@article{tomkins_yuter_miller_2024, title={Dual adaptive differential threshold method for automated detection of faint and strong echo features in radar observations of winter storms}, url={https://doi.org/10.5194/egusphere-2023-2888}, DOI={10.5194/egusphere-2023-2888}, abstractNote={Abstract. Radar observations of winter storms often exhibit locally-enhanced linear features in reflectivity, sometimes labeled as snow bands. We have developed a new, objective method for detecting locally-enhanced echo features in radar data from winter storms. In comparison to convective cells in warm season precipitation, these features are usually less distinct from the background echo and often have more fuzzy or feathered edges. This technique identifies both prominent, strong features and more subtle, faint features. A key difference from previous radar reflectivity feature detection algorithms is the combined use of two adaptive differential thresholds, one that decreases with increasing background values and one that increases with increasing background values. The algorithm detects features within a snow rate field that is rescaled from reflectivity and incorporates an under and over estimate to account for uncertainties in the detection. We demonstrate the technique on several examples from the US National Weather Service operational radar network. The feature detection algorithm is highly customizable and can be tuned for a variety of datasets and applications.
                        }, author={Tomkins, Laura Mary and Yuter, Sandra E. and Miller, Matthew A.}, year={2024}, month={Jan} }
 @article{tomkins_yuter_miller_2024, title={Dual adaptive differential threshold method for automated detection of faint and strong echo features in radar observations of winter storms}, volume={17}, ISSN={["1867-8548"]}, url={https://doi.org/10.5194/amt-17-3377-2024}, DOI={10.5194/amt-17-3377-2024}, abstractNote={Abstract. Radar observations of winter storms often exhibit locally enhanced linear features in reflectivity, sometimes labeled as snow bands. We have developed a new, objective method for detecting locally enhanced echo features in radar data from winter storms. In comparison to convective cells in warm season precipitation, these features are usually less distinct from the background echo and often have more fuzzy or feathered edges. This technique identifies both prominent, strong features and more subtle, faint features. A key difference from previous radar reflectivity feature detection algorithms is the combined use of two adaptive differential thresholds, one that decreases with increasing background values and one that increases with increasing background values. The algorithm detects features within a snow rate field rather than reflectivity and incorporates an underestimate and overestimate of feature areas to account for uncertainties in the detection. We demonstrate the technique on several examples from the US National Weather Service operational radar network. The feature detection algorithm is highly customizable and can be tuned for a variety of data sets and applications.}, number={11}, journal={ATMOSPHERIC MEASUREMENT TECHNIQUES}, author={Tomkins, Laura M. and Yuter, Sandra E. and Miller, Matthew A.}, year={2024}, month={Jun}, pages={3377–3399} }
 @article{allen_yuter_miller_tomkins_2024, title={Objective identification of pressure wave events from networks of 1 Hz, high-precision sensors}, volume={17}, ISSN={["1867-8548"]}, url={https://doi.org/10.5194/amt-17-113-2024}, DOI={10.5194/amt-17-113-2024}, abstractNote={Abstract. Mesoscale pressure waves, including atmospheric gravity waves, outflow and frontal passages, and wake lows, are outputs of and can potentially modify clouds and precipitation. The vertical motions associated with these waves can modify the temperature and relative humidity of air parcels and thus yield potentially irreversible changes to the cloud and precipitation content of those parcels. A wavelet-based method for identifying and tracking these types of wave signals in time series data from networks of low-cost, high-precision (0.8 Pa noise floor, 1 Hz recording frequency) pressure sensors is demonstrated. Strong wavelet signals are identified using a wave-period-dependent (i.e., frequency-dependent) threshold, and then those signals are extracted by inverting the wavelet transform. Wave periods between 1 and 120 min were analyzed – a range which could capture acoustic, acoustic-gravity, and gravity wave modes. After extracting the signals from a network of pressure sensors, the cross-correlation function is used to estimate the time difference between the wave passage at each pressure sensor. From those time differences, the wave phase velocity vector is calculated using a least-squares fit. If the fitting error is sufficiently small (thresholds of RMSE < 90 s and NRMSE < 0.1 were used), then a wave event is considered robust and trackable. We present examples of tracked wave events, including a Lamb wave caused by the Hunga Tonga volcanic eruption in January 2020, a gravity wave train, an outflow boundary passage, a frontal passage, and a cold front passage. The data and processing techniques presented here can have research applications in wave climatology and testing associations between waves and atmospheric phenomena.
                    }, number={1}, journal={ATMOSPHERIC MEASUREMENT TECHNIQUES}, author={Allen, Luke R. and Yuter, Sandra E. and Miller, Matthew A. and Tomkins, Laura M.}, year={2024}, month={Jan}, pages={113–134} }
 @article{allen_yuter_miller_tomkins_2024, title={Objectively identified mesoscale surface air pressure waves in the context of winter storm environments and radar reflectivity features: a 3+ year analysis}, url={https://doi.org/10.5194/egusphere-2024-2160}, DOI={10.5194/egusphere-2024-2160}, abstractNote={Abstract. Atmospheric gravity waves (i.e., buoyancy waves) can occur within stable layers when vertical oscillations are triggered by localized heating, flow over terrain, or imbalances in upper level flow. Case studies of winter storms have associated gravity waves with heavier surface snowfall, but the representativeness of those findings for settings without orographic precipitation has not been previously addressed. To detect gravity waves, we deployed networks of high precision pressure sensors from January 2020 to April 2023 in and around Toronto, ON, Canada, and New York, NY, USA, two regions without strong topographic forcing. Pressure wave events were identified when at least 4 sensors in a network detected propagating pressure waves with wave periods ≤ 67 min, wavelengths ≤ 170 km, and amplitudes ≥ 0.45 hPa. We detected 33 pressure wave events across 40 months of data, of which 23 were gravity waves and the rest were frontal passages, outflow boundary passages, or a wake low. Reanalysis model output and operational weather observations provided environmental context for each gravity wave event. Consistent with previous work, most gravity wave events occurred with strong upper-level flow imbalance to the south or west of their location. Of the 79 winter storms with snow that occurred over our 40 months of observations, only 6 had detectable gravity waves. For New York City, the typical offshore cyclone low center track means the metro area is usually in a location where gravity waves are not expected to occur.}, author={Allen, Luke R. and Yuter, Sandra E. and Miller, Matthew A. and Tomkins, Laura M.}, year={2024}, month={Aug} }
 @article{allen_yuter_miller_tomkins_2023, title={Objective identification of pressure wave events from networks of 1-Hz, high-precision sensors}, url={https://doi.org/10.5194/egusphere-2023-1600}, DOI={10.5194/egusphere-2023-1600}, abstractNote={Abstract. Mesoscale pressure waves including atmospheric gravity waves, outflow and frontal passages, and wake lows are outputs of and can potentially modify clouds and precipitation. A wavelet-based method for identifying and tracking these types of wave signals in time series data from networks of low-cost, high-precision (0.8-Pa noise floor, 1-Hz recording frequency) pressure sensors is demonstrated. Strong wavelet signals are identified using a wave period-dependent (i.e., frequency-dependent) threshold, then those signals are extracted by inverting the wavelet transform. Wave periods between 1 minute and 120 minutes were analyzed, a range which would include several types of mesoscale disturbances in the troposphere. After extracting the signals from a network of pressure sensors, the cross-correlation function is used to estimate the time difference between the wave passage at each pressure sensor. From those time differences, the wave phase velocity vector is calculated using a least-squares fit. If the fitting error is sufficiently small (thresholds of RMSE < 90 s and NRMSE < 0.1 were used), then a wave event is considered robust and trackable.
                        }, author={Allen, Luke Robert and Yuter, Sandra E. and Miller, Matthew Allen and Tomkins, Laura M.}, year={2023}, month={Sep} }
 @article{miller_yuter_hoban_tomkins_colle_2022, title={Detecting wave features in Doppler radial velocity radar observations}, volume={15}, ISSN={["1867-8548"]}, url={https://doi.org/10.5194/amt-15-1689-2022}, DOI={10.5194/amt-15-1689-2022}, abstractNote={Abstract. Mesoscale, wave-like perturbations in horizontal air motions in the troposphere (velocity waves) are associated with vertical velocity, temperature, and pressure perturbations that can initiate or enhance precipitation within clouds. The ability to detect velocity waves from horizontal wind information is an important tool for atmospheric research and weather forecasting. This paper presents a method to routinely detect velocity waves using Doppler radial velocity data from a scanning weather radar. The method utilizes the difference field between consecutive position plan indicator (PPI) scans at a given elevation angle. Using the difference between fields a few minutes apart highlights small-scale perturbations associated with waves because the larger-scale wind field changes more slowly. Image filtering retains larger contiguous velocity bands and discards noise. Wave detection scales are limited by the size of the temporal difference relative to the wave motion and the radar resolution volume size.
                    }, number={6}, journal={ATMOSPHERIC MEASUREMENT TECHNIQUES}, publisher={Copernicus GmbH}, author={Miller, Matthew A. and Yuter, Sandra E. and Hoban, Nicole P. and Tomkins, Laura M. and Colle, Brian A.}, year={2022}, month={Mar}, pages={1689–1702} }
 @article{tomkins_yuter_miller_allen_2022, title={Image Muting of Mixed Precipitation to Improve Identification of Regions of Heavy Snow in Radar Data}, url={https://doi.org/10.5194/amt-2022-160}, DOI={10.5194/amt-2022-160}, abstractNote={Abstract. In winter storms, enhanced radar reflectivity is often associated with heavy snow; however, some higher reflectivities are the result of melting and mixed precipitation. The correlation coefficient (a dual-polarization radar variable) can identify regions of and mixed precipitation, but this information is usually presented separately from reflectivity. Especially under time pressure, even experienced meteorologists can mistake regions of mixed precipitation for heavy snow because of the high cognitive load associated with comparing data in two fields while simultaneously attempting to discount a portion of the high reflectivity values. We developed an image muting method for regional radar maps that visually deemphasizes the high reflectivity values associated with mixed precipitation. These image muted depictions of winter storm precipitation structures are useful for monitoring real-time weather conditions and for analyzing storms.
                        }, author={Tomkins, Laura M. and Yuter, Sandra E. and Miller, Matthew A. and Allen, Luke R.}, year={2022}, month={May} }
 @article{tomkins_yuter_miller_allen_2022, title={Image muting of mixed precipitation to improve identification of regions of heavy snow in radar data}, volume={15}, ISSN={["1867-8548"]}, url={https://doi.org/10.5194/amt-15-5515-2022}, DOI={10.5194/amt-15-5515-2022}, abstractNote={Abstract. In winter storms, enhanced radar reflectivity is often associated with heavy snow. However, some higher reflectivities are the result of mixed precipitation including melting snow. The correlation coefficient (a dual-polarization radar variable) can identify regions of mixed precipitation, but this information is usually presented separately from reflectivity. Especially under time pressure, radar data users can mistake regions of mixed precipitation for heavy snow because of the high cognitive load associated with comparing data in two fields while simultaneously attempting to discount a portion of the high reflectivity values. We developed an image muting method for regional radar maps that visually de-emphasizes the high reflectivity values associated with mixed precipitation. These image muted depictions of winter storm precipitation structures are useful for analyzing regions of heavy snow and monitoring real-time weather conditions.}, number={18}, journal={ATMOSPHERIC MEASUREMENT TECHNIQUES}, author={Tomkins, Laura M. and Yuter, Sandra E. and Miller, Matthew A. and Allen, Luke R.}, year={2022}, month={Sep}, pages={5515–5525} }
 @article{hueholt_yuter_miller_2022, title={Revisiting Diagrams of Ice Growth Environments}, volume={103}, ISSN={["1520-0477"]}, DOI={10.1175/BAMS-D-21-0271.1}, abstractNote={Abstract
Ice habit diagrams published prior to 2009—and many since—do not accurately describe in situ observations of ice shapes as a function of temperature and moisture. Laboratory studies and analysis of field observations by Bailey and Hallett in a series of papers in 2002, 2004, and 2009 corrected several errors from earlier studies, but their work has not been widely disseminated. We present a new, simplified diagram based on Bailey and Hallett’s work that focuses on several ice growth forms arising from the underlying surface processes by which mass is added to a crystal: tabular, columnar, branched, side branched, two types of polycrystalline forms, and a multiple growth regime at low ice supersaturations. To aid interpretation for a variety of applications, versions of the ice growth diagram are presented in terms of relative humidity with respect to water as well as the traditional formats of relative humidity with respect to ice and vapor density excess. These diagrams are intended to be understandable and useful in classroom settings at the sophomore undergraduate level and above. The myriad shapes of pristine snow crystals can be described as the result of either a single growth form or a sequence of growth forms. Overlays of data from upper-air soundings on the ice growth diagrams aid interpretation of expected physical properties and processes in conditions of ice growth.}, number={11}, journal={BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY}, author={Hueholt, Daniel M. and Yuter, Sandra E. and Miller, Matthew A.}, year={2022}, month={Nov}, pages={E2584–E2603} }
 @article{miller_yuter_hoban_tomkins_colle_2021, title={Detecting Wave Features in Doppler Radial Velocity Radar Observations}, volume={10}, url={https://doi.org/10.5194/amt-2021-256}, DOI={10.5194/amt-2021-256}, abstractNote={Abstract. Mesoscale, wave-like perturbations in horizontal air motions in the troposphere (velocity waves) are associated with vertical velocity, temperature, and pressure perturbations that can initiate or enhance precipitation within clouds. The ability to detect velocity waves from horizontal wind information is an important tool for atmospheric research and weather forecasting. This paper presents a method to routinely detect velocity waves using Doppler radial velocity data from a scanning weather radar. The method utilizes the difference field between consecutive PPI scans at a given elevation angle. Using the difference between fields a few minutes apart highlights small scale perturbations associated with waves because the larger scale wind field changes more slowly. Image filtering retains larger contiguous velocity bands and discards noise. Wave detection scales are limited by the size of the temporal difference relative to the wave motion and the radar resolution volume size.
                        }, publisher={Copernicus GmbH}, author={Miller, Matthew A. and Yuter, Sandra E. and Hoban, Nicole P. and Tomkins, Laura M. and Colle, Brian A.}, year={2021}, month={Oct} }
 @article{patel_yuter_miller_rhodes_bain_peele_2021, title={The Diurnal Cycle of Winter Season Temperature Errors in the Operational Global Forecast System (GFS)}, volume={48}, ISSN={["1944-8007"]}, url={https://doi.org/10.1029/2021GL095101}, DOI={10.1029/2021GL095101}, abstractNote={AbstractForecasts from NOAA's Global Forecast System (GFS) and the High‐Resolution Rapid Refresh (HRRR) weather models are matched to surface observations for the winter season of November 2019 to March 2020 at 210 airports across the United States. The 2‐m temperature errors, conditioned on observed weather conditions such as cloud cover amount and wind speed, are used to determine the nature of systematic model biases. We observe a strong diurnal cycle in 2‐m temperature errors in the GFS in conditions with 50% and 25% sky cover, with a 1°C warm bias at night and a 2°C cold bias during the day. The HRRR, which uses a different set of physical parameterizations, does not have a clear diurnal cycle in errors under the same conditions. These results highlight the utility of weather‐conditional comparisons across the diurnal cycle to diagnose sources of model weaknesses and to target model improvements.}, number={20}, journal={GEOPHYSICAL RESEARCH LETTERS}, publisher={American Geophysical Union (AGU)}, author={Patel, Ronak N. and Yuter, Sandra E. and Miller, Matthew A. and Rhodes, Spencer R. and Bain, Lily and Peele, Toby W.}, year={2021}, month={Oct} }
 @article{perry_yuter_matthews_wagnon_khadka_aryal_shrestha_tait_miller_o'neill_et al._2021, title={Direct observations of a Mt Everest snowstorm from the world's highestsurface-basedradar observations}, volume={76}, ISSN={["1477-8696"]}, DOI={10.1002/wea.3854}, abstractNote={L. Baker Perry1 , Sandra E. Yuter2, Tom Matthews3 , Patrick Wagnon4, Arbindra Khadka5,6 , Deepak Aryal5, Dibas Shrestha5, Alex Tait7, Matthew A. Miller2, Alex O’Neill1, Spencer R. Rhodes2, Inka Koch6, Tenzing G. Sherpa8, Subash Tuladhar9, Saraju K. Baidya9, Sandra Elvin7, Aurora C. Elmore7, Ananta Gajurel5 and Paul A. Mayewski10 1Appalachian State University, Boone, North Carolina, USA 2North Carolina State University, Raleigh, North Carolina, USA 3Loughborough University, Loughborough, UK 4University Grenoble Alpes, CNRS, IRD, IGE, Grenoble, France 5Tribhuvan University, Kirtipur, Nepal 6ICIMOD, Patan, Nepal 7National Geographic Society, Washington, USA 8Khumbu Climbing Center, Khumjung, Nepal 9Department of Hydrology and Meteorology, Kathmandu, Nepal 10University of Maine, Orono, Maine, USA}, number={2}, journal={WEATHER}, publisher={Wiley}, author={Perry, L. Baker and Yuter, Sandra E. and Matthews, Tom and Wagnon, Patrick and Khadka, Arbindra and Aryal, Deepak and Shrestha, Dibas and Tait, Alex and Miller, Matthew A. and O'Neill, Alex and et al.}, year={2021}, month={Feb}, pages={57–59} }
 @article{yuter_hader_miller_mechem_2018, title={Abrupt cloud clearing of marine stratocumulus in the subtropical southeast Atlantic}, volume={361}, ISSN={["1095-9203"]}, DOI={10.1126/science.aar5836}, abstractNote={A shrinking marine refrigerator
          
            Low subtropical marine clouds scatter solar radiation back to space and thereby cool the climate system. Most work on understanding changes in the coverage of these types of clouds has focused on the effects of sea surface temperatures or on aerosols. Yuter
            et al.
            show that dynamic effects due to atmospheric gravity waves are responsible for the rapid clearing of large areas of these clouds. This phenomenon also has implications for marine ecology and biogeochemistry.
          
          
            Science
            , this issue p.
            697
          }, number={6403}, journal={SCIENCE}, publisher={American Association for the Advancement of Science (AAAS)}, author={Yuter, Sandra E. and Hader, John D. and Miller, Matthew A. and Mechem, David B.}, year={2018}, month={Aug}, pages={697-+} }
 @article{mechem_wittman_miller_yuter_de szoeke_2018, title={Joint Synoptic and Cloud Variability over the Northeast Atlantic near the Azores}, volume={57}, ISSN={["1558-8432"]}, DOI={10.1175/jamc-d-17-0211.1}, abstractNote={AbstractMarine boundary layer clouds are modified by processes at different spatial and temporal scales. To isolate the processes governing aerosol–cloud–precipitation interactions, multiday synoptic variability of the environment must be accounted for. Information on the location of low clouds relative to the ridge–trough pattern gives insight into how cloud properties vary as a function of environmental subsidence and stability. The technique of self-organizing maps (SOMs) is employed to objectively classify the 500-hPa geopotential height patterns for 33 years of reanalysis fields (ERA-Interim) into pretrough, trough, posttrough, ridge, and zonal-flow categories. The SOM technique is applied to a region of prevalent marine low cloudiness over the eastern North Atlantic Ocean that is centered on the Azores island chain, the location of a long-term U.S. Department of Energy observation site. The Azores consistently lie in an area of substantial variability in synoptic configuration, thermodynamic environment, and cloud properties. The SOM method was run in two ways to emphasize multiday and seasonal variability separately. Over and near the Azores, there is an east-to-west sloshing back and forth of the western edge of marine low clouds associated with different synoptic states. The different synoptic states also exhibit substantial north–south variability in the position of high clouds. For any given month of the year, there is large year-to-year variability in the occurrence of different synoptic states. Hence, estimating the climatological behavior of clouds from short-term field campaigns has large uncertainties. This SOM approach is a robust method that is broadly applicable to characterizing synoptic regimes for any location.}, number={6}, journal={JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY}, publisher={American Meteorological Society}, author={Mechem, David B. and Wittman, Carly S. and Miller, Matthew A. and Yuter, Sandra E. and De Szoeke, Simon P.}, year={2018}, month={Jun}, pages={1273–1290} }
 @article{wilbanks_yuter_szoeke_brewer_miller_hall_burleyson_2015, title={Near-Surface Density Currents Observed in the Southeast Pacific Stratocumulus-Topped Marine Boundary Layer*}, volume={143}, ISSN={["1520-0493"]}, DOI={10.1175/mwr-d-14-00359.1}, abstractNote={Abstract
               Density currents (i.e., cold pools or outflows) beneath marine stratocumulus clouds are characterized using 30 days of ship-based observations obtained during the 2008 Variability of American Monsoon Systems (VAMOS) Ocean–Cloud–Atmosphere–Land Study Regional Experiment (VOCALS-REx) in the southeast Pacific. An air density increase criterion applied to the Improved Meteorological (IMET) sensor data identified 71 density current front, core (peak density), and tail (dissipating) zones. The similarity in speeds of the mean density current propagation speed (1.8 m s−1) and the mean cloud-level advection relative to the surface layer wind (1.9 m s−1) allowed drizzle cells to deposit elongated density currents in their wakes. Scanning Doppler lidar captured prefrontal updrafts with a mean intensity of 0.91 m s−1 and an average vertical extent of 800 m. Updrafts were often surmounted by low-lying shelf clouds not connected to the overlying stratocumulus cloud. The observed density currents were 5–10 times thinner and weaker than typical continental thunderstorm cold pools. Nearly 90% of density currents were identified when C-band radar estimated areal average rain rates exceeded 1 mm day−1 over a 30-km diameter. Rather than peaking when rain rates were highest overnight, density current occurrence peaks between 0600 and 0800 local solar time when enhanced local drizzle co-occurred with shallow subcloud dry and stable layers. The dry layers may have contributed to density current formation by enhancing subcloud evaporation of drizzle. Density currents preferentially occurred in a large region of predominantly open cells but also occurred in regions of closed cells.}, number={9}, journal={MONTHLY WEATHER REVIEW}, publisher={American Meteorological Society}, author={Wilbanks, Matt C. and Yuter, Sandra E. and Szoeke, Simon P. and Brewer, W. Alan and Miller, Matthew A. and Hall, Andrew M. and Burleyson, Casey D.}, year={2015}, month={Sep}, pages={3532–3555} }
 @article{wood_wyant_bretherton_remillard_kollias_fletcher_stemmler_szoeke_yuter_miller_et al._2015, title={CLOUDS, AEROSOLS, AND PRECIPITATION IN THE MARINE BOUNDARY LAYER An ARM Mobile Facility Deployment}, volume={96}, ISSN={["1520-0477"]}, DOI={10.1175/bams-d-13-00180.1}, abstractNote={Abstract
               The Clouds, Aerosol, and Precipitation in the Marine Boundary Layer (CAP-MBL) deployment at Graciosa Island in the Azores generated a 21-month (April 2009–December 2010) comprehensive dataset documenting clouds, aerosols, and precipitation using the Atmospheric Radiation Measurement Program (ARM) Mobile Facility (AMF). The scientific aim of the deployment is to gain improved understanding of the interactions of clouds, aerosols, and precipitation in the marine boundary layer.
               Graciosa Island straddles the boundary between the subtropics and midlatitudes in the northeast Atlantic Ocean and consequently experiences a great diversity of meteorological and cloudiness conditions. Low clouds are the dominant cloud type, with stratocumulus and cumulus occurring regularly. Approximately half of all clouds contained precipitation detectable as radar echoes below the cloud base. Radar and satellite observations show that clouds with tops from 1 to 11 km contribute more or less equally to surface-measured precipitation at Graciosa. A wide range of aerosol conditions was sampled during the deployment consistent with the diversity of sources as indicated by back-trajectory analysis. Preliminary findings suggest important two-way interactions between aerosols and clouds at Graciosa, with aerosols affecting light precipitation and cloud radiative properties while being controlled in part by precipitation scavenging.
               The data from Graciosa are being compared with short-range forecasts made with a variety of models. A pilot analysis with two climate and two weather forecast models shows that they reproduce the observed time-varying vertical structure of lower-tropospheric cloud fairly well but the cloud-nucleating aerosol concentrations less well. The Graciosa site has been chosen to be a permanent fixed ARM site that became operational in October 2013.}, number={3}, journal={BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY}, author={Wood, Robert and Wyant, Matthew and Bretherton, Christopher S. and Remillard, Jasmine and Kollias, Pavlos and Fletcher, Jennifer and Stemmler, Jayson and Szoeke, Simone and Yuter, Sandra and Miller, Matthew and et al.}, year={2015}, month={Mar}, pages={419–439} }
 @article{yuter_miller_parker_markowski_richardson_brooks_straka_2013, title={Comment on "Why do tornados and hailstorms rest on weekends?" by D. Rosenfeld and T. Bell}, volume={118}, ISSN={["2169-8996"]}, DOI={10.1002/jgrd.50526}, abstractNote={[1] The paper “Why do tornados and hailstorms rest on weekends?” [Rosenfeld and Bell, 2011] (hereinafter RB2011) contains key misunderstandings of US spring and summer tornadoes, supercell storms, and their environments. In this comment, we show that (1) there is not a robust weekly cycle or midweek maximum in tornado occurrence or tornado days, (2) RB2011’s physical explanation for how increased aerosol concentrations would cause increased frequency and severity of tornadoes and hail in supercells is inconsistent with actual supercell storm structures and their environments, and (3) RB2011’s method of averaging aerosol and tornado data from 100 W eastward conflates an aerosol weekly cycle in one geographic location with tornado occurrence in another.}, number={13}, journal={JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES}, publisher={American Geophysical Union (AGU)}, author={Yuter, Sandra E. and Miller, Matthew A. and Parker, Matthew D. and Markowski, Paul M. and Richardson, Yvette and Brooks, Harold and Straka, Jerry M.}, year={2013}, month={Jul}, pages={7332–7338} }
 @article{miller_yuter_2013, title={Detection and characterization of heavy drizzle cells within subtropical marine stratocumulus using AMSR-E 89-GHz passive microwave measurements}, volume={6}, ISSN={["1867-8548"]}, DOI={10.5194/amt-6-1-2013}, abstractNote={Abstract. This empirical study demonstrates the feasibility of using 89-GHz Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) passive microwave brightness temperature data to detect heavily drizzling cells within subtropical marine stratocumulus. For the purpose of this paper, we define heavily drizzling cells as areas ≥ 6 km × 4 km with C-band Z > 0 dBZ; equivalent to > 0.084 mm h−1. A binary heavy drizzle product is described that can be used to determine areal and feature statistics of drizzle cells within the major marine stratocumulus regions. Current satellite liquid water path (LWP) and cloud radar products capable of detecting drizzle are either lacking in resolution (AMSR-E LWP), diurnal coverage (MODIS LWP), or spatial coverage (CloudSat). The AMSR-E 89-GHz data set at 6 km × 4 km spatial resolution is sufficient for resolving individual heavily drizzling cells. Radiant emission at 89 GHz by liquid-water cloud and precipitation particles from drizzling cells in marine stratocumulus regions yields local maxima in brightness temperature against an otherwise cloud-free background brightness temperature. The background brightness temperature is primarily constrained by column-integrated water vapor for moderate sea surface temperatures. Clouds containing ice are screened out. Once heavily drizzling pixels are identified, connected pixels are grouped into discrete drizzle cell features. The identified drizzle cells are used in turn to determine several spatial statistics for each satellite scene, including drizzle cell number and size distribution. The identification of heavily drizzling cells within marine stratocumulus regions with satellite data facilitates analysis of seasonal and regional drizzle cell occurrence and the interrelation between drizzle and changes in cloud fraction.
                    }, number={1}, journal={ATMOSPHERIC MEASUREMENT TECHNIQUES}, author={Miller, M. A. and Yuter, S. E.}, year={2013}, pages={1–13} }
 @article{miller_yuter_2012, title={Detection and characterization of drizzle cells within marine stratocumulus using AMSR-E 89 GHz passive microwave measurements}, volume={7}, DOI={10.5194/amtd-5-4571-2012}, abstractNote={Abstract. This empirical study demonstrates the feasibility of using 89 GHz Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) passive microwave brightness temperature data to detect heavily drizzling cells within marine stratocumulus. A binary heavy drizzle product is described that can be used to determine areal and feature statistics of drizzle cells within the major marine stratocumulus regions. Current satellite liquid water path (LWP) and cloud radar products capable of detecting drizzle are either lacking in resolution (AMSR-E LWP), diurnal coverage (MODIS LWP), or spatial coverage (CloudSat). The AMSR-E 89 GHz data set at 6 × 4 km spatial resolution is sufficient for resolving individual heavily drizzling cells. Radiant emission at 89 GHz by liquid-water cloud and precipitation particles from drizzling cells in marine stratocumulus regions yields local maxima in brightness temperature against an otherwise cloud-free background brightness temperature. The background brightness temperature is primarily constrained by column-integrated water vapor and sea surface temperature. Clouds containing ice are screened out. Once heavily drizzling pixels are identified, connected pixels are grouped into discrete drizzle cell features. The identified drizzle cells are used in turn to determine several spatial statistics for each satellite scene, including drizzle cell number and size distribution. The identification of heavily drizzling cells within marine stratocumulus regions with satellite data facilitates analysis of seasonal and regional drizzle cell occurrence and the interrelation between drizzle and changes in cloud fraction.
                        }, publisher={Copernicus GmbH}, author={Miller, M. A. and Yuter, S. E.}, year={2012}, month={Jul} }
 @article{whelan_galbraith_weller_farrar_grant_grados_szoeke_moffat_zappa_yang_et al._2009, title={Stratus 9/VOCALS ninth setting of the Stratus Ocean Reference Station and VOCALS Regional Experiment}, DOI={10.1575/1912/2841}, abstractNote={Abstract : The Woods Hole Oceanographic Institution (WHOI) Hawaii Ocean Timeseries (HOT) Site (WHOTS), 100 km north of Oahu, Hawaii, is intended to provide long-term, high-quality air-sea fluxes as a part of the NOAA Climate Observation Program. The WHOTS mooring also serves as a coordinated part of the HOT program, contributing to the goals of observing heat, fresh water and chemical fluxes at a site representative of the oligotrophic North Pacific Ocean. The approach is to maintain a surface mooring outfitted for meteorological and oceanographic measurements by successive mooring turnarounds. These observations will be used to investigate air-sea interaction processes related to climate variability. This report documents recovery of the WHOTS-4 mooring and deployment of the fifth mooring (WHOTS-5). Both moorings used Surlyn foam buoys as the surface element and were outfitted with two Air-Sea Interaction Meteorology (ASIMET) systems. Each ASIMET system measures, records, and transmits via Argos satellite the surface meteorological variables necessary to compute air-sea fluxes of heat, moisture and momentum. The upper 155 m of the moorings were outfitted with oceanographic sensors for the measurement of temperature, conductivity and velocity. A pCO2 system was installed on the WHOTS-5 buoy. The WHOTS mooring turnaround was done between 3 and 11 June 2008. Operations began with deployment of the WHOTS-5 mooring. This was followed by meteorological intercomparisons and CTDs at the WHOTS-4 site. A period of calmer weather was taken advantage of to recover WHOTS-4 on 6 June 2008. The Kilo Moana then returned to the WHOTS-5 mooring for CTD operations and meteorological intercomparisons. This report describes these cruise operations, as well as some of the in-port operations and pre-cruise buoy preparations.}, publisher={Woods Hole Oceanographic Institution}, author={Whelan, Sean P. and Galbraith, Nancy and Weller, Robert and Farrar, John T. and Grant, David and Grados, Carmen and Szoeke, Simon P. and Moffat, Carlos and Zappa, Chris and Yang, Mingxi and et al.}, year={2009} }
 @article{miller_yuter_2008, title={Lack of correlation between chlorophyll a and cloud droplet effective radius in shallow marine clouds}, volume={35}, ISSN={["1944-8007"]}, DOI={10.1029/2008gl034354}, abstractNote={The hypothesis that areas of high oceanic productivity affect the physical properties of shallow marine clouds via the production of secondary organic aerosols is evaluated using satellite data. The correlation between chlorophyll a concentrations, an indication of oceanic productivity, and low cloud droplet liquid phase effective radius (Re) is examined for several ocean regions and time periods. While a strong correlation between chlorophyll a and low Re can occur for specific periods in some locations, the correlation is not reproducible in other regions and time periods. The intermittent correlation between high concentrations of chlorophyll a and low Re is a coincidence and is not representative of a dominant, monotonic, causative relation between secondary organic aerosols and marine shallow cloud properties.}, number={13}, journal={GEOPHYSICAL RESEARCH LETTERS}, publisher={American Geophysical Union (AGU)}, author={Miller, Matthew A. and Yuter, Sandra E.}, year={2008}, month={Jul} }