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Arun Kumar and Martin P. Hoerling

A thought experiment on atmospheric interannual variability associated with El Nino is formulated and is used to investigate the seasonal predictability as it relates to the practice of generating ensemble GCM predictions. The purpose of the study is to gain insight on two important issues within seasonal climate forecasting: (i) the dependence of seasonal forecast skill on a GCM's ensemble size, and the benefits to be expected from using increasingly larger ensembles, and (ii) the merits of dynamical GCM techniques relative to empirical statistical ones for making seasonal forecasts, and the scenarios under which the former may be the superior tool.

It is first emphasized that seasonal predictability is an intrinsic property of the observed system, and is inherently limited owing to the nonzero spread of seasonally averaged atmospheric states subjected to identical SST boundary forcing. Further, such boundary forced predictability can be diagnosed from the change in the statistical distribution of the atmospheric states with respect to different SSTs. The GCM prediction problem is thus cast as one of determining this statistical distribution, and its variation with respect to SST forcing.

For a perfect GCM, the skill of the seasonal prediction based on the ensemble mean is shown to be always greater than that based on a single realization, consistent with the results of other studies. However, prediction skill for larger ensembles cannot exceed the observed system's inherent predictability. It is argued that the very necessity for larger ensembles is a testimony for the low predictability of the system.

The advantage of perfect GCM-based seasonal predictions versus ones based on empirical methods is argued to depend on the nonlinearity of the observed atmosphere to SST forcings. If such nonlinearity is high, GCM methods will in principle yield superior seasonal forecast skill. On the other hand, in the absence of nonlinearity, empirical methods trained on the instrumental record may be equally skillful.

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William Krasner and Julius London

A method is devised (based on the theory of Bjerknes and Holmboe on the deepening of cyclones), for the forecasting of the deepening of tropical cyclones. It is shown that the distribution of divergence around an easterlies trough (“easterly wave”), as revealed through consideration of the vorticity equation agrees with this application to the deepening of tropical cyclones. The conclusion is reached that an estward tilt of the pressure, or streamline, axis of a trough or developing tropical disturbance is most favorable for deepening; whereas, in deep easterlies, a westward tilt of the axis would result in filling and dissipation. A technique is also suggested for a quick qualitative evaluation of the direction of slope of the axis of such a pressure or streamline system.

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A. F. Hasler, M. desJardins, and A. J. Negri

The innate capability to perceive 3-dimensional stereo imagery has been exploited to present multidimensional meteorological data fields. Variations on an artificial stereo technique first discussed by Pichel et al. 1973 are used to display single and multispectral images in a vivid and easily assimilated manner. Examples of visible/infrared artificial stereo are given for Hurricane Allen (cover) and for severe thunderstorms on 10 April 1979. Three-dimensional output from a mesoscale model also is presented.

The images may be viewed through the glasses inserted in the February 1981 issue of the Bulletin, with the red lens over the right eye. The images have been produced on the interactive Atmospheric and Oceanographic Information Processing System (AOIPS) at Goddard Space Flight Center.

Stereo presentation is an important aid in understanding meteorological phenomena for operational weather forecasting, research case studies, and model simulations.

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W. Paul Menzel

Tetsuya (Ted) Fujita was a pioneer in remote sensing of atmospheric motion. When meteorological satellites were introduced, he developed techniques for precise analysis of satellite measurements (sequences of images from polar orbiting platforms first and then from geostationary platforms). Soon after his initial work, the ability to track clouds and relate them to flow patterns in the atmosphere was transferred into routine operations at the national forecast centers. Cloud motion vectors derived from geostationary satellite imagery have evolved into an important data source of meteorological information, especially over the oceans. The current National Environmental Satellite, Data, and Information Service operational production of Geostationary Operational Environmental Satellite cloud and water vapor motion winds continues to perform well; rms differences with respect to raob's are found to be 6.5–7.5 m s−1

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Douglas A. May, Michelle M. Parmeter, Daniel S. Olszewski, and Bruce D. McKenzie

A complete overview of the national Shared Processing Program (SPP) satellite sea surface temperature (SST) retrieval product is presented. This paper summarizes the operational processing of digital Advanced Very High Resolution Radiometer (AVHRR) satellite data into a global SST retrieval product at the Naval Oceanographic Office (NAVOCEANO). Satellite SST generation is described, detailing data processing procedures, algorithms used, and quality control techniques. User interaction and data monitoring through the SPP algorithm research panel for SST is presented along with SST products and information available to users. The NAVOCEANO national SST product consists of more than 150 000 global retrievals per day and demonstrates monthly bias errors less than 0.1 °C and root-mean-square difference errors less than 0.6°C relative to global drifting-buoy measurements. The product is important to and operationally utilized within thermal structure analyses, civilian and military maritime activities, and numerical weather prediction forecasts.

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James A. Schiavone and Thomas V. Papathomas

The extensive growth in meteorological data over the last several decades is imposing severe strains on the meteorologist's ability to fully exploit the data's ultimate value within the time constraints of forecasting operations. Fortunately, the price/performance ratio of the hardware destined to graphically display these datasets is approaching the level of affordability by a significant population of meteorologists. However, this now places a burden on software developers, who are challenged to creatively exploit the hardware for displaying complex meteorological datasets in a mode that exhibits their three-dimensional (3-D), time-evolving nature.

In this paper, we address this challenge by fostering an awareness of the valuable interdisciplinary work that might benefit meteorology. Our goals are to promote software progress through reuse of existing techniques rather than by reinventing “new” ones, and to stimulate ideas for creating new tools and methods. Our review focuses on 3-D display techniques for time-evolving phenomena, presented in the context of both sides of the human/computer interface. We hope that some of this interdisciplinary knowledge accrued will be applied to meteorology.

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Edwin Kessler and James W. Wilson

Appropriate uses of radar in a national weather system within the next 10–15 years are considered. A radar network sensing precipitation reflectivity and utilizing many automatic techniques for acquiring and processing data, preparing forecasts, and communicating precipitation characteristics represents a worthwhile goal practically achievable by 1980. A suitable system would combine the information provided by radar and other sensors, would provide users with the specialized information they require at reasonable cost, and would promote effective interpersonal and man-machine relationships. It would also readily admit new instruments and techniques as their worth is demonstrated.

The meteorological applications of reflectivity data are listed and radar data flow rates corresponding to low, moderate, and high load configurations in the envisioned system are presented. Increasing flow rates correspond to increasing proportions of automatic as opposed to manual operations in the system.

The system outlined represents a preliminary goal which should be modified as new knowledge is acquired from field tests within the operational radar system and from other research.

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Gary L. Hufford, Leonard J. Salinas, James J. Simpson, Elliott G. Barske, and David C. Pieri

Volcanic ash clouds pose a real threat to aircraft safety. The ash is abrasive and capable of causing serious damage to aircraft engines, control surfaces, windshields, and landing lights. In addition, ash can clog the pitot-static systems, which determine wind speed and altitude, and damage sensors used to fly the aircraft. To ensure aviation safety, a warning system should be capable of a 5-min response time once an eruption has been detected. Pilots are the last link in the chain of safety actions to avoid or mitigate encounters with volcanic ash. For the pilots to be effective, the warning and safety system must meet their needs. The ability to issue accurate and timely warnings, advisories, and forecasts requires a rapid means to detect and continually track the ash cloud and smooth coordination between many agencies. The current operational ash detection technique uses satellite remote sensing. Potential problems with this technique and the potential impact of these problems on aircraft safety are discussed.

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James W. Wilson and Edward A. Brandes

Radar can produce detailed precipitation information for large areas from a single location in real time. Although radar has been used experimentally for nearly 30 years to measure rainfall, operational implementation has been slow. Today we find that data are underutilized and both confusion and misunderstanding exist about the inherent ability of radar to measure rainfall, about factors that contribute to errors, and about the importance of careful calibration and signal processing.

Areal and point rainfall estimates are often in error by a factor of two or more. Error sources reside in measurement of radar reflectivity factor, evaporation and advection of precipitation before reaching the ground, and variations in the drop-size distribution and vertical air motions. Nevertheless, radar can be of lifesaving usefulness by alerting forecasters to the potential for flash flooding.

The most successful technique for improving the radar rainfall estimates has been to “calibrate” the radar with rain gages. Simple techniques that combine sparse gage reports (one gage per 1000–2000 km2) with radar produce smaller measurement errors (10–30%) than either system alone. When high accuracy rainfall measurements are needed (average error less than about 10–20%) the advantage of radar is diminished, since the number of gages required for calibration is itself sufficient to provide the desired accuracy.

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Carl W. Kreitzberg

This paper outlines a mesoscale forecast system that could be implemented within a few years in spite of relatively sparse direct observations. Methods are discussed of using satellite information on large mesoscale features to initiate numerical models. The models develop further mesoscale structures through the influence of mesoscale geographic features and organized convective systems. The output of the numerical model serves as the physical foundation upon which the latest detailed satellite data can be interpreted.

Although many of the techniques described are not off-the-shelf items today, they are entirely feasible. It is important that the components of the forecast system be developed in parallel, rather than in series, if the system is to be completed within five years. The components include: polar-orbiting satellites for high latitudes; geosynchronous satellites for low latitudes; a mesoclimatological data base largely from satellite data; a mesoscale numerical prediction model with lateral boundary data supplied from a conventional large-scale model; and a variety of simple models and empirical schemes for treating special mesoscale phenomena.

A review of current activities in mesometeorology provides substantial evidence that the revolution in large-scale weather prediction in the past decade will be followed by a similar revolution on the mesoscale in the next five years.

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