| NSF Org: |
AGS Division of Atmospheric and Geospace Sciences |
| Recipient: |
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| Initial Amendment Date: | March 29, 2019 |
| Latest Amendment Date: | March 29, 2019 |
| Award Number: | 1841246 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Nicholas Anderson
nanderso@nsf.gov (703)292-4715 AGS Division of Atmospheric and Geospace Sciences GEO Directorate for Geosciences |
| Start Date: | April 1, 2019 |
| End Date: | March 31, 2023 (Estimated) |
| Total Intended Award Amount: | $214,562.00 |
| Total Awarded Amount to Date: | $214,562.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
660 PARRINGTON OVAL RM 301 NORMAN OK US 73019-3003 (405)325-4757 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
120 David L. Boren Blvd Norman OK US 73072-7268 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Physical & Dynamic Meteorology |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.050 |
ABSTRACT
Winter storms cause major disruptions to transportation, commerce, the power grid and public safety. The goal of this research project is to provide better information on the properties of clouds and precipitation during winter storms so that these events can be better forecast. The researchers will use advanced radar observations, and techniques to analyze that data, to study a variety of processes in winter storms that affect the types and amount of precipitation that falls at the surface. Better predictions of snowstorms are especially important for ground and air travel and this work has connections to the relevant operational forecast agencies. In addition, students will be trained in radar meteorology techniques, ensuring the advancement of the next generation of scientists.
The research team will investigate the microphysics of cold-season storms, with a focus on the use of multi-frequency, dual-polarimetric radar observations. A unique combination of state-of-the-art tools in modern weather radar technology and explicit microphysical modeling will be used to derive a better understanding of the microphysics in winter storms, including dendritic growth, riming, aggregation aloft, and processes in the melting layer. The main observational tools will be the radar facilities at Stony Brook University, which are highlighted by the Ka-band scanning radar and W- and Ku-band profiling radars. Ground measurements will include the Multi-Angle Snowflake Camera (MASC), disdrometers, and Community Collaborative Rail, Hail and Snow Network (CoCoRaHS) observations.
The main objectives of the work are to: 1) Explore synergy among polarimetric, multi-frequency, and Doppler radar measurements using innovative techniques for processing and representing multi-parameter radar data to provide information about microphysical and kinematic processes in ice and mixed-phase clouds, 2) Investigate novel radar methods for quantification of ice hydrometers, and 3) Develop a 1D cloud spectral bin model combined with a forward radar operator to simulate key microphysical processes of snow formation and provide recommendations for improving the parameterization these processes in large bulk and spectral bin models.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Winter precipitation hazards are notoriously difficult to diagnose and forecast because very subtle changes in the thermodynamic conditions in the atmosphere may cause dramatic changes in the precipitation types ranging from heavy snowfall to ice pellets and freezing rain that can cause high impacts on general public and transportation due to high accumulations of snow, icy roads, and low visibility. Icing hazard to aviation is very common during a cold season as well.
Dual-polarization radars emerge as an efficient tool to classify hydrometeor types and quantify ice and snow amounts. However, the radar measurements should be complemented by cloud modeling for reliable detection and nowcasting of the inclement winter weather phenomena. This is why synergetic use of radar observations and simple cloud models of snow formation was in the focus of this research. One of the primary concepts was that the microphysical properties of ice and snow have to be retrieved aloft in the cloud (particularly in the dendritic growth layer (DGL) where a bulk of snow is formed) using radar measurements and fallout of this ice and snow down to the surface is predicted using cloud model considerations.
Following this strategy, novel polarimetric algorithms for ice retrievals in a full depth of the atmosphere have been developed and tested using in situ observations of snow at the surface and onboard research aircrafts during field campaigns IMPACTS and ICICLE. The radar retrieval algorithms originally developed for centimeter-wavelength radars have been modified for utilization with millimeter-wavelength radars and evaluated using the observations with Ka-band surveillance polarimetric radar (KASPR) deployed at the Stony Brook University Radar Observatory.
Our 1D model of snow formation with spectral bin microphysics was refined by incorporating additional microphysical processes such as collision / coalescence and diabatic cooling / warming and drying / moistening. As a result, this model initialized by results of polarimetric radar measurements aloft was able to nowcast the type and amount of snow precipitation with a lead time of an hour. The attached image demonstrates how the quality of precipitation classification (HCA + SBC) was improved compared to the output of the existing hydrometeor classification algorithm (HCA) currently implemented on the NEXRAD weather radar network for two winter storms. Two additional classes of precipitation have been added: freezing rain (dark blue) and ice pellets (red) that agree much better with ground truth (right panels) than the output from the current HCA that erroneously qualifies precipitation as rain (green in the middle panels).
During the project, a polarimetric algorithm for snow measurement was refined and validated for a number of snow events observed by the NEXRAD radars. A principally novel polarimetric radar technique for estimating visibility in snow was suggested and tested for selected snowstorms.
The research under this project significantly contributed to the understanding of the microphysics of ice and snow formation. The results of microphysical retrievals in ice using novel polarimetric radar techniques can serve as an observational reference for validating microphysical parameterization schemes utilized in the numerical weather prediction models.
Last Modified: 08/02/2023
Modified by: Alexander V Ryzhkov
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