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

Abstract

In this paper, the overall quality of the water vapor profiles of global operational radiosonde data for the period 2000-2009 is investigated using upper tropospheric humidity (UTH) retrieved from microwave satellite data. Overall, the nighttime radiosonde data showed a dry bias (−5% to −15%) over Europe, Australia, and New Zealand and systematically moist bias (greater than 30%) over China and the former Soviet Union. The nighttime sonde data from the U.S. and Canada showed a bias between −10% and 20%. Most stations indicated a daytime radiation dry bias, except for a few stations from the U.S. and the former Soviet Union. A sensorwise comparison showed a large nighttime wet bias for the Russian (MRZ-3A and MARS) and Chinese GZZ-2 sensors, a relatively small nighttime wet bias for the U.S. Sippican and VIZ-B2 sensors, and a nighttime dry bias for the Chinese GTS1, Vaisala (RS80-A, RS80-H, RS90, RS92K, and RS92-SGP), and the U.S. VIZ-MKII sensors. All sensors had a daytime radiation dry bias, except for the Russian MRZ-3A sensor that had a daytime radiation wet bias that could be because of the daytime radiation bias correction. Because of the large differences between different radiosonde sensors, it is essential for UTH studies to only use the data measured using a single type of sensor at any given station.Index Terms-Microwave remote sensing (RS), radiosonde data, satellite data, tropospheric humidity, water vapor.

Assessing the quality of humidity measurements from global operational radiosonde sensors

“…Furthermore, similar sensor types operating in different regions have the same bias, e.g., Vaisala sensors used in the U.S. and Scandinavian countries have similar biases. The spatial inhomogeneity in sonde data, especially in the UT, has been discussed in more detail in the earlier studies [ Elliott and Gaffen , ; Elliott et al , ; Ross and Elliot , ; Moradi et al , ; Soden and Lanzante , ].…”

“…[3] Previous studies show very large spatio-temporal inhomogeneities in global sonde data [Elliott and Gaffen, 1991;Elliott et al, 1998;Ross and Elliot, 2001;Moradi et al, 2013a;Soden and Lanzante, 1996]. Other sensor intercomparison studies also show that even under controlled conditions, different sensor types show different accuracies throughout the troposphere, especially in mid-upper troposphere [Nash et al, 2010;Miloshevich et al, 2001].…”

A new climate data record of upper-tropospheric humidity from microwave observations

“…We chose the comparison to an existing UTH data record over a comparison with in-situ measurements from radiosondes for two main reasons. Firstly, humidity measurements from many types of radiosondes are subject to significant biases in the upper troposphere 31,32 and these biases strongly depend on the sensor type 33 . Hence, combining different sensor types, which would be required to get a sufficient temporal and spatial coverage for the validation of a UTH CDR, is problematic.…”

The AMSU-based Hydrological Bundle Climate Data Record – Description and Comparison with Other Data Sets

“…These sensors have been in operation since 1998 with the launch of NOAA-15, and are also on NOAA-16, -17, -18, -19 and the MetOp-A and -B satellites. A data set called the "Hydrological Bundle" is a Climate Data Record (CDR) that utilizes brightness temperatures from Fundamental CDRs (FCDR) to generate Thematic CDRs (TCDR) [3][4]. The TCDRs are generated using the Microwave Surface and Precipitation Products System (MSPPS) package, and produces a set of products including: Total Precipitable Water (TPW), Cloud Liquid Water (CLW), Sea-Ice concentration (SIC), Land surface temperature (LST), Land surface emissivity (LSE) for 23, 31, 50 GHz, rain rate (RR), snow cover (SC), ice water path (IWP), and snow water equivalent (SWE) [5].…”