The generation and assimilation of continuous AMVs with 4DVar
2011; Volume: 61; Issue: 2 Linguagem: Inglês
10.22499/2.6102.004
ISSN1836-716X
AutoresJ. Le Marshall, Rolf Seecamp, Yi Xiao, Peter Steinle, H Sims, Terry Skinner, Jim Jung, Tan Le,
Tópico(s)Electric Vehicles and Infrastructure
ResumoContinuous (hourly) atmospheric motion vectors (AMVs) have been generated from Multi-functional Transport Satellite 1 Replacement (MTSAT-1R) and at times MTSAT-2 radiance data (imagery) since 2005.MTSAT-1R is the primary geostationary meteorological satellite observing the Western Pacific, Asia and the Australian region.The calibrated and navigated radiance data have been used to calculate AMVs.The continuous AMVs have been error characterized and used in near real-time trials to gauge their impact on operational regional Numerical Weather Prediction (NWP) using four-dimensional variational assimilation (4DVar).The beneficial impact of these data on the Bureau of Meteorology's (BoM's) current operational system is described below.The vectors are used operationally, for analysis in the Darwin Regional Forecast Office and, after these trials, the AMVs were approved for introduction into the Bureau's National Meteorological and Oceanographic Centre's (NMOC's) operational NWP suite for use by the operational Australian Community Climate Earth System Simulator (ACCESS) regional model ACCESS-R.The utility of these continuous wind data for tropical cyclone prediction has already been demonstrated in a number of studies.In recent studies using ACCESS Global and Regional models, locally generated high spatial and temporal resolution (hourly) AMVs from MTSAT-1R have been employed with 4DVar and their beneficial impact in the Australian region and with tropical cyclone track prediction, has been recorded.vectors available from geostationary satellite observations for operational forecasts have been long quantified (Le Marshall et al. 1996a).The utility of using locally generated AMVs from MTSAT-1R observations in operational regional numerical weather prediction has also been recently demonstrated (Le Marshall et al. 2008
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