Total column water vapour measurements from GOME-2 MetOp-A and MetOp-B

Abstract

Abstract. The knowledge of the total column water vapour (TCWV) global distribution is fundamental for climate analysis and weather monitoring. In this work, we present the retrieval algorithm used to derive the operational TCWV from the GOME-2 sensors and perform an extensive inter-comparison and validation in order to estimate their absolute accuracy and long-term stability. We use the recently reprocessed data sets retrieved by the GOME-2 instruments aboard EUMETSAT's MetOp-A and MetOp-B satellites and generated by DLR in the framework of the O3M-SAF using the GOME Data Processor (GDP) version 4.7. The retrieval algorithm is based on a classical Differential Optical Absorption Spectroscopy (DOAS) method and combines H2O/O2 retrieval for the computation of the trace gas vertical column density. We introduce a further enhancement in the quality of the H2O column by optimizing the cloud screening and developing an empirical correction in order to eliminate the instrument scan angle dependencies. We evaluate the overall consistency between about 8 months measurements from the newer GOME-2 instrument on the MetOp-B platform with the GOME-2/MetOp-A data in the overlap period. Furthermore, we compare GOME-2 results with independent TCWV data from ECMWF and with SSMIS satellite measurements during the full period January 2007–August 2013 and we perform a validation against the combined SSM/I + MERIS satellite data set developed in the framework of the ESA DUE GlobVapour project. We find global mean biases as small as ± 0.03 g cm−2 between GOME-2A and all other data sets. The combined SSM/I-MERIS sample is typically drier than the GOME-2 retrievals (−0.005 g cm−2), while on average GOME-2 data overestimate the SSMIS measurements by only 0.028 g cm−2. However, the size of some of these biases are seasonally dependent. Monthly average differences can be as large as 0.1 g cm−2, based on the analysis against SSMIS measurements, but are not as evident in the validation with the ECMWF and the SSM/I + MERIS data. Studying two exemplary months, we estimate regional differences and identify a very good agreement between GOME-2 total columns and all three independent data sets, especially for land areas, although some discrepancies over ocean and over land areas with high humidity and a relatively large surface albedo are also present.

Validation of GOME-2/MetOp-A total water vapour column using reference radiosonde data from the GRUAN network

“…Although the GDP retrieval algorithm provides a "H2O flag" for heavy cloudy conditions that invalidates the AMF determination and, consequently, the retrieved TWVC data, it does not apply any cloud correction method for the remaining cloudy cases Grossi et al, 2014). Therefore, it is expected that the TWVC data derived from the GDP algorithm presents a larger dependence on cloud properties than other satellite retrieval algorithms with some implemented cloud correction method.…”

“…Additionally, all GOME-2 TWVC data used in this work correspond to those cases which are not flagged by the GDP 4.6-4.7 retrieval algorithms as contaminated by heavy clouds (large fraction of pixel covered by clouds and, simultaneously, with high cloud albedo). Thus, the "H2O flag" is set when cloud albedo × cloud fraction > 0.6 or when the O 2 absorption is too small Grossi et al, 2014). The TWVC data for the remaining cloudy cases are provided by the satellite algorithms.…”

“…A detailed description of GDP can be found in the Algorithm Theoretical Basis Document of Valks et al (2011) and in the work of Grossi et al (2014). Here a brief description is presented.…”

“…According to Valks et al (2011) and Grossi et al (2014), the error budget in the TWVC retrieved from GOME-2 can be separated in errors affecting the retrieval of the slant columns (DOAS-related errors), and errors affecting the conversion of the slant into vertical column (AMF-related errors). Additionally, the general error contribution (e.g.…”

“…Although some validation exercises of GOME-2 TWVC data have been separately carried out before (e.g. Kalakoski et al, 2011Kalakoski et al, , 2014Schröder and Schneider, 2012;Grossi et al, 2013Grossi et al, , 2014, the present study should be considered as complementary since it works with a homogeneous high-quality data sets as reference (RS92 GRUAN Data Product, RS92-GDP) and with a focus on the analysis of the effects of cloudiness and geometrical properties that have not been studied in detail up to now. It is therefore expected that this paper will improve the understanding of the quality of the GOME-2 TWVC data retrieved by the GDP retrieval algorithm.…”