The Impact of Ensemble Kalman Filter Assimilation of Near-Surface Observations on the Predictability of Atmospheric Conditions over Complex Terrain: Results from Recent MATERHORN Field Program
Abstract
Near-surface atmospheric observations are the main conventional observations for weather forecasts. However, in modern numerical weather prediction, the use of surface observations, especially those data over complex terrain, remains a unique challenge. There are fundamental difficulties in assimilating surface observations with three-dimensional variational data assimilation (3DVAR). In our early study[1] (Pu et al. 2013), a series of observing system simulation experiments was performed with the ensemble Kalman filter (EnKF) and compared with 3DVAR for its ability to assimilate surface observations with 3DVAR. Using the advanced research version of the Weather Research and Forecasting (WRF) model, results demonstrate that the EnKF can overcome some fundamental limitations that 3DVAR has in assimilating surface observations over complex terrain. Specifically, through its flow-dependent background error term, the EnKF produces more realistic analysis increments over complex terrain in general. Over complex terrain, the EnKF clearly performs better than 3DVAR, because it is more capable of handling surface data in the presence of terrain misrepresentation. With this presentation, we further examine the impact of EnKF data assimilation on the predictability of atmospheric conditions over complex terrain with the WRF model and the observations obtained from the most recent field experiments of the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program. The MATERHORN program provides comprehensive observations over mountainous regions, allowing the opportunity to study the predictability of atmospheric conditions over complex terrain in great details. Specifically, during fall 2012 and spring 2013, comprehensive observations were collected of soil states, surface energy budgets, near-surface atmospheric conditions, and profiling measurements from multiple platforms (e.g., balloon, lidar, radiosondes, etc.) over Dugway Proving Ground (DPG), Utah. With the near-surface observations and sounding data obtained during the MATERHORN fall 2012 field experiment, a month-long cycled EnKF analysis and forecast was produced with the WRF model and an advanced EnKF data assimilation system. Results are compared with the WRF near real-time forecasting during the same month and a set of analysis with 3DVAR data assimilation. Overall evaluation suggests some useful insights on the impacts of different data assimilation methods, surface and soil states, terrain representation on the predictability of atmospheric conditions over mountainous terrain. Details will be presented. References [1] Pu, Z., H. Zhang, and J. A. Anderson,. 'Ensemble Kalman filter assimilation of near-surface observations over complex terrain: Comparison with 3DVAR for short-range forecasts.' Tellus A, vol. 65,19620. 2013. http://dx.doi.org/10.3402/tellusa.v65i0. 19620.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2013
- Bibcode:
- 2013AGUFM.A14D..09P
- Keywords:
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- 3315 ATMOSPHERIC PROCESSES Data assimilation;
- 3307 ATMOSPHERIC PROCESSES Boundary layer processes