Evaluating tropospheric humidity from GPS radio occultation, radiosonde, and AIRS from high-resolution time series

Abstract

Abstract. While water vapor is the most important tropospheric greenhouse gas, it is also highly variable in both space and time, and water vapor concentrations range over 3 orders of magnitude in the troposphere. These properties challenge all observing systems to accurately measure and resolve the vertical structure and variability of tropospheric humidity. In this study we characterize the humidity measurements of various observing techniques, including four separate Global Positioning System (GPS) radio occultation (RO) humidity retrievals (University Corporation for Atmospheric Research (UCAR) direct, UCAR one-dimensional variational retrieval (1D-Var), Wegener Center for Climate and Global Change (WEGC) 1D-Var, Jet Propulsion Laboratory (JPL) direct), radiosonde, and Atmospheric Infrared Sounder (AIRS) data. Furthermore, we evaluate how well the ERA-Interim reanalysis and NCEP Global Forecast System (GFS) model perform in analyzing water vapor at different levels. To investigate detailed vertical structure, we analyzed time–height cross sections over four radiosonde stations in the tropical and subtropical western Pacific for the year 2007. We found that the accuracy of RO humidity is comparable to or better than both radiosonde and AIRS humidity over 800 to 400 hPa, as well as below 800 hPa if super-refraction is absent. The various RO retrievals of specific humidity agree within 20 % in the 1000–400 hPa layer, and differences are most pronounced above 600 hPa.

Sensitivity of Forward-Modeled Bending Angles to Vertical Interpolation of Refractivity for Radio Occultation Data Assimilation

“…We chose this location and a month in the deep tropics because of the high vertical variability and extreme (high and low) values of water vapor in this region. Rieckh et al (2017Rieckh et al ( , 2018 and Anthes and Rieckh (2018) characterized the variability and errors associated with RO in this same region and compared RO with other datasets. Upon our investigation into the sample size, we conclude that the sample size does not have a significant effect on our results.…”

Completeness of radiosonde humidity observations based on the Integrated Global Radiosonde Archive

“…The trouble in measuring upper-air humidity affects the completeness of observations in several ways: the vertical extent of humidity soundings varies much among radiosonde stations and over time owing to sensor limitations in very cold air; vertical gaps in low-humidity regions are expected, due to cutoff of RH below sensors' measuring capability; likewise, missing days in radiosonde humidity records may originate from adverse conditions (dry days, wet days, cold days) at individual stations (Garand et al, 1992;Ross and Elliott, 1996;McCarthy et al, 2009;Dai et al, 2011). As explained above, the actual extent of missing data depends on the observing practices combined with sensor limitations.…”

“…Usually, the precipitable water vapor (column-integrated water vapor mass per unit surface area) is estimated from the profile of water vapor mixing ratio between the surface and the 500 hPa level -i.e., the layer where ∼ 95 % of the columnar mass of water vapor is and where humidity data from radiosondes are more often available and generally more accurate (Elliot et al, 1991;Gaffen et al, 1992;Ross and Elliott, 1996;Durre et al, 2009). In this paper, a humidity profile is considered eligible to estimate precipitable water vapor under the following conditions:…”

A New Algorithm for the Retrieval of Atmospheric Profiles from GNSS Radio Occultation Data in Moist Air and Comparison to 1DVar Retrievals

“…The new scheme is implemented since 2013 already-in line with its initial design with refractivityequation closure for pressure retrieval [35] and in a basic form with static input uncertainty profiles-in the Wegener Center for Climate and Global Change (WEGC) current Occultation Processing System version 5.6 (OPS v5.6). It has shown reliable results for entire climate records in several studies [24,25,[36][37][38].…”

“…The usual selection of y o in moist-air retrieval by 1DVar is the observed refractivity profile from which temperature, humidity and surface pressure are retrieved as state x r [17][18][19][20]. Currently, the RO data processing centers Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) Data Analysis and Archive Center (CDAAC), University Corporation for Atmospheric Research (UCAR) Boulder, Radio Occultation Meteorology Satellite Application Facility (ROM-SAF), Danish Meteorological Institute (DMI) Copenhagen, and National Oceanic and Atmospheric Administration (NOAA) Center for Satellite Applications and Research (STAR) Maryland, use 1DVar algorithm implementations for their (operational) moist air retrievals [20][21][22][23][24][25]. Both ROM-SAF and CDAAC moist air profiles are used for our evaluation of the new algorithm in this study.…”