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J. A. Flueck, W. L. Woodley, A. G. Barnston, and T. J. Brown

Abstract

The Florida Area Cumulus Experiment (FACE) was a two-stage program dedicated to assessing the potential of “dynamic seeding” for enhancing convective rainfall in a fixed target area. FACE-1 (1970–76) was an exploratory cloud seeding experiment that produced substantial indications of a positive treatment effect on rain at the ground, and FACE-2 (1978–80) was a confirmatory experiment that did not confirm the treatment effect results of FACE-1.

This article presents some new analyses of both the FACE-1 and FACE-2 data in an effort to better understand the role of meteorological and treatment factors on rainfall in the days selected for experimentation in Florida. The analyses rely upon a guided exploratory linear modeling of the natural target area rainfall and the potential treatment effects. In particular, a conceptual model of natural Florida rainfall is utilized to guide the construction of the exploratory linear model. After the form of the model is selected it is fitted to both the FACE-1 and the FACE-2 data sets in an attempt to reassess the effects of treatment.

Two approaches are taken to assessing the treatment effects in FACE-1 and in FACF-2: cross-comparison and cross-validation. Both techniques suggest a positive treatment effect in each stage of FACE (i.e., 30–45% in FACE-1 and 10–15% in FACF-2). However, the conventional 0.05 unadjusted statistical level of support is only present in the FACE-1 data. The question of whether FACE-1 results were different from FACE-2 is unresolved. These results continue to emphasize the need to better account for the natural convective precipitation processes in south Florida prior to conducting a cloud seeding project.

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Anthony G. Barnston, William L. Woodley, John A. Flueck, and Michael H. Brown

Abstract

The Florida Area Cumulus Experiment (FACE) is a single area, randomized experiment designed to assess the ground-level rainfall effects of dynamic cloud seeding in summer on the south Florida peninsula. The second phase of FACE (FACE-2), an attempt to confirm the indication of seeding-induced rain increases in FACE-1, has been completed. A description of the FACE-2 program design and how well it was implemented in the summers of 1978, 1979 and 1980 is provided. The data reduction process and its rationale are described both for the basic rainfall data and for the predictor variables to be used in the covariate analyses. The resulting FACE-2 rainfall and covariate data are presented for each of the 61 days of experimentation without knowledge of whether actual seeding (using silver iodide) took place. (Part II will contain the confirmatory and replicated analyses of the effects of seeding, and Part III will present a number of exploratory analyses of the FACE-1 and FACE-2 data.)

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Anthony G. Barnston, Michael K. Tippett, Huug M. van den Dool, and David A. Unger

Abstract

Since 2002, the International Research Institute for Climate and Society, later in partnership with the Climate Prediction Center, has issued an ENSO prediction product informally called the ENSO prediction plume. Here, measures to improve the reliability and usability of this product are investigated, including bias and amplitude corrections, the multimodel ensembling method, formulation of a probability distribution, and the format of the issued product. Analyses using a subset of the current set of plume models demonstrate the necessity to correct individual models for mean bias and, less urgent, also for amplitude bias, before combining their predictions. The individual ensemble members of all models are weighted equally in combining them to form a multimodel ensemble mean forecast, because apparent model skill differences, when not extreme, are indistinguishable from sampling error when based on a sample of 30 cases or less. This option results in models with larger ensemble numbers being weighted relatively more heavily. Last, a decision is made to use the historical hindcast skill to determine the forecast uncertainty distribution rather than the models’ ensemble spreads, as the spreads may not always reproduce the skill-based uncertainty closely enough to create a probabilistically reliable uncertainty distribution. Thus, the individual model ensemble members are used only for forming the models’ ensemble means and the multimodel forecast mean. In other situations, the multimodel member spread may be used directly. The study also leads to some new formats in which to more effectively show both the mean ENSO prediction and its probability distribution.

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