1,145 research outputs found
Diagnosing atmospheric motion vector observation errors for an operational high resolution data assimilation system
Atmospheric motion vectors (AMVs) are wind observations derived by tracking cloud or water vapour features in consecutive satellite images. These observations are incorporated into Numerical Weather Prediction (NWP) through data assimilation. In the assimilation algorithm, the weighting given to an observation is determined by the uncertainty associated with its measurement and representation. Previous studies assessing AMV uncertainty have used direct comparisons between AMVs with co-located radiosonde data and AMVs derived from Observing System Simulation Experiments (OSSEs). These have shown that AMV error is horizontally correlated with characteristic length scale up to 200 km. In this work, we take an alternative approach and estimate AMV error variance and horizontal error correlation using background and analysis residuals obtained from the Met Office limited area, 3 km horizontal grid length data assimilation system. The results show that the observation error variance profile ranges from 5.2 to 14.1 s m2s− 2, with the highest values occurring at high and medium heights. This is indicative that the maximum error variance occurs where wind speed and shear, in combination, are largest. With the exception of AMVs derived from the High Resolution Visible channel, the results show horizontal observation error correlations at all heights in the atmosphere, with correlation lengthscales ranging between 140 and 200 km. These horizontal lengthscales are significantly larger than current AMV observation thinning distances used in the Met Office high resolution assimilation
On the Predictability of Hub Height Winds
Wind energy is a major source of power in over 70 countries across the world, and the worldwide share of wind energy in electricity consumption is growing. The introduction of signicant amounts of wind energy into power systems makes accurate wind forecasting a crucial element of modern electrical grids. These systems require forecasts with temporal scales of tens of minutes to a few days in advance at wind farm locations. Traditionally these forecasts predict the wind at turbine hub heights; this information is then converted by transmission system operators and energy companies into predictions of power output at wind farms. Since the power available in the wind is proportional to the wind speed cubed, even small wind forecast errors result in large power prediction errors. Accurate wind forecasts are worth billions of dollars annually; forecast improvements will result in reduced costs to consumers due to better integration of wind power into the power grid and more effcient trading of wind power on energy markets.This thesis is a scientic contribution to the advancement of wind energy forecasting with mesoscale numerical weather prediction models. After an economic and theoretical overview of the importance of wind energy forecasts, this thesis continues with an analysis of wind speed predictions at hub height using the Weather Research and Forecasting (WRF) model. This analysis demonstrates the need for more detailed analyses of wind speeds and it is shown that wind energy forecasting cannot be reduced solely to forecasting winds at hub height. Calculating only the power output from hub height winds can result in erroneous estimates due to the vertical wind shear in the atmospheric boundary layer (PBL). Results show that the accuracy of modeled wind conditions and wind proles in the PBL depends on the PBL scheme adopted and is different under varying atmospheric stability conditions, among other modeling factors. This has important implications for wind energy applications: shallow stable boundary layers can result in excessive wind shear, which is detrimental for wind energy applications. This is particularly relevant with offshore facilities, which represent a significant portion of new wind farms being constructed. Furthermore, a novel aspect to this study is the presentation of a verification methodology that takes into account wind at different heights where turbines operate.The increasing number of wind farm deployments represents a novel and unique data source for improving mesoscale wind forecasts for wind energy applications. These new measurements include nacelle wind speeds and the turbines' angle of rotation into the wind (yaw angles). This thesis continues with an extensive description of this new data set and its challenges in data assimilation, focusing on data from the Horns Rev I wind farm. Since wind farm data are such a dense data set there is need to derive representative information from the measurements, i.e., thin the data. Different thinning strategies and their impact on improving wind forecasts for wind power predictions are investigated with the WRF Four-Dimensional Data Assimilation system. The median of the whole wind farm was found to be the most successful thinning strategy. Nacelle winds and yaw angles are a promising data set to improve wind predictions downstream of a wind farm as well as at the wind farm itself: Their impact lasted up to 5 hours and depends on time of the day, forecast lead time and weather situation
Data compression in the presence of observational error correlations
Numerical weather prediction (NWP) models are moving towards km-scale (and smaller) resolutions in order to forecast high-impact weather. As the resolution of NWP models increase the need for high-resolution observations to constrain these models also increases. A major hurdle to the assimilation of dense observations in NWP is the presence of non-negligible observation error correlations (OECs). Despite the difficulty in estimating these error correlations, progress is being made, with centres around the world now explicitly accounting for OECs in a variety of observation types. This paper explores how to make efficient use of this potentially dramatic increase in the amount of data available for assimilation.
In an idealised framework it is illustrated that as the length-scales of the OECs increase the scales that the analysis is most sensitive to the observations become smaller. This implies that a denser network of observations is more beneficial with increasing OEC length-scales. However, the computational and storage burden associated with such a dense network may not be feasible. To reduce the amount of data, a compression technique based on retaining the maximum information content of the observations can be used. When the OEC length-scales are large (in comparison to the prior error correlations), the data compression will select observations of the smaller scales for assimilation whilst throwing out the larger scale information. In this case it is shown that there is a discrepancy between the observations with the maximum information and those that minimise the analysis error variances.
Experiments are performed using the Ensemble Kalman Filter and the Lorenz-1996 model, comparing different forms of data reduction. It is found that as the OEC length-scales increase the assimilation becomes more sensitive to the choice of data reduction technique
Data assimilation with correlated observation errors: experiments with a 1-D shallow water model
Remote sensing observations often have correlated errors, but the correlations are typically ignored in data assimilation for numerical weather prediction. The assumption of zero correlations is often used with data thinning methods, resulting in a loss of information. As operational centres move towards higher-resolution forecasting, there is a requirement to retain data providing detail on appropriate scales. Thus an alternative approach to dealing with observation error correlations is needed. In this article, we consider several approaches to approximating observation error correlation matrices: diagonal approximations, eigendecomposition approximations and Markov matrices. These approximations are applied in incremental variational assimilation experiments with a 1-D shallow water model using synthetic observations. Our experiments quantify analysis accuracy in comparison with a reference or ‘truth’ trajectory, as well as with analyses using the ‘true’ observation error covariance matrix. We show that it is often better to include an approximate correlation structure in the observation error covariance matrix than to incorrectly assume error independence. Furthermore, by choosing a suitable matrix approximation, it is feasible and computationally cheap to include error correlation structure in a variational data assimilation algorithm
Information-based data selection for ensemble data assimilation
Ensemble-based data assimilation is rapidly proving itself as a computationally-efficient and skilful assimilation method for numerical weather prediction, which can provide a viable alternative to more established variational assimilation techniques. However, a fundamental shortcoming of ensemble techniques is that the resulting analysis increments can only span a limited subspace of the state space, whose dimension is less than the ensemble size. This limits the amount of observational information that can effectively constrain the analysis. In this paper, a data selection strategy that aims to assimilate only the observational components that matter most and that can be used with both stochastic and deterministic ensemble filters is presented. This avoids unnecessary computations, reduces round-off errors and minimizes the risk of importing observation bias in the analysis. When an ensemble-based assimilation technique is used to assimilate high-density observations, the data-selection procedure allows the use of larger localization domains that may lead to a more balanced analysis. Results from the use of this data selection technique with a two-dimensional linear and a nonlinear advection model using both in situ and remote sounding observations are discussed
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