Comparison of initial perturbation methods for ensemble prediction at convective scale

Raynaud, Laure; Bouttier, François

doi:10.1002/qj.2686

Cited by 69 publications

(66 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the cool-start analysis systems, whereby the high-resolution backgrounds were spun up over 6 h, could potentially be improved. It is possible that the spinup process was incomplete at 6 h (e.g., Raynaud and Bouttier 2016), which may have negatively impacted the cool-start analyses. Thus, further work might examine spinup characteristics more closely and potentially lengthen the spinup period.…”

Section: Discussion and Summarymentioning

confidence: 98%

Improving Large-Domain Convection-Allowing Forecasts with High-Resolution Analyses and Ensemble Data Assimilation

Schwartz

2016

Mon. Wea. Rev.

View full text Add to dashboard Cite

Analyses with 20-km horizontal grid spacing were produced from continuously cycling three-dimensional variational (3DVAR), ensemble square root Kalman filter (EnSRF), and ''hybrid'' variational-ensemble data assimilation (DA) systems over a domain spanning the conterminous United States. These analyses initialized 36-h Weather Research and Forecasting Model forecasts containing a large convection-allowing 4-km nested domain, where downscaled 20-km 3DVAR, EnSRF, and hybrid analyses initialized the 4-km forecasts. Overall, hybrid analyses initialized the best 4-km precipitation forecasts.Furthermore, whether 4-km precipitation forecasts could be improved by initializing them with true 4-km analyses was assessed. As it was computationally infeasible to produce 4-km continuously cycling ensembles over the large 4-km domain, several ''dual-resolution'' hybrid DA configurations were adopted where 4-km backgrounds were combined with 20-km ensembles to produce 4-km hybrid analyses. Additionally, 4-km 3DVAR analyses were produced.In both hybrid and 3DVAR frameworks, initializing 4-km forecasts with true 4-km analyses, rather than downscaled 20-km analyses, yielded superior precipitation forecasts over the first 12 h. Differences between forecasts initialized from 4-km and downscaled 20-km hybrid analyses were smaller for 18-36-h forecasts, but there were occasionally meaningful differences. Continuously cycling the 4-km backgrounds and using static background error covariances with larger horizontal length scales in the hybrid led to better forecasts. All hybrid-initialized forecasts, including those initialized from downscaled 20-km analyses, were more skillful than forecasts initialized from 4-km 3DVAR analyses, suggesting the analysis method was more important than analysis resolution.

show abstract

Section: Discussion and Summarymentioning

confidence: 98%

Improving Large-Domain Convection-Allowing Forecasts with High-Resolution Analyses and Ensemble Data Assimilation

Schwartz

2016

Mon. Wea. Rev.

View full text Add to dashboard Cite

show abstract

“…It also has the advantage of being simple to implement and with low CPU costs. However, when compared to other CPU consuming methods it suffers from a spin‐up time of about 9–12 hr (Raynaud & Bouttier, ; Vié et al, ).…”

Section: Methodsmentioning

confidence: 99%

Reforecasting the Flooding of Florence of 4 November 1966 With Global and Regional Ensembles

Capecchi

Buizza

2019

JGR Atmospheres

View full text Add to dashboard Cite

Providing skilful predictions of high‐impact weather up to 2 weeks ahead is on the agenda of international weather centers. Evaluating the capabilities of current numerical systems in predicting past events can bring extremely valuable contributions to the assessment of the information content available today with operational models. In the framework of the activities for the fiftieth anniversary of the extreme precipitation event that occurred in Italy in November 1966, this paper investigates its predictability using state‐of‐the‐art global and regional ensemble simulations. The first goal is to assess if and how many days in advance, this event can be predicted by current European Centre for Medium‐Range Weather Forecasts (ECMWF) global ensembles. A second goal is to evaluate the potential added value of running nested higher‐resolution and convection‐permitting ensembles. It is shown that ECMWF ensembles are able to provide valuable information up to 3 days before the event. Within this forecast range, convection‐permitting simulations can provide more accurate estimations of precipitation maxima. However, the results indicate also a strong underestimation of rainfall amounts with both global and regional models even at short forecast range. To partially explain this shortcoming, we discuss how the scarcity of observations available in 1966 for the analysis process limits the quality of the ensemble initial conditions and we adopt a method to obtain more reliable ensemble forecasts. The paper concludes with a comparison with previous similar works; results indicate a gain in predictability of up to 12 hr with respect to numerical revisitations performed to mark the fortieth anniversary of the event.

show abstract

“…9 are a maximum of only ten hours old, this may explain why a small-scale (3D-like) energy spectrum is not seen. Raynaud and Bouttier (2016) also showed that perturbations downscaled into the 2.5 km grid-length AROME EPS took 9-12 hours to develop into realistic small-scale structures. This issue may be particularly relevant to our case study due to the passage 25 of a cold front, as opposed to a case where convection is the dominant source of precipitation (the latter of which may generate 3D motion more quickly).…”

Section: ē (N )mentioning

confidence: 96%

“…Raynaud and Bouttier (2016)). Alternatively, a set of ensemble members that sample a climatological PDF may be taken from one long (e.g.…”

Section: Generating the 93 Convective-scale Ensemble Membersmentioning

confidence: 99%

Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble

Bannister

Migliorini

Rudd

et al. 2017

Preprint

View full text Add to dashboard Cite

Abstract. Ensemble-based predictions are increasingly used as an aid to weather forecasting and to data assimilation, where the aim is to capture the range of possible outcomes consistent with the underlying uncertainties. Constraints on computing resources mean that ensembles have a relatively small size, which can lead to an incomplete range of possible outcomes, and to inherent sampling errors. This paper discusses how an existing ensemble can be relatively easily increased in size, it develops a range of standard and extended diagnostics to help determine whether a given ensemble is 'large enough' to 5 be useful for forecasting and data assimilation purposes, and it applies the diagnostics to a convective-scale case study for illustration. Diagnostics include the effect of ensemble size on various aspects of rainfall forecasts, kinetic energy spectra, and (co)-variance statistics in the spatial and spectral domains.The work here extends the Met Office's 24 ensemble members to 93. It is found that the extra members do develop a significant degree of linear independence, they increase the ensemble spread (although with caveats to do with non-Gaussianity), 10 they reduce sampling error in many statistical quantities (namely variances, correlations, and length-scales), and improve the effective spatial resolution of the ensemble. The extra members though do not improve the probabilistic rain rate forecasts.It is assumed that the 93-member ensemble approximates the error-free statistics, which is a practical assumption, but the data suggests that this number of members is ultimately not enough to justify this assumption, and therefore more ensembles are likely required for such convective-scale systems to further reduce sampling errors, especially for ensemble data assimilation 15 purposes.Copyright statement.

show abstract

Comparison of initial perturbation methods for ensemble prediction at convective scale

Cited by 69 publications

References 33 publications

Improving Large-Domain Convection-Allowing Forecasts with High-Resolution Analyses and Ensemble Data Assimilation

Improving Large-Domain Convection-Allowing Forecasts with High-Resolution Analyses and Ensemble Data Assimilation

Reforecasting the Flooding of Florence of 4 November 1966 With Global and Regional Ensembles

Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble

Contact Info

Product

Resources

About