Skip to main content
Have a personal or library account? Click to login
The importance of simulated errors in observing system simulation experiments Cover

The importance of simulated errors in observing system simulation experiments

Open Access
|Jan 2021

Figures & Tables

Fig. 1.

Horizontal correlation of AMSU-A NOAA-18 channel 5 observation innovations as a function of distance (km) calculated for the month of July. Heavy line, Real case; thin line, Corr case; dashed line, NoCorr case. Markers indicate sample size less than 100 observations per month.

Fig. 2.

Vertical correlation of rawinsonde temperature observation innovations as a function of pressure (hPa) for correlations against innovations at 600 hPa. Twice daily data for the month of July. Heavy line, Real case; thin line, Corr case; dashed line, NoCorr case.

Fig. 3.

Channel correlations for IASI metop-a observation innovations. Twice daily data for the month of July. (a) Real case (O-F); (b) NoCorr case (O-F); (c) simulated error correlations added for Corr case; (d) Corr case (O-F).

Fig. 4.

Top, Standard deviations of observation innovations (O-F). Twice daily data for the month of July. Real case, open circles; Corr case, solid dots; NoCorr case, open stars. Bottom, comparison of the magnitude of simulated added errors (open squares) and the R observation error weighting used by the DAS (dark squares). (a, c) AMSU-A noaa-18 as a function of channel; (b, d) rawinsonde temperature as a function of height.

Fig. 5.

Zonal means of temporal root mean square analysis increments (A, B). Twice daily data for the month of July. (a, d, g) Real case; (b, e, h) Corr case; (c, f, i) NoCorr case. (a, b, c) T (K); (d, e, f) zonal wind (m s–1); (g, h, i) specific humidity (kg kg–1).

Fig. 6.

Zonal means of root temporal mean square background errors. (a, d) Temperature (K); (b, e) specific humidity (kg kg–1); (c, f) zonal wind (m s–1). (a, b, c) Left panels, Corr case; (d, e, f) difference between NoCorr and Corr cases.

Fig. 7.

Zonal means of temporal mean differences in absolute analysis and background errors, |ANR||BNR|. (a, d) Temperature (K); (b, e) specific humidity (kg kg–1); (c, f) zonal wind (m s–1). (a, b, c) Corr case; (d, e, f) difference between NoCorr and Corr cases.

Fig. 8.

Zonal means of root temporal mean square analysis errors. (a, d) Temperature (K); (b, e) specific humidity (kg kg–1); (c, f) zonal wind (m s–1). (a, b, c) Corr case; (d, e, f) difference between NoCorr and Corr cases.

Fig. 9.

Fractional difference in areal mean temporal-RMS forecast error, (NoCorr-Corr)/Corr, month of July. Stippling indicates significant difference at the 90th percentile confidence level. (a, b, c), temperature error; (d, e, f) specific humidity error; (g, h, i) zonal wind error. (a, d, g) NHEX (20N-90N); (b, e, h) SHEX (90S-20S); (c, f, i) Tropics (20N-20S).

Fig. 10.

Daily mean FSO estimates of observation impacts on 24 hour forecast skill for a total wet energy norm, self-analysis verification. Left panel, comparison of net daily impact with whiskers indicating confidence interval at the 95th percentile; right panel, difference in impacts between NoCorr and Corr cases, with whiskers indicating the confidence interval of paired differences at the 95th percentile.

Table 1.

List of simulated correlated errors added to each synthetic observation type.

Error correlationObservationHorizontalVerticalChannelNone AIRSX AMSU-AX ATMSXCrISX HIRS4X IASIX MHSX SSMISXSurface conventionalX AMVXX AircraftX ScatterometerX RAOBX GPSROX

[i] X denotes the addition of simulated errors with the indicated correlation type.

Language: English
Page range: 1886795 - 1886795
Published on: Jan 1, 2021
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2021 Nikki C. Privé, Ronald M. Errico, Will McCarty, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.