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Abstract Weather forecasts made with imperfect models contain state‐dependent errors. Data assimilation (DA) partially corrects these errors with new information from observations. As such, the corrections, or “analysis increments,” produced by the DA process embed information about model errors. An attempt is made here to extract that information to improve numerical weather prediction. Neural networks (NNs) are trained to predict corrections to the systematic error in the National Oceanic and Atmospheric Administration's FV3‐GFS model based on a large set of analysis increments. A simple NN focusing on an atmospheric column significantly improves the estimated model error correction relative to a linear baseline. Leveraging large‐scale horizontal flow conditions using a convolutional NN, when compared to the simple column‐oriented NN, does not improve skill in correcting model error. The sensitivity of model error correction to forecast inputs is highly localized by vertical level and by meteorological variable, and the error characteristics vary across vertical levels. Once trained, the NNs are used to apply an online correction to the forecast during model integration. Improvements are evaluated both within a cycled DA system and across a collection of 10‐day forecasts. It is found that applying state‐dependent NN‐predicted corrections to the model forecast improves the overall quality of DA and improves the 10‐day forecast skill at all lead times.
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Estimation of Systematic Errors in the GFS Using Analysis Increments: Estimation of systematic errors in GFS
We estimate the effect of model deficiencies in the Global Forecast System that lead to systematic forecast errors, as a first step toward correcting them online (i.e., within the model) as in Danforth & Kalnay (2008a, 2008b). Since the analysis increments represent the corrections that new observations make on the 6 h forecast in the analysis cycle, we estimate the model bias corrections from the time average of the analysis increments divided by 6 h, assuming that initial model errors grow linearly and first ignoring the impact of observation bias. During 2012–2016, seasonal means of the 6 h model bias are generally robust despite changes in model resolution and data assimilation systems, and their broad continental scales explain their insensitivity to model resolution. The daily bias dominates the submonthly analysis increments and consists primarily of diurnal and semidiurnal components, also requiring a low dimensional correction. Analysis increments in 2015 and 2016 are reduced over oceans, which we attribute to improvements in the specification of the sea surface temperatures. These results provide support for future efforts to make online correction of the mean, seasonal, and diurnal and semidiurnal model biases of Global Forecast System to reduce both systematic and random errors, as suggested by Danforth & Kalnay (2008a, 2008b). It also raises the possibility that analysis increments could be used to provide guidance in testing new physical parameterizations.
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- Award ID(s):
- 1635470
- PAR ID:
- 10059315
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Atmospheres
- ISSN:
- 2169-897X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/2017JD027423
