Ensemble Kalman filtering

Houtekamer, P. L.; Mitchell, Herschel L.

doi:10.1256/qj.05.135

Cited by 339 publications

(212 citation statements)

References 72 publications

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“…This contrasts from numerical weather prediction (NWP), where there are currently a number of operational, or semi-operational, EnKF systems (Houtekamer and Mitchell, 2006;Torn and Hakim, 2008;Bonavita et al, 2008;Compo et al, 2011). Ocean forecasting differs from NWP in several respects.…”

Section: Introductionmentioning

confidence: 99%

TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic

et al. 2012

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Abstract. We present a detailed description of TOPAZ4, the latest version of TOPAZ -a coupled ocean-sea ice data assimilation system for the North Atlantic Ocean and Arctic. It is the only operational, large-scale ocean data assimilation system that uses the ensemble Kalman filter. This means that TOPAZ features a time-evolving, state-dependent estimate of the state error covariance. Based on results from the pilot MyOcean reanalysis for 2003-2008, we demonstrate that TOPAZ4 produces a realistic estimate of the ocean circulation in the North Atlantic and the sea-ice variability in the Arctic. We find that the ensemble spread for temperature and sea-level remains fairly constant throughout the reanalysis demonstrating that the data assimilation system is robust to ensemble collapse. Moreover, the ensemble spread for ice concentration is well correlated with the actual errors. This indicates that the ensemble statistics provide reliable state-dependent error estimates -a feature that is unique to ensemble-based data assimilation systems. We demonstrate that the quality of the reanalysis changes when different sea surface temperature products are assimilated, or when in-situ profiles below the ice in the Arctic Ocean are assimilated. We find that data assimilation improves the match to independent observations compared to a free model. Improvements are particularly noticeable for ice thickness, salinity in the Arctic, and temperature in the Fram Strait, but not for transport estimates or underwater temperature. At the same time, the pilot reanalysis has revealed several flaws in the system that have degraded its performance. Finally, we show that a simple bias estimation scheme can effectively detect the seasonal or constant bias in temperature and sea-level.

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Section: Introductionmentioning

confidence: 99%

TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic

et al. 2012

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“…However, whereas the UKF relies on a systematic way of distributing the particles such that the mean and covariance of the conditional probability distribution p [x k |Z k ] are equal to that of the particles, the EnKF relies 5 on random realizations, without guarantees that the mean and covariance are captured accurately. Though, the EnKF has been shown to work well in a number of applications, with typically far fewer particles than states, i.e., Y N (e.g., Houtekamer and Mitchell, 2005;Gillijns et al, 2006). The forecast and update step are very similar to that of the UKF, namely:…”

Section: The Ensemble Kalman Filter (Enkf)mentioning

confidence: 99%

Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control

Doekemeijer¹,

Boersma²,

Pao³

et al. 2018

Preprint

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Abstract. Wind farm control often relies on computationally inexpensive surrogate models to predict the dynamics inside a farm. However, the reliability of these models over the spectrum of wind farm operation remains questionable due to the many uncertainties in the atmospheric conditions and tough-to-model flow dynamics at a range of spatial and temporal scales relevant for control. A closed-loop control framework is proposed in which a simplified dynamical LES model is calibrated and used for optimization in real time. This paper presents an estimation solution with an Ensemble Kalman filter (EnKF) at its core, 5 which calibrates the surrogate model to the actual atmospheric conditions. The estimator is tested in high-fidelity simulations of a nine-turbine wind farm. Using exclusively turbine SCADA measurements, the adaptability to modeling errors and changes in atmospheric conditions (TI, wind speed) is shown. Convergence is reached within 400 seconds of operation, after which the estimation error in flow fields is negligible. At a low computational cost of 1.2 s on an 8-core CPU, this algorithm shows comparable accuracy to the state of the art from the literature while being approximately two orders of magnitude faster. Using 10 the calibration solution presented, the surrogate model can be used for accurate forecasting and optimization.

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“…Each EDA member is an independent data assimilation, using the same set of observations, but introducing perturbations to these observations consistent with the known observation errors. The Meteorological Service of Canada (MSC) uses perturbed observations and an ensemble approach, the ensemble Kalman filter (EnKF, Houtekamer and Mitchell 2005;Houtekamer et al 2009Houtekamer et al , 2014, to provide an ensemble of initial conditions. It should be noted that for both the EDA and EnKF, it is necessary to take account of model uncertainties (see below) as well as the observation uncertainties to generate appropriate initial perturbations.…”

Section: Initial Condition Uncertaintiesmentioning

confidence: 99%

Medium- and Extended-Range Ensemble Weather Forecasting

Richardson

2018

Weather &Amp; Climate Services for the Energy Industry

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The chapter provides an overview of ensemble weather forecasting for the medium-and extended-range (days to weeks ahead). It reviews the methods used to account for uncertainties in the initial conditions and in the forecast models themselves. The chapter explores the challenges of making useful forecasts for the sub-seasonal timescale, beyond the typical limit for skilful day-to-day forecasts, and considers some of the sources of predictability such as the Madden-Julian oscillation (MJO) that make this possible. It then introduces some of the ensemble-based forecast products and concludes with a case study for a European heat wave that demonstrates how ensemble weather forecasts can be used to guide decision making for weather-dependent activities.

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Ensemble Kalman filtering

Cited by 339 publications

References 72 publications

TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic

TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic

Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control

Medium- and Extended-Range Ensemble Weather Forecasting

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