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- Author or Editor: David B. Wuertz x
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Abstract
Comparisons of horizontal wind component measurements from a rawinsonde and a UHF wind profiler radar, obtained twice daily over a period of nearly 2 years (from mid-January 1984 through October 1985), showed differences with a standard deviation of about 2.5 m s−1, mainly due to meteorological variability in the winds.
Abstract
Comparisons of horizontal wind component measurements from a rawinsonde and a UHF wind profiler radar, obtained twice daily over a period of nearly 2 years (from mid-January 1984 through October 1985), showed differences with a standard deviation of about 2.5 m s−1, mainly due to meteorological variability in the winds.
Abstract
This paper presents a description of the fully automated quality-assurance (QA) procedures that are being applied to temperatures in the Integrated Global Radiosonde Archive (IGRA). Because these data are routinely used for monitoring variations in tropospheric temperature, it is of critical importance that the system be able to detect as many errors as possible without falsely identifying true meteorological events as erroneous. Three steps were taken to achieve such robust performance. First, 14 tests for excessive persistence, climatological outliers, and vertical and temporal inconsistencies were developed and arranged into a deliberate sequence so as to render the system capable of detecting a variety of data errors. Second, manual review of random samples of flagged values was used to set the “thresholds” for each individual check so as to minimize the number of valid values that are mistakenly identified as errors. The performance of the system as a whole was also assessed through manual inspection of random samples of the quality-assured data. As a result of these efforts, the IGRA temperature QA procedures effectively remove the grossest errors while maintaining a false-positive rate of approximately 10%.
Abstract
This paper presents a description of the fully automated quality-assurance (QA) procedures that are being applied to temperatures in the Integrated Global Radiosonde Archive (IGRA). Because these data are routinely used for monitoring variations in tropospheric temperature, it is of critical importance that the system be able to detect as many errors as possible without falsely identifying true meteorological events as erroneous. Three steps were taken to achieve such robust performance. First, 14 tests for excessive persistence, climatological outliers, and vertical and temporal inconsistencies were developed and arranged into a deliberate sequence so as to render the system capable of detecting a variety of data errors. Second, manual review of random samples of flagged values was used to set the “thresholds” for each individual check so as to minimize the number of valid values that are mistakenly identified as errors. The performance of the system as a whole was also assessed through manual inspection of random samples of the quality-assured data. As a result of these efforts, the IGRA temperature QA procedures effectively remove the grossest errors while maintaining a false-positive rate of approximately 10%.
Abstract
This paper provides a general description of the Integrated Global Radiosonde Archive (IGRA), a new radiosonde dataset from the National Climatic Data Center (NCDC). IGRA consists of radiosonde and pilot balloon observations at more than 1500 globally distributed stations with varying periods of record, many of which extend from the 1960s to present. Observations include pressure, temperature, geopotential height, dewpoint depression, wind direction, and wind speed at standard, surface, tropopause, and significant levels.
IGRA contains quality-assured data from 11 different sources. Rigorous procedures are employed to ensure proper station identification, eliminate duplicate levels within soundings, and select one sounding for every station, date, and time. The quality assurance algorithms check for format problems, physically implausible values, internal inconsistencies among variables, runs of values across soundings and levels, climatological outliers, and temporal and vertical inconsistencies in temperature. The performance of the various checks was evaluated by careful inspection of selected soundings and time series.
In its final form, IGRA is the largest and most comprehensive dataset of quality-assured radiosonde observations freely available. Its temporal and spatial coverage is most complete over the United States, western Europe, Russia, and Australia. The vertical resolution and extent of soundings improve significantly over time, with nearly three-quarters of all soundings reaching up to at least 100 hPa by 2003. IGRA data are updated on a daily basis and are available online from NCDC as both individual soundings and monthly means.
Abstract
This paper provides a general description of the Integrated Global Radiosonde Archive (IGRA), a new radiosonde dataset from the National Climatic Data Center (NCDC). IGRA consists of radiosonde and pilot balloon observations at more than 1500 globally distributed stations with varying periods of record, many of which extend from the 1960s to present. Observations include pressure, temperature, geopotential height, dewpoint depression, wind direction, and wind speed at standard, surface, tropopause, and significant levels.
IGRA contains quality-assured data from 11 different sources. Rigorous procedures are employed to ensure proper station identification, eliminate duplicate levels within soundings, and select one sounding for every station, date, and time. The quality assurance algorithms check for format problems, physically implausible values, internal inconsistencies among variables, runs of values across soundings and levels, climatological outliers, and temporal and vertical inconsistencies in temperature. The performance of the various checks was evaluated by careful inspection of selected soundings and time series.
In its final form, IGRA is the largest and most comprehensive dataset of quality-assured radiosonde observations freely available. Its temporal and spatial coverage is most complete over the United States, western Europe, Russia, and Australia. The vertical resolution and extent of soundings improve significantly over time, with nearly three-quarters of all soundings reaching up to at least 100 hPa by 2003. IGRA data are updated on a daily basis and are available online from NCDC as both individual soundings and monthly means.
Abstract
Radar wind profilers (RWPs) sense the mean and turbulent motion of the clear air through Doppler shifts induced along several (3–5) upward-looking beams. RWP signals, like all radars signals, are often contaminated. The contamination is clearly evident in the associated Doppler spectra, and automatic routines designed to extract meteorological quantities from these spectra often yield inaccurate results. Much of the observed contamination is due to an aliasing of higher frequency signals into the clear-air portion of the spectrum and a broadening of the spectral contaminants caused by the relatively short time series used to generate Doppler spectra. In the past, this source of contamination could not be avoided because of limitations on the size and speed of RWP processing computers. Today’s computers, however, are able to process larger amounts of data at greatly increased speeds. Here it is shown how standard and well-known spectral processing methods can be applied to significantly longer time series to reduce contamination in the radar spectra and thereby improve the accuracy and the reliability of meteorological products derived from RWP systems. In particular, spectral processing methods to identify and remove contamination that is often aliased into the clear-air portion of the spectrum are considered. Optimal techniques for combining multiple spectra to produce averaged spectra are also discussed.
Abstract
Radar wind profilers (RWPs) sense the mean and turbulent motion of the clear air through Doppler shifts induced along several (3–5) upward-looking beams. RWP signals, like all radars signals, are often contaminated. The contamination is clearly evident in the associated Doppler spectra, and automatic routines designed to extract meteorological quantities from these spectra often yield inaccurate results. Much of the observed contamination is due to an aliasing of higher frequency signals into the clear-air portion of the spectrum and a broadening of the spectral contaminants caused by the relatively short time series used to generate Doppler spectra. In the past, this source of contamination could not be avoided because of limitations on the size and speed of RWP processing computers. Today’s computers, however, are able to process larger amounts of data at greatly increased speeds. Here it is shown how standard and well-known spectral processing methods can be applied to significantly longer time series to reduce contamination in the radar spectra and thereby improve the accuracy and the reliability of meteorological products derived from RWP systems. In particular, spectral processing methods to identify and remove contamination that is often aliased into the clear-air portion of the spectrum are considered. Optimal techniques for combining multiple spectra to produce averaged spectra are also discussed.
Abstract
Two climate indices that are useful for monitoring the impact of weather and climate on energy usage and crop yields in the United States have been developed at the National Climatic Data Center. The residential energy-demand temperature index (REDTI), which is based on total population-weighted heating and cooling degree days in the contiguous United States, provides quantitative information on the impact of seasonal temperatures on residential energy demand. The moisture stress index (MSI) is based on the effect of severe to catastrophic drought (Palmer Z index values ≤−2) or catastrophic wetness (Z ≥ +5) on crop productivity within two crop-growing regions (corn and soybeans). Using climate data that extends into the late nineteenth century and operational updates of near-real-time data, the indices provide information that places the impact of weather and climate on energy-supply and crop-production sectors of the economy during the most recent season into historical perspective.
Abstract
Two climate indices that are useful for monitoring the impact of weather and climate on energy usage and crop yields in the United States have been developed at the National Climatic Data Center. The residential energy-demand temperature index (REDTI), which is based on total population-weighted heating and cooling degree days in the contiguous United States, provides quantitative information on the impact of seasonal temperatures on residential energy demand. The moisture stress index (MSI) is based on the effect of severe to catastrophic drought (Palmer Z index values ≤−2) or catastrophic wetness (Z ≥ +5) on crop productivity within two crop-growing regions (corn and soybeans). Using climate data that extends into the late nineteenth century and operational updates of near-real-time data, the indices provide information that places the impact of weather and climate on energy-supply and crop-production sectors of the economy during the most recent season into historical perspective.
This paper describes the new release of the Merged Land–Ocean Surface Temperature analysis (MLOST version 3.5), which is used in operational monitoring and climate assessment activities by the NOAA National Climatic Data Center. The primary motivation for the latest version is the inclusion of a new land dataset that has several major improvements, including a more elaborate approach for addressing changes in station location, instrumentation, and siting conditions. The new version is broadly consistent with previous global analyses, exhibiting a trend of 0.076°C decade−1 since 1901, 0.162°C decade−1 since 1979, and widespread warming in both time periods. In general, the new release exhibits only modest differences with its predecessor, the most obvious being very slightly more warming at the global scale (0.004°C decade−1 since 1901) and slightly different trend patterns over the terrestrial surface.
This paper describes the new release of the Merged Land–Ocean Surface Temperature analysis (MLOST version 3.5), which is used in operational monitoring and climate assessment activities by the NOAA National Climatic Data Center. The primary motivation for the latest version is the inclusion of a new land dataset that has several major improvements, including a more elaborate approach for addressing changes in station location, instrumentation, and siting conditions. The new version is broadly consistent with previous global analyses, exhibiting a trend of 0.076°C decade−1 since 1901, 0.162°C decade−1 since 1979, and widespread warming in both time periods. In general, the new release exhibits only modest differences with its predecessor, the most obvious being very slightly more warming at the global scale (0.004°C decade−1 since 1901) and slightly different trend patterns over the terrestrial surface.
