The use of CCMP data is very doubtful since the time series before 2009 date of the injection of QUIKSCAT data (1999QUIKSCAT data ( -2009 in the variational analysis underestimates the intensity of the winds and overestimates the wind after 1999. In this context, and to be able to conclude on the trend, the authors have to use other data sources, especially ASCAT, although the series is short, but it allows to validate or correct the data series.Thanks for the remark. We performed a correlation study between the CCMP V1.1 (PODAAC) database we used in our analysis and the new CCMP V2.0 database (released by REMSS in 2017). This reprocessing is an update of CCMP. It uses the most current and complete RSS cross-calibrated wind datasets, including ASCAT, and C1
Global change is one of the outstanding problems nowadays. This is the reason why considerable attention, and economic resources to monitor climate variables have increased. Wind data constitute one of the key elements that determine the local climate. In this paper, the performance of a shallow neural net (SNN) is tested to simulate remote sensing wind intensity data from reanalysis data from nearby location. As a result, a sequence of wind data with more spatial resolution can be achieved, allowing the availability of more data at the local scale.
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