The Spatial Structure of Observation Errors in Atmospheric Motion Vectors from Geostationary Satellite Data

Abstract

This study investigates and quantifies in detail the spatial correlations of random errors in atmospheric motion vectors (AMVs) derived by tracking structures in imagery from geostationary satellites. 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, tracking of similar cloud structures, or quality control procedures may introduce spatially correlated errors. The spatial structure of the error correlations is investigated based on a 1-yr 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 infrared and water vapor wind datasets from Meteosat-5 and -7, Geostationary Operational Environmental Satellite-8 and -10 (GOES-8 and -10), and Geostationary Meteorological Satellite-5 (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. Even broader correlations are found for tropical regions. The correlations exhibit considerable anisotropic structures with, for instance, longer correlation scales in the south?north direction for the ?-wind component, and are comparable to error correlations for short-term forecasts. The study estimates the spatially correlated part of the annual mean AMV wind component error for high-level Northern Hemisphere winds to be about 2.7?3.5 m s?1. Some seasonal variation is found for these errors with larger values in winter. The findings have a number of important implications for the use of AMVs in data assimilation.