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Section: Wildfire Atmospheric Soil and Vegetation Characteristicssupporting

confidence: 85%

Wildfire Scenarios for Assessing Risk of Cover Loss in a Megadiverse Zone within the Colombian Caribbean

Cabrera,

Ferro,

Casallas

et al. 2024

Sustainability

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“…Despite several limitations, solar forecasting has made significant contributions to energy resource harvesting and allocation. Thus, to improve the reliability and dependability of solar forecasting, post-processing techniques are introduced [13][14][15]. While solar forecasting involves predicting the amount of solar energy that will be generated, postprocessing techniques are used to increase the accuracy [16] of solar forecasts by refining raw forecast data.…”

Section: Introductionmentioning

confidence: 99%

Refining the Selection of Historical Period in Analog Ensemble Technique

del Pozo,

Kim,

Kim

2023

Energies

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A precise estimate of solar energy output is essential for its efficient integration into the power grid as solar energy becomes a more significant renewable energy source. Contrarily, the creation of solar energy involves fluctuation and uncertainty. The integration and operation of energy systems are complicated by the uncertainty in solar energy projection. As a post-processing technique to lower systematic and random errors in the operational meteorological forecast model, the analog ensemble algorithm will be introduced in this study. When determining the appropriate historical and predictive data required to use the approach, an optimization is conducted for the historical period in order to further maximize the capabilities of the analog ensemble. To determine statistical consistency and spread skill, the model is evaluated against both the raw forecast model and observations. The outcome lowers the uncertainty in the predicted data by demonstrating that statistical findings improve significantly even with 1-month historical data. Nevertheless, the optimization with a year’s worth of historical data demonstrates a notable decrease in the outcomes, limiting overestimation and lowering uncertainty. Specifically, analog ensemble algorithms calibrate analog forecasts that are equivalent to the latest target forecasts within a set of previous deterministic forecasts. Overall, we conclude that analog ensembles assuming a 1-year historical period offer a comprehensive method to minimizing uncertainty and that they should be carefully assessed given the specific forecasting aims and limits.

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Overview of extreme weather events, impacts and forecasting techniques

Cited by 2 publications

References 245 publications

Wildfire Scenarios for Assessing Risk of Cover Loss in a Megadiverse Zone within the Colombian Caribbean

Wildfire Scenarios for Assessing Risk of Cover Loss in a Megadiverse Zone within the Colombian Caribbean

Refining the Selection of Historical Period in Analog Ensemble Technique

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