The Heidelberg iterative cloud retrieval utilities (HICRU) and its application to GOME data

Information about clouds, in particular the accurate identification of cloud free pixels, is crucial for the retrieval of tropospheric vertical column densities from space. The Heidelberg Iterative Cloud Retrieval Utilities (HICRU) retrieve effective cloud fraction using spectra of two instruments designed for trace gas retrievals from space: The Global Ozone Monitoring Experiment (GOME) on the European Remote Sensing Satellite (ERS-2) and the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) on ENVISAT. HICRU applies the widely used threshold method to the so-called Polarization Monitoring Devices (PMDs) with higher spatial resolution compared to the channels used for trace gas retrievals. Cloud retrieval and in particular the identification of cloud free pixels is improved by HICRU through a sophisticated, iterative retrieval of the thresholds which takes their dependency on different instrumental and geometrical parameters into account. The lower thresholds, which represent the surface albedo and strongly affect the results of the algorithm, are retrieved accurately through a four stage classification scheme using image sequence analysis. The design and the results of the algorithm applied to GOME data are described and compared to several other cloud algorithms for GOME. The differences to other cloud algorithms are discussed with respect to the particular characteristics of the algorithms.

[1]  Eleonora P. Zege,et al.  A semianalytical cloud retrieval algorithm using backscattered radiation in 0.4–2.4 μm spectral region , 2003 .

[2]  Kelly Chance,et al.  Cloud retrieval algorithm for the European Space Agency's Global Ozone Monitoring Experiment , 1998, Remote Sensing.

[3]  John P. Burrows,et al.  The ring effect in the cloudy atmosphere , 2001 .

[4]  Ulrich Platt,et al.  Differential optical absorption spectroscopy (DOAS) , 1994 .

[5]  Steffen Beirle,et al.  El Niño induced anomalies in global data sets of total column precipitable water and cloud cover derived from GOME on ERS‐2 , 2005 .

[6]  N. Fournier,et al.  Determination of Cloud Parameters from SCIAMACHY Data for the Correction of Tropospheric Trace Gases , 2005 .

[7]  J. Hovenier,et al.  Global distributions of effective cloud fraction and cloud top pressure derived from oxygen A band spectra measured by the Global Ozone Monitoring Experiment: Comparison to ISCCP data , 2002 .

[8]  L. Friberg.  [Air pollution]. , 1984, Svenska lakartidningen.

[9]  Steffen Beirle,et al.  Estimate of nitrogen oxide emissions from shipping by satellite remote sensing , 2004 .

[10]  Steffen Beirle,et al.  Highly resolved global distribution of tropospheric NO 2 using GOME narrow swath mode data , 2004 .

[11]  N. Fournier,et al.  Improving cloud information over deserts from SCIAMACHY O 2 A-band , 2005 .

[12]  Steffen Beirle,et al.  Weekly cycle of NO 2 by GOME measurements: a signature of anthropogenic sources , 2003 .

[13]  W. Rossow,et al.  The International Satellite Cloud Climatology Project (ISCCP): The First Project of the World Climate Research Programme , 1983 .

[14]  Richard A. Frey,et al.  The effect of sensor resolution on the number of cloud-free observations from space , 2006 .

[15]  J. Hovenier,et al.  A fast method for retrieval of cloud parameters using oxygen A band measurements from the Global Ozone Monitoring Experiment , 2001 .

[16]  Piet Stammes,et al.  Cloud pressure retrieval using the O2‐O2 absorption band at 477 nm , 2004 .

[17]  James F. Gleason,et al.  Retrieval of cloud pressure and oceanic chlorophyll content using Raman scattering in GOME ultraviolet spectra , 2004 .

[18]  Diego G. Loyola,et al.  Automatic cloud analysis from polar-orbiting satellites using neural network and data fusion techniques , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[19]  Diego Loyola,et al.  A new cloud recognition algorithm for optical sensors , 1998, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174).

[20]  N. Fournier,et al.  Improving cloud information over deserts from SCIAMACHY Oxygen A-band measurements , 2006 .

[21]  A. Kokhanovsky,et al.  Semianalytical cloud retrieval algorithm as applied to the cloud top altitude and the cloud geometrical thickness determination from top‐of‐atmosphere reflectance measurements in the oxygen A band , 2004 .

[22]  Ulrich Platt,et al.  Monitoring Of Trace Gas Emissions From Space: Tropospheric Abundances Of BrO, NO2, H2CO, SO2, H2O And O4 As Measured By GOME , 2002 .

[23]  Ilse Aben,et al.  Validation of GOME polarization measurements by method of limiting atmospheres , 2005 .

[24]  Akihiko Kuze,et al.  Analysis of cloud top height and cloud coverage from satellites using the O2 A and B bands , 1994 .

[25]  Michael Eisinger,et al.  Refinement of a Database of Spectral Surface Reflectivity in the Range 335-772 nm Derived from 5.5 Years of GOME Observations , 2003 .

[26]  Peter Builtjes,et al.  Retrieval methods of effective cloud cover from the GOME instrument: an intercomparison , 2002 .

[27]  Ulrich Platt,et al.  Case Studies for the Investigation of Cloud Sensitive Parameters as Measured by GOME , 2004 .

[28]  R. B. A. Koelemeijer,et al.  Validation of Global Ozone Monitoring Experiment cloud fractions relevant for accurate ozone column retrieval , 1999 .

[29]  Ulrich Platt,et al.  Cloud classification analyzing image sequences , 2000 .

[30]  Piet Stammes,et al.  A novel approach to the polarization correction of spaceborne spectrometers , 2003 .

[31]  Michael Eisinger,et al.  The Global Ozone Monitoring Experiment (GOME): Mission Concept and First Scientific Results , 1999 .