Development and verification of a new wind speed forecasting system using an ensemble Kalman filter data assimilation technique in a fully coupled hydrologic and atmospheric model

Abstract

[1] Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its inherently intermittent nature. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. We have adapted the Data Assimilation Research Testbed (DART), a community software facility which includes the ensemble Kalman filter (EnKF) algorithm, to expand our capability to use observational data to improve forecasts produced with a fully coupled hydrologic and atmospheric modeling system, the ParFlow (PF) hydrologic model and the Weather Research and Forecasting (WRF) mesoscale atmospheric model, coupled via mass and energy fluxes across the land surface, and resulting in the PF.WRF model. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. We have used the PF.WRF model to explore the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture, and wind speed and demonstrated that reductions in uncertainty in these coupled fields realized through assimilation of soil moisture observations propagate through the hydrologic and atmospheric system. The sensitivities found in this study will enable further studies to optimize observation strategies to maximize the utility of the PF.WRF-DART forecasting system. Citation: Williams, J. L., III, R. M. Maxwell, and L. Delle Monache (2013), Development and verification of a new wind speed forecasting system using an ensemble Kalman filter data assimilation technique in a fully coupled hydrologic and atmospheric model, J. Adv. Model. Earth Syst., 5, 785-800,

Investigation of boundary-layer wind predictions during nocturnal low-level jet events using the Weather Research and Forecasting model

“…Similar to the methodology of Yang et al , we chose three schemes representing fundamentally different formulations of mixing in the stable regime, during which the LLJ scenarios investigated herein occurred. The three schemes chosen are among the six examined by Deppe et al , who investigated hub‐height wind forecasts using the WRF model in nearby Iowa, an area that also frequently experiences LLJs. Here, we briefly highlight key differences among the formulations, with further information available in the references.…”

“…Mesoscale forecasting has benefited from development of real‐time four‐dimensional data assimilation (Liu et al ) and ensemble forecast systems (e.g. Deppe et al ; Williams et al ; Edmunds et al ). WRF's LES capabilities have been enhanced via introduction of both advanced LES subgrid models (Mirocha et al ; Kirkil et al ), and development of mesoscale‐to‐LES downscaling algorithms for multi‐scale simulations (Muñoz‐Esparza et al ; Mirocha et al ; Talbot et al ; Liu et al ).…”

Soil moisture background error covariance and data assimilation in a coupled land‐atmosphere model

“…We develop a variational data assimilation system for direct assimilation of soil moisture using the WRF model with the Noah land surface scheme. Unlike recent studies using ensemble filtering for soil moisture data assimilation with a coupled model [ Rasmy et al ., ; Williams et al ., ; Schneider et al ., ], this study has the following unique features: (1) estimation of spatiotemporally varying soil moisture background error covariance using the NMC method and (2) development of a one‐dimensional variational data assimilation (1D‐Var) scheme to assimilate remotely sensed soil moisture retrievals into the WRF‐Noah model. We use the NCEP final analysis (FNL) data set to derive the initial conditions for the WRF‐Noah model.…”

Wind and solar resource data sets

“…Data assimilation takes many different forms and uses many different data sources—e.g., observations from national weather station networks, radio soundings, and some satellite observations. Data assimilation usually has a local impact, but it can have an effect beyond the area where the measurements are available if the measurements are of good quality and include surface observations and upper air observations . Adjoint sensitivity studies can help effectively determine where to site instruments that will be used in data assimilation …”

“…Data assimilation usually has a local impact, but it can have an effect beyond the area where the measurements are available if the measurements are of good quality and include surface observations and upper air observations. [16][17][18] Adjoint sensitivity studies can help effectively determine where to site instruments that will be used in data assimilation. 19,20 The data used in data sets can also be improved through statistical post-processing techniques and adjustments-e.g., by scaling wind speeds or irradiance estimates to local observations.…”