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IMERG V06: Changes to the Morphing Algorithm

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  • 1 Universities Space Research Association, Columbia, and NASA Goddard Space Flight Center, Greenbelt, Maryland
  • | 2 NASA Goddard Space Flight Center, Greenbelt, Maryland
  • | 3 Science Systems and Applications, Inc., Lanham, and NASA Goddard Space Flight Center, Greenbelt, Maryland
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Abstract

As the U.S. Science Team’s globally gridded precipitation product from the NASA–JAXA Global Precipitation Measurement (GPM) mission, the Integrated Multi-Satellite Retrievals for GPM (IMERG) estimates the surface precipitation rates at 0.1° every half hour using spaceborne sensors for various scientific and societal applications. One key component of IMERG is the morphing algorithm, which uses motion vectors to perform quasi-Lagrangian interpolation to fill in gaps in the passive microwave precipitation field using motion vectors. Up to IMERG V05, the motion vectors were derived from the large-scale motions of infrared observations of cloud tops. This study details the changes introduced in IMERG V06 to derive motion vectors from large-scale motions of selected atmospheric variables in numerical models, which allow IMERG estimates to be extended from the 60°N–60°S latitude band to the entire globe. Evaluation against both instantaneous passive microwave retrievals and ground measurements demonstrates the general improvement in the precipitation field of the new approach. Most of the model variables tested exhibited similar performance, but total precipitable water vapor was chosen as the source of the motion vectors for IMERG V06 due to its competitive performance and global completeness. Continuing assessments will provide further insights into possible refinements of this revised morphing scheme in future versions of IMERG.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JTECH-D-19-0114.s1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is included in the Global Precipitation Measurement (GPM) special collection.

Corresponding author: Jackson Tan, jackson.tan@nasa.gov

Abstract

As the U.S. Science Team’s globally gridded precipitation product from the NASA–JAXA Global Precipitation Measurement (GPM) mission, the Integrated Multi-Satellite Retrievals for GPM (IMERG) estimates the surface precipitation rates at 0.1° every half hour using spaceborne sensors for various scientific and societal applications. One key component of IMERG is the morphing algorithm, which uses motion vectors to perform quasi-Lagrangian interpolation to fill in gaps in the passive microwave precipitation field using motion vectors. Up to IMERG V05, the motion vectors were derived from the large-scale motions of infrared observations of cloud tops. This study details the changes introduced in IMERG V06 to derive motion vectors from large-scale motions of selected atmospheric variables in numerical models, which allow IMERG estimates to be extended from the 60°N–60°S latitude band to the entire globe. Evaluation against both instantaneous passive microwave retrievals and ground measurements demonstrates the general improvement in the precipitation field of the new approach. Most of the model variables tested exhibited similar performance, but total precipitable water vapor was chosen as the source of the motion vectors for IMERG V06 due to its competitive performance and global completeness. Continuing assessments will provide further insights into possible refinements of this revised morphing scheme in future versions of IMERG.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JTECH-D-19-0114.s1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is included in the Global Precipitation Measurement (GPM) special collection.

Corresponding author: Jackson Tan, jackson.tan@nasa.gov

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