John Michalakes

Prediction of Landfalling Hurricanes with the Advanced Hurricane WRF Model

Abstract Real-time forecasts of five landfalling Atlantic hurricanes during 2005 using the Advanced Research Weather Research and Forecasting (WRF) (ARW) Model at grid spacings of 12 and 4 km revealed performance generally competitive with, and occasionally superior to, other operational forecasts for storm position and intensity. Recurring errors include 1) excessive intensification prior to landfall, 2) insufficient momentum exchange with the surface, and 3) inability to capture rapid intensification when observed. To address these errors several augmentations of the basic community model have been designed and tested as part of what is termed the Advanced Hurricane WRF (AHW) model. Based on sensitivity simulations of Katrina, the inner-core structure, particularly the size of the eye, was found to be sensitive to model resolution and surface momentum exchange. The forecast of rapid intensification and the structure of convective bands in Katrina were not significantly improved until the grid spacing ap...

THE WEATHER RESEARCH AND FORECAST MODEL: SOFTWARE ARCHITECTURE AND PERFORMANCE

The first non-beta release of the Weather Research and Forecast (WRF) modeling system in May, 2004 represented a key milestone in the effort to design and implement a fullyfunctioning, next-generation modeling system for the atmospheric research and operational NWP user communities. With efficiency, portability, maintainability, and extensibility as bedrock requirements, the WRF software framework has allowed incremental and reasonably rapid development while maintaining overall consistency and adherence to the architecture and its interfaces. The WRF 2.0 release supports the fullrange of functionality envisioned for the model including efficient scalable performance on a range of high-performance computing platforms, multiple dynamic cores and physics options, low-overhead two-way interactive nesting, moving nests, model coupling, and interoperability with other common model infrastructure efforts such as ESMF.

GPU ACCELERATION OF NUMERICAL WEATHER PREDICTION

Weather and climate prediction software has enjoyed the benefits of exponentially increasing processor power for almost 50 years. Even with the advent of large-scale parallelism in weather models, much of the performance increase has come from increasing processor speed rather than increased parallelism. This free ride is nearly over. Recent results also indicate that simply increasing the use of large- scale parallelism will prove ineffective for many scenarios. We present an alternative method of scaling model performance by exploiting emerging architectures using the fine-grain parallelism once used in vector machines. The paper shows the promise of this approach by demonstrating a 20 times speedup for a computationally intensive portion of the Weather Research and Forecast (WRF) model on an NVIDIA 8800 GTX graphics processing unit (GPU). We expect an overall 1.3 times speedup from this change alone.

Development of a next-generation regional weather research and forecast model.

The Weather Research and Forecast (WRF) project is a multi-institutional effort to develop an advanced mesoscale forecast and data assimilation system that is accurate, efficient, and scalable across a range of scales and over a host of computer platforms. The first release, WRF 1.0, was November 30, 2000, with operational deployment targeted for the 2004-05 time frame. This paper provides an overview of the project and current status of the WRF development effort in the areas of numerics and physics, software and data architecture, and single-source parallelism and performance portability.

A numerical study of the effects of atmospheric and wake turbulence on wind turbine dynamics

Although the atmospheric sciences community has been studying the effects of atmospheric stability and surface roughness on the planetary boundary layer for some time, their effects on wind turbine dynamics have not been well studied. In this study, we performed numerical experiments to explore some of the effects of atmospheric stability and surface roughness on wind turbine dynamics. We used large-eddy simulation to create atmospheric winds and compute the wind turbine flows, and we modeled the wind turbines as revolving and flexible actuator lines coupled to a wind turbine structural and system dynamic model. We examined the structural moments about the wind turbine blade, low-speed shaft, and nacelle; power production; and wake evolution when large 5-MW turbines are subjected to winds generated from low- and high-surface roughness levels representative of offshore and onshore conditions, respectively, and also neutral and unstable atmospheric conditions. In addition, we placed a second turbine 7 rotor...

Four-Dimensional Variational Data Assimilation for WRF: Formulation and Preliminary Results

Abstract The Weather Research and Forecasting (WRF) model–based variational data assimilation system (WRF-Var) has been extended from three- to four-dimensional variational data assimilation (WRF 4D-Var) to meet the increasing demand for improving initial model states in multiscale numerical simulations and forecasts. The initial goals of this development include operational applications and support to the research community. The formulation of WRF 4D-Var is described in this paper. WRF 4D-Var uses the WRF model as a constraint to impose a dynamic balance on the assimilation. It is shown to implicitly evolve the background error covariance and to produce the flow-dependent nature of the analysis increments. Preliminary results from real-data 4D-Var experiments in a quasi-operational setting are presented and the potential of WRF 4D-Var in research and operational applications are demonstrated. A wider distribution of the system to the research community will further develop its capabilities and to encoura...

Application of a multiscale, coupled MM5/chemistry model to the complex terrain of the VOTALP valley campaign

Abstract A coupled complex meteorology/chemistry model has been used to simulate the flow field and the concentration fields of atmospheric pollutants in Alpine valleys during the VOTALP (Vertical Ozone Transports in the ALPs) Valley Campaign in August 1996 in southern Switzerland. This paper starts with a description of a coupled numerical model (MCCM, Multiscale Climate Chemistry Model), which is based on the Penn State/NCAR nonhydrostatic mesoscale model (MM5) and the RADM2 gas-phase chemical reaction scheme. The second part of the paper presents a simulation for the Mesolcina Valley, the core region of the VOTALP Valley Campaign, and adjacent regions. The simulation was done using the nesting facility of the coupled meteorology/chemistry model. The horizontal resolution for the innermost nest was 1 km. The simulations depict the daily thermally induced valley and mountain wind system and the advection of pollutants with this wind system. It becomes obvious that in the model simulation highly polluted air from the Po Basin is transported into the Alpine valleys during the day. During the night cleaner air is brought downward with the mountain winds. Cross sections from the high-resolution model results give a closer look at the inflow and outflow of pollutants into and from the Mesolcina Valley.

The Weather Research and Forecasting Model's Community Variational/Ensemble Data Assimilation System: WRFDA

Data assimilation is the process by which observations are combined with short-range NWP model output to produce an analysis of the state of the atmosphere at a specified time. Since its inception in the late 1990s, the multiagency Weather Research and Forecasting (WRF) model effort has had a strong data assimilation component, dedicating two working groups to the subject. This article documents the history of the WRF data assimilation effort, and discusses the challenges associated with balancing academic, research, and operational data assimilation requirements in the context of the WRF effort to date. The WRF Models Community Variational/Ensemble Data Assimilation System (WRFDA) has evolved over the past 10 years, and has resulted in over 30 refereed publications to date, as well as implementation in a wide range of real-time and operational NWP systems. This paper provides an overview of the scientific capabilities of WRFDA, and together with results from sample operation implementations at the U.S. ...

Local and Mesoscale Impacts of Wind Farms as Parameterized in a Mesoscale NWP Model

A new wind farm parameterization has been developed for the mesoscale numerical weather prediction model, the WeatherResearchand Forecasting model (WRF). The effects of wind turbinesare represented by imposinga momentum sink on themeanflow;transferringkinetic energyintoelectricity andturbulent kinetic energy (TKE). The parameterization improves upon previous models, basing the atmospheric drag of turbines on the thrust coefficient of a modern commercial turbine. In addition, the source of TKE varies with wind speed, reflecting the amount of energy extracted from the atmosphere by the turbines that does not produce electrical energy. Analyses of idealized simulations of a large offshore wind farm are presented to highlight the perturbation induced by the wind farm and its interaction with the atmospheric boundary layer (BL). A wind speed deficit extended throughout the depth of the neutral boundary layer, above and downstream from the farm, with alongwakeof60-kme-foldingdistance.Withinthefarmthewindspeeddeficitreachedamaximum reduction of 16%. A maximum increase of TKE, by nearly a factor of 7, was located within the farm. The increase in TKE extended to the top of the BL above the farm due to vertical transport and wind shear, significantly enhancing turbulent momentum fluxes. The TKE increased by a factor of 2 near the surface within the farm. Near-surface winds accelerated by up to 11%. These results are consistent with the few results available from observations and large-eddy simulations, indicating this parameterization provides a reasonable means of exploring potential downwind impacts of large wind farms.

Design and performance of a scalable parallel community climate model

Abstract We describe the design of a parallel global atmospheric circulation model, PCCM2. This parallel model is functionally equivalent to the National Center for Atmospheric Researchs Community Climate Model, CCM2, but is structured to exploit distributed memory multi-computers. PCCM2 incorporates parallel spectral transform, semi-Lagrangian transport, and load balancing algorithms. We present detailed performance results on the IBM SP2 and Intel Paragon. These results provide insights into the scalability of the individual parallel algorithms and of the parallel model as a whole.