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Proactive QC: A Fully Flow-Dependent Quality Control Scheme Based on EFSO

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  • 1 University of Maryland, College Park, College Park, Maryland, and Japan Meteorological Agency, Tokyo, Japan
  • 2 University of Maryland, College Park, College Park, Maryland
  • 3 Japan Meteorological Agency, Tokyo, Japan
  • 4 University of Maryland, College Park, College Park, Maryland, and RIKEN Advanced Institute for Computational Science, Kobe, Japan
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Abstract

Despite dramatic improvements over the last decades, operational NWP forecasts still occasionally suffer from abrupt drops in their forecast skill. Such forecast skill “dropouts” may occur even in a perfect NWP system because of the stochastic nature of NWP but can also result from flaws in the NWP system. Recent studies have shown that dropouts occur due not to a model’s deficiencies but to misspecified initial conditions, suggesting that they could be mitigated by improving the quality control (QC) system so that the observation-minus-background (O-B) innovations that would degrade a forecast can be detected and rejected. The ensemble forecast sensitivity to observations (EFSO) technique enables for the quantification of how much each observation has improved or degraded the forecast. A recent study has shown that 24-h EFSO can detect detrimental O-B innovations that caused regional forecast skill dropouts and that the forecast can be improved by not assimilating them. Inspired by that success, a new QC method is proposed, termed proactive QC (PQC), that detects detrimental innovations 6 h after the analysis using EFSO and then repeats the analysis and forecast without using them. PQC is implemented and tested on a lower-resolution version of NCEP’s operational global NWP system. It is shown that EFSO is insensitive to the choice of verification and lead time (24 or 6 h) and that PQC likely improves the analysis, as attested to by forecast improvements of up to 5 days and beyond. Strategies for reducing the computational costs and further optimizing the observation rejection criteria are also discussed.

Denotes content that is immediately available upon publication as open access.

© 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: Daisuke Hotta, dhotta@mri-jma.go.jp

This article is included in the Sixth WMO Data Assimilation Symposium Special Collection.

Abstract

Despite dramatic improvements over the last decades, operational NWP forecasts still occasionally suffer from abrupt drops in their forecast skill. Such forecast skill “dropouts” may occur even in a perfect NWP system because of the stochastic nature of NWP but can also result from flaws in the NWP system. Recent studies have shown that dropouts occur due not to a model’s deficiencies but to misspecified initial conditions, suggesting that they could be mitigated by improving the quality control (QC) system so that the observation-minus-background (O-B) innovations that would degrade a forecast can be detected and rejected. The ensemble forecast sensitivity to observations (EFSO) technique enables for the quantification of how much each observation has improved or degraded the forecast. A recent study has shown that 24-h EFSO can detect detrimental O-B innovations that caused regional forecast skill dropouts and that the forecast can be improved by not assimilating them. Inspired by that success, a new QC method is proposed, termed proactive QC (PQC), that detects detrimental innovations 6 h after the analysis using EFSO and then repeats the analysis and forecast without using them. PQC is implemented and tested on a lower-resolution version of NCEP’s operational global NWP system. It is shown that EFSO is insensitive to the choice of verification and lead time (24 or 6 h) and that PQC likely improves the analysis, as attested to by forecast improvements of up to 5 days and beyond. Strategies for reducing the computational costs and further optimizing the observation rejection criteria are also discussed.

Denotes content that is immediately available upon publication as open access.

© 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: Daisuke Hotta, dhotta@mri-jma.go.jp

This article is included in the Sixth WMO Data Assimilation Symposium Special Collection.

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