Search Results
You are looking at 1 - 10 of 81 items for
- Author or Editor: David D. Turner x
- Refine by Access: All Content x
Abstract
The information content of high-spectral-resolution midinfrared (MIR; 650–2300 cm−1) and far-infrared (FIR; 200–685 cm−1) upwelling radiance spectra is calculated for clear-sky temperature and water vapor profiles. The wavenumber ranges of the two spectral bands overlap at the central absorption line in the CO2 ν 2 absorption band, and each contains one side of the full absorption band. Each spectral band also includes a water vapor absorption band; the MIR contains the first vibrational–rotational absorption band, while the FIR contains the rotational absorption band. The upwelling spectral radiances are simulated with the line-by-line radiative transfer model (LBLRTM), and the retrievals and information content analysis are computed using standard optimal estimation techniques. Perturbations in the surface temperature and in the trace gases methane, ozone, and nitrous oxide (CH4, O3, and N2O) are introduced to represent forward-model errors. Each spectrum is observed by a simulated infrared spectrometer, with a spectral resolution of 0.5 cm−1, with realistic spectrally varying sensor noise levels. The modeling and analysis framework is applied identically to each spectral range, allowing a quantitative comparison. The results show that for similar sensor noise levels, the FIR shows an advantage in water vapor profile information content and less sensitivity to forward-model errors. With a higher noise level in the FIR, which is a closer match to current FIR detector technology, the FIR information content drops and shows a disadvantage relative to the MIR.
Abstract
The information content of high-spectral-resolution midinfrared (MIR; 650–2300 cm−1) and far-infrared (FIR; 200–685 cm−1) upwelling radiance spectra is calculated for clear-sky temperature and water vapor profiles. The wavenumber ranges of the two spectral bands overlap at the central absorption line in the CO2 ν 2 absorption band, and each contains one side of the full absorption band. Each spectral band also includes a water vapor absorption band; the MIR contains the first vibrational–rotational absorption band, while the FIR contains the rotational absorption band. The upwelling spectral radiances are simulated with the line-by-line radiative transfer model (LBLRTM), and the retrievals and information content analysis are computed using standard optimal estimation techniques. Perturbations in the surface temperature and in the trace gases methane, ozone, and nitrous oxide (CH4, O3, and N2O) are introduced to represent forward-model errors. Each spectrum is observed by a simulated infrared spectrometer, with a spectral resolution of 0.5 cm−1, with realistic spectrally varying sensor noise levels. The modeling and analysis framework is applied identically to each spectral range, allowing a quantitative comparison. The results show that for similar sensor noise levels, the FIR shows an advantage in water vapor profile information content and less sensitivity to forward-model errors. With a higher noise level in the FIR, which is a closer match to current FIR detector technology, the FIR information content drops and shows a disadvantage relative to the MIR.
Abstract
The Method of Object-based Diagnostic Evaluation (MODE) is used to perform an object-based verification of approximately 1400 forecasts of composite reflectivity from the operational HRRR during April–September 2019. In this study, MODE is configured to prioritize deep, moist convective storm cells typical of those that produce severe weather across the central and eastern United States during the warm season. In particular, attributes related to distance and size are given the greatest attribute weights for computing interest in MODE. HRRR tends to overforecast all objects, but substantially overforecasts both small objects at low-reflectivity thresholds and large objects at high-reflectivity thresholds. HRRR tends to either underforecast objects in the southern and central plains or has a correct frequency bias there, whereas it overforecasts objects across the southern and eastern United States. Attribute comparisons reveal the inability of the HRRR to fully resolve convective-scale features and the impact of data assimilation and loss of skill during the initial hours of the forecasts. Scalar metrics are defined and computed based on MODE output, chiefly relying on the interest value. The object-based threat score (OTS), in particular, reveals similar performance of HRRR forecasts as does the Heidke skill score, but with differing magnitudes, suggesting value in adopting an object-based approach to forecast verification. The typical distance between centroids of objects is also analyzed and shows gradual degradation with increasing forecast length.
Abstract
The Method of Object-based Diagnostic Evaluation (MODE) is used to perform an object-based verification of approximately 1400 forecasts of composite reflectivity from the operational HRRR during April–September 2019. In this study, MODE is configured to prioritize deep, moist convective storm cells typical of those that produce severe weather across the central and eastern United States during the warm season. In particular, attributes related to distance and size are given the greatest attribute weights for computing interest in MODE. HRRR tends to overforecast all objects, but substantially overforecasts both small objects at low-reflectivity thresholds and large objects at high-reflectivity thresholds. HRRR tends to either underforecast objects in the southern and central plains or has a correct frequency bias there, whereas it overforecasts objects across the southern and eastern United States. Attribute comparisons reveal the inability of the HRRR to fully resolve convective-scale features and the impact of data assimilation and loss of skill during the initial hours of the forecasts. Scalar metrics are defined and computed based on MODE output, chiefly relying on the interest value. The object-based threat score (OTS), in particular, reveals similar performance of HRRR forecasts as does the Heidke skill score, but with differing magnitudes, suggesting value in adopting an object-based approach to forecast verification. The typical distance between centroids of objects is also analyzed and shows gradual degradation with increasing forecast length.
Retrieving Mean Temperature of Atmospheric Liquid Water Layers Using Microwave Radiometer MeasurementsAbstract
A remote sensing method to retrieve the mean temperature of cloud liquid using ground-based microwave radiometer measurements is evaluated and tested by comparisons with direct cloud temperature information inferred from ceilometer cloud-base measurements and temperature profiles from radiosonde soundings. The method is based on the dependence of the ratio of cloud optical thicknesses at W-band (~90 GHz) and Ka-band (~30 GHz) frequencies on cloud liquid temperature. This ratio is obtained from total optical thicknesses inferred from radiometer measurements of brightness temperatures after accounting for the contributions from oxygen and water vapor. This accounting is done based on the radiometer-based retrievals of integrated water vapor amount and temperature and pressure measurements at the surface. The W–Ka-band ratio method is applied to the measurements from a three-channel (90, 31.4, and 23.8 GHz) microwave radiometer at the U.S. Department of Energy Atmospheric Radiation Measurement Mobile Facility at Oliktok Point, Alaska. The analyzed events span conditions from warm stratus clouds with temperatures above freezing to mixed-phase clouds with supercooled liquid water layers. Intercomparisons of radiometer-based cloud liquid temperature retrievals with estimates from collocated ceilometer and radiosonde measurements indicated on average a standard deviation of about 3.5°C between the two retrieval types in a wide range of cloud temperatures, from warm liquid clouds to mixed-phase clouds with supercooled liquid and liquid water paths greater than 50 g m−2. The three-channel microwave radiometer–based method has a broad applicability, since it requires neither the use of active sensors to locate the boundaries of liquid cloud layers nor information on the vertical profile of temperature.
Abstract
A remote sensing method to retrieve the mean temperature of cloud liquid using ground-based microwave radiometer measurements is evaluated and tested by comparisons with direct cloud temperature information inferred from ceilometer cloud-base measurements and temperature profiles from radiosonde soundings. The method is based on the dependence of the ratio of cloud optical thicknesses at W-band (~90 GHz) and Ka-band (~30 GHz) frequencies on cloud liquid temperature. This ratio is obtained from total optical thicknesses inferred from radiometer measurements of brightness temperatures after accounting for the contributions from oxygen and water vapor. This accounting is done based on the radiometer-based retrievals of integrated water vapor amount and temperature and pressure measurements at the surface. The W–Ka-band ratio method is applied to the measurements from a three-channel (90, 31.4, and 23.8 GHz) microwave radiometer at the U.S. Department of Energy Atmospheric Radiation Measurement Mobile Facility at Oliktok Point, Alaska. The analyzed events span conditions from warm stratus clouds with temperatures above freezing to mixed-phase clouds with supercooled liquid water layers. Intercomparisons of radiometer-based cloud liquid temperature retrievals with estimates from collocated ceilometer and radiosonde measurements indicated on average a standard deviation of about 3.5°C between the two retrieval types in a wide range of cloud temperatures, from warm liquid clouds to mixed-phase clouds with supercooled liquid and liquid water paths greater than 50 g m−2. The three-channel microwave radiometer–based method has a broad applicability, since it requires neither the use of active sensors to locate the boundaries of liquid cloud layers nor information on the vertical profile of temperature.
Abstract
A trend analysis was applied to a 14-yr time series of downwelling spectral infrared radiance observations from the Atmospheric Emitted Radiance Interferometer (AERI) located at the Atmospheric Radiation Measurement Program (ARM) site in the U.S. Southern Great Plains. The highly accurate calibration of the AERI instrument, performed every 10 min, ensures that any statistically significant trend in the observed data over this time can be attributed to changes in the atmospheric properties and composition, and not to changes in the sensitivity or responsivity of the instrument. The measured infrared spectra, numbering more than 800 000, were classified as clear-sky, thin cloud, and thick cloud scenes using a neural network method. The AERI data record demonstrates that the downwelling infrared radiance is decreasing over this 14-yr period in the winter, summer, and autumn seasons but it is increasing in the spring; these trends are statistically significant and are primarily due to long-term change in the cloudiness above the site. The AERI data also show many statistically significant trends on annual, seasonal, and diurnal time scales, with different trend signatures identified in the separate scene classifications. Given the decadal time span of the dataset, effects from natural variability should be considered in drawing broader conclusions. Nevertheless, this dataset has high value owing to the ability to infer possible mechanisms for any trends from the observations themselves and to test the performance of climate models.
Abstract
A trend analysis was applied to a 14-yr time series of downwelling spectral infrared radiance observations from the Atmospheric Emitted Radiance Interferometer (AERI) located at the Atmospheric Radiation Measurement Program (ARM) site in the U.S. Southern Great Plains. The highly accurate calibration of the AERI instrument, performed every 10 min, ensures that any statistically significant trend in the observed data over this time can be attributed to changes in the atmospheric properties and composition, and not to changes in the sensitivity or responsivity of the instrument. The measured infrared spectra, numbering more than 800 000, were classified as clear-sky, thin cloud, and thick cloud scenes using a neural network method. The AERI data record demonstrates that the downwelling infrared radiance is decreasing over this 14-yr period in the winter, summer, and autumn seasons but it is increasing in the spring; these trends are statistically significant and are primarily due to long-term change in the cloudiness above the site. The AERI data also show many statistically significant trends on annual, seasonal, and diurnal time scales, with different trend signatures identified in the separate scene classifications. Given the decadal time span of the dataset, effects from natural variability should be considered in drawing broader conclusions. Nevertheless, this dataset has high value owing to the ability to infer possible mechanisms for any trends from the observations themselves and to test the performance of climate models.
Abstract
Two-dimensional water vapor fields were retrieved by simulated measurements from multiple ground-based microwave radiometers using a tomographic approach. The goal of this paper was to investigate how the various aspects of the instrument setup (number and spacing of elevation angles and of instruments, number of frequencies, etc.) affected the quality of the retrieved field. This was done for two simulated atmospheric water vapor fields: 1) an exaggerated turbulent boundary layer and 2) a simplified water vapor front. An optimal estimation algorithm was used to obtain the tomographic field from the microwave radiometers and to evaluate the fidelity and information content of this retrieved field.
While the retrieval of the simplified front was reasonably successful, the retrieval could not reproduce the details of the turbulent boundary layer field even using up to nine instruments and 25 elevation angles. In addition, the vertical profile of the variability of the water vapor field could not be captured. An additional set of tests was performed using simulated data from a Raman lidar. Even with the detailed lidar measurements, the retrieval did not succeed except when the lidar data were used to define the a priori covariance matrix. This suggests that the main limitation to obtaining fine structures in a retrieved field using tomographic retrievals is the definition of the a priori covariance matrix.
Abstract
Two-dimensional water vapor fields were retrieved by simulated measurements from multiple ground-based microwave radiometers using a tomographic approach. The goal of this paper was to investigate how the various aspects of the instrument setup (number and spacing of elevation angles and of instruments, number of frequencies, etc.) affected the quality of the retrieved field. This was done for two simulated atmospheric water vapor fields: 1) an exaggerated turbulent boundary layer and 2) a simplified water vapor front. An optimal estimation algorithm was used to obtain the tomographic field from the microwave radiometers and to evaluate the fidelity and information content of this retrieved field.
While the retrieval of the simplified front was reasonably successful, the retrieval could not reproduce the details of the turbulent boundary layer field even using up to nine instruments and 25 elevation angles. In addition, the vertical profile of the variability of the water vapor field could not be captured. An additional set of tests was performed using simulated data from a Raman lidar. Even with the detailed lidar measurements, the retrieval did not succeed except when the lidar data were used to define the a priori covariance matrix. This suggests that the main limitation to obtaining fine structures in a retrieved field using tomographic retrievals is the definition of the a priori covariance matrix.
Abstract
Various methods have been developed to characterize cloud type, otherwise referred to as cloud regime. These include manual sky observations, combining radiative and cloud vertical properties observed from satellite, surface-based remote sensing, and digital processing of sky imagers. While each method has inherent advantages and disadvantages, none of these cloud-typing methods actually includes measurements of surface shortwave or longwave radiative fluxes. Here, a method that relies upon detailed, surface-based radiation and cloud measurements and derived data products to train a random-forest machine-learning cloud classification model is introduced. Measurements from five years of data from the ARM Southern Great Plains site were compiled to train and independently evaluate the model classification performance. A cloud-type accuracy of approximately 80% using the random-forest classifier reveals that the model is well suited to predict climatological cloud properties. Furthermore, an analysis of the cloud-type misclassifications is performed. While physical cloud types may be misreported, the shortwave radiative signatures are similar between misclassified cloud types. From this, we assert that the cloud-regime model has the capacity to successfully differentiate clouds with comparable cloud–radiative interactions. Therefore, we conclude that the model can provide useful cloud-property information for fundamental cloud studies, inform renewable energy studies, and be a tool for numerical model evaluation and parameterization improvement, among many other applications.
Abstract
Various methods have been developed to characterize cloud type, otherwise referred to as cloud regime. These include manual sky observations, combining radiative and cloud vertical properties observed from satellite, surface-based remote sensing, and digital processing of sky imagers. While each method has inherent advantages and disadvantages, none of these cloud-typing methods actually includes measurements of surface shortwave or longwave radiative fluxes. Here, a method that relies upon detailed, surface-based radiation and cloud measurements and derived data products to train a random-forest machine-learning cloud classification model is introduced. Measurements from five years of data from the ARM Southern Great Plains site were compiled to train and independently evaluate the model classification performance. A cloud-type accuracy of approximately 80% using the random-forest classifier reveals that the model is well suited to predict climatological cloud properties. Furthermore, an analysis of the cloud-type misclassifications is performed. While physical cloud types may be misreported, the shortwave radiative signatures are similar between misclassified cloud types. From this, we assert that the cloud-regime model has the capacity to successfully differentiate clouds with comparable cloud–radiative interactions. Therefore, we conclude that the model can provide useful cloud-property information for fundamental cloud studies, inform renewable energy studies, and be a tool for numerical model evaluation and parameterization improvement, among many other applications.
Abstract
An absorbing load in a liquid nitrogen bath is commonly used as a radiance standard for calibrating radiometers operating at microwave to infrared wavelengths. It is generally assumed that the physical temperature of the load is stable and equal to the boiling point temperature of pure N2 at the ambient atmospheric pressure. However, this assumption will fail to hold when air movement, as encountered in outdoor environments, allows O2 gas to condense into the bath. Under typical conditions, initial boiling point drift rates of order 25 mK min−1 can occur, and the boiling point of a bath maintained by repeated refilling with pure N2 can eventually shift by approximately 2 K. Laboratory bench tests of a liquid nitrogen bath under simulated wind conditions are presented together with an example of an outdoor radiometer calibration that demonstrates the effect, and the physical processes involved are explained in detail. A key finding is that in windy conditions, changes in O2 volume fraction are related accurately to fractional changes in bath volume due to boiloff, independent of wind speed. This relation can be exploited to ensure that calibration errors due to O2 contamination remain within predictable bounds.
Abstract
An absorbing load in a liquid nitrogen bath is commonly used as a radiance standard for calibrating radiometers operating at microwave to infrared wavelengths. It is generally assumed that the physical temperature of the load is stable and equal to the boiling point temperature of pure N2 at the ambient atmospheric pressure. However, this assumption will fail to hold when air movement, as encountered in outdoor environments, allows O2 gas to condense into the bath. Under typical conditions, initial boiling point drift rates of order 25 mK min−1 can occur, and the boiling point of a bath maintained by repeated refilling with pure N2 can eventually shift by approximately 2 K. Laboratory bench tests of a liquid nitrogen bath under simulated wind conditions are presented together with an example of an outdoor radiometer calibration that demonstrates the effect, and the physical processes involved are explained in detail. A key finding is that in windy conditions, changes in O2 volume fraction are related accurately to fractional changes in bath volume due to boiloff, independent of wind speed. This relation can be exploited to ensure that calibration errors due to O2 contamination remain within predictable bounds.
Abstract
Mobile systems equipped with remote sensing instruments capable of simultaneous profiling of temperature, moisture, and wind at high temporal resolutions can offer insights into atmospheric phenomena that the operational network cannot. Two recently developed systems, the Space Science and Engineering Center (SSEC) Portable Atmospheric Research Center (SPARC) and the Collaborative Lower Atmosphere Profiling System (CLAMPS), have already experienced great success in characterizing a variety of phenomena. Each system contains an Atmospheric Emitted Radiance Interferometer for thermodynamic profiling and a Halo Photonics Stream Line Doppler wind lidar for kinematic profiles. These instruments are augmented with various in situ and remote sensing instruments to provide a comprehensive assessment of the evolution of the lower troposphere at high temporal resolution (5 min or better). While SPARC and CLAMPS can be deployed independently, the common instrument configuration means that joint deployments with well-coordinated data collection and analysis routines are easily facilitated.
In the past several years, SPARC and CLAMPS have participated in numerous field campaigns, which range from mesoscale campaigns that require the rapid deployment and teardown of observing systems to multiweek fixed deployments, providing crucial insights into the behavior of many different atmospheric boundary layer processes while training the next generation of atmospheric scientists. As calls for a nationwide ground-based profiling network continue, SPARC and CLAMPS can play an important role as test beds and prototype nodes for such a network.
Abstract
Mobile systems equipped with remote sensing instruments capable of simultaneous profiling of temperature, moisture, and wind at high temporal resolutions can offer insights into atmospheric phenomena that the operational network cannot. Two recently developed systems, the Space Science and Engineering Center (SSEC) Portable Atmospheric Research Center (SPARC) and the Collaborative Lower Atmosphere Profiling System (CLAMPS), have already experienced great success in characterizing a variety of phenomena. Each system contains an Atmospheric Emitted Radiance Interferometer for thermodynamic profiling and a Halo Photonics Stream Line Doppler wind lidar for kinematic profiles. These instruments are augmented with various in situ and remote sensing instruments to provide a comprehensive assessment of the evolution of the lower troposphere at high temporal resolution (5 min or better). While SPARC and CLAMPS can be deployed independently, the common instrument configuration means that joint deployments with well-coordinated data collection and analysis routines are easily facilitated.
In the past several years, SPARC and CLAMPS have participated in numerous field campaigns, which range from mesoscale campaigns that require the rapid deployment and teardown of observing systems to multiweek fixed deployments, providing crucial insights into the behavior of many different atmospheric boundary layer processes while training the next generation of atmospheric scientists. As calls for a nationwide ground-based profiling network continue, SPARC and CLAMPS can play an important role as test beds and prototype nodes for such a network.
Abstract
Land–atmosphere feedbacks are a critical component of the hydrologic cycle. Vertical profiles of boundary layer temperature and moisture, together with information about the land surface, are used to compute land–atmosphere coupling metrics. Ground-based remote sensing platforms, such as the Atmospheric Emitted Radiance Interferometer (AERI), can provide high-temporal-resolution vertical profiles of temperature and moisture. When collocated with soil moisture, surface flux, and surface meteorological observations such as at the Atmospheric Radiation Measurement (ARM) Southern Great Plains site, it is possible to observe both the terrestrial and atmospheric legs of land–atmosphere feedbacks. In this study, we compare a commonly used coupling metric computed from radiosonde-based data with that obtained from the AERI to characterize the accuracy and uncertainty in the metric derived from the two distinct platforms. This approach demonstrates the AERI’s utility where radiosonde observations are absent in time and/or space. Radiosonde- and AERI-based observations of the convective triggering potential and low-level humidity index (collectively referred to as CTP-HIlow) were computed during the 1200 UTC observation time and displayed good agreement during both the 2017 and 2019 warm seasons. Radiosonde- and AERI-derived metrics diagnosed the same atmospheric preconditioning based upon the CTP-HIlow framework a majority of the time. When retrieval uncertainty was considered, even greater agreement was found between radiosonde- and AERI-derived classification. The AERI’s ability to represent this coupling metric well enabled novel exploration of temporal variability within the overnight period in CTP and HIlow. Observations of CTP-HIlow computed within a few hours of 1200 UTC were essentially equivalent; however, with greater differences in time there arose greater differences in CTP and HIlow.
Abstract
Land–atmosphere feedbacks are a critical component of the hydrologic cycle. Vertical profiles of boundary layer temperature and moisture, together with information about the land surface, are used to compute land–atmosphere coupling metrics. Ground-based remote sensing platforms, such as the Atmospheric Emitted Radiance Interferometer (AERI), can provide high-temporal-resolution vertical profiles of temperature and moisture. When collocated with soil moisture, surface flux, and surface meteorological observations such as at the Atmospheric Radiation Measurement (ARM) Southern Great Plains site, it is possible to observe both the terrestrial and atmospheric legs of land–atmosphere feedbacks. In this study, we compare a commonly used coupling metric computed from radiosonde-based data with that obtained from the AERI to characterize the accuracy and uncertainty in the metric derived from the two distinct platforms. This approach demonstrates the AERI’s utility where radiosonde observations are absent in time and/or space. Radiosonde- and AERI-based observations of the convective triggering potential and low-level humidity index (collectively referred to as CTP-HIlow) were computed during the 1200 UTC observation time and displayed good agreement during both the 2017 and 2019 warm seasons. Radiosonde- and AERI-derived metrics diagnosed the same atmospheric preconditioning based upon the CTP-HIlow framework a majority of the time. When retrieval uncertainty was considered, even greater agreement was found between radiosonde- and AERI-derived classification. The AERI’s ability to represent this coupling metric well enabled novel exploration of temporal variability within the overnight period in CTP and HIlow. Observations of CTP-HIlow computed within a few hours of 1200 UTC were essentially equivalent; however, with greater differences in time there arose greater differences in CTP and HIlow.
