Practical Ensemble-Based Approaches to Estimate Atmospheric Background Error Covariances for Limited-Area Deterministic Data Assimilation

Bédard, Joël; Buehner, Mark; Caron, Jean‐François; Baek, Seung-Jong; Fillion, Luc

doi:10.1175/mwr-d-18-0145.1

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…have yielded mixed results and provide scant overall evidence that BECs coarser than the deterministic background systematically degrade subsequent forecasts (e.g., Schwartz et al 2015b;Schwartz 2016;Lei and Whitaker 2017;Bédard et al 2018Bédard et al , 2020Kay and Wang 2020). Furthermore, some ensemble-based DA systems use simpler procedures to update ensemble perturbations relative to methods for updating deterministic backgrounds, finding few adverse impacts from the simplifications and implicitly acknowledging the overriding importance of central states (e.g., Buehner et al 2017;Lorenc et al 2017;Bédard et al 2018). Therefore, past research collectively suggests that ensemble perturbation resolution likely has secondary importance relative to resolution of deterministic backgrounds in dual-resolution EnVar DA systems, consistent with our results for short-term CAE forecasts.…”

Section: A Connection To Dual-resolution Ensemble-variational Da Systemsmentioning

confidence: 99%

Short-Term Convection-Allowing Ensemble Precipitation Forecast Sensitivity to Resolution of Initial Condition Perturbations and Central Initial States

Schwartz

Poterjoy

Romine

et al. 2022

Weather and Forecasting

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Nine sets of 36-h, 10-member, convection-allowing ensemble (CAE) forecasts with 3-km horizontal grid spacing were produced over the conterminous United States for a 4-week period. These CAEs had identical configurations except for their initial conditions (ICs), which were constructed to isolate CAE forecast sensitivity to resolution of IC perturbations and central initial states about which IC perturbations were centered. The IC perturbations and central initial states were provided by limited-area ensemble Kalman filter (EnKF) analyses with both 15- and 3-km horizontal grid spacings, as well as from NCEP’s Global Forecast System (GFS) and Global Ensemble Forecast System. Given fixed-resolution IC perturbations, reducing horizontal grid spacing of central initial states improved ∼1–12-h precipitation forecasts. Conversely, for constant-resolution central initial states, reducing horizontal grid spacing of IC perturbations led to comparatively smaller short-term forecast improvements or none at all. Overall, all CAEs initially centered on 3-km EnKF mean analyses produced objectively better ∼1–12-h precipitation forecasts than CAEs initially centered on GFS or 15-km EnKF mean analyses regardless of IC perturbation resolution, strongly suggesting it is more important for central initial states to possess fine-scale structures than IC perturbations for short-term CAE forecasting applications, although fine-scale perturbations could potentially be critical for data assimilation purposes. These findings have important implications for future operational CAE forecast systems and suggest CAE IC development efforts focus on producing the best possible high-resolution deterministic analyses that can serve as central initial states for CAEs.

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Section: A Connection To Dual-resolution Ensemble-variational Da Systemsmentioning

confidence: 99%

Short-Term Convection-Allowing Ensemble Precipitation Forecast Sensitivity to Resolution of Initial Condition Perturbations and Central Initial States

Schwartz

Poterjoy

Romine

et al. 2022

Weather and Forecasting

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“…The specific configuration of the EnKF-CV is similar to the system described by Bédard et al (2018), except for a reduced ensemble size of 128 members and more severe spatial localization. The localization function is the same function from Gaspari and Cohn (1999) already mentioned, such that the horizontal covariances are forced to zero at a horizontal distance of 1400 km for vertical levels below 400 hPa, which increases to a distance of 2000 km for levels above 14 hPa.…”

Section: Experiments With a Regional Nwp Model A Experimental Setupmentioning

confidence: 99%

Local Ensemble Transform Kalman Filter with Cross Validation

Buehner

2020

Monthly Weather Review

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Many ensemble data assimilation (DA) approaches suffer from the so-called inbreeding problem. As a consequence, there is an excessive reduction in ensemble spread by the DA procedure, causing the analysis ensemble spread to systematically underestimate the uncertainty of the ensemble mean analysis. The stochastic EnKF used for operational NWP in Canada largely avoids this problem by applying cross validation, that is, using an independent subset of ensemble members for updating each member. The goal of the present study is to evaluate two new variations of the local ensemble transform Kalman filter (LETKF) that also incorporate cross validation. In idealized numerical experiments with Gaussian-distributed background ensembles, the two new LETKF approaches are shown to produce reliable analysis ensembles such that the ensemble spread closely matches the uncertainty of the ensemble mean, without any ensemble inflation. In ensemble DA experiments with highly nonlinear idealized forecast models, the deterministic version of the LETKF with cross validation quickly diverges, but the stochastic version produces better results, nearly identical to the stochastic EnKF with cross validation. In the context of a regional NWP system, ensemble DA experiments are performed with the two new LETKF-based approaches with cross validation, the standard LETKF, and the stochastic EnKF. All approaches with cross validation produce similar ensemble spread at the first analysis time, though the amplitude of the changes to the individual members is larger with the stochastic approaches. Over the 10-day period of the experiments, the fit of the ensemble mean background state to radiosonde observations is statistically indistinguishable for all approaches evaluated.

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“…However, such improvements can only be realized by reducing the amount of thinning up to the point where the error correlation between the remaining observations reaches an empirically determined value of 0.2 (Liu and Rabier, 2002). As an example, in all Environment and Climate Change Canada (ECCC) operational NWP systems, although satellite brightness temperature observations are spatially thinned such that the minimum distance between assimilated observations is approximately 150 km (Bédard et al, 2018), the error variances are still inflated (Heilliette and Garand, 2015) to compensate for remaining unaccounted-for error correlations. The observation error variance inflation and spatial thinning approaches disadvantage small scales and the present study aims at developing a practical assimilation approach to extract smaller-scale information from observations with spatially correlated errors, while still assuming spatially uncorrelated observation errors within the data assimilation system.…”

Section: Introductionmentioning

confidence: 99%

A practical assimilation approach to extract smaller‐scale information from observations with spatially correlated errors: An idealized study

Bédard

Buehner

2019

Quart J Royal Meteoro Soc

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It is still common to neglect the spatial error correlations of assimilated observations in numerical weather prediction systems because no practical approach is available to account for them when the number of observations with correlated error is large or when these observations are non‐uniformly distributed. Instead, it is common practice to inflate observation error variances to avoid overfitting large scales and spatially thin observations to reduce error correlations between remaining observations, although both methods generally sacrifice small‐scale information. Inspired by previous work on assimilating the difference between adjacent observations (so‐called spatial difference observations), this study aims at combining direct observations with spatial difference observations in the assimilation to extract both large‐ and smaller‐scale information from observations with spatially correlated errors, while still neglecting these error correlations. Experiments performed in a simplified 1D context over a periodic domain show that the combined approach is numerically equivalent to directly assimilating observations available at every grid point using non‐diagonal observation error covariances based on a first‐order autoregressive correlation function. In a case where observation error correlations have a different structure (e.g. Gaussian), the true observation error correlations are not perfectly taken into account by the combined approach, but it still efficiently extracts information to correct the scales that have the most errors. Combining direct observations with spatial difference observations proves complementary and experimental results show lower analysis errors for both large and intermediate scales. More specifically, while neglecting spatial observation error correlations, the combined approach provides results with lower analysis error than the direct approach, especially when spatial error correlations are large and when direct observations are spatially thinned.

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Practical Ensemble-Based Approaches to Estimate Atmospheric Background Error Covariances for Limited-Area Deterministic Data Assimilation

Cited by 5 publications

References 30 publications

Short-Term Convection-Allowing Ensemble Precipitation Forecast Sensitivity to Resolution of Initial Condition Perturbations and Central Initial States

Short-Term Convection-Allowing Ensemble Precipitation Forecast Sensitivity to Resolution of Initial Condition Perturbations and Central Initial States

Local Ensemble Transform Kalman Filter with Cross Validation

A practical assimilation approach to extract smaller‐scale information from observations with spatially correlated errors: An idealized study

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