Globally mapped terrestrial chlorophyll fluorescence retrievals are of high interest because they can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. Previous satellite retrievals of fluorescence have relied solely upon the filling-in of solar Fraunhofer lines that are not significantly affected by atmospheric absorption. Although these measurements provide near-global coverage on a monthly basis, they suffer from relatively low precision and sparse spatial sampling. Here, we describe a new methodology to retrieve global far-red fluorescence information; we use hyperspectral data with a simplified radiative transfer model to disentangle the spectral signatures of three basic components: atmospheric absorption, surface reflectance, and fluorescence radiance. An empirically based principal component analysis approach is employed, primarily using cloudy data over ocean, to model and solve for the atmospheric absorption. Through detailed simulations, we demonstrate the feasibility of the approach and show that moderate-spectral-resolution measurements with a relatively high signal-to-noise ratio can be used to retrieve far-red fluorescence information with good precision and accuracy. The method is then applied to data from the Global Ozone Monitoring Instrument 2 (GOME-2). The GOME-2 fluorescence retrievals display similar spatial structure as compared with those from a simpler technique applied to the Greenhouse gases Observing SATellite (GOSAT). GOME-2 enables global mapping of far-red fluorescence with higher precision over smaller spatial and temporal scales than is possible with GOSAT. Near-global coverage is provided within a few days. We are able to show clearly for the first time physically plausible variations in fluorescence over the course of a single month at a spatial resolution of 0.5° × 0.5°. We also show some significant differences between fluorescence and coincident normalized difference vegetation indices (NDVI) retrievals.

The Global Ozone Monitoring Experiment-2 (GOME-2) flies on the Metop series of satellites, the space component of the EUMETSAT Polar System. In this paper we will provide an overview of the instrument design, the on-ground calibration and characterization activities, in-flight calibration, and level 0 to 1 data processing. The current status of the level 1 data is presented and points of specific relevance to users are highlighted. Long-term level 1 data consistency is also discussed and plans for future work are outlined. The information contained in this paper summarizes a large number of technical reports and related documents containing information that is not currently available in the published literature. These reports and documents are however made available on the EUMETSAT web pages and readers requiring more details than can be provided in this overview paper will find appropriate references at relevant points in the text.

A retrieval of total column water vapour (TCWV) from MODIS (Moderate-resolution Imaging Spectroradiometer) measurements is presented. The algorithm is adapted from a retrieval for MERIS (Medium Resolution Imaging Spectrometer) from Lindstrot et al. (2012). It obtains the TCWV for cloud-free scenes above land at a spatial resolution of 1 km × 1 km and provides uncertainties on a pixel-by-pixel basis. The algorithm has been extended by introducing empirical correction coefficients for the transmittance calculation within the forward operator. With that, a wet bias of the MODIS algorithm against ARM microwave radiometer data has been eliminated. The validation against other ground-based measurements (GNSS water vapour stations, GUAN radiosondes, and AERONET sun photometers) on a global scale reveals a bias between −0.8 and −1.6 mm and root mean square deviations between 0.9 and 2 mm. This is an improvement in comparison to the operational TCWV Level 2 product (bias between −1.9 and −3.2 mm and root mean square deviations between 1.9 and 3.4 mm). The comparison to MERIS TCWV for an example overpass exposes a systematic dry bias.

A new scheme for the retrieval of total columnar water vapour from measurements of MERIS (Medium Resolution Imaging Spectrometer) on ENVISAT (ENVIronmental SATellite) is presented. The algorithm is based on a fast forward model of the water vapour transmittance around 900nm, including a correction for atmospheric scattering and the influence of the temperature- and pressure-profile on the water vapour absorption lines. It provides the water vapour column amount for cloud-free scenes above land and ocean at a spatial resolution of 0.25 km × 0.3 km and 1 km × 1.2 km, depending on whether applied to the "full resolution" or the operational "reduced resolution" mode of MERIS. Uncertainties are provided on a pixel-by-pixel basis, taking into account all relevant sources of error. An extensive validation against various sources of ground-based reference data reveals a high accuracy of MERIS water vapour above land (root mean square deviations between 1.4 mm and 3.7 mm), apart from a wet bias of MERIS between 5 and 10% that is found in all comparison studies. This wet bias might be caused by spectroscopic uncertainties, such as the description of the water vapour continuum. Above ocean the accuracy is reduced, due to the uncertainty introduced by the unknown atmospheric scattering. Consequently, an increased root mean square deviation of ≥5 mm was found by comparing MERIS total columnar water vapour above ocean against SSM/I and ENVISAT MWR data. An increased wet bias of 2–3 mm is found over ocean, potentially due to a not properly working atmospheric correction scheme.

A global time series of total columnar water vapour from combined data of the Medium Resolution Imaging Spectrometer (MERIS) onboard ESA's Environmental Satellite (ENVISAT) and the Special Sensor Microwave/Imager (SSM/I) onboard the satellite series of the US Defense Meteorological Satellite Program (DMSP) is presented. The unique data set, generated in the framework of the ESA Data User Element (DUE) GlobVapour project, combines atmospheric water vapour observations over land and ocean, derived from measurements in the near-infrared and the microwave range, respectively. Daily composites and monthly means of total columnar water vapour are available as global maps on rectangular latitude–longitude grids with a spatial resolution of 0.05° × 0.05° over land and 0.5° × 0.5° over ocean for the years 2003 to 2008. The data are stored in NetCDF files and is fully compliant with the NetCDF Climate Forecast convention. Through the combination of high-quality microwave observations and near-infrared observations over ocean and land surfaces, respectively, the data set provides global coverage. The combination of both products is carried out such that the individual properties of the microwave and near-infrared products, in particular their uncertainties, are not modified by the merging process and are therefore well defined. Due to the global coverage and the provided uncertainty estimates this data set is potentially of high value for climate research. The SSM/I-MERIS TCWV data set is freely available via the GlobVapour project web page (www.globvapour.info) with associated doi:10.5676/DFE/WV_COMB/FP. In this paper, the details of the data set generation, i.e. the satellite data used, the retrieval techniques and merging approaches, are presented. The derived level 3 products are compared to global radiosonde data from the GCOS upper air network (GUAN), showing a high agreement with a root-mean-square deviation of roughly 4.4 kg m⁻² and a small wet bias well below 1 kg m⁻². Furthermore, the data set is shown to be free of seasonal biases. The consistency of the MERIS and SSM/I retrievals is demonstrated by applying the MERIS retrieval to sun glint areas over ocean.

Reflected solar radiation measured by the Medium Resolution Imaging Spectrometer (MERIS) on the Environmental Satellite (Envisat) is currently used within the European Space Agency’s ground segment for the retrieval of cloud-top pressure. The algorithm is based on the analysis of the gaseous absorption of solar radiation in the oxygen A band at 761 nm. The strength of absorption is directly related to the average photon pathlength, which is mainly determined by the cloud-top pressure. However, it additionally depends on surface and cloud properties, like cloud thickness and microphysics. The interpretation of the measurements is further complicated by the temperature dependence of the absorption line shapes and the sensitivity to the spectral properties of the spectrometer like spectral position and width. This paper is focused on results of sensitivity studies using the Matrix Operator Model (MOMO) radiative transfer model that examine the most important parameters affecting the measurements of MERIS or similar instruments. The cloud-top pressure retrieval scheme is briefly presented. An analysis of the information content and the degrees of freedom of measurements within the oxygen A band is included in this study.