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Abstract
Diurnal variations in tropical cold cloudiness are examined for the period 1986–90 for each 2.5° latitude–longitude area in the global Tropics. The fractional coverage of cold cloudiness, as determined from various IR brightness temperature thresholds, has been used as a proxy for tropical convective precipitation, as direct observations of rainfall are unavailable for much of the earth, especially over the oceans. Variations in fractional coverage of cold cloud for three different temperature thresholds are examined: 235.225, and 215 K. The results of this study indicate that cold cloud is most frequently observed over land between 1800 and 2100 local time and is independent of the temperature threshold used. Over the tropical oceans, however, the time of maximum occurrence of cold cloud varies substantially with the temperature threshold employed. Coldest cloud-top temperatures (< 215 K) are found to occur much earlier in the day than warmer cloud tops and peak between 0300 and 0600 local time, which is consistent with many earlier limited-area studios. This observation is further confirmed from precipitation intensity differences between morning and evening observations from microwave satellite data. An interesting out-of-phase relationship between oceanic and continental convection is also discussed.
Ship reports of weather type from the Comprehensive Ocean Atmosphere Data Set are examined as are hourly rainfall amounts from optical rain gauges on moored buoys that were deployed for the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. Both of these data sources also indicate the preference for predawn oceanic heavy rainfall and convective activity. A cursory examination of the diurnal variations in short-range (6 h) rainfall forecasts from the National Meteorological Center Medium-Range Forecast Model are compared with the satellite and in situ results. The daily variations of these forecasts, which are made four times daily, indicate that the diurnal behavior of the model is in reasonable agreement with that of the satellite and in situ observations.
Abstract
Diurnal variations in tropical cold cloudiness are examined for the period 1986–90 for each 2.5° latitude–longitude area in the global Tropics. The fractional coverage of cold cloudiness, as determined from various IR brightness temperature thresholds, has been used as a proxy for tropical convective precipitation, as direct observations of rainfall are unavailable for much of the earth, especially over the oceans. Variations in fractional coverage of cold cloud for three different temperature thresholds are examined: 235.225, and 215 K. The results of this study indicate that cold cloud is most frequently observed over land between 1800 and 2100 local time and is independent of the temperature threshold used. Over the tropical oceans, however, the time of maximum occurrence of cold cloud varies substantially with the temperature threshold employed. Coldest cloud-top temperatures (< 215 K) are found to occur much earlier in the day than warmer cloud tops and peak between 0300 and 0600 local time, which is consistent with many earlier limited-area studios. This observation is further confirmed from precipitation intensity differences between morning and evening observations from microwave satellite data. An interesting out-of-phase relationship between oceanic and continental convection is also discussed.
Ship reports of weather type from the Comprehensive Ocean Atmosphere Data Set are examined as are hourly rainfall amounts from optical rain gauges on moored buoys that were deployed for the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. Both of these data sources also indicate the preference for predawn oceanic heavy rainfall and convective activity. A cursory examination of the diurnal variations in short-range (6 h) rainfall forecasts from the National Meteorological Center Medium-Range Forecast Model are compared with the satellite and in situ results. The daily variations of these forecasts, which are made four times daily, indicate that the diurnal behavior of the model is in reasonable agreement with that of the satellite and in situ observations.
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
Multivariate statistical analyses are employed to identify the source areas of the fine particulates and sulfate, which are the primary components of summer haze in the Blue Ridge Mountains of Virginia. These analyses include principal component analysis followed by stepwise multiple regression analysis. The results indicate that most of the fine particles and sulfates originate in the Midwest. The most important factor for both parameters is the residence time of the air parcels over the Midwest. The results also indicate that the sulfate is formed by photochemically initiated reactions. Production of organic aerosols from natural hydrocarbon emissions is also identified as a minor source of fine particles in the Blue Ridge Mountains area.
Abstract
Multivariate statistical analyses are employed to identify the source areas of the fine particulates and sulfate, which are the primary components of summer haze in the Blue Ridge Mountains of Virginia. These analyses include principal component analysis followed by stepwise multiple regression analysis. The results indicate that most of the fine particles and sulfates originate in the Midwest. The most important factor for both parameters is the residence time of the air parcels over the Midwest. The results also indicate that the sulfate is formed by photochemically initiated reactions. Production of organic aerosols from natural hydrocarbon emissions is also identified as a minor source of fine particles in the Blue Ridge Mountains area.
Abstract
The relationship between the fractional time raining and tropical rainfall amount is investigated using raingage data and a point process model of tropical rainfall. Both the strength and the nature of the relationship are dependent upon the resolution of the data used to estimate the fractional time raining. It is found that highly accurate estimates of rainfall amounts over periods of one month or greater can be obtained from the fractional time raining so long as high-time-resolution data are used. It is demonstrated that the relationship between the fractional time raining and monthly atoll rainfall is quasi-homogeneous within the monsoon trough region of the equatorial western Pacific.
Abstract
The relationship between the fractional time raining and tropical rainfall amount is investigated using raingage data and a point process model of tropical rainfall. Both the strength and the nature of the relationship are dependent upon the resolution of the data used to estimate the fractional time raining. It is found that highly accurate estimates of rainfall amounts over periods of one month or greater can be obtained from the fractional time raining so long as high-time-resolution data are used. It is demonstrated that the relationship between the fractional time raining and monthly atoll rainfall is quasi-homogeneous within the monsoon trough region of the equatorial western Pacific.
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 goal of this study is to improve understanding of the optimization criteria for radar rainfall (RR) products. Conditional bias (CB) is formally defined and discussed. The CB is defined as the difference between a given rain rate and the conditional average of its estimates. A simple analytical model is used to study the behavior of CB and its effect on the relationship between the estimates and the truth. This study shows the measurement errors of near-surface radar reflectivity and the natural reflectivity–rainfall rate variability can affect CB. This RR estimation error component is also compared with the commonly used mean-square error (MSE). A dilemma between the minimization of these two errors is demonstrated. Removing CB from the estimates significantly increases MSE, but minimizing MSE results in a large CB that manifests itself in underestimation of strong rainfalls.
Abstract
The goal of this study is to improve understanding of the optimization criteria for radar rainfall (RR) products. Conditional bias (CB) is formally defined and discussed. The CB is defined as the difference between a given rain rate and the conditional average of its estimates. A simple analytical model is used to study the behavior of CB and its effect on the relationship between the estimates and the truth. This study shows the measurement errors of near-surface radar reflectivity and the natural reflectivity–rainfall rate variability can affect CB. This RR estimation error component is also compared with the commonly used mean-square error (MSE). A dilemma between the minimization of these two errors is demonstrated. Removing CB from the estimates significantly increases MSE, but minimizing MSE results in a large CB that manifests itself in underestimation of strong rainfalls.
Abstract
This study provides an intensive evaluation of the Global Precipitation Climatology Project (GPCP) 1° daily (1DD) rainfall products over the Mississippi River basin, which covers 435 1° latitude × 1° longitude grids for the period of January 1997–December 2000 using radar-based precipitation estimates. The authors’ evaluation criteria include unconditional continuous, conditional (quasi) continuous, and categorical statistics, and their analyses cover annual and seasonal time periods. The authors present spatial maps that reflect the results for the 1° grids and a summary of the results for three selected regions. They also develop a statistical framework that partitions the GPCP–radar difference statistics into GPCP error and radar error statistics. They further partition the GPCP error statistics into sampling error and retrieval error statistics and estimate the sampling error statistics using a data-based resampling experiment. Highlights of the results include the following: 1) the GPCP 1DD product captures the spatial and temporal variability of rainfall to a high degree, with more than 80% of the variance explained, 2) the GPCP 1DD product proficiently detects rainy days at a large range of rainfall thresholds, and 3) in comparison with radar-based estimates the GPCP 1DD product overestimates rainfall.
Abstract
This study provides an intensive evaluation of the Global Precipitation Climatology Project (GPCP) 1° daily (1DD) rainfall products over the Mississippi River basin, which covers 435 1° latitude × 1° longitude grids for the period of January 1997–December 2000 using radar-based precipitation estimates. The authors’ evaluation criteria include unconditional continuous, conditional (quasi) continuous, and categorical statistics, and their analyses cover annual and seasonal time periods. The authors present spatial maps that reflect the results for the 1° grids and a summary of the results for three selected regions. They also develop a statistical framework that partitions the GPCP–radar difference statistics into GPCP error and radar error statistics. They further partition the GPCP error statistics into sampling error and retrieval error statistics and estimate the sampling error statistics using a data-based resampling experiment. Highlights of the results include the following: 1) the GPCP 1DD product captures the spatial and temporal variability of rainfall to a high degree, with more than 80% of the variance explained, 2) the GPCP 1DD product proficiently detects rainy days at a large range of rainfall thresholds, and 3) in comparison with radar-based estimates the GPCP 1DD product overestimates rainfall.
Abstract
Next-Generation Weather Radar (NEXRAD) multisensor precipitation estimates will be used for a host of applications that include operational streamflow forecasting at the National Weather Service River Forecast Centers (RFCs) and nonoperational purposes such as studies of weather, climate, and hydrology. Given these expanding applications, it is important to understand the quality and error characteristics of NEXRAD multisensor products. In this paper, the issues involved in evaluating these products are examined through an assessment of a 5.5-yr record of multisensor estimates from the Arkansas–Red Basin RFC. The objectives were to examine how known radar biases manifest themselves in the multisensor product and to quantify precipitation estimation errors. Analyses included comparisons of multisensor estimates based on different processing algorithms, comparisons with gauge observations from the Oklahoma Mesonet and the Agricultural Research Service Micronet, and the application of a validation framework to quantify error characteristics. This study reveals several complications to such an analysis, including a paucity of independent gauge data. These obstacles are discussed and recommendations are made to help to facilitate routine verification of NEXRAD products.
Abstract
Next-Generation Weather Radar (NEXRAD) multisensor precipitation estimates will be used for a host of applications that include operational streamflow forecasting at the National Weather Service River Forecast Centers (RFCs) and nonoperational purposes such as studies of weather, climate, and hydrology. Given these expanding applications, it is important to understand the quality and error characteristics of NEXRAD multisensor products. In this paper, the issues involved in evaluating these products are examined through an assessment of a 5.5-yr record of multisensor estimates from the Arkansas–Red Basin RFC. The objectives were to examine how known radar biases manifest themselves in the multisensor product and to quantify precipitation estimation errors. Analyses included comparisons of multisensor estimates based on different processing algorithms, comparisons with gauge observations from the Oklahoma Mesonet and the Agricultural Research Service Micronet, and the application of a validation framework to quantify error characteristics. This study reveals several complications to such an analysis, including a paucity of independent gauge data. These obstacles are discussed and recommendations are made to help to facilitate routine verification of NEXRAD products.
