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- Author or Editor: Mark Morrissey x
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Abstract
The quality of ship data within the equatorial western Pacific is investigated using statistical analyses, and by comparison with data from neighboring island stations extracted from National Weather Service analyses. Results indicate that ship-measured sea surface temperature has an inherently small spatial scale. Surface pressure, on the other hand, has an inherently large spatial scale, which allows sparse measurements to record large-scale variations precisely. On the average, ship-measured wind, spatially averaged within a lane located near 150°E, is as good a measure of the large-scale wind flow as are the winds recorded at the sparse island stations within the western Pacific. Inaccuracies in the spatially averaged ship elements indicate that further smoothing of the data is required.
Abstract
The quality of ship data within the equatorial western Pacific is investigated using statistical analyses, and by comparison with data from neighboring island stations extracted from National Weather Service analyses. Results indicate that ship-measured sea surface temperature has an inherently small spatial scale. Surface pressure, on the other hand, has an inherently large spatial scale, which allows sparse measurements to record large-scale variations precisely. On the average, ship-measured wind, spatially averaged within a lane located near 150°E, is as good a measure of the large-scale wind flow as are the winds recorded at the sparse island stations within the western Pacific. Inaccuracies in the spatially averaged ship elements indicate that further smoothing of the data is required.
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.
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.
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.
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.
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.
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.
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.
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.
