Second SNPP Cal/Val campaign: environmental data retrieval analysis

Daniel K. Zhou; Allen M. Larar; Xu Liu; Jialin Tian; William L. Smith; Susan H. Kizer; Mitch D. Goldberg

doi:10.1117/12.2223074

7 May 2016 Second SNPP Cal/Val campaign: environmental data retrieval analysis

Daniel K. Zhou, Allen M. Larar, Xu Liu, Jialin Tian, William L. Smith, Susan H. Kizer, Mitch D. Goldberg

Proceedings Volume 9880, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VI; 988008 (2016) https://doi.org/10.1117/12.2223074
Event: SPIE Asia-Pacific Remote Sensing, 2016, New Delhi, India

Abstract

Satellite ultraspectral infrared sensors provide key data records essential for weather forecasting and climate change science. The Suomi National Polar-orbiting Partnership (Soumi NPP) satellite Environmental Data Records (EDRs) are retrieved from calibrated ultraspectral radiance or Sensor Data Records (SDRs). Understanding the accuracy of retrieved EDRs is critical. The second Suomi NPP Calibration/Validation field campaign was conducted during March 2015 with flights over Greenland. The NASA high-altitude ER-2 aircraft carrying ultraspectral interferometer sounders such as the National Airborne Sounder Testbed-Interferometer (NAST-I) flew under the Suomi NPP satellite that carries the Crosstrack Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS). Herein we inter-compare the EDRs produced from different retrieval algorithms employed on these satellite and aircraft campaign data. The available radiosonde measurements together with the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses are used to assess atmospheric temperature and moisture retrievals from the aircraft and satellite platforms. Preliminary results of this experiment under a winter, Arctic environment are presented.

Citation Download Citation

Daniel K. Zhou, Allen M. Larar, Xu Liu, Jialin Tian, William L. Smith, Susan H. Kizer, and Mitch D. Goldberg "Second SNPP Cal/Val campaign: environmental data retrieval analysis", Proc. SPIE 9880, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VI, 988008 (7 May 2016); https://doi.org/10.1117/12.2223074

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