Revision of the Stochastically Perturbed Parametrisations model uncertainty scheme in the Integrated Forecasting System

Abstract. We report on an evaluation of tropospheric ozone and its precursor gases in three atmospheric chemistry versions as implemented in ECMWF’s Integrated Forecasting System (IFS), referred to as IFS(CB05BASCOE), IFS(MOZART) and IFS(MOCAGE). While the model versions were forced with the same overall meteorology, emissions, transport and deposition schemes, they vary largely in their parameterizations describing atmospheric chemistry, including the organics degradation, heterogeneous chemistry and photolysis, as well as chemical solver. The model results from the three chemistry versions are compared against a range of aircraft field campaigns, ozone sondes and satellite observations, which provides quantification of the overall model uncertainty driven by the chemistry parameterizations. We find that they produce similar patterns and magnitudes for carbon monoxide (CO) and ozone (O3), as well as a range of non-methane hydrocarbons (NMHCs), with averaged differences for O3 (CO) within 10 % (20 %) throughout the troposphere. Most of the divergence in the magnitude of NMHCs can be explained by differences in OH concentrations, which can reach up to 50 % particularly at high latitudes. Also comparatively large discrepancies between model versions exist for NO2, SO2 and HNO3, which are strongly influenced by secondary chemical production and loss. Other, common biases in CO and NMHCs are mainly attributed to uncertainties in their emissions. This configuration of having various chemistry versions within IFS provides a quantification of uncertainties induced by chemistry modeling in the main CAMS global trace gas products beyond those that are constrained by data-assimilation.

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Representation of model error through process-based perturbations for ensemble prediction : application to turbulence and shallow convection parametrisations

10.5194/egusphere-egu21-15167 ◽

2021 ◽

Author(s):

Axelle Fleury ◽

François Bouttier

Keyword(s):

Model Uncertainty ◽

Perturbation Methods ◽

Stochastic Perturbation ◽

Research Field ◽

Ensemble Prediction ◽

Model Errors ◽

Shallow Convection ◽

Physical Processes ◽

Closure Condition ◽

Stochastically Perturbed

<p>The boundary layer is the place of many complex physical processes spanning various time and space scales, part of which need to be parametrised in NWP models. These parametrisations are known sources of uncertainty in the models, due to the difficulty of accurately representing the processes, and the resulting simplifications and approximations that have to be done. Model uncertainty is part of what ensemble prediction systems seek to represent. This can be achieved in particular by using stochastic perturbation methods, where noise is introduced during model computations to change its state and produce different simulations. Well-known and widely used perturbation schemes like the Stochastically Perturbed Parametrisation Tendencies (SPPT) scheme have shown their effectiveness and their interest in building ensembles. However, part of the model uncertainty is not yet well represented in current ensemble systems, while some of the assumptions made by SPPT can be questioned. This argues for a diversity of approaches to represent model errors. In this active research field, alternative perturbation methods are investigated, such as the Stochastically Perturbed Parametrisations (SPP) method, or other methods focusing on the perturbation of particular physical processes. The work presented here focuses on the last ones. Based on two examples of methods published in the literature, perturbations have been applied to the turbulence and shallow convection parametrisation schemes of the mesoscale NWP model Arome from M&#233;t&#233;o-France. The perturbation of turbulence is based on the use of subgrid-scale variances to regulate the amplitude of an additive noise, while shallow convection is perturbed through a stochastic closure condition of the scheme. A simplified 1D framework has been used, in order to assess the ability of the method to produce an ensemble with sufficient dispersion and to compare its results with those from existing methods like SPPT.</p>

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Treatment of model uncertainty from radiation by the Stochastically Perturbed Parametrization Tendencies (SPPT) scheme and associated revisions in the ECMWF ensembles

Quarterly Journal of the Royal Meteorological Society ◽

10.1002/qj.3570 ◽

2019 ◽

Vol 145 (S1) ◽

pp. 75-89 ◽

Cited By ~ 5

Author(s):

Sarah‐Jane Lock ◽

Simon T. K. Lang ◽

Martin Leutbecher ◽

Robin J. Hogan ◽

Frederic Vitart

Keyword(s):

Model Uncertainty ◽

Stochastically Perturbed

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The Arctic Carbon Monitoring and Forecasting System: a novel forecasting framework to build new understanding and reduce model uncertainty of the permafrost carbon-climate feedback.

10.1002/essoar.10509224.1 ◽

2021 ◽

Author(s):

ELCHIN JAFAROV ◽

Helene Genet ◽

Valeria Briones ◽

Brendan Rogers ◽

Jennifer Watts ◽

...

Keyword(s):

Model Uncertainty ◽

Climate Feedback ◽

The Arctic ◽

System A ◽

Monitoring And Forecasting ◽

Forecasting System

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

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2013.0290 ◽

2014 ◽

Vol 372 (2018) ◽

pp. 20130290 ◽

Cited By ~ 55

Author(s):

Antje Weisheimer ◽

Susanna Corti ◽

Tim Palmer ◽

Frederic Vitart

Keyword(s):

Time Scales ◽

Weather Prediction ◽

Seasonal Forecasting ◽

Near Surface ◽

Weather Forecasts ◽

European Centre ◽

Medium Range ◽

Forecasting System ◽

The Impact ◽

Stochastically Perturbed

The finite resolution of general circulation models of the coupled atmosphere–ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere–ocean climate system in operational forecast mode, and the latest seasonal forecasting system—System 4—has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981–2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden–Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid-latitude circulation regimes over the Pacific–North America region.

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Development and Operational Status of Wind Power Forecasting System

IEEJ Transactions on Power and Energy ◽

10.1541/ieejpes.133.366 ◽

2013 ◽

Vol 133 (4) ◽

pp. 366-372 ◽

Cited By ~ 4

Author(s):

Isao Aoki ◽

Ryoichi Tanikawa ◽

Nobuyuki Hayasaki ◽

Mitsuhiro Matsumoto ◽

Shigero Enomoto

Keyword(s):

Wind Power ◽

Wind Power Forecasting ◽

Forecasting System ◽

Power Forecasting

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The use of Identified Model Uncertainty in Control Design for the ASTREX Structure

1993 American Control Conference ◽

10.23919/acc.1993.4793457 ◽

1993 ◽

Author(s):

Henk Aling ◽

Robert L. Kosut

Keyword(s):

Model Uncertainty ◽

Control Design

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Implementasi Metode Winter untuk Forecasting Pertumbuhan Jumlah Penduduk: Studi Kasus Wilayah Provinsi NTB

Jurnal Pemikiran dan Penelitian Pendidikan Matematika (JP3M) ◽

10.36765/jp3m.v3i1.243 ◽

2020 ◽

Vol 3 (1) ◽

pp. 51-61

Author(s):

Syaharuddin ◽

Abdul Adhiim Rizky ◽

Lutfi Jauhari ◽

Siti Fatimah ◽

Wahyu Ningsih ◽

...

Keyword(s):

Time Series Data ◽

Optimal Parameter ◽

Series Data ◽

Percentage Error ◽

Average Error ◽

Ratio Data ◽

Female Data ◽

Forecasting System ◽

Conducting Research ◽

And Gender

This research aims to analyse the acceleration of population growth based on gender in West Nusa Tenggara Province (NTB) using the Forecasting system by constructing the winter's method in the shape of the Multiple Forecasting System (G-MFS) based on Matlab by calculating the period indicator for accuracy to find time series data in the year 2020-2029. At the simulation stage, researchers used the population and gender ratio data in NTB Province in 2009-2019. The method used in conducting research is to use the winter's method. The evaluation of Forecasting results is done by calculating the average error value using the Mean Absolute Percentage Error (MAPE) method. From this study obtained the most optimal parameter value on male data namely ʌ, β and γ sequential values of 0.9, 0.5 and 0.9 while in female data, the value of ʌ, β and γ respectively, 0.2, 0.1 and 0.5. Then with the value of the parameter obtained MAPE value in male data of 1.7785% and in female data of 0.89034%.

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