Heat waves: Physical understanding and scientific challenges
Heat waves (HWs) can cause large socioeconomic and environmental impacts. The
observed increases in their frequency, intensity and duration are projected to continue with …
observed increases in their frequency, intensity and duration are projected to continue with …
[HTML][HTML] Statistical postprocessing for weather forecasts: Review, challenges, and avenues in a big data world
Statistical postprocessing techniques are nowadays key components of the forecasting
suites in many national meteorological services (NMS), with, for most of them, the objective …
suites in many national meteorological services (NMS), with, for most of them, the objective …
Using explainable machine learning forecasts to discover subseasonal drivers of high summer temperatures in western and central Europe
Reliable subseasonal forecasts of high summer temperatures would be very valuable for
society. Although state-of-the-art numerical weather prediction (NWP) models have become …
society. Although state-of-the-art numerical weather prediction (NWP) models have become …
Seasonal prediction of European summer heatwaves
Under the influence of global warming, heatwaves are becoming a major threat in many
parts of the world, affecting human health and mortality, food security, forest fires …
parts of the world, affecting human health and mortality, food security, forest fires …
Subseasonal predictability of onset, duration, and intensity of European heat extremes
M Pyrina, DIV Domeisen - Quarterly Journal of the Royal …, 2023 - Wiley Online Library
Successful weather forecasts on subseasonal time‐scales can support societal
preparedness and mitigate the impacts of extreme events. Heatwaves in particular can, in …
preparedness and mitigate the impacts of extreme events. Heatwaves in particular can, in …
Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts
A barrier to utilizing machine learning in seasonal forecasting applications is the limited
sample size of observational data for model training. To circumvent this issue, here we …
sample size of observational data for model training. To circumvent this issue, here we …
Assessing present and future risk of water damage using building attributes, meteorology, and topography
C Heinrich-Mertsching, JC Wahl… - Journal of the Royal …, 2023 - academic.oup.com
Weather-related risk makes the insurance industry inevitably concerned with climate and
climate change. Buildings hit by pluvial flooding is a key manifestation of this risk, giving rise …
climate change. Buildings hit by pluvial flooding is a key manifestation of this risk, giving rise …
Correcting Subseasonal Forecast Errors with an Explainable ANN to Understand Misrepresented Sources of Predictability of European Summer Temperatures
Subseasonal forecasts are challenging for numerical weather prediction (NWP) and
machine learning models alike. Forecasting 2-m temperature (t2m) with a lead time of 2 or …
machine learning models alike. Forecasting 2-m temperature (t2m) with a lead time of 2 or …
Post-processing sub-seasonal precipitation forecasts at various spatiotemporal scales across China during boreal summer monsoon
Post-processing outputs from Coupled Global Circulation Models (CGCM) is required to
generate skillful and reliable sub-seasonal precipitation forecasts. However, it is not …
generate skillful and reliable sub-seasonal precipitation forecasts. However, it is not …
Introducing long‐term trends into subseasonal temperature forecasts through trend‐aware postprocessing
Skilful subseasonal forecasts are crucial for issuing early warnings of extreme weather
events, such as heatwaves and floods. Operational subseasonal climate forecasts are often …
events, such as heatwaves and floods. Operational subseasonal climate forecasts are often …