Comparisons of the tropospheric specific humidity from GPS radio occultations with ERA–Interim, NASA MERRA and AIRS data

Abstract

Abstract. We construct a 9–year data record (2007–2015) of the tropospheric specific humidity (SH) using Global Positioning System radio occultation (GPS RO) observations from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission. This record covers the ±40° latitude belt and includes estimates of the zonally averaged monthly mean SH from 700 hPa up to 400 hPa. It includes three major climate zones: a) the deep tropics (±15°), b) the trade winds belts (±15–30°), and c) the subtropics (±30–40°). Our objective is to compare the RO observations with the European Center for Medium-range Weather Forecasts Re-Analysis Interim (ERA-Interim), the Modern-Era Retrospective analysis for Research and Applications (MERRA), and the Atmospheric Infrared Sounder (AIRS) to examine the consistency among the data sets. We present RO SHs from both JPL and UCAR processing centers to provide an estimate of the structural uncertainty of the RO SH products. The results show that the RO observations capture the seasonal and interannual SH variability as all other data sets. On average, the JPL-RO SH agrees with both reanalyses to within 10 %, is overall larger than all data sets, having maximum differences with AIRS by ~ 10–30 %, and is almost twice as wet as all other data sets in the middle-to-upper troposphere at the subtropics. The UCAR-RO SH also agrees with both reanalyses and AIRS, but is systematically drier than all other data sets. Provided the estimated differences between the RO observations and the rest of the data sets, together with the retrieval uncertainty of the SH products from all data sets, we conclude that RO observations are a valuable independent observing system, which could augment independent reanalyses and satellite platforms. We anticipate that the COSMIC-2 mission will increase the observational sampling; thus, improving the coverage and quality of the observed SH climatology.

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

“…Our results support the findings of Vergados et al (2017), e.g. the relative dryness of the UCAR 1D-Var and wetness of the JPL RO humidity retrieval, and the dry bias of AIRS.…”

“…the relative dryness of the UCAR 1D-Var and wetness of the JPL RO humidity retrieval, and the dry bias of AIRS. While Vergados et al (2017) draw their conclusions from large-scale 5 multi-year climatologies, we use high resolution time series to depict the short-term and small scale variability of humidity, and add results below 700 hPa, where the tropospheric water vapor content is highest.…”

Trend analysis of tropospheric specific humidity over Iran during 1979–2016

“…The primary data sets assimilated in ERA‐Interim are radiosonde humidity observations, AIRS and microwave radiances, and, as of November 2006, the GPS RO bending angle profiles (Vergados et al , ). The ERA‐Interim uses a 4D variational assimilation technique (Simmons et al , ) to analyse a variety of observational data sets to predict the state of the atmosphere with accuracy similar to what is theoretically possible based on the error characteristics of the assimilated data (Simmons and Hollingsworth, ).…”