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Juhani Rinne
and
Heikki Järvinen

Abstract

This paper presents an estimate of the Cressman term that appears in the divergent barotropic model as a corrector of planetary-wave retrogression. In contrast with earlier studies, the term varies as a function of geographical location. The estimation is performed using the adjoint model.

The results deviate from those earlier derived theoretically and applied in routine forecasting. The Cressman term can now be viewed as a corrector of the systematic error or as a baroclinity parameter. The proposed form of the Cressman term can also be interpreted as a forcing parameter, maintaining the troughs and ridges of the main circulation by affecting the free long waves.

Parameter estimation using the adjoint model has shown its potential in these experiments. Not only are the parameter values determined but new interpretations and approaches have been found.

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Juhani Rinne
and
Heikki Järvinen

Abstract

The chaotic structure of two-dimensional atmospheric flow is illustrated. It is shown that certain errors in numerical approximations can Prevent the correct prediction of chaotic processes. This is the case when the numerical approximations do not sufficiently allow air parcels to deviate from each other. The error mechanism is described with a case study and is proposed as one explanation for the errors observed when forecasting the development of blocking highs. It can explain why the errors in blocking highs are similarly found in different models from different centers, why they appear in medium-range forecasts but not in short-range forecasts, and why the error decreases only slowly with increasing resolution.

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Lauri Tuppi
,
Madeleine Ekblom
,
Pirkka Ollinaho
, and
Heikki Järvinen

Abstract

Numerical weather prediction models contain parameters that are inherently uncertain and cannot be determined exactly. It is thus desirable to have reliable objective approaches for estimation of optimal values and uncertainties of these parameters. Traditionally, the parameter tuning has been done manually, which can lead to the tuning process being a maze of subjective choices. In this paper we present how to optimize 20 key physical parameters in the atmospheric model Open Integrated Forecasting System (OpenIFS) that have a strong impact on forecast quality. The results show that simultaneous optimization of O(20) parameters is possible with O(100) algorithm steps using an ensemble of O(20) members; the results also show that the optimized parameters lead to substantial enhancement of predictive skill. The enhanced predictive skill can be attributed to reduced biases in low-level winds and upper-tropospheric humidity in the optimized model. We find that the optimization process is dependent on the starting values of the parameters that are optimized (starting from better-suited values results in a better model). The results show also that the applicability of the tuned parameter values across different model resolutions is somewhat limited because of resolution-dependent model biases, and we also found that the parameter covariances provided by the tuning algorithm seem to be uninformative.

Significance Statement

The purpose of this work is to show how to use algorithmic methods to optimize a weather model in a computationally efficient manner. Traditional manual model tuning is an extremely laborious and time-consuming process, so algorithmic methods have strong potential for saving the model developers’ time and accelerating development. This paper shows that algorithmic optimization is possible and that weather forecasts can be improved. However, potential issues related to the use of the optimized parameter values across different model resolutions are discussed as well as other shortcomings related to the tuning process.

Open access
Judith Berner
,
Ulrich Achatz
,
Lauriane Batté
,
Lisa Bengtsson
,
Alvaro de la Cámara
,
Hannah M. Christensen
,
Matteo Colangeli
,
Danielle R. B. Coleman
,
Daan Crommelin
,
Stamen I. Dolaptchiev
,
Christian L. E. Franzke
,
Petra Friederichs
,
Peter Imkeller
,
Heikki Järvinen
,
Stephan Juricke
,
Vassili Kitsios
,
François Lott
,
Valerio Lucarini
,
Salil Mahajan
,
Timothy N. Palmer
,
Cécile Penland
,
Mirjana Sakradzija
,
Jin-Song von Storch
,
Antje Weisheimer
,
Michael Weniger
,
Paul D. Williams
, and
Jun-Ichi Yano

Abstract

The last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to represent model inadequacy better and to improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing long-standing climate biases and is relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups that show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface, and cryosphere of comprehensive weather and climate models 1) gives rise to more reliable probabilistic forecasts of weather and climate and 2) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics, and turbulence is reviewed; its relevance for the climate problem is demonstrated; and future research directions are outlined.

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