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David W. Martin, Brian Auvine, and David Suchman

Abstract

An evolutionary view is sought Of a single cloud cluster. This cluster was chosen less for intensity than for comprehensive observations. The aim is to describe the principal outside controls on the cluster, including its relationship with nearby clusters. This is accomplished by combining observations from satellites with those from ships and aircraft.

The cluster represented the deepest of four overlapping layers of moist convection present on this day. It—and its neighbors—tended to occur along rings of cumulus clouds, somewhat larger in size, which were formed by the collapse of older clusters. There was no evidence of a migratory cyclonic synoptic disturbance in the lower troposphere. On the contrary, the cluster occurred entirely within southwest monsoon flow. Abruptly, early in the afternoon, as its cumulonimbus towers became aligned across the front face, the cluster accelerated and intensified. It is argued that this change toward squall line structure and behavior was due to strengthening vertical shear in the upper troposphere, which, together with a layer of dry northeasterlies near 600 mb, increased the strength of evaporationally forced downdrafts under the cirrus shield. The change, a kind of metamorphosis, points to more variability in tropical cloud clusters than has commonly been recognized.

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David Suchman, Brian Auvine, and Barry Hinton

To examine whether the addition of satellite data to forecasting procedures helps forecasters make better forecasts, we studied a meteorological consulting firm and its clients before and after satellite data were used in the preparation of weather forecasts, and whether the clients benefited from this new data source. We found that the satellite data were most valuable when they could be looped to show evolving cloud patterns and enhanced to show brightness differences. The satellite data would have been even more useful if the dissemination system were more flexible and the images were not pregridded.

Our main conclusions are:

  1. Satellite data are most useful to forecasters in data-poor areas and also help to fine-tune forecasts in data-rich areas. Because even slight improvements in forecast accuracy can result in sizable savings for clients, the use of satellite data can produce a significant economic benefit.
  2. Working with satellite data is a valuable educational experience for forecasters and undoubtedly improves their forecasting skills.
  3. Any future satellite data delivery system should take into account the needs and facilities of the user community.

Finally, we have shown that it is possible, using real data in actual situations, to help determine some of the economic effects of a new tool and the ways it can be used to bring about greater public benefits.

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David Suchman, Brian A. Auvine, and Barry H. Hinton

The clients of a meteorological consulting firm were studied to determine the effects of weather forecasts on their operations. We determined what weather conditions triggered certain operational decisions in three groups of clients—governmental bodies, gas utilities, and electric utilities. Then, using actual forecasts over a 2-year period, we calculated the monetary losses incurred as a result of incorrect forecasts. The results generally show losses in the thousands of dollars for each erroneous forecast. Thus, if the weather service is able to prevent even one set of poor decisions based on a forecast, the cost of the service would be returned and in many cases greatly exceeded. Other effects of the clients' use of the forecast are discussed qualitatively. These include nonmonetary gains to the clients and their customers through increased convenience, easier planning, and fewer breakdowns in service. At least some clients fail to realize these advantages through inefficient use of the forecast.

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JenéD. Michaud, Brian A. Auvine, and Olga C. Penalba

Abstract

This study examines the spatial variability of mean monthly summer rainfall in the southwestern United States, with special attention given to the effect elevation. Rain gauge data from a consistent 60-yr period show that mean rainfall increases linearly with elevation within a local area. A simple model (rain = normalized rainfall as a function of latitude and longitude + elevation coefficient × elevation) explains a large part of the spatial variability of mean rainfall. The rainfall model (the MSWR model) and digital elevation data were used to produce a 1° × 1° gridded rainfall climatology for July, August, and September. Regional rainfall estimated with this model is 9.3% higher than an estimate based on arithmetic averaging of gauge data over 2° × 2° areas. For individual 2° × 2° cells, the difference between model rainfall and the arithmetic mean of gauge rainfall ranged from −250% to +41%.

The MSWR model was used to remove orographic effects from regional rainfall fields. When rainfall is normalized to sea level, two rainfall maximums emerge: one in south-central Arizona associated with the Mexican monsoon maximum and one in southeastern New Mexico associated with the Gulf of Mexico. Detrended block kriging (using the MSWR model as an estimate of the long-term trend) and monthly rain gauge data were used to produce unbiased areal rainfall estimates that were compared to 1° × 1° satellite-based rainfall estimates. On a month-by-month basis, there were large differences between the two estimates, although the comparison improved after temporal averaging.

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David W. Martin, Barry B. Hinton, and Brian A. Auvine

Monthly rain falling on the Indian Ocean is mapped for the period 1979 through 1981 by means of observations of the Nimbus-7 Scanning Multichannel Microwave Radiometer. Both stationary and mobile parts were found in the pattern of rain. The stationary part consisted of three zonal and two meridional bands. Only one, the band along and south of the equator, maintained a strong presence through all seasons. A north equatorial counterpart to this south equatorial band also was persistent, but weak. The mobile part of the pattern took the form of a wave. The locus of this wave was an eastward-tilted figure eight, which straddled the equator. The wave moved clockwise along the north loop of the figure eight, counterclockwise along the south loop. The crest of the wave crossed the equator from south to north in May or June and crossed the equator from north to south between August and October. Along its path the equatorial bands were alternately amplified and damped, and the transient bands were activated and suppressed. The effect of the bands and wave was to produce a strong “monsoon” (annual cycle, summer peak) signature in rain falling over both the northeastern and southwestern reaches of the Indian Ocean.

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Barry B. Hinton, William S. Olson, David W. Martin, and Brian Auvine

Abstract

This study discusses a rainfall algorithm utilizing six channels of microwave radiance data from the Nimbus-7 Scanning Multifrequency Microwave Radiometer. The algorithm is intended for short-term climate studies over the ocean at low latitudes. To find a set of functional relationships, rain ratios are regressed on brightness temperatures for each channel. Next, these functions are integrated over a class of rain-rate distributions to find relations between mean brightness temperatures and mean rain rates. This step accounts for beam filling. Finally, weights are obtained for combining the rain rates from the individual channels. The weights vary with the rain rates, so that the optimum combination of channels is always used. Results are stored in a database grid 1° latitude × 1° longitude by one month. To test the algorithm, three years (1979–81) of data from the Indian Ocean are processed. These show spatial patterns very similar to previous climatological studies and to expected seasonal variations as determined at climatological observing stations. In addition, the results are compared with another microwave algorithm and with an infrared threshold method substantially the same as the GOES precipitation index.

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