Werner Thomas

Detection of biomass burning combustion products in Southeast Asia from backscatter data taken by the GOME spectrometer

We show that atmospheric UV/visible backscatter spectra obtained by the Global Ozone Monitoring Experiment (GOME) spectrometer on board the ESA ERS-2 satellite may be used to retrieve column amounts of key trace species associated with smoke cloud combustion from biomass burning events. This paper focuses on the recent rain forest burning in SE Asia (August–October 1997). For ground scenes with low cloudiness, differential absorption fitting applied to backscatter spectra yields column distributions of NO2 and H2CO in and around smoke-polluted regions. A two-fold increase in the vertical NO2 content is apparent over large parts of the smoke cloud; this clearly indicates the ability of GOME to measure tropospheric NO2 content. H2CO is detected only in areas closest to combustion sources. Slant column amounts in the range 2.5 - 4 × 1016 mol cm−2 have been determined; these correspond with previous estimations of vertical columns of H2CO for biomass Savannah burning.

Cloud Properties Derived From GOME/ERS-2 Backscatter Data for Trace Gas Retrieval

We focus on the retrieval of cloud properties appropriate for trace gas retrieval from sun-normalized ultraviolet/visible backscatter spectra obtained from the Global Ozone Monitoring Experiment (GOME) onboard the European Space Agencys European Remote Sensing 2 Satellite (ERS-2). Retrieved quantities are the fractional cloud coverage of the GOME footprint, the cloud-top albedo, and the cloud-top height. A data fusion technique is applied to calculate the fractional cloud cover of GOME footprints from GOMEs polarization measurement devices. Furthermore, cloud-top albedo and cloud-top height are retrieved simultaneously from GOME measurements around the oxygen A-band by a neural network approach. We compare our results with corresponding results from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) radiometer onboard the first European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) METEOSAT Second Generation 1 geostationary spacecraft. Our analysis revealed that GOME-derived basic cloud properties are of remarkably high quality. GOME slightly underestimates the cloud coverage of footprints, which was expected since GOME is mainly sensitive to optically thick water clouds. GOME measurements are limited to the ultraviolet and visible part of the solar spectrum, which hampers the detection of optically thin clouds. For both the cloud-top height and the cloud-top albedo, we found a small bias relative to SEVIRI results. The overall uncertainty of retrieved total ozone columns with respect to cloud parameters is about 1%-2%. Our approach is applied to the operational processing of GOME/ERS-2 and will be applied to GOME-2/METOP (launched in 2006) in the framework of EUMETSATs Satellite Application Facility on Ozone and Atmospheric Chemistry Monitoring (O3M-SAF).

The CM-SAF and FUB Cloud Detection Schemes for SEVIRI: Validation with Synoptic Data and Initial Comparison with MODIS and CALIPSO

Abstract The Satellite Application Facility on Climate Monitoring (CM-SAF) is aiming to retrieve satellite-derived geophysical parameters suitable for climate monitoring. CM-SAF started routine operations in early 2007 and provides a climatology of parameters describing the global energy and water cycle on a regional scale and partially on a global scale. Here, the authors focus on the performance of cloud detection methods applied to measurements of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on the first Meteosat Second Generation geostationary spacecraft. The retrieved cloud mask is the basis for calculating the cloud fractional coverage (CFC) but is also mandatory for retrieving other geophysical parameters. Therefore, the quality of the cloud detection directly influences climate monitoring of many other parameters derived from spaceborne sensors. CM-SAF products and results of an alternative cloud coverage retrieval provided by the Institut fur Weltraumwissenschaften of the Freie Univ...

Global patterns in daytime cloud properties derived from GOME backscatter UV-VIS measurements

In this paper, we present an overview of the cloud property data set derived from 8 years of reflected solar ultraviolet-visible (UV-VIS) measurements taken by the global ozone monitoring experiment (GOME) instrument from April 1996 to June 2003. We consider four such properties: cloud amount, cloud-top pressure, cloud optical thickness and cloud type. Cloud amounts are generated from GOME broadband polarization data using data fusion techniques, while cloud-top height (pressure) and cloud-top albedo are retrieved from GOME backscatter measurements in the oxygen (O2) A-band via neural network inversion of simulated reflectances. Cloud optical thickness is derived as an additional parameter from the cloud-top albedo and radiative transfer model simulations, and cloud type is determined from the cloud-top pressure and optical thickness. We analyse global and seasonal patterns for these properties, looking at monthly means, standard deviations and the 8-year average values. We compare GOME results with the longer-period multisatellite international satellite cloud climatology project (ISCCP) D-series cloud climatology. The overall good agreement demonstrates that GOME provides accurate and complementary cloud information. Differences in cloud amount, cloud-top height and optical thickness values are due primarily to contrasting measurement strategies (GOME measures daytime-only UV-VIS backscatter, ISCCP is based on several day and night infrared satellite observations). We look forward to the extension of this UV-VIS cloud parameter series with the advent of more recent backscatter atmospheric composition instruments such as the scanning imaging absorption spectrometer for atmospheric cartography (SCIAMACHY) on-board the environmental satellite (ENVISAT) and the GOME-2 series on the MetOp platforms.

GOME level 1-to-2 data processor version 3.0: a major upgrade of the GOME/ERS-2 total ozone retrieval algorithm

The global ozone monitoring experiment (GOME) was launched in April 1995, and the GOME data processor (GDP) retrieval algorithm has processed operational total ozone amounts since July 1995. GDP level 1-to-2 is based on the two-step differential optical absorption spectroscopy (DOAS) approach, involving slant column fitting followed by air mass factor (AMF) conversions to vertical column amounts. We present a major upgrade of this algorithm to version 3.0. GDP 3.0 was implemented in July 2002, and the 9-year GOME data record from July 1995 to December 2004 has been processed using this algorithm. The key component in GDP 3.0 is an iterative approach to AMF calculation, in which AMFs and corresponding vertical column densities are adjusted to reflect the true ozone distribution as represented by the fitted DOAS effective slant column. A neural network ensemble is used to optimize the fast and accurate parametrization of AMFs. We describe results of a recent validation exercise for the operational version of the total ozone algorithm; in particular, seasonal and meridian errors are reduced by a factor of 2. On a global basis, GDP 3.0 ozone total column results lie between -2% and +4% of ground-based values for moderate solar zenith angles lower than 70 degrees. A larger variability of about +5% and -8% is observed for higher solar zenith angles up to 90 degrees.

A complex study of Etna's volcanic plume from ground‐based, in situ and space‐borne observations

Two periods of transboundary transport of volcanic aerosols and debris following recent eruptions of Mount Etna, Italy, were examined using ground‐based and satellite spectrophotometric measurements together with Light Detection And Ranging (LiDAR) and aerosol filter observations in Athens and Thessaloniki, Greece. Independent columnar SO2 measurements from ground and space identified peaks at Greek sites after the volcanic eruptions. LiDAR measurements of the aerosol extinction at Thessaloniki and Athens performed in July 2001 have shown the height of the volcanic plume to be about 3.5 km asl and the optical thickness of the dust layer to be of the order of 3×10−3 at 532 nm. Strong ozone depletion observed at the volcano plume level by using ozonesonde ascents may be attributed to the in‐plume processes that generate reactive halogens, which in turn destroy ozone. The chemical and elemental composition of aerosol samples, taken at the Earths surface, was analysed and confirmed the volcanic origin of the dust.

A three-dimensional model for calculating reflection functions of inhomogeneous and orographically structured natural landscapes

Abstract A three-dimensional reflection model has been developed for calculating the reflection functions of inhomogeneous and orographically structured natural land surfaces. The site of an area whose reflection properties are to be modeled can be freely chosen; the task is limited, however, by its allocated CPU time. The surface structure of such an area is taken from a digital terrain model. The known anisotropic reflection properties of various flat surface types, for example, coniferous forest, hardwood forest, grassland, and agricultural cover types, are taken into account. The effects of multiple reflection in the subscale orographic structure of an area are calculated up to the second order of reflection. For the test sites, a typical difference of 25% in the reflection factors of a vertically structured surface and a corresponding flat surface was found, with the higher values for a flat surface. The model is validated by comparing simulated radiances with measured radiances in the solar channels of the AVHRR onboard NOAA-11 and NOAA-12 for several test sites in the German alpine region. The simulated radiances are calculated with an angular-dependent radiative transfer model with the modeled reflection functions as the lower boundary layer of the coupled Earth-atmosphere system. The results show good agreement between measured and simulated satellite data, especially in Channel 2, if the vertical structure of the surface is considered. A statistically significant influence of the orographic structure was found in the simulated radiances as compared with radiances calculated for a corresponding flat surface with the same cover types. It is concluded that the developed reflection model is able to simulate correctly the anisotropic reflection properties of vertically structured natural landscape as it can be found in mountainous terrain.

A Method for Estimating the Sampling Error Applied to CM-SAF Monthly Mean Cloud Fractional Cover Data Retrieved From MSG SEVIRI

Averaging a set of individual measurements can reduce the stochastic error but can introduce a sampling error particularly for irregularly sampled data. We present a general method to estimate the total error of an averaged quantity as a combination of the measurement error and the sampling error without knowledge about the true average value of the distribution. Our approach requires covariance matrices connecting the retrieved measurement values to an independent reference data set. These covariance matrices can be obtained from a representative validation data set. We confirm the validity of the method by estimating the temporal sampling error of monthly mean cloud fractional cover (CFC) data derived from the Spinning-Enhanced Visible and Infrared Imager radiometer onboard the METEOSAT Second Generation (MSG) spacecraft, operated by the European Organization for the Exploitation of Meteorological Satellites. The estimated sampling errors are then compared with the true sampling errors calculated from an hourly sampled complete data set. For this purpose, we use ten sampling scenarios. Some of them address typical sampling problems like systematic over- and undersampling as well as hourly, daily, and random data gaps. Two additional sampling scenarios are directly related to the satellite application facility on climate monitoring monthly mean CFC data record. These are used to estimate the worst case sampling errors of this data record. The estimated total and sampling errors agree well with corresponding calculated values. We derive the needed covariance matrices by analyzing synoptic observations of the cloud fraction which are MSG diskwide available, the majority of them over European land surfaces. The method is not limited to temporal averaging cloud fraction data. Moreover, it is a general method that is also applicable to temporal and spatial averaging of other parameters as long as appropriate covariance matrices are available.

SCIAMACHY near real time products and algorithms

SCIAMACHY, which is part of the ENVISAT-1 scientific payload, is a double grating spectrometer designed to observe the light reflected, transmitted and back-scattered by the Earths atmosphere. Its broad spectral coverage (240 to 2400 nm) combined with multiple viewing geometries (nadir, limb, solar and lunar occultation) enable the measurement of numerous atmospheric constituents in the stratosphere as well as in the troposphere. The list of trace gases includes O/sub 3/, NO/sub 2/, BrO, SO/sub 2/, OClO, H/sub 2/CO in the UV and visible regions and H/sub 2/O, N/sub 2/O, CO, CH/sub 4/ and CO/sub 2/ in the IR regions. Geolocated engineering calibrated products (level 1b) as well as geolocated geophysical products (level 2) containing the total column amounts for the above species (excluding CO/sub 2/) are produced by a near real time (NRT) processor and are available to users three hours after data acquisition. An enhanced off-line processor additionally produces (several days after acquisition) stratospheric profiles of most of the above molecules as well as O/sub 3/ profiles down to the troposphere. As an Expert Support Laboratory, the DLR-DFD in Oberpfaffenhofen has specified the algorithms required for the NRT processing chain. This paper summarises these NRT algorithms and the resulting level 1b and 2 products distributed to users.

Detection of minor trace species in the atmosphere

The Global Ozone Monitoring Experiment (GOME) is an atmospheric chemistry instrument on-board the ERS-2 satellite which is able to measure a range of important atmospheric trace constituents on a global scale. Atmospheric UV/visible backscatter spectra obtained by the GOME spectrometer were used to retrieve column amounts of key trace species associated with biomass burning events and ozone hole chemistry. In particular, the column distributions of ozone (O3), nitrogen dioxide (NO2), formaldehyde (CH2O), and bromine-monoxide (BrO) were retrieved on an operational basis. The differential optical absorption spectroscopy technique (DOAS) is applied to backscatter spectra and yields slant column distributions of the aforementioned species. Additionally, the vertical columns of O3 and NO2 are provided. A strong enhancement of both the NO2 and CH2O contents were detected during the severe biomass burning event in September 1997 in SE Asia. A higher NO2 content is apparent over a large area within the smoke clouds, where formaldehyde is detected only in areas closest to combustion sources. BrO has been detected on a global scale and under Antarctic winter (ozone hole) conditions. The knowledge about the spatial distribution and the amount of BrO is of high relevance because BrO is a key species for the depletion of stratospheric ozone.