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Environmental Verification and Analysis Center (EVAC) at the University of Oklahoma houses the Comprehensive Pacific Rainfall Database (PACRAIN). The PACRAIN database is the most extensive Pacific island rain gauge database in the world. Data have been collected from hundreds of Pacific island stations, with some records going back as far as the 1800s. (It is currently available to scientists online at http://pacrain.evac.ou.edu .) The goal of the PACRAIN database is to satisfy the need for a single source
Environmental Verification and Analysis Center (EVAC) at the University of Oklahoma houses the Comprehensive Pacific Rainfall Database (PACRAIN). The PACRAIN database is the most extensive Pacific island rain gauge database in the world. Data have been collected from hundreds of Pacific island stations, with some records going back as far as the 1800s. (It is currently available to scientists online at http://pacrain.evac.ou.edu .) The goal of the PACRAIN database is to satisfy the need for a single source
analysis and model comparison studies ( Trenberth et al. 2007 ). Here, a database is described whose aim is to address the need for daily climate data over global land areas. The database, known as the Global Historical Climatology Network (GHCN)-Daily dataset, contains daily data from over 80 000 surface stations worldwide, about two-thirds of which are for precipitation only. Like its counterpart for monthly data ( Peterson and Vose 1997 ; Peterson et al. 1998 ; Lawrimore et al. 2011 ), GHCN
analysis and model comparison studies ( Trenberth et al. 2007 ). Here, a database is described whose aim is to address the need for daily climate data over global land areas. The database, known as the Global Historical Climatology Network (GHCN)-Daily dataset, contains daily data from over 80 000 surface stations worldwide, about two-thirds of which are for precipitation only. Like its counterpart for monthly data ( Peterson and Vose 1997 ; Peterson et al. 1998 ; Lawrimore et al. 2011 ), GHCN
1. Introduction This paper details efforts to reanalyze the National Hurricane Center (NHC)’s second-generation North Atlantic Hurricane Database (HURDAT2; Landsea and Franklin 2013 ), also called the “best track” since they are the “best” postseason determination of tropical cyclone (TC) tracks and intensities for the period from 1931 to 1943. Previous work as part of the Atlantic Hurricane Database Reanalysis Project that has been officially included in the HURDAT2 dataset includes the
1. Introduction This paper details efforts to reanalyze the National Hurricane Center (NHC)’s second-generation North Atlantic Hurricane Database (HURDAT2; Landsea and Franklin 2013 ), also called the “best track” since they are the “best” postseason determination of tropical cyclone (TC) tracks and intensities for the period from 1931 to 1943. Previous work as part of the Atlantic Hurricane Database Reanalysis Project that has been officially included in the HURDAT2 dataset includes the
1. Introduction a. The uncertainty quantification problem The National Digital Forecast Database (NDFD) was designed by the National Weather Service (NWS) to store the official forecasts of the sensible weather elements produced by the NWS field offices throughout the United States ( Glahn and Ruth 2003 ). The official forecasts are subjective in that they are made judgmentally by human forecasters with the support of software systems and are based on information from multiple sources
1. Introduction a. The uncertainty quantification problem The National Digital Forecast Database (NDFD) was designed by the National Weather Service (NWS) to store the official forecasts of the sensible weather elements produced by the NWS field offices throughout the United States ( Glahn and Ruth 2003 ). The official forecasts are subjective in that they are made judgmentally by human forecasters with the support of software systems and are based on information from multiple sources
probability is associated with greater cold cloud area and raining area, especially in the eastern North Pacific Ocean. Using a limited subset of Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and Weather Surveillance Radar-1988 Doppler (WSR-88D) data, Kelley et al. (2004 , 2005) found that the chance of TC intensification increases when one or more hot towers exist in the eyewall. Yet uncertainties remain and additional quantification with a larger database is highly desirable. In
probability is associated with greater cold cloud area and raining area, especially in the eastern North Pacific Ocean. Using a limited subset of Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and Weather Surveillance Radar-1988 Doppler (WSR-88D) data, Kelley et al. (2004 , 2005) found that the chance of TC intensification increases when one or more hot towers exist in the eyewall. Yet uncertainties remain and additional quantification with a larger database is highly desirable. In
1. Introduction “Best tracks” are National Hurricane Center (NHC) poststorm analyses of the intensity, central pressure, position, and size of tropical and subtropical cyclones ( Jarvinen et al. 1984 ), and represent the official historical record for each storm. These analyses (apart from those for size) make up the database known as the hurricane database (HURDAT) and have been used for a wide variety of applications: verification of official and model predictions of track and intensity
1. Introduction “Best tracks” are National Hurricane Center (NHC) poststorm analyses of the intensity, central pressure, position, and size of tropical and subtropical cyclones ( Jarvinen et al. 1984 ), and represent the official historical record for each storm. These analyses (apart from those for size) make up the database known as the hurricane database (HURDAT) and have been used for a wide variety of applications: verification of official and model predictions of track and intensity
1. Introduction This paper details efforts to reanalyze the National Hurricane Center’s (NHC) North Atlantic Hurricane Database (or HURDAT; Jarvinen et al. 1984 ), also called the “best track” since they are the “best” 1 postseason determinations of tropical cyclone (TC) tracks and intensities for the period from 1921 to 1930. Previous work on the reanalysis that has been officially included into the HURDAT dataset includes the periods from 1851 to 1910 ( Landsea et al. 2004a ), 1911 to 1920
1. Introduction This paper details efforts to reanalyze the National Hurricane Center’s (NHC) North Atlantic Hurricane Database (or HURDAT; Jarvinen et al. 1984 ), also called the “best track” since they are the “best” 1 postseason determinations of tropical cyclone (TC) tracks and intensities for the period from 1921 to 1930. Previous work on the reanalysis that has been officially included into the HURDAT dataset includes the periods from 1851 to 1910 ( Landsea et al. 2004a ), 1911 to 1920
first step; consequently, this paper summarizes the history and technical development of the CMA TC database. In response to a request for increased forecasting and research activity in China, a TC reanalysis project was sponsored between 1969 and 1972. Meteorologists and experts from within and outside the CMA were brought together by this project, and focused on the construction of the official CMA TC database, which included the best-track data and TC-induced wind and precipitation observations
first step; consequently, this paper summarizes the history and technical development of the CMA TC database. In response to a request for increased forecasting and research activity in China, a TC reanalysis project was sponsored between 1969 and 1972. Meteorologists and experts from within and outside the CMA were brought together by this project, and focused on the construction of the official CMA TC database, which included the best-track data and TC-induced wind and precipitation observations
aerosol property retrievals and aerosol radiative forcing estimates. To obtain robust observational constraints on the climatic impacts of aerosol particles, there is a pressing need to derive the optical properties of aerosol particles based on realistic particle shapes and inherent optical characteristics (i.e., the real and imaginary parts of the index of refraction). This study develops a database [Texas A&M University dust 2020 (TAMUdust2020)] of the optical properties of irregular aerosol
aerosol property retrievals and aerosol radiative forcing estimates. To obtain robust observational constraints on the climatic impacts of aerosol particles, there is a pressing need to derive the optical properties of aerosol particles based on realistic particle shapes and inherent optical characteristics (i.e., the real and imaginary parts of the index of refraction). This study develops a database [Texas A&M University dust 2020 (TAMUdust2020)] of the optical properties of irregular aerosol
are fundamental to the development of many applications involving ice clouds. The bulk optical properties of ice clouds are the counterparts of an ensemble of ice crystals with certain size and shape distributions (e.g., Baum et al. 2005 ). The bulk ice cloud optical properties are used for radiative flux simulations involving ice clouds in general circulation models (GCMs) and remote sensing of ice clouds from satellite measurements. At present, several single-scattering property databases of
are fundamental to the development of many applications involving ice clouds. The bulk optical properties of ice clouds are the counterparts of an ensemble of ice crystals with certain size and shape distributions (e.g., Baum et al. 2005 ). The bulk ice cloud optical properties are used for radiative flux simulations involving ice clouds in general circulation models (GCMs) and remote sensing of ice clouds from satellite measurements. At present, several single-scattering property databases of
