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Aronne Merrelli and David D. Turner

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.

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Jeffrey D. Duda and David D. Turner

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 from 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 US 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 over-forecast all objects, but substantially over-forecasts both small objects at low reflectivity thresholds and large objects at high reflectivity thresholds. HRRR tends to either under-forecast objects in the southern and central Plains or has a correct frequency bias there, whereas it over-forecasts objects across the southern and eastern US. 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.

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P. Jonathan Gero and David D. Turner

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.

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Véronique Meunier, David D. Turner, and Pavlos Kollias

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.

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Sergey Y. Matrosov and David D. Turner

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.

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David D. Turner, P. Jonathan Gero, and David C. Tobin
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Joseph Sedlar, Laura D. Riihimaki, Kathleen Lantz, and David D. Turner

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.

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Jianhao Zhang, Paquita Zuidema, David D. Turner, and Maria P. Cadeddu

Abstract

The interactions between equatorial convection and humidity as a function of height, at a range of time scales, remain an important research frontier. The ability of surface-based microwave radiometry to contribute to such research is assessed using retrievals of the vertical structure of atmospheric humidity above the equatorial Indian Ocean, developed as part of the Dynamics of Madden–Julian Oscillation field campaign. The optimally estimated humidity retrievals are based on radiances at five frequencies spanning 20–30 GHz and are constrained by radiometer-derived water vapor paths that compare well to radiosonde values except in highly convective conditions. The moisture retrievals possess a robust 2 degrees of freedom, allowing the atmosphere to be treated as two independent layers. A mean bias of 1 g kg−1 contains a vertical structure that is removed in the assessments. The retrieved moisture profiles are able to capture humidity variability within two layer averages at intraseasonal, synoptic, and daily time scales. The retrieved humidity profiles at hourly scales are qualitatively correct under synoptically suppressed conditions but with an exaggerated vertical bimodality. The retrievals do not match radiosonde profiles within most of the day prior to/after convection. This analysis serves to better delineate applications for radiometers. Radiometers can usefully augment more expensive radiosonde networks for longer-term monitoring given careful cross-instrument calibration. At shorter time scales, a synergism with additional instruments can likely increase the realism of the retrievals.

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Sarah M. Borg, Steven M. Cavallo, and David D. Turner

Abstract

Tropopause polar vortices (TPVs) are long-lived, coherent vortices that are based on the dynamic tropopause and characterized by potential vorticity anomalies. TPVs exist primarily in the Arctic, with potential impacts ranging from surface cyclone generation and Rossby wave interactions to dynamic changes in sea ice. Previous analyses have focused on model output indicating the importance of clear-sky and cloud-top radiative cooling in the maintenance and evolution of TPVs, but no studies have focused on local observations to confirm or deny these results. This study uses cloud and atmospheric state observations from Summit Station, Greenland, combined with single-column experiments using the Rapid Radiative Transfer Model to investigate the effects of clear-sky, ice-only, and all-sky radiative cooling on TPV intensification. The ground-based observing system combined with temperature and humidity profiles from the European Centre for Medium-Range Weather Forecasts’s fifth major global reanalysis dataset, which assimilates the twice-daily soundings launched at Summit, provides novel details of local characteristics of TPVs. Longwave radiative contributions to TPV diabatic intensity changes are analyzed with these resources, starting with a case study focusing on observed cloud properties and associated radiative effects, followed by a composite study used to evaluate observed results alongside previously simulated results. Stronger versus weaker vertical gradients in anomalous clear-sky radiative heating rates, contributing to Ertel potential vorticity changes, are associated with strengthening versus weakening TPVs. Results show that clouds are sometimes influential in the intensification of a TPV, and composite results share many similarities to modeling studies in terms of atmospheric state and radiative structure.

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Timothy J. Wagner, Petra M. Klein, and David D. Turner

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.

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