The ESA research project "Application of KLIMA Algorithm to CO Retrieval from IASI/METOP-A Observations and Comparison with TANSO-FTS/GOSAT Products" aims to develop a dedicated software, based on the KLIMA inversion algorithm (originally proposed by IFAC-CNR for the 6 cycle of ESA Earth Explorer Core Missions), suited for CO retrieval and integrated into the ESA grid-based operational environment Grid Processing On-Demand (G-POD) to process Level 1 data acquired by the infrared atmospheric sounding interferometer (IASI) and to perform a comparison with Thermal And Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer (TANSO-FTS), on board of the Greenhouse gases Observing SATellite (GOSAT), Level 2 data. In order to obtain a reasonable capacity to bulk processing IASI data, we choose to integrate the KLIMA code into the G-POD system. For this reason, we investigated an optimized version of the KLIMA algorithm, aiming at developing a nonoperational retrieval code with ad- equate features for the integration on the G-POD system. The optimized version of KLIMA retrieval code has been completed and integrated on the G-POD operational environment and is available for bulk processing of IASI data. Using the KLIMA inversion code integrated into the ESA G-POD, it was possible to perform an extensive comparison of a selected set of IASI measurements collocated with TANSO-FTS observations. We performed an extensive comparison of the column-average CO dry air mole fraction (XCO ) retrieved from IASI measurements by using the KLIMA/G-POD inversion code with the operational Level 2 SWIR products (Version 01.xx and Version 02.xx) from collocated TANSO-FTS observations. In this work, we describe the strategy adopted for the comparison and we show the results of this activity.

Comparison of Column-Averaged Volume Mixing Ratios of Carbon Dioxide Retrieved From IASI/METOP-A Using KLIMA Algorithm and TANSO-FTS/GOSAT Level 2 Products

Lucia Maria Laurenza;S Del Bianco;M Gai;F Barbara;U Cortesi
2014

Abstract

The ESA research project "Application of KLIMA Algorithm to CO Retrieval from IASI/METOP-A Observations and Comparison with TANSO-FTS/GOSAT Products" aims to develop a dedicated software, based on the KLIMA inversion algorithm (originally proposed by IFAC-CNR for the 6 cycle of ESA Earth Explorer Core Missions), suited for CO retrieval and integrated into the ESA grid-based operational environment Grid Processing On-Demand (G-POD) to process Level 1 data acquired by the infrared atmospheric sounding interferometer (IASI) and to perform a comparison with Thermal And Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer (TANSO-FTS), on board of the Greenhouse gases Observing SATellite (GOSAT), Level 2 data. In order to obtain a reasonable capacity to bulk processing IASI data, we choose to integrate the KLIMA code into the G-POD system. For this reason, we investigated an optimized version of the KLIMA algorithm, aiming at developing a nonoperational retrieval code with ad- equate features for the integration on the G-POD system. The optimized version of KLIMA retrieval code has been completed and integrated on the G-POD operational environment and is available for bulk processing of IASI data. Using the KLIMA inversion code integrated into the ESA G-POD, it was possible to perform an extensive comparison of a selected set of IASI measurements collocated with TANSO-FTS observations. We performed an extensive comparison of the column-average CO dry air mole fraction (XCO ) retrieved from IASI measurements by using the KLIMA/G-POD inversion code with the operational Level 2 SWIR products (Version 01.xx and Version 02.xx) from collocated TANSO-FTS observations. In this work, we describe the strategy adopted for the comparison and we show the results of this activity.
2014
Istituto di Fisica Applicata - IFAC
Atmospheric modeling
carbon dioxide
data validation
Greenhouse gases Observing SATellite (GOSAT)
hyperspectral sensors
infrared atmospheric sounding interferom- eter (IASI)
KLIMA algorithm
remote sensing
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/248601
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