Search Results (2)

Here, we examine extreme ocean winds associated with intense wintertime extratropical windstorms over the North Pacific. The study was mainly based on NASA Soil Moisture Active Passive (SMAP) L-band radiometer observations allowing the retrieval of ocean wind speeds up to 70 m/s regardless of precipitation intensity. Additionally, we assessed the ability of atmospheric reanalysis ERA5 and the Climate Forecast System Version 2 (CFSv2) to reproduce high-wind features within severe windstorms, particularly those associated with “explosive” cyclogenesis. The analysis identified 145 windstorm events with hurricane-force (HF) wind zones within the SMAP L-band radiometer swath from 2015 to 2023. These windstorms develop most frequently over two areas: southeast of Kamchatka and south of Alaska, spanning 40–47°N latitudes. Both reanalysis datasets significantly underestimated HF wind speeds compared to SMAP measurements, but CFSv2 tends to reproduce more-intense windstorms than ERA5. Among the notable new findings is that the SMAP data revealed two distinct groups in maximum wind speed distribution, indicating the existence of a separate class of severe windstorm events with a distinct mechanism for extreme wind formation related probably to a Shapiro–Keyser cyclogenesis and the presence of sting jet (SJ) feature. The study highlights the potential of SMAP measurements to study wind extremes and underscores the need for improvements in operational predictive models to better reproduce the formation of SJ windstorms. Full article

Windstorms may have negative consequences on forest ecosystems, industries, and societies. Extreme events related to extra-tropical cyclonic systems remind us that better recognition and understanding of the factors driving forest damage are needed for more efficient risk management and planning. In the present study, we statistically modelled forest damage caused by the windstorm Klaus in south-west France. This event occurred on 24 January 2009 and caused severe damage to maritime pine (Pinus pinaster) forest stands. We aimed at isolating the best potential predictors that can help to build better predictive models of forest damage. We applied the random forest (RF) technique to find the best classifiers of the forest damage binary response variable. Five-fold spatial block cross-validation, repeated five times, and forward feature selection (FFS) were applied to the control for model over-fitting. In addition, variable importance (VI) and accumulated local effect (ALE) plots were used as model performance metrics. The best RF model was used for spatial prediction and forest damage probability mapping. The ROC AUC of the best RF model was 0.895 and 0.899 for the training and test set, respectively, while the accuracy of the RF model was 0.820 for the training and 0.837 for the test set. The FFS allowed us to isolate the most important predictors, which were the distance from the windstorm trajectory, soil sand fraction content, the MODIS normalized difference vegetation index (NDVI), and the wind exposure index (WEI). In general, their influence on the forest damage probability was positive for a wide range of the observed values. The area of applicability (AOA) confirmed that the RF model can be used to construct a probability map for almost the entire study area. Full article