Land Surface Parameter and State Perturbations in the Global Ensemble Forecast System

Gehne, Maria; Hamill, Thomas M.; Bates, Gary T.; Pegion, Philip; Kolczynski, Walter C.

doi:10.1175/mwr-d-18-0057.1

Cited by 4 publications

(2 citation statements)

References 66 publications

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“…Gehne et al . (2019) study the stochastic representation of uncertainties at process level for the land‐surface parametrisation in the Global Ensemble Forecast System (GEFS) of the National Centers for Environmental Prediction (NCEP).…”

Section: Introductionmentioning

confidence: 99%

“…Hirt et al (2019) describe stochastic perturbations for the subgrid turbulence parametrisation in convection-permitting ensembles using the Consortium for Small-scale Modeling (COSMO) model; their so-called physically based perturbations (PSP) target the initiation of convection. Gehne et al (2019) study the stochastic representation of uncertainties at process level for the land-surface parametrisation in the Global Ensemble Forecast System (GEFS) of the National Centers for Environmental Prediction (NCEP). Ollinaho et al (2017) introduce a comprehensive process-level stochastic representation of model uncertainties for the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF).…”

Section: Introductionmentioning

confidence: 99%

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Revision of the Stochastically Perturbed Parametrisations model uncertainty scheme in the Integrated Forecasting System

Lang

Lock

Leutbecher

et al. 2021

Quart J Royal Meteoro Soc

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The Stochastically Perturbed Parametrisations scheme (SPP) represents model uncertainty in numerical weather prediction by introducing stochastic perturbations into the physical parametrisation schemes. The perturbations are constructed in such a way that the internal consistency of the physical parametrisation schemes is preserved. We developed a revised version of SPP for the Integrated Forecasting System of the European Centre for Medium‐Range Weather Forecasts (ECMWF). The revised version introduces perturbations to additional quantities and modifies the probability distributions sampled by the scheme. Medium‐range ensemble forecasts with the revised SPP are considerably more skilful than ensemble forecasts with the original implementation of SPP. The revised version of SPP is similar, in terms of forecast skill, to the Stochastically Perturbed Parametrisation Tendency scheme (SPPT), which is currently used to represent model uncertainty in the operational ECMWF ensemble forecasts.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Revision of the Stochastically Perturbed Parametrisations model uncertainty scheme in the Integrated Forecasting System

Lang

Lock

Leutbecher

et al. 2021

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

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Accounting for land model error in numerical weather prediction ensemble systems: toward ensemble-based coupled land/atmosphere data assimilation

Draper

2021

Journal of Hydrometeorology

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The ensembles used in the NOAA National Centers for Environmental Prediction (NCEP) global data assimilation and numerical weather prediction (NWP) system are under-dispersed at and near the land surface, preventing their use in ensemble-based land data assimilation. Comparison to offline (land-only) data assimilation ensemble systems suggests that while the relevant atmospheric fields are under-dispersed in NCEP’s system, this alone cannot explain the under-dispersed land component, and an additional scheme is required to explicitly account for land model error. This study then investigates several schemes for perturbing the soil (moisture and temperature) states in NCEP’s system, qualitatively comparing the induced ensemble spread to independent estimates of the forecast error standard deviation in soil moisture, soil temperature, 2m temperature, and 2m humidity. Directly adding perturbations to the soil states, as is commonly done in offline systems, generated unrealistic spatial patterns in the soil moisture ensemble spread. Application of a Stochastically Perturbed Physics Tendencies scheme to the soil states is inherently limited in the amount of soil moisture spread that it can induce. Perturbing the land model parameters, in this case vegetation fraction, generated a realistic distribution in the ensemble spread, while also inducing perturbations in the land (soil states) and atmosphere (2m states) that are consistent with errors in the land/atmosphere fluxes. The parameter perturbation method is then recommended for NCEP’s ensemble system, and it is currently being refined within the development of an ensemble-based coupled land/atmosphere data assimilation for NCEP’s NWP system.

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Improving Ensemble Weather Prediction System Initialization: Disentangling the Contributions from Model Systematic Errors and Initial Perturbation Size

Hamill

Scheuerer

2021

Monthly Weather Review

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Characteristics of the European Centre for Medium-Range Weather Forecast’s (ECMWF’s) 00 UTC diagnosed 2-meter temperatures (T2m) from 4D-Var and global ensemble forecasts initial conditions were examined in 2018 over the contiguous US at 1/2-degree grid spacing. These were compared against independently generated, upscaled high-resolution T2m analyses that were created with a somewhat novel data assimilation methodology, an extension of classical optimal interpolation (OI) to surface data analysis. The analysis used a high-resolution, spatially detailed climatological background and was statistically unbiased. Differences of the ECMWF 4D-Var T2m initial states from the upscaled OI reference were decomposed into a systematic component and a residual component. The systematic component was determined by applying a temporal smoothing to the time series of differences between the ECMWF T2m analyses and the OI analyses. Systematic errors at 00 UTC were commonly 1 K or more and larger in the mountainous western US, with the ECMWF analyses cooler than the reference. The residual error is regarded as random in character and should be statistically consistent with the spread of the ensemble of initial conditions after inclusion of OI analysis uncertainty. This analysis uncertainty was large in the western US, complicating interpretation. There were some areas suggestive of an over-spread initial ensemble, others under-spread. Assimilation of more observations in the reference OI analysis would reduce analysis uncertainty, facilitating more conclusive determination of initial-condition ensemble spread characteristics.

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Land Surface Parameter and State Perturbations in the Global Ensemble Forecast System

Cited by 4 publications

References 66 publications

Revision of the Stochastically Perturbed Parametrisations model uncertainty scheme in the Integrated Forecasting System

Revision of the Stochastically Perturbed Parametrisations model uncertainty scheme in the Integrated Forecasting System

Accounting for land model error in numerical weather prediction ensemble systems: toward ensemble-based coupled land/atmosphere data assimilation

Improving Ensemble Weather Prediction System Initialization: Disentangling the Contributions from Model Systematic Errors and Initial Perturbation Size

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