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Title: Improving Wind Predictions in the Marine Atmospheric Boundary Layer Through Parameter Estimation in a Single Column Model

Abstract

A current barrier to greater deployment of offshore wind turbines is the poor quality of numerical weather prediction model wind and turbulence forecasts over open ocean. The bulk of development for atmospheric boundary layer (ABL) parameterization schemes has focused on land, partly due to a scarcity of observations over ocean. The 100-m FINO1 tower in the North Sea is one of the few sources worldwide of atmospheric profile observations from the sea surface to turbine hub height. These observations are crucial to developing a better understanding and modeling of physical processes in the marine ABL. In this paper we use the WRF single column model (SCM), coupled with an ensemble Kalman filter from the Data Assimilation Research Testbed (DART), to create 100-member ensembles at the FINO1 location. The goal of this study is to determine the extent to which model parameter estimation can improve offshore wind forecasts. Combining two datasets that provide lateral forcing for the SCM and two methods for determining z 0, the time-varying sea-surface roughness length, we conduct four WRF-SCM/DART experiments over the October-December 2006 period. The two methods for determining z 0 are the default Fairall-adjusted Charnock formulation in WRF, and using parameter estimation techniques tomore » estimate z 0 in DART. Using DART to estimate z 0 is found to reduce 1-h forecast errors of wind speed over the Charnock-Fairall z 0 ensembles by 4%–22%. Finally, however, parameter estimation of z 0 does not simultaneously reduce turbulent flux forecast errors, indicating limitations of this approach and the need for new marine ABL parameterizations.« less

Authors:
 [1];  [1];  [1];  [1];  [2];  [1];  [3]
  1. National Center for Atmospheric Research, Boulder, CO (United States). Research Applications Lab.
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  3. National Renewable Energy Center (CENER), Sarriguren (Spain)
Publication Date:
Research Org.:
National Center for Atmospheric Research, Boulder, CO (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
Contributing Org.:
National Renewable Energy Center (CENER), Sarriguren (Spain)
OSTI Identifier:
1325250
Grant/Contract Number:
EE0005374
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Monthly Weather Review
Additional Journal Information:
Journal Name: Monthly Weather Review; Journal ID: ISSN 0027-0644
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY

Citation Formats

Lee, Jared A., Hacker, Joshua P., Monache, Luca Delle, Kosovic, Branko, Clifton, Andrew, Vandenberghe, Francois, and Rodrigo, Javier Sanz. Improving Wind Predictions in the Marine Atmospheric Boundary Layer Through Parameter Estimation in a Single Column Model. United States: N. p., 2016. Web. doi:10.1175/MWR-D-16-0063.1.
Lee, Jared A., Hacker, Joshua P., Monache, Luca Delle, Kosovic, Branko, Clifton, Andrew, Vandenberghe, Francois, & Rodrigo, Javier Sanz. Improving Wind Predictions in the Marine Atmospheric Boundary Layer Through Parameter Estimation in a Single Column Model. United States. doi:10.1175/MWR-D-16-0063.1.
Lee, Jared A., Hacker, Joshua P., Monache, Luca Delle, Kosovic, Branko, Clifton, Andrew, Vandenberghe, Francois, and Rodrigo, Javier Sanz. Wed . "Improving Wind Predictions in the Marine Atmospheric Boundary Layer Through Parameter Estimation in a Single Column Model". United States. doi:10.1175/MWR-D-16-0063.1. https://www.osti.gov/servlets/purl/1325250.
@article{osti_1325250,
title = {Improving Wind Predictions in the Marine Atmospheric Boundary Layer Through Parameter Estimation in a Single Column Model},
author = {Lee, Jared A. and Hacker, Joshua P. and Monache, Luca Delle and Kosovic, Branko and Clifton, Andrew and Vandenberghe, Francois and Rodrigo, Javier Sanz},
abstractNote = {A current barrier to greater deployment of offshore wind turbines is the poor quality of numerical weather prediction model wind and turbulence forecasts over open ocean. The bulk of development for atmospheric boundary layer (ABL) parameterization schemes has focused on land, partly due to a scarcity of observations over ocean. The 100-m FINO1 tower in the North Sea is one of the few sources worldwide of atmospheric profile observations from the sea surface to turbine hub height. These observations are crucial to developing a better understanding and modeling of physical processes in the marine ABL. In this paper we use the WRF single column model (SCM), coupled with an ensemble Kalman filter from the Data Assimilation Research Testbed (DART), to create 100-member ensembles at the FINO1 location. The goal of this study is to determine the extent to which model parameter estimation can improve offshore wind forecasts. Combining two datasets that provide lateral forcing for the SCM and two methods for determining z0, the time-varying sea-surface roughness length, we conduct four WRF-SCM/DART experiments over the October-December 2006 period. The two methods for determining z0 are the default Fairall-adjusted Charnock formulation in WRF, and using parameter estimation techniques to estimate z0 in DART. Using DART to estimate z0 is found to reduce 1-h forecast errors of wind speed over the Charnock-Fairall z0 ensembles by 4%–22%. Finally, however, parameter estimation of z0 does not simultaneously reduce turbulent flux forecast errors, indicating limitations of this approach and the need for new marine ABL parameterizations.},
doi = {10.1175/MWR-D-16-0063.1},
journal = {Monthly Weather Review},
number = ,
volume = ,
place = {United States},
year = {Wed Aug 03 00:00:00 EDT 2016},
month = {Wed Aug 03 00:00:00 EDT 2016}
}

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  • A current barrier to greater deployment of offshore wind turbines is the poor quality of numerical weather prediction model wind and turbulence forecasts over open ocean. The bulk of development for atmospheric boundary layer (ABL) parameterization schemes has focused on land, partly due to a scarcity of observations over ocean. The 100-m FINO1 tower in the North Sea is one of the few sources worldwide of atmospheric profile observations from the sea surface to turbine hub height. These observations are crucial to developing a better understanding and modeling of physical processes in the marine ABL. In this study, we usemore » the WRF single column model (SCM), coupled with an ensemble Kalman filter from the Data Assimilation Research Testbed (DART), to create 100-member ensembles at the FINO1 location. The goal of this study is to determine the extent to which model parameter estimation can improve offshore wind forecasts.« less
  • Results are presented of the GASS/EUCLIPSE single-column model inter-comparison study on the subtropical marine low-level cloud transition. A central goal is to establish the performance of state-of-the-art boundary-layer schemes for weather and climate mod- els for this cloud regime, using large-eddy simulations of the same scenes as a reference. A novelty is that the comparison covers four different cases instead of one, in order to broaden the covered parameter space. Three cases are situated in the North-Eastern Pa- cific, while one reflects conditions in the North-Eastern Atlantic. A set of variables is considered that reflects key aspects of the transitionmore » process, making use of simple met- rics to establish the model performance. Using this method some longstanding problems in low level cloud representation are identified. Considerable spread exists among models concerning the cloud amount, its vertical structure and the associated impact on radia- tive transfer. The sign and amplitude of these biases differ somewhat per case, depending on how far the transition has progressed. After cloud breakup the ensemble median ex- hibits the well-known “too few too bright” problem. The boundary layer deepening rate and its state of decoupling are both underestimated, while the representation of the thin capping cloud layer appears complicated by a lack of vertical resolution. Encouragingly, some models are successful in representing the full set of variables, in particular the verti- cal structure and diurnal cycle of the cloud layer in transition. An intriguing result is that the median of the model ensemble performs best, inspiring a new approach in subgrid pa- rameterization.« less
  • The parameterization of the stably stratified atmospheric boundary layer is a difficult issue, having a significant impact on medium-range weather forecasts and climate integrations. To pursue this further, a moderately stratified Arctic case is simulated by nineteen single-column turbulence schemes. Statistics from a large-eddy simulation intercomparison made for the same case by eleven different models are used as a guiding reference. The single-column parameterizations include research and operational schemes from major forecast and climate research centers. Results from first-order schemes, a large number of turbulence kinetic energy closures, and other models were used. There is a large spread in themore » results; in general, the operational schemes mix over a deeper layer than the research schemes, and the turbulence kinetic energy and other higher-order closures give results closer to the statistics obtained from the large-eddy simulations. The sensitivities of the schemes to the parameters of their turbulence closures are partially explored.« less
  • Reliability of column experiment data interpretation by fitting the data to a transport model using the nonlinear least squares method is of concern when the complexity of the experiment increases. Misfits often occur and are accepted with the excuse of system complexity. More often, overfits are published and accepted uncritically. The objective of this work was to improve the reliability of column experiment data interpretation to provide insights for future investigation and select estimates for application prediction. We proposed to achieve this goal by evaluating and comparing various estimates that consider the uncertainties in experimental conditions, sensitivities of model parameters,more » alternative parameterizations and models. Two examples from the literature were selected for the illustration. Sensitivity analysis showed that the experimentally-controlled parameters (water content, flow rate and pulse volume) are much more sensitive than the transport parameters (dispersivity and mass transfer coefficient) in breakthrough curve fitting. Fixing these sensitive parameters may lead to misfit or biased estimate. Estimating sensitive parameters helps diagnose and remediate the misfit. The dispersion coefficient was parameterized as a function of solute molecular diffusion coefficient, medium dispersivity and average pore water velocity to reduce the number of estimated parameters for multiple tracers. Nonlinear least squares statistics were used to evaluate the goodness-of-fit for the multiple estimates obtained using different fitting schemes (e.g., fixing or estimating parameters), parameterizations and models (e.g., equilibrium or nonequilibrium). The F-test and Akaike s Information Criteria were used for the discrimination of statistically reasonable estimates. Our results suggest that sensitivity/uncertainty analysis and model evaluation /discrimination are useful to improve the reliability of column experiment data interpretation.« less
  • The filtering skill for turbulent signals from nature is often limited by model errors created by utilizing an imperfect model for filtering. Updating the parameters in the imperfect model through stochastic parameter estimation is one way to increase filtering skill and model performance. Here a suite of stringent test models for filtering with stochastic parameter estimation is developed based on the Stochastic Parameterization Extended Kalman Filter (SPEKF). These new SPEKF-algorithms systematically correct both multiplicative and additive biases and involve exact formulas for propagating the mean and covariance including the parameters in the test model. A comprehensive study is presented ofmore » robust parameter regimes for increasing filtering skill through stochastic parameter estimation for turbulent signals as the observation time and observation noise are varied and even when the forcing is incorrectly specified. The results here provide useful guidelines for filtering turbulent signals in more complex systems with significant model errors.« less