Targeted goes satellite observations to improve hurricane track forecast : A case study of hurricane floyd
This study performs a set of Observing System Simulation Experiments (OSSEs) using Geostationary Operational Environmental Satellite (GOES) soundings. The primary objective of the OSSEs is to demonstrate that targeted observations can improve forecast accuracy by enhancing the initial conditions and...
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Veröffentlicht in: | Pure and applied geophysics 2007-10, Vol.164 (10), p.2083-2100 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | This study performs a set of Observing System Simulation Experiments (OSSEs) using Geostationary Operational Environmental Satellite (GOES) soundings. The primary objective of the OSSEs is to demonstrate that targeted observations can improve forecast accuracy by enhancing the initial conditions and mitigating their uncertainties. Hurricane Floyd (1999) is chosen as a study case. The main reason for choosing hurricane Floyd as a test case is that the movement of the storm was dictated by a mid-level complex polar jet steering flow region. This well-defined feature allowed us to examine the inaccuracy of analysis over the steering flow area using GOES soundings as targeted observations and its impact on the forecast track error. The set of experiments starts from a baseline forecast of hurricane Floyd using the Operational Multiscale Environment model with Grid Adaptivity (OMEGA). From GOES satellite soundings, atmospheric vertical profiles were extracted to simulate targeted observations. These data extracts were assimilated in the initial conditions to simulate new forecasts of hurricane Floyd which were then compared against both the baseline track and observed track. It was found that targeted observations in a forecast sensitive area can help to reduce hurricane forecast track error. Assimilation of only the subset of data (about 50 soundings) from the subjectively chosen fully sampled target region produced a considerable reduction of the track forecast errors (about 30%) within the first critical three days of the forecast.[PUBLICATION ABSTRACT] |
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ISSN: | 0033-4553 1420-9136 |
DOI: | 10.1007/s00024-007-0256-x |