2002: The spatial structure of observation errors in atmospheric motion vectors from geostationary satellite data

This study investigates in detail the spatial correlations of random errors in Atmospheric Motion Vectors (AMVs). A good specification of the observation error is essential to assimilate any kind of observation for Numerical Weather Prediction in a near-optimal way. For AMVs, height assignment, quality control procedures, and other factors introduce spatially correlated errors. The spatial structure of the error correlations is investigated based on a one-year dataset of pairs of collocations between AMVs and radiosonde observations. Assuming spatially uncorrelated sonde errors, the spatial AMV error correlations are obtained over dense sonde networks. Results for operational IR and WV wind datasets from METEOSAT-5 and 7, GOES-8 and 10, and GMS-5 are presented. Winds from all five datasets show statistically significant spatial error correlations for distances up to about 800 km, with little difference between satellites, channels, or vertical levels. Broader correlations are found for tropical regions. The correlations exhibit anisotropic structures with, for instance, longer correlation scales in South-North direction for the v-wind component. The study estimates the spatially correlated part of the annual mean AMV wind component error for high-level Northern Hemisphere winds as about 2.7–3.5 m/s. The findings have a number of important implications for the use of AMVs in data assimilation.