Comparing upper tropospheric humidity data from microwave satellite instruments and tropical radiosondes

Abstract

[1] Atmospheric humidity plays an important role in the Earth's climate. Microwave satellite data provide valuable humidity observations in the upper troposphere with global coverage. In this study, we compare upper tropospheric humidity (UTH) retrieved from the Advanced Microwave Sounding Unit and the Microwave Humidity Sounder against radiosonde data measured at four of the central facilities of the Atmospheric Radiation Measurement program. The Atmospheric Radiative Transfer Simulator (ARTS) was used to simulate satellite brightness temperatures from the radiosonde profiles. Strong ice clouds were filtered out, as their influence on microwave measurements leads to incorrect UTH values. Day and night radiosonde profiles were analyzed separately to take into account the radiosonde radiation bias. The comparison between radiosonde and satellite is most meaningful for data in cloud-free, nighttime conditions and with a time difference of less than 2 hr. We found good agreement between the two data sets. The satellite data were slightly moister than the radiosonde data, with a mean difference of 1%-2.3% relative humidity (RH), depending on the radiosonde site. Monthly gridded data were also compared and showed a slightly larger mean difference of up to 3.3% RH, which can be explained by sampling issues.

A multi-instrument comparison of integrated water vapour measurements at a high latitude site

“…The AMSU-B IWV data for our study were extracted from the dataset in the same way as for earlier work where we compared AMSU-B upper tropospheric humidity measurements to radiosonde measurements (Buehler et al, 2004;John and Buehler, 2005;Moradi et al, 2010;Kottayil et al, 2012): for each satellite pass that covers the Kiruna area, IWV from all satellite footprints for which the center is within a target area of 50 km radius around Kiruna airport (67.82 • N, 20.33 • E) were averaged and the standard deviation of this average was calculated (see Fig. 1).…”

Evaluation of satellite-derived agro-climate variables in the Northern Great Plains of the United States

“…Various studies (Buehler et al 2004, Gettelman et al 2010, Bedka et al 2010, Moradi et al 2010 have suggested that the satellite RH measurements are less reliable under elevated cloudiness. Fetzer et al (2006) speculated that cold weather cloud formations could be the cause of an increase in bias in satellite water vapour products.…”

“…In 2002, the atmospheric infrared sounder (AIRS) was launched to measure a suite of geophysical variables including temperature and humidity at various altitudes in the atmosphere. So far the efforts for validation have been primarily focused on vertical profiles, but few studies concentrated on evaluation of the variables at or near the surface, which is critical for agriculture production and decision making (Fetzer et al 2003a, 2003b, Buehler et al 2004, John and Buehler 2005, Moradi et al 2010.…”

Evaluating Instrumental Inhomogeneities in Global Radiosonde Upper Tropospheric Humidity Data Using Microwave Satellite Data

“…However, previous studies show that satellite data have a good accuracy compared to the high-resolution high-quality radiosonde data. For example, Moradi et al [1] compared satellite UTH with the nighttime high-resolution radiosonde data from the Atmospheric Radiation Measurement program measured using Vaisala RS92 sensors and reported that satellite data are always about 8%-12% (0.5-1.5 K) more moist than sonde UTH. It is worthwhile to mention that up to 0.5 K of this difference was attributed to the omission of ozone from the radiative transfer (RT) calculations.…”

“…R ADIOSONDE data have numerous applications in many areas of meteorology, climatology, and hydrology, such as validating satellite products [1]- [3], serving as a priori profiles for retrieving atmospheric profiles from satellite data [4], initializing weather forecast models [5], evaluating climatological features [6], [7], and assimilating into reanalyses [8]. Radiosonde data are prone to many errors, including sensor design, calibration, data processing, and contamination, particularly in the dry and cold conditions of upper troposphere [9]- [11].…”