Use of adjoint sensitivity analysis to diagnose the CMC global analysis performance: A case study

Laroche, Stéphane; Tanguay, Monique; Zadra, Ayrton; Morneau, Josée

doi:10.3137/ao.400404

Cited by 12 publications

(4 citation statements)

References 46 publications

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“…For the same case, Kleist and Morgan [2005b] reduced 46% of forecast error with 12 iterations, and by better representation of dynamic features, the corresponding precipitation forecast was also improved. Laroche et al [2002] applied adjoint sensitivity analysis to a poor 3‐day winter storm forecast of the Global Environmental Multiscale model. Recently, there have been some studies concerning the reality of key analysis error compared to observations [ Isaksen et al , 2005; Caron et al , 2007a, 2007b].…”

Section: Introductionmentioning

confidence: 99%

Moist adjoint‐based forecast sensitivities for a heavy snowfall event over the Korean Peninsula on 4–5 March 2004

Jung

Kim

2009

J. Geophys. Res.

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[1] Adjoint sensitivity analyses are applied to a heavy snowfall event on the Korean Peninsula using the MM5 Adjoint Modeling System. To evaluate the effects of initial conditions on forecast error, adjoint integrations are performed using forecast error as a response function. Initial adjoint sensitivities are located in the middle to lower troposphere with horizontally isolated upshear-tilted structures in the upstream regions (i.e., southern Mongolia). In addition, due to the effect of moist physics in adjoint model integration, vertically striped structures in the lower troposphere are also detected in the southern sea of the Korean Peninsula. Both initial adjoint sensitivity structures match the individual singular vector structures. The new 36-h forecast using the initial condition perturbed with scaled adjoint sensitivities (i.e., key analysis error) shows much improvement in the intensity and location of the surface cyclone as well as the upper trough, and reduces 42.9% of the forecast error compared to the control forecast. In addition, the forecasts using the initial condition perturbed by the key analysis error deviate slightly from the observations than the forecasts using the control analysis in the first 12 h and are much closer to the observations than the control forecasts in the later 12 h. The linear and nonlinear evolutions of temperature perturbations over the large adjoint sensitivity regions show similar growth rates and spatial distributions. Additional experiments with and without specific moist physics for linear model integrations show that disagreement of moist physics in the nonlinear and linear model integrations degrades the linearity.

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Section: Introductionmentioning

confidence: 99%

Moist adjoint‐based forecast sensitivities for a heavy snowfall event over the Korean Peninsula on 4–5 March 2004

Jung

Kim

2009

J. Geophys. Res.

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“…This would correspond to a posteriori sensitivity functions often used to trace back the key analysis errors that can explain forecast error at a given lead time (Klinker et al 1998;Laroche et al 2002). So we know after the fact what should be the structure of the correction to the analysis that would impact the forecast the most.…”

Section: Example Based On 1d-var Experimentsmentioning

confidence: 99%

“…The method employed is explained in Laroche et al (2002) and Caron et al (2007a). Four types of structure functions will be considered in our study: d a global sensitivity, for which the error is measured globally, d a local sensitivity, for which the error is measured over an area on the east coast of North America, d a hemispheric sensitivity function computed over the latitudinal band 258-908N, d a sensitivity function, for which the control variable is potential vorticity (PV), which constrains the sensitivity to be more dynamically balanced, hereinafter called PV-bal (Caron et al 2007b).…”

Section: March 2011 L U P U a N D G A U T H I E Rmentioning

confidence: 99%

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Observability of Flow-Dependent Structure Functions for Use in Data Assimilation

Lupu

Gauthier

2011

Monthly Weather Review

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One of the objectives of data assimilation is to produce initial conditions that will improve the quality of forecasts. Studies on singular vectors and sensitivity studies have shown that small changes to the initial conditions can sometimes lead to exponential error growth. This has motivated research to include flowdependent structures within the assimilation that would have the characteristics to correctly predict the growth or decay of meteorological systems. This relates to the characterization of precursors to atmospheric instability. In this paper, the observability of such structures by observations is discussed. Several studies have shown that deploying observations over regions where changes in the initial conditions may impact the forecast the most do not lead to the expected benefit. In this paper, it is shown that given the small magnitude of the signal to be detected, it is important to take into account the accuracy of the observations. If the signalto-noise ratio is too low, observations cannot detect and characterize precursors to forecast error growth. From that perspective, the assimilation only has the possibility to extract information about evolved structures of error growth. Experiments with a simple one-dimensional variational data assimilation (1D-Var) system are presented and, then, an adapted three-dimensional variational data assimilation (3D-Var) system with different sensitivity structure functions is used. The results have been obtained by adapting the variational assimilation system of Environment Canada.

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Further investigation on adjoint sensitivity of surface precipitation to initial conditions

Mahfouf¹,

Bilodeau²

2007

metz

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Use of adjoint sensitivity analysis to diagnose the CMC global analysis performance: A case study

Cited by 12 publications

References 46 publications

Moist adjoint‐based forecast sensitivities for a heavy snowfall event over the Korean Peninsula on 4–5 March 2004

Moist adjoint‐based forecast sensitivities for a heavy snowfall event over the Korean Peninsula on 4–5 March 2004

Observability of Flow-Dependent Structure Functions for Use in Data Assimilation

Further investigation on adjoint sensitivity of surface precipitation to initial conditions

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