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Abstract
Winter-weather conditions pose an extreme hazard to motorists, resulting in approximately 1000 fatalities annually on U.S. roadways. Minimizing adverse impacts of winter weather requires (i) the identification of hazardous weather conditions leading up to and at the time of fatal crashes, and (ii) effective, targeted messaging of those hazards to motorists. The first objective is addressed by matching motor-vehicle-related fatalities from 2008 to 2019 to nearby weather reports to determine how precipitation types and other observable weather conditions (i.e., precipitation intensity, obscurations, and visibility) change leading up to crashes. One-half of fatalities occur in snow, with 75% occurring in ongoing snowfall. Of fatalities during freezing precipitation, 41% occur near the onset of freezing precipitation. In addition, 42% of fatalities have deteriorating weather conditions prior to the crash, primarily visibility reductions of ≥25%. The second objective is addressed by examining language currently used in National Weather Service Winter Weather Warning, Watch, or Advisory (WSW) issuances for fatal crashes. Only one-third of fatalities have a WSW. These WSWs both identify a road hazard (e.g., “roads will become slick”) and provide an action item for motorists (e.g., “slow down and use caution while driving”) but do not clearly convey tiered road-hazard ratings. Examination of non-weather-related attributes of fatal crashes suggest that variable-message signs along highways may be useful to communicate road hazards, and that future messaging should urge motorists to leave additional space around their vehicles, slow down, prepare for rapidly deteriorating conditions, and teach strategies to regain control of their vehicle.
Significance Statement
We find that approximately 1000 fatalities occur each year on U.S. roadways during winter weather. To inform how to reduce fatalities in the future, we identify weather conditions leading up to and at the time of fatal crashes and determine whether road hazards were publicly messaged alongside weather warnings and advisories. Ongoing snowfall, the onset of freezing precipitation, and visibility reductions were prominent factors found in many fatal crashes, suggesting that these may be important factors to address in future safety studies. Winter-weather warnings and advisories often contain language cautioning road hazards, yet only one-third of fatalities occur during conditions with such official statements. However, these statements do not clearly indicate how hazardous roads will be.
Abstract
Winter-weather conditions pose an extreme hazard to motorists, resulting in approximately 1000 fatalities annually on U.S. roadways. Minimizing adverse impacts of winter weather requires (i) the identification of hazardous weather conditions leading up to and at the time of fatal crashes, and (ii) effective, targeted messaging of those hazards to motorists. The first objective is addressed by matching motor-vehicle-related fatalities from 2008 to 2019 to nearby weather reports to determine how precipitation types and other observable weather conditions (i.e., precipitation intensity, obscurations, and visibility) change leading up to crashes. One-half of fatalities occur in snow, with 75% occurring in ongoing snowfall. Of fatalities during freezing precipitation, 41% occur near the onset of freezing precipitation. In addition, 42% of fatalities have deteriorating weather conditions prior to the crash, primarily visibility reductions of ≥25%. The second objective is addressed by examining language currently used in National Weather Service Winter Weather Warning, Watch, or Advisory (WSW) issuances for fatal crashes. Only one-third of fatalities have a WSW. These WSWs both identify a road hazard (e.g., “roads will become slick”) and provide an action item for motorists (e.g., “slow down and use caution while driving”) but do not clearly convey tiered road-hazard ratings. Examination of non-weather-related attributes of fatal crashes suggest that variable-message signs along highways may be useful to communicate road hazards, and that future messaging should urge motorists to leave additional space around their vehicles, slow down, prepare for rapidly deteriorating conditions, and teach strategies to regain control of their vehicle.
Significance Statement
We find that approximately 1000 fatalities occur each year on U.S. roadways during winter weather. To inform how to reduce fatalities in the future, we identify weather conditions leading up to and at the time of fatal crashes and determine whether road hazards were publicly messaged alongside weather warnings and advisories. Ongoing snowfall, the onset of freezing precipitation, and visibility reductions were prominent factors found in many fatal crashes, suggesting that these may be important factors to address in future safety studies. Winter-weather warnings and advisories often contain language cautioning road hazards, yet only one-third of fatalities occur during conditions with such official statements. However, these statements do not clearly indicate how hazardous roads will be.
Abstract
In line with the continued focus of the National Weather Service (NWS) to provide impact-based decision support services (IDSS) and effectively communicate potential impacts, a new IDSS forecasting tool for surface-transportation hazards is in development at the Weather Prediction Center: the hourly winter storm severity index (WSSI-H). This second part of the series outlines the current algorithms and thresholds for the components of the WSSI-H, which has been developed in line with the approach and considerations discussed in Part I of this series. These components—snow amount, ice accumulation, snow rate, liquid rate, and blowing and drifting snow—each address a specific hazard for motorists. The inclusion of metrics related to driving conditions for untreated road surfaces and time-of-day factoring for active precipitation types helps directly tie forecasted weather conditions to transportation impacts. Impact severity level thresholds are approximately in line with thresholds used by transportation agencies when considering various mitigation strategies (e.g., imposing speed restrictions or closing roadways). Whereas the product is not meant to forecast specific impacts (e.g., road closure or pileup), impact severity levels are designed to scale with increasingly poor travel conditions, which can prompt various mitigation efforts from motorists or transportation agencies to maintain safety. WSSI-H outputs for three winter events are discussed in depth to highlight the potential utility of the product. Overall, the WSSI-H is intended to provide high-resolution situational awareness of potential surface-transportation-related impacts and aid in enhanced collaborations between NWS forecasters and stakeholders like transportation agencies to improve motorist safety.
Significance Statement
A new impact-based forecast product designed to aid in situational awareness of potential impacts from surface-transportation-related hazards is in development. In this second part of the series, we outline the algorithms and thresholds for the various components of the product, where each component addresses a unique hazard. Product outputs for three winter events are presented to highlight the potential utility of the product in an operational forecast setting. Ultimately, enhanced collaboration between forecasters and transportation agencies alongside guidance from this product will bolster consistent messaging to motorists and improve safety and mobility on roads.
Abstract
In line with the continued focus of the National Weather Service (NWS) to provide impact-based decision support services (IDSS) and effectively communicate potential impacts, a new IDSS forecasting tool for surface-transportation hazards is in development at the Weather Prediction Center: the hourly winter storm severity index (WSSI-H). This second part of the series outlines the current algorithms and thresholds for the components of the WSSI-H, which has been developed in line with the approach and considerations discussed in Part I of this series. These components—snow amount, ice accumulation, snow rate, liquid rate, and blowing and drifting snow—each address a specific hazard for motorists. The inclusion of metrics related to driving conditions for untreated road surfaces and time-of-day factoring for active precipitation types helps directly tie forecasted weather conditions to transportation impacts. Impact severity level thresholds are approximately in line with thresholds used by transportation agencies when considering various mitigation strategies (e.g., imposing speed restrictions or closing roadways). Whereas the product is not meant to forecast specific impacts (e.g., road closure or pileup), impact severity levels are designed to scale with increasingly poor travel conditions, which can prompt various mitigation efforts from motorists or transportation agencies to maintain safety. WSSI-H outputs for three winter events are discussed in depth to highlight the potential utility of the product. Overall, the WSSI-H is intended to provide high-resolution situational awareness of potential surface-transportation-related impacts and aid in enhanced collaborations between NWS forecasters and stakeholders like transportation agencies to improve motorist safety.
Significance Statement
A new impact-based forecast product designed to aid in situational awareness of potential impacts from surface-transportation-related hazards is in development. In this second part of the series, we outline the algorithms and thresholds for the various components of the product, where each component addresses a unique hazard. Product outputs for three winter events are presented to highlight the potential utility of the product in an operational forecast setting. Ultimately, enhanced collaboration between forecasters and transportation agencies alongside guidance from this product will bolster consistent messaging to motorists and improve safety and mobility on roads.
Abstract
Development of an impact-based decision support forecasting tool for surface-transportation hazards requires consideration for what impacts the product is intended to capture and how to scale forecast information to impacts to then categorize impact severity. In this first part of the series, we discuss the motivation and intent of such a product, in addition to outlining the approach we take to leverage existing and new research to develop the product. Traffic disruptions (e.g., crashes, increased travel times, roadway restrictions, or closures) are the intended impacts, where impact severity levels are intended to scale to reflect the increasing severity of adverse driving conditions that can correlate with a need for enhanced mitigation efforts by motorists and/or transportation agencies (e.g., slowing down, avoiding travel, and imposing roadway restrictions or closures). Previous research on how weather and road conditions impact transportation and novel research herein to create a metric for crash impact based on precipitation type and local hour of the day are both intended to help scale weather forecasts to impacts. Impact severity classifications can ultimately be determined through consideration of any thresholds used by transportation agencies, in conjunction with the scaling metrics.
Significance Statement
Weather can profoundly impact surface transportation and motorist safety. Because of this and because there are no explicit tools available to forecasters to identify and communicate potential impacts to surface transportation, there is a desire for the development of such a forecast product. However, doing so requires careful consideration for what impacts are intended to be included, how weather corresponds to impacts, and how thresholds for impact severity should be defined. In this first part of the paper series, we outline each of these aspects and present novel research and approaches for the development of an impact-based forecast product specifically tailored to surface-transportation hazards. The product is ultimately intended to improve motorist safety and mobility on roads.
Abstract
Development of an impact-based decision support forecasting tool for surface-transportation hazards requires consideration for what impacts the product is intended to capture and how to scale forecast information to impacts to then categorize impact severity. In this first part of the series, we discuss the motivation and intent of such a product, in addition to outlining the approach we take to leverage existing and new research to develop the product. Traffic disruptions (e.g., crashes, increased travel times, roadway restrictions, or closures) are the intended impacts, where impact severity levels are intended to scale to reflect the increasing severity of adverse driving conditions that can correlate with a need for enhanced mitigation efforts by motorists and/or transportation agencies (e.g., slowing down, avoiding travel, and imposing roadway restrictions or closures). Previous research on how weather and road conditions impact transportation and novel research herein to create a metric for crash impact based on precipitation type and local hour of the day are both intended to help scale weather forecasts to impacts. Impact severity classifications can ultimately be determined through consideration of any thresholds used by transportation agencies, in conjunction with the scaling metrics.
Significance Statement
Weather can profoundly impact surface transportation and motorist safety. Because of this and because there are no explicit tools available to forecasters to identify and communicate potential impacts to surface transportation, there is a desire for the development of such a forecast product. However, doing so requires careful consideration for what impacts are intended to be included, how weather corresponds to impacts, and how thresholds for impact severity should be defined. In this first part of the paper series, we outline each of these aspects and present novel research and approaches for the development of an impact-based forecast product specifically tailored to surface-transportation hazards. The product is ultimately intended to improve motorist safety and mobility on roads.
Abstract
Thousands of comparisons between total precipitable water vapor (PWV) obtained from radiosonde (Vaisala RS80-H) profiles and PWV retrieved from a collocated microwave radiometer (MWR) were made at the Atmospheric Radiation Measurement (ARM) Program's Southern Great Plains Cloud and Radiation Testbed (SGP CART) site in northern Oklahoma from 1994 to 2000. These comparisons show that the RS80-H radiosonde has an approximate 5% dry bias compared to the MWR. This observation is consistent with interpretations of Vaisala RS80 radiosonde data obtained during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE). In addition to the dry bias, analysis of the PWV comparisons as well as of data obtained from dual-sonde soundings done at the SGP show that the calibration of the radiosonde humidity measurements varies considerably both when the radiosondes come from different calibration batches and when the radiosondes come from the same calibration batch. This variability can result in peak-to-peak differences between radiosondes of greater than 25% in PWV. Because accurate representation of the vertical profile of water vapor is critical for ARM's science objectives, an empirical method for correcting the radiosonde humidity profiles is developed based on a constant scaling factor. By using an independent set of observations and radiative transfer models to test the correction, it is shown that the constant humidity scaling method appears both to improve the accuracy and reduce the uncertainty of the radiosonde data. The ARM data are also used to examine a different, physically based, correction scheme that was developed recently by scientists from Vaisala and the National Center for Atmospheric Research (NCAR). This scheme, which addresses the dry bias problem as well as other calibration-related problems with the RS80-H sensor, results in excellent agreement between the PWV retrieved from the MWR and integrated from the corrected radiosonde. However, because the physically based correction scheme does not address the apparently random calibration variations observed, it does not reduce the variability either between radiosonde calibration batches or within individual calibration batches.
Abstract
Thousands of comparisons between total precipitable water vapor (PWV) obtained from radiosonde (Vaisala RS80-H) profiles and PWV retrieved from a collocated microwave radiometer (MWR) were made at the Atmospheric Radiation Measurement (ARM) Program's Southern Great Plains Cloud and Radiation Testbed (SGP CART) site in northern Oklahoma from 1994 to 2000. These comparisons show that the RS80-H radiosonde has an approximate 5% dry bias compared to the MWR. This observation is consistent with interpretations of Vaisala RS80 radiosonde data obtained during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE). In addition to the dry bias, analysis of the PWV comparisons as well as of data obtained from dual-sonde soundings done at the SGP show that the calibration of the radiosonde humidity measurements varies considerably both when the radiosondes come from different calibration batches and when the radiosondes come from the same calibration batch. This variability can result in peak-to-peak differences between radiosondes of greater than 25% in PWV. Because accurate representation of the vertical profile of water vapor is critical for ARM's science objectives, an empirical method for correcting the radiosonde humidity profiles is developed based on a constant scaling factor. By using an independent set of observations and radiative transfer models to test the correction, it is shown that the constant humidity scaling method appears both to improve the accuracy and reduce the uncertainty of the radiosonde data. The ARM data are also used to examine a different, physically based, correction scheme that was developed recently by scientists from Vaisala and the National Center for Atmospheric Research (NCAR). This scheme, which addresses the dry bias problem as well as other calibration-related problems with the RS80-H sensor, results in excellent agreement between the PWV retrieved from the MWR and integrated from the corrected radiosonde. However, because the physically based correction scheme does not address the apparently random calibration variations observed, it does not reduce the variability either between radiosonde calibration batches or within individual calibration batches.
Abstract
Research funded by the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) program has led to significant improvements in longwave radiative transfer modeling over the last decade. These improvements, which have generally come in small incremental changes, were made primarily in the water vapor self- and foreign-broadened continuum and the water vapor absorption line parameters. These changes, when taken as a whole, result in up to a 6 W m−2 improvement in the modeled clear-sky downwelling longwave radiative flux at the surface and significantly better agreement with spectral observations. This paper provides an overview of the history of ARM with regard to clear-sky longwave radiative transfer, and analyzes remaining related uncertainties in the ARM state-of-the-art Line-by-Line Radiative Transfer Model (LBLRTM).
A quality measurement experiment (QME) for the downwelling infrared radiance at the ARM Southern Great Plains site has been ongoing since 1994. This experiment has three objectives: 1) to validate and improve the absorption models and spectral line parameters used in line-by-line radiative transfer models, 2) to assess the ability to define the atmospheric state, and 3) to assess the quality of the radiance observations that serve as ground truth for the model. Analysis of data from 1994 to 1997 made significant contributions to optimizing the QME, but is limited by small but significant uncertainties and deficiencies in the atmospheric state and radiance observations. This paper concentrates on the analysis of QME data from 1998 to 2001, wherein the data have been carefully selected to address the uncertainties in the 1994–97 dataset. Analysis of this newer dataset suggests that the representation of self-broadened water vapor continuum absorption is 3%–8% too strong in the 750–1000 cm−1 region. The dataset also provides information on the accuracy of the self- and foreign-broadened continuum absorption in the 1100–1300 cm−1 region. After accounting for these changes, remaining differences in modeled and observed downwelling clear-sky fluxes are less than 1.5 W m−2 over a wide range of atmospheric states.
Abstract
Research funded by the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) program has led to significant improvements in longwave radiative transfer modeling over the last decade. These improvements, which have generally come in small incremental changes, were made primarily in the water vapor self- and foreign-broadened continuum and the water vapor absorption line parameters. These changes, when taken as a whole, result in up to a 6 W m−2 improvement in the modeled clear-sky downwelling longwave radiative flux at the surface and significantly better agreement with spectral observations. This paper provides an overview of the history of ARM with regard to clear-sky longwave radiative transfer, and analyzes remaining related uncertainties in the ARM state-of-the-art Line-by-Line Radiative Transfer Model (LBLRTM).
A quality measurement experiment (QME) for the downwelling infrared radiance at the ARM Southern Great Plains site has been ongoing since 1994. This experiment has three objectives: 1) to validate and improve the absorption models and spectral line parameters used in line-by-line radiative transfer models, 2) to assess the ability to define the atmospheric state, and 3) to assess the quality of the radiance observations that serve as ground truth for the model. Analysis of data from 1994 to 1997 made significant contributions to optimizing the QME, but is limited by small but significant uncertainties and deficiencies in the atmospheric state and radiance observations. This paper concentrates on the analysis of QME data from 1998 to 2001, wherein the data have been carefully selected to address the uncertainties in the 1994–97 dataset. Analysis of this newer dataset suggests that the representation of self-broadened water vapor continuum absorption is 3%–8% too strong in the 750–1000 cm−1 region. The dataset also provides information on the accuracy of the self- and foreign-broadened continuum absorption in the 1100–1300 cm−1 region. After accounting for these changes, remaining differences in modeled and observed downwelling clear-sky fluxes are less than 1.5 W m−2 over a wide range of atmospheric states.
The Global Space-based Inter-Calibration System (GSICS) is a new international program to assure the comparability of satellite measurements taken at different times and locations by different instruments operated by different satellite agencies. Sponsored by the World Meteorological Organization and the Coordination Group for Meteorological Satellites, GSICS will intercalibrate the instruments of the international constellation of operational low-earth-orbiting (LEO) and geostationary earth-orbiting (GEO) environmental satellites and tie these to common reference standards. The intercomparability of the observations will result in more accurate measurements for assimilation in numerical weather prediction models, construction of more reliable climate data records, and progress toward achieving the societal goals of the Global Earth Observation System of Systems. GSICS includes globally coordinated activities for prelaunch instrument characterization, onboard routine calibration, sensor intercomparison of near-simultaneous observations of individual scenes or overlapping time series, vicarious calibration using Earth-based or celestial references, and field campaigns. An initial strategy uses high-accuracy satellite instruments, such as the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infrared Sounder (AIRS) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)'s Centre National d'Études Spatiales (CNES) Infrared Atmospheric Sounding Interferometer (IASI), as space-based reference standards for intercalibrating the operational satellite sensors. Examples of initial intercalibration results and future plans are presented. Agencies participating in the program include the Centre National d'Études Spatiales, China Meteorological Administration, EUMETSAT, Japan Meteorological Agency, Korea Meteorological Administration, NASA, National Institute of Standards and Technology, and NOAA.
The Global Space-based Inter-Calibration System (GSICS) is a new international program to assure the comparability of satellite measurements taken at different times and locations by different instruments operated by different satellite agencies. Sponsored by the World Meteorological Organization and the Coordination Group for Meteorological Satellites, GSICS will intercalibrate the instruments of the international constellation of operational low-earth-orbiting (LEO) and geostationary earth-orbiting (GEO) environmental satellites and tie these to common reference standards. The intercomparability of the observations will result in more accurate measurements for assimilation in numerical weather prediction models, construction of more reliable climate data records, and progress toward achieving the societal goals of the Global Earth Observation System of Systems. GSICS includes globally coordinated activities for prelaunch instrument characterization, onboard routine calibration, sensor intercomparison of near-simultaneous observations of individual scenes or overlapping time series, vicarious calibration using Earth-based or celestial references, and field campaigns. An initial strategy uses high-accuracy satellite instruments, such as the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infrared Sounder (AIRS) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)'s Centre National d'Études Spatiales (CNES) Infrared Atmospheric Sounding Interferometer (IASI), as space-based reference standards for intercalibrating the operational satellite sensors. Examples of initial intercalibration results and future plans are presented. Agencies participating in the program include the Centre National d'Études Spatiales, China Meteorological Administration, EUMETSAT, Japan Meteorological Agency, Korea Meteorological Administration, NASA, National Institute of Standards and Technology, and NOAA.
An intercomparison of radiation codes used in retrieving upper-tropospheric humidity (UTH) from observations in the ν2 (6.3 μm) water vapor absorption band was performed. This intercomparison is one part of a coordinated effort within the Global Energy and Water Cycle Experiment Water Vapor Project to assess our ability to monitor the distribution and variations of upper-tropospheric moisture from spaceborne sensors. A total of 23 different codes, ranging from detailed line-by-line (LBL) models, to coarser-resolution narrowband (NB) models, to highly parameterized single-band (SB) models participated in the study. Forward calculations were performed using a carefully selected set of temperature and moisture profiles chosen to be representative of a wide range of atmospheric conditions. The LBL model calculations exhibited the greatest consistency with each other, typically agreeing to within 0.5 K in terms of the equivalent blackbody brightness temperature (Tb ). The majority of NB and SB models agreed to within ±1 K of the LBL models, although a few older models exhibited systematic Tb biases in excess of 2 K. A discussion of the discrepancies between various models, their association with differences in model physics (e.g., continuum absorption), and their implications for UTH retrieval and radiance assimilation is presented.
An intercomparison of radiation codes used in retrieving upper-tropospheric humidity (UTH) from observations in the ν2 (6.3 μm) water vapor absorption band was performed. This intercomparison is one part of a coordinated effort within the Global Energy and Water Cycle Experiment Water Vapor Project to assess our ability to monitor the distribution and variations of upper-tropospheric moisture from spaceborne sensors. A total of 23 different codes, ranging from detailed line-by-line (LBL) models, to coarser-resolution narrowband (NB) models, to highly parameterized single-band (SB) models participated in the study. Forward calculations were performed using a carefully selected set of temperature and moisture profiles chosen to be representative of a wide range of atmospheric conditions. The LBL model calculations exhibited the greatest consistency with each other, typically agreeing to within 0.5 K in terms of the equivalent blackbody brightness temperature (Tb ). The majority of NB and SB models agreed to within ±1 K of the LBL models, although a few older models exhibited systematic Tb biases in excess of 2 K. A discussion of the discrepancies between various models, their association with differences in model physics (e.g., continuum absorption), and their implications for UTH retrieval and radiance assimilation is presented.
The Arm Program's Water Vapor Intensive Observation Periods
Overview, Initial Accomplishments, and Future Challenges
A series of water vapor intensive observation periods (WVIOPs) were conducted at the Atmospheric Radiation Measurement (ARM) site in Oklahoma between 1996 and 2000. The goals of these WVIOPs are to characterize the accuracy of the operational water vapor observations and to develop techniques to improve the accuracy of these measurements.
The initial focus of these experiments was on the lower atmosphere, for which the goal is an absolute accuracy of better than 2% in total column water vapor, corresponding to ~1 W m−2 of infrared radiation at the surface. To complement the operational water vapor instruments during the WVIOPs, additional instrumentation including a scanning Raman lidar, microwave radiometers, chilled-mirror hygrometers, a differential absorption lidar, and ground-based solar radiometers were deployed at the ARM site. The unique datasets from the 1996, 1997, and 1999 experiments have led to many results, including the discovery and characterization of a large (> 25%) sonde-to-sonde variability in the water vapor profiles from Vaisala RS-80H radiosondes that acts like a height-independent calibration factor error. However, the microwave observations provide a stable reference that can be used to remove a large part of the sonde-to-sonde calibration variability. In situ capacitive water vapor sensors demonstrated agreement within 2% of chilled-mirror hygrometers at the surface and on an instrumented tower. Water vapor profiles retrieved from two Raman lidars, which have both been calibrated to the ARM microwave radiometer, showed agreement to within 5% for all altitudes below 8 km during two WVIOPs. The mean agreement of the total precipitable water vapor from different techniques has converged significantly from early analysis that originally showed differences up to 15%. Retrievals of total precipitable water vapor (PWV) from the ARM microwave radiometer are now found to be only 3% moister than PWV derived from new GPS results, and about 2% drier than the mean of radiosonde data after a recently defined sonde dry-bias correction is applied. Raman lidar profiles calibrated using tower-mounted chilled-mirror hygrometers confirm the expected sensitivity of microwave radiometer data to water vapor changes, but it is drier than the microwave radiometer (MWR) by 0.95 mm for all PWV amounts. However, observations from different collocated microwave radiometers have shown larger differences than expected and attempts to resolve the remaining inconsistencies (in both calibration and forward modeling) are continuing.
The paper concludes by outlining the objectives of the recent 2000 WVIOP and the ARM–First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Water Vapor Experiment (AFWEX), the latter of which switched the focus to characterizing upper-tropospheric humidity measurements.
A series of water vapor intensive observation periods (WVIOPs) were conducted at the Atmospheric Radiation Measurement (ARM) site in Oklahoma between 1996 and 2000. The goals of these WVIOPs are to characterize the accuracy of the operational water vapor observations and to develop techniques to improve the accuracy of these measurements.
The initial focus of these experiments was on the lower atmosphere, for which the goal is an absolute accuracy of better than 2% in total column water vapor, corresponding to ~1 W m−2 of infrared radiation at the surface. To complement the operational water vapor instruments during the WVIOPs, additional instrumentation including a scanning Raman lidar, microwave radiometers, chilled-mirror hygrometers, a differential absorption lidar, and ground-based solar radiometers were deployed at the ARM site. The unique datasets from the 1996, 1997, and 1999 experiments have led to many results, including the discovery and characterization of a large (> 25%) sonde-to-sonde variability in the water vapor profiles from Vaisala RS-80H radiosondes that acts like a height-independent calibration factor error. However, the microwave observations provide a stable reference that can be used to remove a large part of the sonde-to-sonde calibration variability. In situ capacitive water vapor sensors demonstrated agreement within 2% of chilled-mirror hygrometers at the surface and on an instrumented tower. Water vapor profiles retrieved from two Raman lidars, which have both been calibrated to the ARM microwave radiometer, showed agreement to within 5% for all altitudes below 8 km during two WVIOPs. The mean agreement of the total precipitable water vapor from different techniques has converged significantly from early analysis that originally showed differences up to 15%. Retrievals of total precipitable water vapor (PWV) from the ARM microwave radiometer are now found to be only 3% moister than PWV derived from new GPS results, and about 2% drier than the mean of radiosonde data after a recently defined sonde dry-bias correction is applied. Raman lidar profiles calibrated using tower-mounted chilled-mirror hygrometers confirm the expected sensitivity of microwave radiometer data to water vapor changes, but it is drier than the microwave radiometer (MWR) by 0.95 mm for all PWV amounts. However, observations from different collocated microwave radiometers have shown larger differences than expected and attempts to resolve the remaining inconsistencies (in both calibration and forward modeling) are continuing.
The paper concludes by outlining the objectives of the recent 2000 WVIOP and the ARM–First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Water Vapor Experiment (AFWEX), the latter of which switched the focus to characterizing upper-tropospheric humidity measurements.
Abstract
A ground-based Fourier transform spectrometer has been developed to measure the atmospheric downwelling infrared radiance spectrum at the earth's surface with high absolute accuracy. The Atmospheric Emitted Radiance Interferometer (AERI) instrument was designed and fabricated by the University of Wisconsin Space Science and Engineering Center (UW-SSEC) for the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program. This paper emphasizes the key features of the UW-SSEC instrument design that contribute to meeting the AERI instrument requirements for the ARM Program. These features include a highly accurate radiometric calibration system, an instrument controller that provides continuous and autonomous operation, an extensive data acquisition system for monitoring calibration temperatures and instrument health, and a real-time data processing system. In particular, focus is placed on design issues crucial to meeting the ARM requirements for radiometric calibration, spectral calibration, noise performance, and operational reliability. The detailed performance characteristics of the AERI instruments built for the ARM Program are described in a companion paper.
Abstract
A ground-based Fourier transform spectrometer has been developed to measure the atmospheric downwelling infrared radiance spectrum at the earth's surface with high absolute accuracy. The Atmospheric Emitted Radiance Interferometer (AERI) instrument was designed and fabricated by the University of Wisconsin Space Science and Engineering Center (UW-SSEC) for the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program. This paper emphasizes the key features of the UW-SSEC instrument design that contribute to meeting the AERI instrument requirements for the ARM Program. These features include a highly accurate radiometric calibration system, an instrument controller that provides continuous and autonomous operation, an extensive data acquisition system for monitoring calibration temperatures and instrument health, and a real-time data processing system. In particular, focus is placed on design issues crucial to meeting the ARM requirements for radiometric calibration, spectral calibration, noise performance, and operational reliability. The detailed performance characteristics of the AERI instruments built for the ARM Program are described in a companion paper.
