Impact of analysis uncertainty upon regional atmospheric moisture flux
- 20 March 1996
- journal article
- Published by American Geophysical Union (AGU) in Journal of Geophysical Research: Atmospheres
- Vol. 101 (D3) , 7291-7303
- https://doi.org/10.1029/95jd02896
Abstract
Horizontal fluxes of atmospheric water vapor are studied for summer months during 1989 and 1992 over North and South America and are based on analyses from European Centre for Medium Range Weather Forecasts, U.S. National Meteorological Center, and United Kingdom Meteorological Office. The calculations are performed over 20° × 20° box‐shaped midlatitude domains located to the east of the Rocky Mountains in North America and to the east of the Andes Mountains in South America. The fluxes are determined from operational center gridded analyses of wind and moisture. Differences in the monthly mean moisture flux divergence determined from these analyses are as large as 7 cm/month precipitable water equivalent over South America and 3 cm/month over North America. Gridded analyses at higher spatial and temporal resolution exhibit better agreement in the moisture budget study. However, significant discrepancies of the moisture flux divergence computed from different gridded analyses still exist. The conclusion is more pessimistic than Rasmusson's [1968] estimate based on station data. Further analysis reveals that the most significant sources of error result from model surface elevation fields, gaps in the data archive, and uncertainties in the wind and specific humidity analyses. Uncertainties in the wind analyses are the most important problem. The low‐level jets, in particular, are substantially different in the different data archives. Part of the reason for this may be due to the way the different analysis models parameterize physical processes affecting low‐level jets. The results support the inference that the noise/signal ratio of the moisture budget may be improved more rapidly by providing better wind observations and analyses than by providing better moisture data.Keywords
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