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Vanessa Caicedo, Ruben Delgado, Ricardo Sakai, Travis Knepp, David Williams, Kevin Cavender, Barry Lefer, and James Szykman

Abstract

A unique automated planetary boundary layer (PBL) retrieval algorithm is proposed as a common cross-platform method for use with commercially available ceilometers for implementation under the redesigned U.S. Environmental Protection Agency Photochemical Assessment Monitoring Stations program. This algorithm addresses instrument signal quality and screens for precipitation and cloud layers before the implementation of the retrieval method using the Haar wavelet covariance transform. Layer attribution for the PBL height is supported with the use of continuation and time-tracking parameters, and uncertainties are calculated for individual PBL height retrievals. Commercial ceilometer retrievals are tested against radiosonde PBL height and cloud-base height during morning and late-afternoon transition times, critical to air quality model prediction and when retrieval algorithms struggle to identify PBL heights. A total of 58 radiosonde profiles were used, and retrievals for nocturnal stable layers, residual layers, and mixing layers were assessed. Overall good agreement was found for all comparisons, with one system showing limitations for the cases of nighttime surface stable layers and daytime mixing layer. It is recommended that nighttime shallow stable-layer retrievals be performed with a recommended minimum height or with additional verification. Retrievals of residual-layer heights and mixing-layer comparisons revealed overall good correlations with radiosonde heights (square of correlation coefficients r 2 ranging from 0.89 to 0.96, and bias ranging from approximately −131 to +63 m for the residual layer and r 2 from 0.88 to 0.97 and bias from −119 to +101 m for the mixing layer).

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David G. Murcray, James N. Brooks, Frank H. Murcray, and Walter J. Williams

Abstract

A balloon flight made 4 February 1970 with an infrared spectral radiometer system scanning from 10 to 13 μm ascended through an extensive cirrus cloud. This allowed measurements to be made of the spectral radiance of the cloud both underneath and in the cloud as the balloon ascended. The spectra obtained are presented and the data were used to determine the emissivity of the cloud at 11 μm. The value ∈=0.38 is in reasonable agreement with values found by Kuhn and Weickmann.

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Leah R. Williams, Jeffrey A. Manion, David M. Golden, and Margaret A. Tolbert

Abstract

Increasing evidence from field, modeling, and laboratory studies suggests that heterogeneous reactions on stratospheric sulfate aerosol particles may contribute to global ozone depiction. Using a Knudsen cell reactor technique, the authors have studied the uptake, reactivity, and solubility of several trace atmospheric species on cold sulfuric acid surfaces representative of stratospheric aerosol particles. The results suggest that the heterogeneous conversion of N2O5 to HNO3 is fast enough to significantly affect the partitioning of nitrogen species in the global stratosphere and thus contribute to global ozone depletion. The hydrolysis of ClONO2 is slower and unlikely to be important under normal conditions at midlatitudes. The solubilities of HCl and HNO3 in sulfuric acid down to 200 K were found to be quite low. For HCl, this means that little HCl is available for reaction on the surfaces of stratospheric sulfate aerosol particles. The low solubility of HNO3 means that this product of heterogeneous reactions will enter the gas phase, and the denitrification observed in polar regions is unlikely to occur in the global stratosphere.

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Anthony C. Riddle, Leslie M. Hartten, David A. Carter, Paul E. Johnston, and Christopher R. Williams

Abstract

One limiting factor in atmospheric radar observations is the inability to distinguish the often weak atmospheric signals from fluctuations of the noise. This study presents a minimum threshold of usability, SNRmin, for signal-to-noise ratios obtained from wind profiling radars. The basic form arises from theoretical considerations of radar noise; the final form includes empirical modifications based on radar observations. While SNRmin was originally developed using data from the 50-MHz profiler at Poker Flat, Alaska, it works well with data collected from a wide range of locations, frequencies, and parameter settings. It provides an objective criterion to accept or reject individual spectra, can be quickly applied to a large quantity of data, and has a false-alarm rate of approximately 0.1%. While this threshold’s form depends on the methods used to calculate SNR and spectral moments, variations of the threshold could be developed for use with data processed by other methods.

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Kenneth S. Gage, Christopher R. Williams, Wallace L. Clark, Paul E. Johnston, and David A. Carter

Abstract

Doppler radar profilers are widely used for routine measurement of wind, especially in the lower troposphere. The same profilers with minor modifications are useful tools for precipitation research. Specifically, the profilers are now increasingly being used to explore the structure of precipitating cloud systems and to provide calibration and validation of other instruments used in precipitation research, including scanning radars and active and passive satellite-borne sensors. A vertically directed profiler is capable of resolving the vertical structure of precipitating cloud systems that pass overhead. Standard profiler measurements include reflectivity, reflectivity-weighted Doppler velocity, and spectral width. This paper presents profiler observations of precipitating cloud systems observed during Tropical Rainfall Measuring Mission (TRMM) Ground Validation field campaigns. The observations show similarities and differences between convective systems observed in Florida; Brazil; and Kwajalein, Republic of the Marshall Islands. In addition, it is shown how a profiler can be calibrated using a collocated Joss–Waldvogel disdrometer, how the profiler can then be used to calibrate a scanning radar, and how the profiler may be used to retrieve drop size distributions.

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David G. Lerach, Steven A. Rutledge, Christopher R. Williams, and Robert Cifelli

Abstract

This study describes the vertical structure of mesoscale convective systems (MCSs) that characterized the 2004 North American monsoon utilizing observations from a 2875-MHz (S band) profiler and a dual-polarimetric scanning Doppler radar. Both instrument platforms operated nearly continuously during the North American Monsoon Experiment (NAME). A technique was developed to identify dominant hydrometeor type using S-band (profiler) reflectivity along with temperature. The simplified hydrometeor identification (HID) algorithm matched polarimetric scanning radar fuzzy logic–based HID results quite well. However, the simplified algorithm lacked the ability to identify ice hydrometeors below the melting layer and on occasion, underestimated the vertical extent of graupel because of a profiler reflectivity bias.

Three of the strongest NAME convective rainfall events recorded by the profiler are assessed in this study. Stratiform rain exhibited a reflectivity bright band and strong Doppler velocity gradient within the melting layer. Convective rainfall exhibited high reflectivity and Doppler velocities exceeding 3 (−10) m s−1 in updrafts (downdrafts). Low-density graupel persisted above the melting layer, often extending to 10 km, with high-density graupel observed near 0°C. Doppler velocity signatures suggested that updrafts and downdrafts were often tilted, though estimating the degree of tilt would have required a more three-dimensional view of the passing storms. Cumulative frequency distributions (CFDs) of reflectivity were created for stratiform and convective rainfall and were found to be similar to results from other tropical locations.

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John R. Mecikalski, John K. Williams, Christopher P. Jewett, David Ahijevych, Anita LeRoy, and John R. Walker

Abstract

The Geostationary Operational Environmental Satellite (GOES)-R convective initiation (CI) algorithm predicts CI in real time over the next 0–60 min. While GOES-R CI has been very successful in tracking nascent clouds and obtaining cloud-top growth and height characteristics relevant to CI in an object-tracking framework, its performance has been hindered by elevated false-alarm rates, and it has not optimally combined satellite observations with other valuable data sources. Presented here are two statistical learning approaches that incorporate numerical weather prediction (NWP) input within the established GOES-R CI framework to produce probabilistic forecasts: logistic regression (LR) and an artificial-intelligence approach known as random forest (RF). Both of these techniques are used to build models that are based on an extensive database of CI events and nonevents and are evaluated via cross validation and on independent case studies. With the proper choice of probability thresholds, both the LR and RF techniques incorporating NWP data produce substantially fewer false alarms than when only GOES data are used. The NWP information identifies environmental conditions (as favorable or unfavorable) for the development of convective storms and improves the skill of the CI nowcasts that operate on GOES-based cloud objects, as compared with when the satellite IR fields are used alone. The LR procedure performs slightly better overall when 14 skill measures are used to quantify the results and notably better on independent case study days.

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James A. Carton, Stuart A. Cunningham, Eleanor Frajka-Williams, Young-Oh Kwon, David P. Marshall, and Rym Msadek
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Kenneth S. Gage, Christopher R. Williams, Paul E. Johnston, Warner L. Ecklund, Robert Cifelli, Ali Tokay, and David A. Carter

Abstract

The National Oceanic and Atmospheric Administration’s Aeronomy Laboratory has modified a standard 915-MHz profiler for use as a precipitation profiler in support of Tropical Rainfall Measuring Mission ground validation field campaigns. This profiler was modified to look vertically with a fixed dish antenna. It was operated during the Texas and Florida Underflights Experiment (TEFLUN) A in south Texas in April–May 1998 and during TEFLUN B in central Florida in August–September 1998. Collocated with the profiler was a Distromet, Inc., RD-69 Joss–Waldvogel disdrometer in Texas and Florida and a two-dimensional video disdrometer in Florida. The disdrometers are used to calibrate the profiler at the lowest range gates. At higher altitudes, the calibrated profiler reflectivities are compared with observations made by scanning radars such as the Weather Surveillance Radar-1988 Doppler in Dickinson, Texas, and Melbourne, Florida, and the S-band Doppler dual-polarization radar in Florida. The authors conclude that it is possible to use profilers as transfer standards to calibrate and to validate the reflectivities measured by the scanning radars.

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David John Gagne II, Amy McGovern, Sue Ellen Haupt, Ryan A. Sobash, John K. Williams, and Ming Xue

Abstract

Forecasting severe hail accurately requires predicting how well atmospheric conditions support the development of thunderstorms, the growth of large hail, and the minimal loss of hail mass to melting before reaching the surface. Existing hail forecasting techniques incorporate information about these processes from proximity soundings and numerical weather prediction models, but they make many simplifying assumptions, are sensitive to differences in numerical model configuration, and are often not calibrated to observations. In this paper a storm-based probabilistic machine learning hail forecasting method is developed to overcome the deficiencies of existing methods. An object identification and tracking algorithm locates potential hailstorms in convection-allowing model output and gridded radar data. Forecast storms are matched with observed storms to determine hail occurrence and the parameters of the radar-estimated hail size distribution. The database of forecast storms contains information about storm properties and the conditions of the prestorm environment. Machine learning models are used to synthesize that information to predict the probability of a storm producing hail and the radar-estimated hail size distribution parameters for each forecast storm. Forecasts from the machine learning models are produced using two convection-allowing ensemble systems and the results are compared to other hail forecasting methods. The machine learning forecasts have a higher critical success index (CSI) at most probability thresholds and greater reliability for predicting both severe and significant hail.

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