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

被引:0
作者
Robert Pincus
Anton Beljaars
Stefan A. Buehler
Gottfried Kirchengast
Florian Ladstaedter
Jeffrey S. Whitaker
机构
[1] University of Colorado,Cooperative Institute for Research in Environmental Sciences
[2] NOAA Earth System Research Lab,Physical Sciences Division
[3] European Centre for Medium-Range Weather Forecasts,Informatics and Natural Sciences Department of Earth Sciences, Meteorological Institute, Faculty of Mathematics
[4] Universitt Hamburg,Wegener Center for Climate and Global Change and Institute for Geophysics, Astrophysics, and Meteorology, Institute of Physics
[5] University of Graz,undefined
来源
Surveys in Geophysics | 2017年 / 38卷
关键词
Water vapor; Satellite; Microwave; Infrared; Radio occultation; Data assimilation; Tropospheric water vapor profiling;
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
收藏
页码:1399 / 1423
页数:24
相关论文
共 409 条
[21]  
Chahine MT(2002)A technical description of atmospheric sounding by GPS occultation J Atmos Solar Terr Phys 64 451-1231
[22]  
Gautier C(2006)Assimilation experiments with CHAMP GPS radio occultation measurements Q J R Meteorol Soc 132 605-45
[23]  
Goldberg MD(2009)H Atmos Chem Phys 9 9433-108
[24]  
Kalnay E(2012)O and HDO measurements with IASI/MetOp J Geophys Res 117 D18111-1643
[25]  
McMillin LM(2015)Reproducibility of GPS radio occultation data for climate monitoring: profile-to-profile inter-comparison of CHAMP climate records 2002 to 2008 from six data centers J Climate 28 2856-1096
[26]  
Revercomb H(2010)Marine boundary layer heights and their longitudinal, diurnal, and interseasonal variability in the southeastern pacific using COSMIC, CALIOP, and radiosonde data Atmos Meas Tech 3 1217-451
[27]  
Rosenkranz PW(2011)Reference quality upper-air measurements: guidance for developing GRUAN data products J Geophys Res 116 1491-627
[28]  
Smith WL(1960)Clear-sky biases in satellite infrared estimates of upper tropospheric humidity and its trends Trans ASME J Basic Eng 82 35-48
[29]  
Staelin DH(1961)A new approach to linear filtering and prediction problems Trans ASME J Basic Eng 83 95-2628
[30]  
Strow LL(2002)New results in linear filtering and prediction theory Bull Am Meteorol Soc 83 1631-23,465