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The Irreplaceable Utility of Sequential Data Assimilation for Numerical Weather Prediction System Development: Lessons Learned from an Experimental HWRF System

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  • 1 University of Maryland, College Park, College Park, Maryland
  • 2 NOAA/Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida
  • 3 Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado
  • 4 NOAA/Earth Systems Research Laboratories, Boulder, Colorado
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Abstract

Limited-area numerical weather prediction models currently run operationally in the United States and follow a “partially cycled” schedule, where sequential data assimilation is periodically interrupted by replacing model states with solutions interpolated from a global model. While this strategy helps overcome several practical challenges associated with real-time regional forecasting, it is no substitute for a robust sequential data assimilation approach for research-to-operations purposes. Partial cycling can mask systematic errors in weather models, data assimilation systems, and data preprocessing techniques, since it introduces information from a different prediction system. It also adds extra heuristics to the model initialization steps outside the general Bayesian filtering framework from which data assimilation methods are derived. This study uses a research-oriented modeling system, which is self-contained in the operational Hurricane Weather Research and Forecasting (HWRF) Model package, to illustrate why next-generation modeling systems should prioritize sequential data assimilation at early stages of development. This framework permits the rigorous examination of all model system components—in a manner that has never been done for the HWRF Model. Examples presented in this manuscript show how sequential data assimilation capabilities can accelerate model advancements and increase academic involvement in operational forecasting systems at a time when the United States is developing a new hurricane forecasting system.

Significance Statement

This study discusses a road map for designing numerical weather predictions systems that are more accessible to the research community. It is based on the premise that the statistical framework used for identifying initial conditions for dynamical models, such as weather prediction models, should play a larger role in model development, observation collection, and uncertainty quantification than currently exists for regional models. While this study uses examples motivated by one current operational weather model, the conclusions have broad implications. Ultimately, the long-term goals set forth by leaders in the atmospheric science community demand a more holistic evaluation of modeling systems than currently exists. This study is timely, considering the advancement of major modeling operational modeling efforts currently under way in the United States.

© 2021 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: Dr. Jonathan Poterjoy, poterjoy@umd.edu

Abstract

Limited-area numerical weather prediction models currently run operationally in the United States and follow a “partially cycled” schedule, where sequential data assimilation is periodically interrupted by replacing model states with solutions interpolated from a global model. While this strategy helps overcome several practical challenges associated with real-time regional forecasting, it is no substitute for a robust sequential data assimilation approach for research-to-operations purposes. Partial cycling can mask systematic errors in weather models, data assimilation systems, and data preprocessing techniques, since it introduces information from a different prediction system. It also adds extra heuristics to the model initialization steps outside the general Bayesian filtering framework from which data assimilation methods are derived. This study uses a research-oriented modeling system, which is self-contained in the operational Hurricane Weather Research and Forecasting (HWRF) Model package, to illustrate why next-generation modeling systems should prioritize sequential data assimilation at early stages of development. This framework permits the rigorous examination of all model system components—in a manner that has never been done for the HWRF Model. Examples presented in this manuscript show how sequential data assimilation capabilities can accelerate model advancements and increase academic involvement in operational forecasting systems at a time when the United States is developing a new hurricane forecasting system.

Significance Statement

This study discusses a road map for designing numerical weather predictions systems that are more accessible to the research community. It is based on the premise that the statistical framework used for identifying initial conditions for dynamical models, such as weather prediction models, should play a larger role in model development, observation collection, and uncertainty quantification than currently exists for regional models. While this study uses examples motivated by one current operational weather model, the conclusions have broad implications. Ultimately, the long-term goals set forth by leaders in the atmospheric science community demand a more holistic evaluation of modeling systems than currently exists. This study is timely, considering the advancement of major modeling operational modeling efforts currently under way in the United States.

© 2021 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: Dr. Jonathan Poterjoy, poterjoy@umd.edu
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