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 because of 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 the WRF single-column model (SCM) is 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 , the time-varying sea surface roughness length, four WRF-SCM/DART experiments are conducted during the October–December 2006 period. The two methods for determining are the default Fairall-adjusted Charnock formulation in WRF and use of the parameter estimation techniques to estimate in DART. Using DART to estimate is found to reduce 1-h forecast errors of wind speed over the Charnock–Fairall ensembles by 4%–22%. However, parameter estimation of does not simultaneously reduce turbulent flux forecast errors, indicating limitations of this approach and the need for new marine ABL parameterizations.

Grand challenges in the science of wind energy

“…Although past use of simplified physical models and basic observational technology has allowed for installation of wind power plants and predictions of performance in a variety of terrain types, there are still major gaps in our knowledge about wind flows in complex terrain or under varying atmospheric stability conditions that can change over the course of a day or season (34,36). Moving to offshore wind power introduces additional coupled physics of the meteorological-oceanographic (i.e., the "metocean") environment, where a nontrivial modeling uncertainty remains, especially with breaking or irregular waves, atmospheric stability, and tropical storms (37).…”

Does Soil Moisture Have an Influence on Near‐Surface Temperature?

“…Sensitivity experiments have seldom been conducted to investigate the relationship between the different variables. Sterk et al (2015) and Lee et al (2017) suggested that compared to the atmospheric three-dimensional dynamics in the WRF model, the WRF-SCM provides a platform for more rapid testing and is a more useful tool to evaluate the sensitivities of various parameters and schemes in NWP models without the interference of three-dimensional atmospheric dynamics. So far, WRF-SCM has been used in some sensitivity experiments (e.g., Baas et al, 2010;Sterk et al, 2015;Svensson et al, 2011), but few of these experiments have paid attention to changes in SM and land use.…”

Toward Improving Short‐Term Predictions of Fine Particulate Matter Over the United States Via Assimilation of Satellite Aerosol Optical Depth Retrievals

“…The responsibility of providing the timely information lies with the air quality managers across the United States, who provide this information by analyzing air quality and weather observations along with Numerical Weather Predictions (NWPs) and PM 2.5 guidance from the National Air Quality Forecasting Capability (NAQFC). The NAQFC uses a state‐of‐the‐science chemistry transport model (CTM) called the Community Multiscale Air Quality (CMAQ) model to predict PM 2.5 (Lee et al, ). CMAQ employs advanced numerical procedures and sophisticated algorithms to process emission inventories and parameterizes a variety of atmospheric physical and chemical processes to predict concentrations of air pollutants including PM 2.5 .…”