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- Author or Editor: Mohar Chattopadhyay x
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Assessing Sensitivity of MERRA-2 to AMSU-A in the Upper StratosphereAbstract
Microwave temperature sounders provide key observations in data assimilation, both in the current and historical global observing systems, as they provide the largest amount of horizontal and vertical temperature information due to their insensitivity to clouds. In the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), microwave sounder radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) are assimilated beginning with NOAA-15 and continuing through the current period. The time series of observation minus background statistics for AMSU-A channels sensitive to the upper stratosphere and lower mesosphere show variabilities due to changes in the AMSU-A constellation in the early AMSU-A period. Noted discrepancies are seen at the onset and exit of AMSU-A observations on the NOAA-15, NOAA-16, NOAA-17, and NASA EOS Aqua satellites. This effort characterizes the sensitivity, both in terms of the observations and the MERRA-2 data. Furthermore, it explores the use of reprocessed and intercalibrated datasets to evaluate whether these homogenized observations can reduce the disparity due to change in instrumental biases against the model background. The results indicate that the AMSU-A radiances used in MERRA-2 are the fundamental cause of this interplatform sensitivity, which can be mitigated by using reprocessed data. The results explore the importance of the reprocessing of the AMSU-A radiances as well as their intercalibration.
Abstract
Microwave temperature sounders provide key observations in data assimilation, both in the current and historical global observing systems, as they provide the largest amount of horizontal and vertical temperature information due to their insensitivity to clouds. In the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), microwave sounder radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) are assimilated beginning with NOAA-15 and continuing through the current period. The time series of observation minus background statistics for AMSU-A channels sensitive to the upper stratosphere and lower mesosphere show variabilities due to changes in the AMSU-A constellation in the early AMSU-A period. Noted discrepancies are seen at the onset and exit of AMSU-A observations on the NOAA-15, NOAA-16, NOAA-17, and NASA EOS Aqua satellites. This effort characterizes the sensitivity, both in terms of the observations and the MERRA-2 data. Furthermore, it explores the use of reprocessed and intercalibrated datasets to evaluate whether these homogenized observations can reduce the disparity due to change in instrumental biases against the model background. The results indicate that the AMSU-A radiances used in MERRA-2 are the fundamental cause of this interplatform sensitivity, which can be mitigated by using reprocessed data. The results explore the importance of the reprocessing of the AMSU-A radiances as well as their intercalibration.
Abstract
The Rapid Scatterometer (RapidScat) was built as a low-cost follow-on to the QuikSCAT mission. It flew on the International Space Station (ISS) and provided data from 3 October 2014 to 20 August 2016. These data allowed for the retrieval of surface wind vectors derived from surface roughness estimates measured from multiple coincident azimuth angles. These measurements were unique to the historical scatterometer record in that the ISS flies in a low inclination, non-sun-synchronous orbit. Scatterometry-derived wind vectors have been routinely assimilated in both forward processing and reanalysis systems run at the Global Modeling and Assimilation Office (GMAO). As the RapidScat retrievals were made available in near–real time, they were assimilated in the forward processing system, and the methods to assimilate and evaluate these retrievals are described. Time series of data statistics are presented first for the near-real-time data assimilated in GMAO forward processing. Second, the full data products provided by the RapidScat team are compared passively to the MERRA-2 reanalysis. Both sets of results show that the root-mean-square (RMS) difference of the observations and the GMAO model background fields increased over the course of the data record. Furthermore, the observations and the backgrounds are shown to be biased for both the zonal and meridional wind components. The retrievals are shown to have had a net forecast error reduction via the forecast sensitivity observation impact (FSOI) metric, which is a quantification of 24-h forecast error reduction, though the impact became neutral as the signal-to-noise ratio of the instrument decreased over its lifespan.
Abstract
The Rapid Scatterometer (RapidScat) was built as a low-cost follow-on to the QuikSCAT mission. It flew on the International Space Station (ISS) and provided data from 3 October 2014 to 20 August 2016. These data allowed for the retrieval of surface wind vectors derived from surface roughness estimates measured from multiple coincident azimuth angles. These measurements were unique to the historical scatterometer record in that the ISS flies in a low inclination, non-sun-synchronous orbit. Scatterometry-derived wind vectors have been routinely assimilated in both forward processing and reanalysis systems run at the Global Modeling and Assimilation Office (GMAO). As the RapidScat retrievals were made available in near–real time, they were assimilated in the forward processing system, and the methods to assimilate and evaluate these retrievals are described. Time series of data statistics are presented first for the near-real-time data assimilated in GMAO forward processing. Second, the full data products provided by the RapidScat team are compared passively to the MERRA-2 reanalysis. Both sets of results show that the root-mean-square (RMS) difference of the observations and the GMAO model background fields increased over the course of the data record. Furthermore, the observations and the backgrounds are shown to be biased for both the zonal and meridional wind components. The retrievals are shown to have had a net forecast error reduction via the forecast sensitivity observation impact (FSOI) metric, which is a quantification of 24-h forecast error reduction, though the impact became neutral as the signal-to-noise ratio of the instrument decreased over its lifespan.
