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Abstract
The spatial variability of 58 precipitation and temperature parameters from the “generation of weather elements for multiple applications” (GEM) weather generator has been investigated over a region of significant complexity in topography and climate. GEM parameters were derived for 80 climate stations in southern Idaho and southeastern Oregon. A technique was developed and used to determine the GEM parameters from high-elevation snowpack telemetry stations that report precipitation in nonstandard 2.5-mm (versus 0.25 mm) increments. Important dependencies were noted between most of these parameters and elevation (both domainwide and local), location, and other factors. The “parameter-elevation regressions on independent slopes model” (PRISM) spatial modeling system was used to develop approximate 4-km gridded data fields of each of these parameters. A feature was developed in PRISM that models temperatures above and below mean inversions differently. Examples of the spatial fields derived from this study and a discussion of the applications of these spatial parameter fields are included.
Abstract
The spatial variability of 58 precipitation and temperature parameters from the “generation of weather elements for multiple applications” (GEM) weather generator has been investigated over a region of significant complexity in topography and climate. GEM parameters were derived for 80 climate stations in southern Idaho and southeastern Oregon. A technique was developed and used to determine the GEM parameters from high-elevation snowpack telemetry stations that report precipitation in nonstandard 2.5-mm (versus 0.25 mm) increments. Important dependencies were noted between most of these parameters and elevation (both domainwide and local), location, and other factors. The “parameter-elevation regressions on independent slopes model” (PRISM) spatial modeling system was used to develop approximate 4-km gridded data fields of each of these parameters. A feature was developed in PRISM that models temperatures above and below mean inversions differently. Examples of the spatial fields derived from this study and a discussion of the applications of these spatial parameter fields are included.
The Cooperative Observer Program (COOP), established over 100 years ago, has become the backbone of temperature and precipitation data that characterize means, trends, and extremes in U.S. climate. However, significant and widespread biases in the way COOP observers measure daily precipitation have been discovered. These include 1) underreporting of light precipitation events (daily totals of less than 0.05 in., or 1.27 mm), and 2) overreporting of daily precipitation amounts evenly divisible by five- and/or ten-hundredths of an inch, that is, 0.10, 0.25, 0.30 in., etc. (2.54, 6.35, 7.62 mm, etc.). Observer biases were found to be highly variable in space and time, which has serious implications for the spatial and temporal trends and variations of commonly used precipitation statistics. In addition, it was found that few COOP stations had sufficiently complete data to allow the calculation of stable precipitation statistics for a stochastic weather simulation model. Out of more than 12,000 COOP stations nationally, only 784 (6%) passed data completeness and observer bias screening tests for the climatological period 1971–2000. Of the 1221 COOP stations selected for the U.S. Historical Climate Network (USHCN), which provides much of the country's official data on climate trends and variability over the past century, only 221 stations (18%) passed these tests. More effective training materials and regular communication with COOP observers could reduce observer bias in the future. However, it is unlikely that observer bias can be eliminated. One solution is to automate the COOP precipitation measurement system, but this is an expensive option, and may increase other biases associated with automated precipitation measurement. Further analyses are needed to better quantify and characterize observer bias, and to develop methods for dealing with its effects.
The Cooperative Observer Program (COOP), established over 100 years ago, has become the backbone of temperature and precipitation data that characterize means, trends, and extremes in U.S. climate. However, significant and widespread biases in the way COOP observers measure daily precipitation have been discovered. These include 1) underreporting of light precipitation events (daily totals of less than 0.05 in., or 1.27 mm), and 2) overreporting of daily precipitation amounts evenly divisible by five- and/or ten-hundredths of an inch, that is, 0.10, 0.25, 0.30 in., etc. (2.54, 6.35, 7.62 mm, etc.). Observer biases were found to be highly variable in space and time, which has serious implications for the spatial and temporal trends and variations of commonly used precipitation statistics. In addition, it was found that few COOP stations had sufficiently complete data to allow the calculation of stable precipitation statistics for a stochastic weather simulation model. Out of more than 12,000 COOP stations nationally, only 784 (6%) passed data completeness and observer bias screening tests for the climatological period 1971–2000. Of the 1221 COOP stations selected for the U.S. Historical Climate Network (USHCN), which provides much of the country's official data on climate trends and variability over the past century, only 221 stations (18%) passed these tests. More effective training materials and regular communication with COOP observers could reduce observer bias in the future. However, it is unlikely that observer bias can be eliminated. One solution is to automate the COOP precipitation measurement system, but this is an expensive option, and may increase other biases associated with automated precipitation measurement. Further analyses are needed to better quantify and characterize observer bias, and to develop methods for dealing with its effects.
Abstract
This study uses the new satellite-based Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission to retrieve tropospheric profiles of temperature and moisture over the data-sparse eastern Pacific Ocean. The COSMIC retrievals, which employ a global positioning system radio occultation technique combined with “first-guess” information from numerical weather prediction model analyses, are evaluated through the diagnosis of an intense atmospheric river (AR; i.e., a narrow plume of strong water vapor flux) that devastated the Pacific Northwest with flooding rains in early November 2006. A detailed analysis of this AR is presented first using conventional datasets and highlights the fact that ARs are critical contributors to West Coast extreme precipitation and flooding events. Then, the COSMIC evaluation is provided. Offshore composite COSMIC soundings north of, within, and south of this AR exhibited vertical structures that are meteorologically consistent with satellite imagery and global reanalysis fields of this case and with earlier composite dropsonde results from other landfalling ARs. Also, a curtain of 12 offshore COSMIC soundings through the AR yielded cross-sectional thermodynamic and moisture structures that were similarly consistent, including details comparable to earlier aircraft-based dropsonde analyses. The results show that the new COSMIC retrievals, which are global (currently yielding ∼2000 soundings per day), provide high-resolution vertical-profile information beyond that found in the numerical model first-guess fields and can help monitor key lower-tropospheric mesoscale phenomena in data-sparse regions. Hence, COSMIC will likely support a wide array of applications, from physical process studies to data assimilation, numerical weather prediction, and climate research.
Abstract
This study uses the new satellite-based Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission to retrieve tropospheric profiles of temperature and moisture over the data-sparse eastern Pacific Ocean. The COSMIC retrievals, which employ a global positioning system radio occultation technique combined with “first-guess” information from numerical weather prediction model analyses, are evaluated through the diagnosis of an intense atmospheric river (AR; i.e., a narrow plume of strong water vapor flux) that devastated the Pacific Northwest with flooding rains in early November 2006. A detailed analysis of this AR is presented first using conventional datasets and highlights the fact that ARs are critical contributors to West Coast extreme precipitation and flooding events. Then, the COSMIC evaluation is provided. Offshore composite COSMIC soundings north of, within, and south of this AR exhibited vertical structures that are meteorologically consistent with satellite imagery and global reanalysis fields of this case and with earlier composite dropsonde results from other landfalling ARs. Also, a curtain of 12 offshore COSMIC soundings through the AR yielded cross-sectional thermodynamic and moisture structures that were similarly consistent, including details comparable to earlier aircraft-based dropsonde analyses. The results show that the new COSMIC retrievals, which are global (currently yielding ∼2000 soundings per day), provide high-resolution vertical-profile information beyond that found in the numerical model first-guess fields and can help monitor key lower-tropospheric mesoscale phenomena in data-sparse regions. Hence, COSMIC will likely support a wide array of applications, from physical process studies to data assimilation, numerical weather prediction, and climate research.
