Validation of the forecast skill of the Global Modeling and Assimilation Office Observing System Simulation Experiment

Privé, Nikki C.; Errico, Ronald M.; Tai, King-Sheng

doi:10.1002/qj.2029

Cited by 29 publications

(28 citation statements)

References 17 publications

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“…The GMAO global OSSE has been extensively calibrated as documented by Prive´et al (2013c). The NR employed is a 13-month free forecast of the ECMWF operational model version c31r1, run at T511 horizontal resolution with 91 vertical levels and 3-hourly output.…”

Section: Methodsmentioning

confidence: 99%

“…A cube-sphere grid with 180 gridpoints along each edge of the cube (approximately equivalent to 0.58 horizontal resolution at the equator) was employed with 72 hybrid vertical coordinate (h) levels. The forecast skill in the OSSE has been validated against the real world in the same manner as described by Prive´et al (2013c), with the forecasts in the OSSE found to have somewhat greater skill than for real data. The increased forecast skill is not expected to significantly impact the results of this study.…”

Section: Methodsmentioning

confidence: 99%

“…Another virtue of the OSSE framework is the ability to directly manipulate the qualities of the simulated observations, particularly the observation errors. Of course, this only provides useful information about the real problem in so far as the OSSE validates Prive´et al, 2013c).…”

Section: Introductionmentioning

confidence: 99%

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Spectral analysis of forecast error investigated with an observing system simulation experiment

Privé¹,

Errico

2015

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C TThe spectra of analysis and forecast error are examined using the observing system simulation experiment framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office. A global numerical weather prediction model, the Global Earth Observing System version 5 with Gridpoint Statistical Interpolation data assimilation, is cycled for 2 months with once-daily forecasts to 336 hours to generate a Control case. Verification of forecast errors using the nature run (NR) as truth is compared with verification of forecast errors using self-analysis; significant underestimation of forecast errors is seen using self-analysis verification for up to 48 hours. Likewise, self-analysis verification significantly overestimates the error growth rates of the early forecast, as well as mis-characterising the spatial scales at which the strongest growth occurs. The NR-verified error variances exhibit a complicated progression of growth, particularly for low wavenumber errors. In a second experiment, cycling of the model and data assimilation over the same period is repeated, but using synthetic observations with different explicitly added observation errors having the same error variances as the control experiment, thus creating a different realisation of the control. The forecast errors of the two experiments become more correlated during the early forecast period, with correlations increasing for up to 72 hours before beginning to decrease.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Spectral analysis of forecast error investigated with an observing system simulation experiment

Privé¹,

Errico

2015

Tellus A: Dynamic Meteorology and Oceanography

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“…The general GMAO OSSE development and validation efforts are described in Errico et al (2013) and Privé et al (2013a). Some improvements and the use of the OSSE to estimate analysis error are presented in Errico and Privé (2014).…”

Section: The Gmao Ossementioning

confidence: 99%

“…At NASA's GMAO, a framework for carefully validating and conducting an observing system simulation experiment (OSSE) has been developed (Errico et al, 2013;Privé et al, 2013a). Within that framework, background error can be precisely calculated because the truth is known explicitly.…”

Section: Introductionmentioning

confidence: 99%

Use of an OSSE to evaluate background‐error covariances estimated by the NMC method

Errico

Privé

2014

Quart J Royal Meteoro Soc

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The NMC method has proven utility for prescribing approximate background-error covariances required by variational data assimilation systems. Here, untuned NMC method estimates are compared with explicitly determined error covariances produced within an OSSE context by exploiting availability of the true simulated states. Such a comparison provides insights into what kind of rescaling is required to render the NMC method estimates usable. It is shown that rescaling of variances and directional correlation lengths depends greatly on both pressure and latitude. In particular, some scaling coefficients appropriate in the Tropics are the reciprocal of those in the Extratropics. Also, the degree of dynamic balance is grossly overestimated by the NMC method. These results agree with previous examinations of the NMC method which used ensembles as an alternative for estimating background-error statistics.

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The influence of observation errors on analysis error and forecast skill investigated with an observing system simulation experiment

2013

Self Cite

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The National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO) observing system simulation experiment (OSSE) framework is used to explore the response of analysis error and forecast skill to observation quality. In an OSSE, synthetic observations may be created that have much smaller error than real observations, and precisely quantified error may be applied to these synthetic observations. Three experiments are performed in which synthetic observations with magnitudes of applied observation error that vary from zero to twice the estimated realistic error are ingested into the Goddard Earth Observing System Model (GEOS‐5) with Gridpoint Statistical Interpolation (GSI) data assimilation for a 1 month period representing July. The analysis increment and observation innovation are strongly impacted by observation error, with much larger variances for increased observation error. The analysis quality is degraded by increased observation error, but the change in root‐mean‐square error of the analysis state is small relative to the total analysis error. Surprisingly, in the 120 h forecast, increased observation error only yields a slight decline in forecast skill in the extratropics and no discernible degradation of forecast skill in the tropics.

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Validation of the forecast skill of the Global Modeling and Assimilation Office Observing System Simulation Experiment

Cited by 29 publications

References 17 publications

Spectral analysis of forecast error investigated with an observing system simulation experiment

Spectral analysis of forecast error investigated with an observing system simulation experiment

Use of an OSSE to evaluate background‐error covariances estimated by the NMC method

The influence of observation errors on analysis error and forecast skill investigated with an observing system simulation experiment

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