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-14-2143-2021

Cited by 6 publications

(6 citation statements)

References 27 publications

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“…This shows that the filter likelihood score is consistent with traditional verification metrics (see e.g. Ollinaho et al 2021) and is expected from previous studies with Lorenz'95 (Solonen and Järvinen 2013). Similar results are obtained for horizontal wind components u and v and specific humidity q (see Figures 5, 6 and 7 in the Appendix).…”

Section: Comparisons Between Different Verification Metricssupporting

confidence: 89%

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Filter Likelihood as an Observation-Based Verification Metric in Ensemble Forecasting

Ekblom

Tuppi

Räty

et al. 2023

Tellus A: Dynamic Meteorology and Oceanography

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In numerical weather prediction (NWP), ensemble forecasting aims to quantify the flow-dependent forecast uncertainty. The focus here is on observation-based verification of the reliability of ensemble forecasting systems. In particular, at short forecast lead times, forecast errors tend to be relatively small compared to observation errors and hence it is very important that the verification metric also accounts for observational uncertainties. This paper studies the so-called filter likelihood score which is deep-rooted in Bayesian estimation theory and fits naturally to the filtering setup of NWP. The filter likelihood score considers observation errors, ensemble mean skill, and ensemble spread in one metric. Importantly, it can be made multivariate and effortlessly expanded to simultaneous verification against all observation types through the observation operators contained in the parental data assimilation scheme.Here observations from the global radiosonde network and satellites (AMSU-A channel 5) are included in the verification of OpenIFS-based ensemble forecasts using different types of initial state perturbations. Our results show that the filter likelihood score is sensitive to the ensemble prediction system quality and compares consistently with other verification metrics such as the relationships between ensemble spread and ensemble mean forecast error, and Dawid-Sebastiani score. Our conclusion is that the filter likelihood score provides a very well-behaving verification metric, that can be made truly multivariate by including covariances, for ensemble prediction systems with a strong foundation in estimation theory.

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Section: Comparisons Between Different Verification Metricssupporting

confidence: 89%

“…The key differences between the two models are: OpenIFS does not include the data-assimilation codes or capabilities of IFS, and the latest release version of OpenIFS is based on the IFS version operational between 2017-2018 (CY43R3), for more details see e.g. Ollinaho et al (2021).…”

Section: Openifs Ensemble Forecastsmentioning

confidence: 99%

Filter Likelihood as an Observation-Based Verification Metric in Ensemble Forecasting

Ekblom

Tuppi

Räty

et al. 2023

Tellus A: Dynamic Meteorology and Oceanography

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“…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, last access: March 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Data assimilation is not explicitly included in OpenIFS, but it is included implicitly because external initial conditions of the atmospheric state (temperature, wind, humidity, and surface pressure) are applied. 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). We simulate time evolution of the atmospheric state with OpenIFS at horizontal resolution T L 639, which corresponds to about 31 km grid spacing at the Equator, and at 91 vertical levels.…”

Section: Weather Modelmentioning

confidence: 99%

Coupling a weather model directly to GNSS orbit determination – case studies with OpenIFS

et al. 2022

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Abstract. Neutral gas atmosphere bends and delays propagation of microwave signals in satellite-based navigation. Weather prediction models can be used to estimate these effects by providing three-dimensional refraction fields to ray-trace the signal delays. In this study, a global numerical weather prediction model (Open Integrated Forecasting System (OpenIFS) licensed for Academic use by the European Centre for Medium-Range Weather Forecast) 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 (Gravity Recovery Object Oriented Programming System) software toolkit of Graz University of Technology) 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. We suspect that removal of the intermediate mapping step allows us 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 modeling errors are not convoluted in the mapping procedures. 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 1-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 spatiotemporal 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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“…For accurate inference of the Earth's gravity potential and derived quantities, such as variations in terrestrial water storage, all non‐gravitational effects on the satellite orbit need to be well understood and separated by physics‐based modeling. A novel approach is taken here to model the influence of Earth radiation on the satellite orbit by using a global numerical weather prediction model of very high forecast skill, the Open Integrated Forecasting System (OpenIFS) model of the European Centre for Medium‐Range Weather Forecasts (ECMWF), and their numerical analyses of global weather (Ollinaho et al., 2021). In weather models, the up‐welling radiation flux, that is, reflected solar radiation and thermal emission, is modeled with reasonable precision on average.…”

Section: Introductionmentioning

confidence: 99%

Weather‐Induced Satellite Orbit Perturbations

Motlaghzadeh,

Vasiuta,

Bister

et al. 2024

JGR Atmospheres

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Satellites in Earth's orbit are exposed to Earth radiation, consisting of reflected solar and emitted thermal radiation, thereby exerting a non‐conservative force that causes acceleration and affects the orbits. Gravity Recovery and Climate Experiment Follow‐On (GRACE‐FO) mission aiming to retrieve the Earth's gravity potential is critically dependent on accounting for this force and all other non‐gravitational forces. There are both diurnal and seasonal variations in the Earth's radiation pressure, of which the seasonal variability can be represented by climatology. Nevertheless, the daily variations in the Earth's radiation pressure, due to the transient changes in the weather; for example, clouds and their properties, are not accounted for in the orbit perturbations studies. We show here that the top‐of‐atmosphere radiation fluxes computed with a numerical weather prediction (NWP) model explain most of the measured short‐term variations in the radial acceleration of the GRACE‐FO satellite. Our physics‐based modeling corrects a hitherto unexplained lack of power spectral density in the measured accelerations. For example, we can accurately model the accelerations associated with a tropical storm in the Indian Ocean in December 2020, which would not be possible when using climatological data. Our results demonstrate that using a global numerical weather prediction model significantly improves the simulation of non‐gravitational effects in the satellites' orbits. In the 7‐day data set, OpenIFS‐simulated acceleration exhibited higher accuracy than climatological‐data‐simulated acceleration (2.5 compared to 2.6 nms−2) and an improved precision (2.6 compared to 3.0 nms−2). This advancement contributes to a more precise orbit determination across various applications in Earth sciences.

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

Cited by 6 publications

References 27 publications

Filter Likelihood as an Observation-Based Verification Metric in Ensemble Forecasting

Filter Likelihood as an Observation-Based Verification Metric in Ensemble Forecasting

Coupling a weather model directly to GNSS orbit determination – case studies with OpenIFS

Weather‐Induced Satellite Orbit Perturbations

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