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Estimation of analysis and forecast error variances Cover

Estimation of analysis and forecast error variances

By:  and    
Open Access
|Dec 2014

Figures & Tables

Fig. 1

Sample mean of true (solid line) and perceived (dashed line) forecast error variances, denoted respectively by x i 2 and by d i 2 as a function of lead time for the three experiments. The analysis error, denoted by x 0 2, is added (dotted line) as a reference. The sample includes 104 cases.

Fig. 2

Time correlation between analysis errors and forecast errors, ρ, as a function of lead time in the true system (continuous line) and in the empirical relationship [eq. (7); dashed line]. The correlation between the analysis errors and the first guess errors, ρ 1, is marked for each experiment.

Table 1. Parameters prescribed to the data assimilation experiments and resulting sample error variances from a time series of 104 cycles.

Prescribed
Experimentσo2ζx12xo2
S20.50.590.57M40.31.030.67L90.34.092.33

[i] σ o 2 is the observation error variance and ζ is the tuning parameter of the background covariance error matrix.

Fig. 3

Error variances and error correlation as a function of lead time for the S experiment using five lead points. Observed perceived error variance (d) and modelled perceived error variance (d fit) (upper panel), forecast error variance (f 2) and estimated forecast error variance (f 2 estimated) (middle panel), true and estimated correlation (lower panel). Error bars in the upper panel are the SEM; for the other panels are the ranges of parameters such that ∣d 2 –d 2 fit∣ < SEM.

Fig. 4

Same as Fig. 3 but for the L experiment.

Fig. 5

Absolute value of difference between the observed perceived error variance and modelled perceived error variance, and the standard error of the mean (SEM) for (a) NCEP, (b) CMC, (c) ECMWF and (d) FNMOC. Abscissa axis corresponds to forecast lead time in units of days.

Table 2. Parameters estimated for the NH 500 hPa inter-comparison of NWP systems: analysis error variance (x02), error growth (α), error doubling times (EDT), correlation between the analysis and first-guess errors (ρ 1) and corresponding explained error variance (EEV).

MODELx02 (m2)α (day1)EDT (days)ρ1EEV (%)
NCEP38.00.252.70.5631CMC29.50.272.60.4722ECMWF11.50.302.30.225FNMOC49.20.262.80.6036
Fig. 6

Forecast (observed perceived, fitted perceives and true) error variances as a function of lead time for four global models: (a) NCEP, (b) CMC, (c) ECMWF and (d) FNMOC for models operational in 2008.

Fig. 7

Resulting correlation of forecast errors and verifying analysis errors for (a) NCEP, (b) CMC, (c) ECMWF, (d) FNMOC.

Fig. 8

(a) Seven-parameter model function applied to TR 10 mU wind forecasts from the NCEP model; (b) resulting analysis-forecast correlation; and (c) misfit compared to SEM (as in Fig. 5).

Language: English
Page range: 21767 - 21767
Submitted on: Jun 17, 2013
Accepted on: Sep 30, 2014
Published on: Dec 1, 2014
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2014 Malaquias Peña, Zoltan Toth, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.