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All-Sky Satellite Radiance Data Assimilation: Methodology and Challenges

  • Milija ZupanskiEmail author
Chapter

Abstract

Assimilation of satellite radiances is the backbone of today’s operational data assimilation. Satellites can cover all parts of globe and provide information in areas not accessible by any other observation type. Of special interest are high-impact weather areas, such as tropical cyclones and severe weather outbreaks, which are mostly covered by clouds. Unfortunately, in current operational practice only clear-sky satellite radiances are assimilated, with only few exceptions. This effectively filters out a potentially useful information from all-sky radiances related to clouds and microphysics, and consequently limits the utility of satellite data. In this paper we will address numerous challenges related to the use of all-sky satellite radiances.All-sky satellite radiances present a formidable challenge for data assimilation as they relate to numerous technical aspects of data assimilation such as: (1) forecast error covariance, (2) correlated observation errors, (3) nonlinearity and non-differentiability, and (4) non-Gaussian errors. Assimilation of all-sky radiances is also challenging from a dynamical/physical point of view, since observing clouds implies a need for better understanding and ultimately simulation of cloud microphysical processes. Given that a reliable prediction of clouds requires a high-resolution cloud-resolving model, assimilation of all-sky radiancesis also a high-dimensional problem that requires addressing computational challenges.

Keywords

Data Assimilation Observation Error Satellite Radiance Cloud Microphysical Process Forecast Error Covariance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was supported by the National Science Foundation Collaboration in Mathematical Geosciences Grant ATM-0930265, and by NOAA NESDIS Grant NA10NES4400012.We would also like to acknowledge high-performance computing support provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Cooperative Institute for Research in the AtmosphereColorado State UniversityFort CollinsUSA

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