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  • Author or Editor: Gerald R. North x
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Gregory R. Markowski
and
Gerald R. North

Abstract

Using a combination of statistical methods and monthly SST anomalies (SSTAs) from one or two ocean regions, relatively strong SSTA–precipitation relationships are found during much of the year in the United States: hindcast-bias-corrected correlation coefficients 0.2–0.4 and 0.3–0.6, on monthly and seasonal timescales, respectively. Improved rigor is central to these results: the most crucial procedure was a transform giving regression residuals meeting statistical validity requirements. Tests on 1994–99 out-of-sample data gave better results than expected: semiquantitative, mapped predictions, and quantitative, Heidke skills, are shown. Correlations are large enough to suggest that substantial skill can be obtained for one to several months' precipitation and climate forecasts using ocean circulation models, or statistical methods alone. Although this study was limited to the United States for simplicity, the methodology is intended as generally applicable. Previous work suggests that similar or better skills should be obtainable over much of earth's continental area. Ways likely to improve skills are noted.

Pacific SSTAs outside the Tropics showed substantial precipitation influence, but the main area of North Pacific variability, that along the subarctic front, did not. Instead, the east–west position of SSTAs appears important. The main variability is likely due to north–south changes in front position and will likely give PC analysis artifacts. SSTAs from some regions, the Gulf of Mexico in particular, gave very strong correlations over large U.S. areas. Tests indicated that they are likely caused by atmospheric forcing. Because unusually strong, they should be useful for testing coupled ocean–atmosphere GCMs. Investigation of differences between ENSO events noted by others showed that they are likely attributable to differing SSTA patterns.

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Bo Li
,
Aditya Murthi
,
Kenneth P. Bowman
,
Gerald R. North
,
Marc G. Genton
, and
Michael Sherman

Abstract

The Taylor hypothesis (TH) as applied to rainfall is a proposition about the space–time covariance structure of the rainfall field. Specifically, it supposes that if a spatiotemporal precipitation field with a stationary covariance Cov(r, τ) in both space r and time τ moves with a constant velocity v, then the temporal covariance at time lag τ is equal to the spatial covariance at space lag r = v τ that is, Cov(0, τ) = Cov(v τ, 0). Qualitatively this means that the field evolves slowly in time relative to the advective time scale, which is often referred to as the frozen field hypothesis. Of specific interest is whether there is a cutoff or decorrelation time scale for which the TH holds for a given mean flow velocity v. In this study, the validity of the TH is tested for precipitation fields using high-resolution gridded Next Generation Weather Radar (NEXRAD) reflectivity data produced by the WSI Corporation by employing two different statistical approaches. The first method is based on rigorous hypothesis testing, while the second is based on a simple correlation analysis, which neglects possible dependencies between the correlation estimates. Radar reflectivity values are used from the southeastern United States with an approximate horizontal resolution of 4 km × 4 km and a temporal resolution of 15 min. During the 4-day period from 2 to 5 May 2002, substantial precipitation occurs in the region of interest, and the motion of the precipitation systems is approximately uniform. The results of both statistical methods suggest that the TH might hold for the shortest space and time scales resolved by the data (4 km and 15 min) but that it does not hold for longer periods or larger spatial scales. Also, the simple correlation analysis tends to overestimate the statistical significance through failing to account for correlations between the covariance estimates.

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