Spatial and temporal varying thresholds for cloud detection in satellite imagery

A new cloud detection technique has been developed and applied to both geostationary and polar orbiting satellite imagery having channels in the thermal infrared and short wave infrared spectral regions. The bispectral composite threshold (BCT) technique uses only the 11 mum and 3.9 mum channels, and composite imagery generated from these channels, in a four-step cloud detection procedure to produce a binary cloud mask at single pixel resolution. A unique aspect of this algorithm is the use of 20-day composites of the 11 mum and the 11-3.9 mum channel difference imagery to represent spatially and temporally varying clear-sky thresholds for the bispectral cloud tests. The BCT cloud detection algorithm has been applied to GOES and MODIS data over the continental United States over the last three years with good success. The resulting products have been validated against "truth" datasets (generated by the manual determination of the sky conditions from available satellite imagery) for various seasons from the 2003-2005 periods. The day and night algorithm has been shown to determine the correct sky conditions 80-90% of the time (on average) over land and ocean areas. Only a small variation in algorithm performance occurs between day-night, land-ocean, and between seasons. The algorithm performs least well during the winter season with only 80% of the sky conditions determined correctly. The algorithm was found to under-determine clouds at night and during times of low sun angle (in geostationary satellite data) and tends to over-determine the presence of clouds during the day, particularly in the summertime. Since the spectral tests use only the short- and long-wave channels common to most multispectral scanners, the application of the BCT technique to a variety of satellite sensors including SEVERI should be straightforward and produce similar performance results.

[1]  A. M. Zavody,et al.  Cloud Clearing over the Ocean in the Processing of Data from the Along-Track Scanning Radiometer (ATSR) , 2000 .

[2]  Jason I. Gobat,et al.  Improved cloud detection in GOES scenes over land , 1995 .

[3]  Gary J. Jedlovec,et al.  GOES Cloud Detection at the Global Hydrology and Climate Center , 2002 .

[4]  Tom Bradshaw,et al.  The NASA Short-term Prediction Research and Transition (SPoRT) Center: A Collaborative Model for Accelerating Research into Operations , 2003 .

[5]  Timothy J. Schmit,et al.  Derived Product Imagery from GOES-8 , 1996 .

[6]  William L. Smith,et al.  A Nonlinear Physical Retrieval Algorithm—Its Application to the GOES-8/9 Sounder , 1999 .

[7]  Alan H. Strahler,et al.  The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research , 1998, IEEE Trans. Geosci. Remote. Sens..

[8]  Christopher M. Hayden,et al.  GOES-VAS Simultaneous Temperature-Moisture Retrieval Algorithm , 1988 .

[9]  Randall J. Alliss The Development of Cloud Retrieval Algorithms Applied to GOES Digital Data , 2000 .

[10]  W. Paul Menzel,et al.  Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS , 2003, IEEE Trans. Geosci. Remote. Sens..

[11]  Donald P. Wylie,et al.  The Diurnal Cycle of Upper-Tropospheric Clouds Measured by GOES-VAS and the ISCCP , 2002 .

[12]  S. Nadon,et al.  High-Resolution Satellite Analysis and Model Evaluation of Clouds and Radiation over the Mackenzie Basin Using AVHRR Data. , 1998 .

[13]  Gary J. Jedlovec,et al.  Cloud Filtering Using a Bi-Spectral Spatial Coherence Approach , 1998 .

[14]  Bruce A. Wielicki,et al.  On the determination of cloud cover from satellite sensors: The effect of sensor spatial resolution , 1992 .

[15]  Chris Darden Bridging the gap between research and operations in the National Weather Service : collaborative activities among the Huntsville Meteorological Community , 2002 .

[16]  G. S. Wade,et al.  Application of GOES-8/9 Soundings to Weather Forecasting and Nowcasting. , 1998 .

[17]  D. Jackson,et al.  Trends in Global Cloud Cover in Two Decades of HIRS Observations , 2005 .

[18]  Christopher J. Merchant,et al.  Probabilistic physically based cloud screening of satellite infrared imagery for operational sea surface temperature retrieval , 2005 .

[19]  W. Menzel,et al.  Introducing GOES-I: The First of a New Generation of Geostationary Operational Environmental Satellites , 1994 .

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

[21]  Gary A. Wick,et al.  Satellite and Skin Layer Effects on the Accuracy of Sea Surface Temperature Measurements from the GOES Satellites , 2002 .

[22]  Ann Henderson-Sellers,et al.  Cloud detection and analysis: A review of recent progress , 1988 .

[23]  W. Rossow,et al.  Cloud Detection Using Satellite Measurements of Infrared and Visible Radiances for ISCCP , 1993 .

[24]  F. Bretherton,et al.  Cloud cover from high-resolution scanner data - Detecting and allowing for partially filled fields of view , 1982 .

[25]  Gary J. Jedlovec,et al.  Spatially Varying Spectrally Thresholds for MODIS Cloud Detection , 2004 .

[26]  Karl-Göran Karlsson,et al.  NWCSAF AVHRR Cloud Detection and Analysis Using Dynamic Thresholds and Radiative Transfer Modeling. Part II: Tuning and Validation , 2005 .

[27]  W. Menzel,et al.  Discriminating clear sky from clouds with MODIS , 1998 .

[28]  M. Derrien,et al.  MSG/SEVIRI cloud mask and type from SAFNWC , 2005 .

[29]  W. Paul Menzel,et al.  A Comparison of Ground and Satellite Observations of Cloud Cover , 1993 .

[30]  Jason I. Gobat,et al.  Improved cloud detection in GOES scenes over the oceans , 1995 .

[31]  W. Paul Menzel,et al.  Observations and trends of clouds based on GOES sounder data , 2001 .

[32]  Kathleen I. Strabala,et al.  Seasonal and Diurnal Changes in Cirrus Clouds as Seen in Four Years of Observations with the VAS , 1992 .

[33]  Robert M. Aune,et al.  NWP Cloud Initialization Using GOES Sounder Data and Improved Modeling of Nonprecipitating Clouds , 2000 .

[34]  W. Paul Menzel,et al.  The MODIS cloud products: algorithms and examples from Terra , 2003, IEEE Trans. Geosci. Remote. Sens..