Articles | Volume 17, issue 1
https://doi.org/10.5194/amt-17-113-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-113-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Jan 2024
Research article |
| 10 Jan 2024
| 10 Jan 2024
Objective identification of pressure wave events from networks of 1 Hz, high-precision sensors
Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, 27695, USA
Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, 27695, USA
Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC, 27695, USA
Matthew A. Miller
Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC, 27695, USA
Laura M. Tomkins
Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, 27695, USA
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We analyzed in-cloud characteristics using in situ measurements from 42 research flights across two field campaigns into non-orographic, non-lake-effect winter storms. Much of the storm volume contains weak vertical motions (a few centimeters per second), and most updrafts ≥ 0.5 m s-1 are small (< 1 km). Within 2 km of cloud radar echo top, stronger vertical motions and conditions for ice particle growth are more common. Overturning air motions near cloud top appear important for the production of snow particles.
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Atmospheric gravity waves (GWs) are air oscillations in which buoyancy is the restoring force, and they may enhance precipitation under certain conditions. We used 3+ seasons of pressure data to identify GWs with wavelengths ≤ 170 km in the Toronto and New York metropolitan areas in the context of snow storms. We found only six GW events during snow storms, suggesting that GWs on those scales are uncommon at the two locations during snow storms and, thus, do not often enhance snowfall.
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Locally higher radar reflectivity values in winter storms can mean more snowfall or a transition from snow to mixtures of snow, partially melted snow, and/or rain. We use the correlation coefficient to de-emphasize regions of mixed precipitation. Visual muting is valuable for analyzing and monitoring evolving weather conditions during winter storm events.
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This study investigates how radar-detected snow bands relate to snowfall rates during winter storms in the northeastern United States. Using over a decade of data, we found that snow bands are not consistently linked to heavy snowfall at the surface, as snow particles are often dispersed by wind before reaching the ground. These findings highlight limitations of using radar reflectivity for predicting snow rates and suggest focusing on radar echo duration to better understand snowfall patterns.
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We analyzed in-cloud characteristics using in situ measurements from 42 research flights across two field campaigns into non-orographic, non-lake-effect winter storms. Much of the storm volume contains weak vertical motions (a few centimeters per second), and most updrafts ≥ 0.5 m s-1 are small (< 1 km). Within 2 km of cloud radar echo top, stronger vertical motions and conditions for ice particle growth are more common. Overturning air motions near cloud top appear important for the production of snow particles.
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Atmospheric gravity waves (GWs) are air oscillations in which buoyancy is the restoring force, and they may enhance precipitation under certain conditions. We used 3+ seasons of pressure data to identify GWs with wavelengths ≤ 170 km in the Toronto and New York metropolitan areas in the context of snow storms. We found only six GW events during snow storms, suggesting that GWs on those scales are uncommon at the two locations during snow storms and, thus, do not often enhance snowfall.
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We have created a new method to better identify enhanced features in radar data from winter storms. Unlike the clear-cut features seen in warm-season storms, features in winter storms are often fuzzier with softer edges. Our technique is unique because it uses two adaptive thresholds that change based on the background radar values. It can identify both strong and subtle features in the radar data and takes into account uncertainties in the detection process.
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Locally higher radar reflectivity values in winter storms can mean more snowfall or a transition from snow to mixtures of snow, partially melted snow, and/or rain. We use the correlation coefficient to de-emphasize regions of mixed precipitation. Visual muting is valuable for analyzing and monitoring evolving weather conditions during winter storm events.
Matthew A. Miller, Sandra E. Yuter, Nicole P. Hoban, Laura M. Tomkins, and Brian A. Colle
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Apparent waves in the atmosphere and similar features in storm winds can be detected by taking the difference between successive Doppler weather radar scans measuring radar-relative storm air motions. Applying image filtering to the difference data better isolates the detected signal. This technique is a useful tool in weather research and forecasting since such waves can trigger or enhance precipitation.
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Short summary
We present a data set of high-precision surface air pressure observations and a method for detecting wave signals from the time series of pressure. A wavelet-based method is used to find wave signals at specific times and wave periods. From networks of pressure sensors spaced tens of kilometers apart, the wave phase speed and direction are estimated. Examples of wave events and their meteorological context are shown using radar data, weather balloon data, and other surface weather observations.
We present a data set of high-precision surface air pressure observations and a method for...
