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EN
In this study seasonal and inter-annual patterns as well as trend in the total precipitable water vapour (TPW) over Malaysia, based on a 30-year data from MERRA-2, have been evaluated using least square regression method. Indicator TPW revealed a pair of minima in February/August and maxima in May/November with highest and lowest long-term means found in East Malaysia. Long-term seasonal variability of TPW exhibited latitudinal dependency in both the NEM and SWM seasons. Indicator TPW showed respective southeast-northwest and southwest-northeast spatial distribution in West and East Malaysia, with the highest statistically significant positive trend found in the former.
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Content available remote Wind wave climate of west Spitsbergen : seasonal variability and extreme events
EN
Waves are the key phenomenon directly influencing coastal morphodynamics. Facing insufficient observations, wind wave climate of the west coast of Spitsbergen can be characterized on the basis of the modelled data. Here we have used the results of spectral wave models: Wave Watch III (WW3) hindcast and WAM in ERA-interim (ERAi) reanalysis. We have observed the presence of seasonal cycle with difference of up to 1 m between significant wave heights in summer and winter. In wave-direction analysis we have noticed the southwestern swell component of remarkably narrow width, thus we expect unidirectional swell impact on the coastline. Extreme events analysis revealed that storms occur mainly in winter, but the most energetic ones (significant wave height of up to 9.5 m) occur in spring and autumn. We have identified positive trends in storms’ frequency (2 storms per decade) and storms’ total duration (4 days per decade) on the south of the study area. More storms can result in the increase of erosion rate on the south-western coasts of Spitsbergen, but this change may be highly dependent on the sea ice characteristics. Wave heights of wind sea and swell are correlated with the relevant atmospheric circulation indices, especially the North Atlantic Oscillation. In the recent decade, the correlation is stronger with WW3 than with ERAi data, at some locations explaining over 50% (over 30%) of the total variance of wind sea (swell) wave heights. In ERAi data, the relationship with circulation indices seems sensitive to the length of the analysis period.
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