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Abramson, B., J.M. Brown, W. Edwards, A. Murphy, and R.L. Winkler, 1996: Hailfinder: A Bayesian System for Forecasting Severe Weather. International Journal of Forecasting, 12, 57-71, Elsevier Press, Amsterdam.

Hailfinder is a Bayesian system that combines meteorological data and models with expert judgment, based on both experience and physical understanding, to forecast severe weather in Northeastern Colorado. The system is based on a model, known as a belief network (BN), that has recently emerged as the basis of some powerful intelligent systems. Hailfinder is the first such system to apply these Bayesian models in the realm of meteorology, a field that has served as the basis of many past investigations of probabilistic forecasting. The design of Hailfinder provides a variety of insights to designers of other BN-based systems, regardless of their fields of application.

Albers, S.C., J.A. McGinley, D.L. Birkenheuer, and J.R. Smart, 1996: The Local Analysis and Prediction System (LAPS): Analyses of Clouds, Precipitation, and Temperature. Weather and Forecasting, 11, 3:273-287.

The Local Analysis and Prediction System (LAPS) combines numerous data sources into a set of analyses and forecasts on a 10-km grid with high temporal resolution. To arrive at an analysis of cloud cover, several input analyses are combined with surface aviation observations and pilot reports of cloud layers. These input analyses are a skin temperature analysis (used to solve for cloud layer heights and coverage) derived from Geostationary Operational Environmental Satellite (GOES) IR 11.24-mm data, other visible and multispectral imagery, a three-dimensional temperature analysis, and a three-dimensional radar reflectivity analysis derived from full volumetric radar data. Use of a model first guess for clouds is currently being phased in. The goal is to combine the data sources to take advantage of their strengths, thereby automating the synthesis similar to that of a human forecaster. The design of the analysis procedures and output displays focuses on forecaster utility. A number of derived fields are calculated including cloud type, liquid water content, ice content, and icing severity, as well as precipitation type, concentration, and accumulation. Results from validating the cloud fields against independent data obtained during the Winter Icing and Storms Project (WISP) are presented. Forecasters can now make use of these analyses in a variety of situations, such as depicting sky cover and radiation characteristics over a region, three-dimensionally delineating visibility and icing conditions for aviation, depicting precipitation type, rain and snow accumulation, etc.

Kelsch, M., and L. Wharton, 1996: Comparing PIREPs with NAWAU Turbulence and Icing Forecasts: Issues and Results. Weather and Forecasting, 11, 3:385-390.

Pilot reports of aircraft turbulence and icing were compared with forecasts of those phenomena from the National Aviation Weather Advisory Unit for a 45-day period in the winter of 1992. An observation-driven approach was used because it is considered more appropriate for accommodating the subjective reporting that is characteristic of the observational dataset. A number of comparisons were done for subsets of the data based on the magnitude of the reported event, aircraft altitude, and type of forecast. Positive observations of events compared with forecasts more favorably for the icing data than for the turbulence data. Observations of nonevents compared with forecasts more favorably for the turbulence data than for the icing data. Positive observations of both icing and turbulence compared with forecasts more favorably below 18000 ft than above 18000 ft. Null reports for icing appear to be more representative of "no icing" than the zero-icing reports.

Rodriguez, B., L. Hart, and T. Henderson, 1996: Parallelizing Operational Weather Forecast Models for Portable and Fast Execution. Journal of Parallel and Distributed Computing, 37, 159-170 (Article No. 0116), Academic Press, Inc.

This paper describes a high-level library (the Nearest Neighbor Tool, NNT) that has been used to parallelize operational weather prediction models. NNT is part of the Scalable Modeling System (SMS), developed at the Forecast Systems Laboratory. Programs written in NNT rely on SMS's run-time system and port between a wide range of computing platforms, performing well in multiprocessor systems. We show, using examples from operational weather models, how large Fortran 77 codes can be parallelized using NNT. We compare the ease of programmability of NNT and High Performance Fortran (HPF). We also discuss optimizations like data move ment overlap (in interprocessor communication and I/O operations), and the minimization of data exchanges through the use of redundant computations. We show that although HPF provides a simpler programming interface, NNT allows for program optimizations that increase performance considerably and still keeps a simple user interface. These optimizations have proven essential to run weather prediction models in real time, and HPF compilers should incorporate them in order to meet operational demands. Throughout the paper, we present performance results of weather models running on a network of workstations, the Intel Paragon, and the SGI Challenge. Finally, we study the cost of programming global address space architectures with NNT's local address space paradigm.

Schwartz, B., 1996: The Quantitative Use of PIREPs in Developing Aviation Weather Guidance Products. Weather and Forecasting, 11, 3:372-384.

An evaluation of the utility of pilot reports (PIREPs) of weather for aviation forecasting product development is presented. Although PIREPs were never intended for quantitative use, this limitation has not prevented developers of improved aviation weather guidance products (such as turbulence or icing forecasting schemes) from using these data. In this paper, an analysis of turbulence reports over the contininental United States and Alaska is employed to show that the reports contained within the PIREPs database are inadequate for defining the actual phenomenology of aviation weather hazards. The results suggest that conclusions regarding the frequency of occurrence and intensity of reported aviation weather phenomena contained within PIREPs should not be based on their reported distribution alone because of the effects of operational constraints and various outside influences. The results also suggest that these data are inadequate for developing, calibrating, and validating aviation weather forecasting guidance products. The procedures and regulations for acquiring PIREPs are included in this report, and a discussion is presented suggesting that improvement to the current voice PIREP reporting system might have a wide range of benefits for both the operational and research aviation meteorological communities.

Slonaker, R.L., B.E. Schwartz, and W.J. Emery, 1996: Occurrence of Nonsurface Superadiabatic Lapse Rates within RAOB Data. Weather and Forecasting, 11, 3:350-359.

As part of creating an atmospheric database for research purposes, 73,497 radiosonde observation (RAOB) soundings from 1983 through 1987 were checked for nonsurface (at least 50 mb above the surface) super-adiabatic lapse rates (SLRs). About 60% of the input profiles contain a nonsurface SLR, most of which are subtle. Some of the superadiabatic reports are extreme, indicating probable RAOB error. These erroneous upper-air data are capable of corrupting derived meteorological parameters and analyses. A check for nonsurface SLRs allows these suspect data to be flagged for deletion or correction. The occurrence of superadiabatic reports is somewhat correlated with season and geographic loca-tion. However, all meteorological conditions are prone to these reports of nonsurface SLRs. A quality control criterion is developed to check for nonsurface SLRs using potential temperature, which is not overly sensitive in thin layers (as opposed to lapse rate). During RAOB ascent, any nonsurface report of a potential temperature decrease of more than 1 K is flagged for superadiabatic quality control failure. This threshold rejects the worst 4.3% of input upper-air profiles, allowing the vast majority of minor occurrences to pass. The meteorological and climatological communities should be aware of the occurrence of nonsurface SLRs within RAOB data.

(To receive a copy of any paper listed here, contact the editor by e-mail, or by fax, (303) 497-3329.)