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 three 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 (UCAR direct, UCAR 1D-Var, WEGC 1D-Var, Jet Propulsion 5 Laboratory (JPL) direct), radiosonde, and Atmospheric Infrared Sounder (AIRS) data. Furthermore, we evaluate how well the ERA-Interim reanalysis and National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model perform in analyzing water vapor at different levels. To investigate detailed vertical structure, we used time-height cross sections over specific locations (radiosonde stations in the tropical and subtropical western Pacific) for the year 2007. We found that RO humidity has comparable or better accuracy than both radiosonde and AIRS humidity over 800 hPa to 400 hPa, 10 as well as below 800 hPa if super-refraction is absent. The various RO retrievals of specific humidity agree within 20 % in the 1000 hPa to 400 hPa layer, and differences are most pronounced above 600 hPa.

Below‐cloud evaporation effect on stable water isotopes in precipitation at the eastern margin of Qinghai‐Tibet Plateau

“…In contrast, the stratification assumption is also mentioned in some cases (Crawford et al, 2017; Zhou et al, 2019), that is, the different heights have various meteorological settings for a falling raindrop. However, the atmospheric relative humidity and air temperature may have great uncertainty to the model which is involved in both measurements and reanalysis (Dai et al, 2011; Rieckh et al, 2018). The Gannan Plateau is a mountainous area, indicating the potential influence of tropography on local temperature and humidity.…”

Evaluating two methods of estimating error variances from multiple data sets using an error model

“…We use the RO-Direct method for computing RO specific humidity (uses GFS temperature to compute specific humidity q from observed RO refractivity). Details about the co-location criteria and specific humidity retrieval are described by Rieckh et al (2018). Data pairs at each level are only used if data are available for all four data sets.…”

“…We use co-located data of radio occultation (RO), RS, NCEP Global Forecast System (GFS), and ERA-Interim (ERA) at Minamidaitojima (Mina), which is located on Okinawa at 25.6°N 131.5°W, and is one of the four RS stations studied by 15 Anthes and Rieckh (2018) and Rieckh et al (2018). We use the RO-Direct method for computing RO specific humidity (uses GFS temperature to compute specific humidity q from observed RO refractivity).…”