Necessary conditions for algorithmic tuning of weather prediction models using OpenIFS as an example

Tuppi, Lauri; Ollinaho, Pirkka; Ekblom, Madeleine; Shemyakin, Vladimir; Järvinen, Heikki

doi:10.5194/gmd-13-5799-2020

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…The OpenIFS release based on IFS CY43R3 includes the stochastically perturbed parameterization tendencies (SPPT) scheme (Buizza et al, 2008), the stochastic kinetic energy backscatter (SKEB) scheme (Berner et al, 2009), and an early version of the stochastically perturbed parametrizations (SPP) scheme (Ollinaho et al, 2017). To assess the skill of ensemble forecasts, it is also important to take biases, analysis uncertainty, and observation errors into account (Yamaguchi et al, 2016). This is something that we plan to do in the future.…”

Section: Fair Continuous Ranked Probability Score (Crps)mentioning

confidence: 99%

Ensemble prediction using a new dataset of ECMWF initial states – OpenEnsemble 1.0

et al. 2021

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Abstract. Ensemble prediction is an indispensable tool in modern numerical weather prediction (NWP). Due to its complex data flow, global medium-range ensemble prediction has almost exclusively been carried out by operational weather agencies to date. Thus, it has been very hard for academia to contribute to this important branch of NWP research using realistic weather models. In order to open ensemble prediction research up to the wider research community, we have recreated all 50+1 operational IFS ensemble initial states for OpenIFS CY43R3. The dataset (OpenEnsemble 1.0) is available for use under a Creative Commons licence and is downloadable from an https server. The dataset covers 1 year (December 2016 to November 2017) twice daily. Downloads in three model resolutions (TL159, TL399, and TL639) are available to cover different research needs. An open-source workflow manager, called OpenEPS, is presented here and used to launch ensemble forecast experiments from the perturbed initial conditions. The deterministic and probabilistic forecast skill of OpenIFS (cycle 40R1) using this new set of initial states is comprehensively evaluated. In addition, we present a case study of Typhoon Damrey from year 2017 to illustrate the new potential of being able to run ensemble forecasts outside of major global weather forecasting centres.

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Section: Fair Continuous Ranked Probability Score (Crps)mentioning

confidence: 99%

Ensemble prediction using a new dataset of ECMWF initial states – OpenEnsemble 1.0

et al. 2021

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Bridging observations, theory and numerical simulation of the ocean using machine learning

Sonnewald

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Jones

et al. 2021

Environ. Res. Lett.

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Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques offers exciting possibilities for advancing the capacity and speed of established methods and for making substantial and serendipitous discoveries. Beyond vast amounts of complex data ubiquitous in many modern scientific fields, the study of the ocean poses a combination of unique challenges that ML can help address. The observational data available is largely spatially sparse, limited to the surface, and with few time series spanning more than a handful of decades. Important timescales span seconds to millennia, with strong scale interactions and numerical modelling efforts complicated by details such as coastlines. This review covers the current scientific insight offered by applying ML and points to where there is imminent potential. We cover the main three branches of the field: observations, theory, and numerical modelling. Highlighting both challenges and opportunities, we discuss both the historical context and salient ML tools. We focus on the use of ML in situ sampling and satellite observations, and the extent to which ML applications can advance theoretical oceanographic exploration, as well as aid numerical simulations. Applications that are also covered include model error and bias correction and current and potential use within data assimilation. While not without risk, there is great interest in the potential benefits of oceanographic ML applications; this review caters to this interest within the research community.

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Necessary conditions for algorithmic tuning of weather prediction models using OpenIFS as an example

Cited by 2 publications

References 23 publications

Ensemble prediction using a new dataset of ECMWF initial states – OpenEnsemble 1.0

Ensemble prediction using a new dataset of ECMWF initial states – OpenEnsemble 1.0

Bridging observations, theory and numerical simulation of the ocean using machine learning

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