Literature DB >> 24842034

Assessing parametrization uncertainty associated with horizontal resolution in numerical weather prediction models.

Glenn Shutts1, Alfons Callado Pallarès2.   

Abstract

The need to represent uncertainty resulting from model error in ensemble weather prediction systems has spawned a variety of ad hoc stochastic algorithms based on plausible assumptions about sub-grid-scale variability. Currently, few studies have been carried out to prove the veracity of such schemes and it seems likely that some implementations of stochastic parametrization are misrepresentations of the true source of model uncertainty. This paper describes an attempt to quantify the uncertainty in physical parametrization tendencies in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System with respect to horizontal resolution deficiency. High-resolution truth forecasts are compared with matching target forecasts at much lower resolution after coarse-graining to a common spatial and temporal resolution. In this way, model error is defined and its probability distribution function is examined as a function of tendency magnitude. It is found that the temperature tendency error associated with convection parametrization and explicit water phase changes behaves like a Poisson process for which the variance grows in proportion to the mean, which suggests that the assumptions underpinning the Craig and Cohen statistical model of convection might also apply to parametrized convection. By contrast, radiation temperature tendency errors have a very different relationship to their mean value. These findings suggest that the ECMWF stochastic perturbed parametrization tendency scheme could be improved since it assumes that the standard deviation of the tendency error is proportional to the mean. Using our finding that the variance error is proportional to the mean, a prototype stochastic parametrization scheme is devised for convective and large-scale condensation temperature tendencies and tested within the ECMWF Ensemble Prediction System. Significant impact on forecast skill is shown, implying its potential for further development.
© 2014 The Author(s) Published by the Royal Society. All rights reserved.

Entities:  

Keywords:  AR1 process; coarse-graining; model error; stochastic parametrization; uncertainty

Year:  2014        PMID: 24842034     DOI: 10.1098/rsta.2013.0284

Source DB:  PubMed          Journal:  Philos Trans A Math Phys Eng Sci        ISSN: 1364-503X            Impact factor:   4.226


  2 in total

1.  Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system.

Authors:  Antje Weisheimer; Susanna Corti; Tim Palmer; Frederic Vitart
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2014-06-28       Impact factor: 4.226

2.  More reliable forecasts with less precise computations: a fast-track route to cloud-resolved weather and climate simulators?

Authors:  T N Palmer
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2014-06-28       Impact factor: 4.226

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.