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Mark L. Morrissey and J. Scott Greene

Abstract

Rainfall estimates for two simple satellite-based rainfall algorithms are verified over the tropical Pacific using a new method that incorporates sparsely distributed raingages. The resulting linear regression relationship between monthly areal rainfall and the highly reflective cloud index agrees with earlier results. However, the GOES precipitation index (GPI), which was calibrated using radar rainfall data obtained from the eastern tropical Atlantic, produces biased areas rainfall estimates over most of the tropical Pacific. However, its precision is greater than the highly reflective cloud index, perhaps due to the GPI's larger spatial dimensions. With the incorporation of calibration coefficients determined in this study, the GPI will produce unbiased estimates of areal rainfall for the tropical Pacific region.

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Mark L. Morrissey and J. Scott Greene

Abstract

The use of the two-parameter Weibull function as an estimator of the wind speed probability density function (PDF) is known to be problematic when a high accuracy of fit is required, such as in the computation of the wind power density function. Various types of nonparametric kernels can provide excellent fits to wind speed histograms but cannot provide tractable analytical expressions. Analytic expressions for the wind speed PDF are needed for many applications, particularly in the downscaling of model or satellite wind speed estimates to the regional or point scale. It is demonstrated that the judicious use of an expansion of orthogonal polynomials can produce more accurate estimates of the wind speed PDF than relatively simply parametric functions, such as the commonly used Weibull function. This study examines four such expansions applied to two different surface wind speed datasets in Oklahoma. The results indicate that the accuracy of fit of a given expansion is strongly related to how close the basis weight function in an expansion resembles the wind speed histogram. It is shown that this basis function, which is the first term in the expansion, acts as a first “best guess” to the true wind speed PDF and that the additional terms act to “adjust” the fit to converge on the true density function. The results indicate that appropriately chosen orthogonal polynomials can provide an excellent fit and are quite tractable.

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Werner E. Cook and J. Scott Greene

Abstract

To provide an analysis tool for areal rainfall estimates, 1° gridded monthly sea level rainfall estimates have been derived from historical atoll rainfall observations contained in the Pacific Rainfall (PACRAIN) database. The PACRAIN database is a searchable repository of in situ rainfall observations initiated and maintained by the University of Oklahoma and supported by a research grant from the National Oceanic and Atmospheric Administration (NOAA)/Climate Program Office/Ocean Observing and Monitoring. The gridding algorithm employs ordinary kriging, a standard geostatistical technique, and selects for nonnegative estimates and for local estimation neighborhoods yielding minimum kriging variance. This methodology facilitates the selection of fixed-size neighborhoods from available stations beyond simply choosing the closest stations, as it accounts for dependence between estimator stations. The number of stations used for estimation is based on bias and standard error exhibited under cross estimation. A cross validation is conducted, comparing estimated and observed rains, as well as theoretical and observed standard errors for the ordinary kriging estimator. The conditional bias of the kriging estimator and the predictive value of kriging standard errors, with respect to observed standard errors, are discussed. Plots of the gridded rainfall estimates are given for sample El Niño and La Niña cases and standardized differences between the estimates produced here and the merged monthly rainfall estimates published by the Global Precipitation Climatology Project (GPCP) are shown and discussed.

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J. Scott Greene, W. Ethan Cook, David Knapp, and Patrick Haines

Abstract

Meteorological models need to be compared to long-term, routinely collected meteorological data. Whenever numerical forecast models are validated and compared, verification winds are normally interpolated to individual model grid points. To be statistically significant, differences between model and verification data must exceed the uncertainty of verification winds due to instrument error, sampling, and interpolation. This paper will describe an approach to examine the uncertainty of interpolated boundary layer winds and illustrate its practical effects on model validation and intercomparison efforts. This effort is part of a joint model validation project undertaken by the Environmental Verification and Analysis Center at the University of Oklahoma (http://www.evac.ou.edu) and the Battlefield Environment Directorate of the Army Research Laboratory. The main result of this study is to illustrate that it is crucial to recognize the errors inherent in gridding verification winds when conducting model validation and intercomparison work. Defendable model intercomparison results may rely on proper scheduling of model tests with regard to seasonal wind climatology and choosing instrument networks and variogram functions capable of providing adequately small errors due to sampling and imperfect modeling. Thus, it is important to quantify verification wind uncertainty when stating forecast errors or differences in the accuracy of forecast models.

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Mark L. Morrissey, Werner E. Cook, and J. Scott Greene

Abstract

The wind power density (WPD) distribution curve is essential for wind power assessment and wind turbine engineering. The usual practice of estimating this curve from wind speed data is to first estimate the wind speed probability density function (PDF) using a nonparametric or parametric method. The density function is then multiplied by one-half the wind speed cubed times the air density. Unfortunately, this means that minor errors in the estimation of the wind speed PDF can result in large errors in the WPD distribution curve because the cubic term in the WPD function magnifies the error. To avoid this problem, this paper presents a new method of estimating the WPD distribution curve through a direct estimation of the curve using a Gauss–Hermite expansion. It is demonstrated that the proposed method provides a much more reliable estimate of the WPD distribution curve.

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Mark L. Morrissey, Angie Albers, J. Scott Greene, and Susan Postawko

Abstract

The wind speed probability density function (PDF) is used in a variety of applications in meteorology, oceanography, and climatology usually as a dataset comparison tool of a function of a quantity such as momentum flux or wind power density. The wind speed PDF is also a function of measurement scale and sampling error. Thus, quantities derived from a function of the wind PDF estimated from measurements taken at different scales may yield vastly different results. This is particularly true in the assessment of wind power density and studies of model subgrid-scale processes related to surface energy fluxes. This paper presents a method of estimating the PDF of wind speed representing a specific scale, whether that is in time, space, or time–space. The concepts used have been developed in the field of nonlinear geostatistics but have rarely been applied to meteorological problems. The method uses an expansion of orthogonal polynomials that incorporates a scaling parameter whose values can be found from the variance of wind speed at the desired scale. Possible uses of this technique are for scale homogenization of model or satellite datasets used in comparison studies, investigations of subgrid-scale processes for development of parameterization schemes, or wind power density assessment.

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Mark L. Morrissey, Angie Albers, J. Scott Greene, and Susan Postawko
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J. Scott Greene, Michael Klatt, Mark Morrissey, and Susan Postawko

Abstract

This paper describes the Comprehensive Pacific Rainfall Database (PACRAIN), which contains daily and monthly precipitation records from the tropical Pacific basin. The database is a collection of observations from a variety of sources, including one, the Schools of the Pacific Rainfall Climate Experiment (SPaRCE), that is unique to PACRAIN. SPaRCE is a cooperative field project and involves schools from various Pacific island and atoll nations.

Recent enhancements to the database, including improved quality control, observation and data entry standardization, expansion of the network, increased collaboration with local meteorological directors, and enhanced high-resolution data (e.g., on hourly or minute time scales), are discussed. This paper also outlines some of the internal data and Web-based access specifics of the database. To illustrate the potential usefulness of the data, two examples of research using the PACRAIN database are provided and discussed. The first is an analysis of temporal changes in the extreme event characteristics of daily precipitation across the region. The second is an illustration of how the PACRAIN database can be used to analyze satellite-based precipitation algorithms.

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Mark L. Morrissey, Howard J. Diamond, Michael J. McPhaden, H. Paul Freitag, and J. Scott Greene

Abstract

The common use of remotely located, buoy-mounted capacitance rain gauges in the tropical oceans for satellite rainfall verification studies provides motivation for an in situ gauge bias assessment. A comparison of the biases in rainfall catchment between Pacific island tipping-bucket rain gauges and capacitance rain gauges mounted on moored buoys in the tropical Pacific is conducted using the relationship between the fractional time in rain and monthly rainfall. This study utilizes the widespread spatial homogeneity of this relationship in the tropics to assess the rain catchment of both types of gauges at given values for the fractional time in rain. The results indicate that the capacitance gauges are not statistically significantly biased relative to the island-based tipping-bucket gauges. In addition, given the relatively small error bounds about the bias estimates any real bias differences among all the tested gauges are likely to be quite small compared to monthly rainfall totals. Underestimates resulting from wind biases, which may be substantial, are not documented in this paper.

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Laurence S. Kalkstein, Paul F. Jamason, J. Scott Greene, Jerry Libby, and Lawrence Robinson

Last summer, Philadelphia, Pennsylvania, instituted a new Hot Weather–Health Watch/Warning System (PWWS) to alert the city's residents of potentially oppressive weather situations that could negatively affect health. In addition, the system was used by the Philadelphia Department of Public Health for guidance in the implementation of mitigation procedures during dangerous weather. The system is based on a synoptic climatological procedure that identifies “oppressive” air masses historically associated with increased human mortality. Airmass occurrence can be predicted up to 48 h in advance with use of model output statistics guidance forecast data. The development and statistical basis of the system are discussed, and an analysis of the procedure's ability to forecast weather situations associated with elevated mortality counts is presented. The PWWS, through greater public awareness of excessive heat conditions, may have played an important role in reducing Philadelphia's total heat-related deaths during the summer of 1995.

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