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

Ollinaho, Pirkka; Carver, G. D.; Lang, Simon; Tuppi, Lauri; Ekblom, Madeleine; Järvinen, Heikki

doi:10.5194/gmd-2020-292

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Data assimilation is not included in OpenIFS, and thus external initial conditions of atmospheric state (temperature, wind, humidity, and surface pressure) are required. In this study, we use operational atmospheric analyses of ECMWF (Ollinaho et al, 2021) and OpenIFS version 43r3v1 that was part of the operational forecasting system at ECMWF from July 2017 to June 2018 (IFS cycle 43r3; ECMWF, 2019).…”

Section: Weather Modelmentioning

confidence: 99%

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Coupling a weather model directly to GNSS orbit determination

Trastoy

Strasser

Tuppi

et al. 2021

Preprint

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Abstract. Neutral atmosphere bends and delays propagation of microwave signals in satellite-based navigation. Weather prediction models can be used to estimate these effects by providing 3-dimensional refraction fields to estimate signal delay in the zenith direction and determine a low-dimensional mapping of this delay to desired azimuth and elevation angles. In this study, a global numerical weather prediction model (OpenIFS licensed for Academic use by ECMWF) is used to generate the refraction fields. The ray-traced slant delays are supplied as such – in contrast to mapping – for an orbit solver (GROOPS software toolkit of TUG) which applies the raw observation method. Here we show that such a close coupling is possible without need for major additional modifications in the solver codes. The main finding here is that the adopted approach provides a very good a priori model for the atmospheric effects on navigation signals, as measured with the midnight discontinuity of GNSS satellite orbits. Our interpretation is that removal of the intermediate mapping step allows to take advantage of the local refraction field asymmetries in the GNSS signal processing. Moreover, the direct coupling helps in identifying deficiencies in the slant delay computation because the modelling errors are not convoluted in the precision-reducing mapping. These conclusions appear robust, despite the relatively small data set of raw code and phase observations covering the core network of 66 ground-based stations of the International GNSS Service over one-month periods in December 2016 and June 2017. More generally, the new configuration enhances our control of geodetic and meteorological aspects of the orbit problem. This is pleasant because we can, for instance, regulate at will the weather model output frequency and increase coverage of spatio-temporal aspects of weather variations. The direct coupling of a weather model in precise GNSS orbit determination presented in this paper provides a unique framework for benefiting even more widely than previously the apparent synergies in space geodesy and meteorology.

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Section: Weather Modelmentioning

confidence: 99%

“…Data availability. Availability of the OpenIFS initial states is described in Ollinaho et al (2021). GNSS observation data are kindly provided by the IGS via its data centers (https://igs.org/data-products-overview).…”

Section: (A)mentioning

confidence: 99%

Coupling a weather model directly to GNSS orbit determination

Trastoy

Strasser

Tuppi

et al. 2021

Preprint

Self Cite

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“…It enables the scientific community to use selected cycles of the ECMWF operational IFS model on local computers at their own institutes. OpenIFS has the same forecasting capability as IFS, it supports the same grid resolutions up to the operational resolution, and it can be used for ensemble forecasting (Ollinaho et al, 2021). The model codes differ, however, as the IFS code for data assimilation and observation processing have been removed from OpenIFS.…”

Section: Introductionmentioning

confidence: 99%

OpenIFS/AC: atmospheric chemistry and aerosol in OpenIFS 43r3

et al. 2022

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Abstract. In this paper, we report on the first implementation of atmospheric chemistry and aerosol as part of the European Centre for Medium-Range Weather Forecasts (ECMWF) OpenIFS model. OpenIFS is a portable version of ECMWF's global numerical weather prediction model. Modules and input data for model cycle CY43R3, which have been developed as part of the Copernicus Atmosphere Monitoring Service (CAMS), have been ported to OpenIFS with the modified CB05 tropospheric chemistry scheme, the bulk bin tropospheric aerosol module, and the option to use Belgian Assimilation System for Chemical ObsErvations (BASCOE)-based stratospheric ozone chemistry. We give an overview of the model, and describe the datasets used for emissions and dry deposition, which are similar to those used in the model configuration applied to create the CAMS reanalysis. We evaluate two reference model configurations with and without the stratospheric chemistry extension against standard observational datasets for tropospheric ozone, surface carbon monoxide (CO), tropospheric nitrogen dioxide (NO2), and aerosol optical depth. The results give basic confidence in the model implementation and configuration. This OpenIFS version with atmospheric composition components is open to the scientific user community under a standard OpenIFS license.

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

et al. 2020

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Abstract. Algorithmic model tuning is a promising approach to yield the best possible forecast performance of multi-scale multi-phase atmospheric models once the model structure is fixed. The problem is to what degree we can trust algorithmic model tuning. We approach the problem by studying the convergence of this process in a semi-realistic case. Let M(x, θ) denote the time evolution model, where x and θ are the initial state and the default model parameter vectors, respectively. A necessary condition for an algorithmic tuning process to converge is that θ is recovered when the tuning process is initialised with perturbed model parameters θ′ and the default model forecasts are used as pseudo-observations. The aim here is to gauge which conditions are sufficient in a semi-realistic test setting to obtain reliable results and thus build confidence on the tuning in fully realistic cases. A large set of convergence tests is carried in semi-realistic cases by applying two different ensemble-based parameter estimation methods and the atmospheric forecast model of the Integrated Forecasting System (OpenIFS) model. The results are interpreted as general guidance for algorithmic model tuning, which we successfully tested in a more demanding case of simultaneous estimation of eight OpenIFS model parameters.

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

Cited by 3 publications

References 15 publications

Coupling a weather model directly to GNSS orbit determination

Coupling a weather model directly to GNSS orbit determination

OpenIFS/AC: atmospheric chemistry and aerosol in OpenIFS 43r3

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

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