Assessing the performance of the Lightning Imaging Sensor (LIS) using Deep Convective Clouds

Abstract The stability of the LIS instrument is examined during a 13 year period (1998–2010) by examining LIS background radiance observations of Deep Convective Clouds (DCCs) which are identified by their cold IR brightness temperature. Pixels in the LIS background image associated with DCCs are identified and analyzed during July and August of each year in the 13 year period. The resulting LIS DCC radiances are found to be stable throughout the period, varying at most by 0.8% from the 13 year mean July August value of 358.1 W sr − 1  m − 2  μm − 1 . The DCC method in this study provides a good approach for evaluating the stability of the future GOES-R Geostationary Lightning Mapper (GLM).

[1]  Ping Yang,et al.  Possibility of the Visible-Channel Calibration Using Deep Convective Clouds Overshooting the TTL , 2009 .

[2]  Patrick Minnis,et al.  On the use of deep convective clouds to calibrate AVHRR data , 2004, SPIE Optics + Photonics.

[3]  J. Bergstrom,et al.  Laboratory Calibration of the Optical Transient Detector and the Lightning Imaging Sensor , 2000 .

[4]  H. Christian.  Global Frequency and Distribution of Lightning as Observed From Space , 2001 .

[5]  Richard J. Blakeslee,et al.  The detection of lightning from geostationary orbit , 1989 .

[6]  Bruce A. Wielicki,et al.  Angular Distribution Models for Top-of-Atmosphere Radiative Flux Estimation from the Clouds and the Earth's Radiant Energy System Instrument on the Tropical Rainfall Measuring Mission Satellite. Part II; Validation , 2003 .

[7]  Richard J. Blakeslee,et al.  Gridded lightning climatology from TRMM-LIS and OTD: Dataset description , 2014 .

[8]  D. Seidel,et al.  Variability and trends in the global tropopause estimated from radiosonde data , 2006 .

[9]  Ping Yang,et al.  Application of deep convective cloud albedo observation to satellite-based study of the terrestrial atmosphere: monitoring the stability of spaceborne measurements and assessing absorption anomaly , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[10]  W. Paul Menzel,et al.  INTRODUCING THE NEXT-GENERATION ADVANCED BASELINE IMAGER ON GOES-R , 2005 .

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

[12]  Richard J. Blakeslee,et al.  The 13 years of TRMM Lightning Imaging Sensor: From individual flash characteristics to decadal tendencies , 2011 .

[13]  William J. Koshak,et al.  The GOES-R GeoStationary Lightning Mapper (GLM) , 2012 .

[14]  Edward J. Zipser,et al.  Diurnal cycles of precipitation, clouds, and lightning in the tropics from 9 years of TRMM observations , 2008 .

[15]  C. Kummerow,et al.  The Tropical Rainfall Measuring Mission (TRMM) Sensor Package , 1998 .

[16]  Christopher J. Schultz,et al.  Lightning and Severe Weather: A Comparison between Total and Cloud-to-Ground Lightning Trends , 2011 .

[17]  Richard J. Blakeslee,et al.  Performance Assessment of the Optical Transient Detector and Lightning Imaging Sensor. Part 2; Clustering Algorithm , 2007 .

[18]  Xiaoxiong Xiong,et al.  The characterization of deep convective cloud albedo as a calibration target using MODIS reflectances , 2010, Asia-Pacific Remote Sensing.

[19]  E. Williams.  The Schumann Resonance: A Global Tropical Thermometer , 1992, Science.

[20]  Byung-Ju Sohn,et al.  Assessment of the calibration performance of satellite visible channels using cloud targets: application to Meteosat-8/9 and MTSAT-1R , 2010 .

[21]  Richard J. Blakeslee,et al.  Performance Assessment of the Optical Transient Detector and Lightning Imaging Sensor. Part I: Predicted Diurnal Variability , 2002 .