Thomas Ruppert

Ten years of GOME/ERS-2 total ozone data- : The new GOME data processor (GDP) version 4: 1. Algorithm description

The Global Ozone Monitoring Instrument (GOME) was launched on European Space Agencys ERS-2 platform in April 1995. The GOME data processor (GDP) operational retrieval algorithm has generated total ozone columns since July 1995. In 2004 the GDP system was given a major upgrade to version 4.0, a new validation was performed, and the 10-year GOME level 1 data record was reprocessed. In two papers, we describe the GDP 4.0 retrieval algorithm and present an error budget and sensitivity analysis (paper 1) and validation of the GDP total ozone product and the overall accuracy of the entire GOME ozone record (paper 2). GDP 4.0 uses an optimized differential optical absorption spectroscopy (DOAS) algorithm, with air mass factor (AMF) conversions calculated using the radiative transfer code linearized discrete ordinate radiative transfer (LIDORT). AMF computation is based on the TOMS version 8 ozone profile climatology, classified by total column, and AMFs are adjusted iteratively to reflect the DOAS slant column result. GDP 4.0 has improved wavelength calibration and reference spectra and includes a new molecular Ring correction to deal with distortion of ozone absorption features due to inelastic rotational Raman scattering effects. Preprocessing for cloud parameter estimation in GDP 4.0 is done using two new cloud correction algorithms: OCRA and ROCINN. For clear and cloudy scenes the precision of the ozone column product is better than 2.4 and 3.3%, respectively, for solar zenith angles up to 80°. Comparisons with ground-based data are generally at the 1-1.5% level or better for all regions outside the poles.

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).

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.

Automated Improvement of Geolocation Accuracy in AVHRR Data Using a Two-Step Chip Matching Approach—A Part of the TIMELINE Preprocessor

The geolocation of Advanced Very High Resolution Radiometer (AVHRR) data is known to be imprecise due to minor satellite position and orbit uncertainties. These uncertainties lead to distortions once the data are projected based on the provided orbit parameters. This can cause geolocation errors of up to 10 km per pixel which is an obstacle for applications such as time series analysis, compositing/mosaicking of images, or the combination with other satellite data. Therefore, a fusion of two techniques to match the data in orbit projection has been developed to overcome this limitation, even without the precise knowledge of the orbit parameters. Both techniques attempt to find the best match between small image chips taken from a reference water mask in the first, and from a median Normalized Difference Vegetation Index (NDVI) mask in the second round. This match is determined shifting around the small image chips until the best correlation between reference and satellite data source is found for each respective image part. Only if both attempts result in the same shift in any direction, the position in the orbit is included in a third order polynomial warping process that will ultimately improve the geolocation accuracy of the AVHRR data. The warping updates the latitude and longitude layers and the contents of the data layers itself remain untouched. As such, original sensor measurements are preserved. An included automated quality assessment generates a quality layer that informs about the reliability of the matching.

Calibration and Pre-processing of a Multi-decadal AVHRR Time Series

Since the early 1980s, the German Remote Sensing Data Centre (DFD) of the German Aerospace Centre (DLR) has received archived and processed Advanced Very High Resolution Radiometer (AVHRR) data from the Polar Orbiting Environmental Satellites (POES) of the National Oceanic and Atmospheric Administration (NOAA). By December 2013, over 237,000 paths over Europe have since been archived at DLR. Based on these High Resolution Picture Transmission (HRPT) raw datasets, an operational pre-processing and value-adding chain has been developed (Dech et al., Aerosp Sci Technol 2(5):335–346, 1998; Tungalagsaikhan et al., Proc. 23th DGPF (12), 2003). In this chapter, the series of AVHRR sensors is introduced, and information on calibration and system correction procedures is given. Next, the pre-processing part of DLR’s processing chain is described, where focus is set on the calibration aspects. Time series examples are provided to show the influence of changes in calibration over time, and to illustrate the need for consistent pre-processing and data harmonization. According to these requirements DLR’s multi-decadal archive of AVHRR data will be re-processed in the frame of the TIMELINE project, providing consistent and well-calibrated time series data.

Challenges and experiences in using heterogeneous, geo-referenced data for automatic creation of driving simulator environments

For the development of advanced driving assistance and automation systems the simulation plays an important role. Urban areas get increasing emphasis, especially in the context of future Car2X-communication. This article describes an approach developed in the project Virtual World. Its goal is to model such virtual three-dimensional (3D) environments and logical road descriptions automatically based on a tool chain from heterogeneous geographic datasets (e.g., cadastral data, road surveying, aerial pictures, and crowd-sourced data). As proof of concept, the urban area of Braunschweig, Germany, was chosen. The article focuses on the generation of a 3D city model with corresponding road network description suitable for driving and traffic simulations as used in research and industry. The article gives technical descriptions of the major work steps and discusses issues regarding the availability of data. It concludes with the current project outcome and further development in the project.

The Integration of an Operational Fire Hot Spots Processing Chain in a Multi-Hazard Emergency Management Service Platform (PHAROS)

The project PHAROS (Project on a Multi-Hazard Open Platform for Satellite Based Downstream Services) designs and implements a multi-hazard open service platform which integrates space-based earth observation, satellite communications and navigation (Galileo/GNSS) assets to provide sustainable (pre-operational) services for a wide variety of users in multi-application domains, such as prediction/early detection of emergencies, population alerting, environmental monitoring and crisis management. While the service platform is designed to be multi-hazard, the specific developments for the pre-operational system and pilot demonstration will be focused on the forest fire scenario. The platform will integrate data from EO satellites and in-situ sensors process it and provide the results to a series of key services for disaster management in its different phases. One of the main concerns is to provide fire hot spots as an input for the PHAROS Simulation Service. nThese fire hot spots (thermal anomalies) are derived automatically and in near real time (NRT) from MODIS data. The MODIS data are available in a high (1d) temporal and in a medium (250m – 1000m) spatial resolution. For the detection of high temperature events (HTE) the MOD14 algorithm is used. The algorithm is based on the shift of the radiances/reflectance to shorter wavelengths (middle infrared) with an increasing surface temperature. MOD14 is well documented and tested in operational services and guarantees comparability and reproducibility as well as a standardized international acknowledged product. The thermal information is collected at 1000 m spatial resolution twice daily by each sensor (Terra and Aqua) providing up to four thermal observations daily. The MODIS images used for fire detection are acquired from two direct broadcast receiving stations from DLR located in Oberpfaffenhofen and Neustrelitz (Germany). nThis Poster will give an overview of the processing chain from the reception, the processing and derivation of the fire hot spots to the dissemination in the Pharos system.

GOME data processor: the first operational DOAS-based algorithm applied to data from a spaceborne sensor

The Global Ozone Monitoring Experiment (GOME) is a new atmospheric chemistry instrument on-board the ERS-2 satellite which was launched in April 1995. The GOME is designed to measure a range of atmospheric trace constituents, with particular emphasis on global ozone distributions. The ground segment for the GOME sensor is with the German Remote Sensing Data Center (DFD). Major components of the GDP are the complete GOME data archive, the Earth-shine spectra calibration step, the total ozone column retrieval process, and the integration into the D-PAF data management system (DMS). Raw GOME data re converted into calibrated radiances during the Level 0 to 1 processing by applying a series of calibration algorithms using in-flight observations and pre-flight instrument calibration parameters. Total column abundances of ozone and other trace gases can be derived from the Level 1 Product, comprising the Earth-shine radiance and the extra- terrestrial solar irradiance, by applying three designated algorithms in the Level 1 to 2 processing step. The Initial Cloud Fitting Algorithm (ICFA) uses the spectral features close to and within the O2 A-band around 760 nm to determine the fractional cloud cover of the pixel scene. The differential optical absorption spectroscopy technique is used for the operational retrieval of ozone and nitrogen dioxide form data in the UV and visible regions of the spectrum. The slant column densities are converted to vertical columns by division with an appropriate Air Mass Factor (AMF), derived from radiative transfer simulations. If clouds are detected by ICFA, an averaged AMF is calculated from the intensity-weighted AMFs to ground and to cloud top. Since the end of July 1996 the GOME data processing is performed operationally at the DFD.