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Patrick King
,
Tsoi-Ching Yip
, and
J. David Steenbergen

Abstract

RAINSAT uses under data to calibrate GOES visible and infra data in terms of probability of rain. It produces probability of rain maps and 3 h forecast probability of rain maps by extrapolation.

An evaluation is made of RAINSAT probability of rain analyses and forecasts for the year 1985, with emphasis on the summer months, using both radar data and raingauge data for verification. In the daytime RAINSAT has skill in separating cloudy areas with near zero probability of rain from cloudy areas with a significant probability of rain. There is some skill in splitting the latter category into different probability levels. Using infrared data only. during day or night, results in a significant drop in skill. Forecasts show some skill out to 6 h.

Season-to-season and within-season comparisons of monthly probability of rain relationships (PoRRs) derived from radar are made. Within-season variability is small, especially in summer and winter. There are large day-to-day variations in the occurrence of rain as a function of visible and infrared values.

Regional variations in PoRRs are assessed in two ways: (1) Regional analyses based on a remote radar PoRR are verified against surface data. (2) Analyses for each region trained on local surface data are compared with those trained on the remote radar. Both approaches support the use of radar data to train the system in regions remote from the radar.

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Lawrence Cheng
,
Tsoi-Ching Yip
, and
Han-Ru Cho

Abstract

The effects of cumulus clouds on the large-scale potential vorticity field are investigated using GATE data. Clouds are found to modify the mean potential vorticity field not only through vertical mixing but also through the generation of potential vorticity by the release of latent beat. Overall, the dynamic effect and the thermodynamic effect of clouds are found to contribute about equally to the large-scale potential vorticity budget.

A diagnostic method is also developed to determine mean cloud vertical vorticity profiles from observed large-scale potential vorticity sources. The method is applied to GATE AIB-scale potential vorticity budgets. The results show that 1) the mean cloud vorticity is of the same order of magnitude as the large-scale mean vorticity, despite the smallness of the horizontal scales of cumulus clouds, and 2) the mean cloud vorticity is smaller than the large-scale mean vorticity in the mean detrainment layer of the cloud population, and larger than the large-scale mean vorticity in the mean cloud entrainment layer. These properties are in agreement with the theoretical analysis presented in Choet al. (1979a).

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