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Iver A. Lund and Donald D. Grantham

Abstract

A total of 511 056 hourly observations of precipitation, taken over a 13-year period at nine stations, were studied to obtain a better understanding of the characteristics of persistence, runs and recurrence. Each hourly precipitation observation was categorized as either none, light, moderate or heavy. Probabilities of each category, except heavy, were estimated from relative frequencies determined from this large data sample and were compared with some theoretical models. The heavy category occurred too infrequently at all of the nine stations to obtain statistically stable relative frequencies from a 13-year period of record. The sample provided sufficient information about the other categories to confidently fit models to the data. The models can be applied to estimate probabilities that precipitation will be observed for sequences of x hours, or more; for exactly x hours; or at time t and also at time t + x hours.

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Iver A. Lund and Donald D. Grantham

Abstract

Hourly observations of precipitation, sky cover, ceiling, visibility, wind speed and temperature, taken over a 13-year period at nine locations along the east coast of the United States, were processed to obtain unconditional and joint relative frequencies of 10 weather events. The relative frequencies were used to develop a model for estimating joint probabilities of weather events from unconditional probabilities and a correlation parameter. The locations range from 9 to 431 mi apart. The probability estimates given by the model were compared with corresponding relative frequencies obtained from the data. The estimates were far superior to estimates based on the assumption that events are statistically independent.

Hourly observations of the same weather elements taken over the same 13-year period at seven locations in the central United States were used to test the model. These locations range from 32 to 678 mi apart. The probability estimates given by the model in the test on independent data using correlation parameters developed from east coast data were also far superior to estimates made on the assumption of independent events. However, some of the estimates were biased. The bias would be eliminated if the correlation parameter for weather events in the central United States were accurately known.

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Iver A. Lund and Donald D. Grantham

Abstract

Hourly observations of precipitation, sky cover, ceiling, visibility, wind speed and temperature, taken over a 13-year period at Washington National Airport, Kennedy International Airport and Raleigh-Durham Airport, were processed to obtain unconditional and recurrence relative frequencies of 10 weather events, in both winter and summer. A model to estimate recurrence probabilities of weather events from unconditional probabilities and a measure of temporal correlation was developed on Washington National Airport data. The model was tested on Kennedy International and Raleigh-Durham data by comparing the probability estimates given by the model with relative frequencies based on observations taken at the two stations. The model estimates for time lags of less than 19 h between observations were always better for all weather events than estimates made on the assumption of statistical independence. They were usually better for all lags of less than 37 h in winter and 63 h in summer.

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Iver A. Lund and Donald D. Grantham

Abstract

A total of 511 056 hourly observations of precipitation, sky cover, ceiling, visibility, wind speed and temperature, taken over a 13-year period at nine locations, were processed to obtain unconditional and two-location joint relative frequencies of 18 weather events, in winter and in summer. A model was developed to estimate joint weather event probabilities from unconditional probabilities and the spatial correlation coefficient. It was developed on data from the 36 pairs of locations ranging from 9 to 431 miles apart. The probability estimates given by the model were compared with the observed relative frequencies and two-thirds of the root-mean-square errors were less than 1%. The largest root-mean-square-error was 2.9%.

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Iver A. Lund, Donald D. Grantham, and Richard E. Davis

Abstract

A model for estimating the probability of obtaining a cloud-free field-of-view as a function of ground-observer-reported total sky cover is presented for earth-to-space viewing applications. The development of the model and examples of its application are described. The model was developed to extend the cloud-free line-of-sight research reported earlier to cover finite field-of-view applications and is based on a subset of the same photographic archive. The archive comprises 2805 whole-sky photographs taken at Columbia, Missouri at 0900, 1200 and 1500 CST during a 39-month period. A grid of 185 annular sectors was used in the analysis of the whole-sky photographs. Statistics of the fractional cloud cover in each sector were computed as a function of total sky cover. Probability estimates were derived and are presented in a matrix format of cloud-free fraction versus reported sky cover for zenith centered areas of 10°, 50°, 90°, 130° and 170° in angular diameter. When the matrix for a given field-of-view is multiplied by a column vector representing the frequencies of observed sky cover at a station, an estimate of the climatic probability that any specified fraction of the field-of-view will be cloud free is obtained. Results of a limited study of the variability of cloud-free field-of-view probabilities as a function of elevation angle are also presented for fields-of-view of 10° and 20°. The results are compared to the cloud-free line-of-sight results.

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