An Adjoint-Based Forecast Impact from Assimilating MISR Winds into the GEOS-5 Data Assimilation and Forecasting System

Kevin J. Mueller Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

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Junjie Liu Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

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Will McCarty NASA Goddard Space Flight Center, Greenbelt, Maryland

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Ron Gelaro NASA Goddard Space Flight Center, Greenbelt, Maryland

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Abstract

This study examines the benefit of assimilating cloud motion vector (CMV) wind observations obtained from the Multiangle Imaging SpectroRadiometer (MISR) within a Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), configuration of the Goddard Earth Observing System-5 (GEOS-5) model data assimilation system (DAS). Available in near–real time (NRT) and with a record dating back to 1999, MISR CMVs boast pole-to-pole coverage and geometric height assignment that is complementary to the suite of atmospheric motion vectors (AMVs) included in the MERRA-2 standard. Experiments spanning September–November of 2014 and March–May of 2015 estimated relative MISR CMV impact on the 24-h forecast error reduction with an adjoint-based forecast sensitivity method. MISR CMV were more consistently beneficial and provided twice as large a mean forecast benefit when larger uncertainties were assigned to the less accurate component of the CMV oriented along the MISR satellite ground track, as opposed to when equal uncertainties were assigned to the eastward and northward components as in previous studies. Assimilating only the cross-track component provided 60% of the benefit of both components. When optimally assimilated, MISR CMV proved broadly beneficial throughout the Earth, with the greatest benefit evident at high latitudes where there is a confluence of more frequent CMV coverage and gaps in coverage from other MERRA-2 wind observations. Globally, MISR represented 1.6% of the total forecast benefit, whereas regionally that percentage was as large as 3.7%.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Junjie Liu, junjie.liu@jpl.nasa.gov

Abstract

This study examines the benefit of assimilating cloud motion vector (CMV) wind observations obtained from the Multiangle Imaging SpectroRadiometer (MISR) within a Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), configuration of the Goddard Earth Observing System-5 (GEOS-5) model data assimilation system (DAS). Available in near–real time (NRT) and with a record dating back to 1999, MISR CMVs boast pole-to-pole coverage and geometric height assignment that is complementary to the suite of atmospheric motion vectors (AMVs) included in the MERRA-2 standard. Experiments spanning September–November of 2014 and March–May of 2015 estimated relative MISR CMV impact on the 24-h forecast error reduction with an adjoint-based forecast sensitivity method. MISR CMV were more consistently beneficial and provided twice as large a mean forecast benefit when larger uncertainties were assigned to the less accurate component of the CMV oriented along the MISR satellite ground track, as opposed to when equal uncertainties were assigned to the eastward and northward components as in previous studies. Assimilating only the cross-track component provided 60% of the benefit of both components. When optimally assimilated, MISR CMV proved broadly beneficial throughout the Earth, with the greatest benefit evident at high latitudes where there is a confluence of more frequent CMV coverage and gaps in coverage from other MERRA-2 wind observations. Globally, MISR represented 1.6% of the total forecast benefit, whereas regionally that percentage was as large as 3.7%.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Junjie Liu, junjie.liu@jpl.nasa.gov
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