Mr. Clement B. Chouinard Profile

Clement B. Chouinard

at Meteorological Serv of Canada

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Proceedings Article | 16 June 2003 Paper

Use of moisture sensitive satellite radiances in the Canadian Meteorological Centre unified 3D-var system

Clement Chouinard, Jacques Halle

Proceedings Volume 4895, (2003) https://doi.org/10.1117/12.466839

KEYWORDS: Satellites, Meteorological satellites, Environmental sensing, Data modeling, 3D modeling, Radiative transfer, Terbium, Temperature metrology, Error analysis, Control systems

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The Canadian Meteorological Centre’s (CMC) three Dimensional Variational (3D-var) is an incremental analysis system that is currently used by both our global and regional models with very little modifications. During the last few years, it has undergone a series of upgrades from isobaric to a terrain-following coordinate, and most importantly the direct assimilation of satellite radiances. The quality control (QC) of observations was also upgraded to a variational quality control whereby the data rejection/acceptance decisions are taken consistently during the minimization problem. In terms of radiance data, the system currently uses so-called raw level-1b AMSU-A that are QC, and bias controlled by the data user and not the producer. The QC and thinning algorithms of the radiance data are more complex and system dependent, but their impact on analyses and forecasts are very large and now comparable to the radiosonde data in the SH. The resolution of NWP forecast/analysis systems is forever increasing and so is the volume of data from various instruments. The volume of satellite data has become quite a challenge even at the level of ingest and QC. One aspect of NWP systems which definitely can benefit from this additional data is the moisture analysis. In that context we have started to experiment with the ingest of water sensitive radiances from the HIRS and AMSU-B instruments onboard NOAA-15 and NOAA-16. As will be shown, the quality of both the temperature and moisture analyses are significantly improved when using these additional satellite data. Preliminary evaluations from 10-day forecasts indicate marked improvements in Quantitative Precipitation Forecast (QPF).

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