The Representation of Tropospheric Water Vapor Over Low-Latitude Oceans in (Re-)analysis: Errors, Impacts, and the Ability to Exploit Current and ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-11

AUTHORS

Robert Pincus, Anton Beljaars, Stefan A. Buehler, Gottfried Kirchengast, Florian Ladstaedter, Jeffrey S. Whitaker

ABSTRACT

This paper addresses the representation of lower tropospheric water vapor in the meteorological analyses—fully detailed estimates of atmospheric state—providing the wide temporal and spatial coverage used in many process studies. Analyses are produced in a cycle combining short forecasts from initial conditions with data assimilation that optimally estimates the state of the atmosphere from the previous forecasts and new observations, providing initial conditions for the next set of forecasts. Estimates of water vapor are among the less certain aspects of the state because the quantity poses special challenges for data assimilation while being particularly sensitive to the details of model parameterizations. Over remote tropical oceans observations of water vapor come from two sources: passive observations at microwave or infrared wavelengths that provide relatively strong constraints over large areas on column-integrated moisture but relatively coarse vertical resolution, and occultations of Global Positioning System provide much higher accuracy and vertical resolution but are relatively spatially coarse. Over low-latitude oceans, experiences with two systems suggest that current analyses reproduce much of the large-scale variability in integrated water vapor but have systematic errors in the representation of the boundary layer with compensating errors in the free troposphere; these errors introduce errors of order 10% in radiative heating rates through the free troposphere. New observations, such as might be obtained by future observing systems, improve the estimates of water vapor but this improvement is lost relatively quickly, suggesting that exploiting better observations will require targeted improvements to global forecast models. More... »

PAGES

1399-1423

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10712-017-9437-z

DOI

http://dx.doi.org/10.1007/s10712-017-9437-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092350229


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