Fast reconstruction of hyperspectral radiative transfer simulations by using small spectral subsets: application to the oxygen A band

Hyperspectral radiative transfer simulations are a versatile tool in remote sensing but can pose a major computational burden. We describe a simple method to construct hyperspectral simulation results by using only a small spectral subsample of the simulated wavelength range, thus leading to major speedups in such simulations. This is achieved by computing principal components for a small number of representative hyperspectral spectra and then deriving a reconstruction matrix for a specific spectral subset of channels to compute the hyperspectral data. The method is applied and discussed in detail using the example of top-of-atmosphere radiances in the oxygen A band, leading to speedups in the range of one to two orders of magnitude when compared to radiative transfer simulations at full spectral resolution

cloud property retrieval using synergistic AATSR and MERIS observations

A newly developed daytime cloud property retrieval algorithm FAME-C (Freie Universität Berlin AATSR MERIS Cloud) is presented. Synergistic observations from AATSR and MERIS, both mounted on the polar orbiting satellite ENVISAT, are used for cloud screening. For cloudy pixels two main steps are carried out in a sequential form. First, a micro-physical cloud property retrieval is performed using an AATSR near-infrared and visible channel. Cloud phase, cloud optical thickness, and effective radius are retrieved, and subsequently cloud water path is computed. Second, two independent cloud top height products are retrieved. For cloud top temperature AATSR brightness temperatures are used, while for cloud top pressure the MERIS oxygen-A absorption channel is used. Results from the micro-physical retrieval serve as input for the two cloud top height retrievals. Introduced are the AATSR and MERIS forward models and auxiliary data needed in FAME-C. Also, the optimal estimation method with uncertainty estimates, which also provides for uncertainty estimated of the retrieved property on a pixel-basis, is presented. Within the frame of the ESA Climate Change Initiative project first global cloud property retrievals have been conducted for the years 2007–2009. For this time period verification efforts are presented comparing FAME-C cloud micro-physical properties to MODIS-TERRA derived cloud micro-physical properties for four selected regions on the globe. The results show reasonable accuracies between the cloud micro- physical retrievals. Biases are generally smallest for marine stratocumulus clouds; −0.28, 0.41μm and −0.18 g m−2 for cloud optical thickness, effective radius and cloud water path, respectively. This is also true for the root mean square error. Also, both cloud top height products are compared to cloud top heights derived from ground-based cloud radars located at several ARM sites. FAME-C mostly shows an underestimation of cloud top heights when compared to radar observations, which is partly attributed to the difficulty of accurate cloud property retrievals for optically thin clouds and multi-layer clouds. The bias is smallest, −0.9 km, for AATSR derived cloud top heights for single- layer clouds

Exploiting the sensitivity of two satellite cloud height retrievals to cloud vertical distribution

This work presents a study on the sensitivity of two satellite cloud height retrievals to cloud vertical distribution. The difference in sensitivity is exploited by relating the difference in the retrieved cloud heights to cloud vertical extent. The two cloud height retrievals, performed within the Freie Universität Berlin AATSR MERIS Cloud (FAME-C) algorithm, are based on independent measurements and different retrieval techniques. First, cloud top temperature (CTT) is retrieved from Advanced Along Track Scanning Radiometer (AATSR) measurements in the thermal infrared. Second, cloud top pressure (CTP) is retrieved from Medium Resolution Imaging Spectrometer (MERIS) measurements in the oxygen-A absorption band. Both CTT and CTP are converted to cloud top height (CTH) using atmospheric profiles from a numerical weather prediction model. A sensitivity study using radiative transfer simulations in the near- infrared and thermal infrared were performed to demonstrate the larger impact of the assumed cloud vertical extinction profile on MERIS than on AATSR top- of-atmosphere measurements. The difference in retrieved CTH (ΔCTH) from AATSR and MERIS are related to cloud vertical extent (CVE) as observed by ground- based lidar and radar at three ARM sites. To increase the impact of the cloud vertical extinction profile on the MERIS-CTP retrievals, single-layer and geometrically thin clouds are assumed in the forward model. The results of the comparison to the ground-based observations were separated into single-layer and multi-layer cloud cases. Analogous to previous findings, the MERIS-CTP retrievals appear to be close to pressure levels in the middle of the cloud. Assuming a linear relationship, the ΔCTH multiplied by 2.5 gives an estimate on the CVE for single-layer clouds. The relationship is weaker for multi-layer clouds. Due to large variations of cloud vertical extinction profiles occurring in nature, a quantitative estimate of the cloud vertical extent is accompanied with large uncertainties. Yet, estimates of the CVE can contribute to the characterization of a cloudy scene. To demonstrate the plausibility of the approach, an estimate of the CVE was applied to a case study. In light of the follow-up mission Sentinel-3 with AATSR and MERIS like instruments, Sea and Land Surface Temperature Radiometer (SLSTR) and (Ocean and Land Colour Instrument) OLCI, respectively, for which the FAME-C algorithm can be easily adapted, a more accurate estimate of the CVE can be expected. OLCI will have three channels in the oxygen-A absorption band, thus providing more pieces of information on the cloud vertical extinction profile

Multichannel analysis of correlation length of SEVIRI images around ground- based cloud observatories to determine their representativeness

Images of measured radiance in different channels of the geostationary Meteosat-9 SEVIRI instrument are analysed with respect to the representativeness of the observations of eight cloud observatories in Europe (e.g. measurements from cloud radars or microwave radiometers). Cloudy situations are selected to get a time series for every pixel in a 300 km × 300 km area centred around each ground station. Then a cross correlation of each time series to the pixel nearest to the corresponding ground site is calculated. In the end a correlation length is calculated to define the representativeness

A global climatology of total columnar water vapour from SSM/I and MERIS

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

A global climatology of total columnar water vapour from SSM/I and MERIS

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

Global Monitoring of Terrestrial Chlorophyll Fluorescence from Moderate-Spectral-Resolution Near-Infrared Satellite Measurements: Methodology, Simulations, and Application to GOME-2

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

Global monitoring of terrestrial chlorophyll fluorescence from moderate spectral resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2

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

On the efficient treatment of temperature profiles for the estimation of atmospheric transmittance under scattering conditions

The vertical temperature profile of the atmosphere has an influence on the width and intensity of gaseous absorption lines. In the visible and near infrared part of the spectrum, this poses a problem for the fast forward simulation of the radiative transfer, needed in algorithms for the retrieval of any atmospheric or surface-related parameter from satellite measurements. We show that the main part of the global variability of temperature profiles can be described by their first 2 to 6 eigenvectors, depending on the accuracy requirement, by performing a Principal Component Analysis (PCA) on a global set of temperature profiles from the Global Forecast System (GFS). Furthermore, we demonstrate the possibility to approximate the atmospheric transmittance in the O<sub>2</sub> A band for any temperature profile with almost perfect accuracy by a linear combination of the transmittances attributed to each of the significant temperature eigenvectors. For the retrieval of surface pressure from O<sub>2</sub> A band measurements, this reduces the global root mean square error from >30 hPa to better than 1 hPa by strongly reducing the regional bias of surface pressure, retrieved on the assumption of an average temperature profile. The technique can be applied under scattering conditions to eliminate temperature-induced errors in, e.g., simulated radiances. In principal, the method can be useful for any problem including gaseous absorption or emission with a significant influence of the temperature profile, such as the retrieval of total water vapour content or sea surface temperature

Quantification of uncertainties of water vapour column retrievals using future instruments

This study presents a quantification of uncertainties of total column water vapour retrievals based on simulated near-infrared measurements of upcoming instruments. The concepts of three scheduled spectrometers were taken into account: OLCI (Ocean and Land Color Instrument) on Sentinel-3, METimage on an EPS-SG (EUMETSAT Polar System – Second Generation) satellite and FCI (Flexible Combined Imager) on MTG (Meteosat Third Generation). Optimal estimation theory was used to estimate the error of a hypothetical total water vapour column retrieval for 27 different atmospheric cases. The errors range from 100% in very dry cases to 2% in humid cases with a very high surface albedo. Generally, the absolute uncertainties increase with higher water vapour column content due to H<sub>2</sub>O-saturation and decrease with a brighter surface albedo. Uncertainties increase with higher aerosol optical thickness, apart from very dark cases. Overall, the METimage channel setting enables the most accurate retrievals. The retrieval using the MTG-FCI build-up has the highest uncertainties apart from very bright cases. <br><br> On the one hand, a retrieval using two absorption channels increases the accuracy, in some cases by one order of magnitude, in comparison to a retrieval using just one absorption channel. On the other hand, a retrieval using three absorption channels has no significant advantage over a two-absorption channel retrieval. <br><br> Furthermore, the optimal position of the absorption channels was determined using the concept of the "information content". For a single channel retrieval, a channel at 900 or 915 nm has the highest mean information content over all cases. The second absorption channel is ideally weakly correlated with the first one, and therefore positioned at 935 nm, in a region with stronger water vapour absorption