Forecast Results from the Local-Domain Mesoscale Model Supporting the 1996 Summer Olympic Games

John S. Snook NOAA Forecast Systems Laboratory, Boulder, CO 80303

Zaphiris Christidis IBM T. J. Watson Research Center, Yorktown Heights, NY

James Edwards NOAA Forecast Systems Laboratory, Boulder, CO 80303 Cooperative Institute for Research in the Atmosphere, Fort Collins, CO

John A. McGinley NOAA Forecast Systems Laboratory, Boulder, CO 80303

1. INTRODUCTION

The National Weather Service (NWS) agreed in 1992 to provide specialized operational weather support to the 1996 Summer Olympic Games in Atlanta (Rothfusz et al. 1996). In response to the high-resolution weather forecast and warning requirements of the Olympic Games, the NWS developed the Olym-pic Weather Support System (OWSS). A key element of the OWSS was the operational implementation of the Forecast Systems Laboratory's (FSL) Local Analysis and Prediction System (LAPS). A three-dimensional data assimilation system, LAPS incorporates all available data sources to provide meso-beta scale analyses and forecasts of the atmosphere covering an area roughly the size of a typical NWS office forecast domain (Albers et al. 1996, McGinley 1995, Snook et al. 1995). Furthermore, the LAPS system is designed to run in the local forecast office using affordable computer workstation technology.

The demonstration of LAPS within the OWSS was important. For the first time, a mesoscale forecast model initialized with comparably high resolution analyses was implemented in an operational environment using technology representative of that planned for the NWS forecast office in the next several years. The LAPS data assimilation and generation of 8-km grid spacing analyses for the Olympic domain is described in a companion paper by Stamus and McGinley (1997). The prediction portion of LAPS used the Regional Atmospheric Modeling System (RAMS, Pielke et al. 1992, Walko et al. 1995). This paper discusses the operational configuration of the forecast modeling system. Forecast verification and benefits to the local forecast office are also presented.

2. COMPUTER HARDWARE SELECTION

The initial LAPS-RAMS forecast system was installed in May 1995 on an IBM RS6000/590 RISC computer workstation located at FSL (Snook 1996). The hardware allowed the generation of one daily 18-h forecast using an 85 X 85 8-km grid and 25 levels. Although the predictions typically required 10 to 12 hours to complete, the forecast results were encouraging and they provided the basis to pursue more powerful computer platforms for this task. At that time, a parallel version of RAMS was being developed as a joint collaboration between FSL and Colorado State University. Test results using 81 nodes on an Intel distributed-memory massively-parallel processor demonstrated that vast improvements in compute time could be attained at a reasonable cost. During early 1996, IBM, as an official sponsor of the Olympic Games, agreed to provide a 30-node RS6000 Scalable Power-parallel (SP2) system as the operational compute engine for the local-domain mesoscale forecast model. The IBM SP2 was installed in the NWS forecast office at Peachtree City, Georgia, during April 1996. Details of the SP2 system are described in a companion paper by Christidis et al. (1997). Implementation of the Scalar Modeling System (SMS) as applied to RAMS, which takes advantage of the SP2's parallel architecture, is discussed in another companion paper by Edwards et al. (1997).

3. OPERATIONAL MODEL DESIGN

The LAPS-RAMS forecast system was designed to have as much flexibility and local control as possible. Hence, two model domains were developed: the first was an 8-km grid covering the full Olympic domain (672 X 672 km^2) and the second domain was a smaller 2-km relocatable grid covering an area of 160 X 160 km^2. Several model features were left under the complete control of the Olympic forecasters. These included model forecast initialization time (any hour, on the hour), model domain grid (either 8 km or 2 km), model domain location (if the 2-km grid was selected), and model forecast length. Typical compute times were 11 minutes per forecast hour for the 85 X 85 X 30 8-km grid and 13 minutes for the 81 X 81 X 37 2-km grid. Generally, forecasters selected the model to run every three hours, which allowed a 16-h forecast for the 8-km domain and a 14-h forecast for the 2-km domain. A typical forecast strategy included: an 8-km forecast initialized at 0600 UTC, a 2-km forecast centered over Savannah, Georgia, initialized at 0900 UTC to support a detailed sea breeze forecast required for the yachting venue, and then 8-km forecasts initialized every three hours starting at 1200 UTC.

RAMS model initialization was provided by LAPS which generated surface analyses every 15 minutes and three-dimensional analyses every 30 minutes (Stamus and McGinley 1997). Forecast lateral boundary conditions were provided by the National Center for Environmental Prediction (NCEP) 29-km, national domain Eta model predictions (Black 1994). Details of the RAMS model initialization and physics can be found in Snook and Pielke (1995), Snook et al. (1995), and Snook (1996). It is important to recognize here that the RAMS model physics were selected to complement the grid scale resolution. Hence, a nonhydrostatic version of the model was employed with a full implementation of liquid and ice microphysics (Walko et al. 1995) that provided an explicit prediction of precipitation, and no cumulus parameterization scheme was implemented.

A comprehensive visualization system that integrates the model output with other guidance and allows the forecaster to rapidly peruse the enormous amounts of data is important because the real-time predictions are useful for only a short time span. The same affordable computer workstation technology has allowed the development of several visualization systems that are capable of meeting these requirements. The OWSS used the N-AWIPS meteorological workstation, developed at NCEP, to ingest, integrate, and display a wide variety of forecaster guidance including the RAMS predictions. Unfortunately, the postprocessing of RAMS data into the format required by N-AWIPS was very compute intensive. The timely transmission of the RAMS output to the N-AWIPS workstation required the dedication of four SP2 nodes to postprocessing at the expense of the model running with four less nodes. These issues are currently being addressed with a new meteorological workstation, under development at FSL and called WFO-Advanced (MacDonald and Wakefield 1996), which is a prototype of the next-generation workstation for the field forecast offices.

Three-dimensional visualization of the RAMS predictions has been successfully demonstrated at FSL as another method to rapidly peruse the model output (Snook et al. 1995). RAMS forecasts were stored every 10 minutes in a format readable by the IBM three-dimen-sional Data Explorer visualization system. Three-dimensional time animations of model output were available to the forecasters and to the Olympic World Wide Web homepage. A companion paper by Treinish and Rothfusz (1997) provides a detailed description of this system.

The complete LAPS system is intended to function wholly in the local forecast office. Hence, an additional design requirement is that the system be as automated as possible. If the system is to be deployed at numerous forecast offices, the human resources needed to run the system must be kept to a minimum. Representatives from FSL and IBM were present to troubleshoot any problems with the LAPS-RAMS system during the operational phases of the Olympic weather support. This human presence proved beneficial as the last few problems were resolved during the first several days of the Games. After this time, the LAPS-RAMS system required very little human interaction outside of the designed local control. As further testament to the minimal amount of required human attention, the LAPS-RAMS system continued to operate for three weeks following the Olympic Games in support of the Paralympic Games, during which no FSL or IBM representatives were present and the system had no software failures.

4. MODEL VERIFICATION

Model validation was conducted through two approaches: quantitative and qualitative. Quantitative model validation was performed automatically on a variety of surface variables including temperature, dew point, and wind. Surface observations were available from the standard NWS observation network and from a special automated network assembled specifically for Olympic Games support (Stamus and McGinley 1997). Approximately 70 surface observations were typically available for comparison with model output that was interpolated to each surface observation location. Because differences exist between the low-level RAMS model height (48 m AGL) and surface observation elevation, several adjustments were made to the interpolated model output. Similarity theory (Louis 1979) is used to adjust model temperatures and wind speeds to the surface temperature observation level of 1.5 m and the surface wind observation level of 10 m. An additional adjustment is made to the model temperature using a standard lapse rate of -6.5 K km^{-1} to account for any difference between the model terrain height and the surface observation elevation at the observation location. No adjustments were made to the model moisture variable. Bias and RMS statistics were computed using every location where surface data were available. Spatial and temporal quality control of the observations was completed by the LAPS operational system.

Quantitative RAMS model validation results are presented in Figure 1a, Figure 1b, and Figure 1c with a comparison to statistics computed from the 10- and 29-km Eta model forecasts provided by NCEP. Similar adjustments to account for differences in model and actual observation heights were completed by NCEP prior to the arrival of the forecasts at Peachtree City. The results are an average of all Eta forecasts initialized at 0300 UTC and all RAMS forecasts initialized at 0600 UTC for the period 2 July - 24 August 1996. The plotted Eta results are displaced by three hours so that the comparisons are displayed at common forecast valid times.

The bias and RMS results indicate a significant improvement with RAMS compared to both 10- and 29-km Eta through 1200 UTC for temperature, dew point, and wind. Significant improvements continue to be noted through 1500 UTC for dew point and RMS improvements of 0.4 - 1.0 m s^{-1} are evident through the entire forecast period for wind. Several experimental forecasts were conducted after the Games in an attempt to explain the cool temperature bias after 1200 UTC (0800 LT). These simulations suggested that the radiation scheme used by RAMS did not properly mix out the boundary layer in the late morning. The overall improvements are likely the result of two features: 1) the improved initialization of RAMS by LAPS that incorporates local data sources which led to improved very short-range (0-6 h) predictions and 2) more sophisticated model physics such as the microphysics and soil model.

The qualitative model evaluation applies the meteorologists' analysis skills and experience with meso-scale weather phenomena to subjectively compare model predictions with physical observations and other visual accounts (e.g. human observations) of the weather. Although not as rigid as a quantitative approach, the qualitative evaluation is useful for subjective comparisons with alternative data sources and other model output. For this report, the qualitative evaluation is the best method for investigating the model's performance of mesoscale precipitation predictions. This important forecast is difficult to evaluate quantitatively due to a lack of high temporal and spatial scale observations, but the operational forecasters can provide subjective insight into the model's performance through comparisons with the radar, satellite, and human observations.

Personal communication with the operational forecasters indicated that, in general, the location and timing of the RAMS precipitation forecasts were quite good. However, there was a very large over-prediction of precipitation amount associated with convection. This is likely the result of the 8-km grid spacing being insufficient to fully resolve the air mass thunderstorms typical during the Georgia summer. The capability of RAMS to even represent mesoscale convection was a noted improvement over the other available forecast models. Added value was also recognized from the ability to restart the model every three hours. Two benefits were evident from this strategy. First, the early morning initialized predictions were often incapable of predicting the subtle mesoscale surface forcings that are important to the prediction of afternoon convection in the tropical environment. But, once these features started to be detected in the later morning LAPS analyses, the RAMS forecasts were able to "latch onto" these features and generate reliable convective guidance. Second, as the forecasters observed common features in repeated predictions they became more confident using these particular forecast features.

The 0900 UTC, 2-km RAMS predictions were designed to enhance the detailed sea breeze forecasts required by the yachting venue. Local buoy observations of sea surface temperature were used in the RAMS model initialization. Special point wind forecasts were generated at half-hour increments for the two yachting event locations. The operational yachting forecasters noted that the timing, penetration, and direction of the sea breeze were well forecast by RAMS. A common theme expressed by all the forecasters was that the RAMS forecasts by themselves were generally good, but the predictions were most useful when viewed in combination with all other available guidance.

5. BENEFITS TO THE LOCAL FORECAST OFFICE

The LAPS-RAMS system has been running quasioperationally at FSL for several years. Results from this system have suggested that the local forecast office could realize many benefits from running a mesoscale analysis and forecasting system on site (Snook and Pielke 1995, Snook 1996). Now, for the first time, these benefits have been demonstrated in a true operational environment.

Some of the more important demonstrated benefits include the local control of the mesoscale model. The ability to interactively select the model domain, the model grid resolution, the model start time and duration, and the frequency of model predictions allowed the operational forecasters to tailor a strategy that would best meet their needs for the local forecast problem. This is a decision that can only be made in the local forecast office.

Locally produced weather analyses and predictions greatly reduce the amount of required communications, from both a data collection standpoint and a model output dissemination standpoint. Local data sources, which may not be available to the NCEP central facility in a timely fashion, can be incorporated into local analyses and prediction. The volume of model output continues to grow exponentially in combination with expanding computer hardware capabilities. Communication of model output over long distances to another computer platform is not necessary when the model runs locally on the same network of computers that controls the operational meteorological workstation. This also eliminates the problem of degrading the frequency and resolution of the model output that frequently occurs when disseminating model data from the NCEP central facility to a local office. Hence, the whole flow of data (from collection into the local analyses, to model initialization, to model computations, to model output visualization) occurs in one location in a timely fashion.

Finally, it is important to understand that the locally-produced local-domain numerical weather prediction effort is not intended to replace any guidance that is available from the NCEP central modeling facility. The local-domain forecasting support is designed to provide an additional mesoscale forecast tool to the suite of products already available on the meteorological workstation. The experiences at Peachtree City and Savannah have successfully demonstrated this synergy.

6. SUMMARY

The LAPS-RAMS meso-beta scale analysis and prediction system was implemented in the OWSS at the NWS Peachtree City forecast office to support the high-resolution weather forecast and warning requirements of the Olympic Games. This was the first operational implementation of the complete LAPS-RAMS system. The capability to generate meso-beta scale analyses and forecasts in the local forecast office using technology typical of that planned for the NWS in the next several years was successfully demonstrated. Local control of the mesoscale model allowed the operational forecasters to tailor the model characteristics to better meet their forecast requirements. Operation of the model in the local environment allowed for more frequent mesoscale forecasts and more timely receipt of the model output. A quantitative and qualitative assessment of the model performance indicates that the local model provided added value to the other guidance available through the OWSS. Furthermore, the demonstration showed that the logistics of running a mesoscale model in the local forecast office can be accomplished with minimal human resources. The OWSS, with LAPS-RAMS included, is an excellent example of the enhanced operational mesoscale forecast capabilities that will be available to the NWS and other forecast offices in the near future.

7. ACKNOWLEDGMENTS

The authors wish to thank Drs. Roger Pielke and William Cotton of Colorado State University and Dr. Craig Tremback of Mission Research Corporation for their permission to use RAMS for this project. Paul Schultz reviewed the article and Nita Fullerton provided technical editing support. The Advanced Computing Group within FSL is acknowledged for their help in parallelizing the RAMS model. RAMS was developed under the support of the National Science Foundation (NSF) and the Army Research Office (ARO).

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