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

Abstract

data record to earlier years. The new definition has the additional advantage that UTH can be calculated directly from given atmospheric profiles of humidity and temperature without a detour via radiative transfer simulations. Detailed uncertainty information in CDRs derived from satellite-based Earth observations are needed to support the application of the data in climate research 7,8. Providing such information on CDR level (level 2 or 3) has mainly been constrained by the availability of uncertainty information in the underlying FCDRs (level 1) in the past. Within the FIDUCEO project four new versions of such FCDRs were created, among them the FIDUCEO Microwave FCDR used as input for our UTH CDR. The FCDR includes information on observational uncertainty on pixel level, which is the result of rigorous uncertainty analyses based on metrological principles 9. These uncertainties are propagated to the spatially and temporally averaged quantities in the UTH CDR. Depending on the spatial and temporal correlation behaviour of the underlying error sources, uncertainties are divided into three different classes, enabling the user to propagate them to spatial and temporal averages of the data. The FIDUCEO UTH CDR is validated against an exisiting microwave UTH data record provided by the Satellite Application Facility on Climate Monitoring (CM-SAF). Differences in monthly tropical mean UTH do not exceed 2% RH and can be attributed to differences in the underlying FCDR and in the CDR processing in approximately equal parts. The structure of this paper is as follows: The Methods chapter introduces the satellite instruments, the UTH retrieval method and the new definition of UTH. Furthermore, a detailed description of the CDR processing is provided. This is followed by the Data Records chapter, which includes a description of the CDR data file format as well as the satellite missions and time periods covered. The subsequent chapter Technical Validation consists of an evaluation of the UTH retrieval performance, the comparison of our CDR with the CM-SAF UTH CDR and a description of uncertainties not estimated in the CDR.

Assessing the consistency of satellite derived upper tropospheric humidity measurements

“…The FIDUCEO UTH (version 1.2) is based on the FIDUCEO fundamental climate data record of recalibrated microwave sounder brightness temperatures (Hans et al, 2019), covering the sensors SSM/T-2, AMSU-B and MHS. It uses a new UTH definition (Lang et al, 2020) based on the concept that the atmospheric emission layer for a water vapor channel is bounded by two characteristic amounts of water vapor integrated from the top of the atmosphere downwards. Using this idea, UTH is defined as the mean relative humidity in a layer between two altitude levels z(IWV1) and z(IWV2), at which the integrated water vapor (IWV) exceeds two viewing angle dependent thresholds IWV1 and IWV2.…”

“…The development of UTH datasets and the examination of temporal and spatial variabilities of UTH have been presented in numerous studies, including both infrared datasets (Soden and Bretherton, 1993;Jackson and Bates, 2001;Shi and Bates, 2011;Iacono et al, 2003;Chung et al, 2007;Gierens et al, 2014;Schröder et al, 2014) and microwave datasets (Brogniez and Pierrehumbert, 2006;Chung et al, 2013;Sohn et al, 2000;Buehler et al, 2008;Lang et al, 2020;Brogniez et al, 2015;Moradi et al, 2016). The variability of UTH is regulated by the large-scale atmospheric circulation.…”

Can machine learning correct microwave humidity radiances for the influence of clouds?

“…This is normally denoted as cloud filtering, in order to obtain data of "clear-sky" character. Such filtering has been applied to derive climate records (Lang et al, 2020) and is essential in studies of the agreement between observations I. Kaur et al: Correcting cloud-affected microwave radiances and simulations (Brogniez et al, 2016) as well as comparing observations of different instruments to validate their calibration (John et al, 2013;Moradi et al, 2015;Berg et al, 2016).…”

Characterization of the High-Resolution Infrared Radiation Sounder Using Lunar Observations

“…The central wavelengths of several channels of HIRS are very similar, but spectral uncertainties remain [8]. This concerns in particular channels [13][14][15][16], which lie between 4.4 and 4.6 µm, and channels 1-7, which lie between 13.3 and 15 µm, i.e., close to the frequency of maximum spectral radiance for an object with a brightness temperature of 300 K. These wavelengths cover absorption bands of nitrous oxide and carbon dioxide in the atmosphere and produce therefore quite different flux values for Earth scenes, which makes it difficult to directly determine the inter-channel homogeneity of the sounding channels in flight.…”

“…Such deeper understanding is essential to produce fundamental climate data records from HIRS with consistent calibration. Such a data set could for example form the basis for new climate data records of upper tropospheric humidity as a complement to the corresponding microwave data set [14].…”