Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications

Can we build stable Climate Data Records (CDRs) spanning several satellite generations? This study outlines how the ClOud Fractional Cover dataset from METeosat First and Second Generation (COMET) of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) was created for the 25-year period 1991–2015. Modern multi-spectral cloud detection algorithms cannot be used for historical Geostationary (GEO) sensors due to their limited spectral resolution. We document the innovation needed to create a retrieval algorithm from scratch to provide the required accuracy and stability over several decades. It builds on inter-calibrated radiances now available for historical GEO sensors. It uses spatio-temporal information and a robust clear-sky retrieval. The real strength of GEO observations—the diurnal cycle of reflectance and brightness temperature—is fully exploited instead of just accounting for single “imagery”. The commonly-used naive Bayesian classifier is extended with covariance information of cloud state and variability. The resulting cloud fractional cover CDR has a bias of 1% Mean Bias Error (MBE), a precision of 7% bias-corrected Root-Mean-Squared-Error (bcRMSE) for monthly means, and a decadal stability of 1%. Our experience can serve as motivation for CDR developers to explore novel concepts to exploit historical sensor data.

[1]  H. Alexandersson.  A homogeneity test applied to precipitation data , 1986 .

[2]  W. Rossow,et al.  Advances in understanding clouds from ISCCP , 1999 .

[3]  M. Hansen,et al.  A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products , 2000 .

[4]  L. Stowe,et al.  AVHRR Pixel Level Clear-Sky Classification Using Dynamic Thresholds (CLAVR-3) , 2001 .

[5]  K. T. Kriebel,et al.  The cloud analysis tool APOLLO: Improvements and validations , 2003 .

[6]  W. Rossow,et al.  The International Satellite Cloud Climatology Project (ISCCP) Web Site An Online Resource for Research , 2004 .

[7]  Yves M. Govaerts,et al.  Operational calibration of the Meteosat radiometer VIS band , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Nadine Gobron,et al.  Coupling diffuse sky radiation and surface albedo , 2005 .

[9]  M. Begert,et al.  Homogeneous temperature and precipitation series of Switzerland from 1864 to 2000 , 2005 .

[10]  Steven Platnick,et al.  Spatially complete global spectral surface albedos: value-added datasets derived from Terra MODIS land products , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Taneil Uttal,et al.  Daytime Global Cloud Typing from AVHRR and VIIRS: Algorithm Description, Validation, and Comparisons , 2005 .

[12]  Karl-Göran Karlsson,et al.  NWCSAF AVHRR Cloud Detection and Analysis Using Dynamic Thresholds and Radiative Transfer Modeling. Part I: Algorithm Description , 2005 .

[13]  Alan H. Strahler,et al.  MODIS bidirectional reflectance distribution function and albedo Climate Modeling Grid products and the variability of albedo for major global vegetation types , 2005 .

[14]  A. Trishchenko,et al.  SPARC: New Cloud, Snow, and Cloud Shadow Detection Scheme for Historical 1-km AVHHR Data over Canada , 2007 .

[15]  A. Evan,et al.  Arguments against a physical long‐term trend in global ISCCP cloud amounts , 2007 .

[16]  Jörg Bendix,et al.  A novel approach to fog/low stratus detection using Meteosat 8 data , 2008 .

[17]  Haruma Ishida,et al.  Development of an unbiased cloud detection algorithm for a spaceborne multispectral imager , 2009 .

[18]  Maximilian Reuter,et al.  The CM-SAF and FUB Cloud Detection Schemes for SEVIRI: Validation with Synoptic Data and Initial Comparison with MODIS and CALIPSO , 2009 .

[19]  Maximilian Reuter,et al.  A Method for Estimating the Sampling Error Applied to CM-SAF Monthly Mean Cloud Fractional Cover Data Retrieved From MSG SEVIRI , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[20]  R. Schiemann,et al.  Reduced space optimal interpolation of daily rain gauge precipitation in Switzerland , 2010 .

[21]  Randal D. Koster,et al.  Assimilation of Satellite-Derived Skin Temperature Observations into Land Surface Models , 2010 .

[22]  Philip Watts,et al.  Joint retrieval of surface reflectance and aerosol optical depth from MSG/SEVIRI observations with an optimal estimation approach: 1. Theory , 2010 .

[23]  Patrick Minnis,et al.  The Global Space-Based Inter-Calibration System , 2011 .

[24]  Johannes Quaas,et al.  Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function , 2011 .

[25]  Marion Mittermaier,et al.  A critical assessment of surface cloud observations and their use for verifying cloud forecasts , 2012 .

[26]  Jörg Trentmann,et al.  Remote sensing of solar surface radiation for climate monitoring — the CM-SAF retrieval in international comparison , 2012 .

[27]  Andi Walther,et al.  A Naive Bayesian Cloud-Detection Scheme Derived fromCALIPSOand Applied within PATMOS-x , 2012 .

[28]  Mark A. Liniger,et al.  A surface radiation climatology across two Meteosat satellite generations , 2013 .

[29]  Xiangqian Wu,et al.  GSICS Inter-Calibration of Infrared Channels of Geostationary Imagers Using Metop/IASI , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Bertrand Theodore,et al.  Land Surface Albedo from Geostationary Satelites: A Multiagency Collaboration within SCOPE-CM , 2013 .

[31]  David A. Santek,et al.  Global satellite composites — 20 years of evolution , 2013 .

[32]  Roger W. Saunders,et al.  Monitoring Satellite Radiance Biases Using NWP Models , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Andrew K. Heidinger,et al.  PATMOS-x: Results from a Diurnally Corrected 30-yr Satellite Cloud Climatology , 2013 .

[34]  Anke Tetzlaff,et al.  The Impact of Time Difference between Satellite Overpass and Ground Observation on Cloud Cover Performance Statistics , 2014, Remote. Sens..

[35]  Andi Walther,et al.  The Pathfinder Atmospheres–Extended AVHRR Climate Dataset , 2014 .

[36]  André Hollstein,et al.  Bayesian cloud detection for MERIS, AATSR, and their combination , 2014 .

[37]  Christoph Neuhaus,et al.  Probabilistic approach to cloud and snow detection on Advanced Very High Resolution Radiometer (AVHRR) imagery , 2014 .

[38]  J. F. Meirink,et al.  The Clouds Climate Change Initiative: Assessment of state-of-the-art cloud property retrieval schemes applied to AVHRR heritage measurements , 2015 .

[39]  Isabel F. Trigo,et al.  Meteosat Land Surface Temperature Climate Data Record: Achievable Accuracy and Potential Uncertainties , 2015, Remote. Sens..

[40]  Isabel F. Trigo,et al.  Comparison of model land skin temperature with remotely sensed estimates and assessment of surface‐atmosphere coupling , 2015 .

[41]  Karl-Göran Karlsson,et al.  Advancing the uncertainty characterisation of cloud masking in passive satellite imagery: Probabilistic formulations for NOAA AVHRR data , 2015 .

[42]  R. Stöckli,et al.  Spatial analysis of sunshine duration in complex terrain by non‐contemporaneous combination of station and satellite data , 2015 .

[43]  Isabel F. Trigo,et al.  Advancing land surface model development with satellite-based Earth observations , 2016 .

[44]  Karl-Göran Karlsson,et al.  CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data , 2016 .

[45]  Peng Zhang,et al.  The Global Space-based Inter-Calibration System (GSICS) , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[46]  Karl-Göran Karlsson,et al.  CLARA-A2: CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data - Edition 2 , 2017 .

[47]  William B. Rossow,et al.  The International Satellite Cloud Climatology Project H-Series climate data record product , 2017 .

[48]  Karl-Göran Karlsson,et al.  Characterization of AVHRR global cloud detection sensitivity based on CALIPSO-CALIOP cloud optical thickness information : demonstration of results based on the CM SAF CLARA-A2 climate data record , 2018 .

[49]  Reto Stöckli,et al.  Performance Assessment of the COMET Cloud Fractional Cover Climatology across Meteosat Generations , 2018, Remote. Sens..

[50]  METEOSAT OBSERVATIONS OF DIURNAL VARIATION OF CLOUD FRACTIONAL COVER , 2019 .

[51]  Tim J. Hewison,et al.  On the Methods for Recalibrating Geostationary Longwave Channels Using Polar Orbiting Infrared Sounders , 2019, Remote. Sens..