Mathematics – Logic
Scientific paper
Oct 2004
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004georl..3119501r&link_type=abstract
Geophysical Research Letters, Volume 31, Issue 19, CiteID L19501
Mathematics
Logic
47
Hydrology: Soil Moisture, Meteorology And Atmospheric Dynamics: Land/Atmosphere Interactions, Meteorology And Atmospheric Dynamics: Numerical Modeling And Data Assimilation, Meteorology And Atmospheric Dynamics: Remote Sensing
Scientific paper
Although surface soil moisture data from different sources (satellite retrievals, ground measurements, and land model integrations of observed meteorological forcing data) have been shown to contain consistent and useful information in their seasonal cycle and anomaly signals, they typically exhibit very different mean values and variability. These biases pose a severe obstacle to exploiting the useful information contained in satellite retrievals through data assimilation. A simple method of bias removal is to match the cumulative distribution functions (cdf) of the satellite and model data. However, accurate cdf estimation typically requires a long record of satellite data. We demonstrate here that by using spatial sampling with a 2 degree moving window we can obtain local statistics based on a one-year satellite record that are a good approximation to those that would be derived from a much longer time series. This result should increase the usefulness of relatively short satellite data records.
Koster Randal D.
Reichle Rolf H.
No associations
LandOfFree
Bias reduction in short records of satellite soil moisture does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Bias reduction in short records of satellite soil moisture, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Bias reduction in short records of satellite soil moisture will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-844107