학술논문

High-resolution satellite-derived dataset of the surface fluxes of heat, freshwater, and momentum for the TOGA COARE IOP
Document Type
Academic Journal
Source
Bulletin of the American Meteorological Society. Oct, 1999, Vol. 80 Issue 10, p2059, 22 p.
Subject
United States
Language
ISSN
0003-0007
Abstract
An integrated approach is presented for determining from several different satellite datasets all of the components of the tropical sea surface fluxes of heat, freshwater, and momentum. The methodology for obtaining the surface turbulent and radiative fluxes uses physical properties of the atmosphere and surface retrieved from satellite observations as inputs into models of the surface turbulent and radiative flux processes. The precipitation retrieval combines analysis of satellite microwave brightness temperatures with a statistical model employing satellite observations of visible/infrared radiances. A high-resolution dataset has been prepared for the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) intensive observation period (IOP), with a spatial resolution of 50 km and temporal resolution of 3 h. The high spatial resolution is needed to resolve the diurnal and mesoscale storm-related variations of the fluxes. The fidelity of the satellite-derived surface fluxes is examined by comparing them with in situ measurements obtained from ships and aircraft during the TOGA COARE IOP and from vertically integrated budgets of heat and freshwater for the atmosphere and ocean. The root-mean-square differences between the satellite-derived and in situ fluxes are dominated by limitations in the satellite sampling; these are reduced when some averaging is done, particularly for the precipitation (which is from a statistical algorithm) and the surface solar radiation (which uses spatially sampled satellite pixels). Nevertheless, the fluxes are determined with a useful accuracy, even at the highest temporal and spatial resolution. By compiling the fluxes at such high resolution, users of the dataset can decide whether and how to average for particular purposes. For example, over time, space, or similar weather events.