Estimation of Systematic Errors in the GFS Using Analysis Increments. Issue 3 (10th February 2018)

• Record Type: Journal Article
• Authors: Bhargava, Kriti; Kalnay, Eugenia; Carton, James A.; Yang, Fanglin
• Is Part Of: Journal of geophysical research. Volume 123:Issue 3(2018)
• Page Start: 1626
• Page End: 1637
• Publication Date: 2018-02-10
• Subjects: bias correction -- data assimilation -- systematic errors -- model bias -- online correction -- weather forecasting; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2017JD027423
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: 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. Key Points: Global Forecast System model has robust, systematic seasonal mean and diurnal forecast errors due to biases of the model and/or observations Six hour analysis increments estimate the model bias before the errors grow nonlinearly and can be used to correct the model "online" Observation and model bias corrections within the analysis increments can be separated by their impact on systematic forecast errors