| NSF Org: |
AGS Division of Atmospheric and Geospace Sciences |
| Recipient: |
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| Initial Amendment Date: | January 16, 2024 |
| Latest Amendment Date: | August 28, 2024 |
| Award Number: | 2336002 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Nicholas Anderson
nanderso@nsf.gov (703)292-4715 AGS Division of Atmospheric and Geospace Sciences GEO Directorate for Geosciences |
| Start Date: | January 15, 2024 |
| End Date: | December 31, 2026 (Estimated) |
| Total Intended Award Amount: | $523,860.00 |
| Total Awarded Amount to Date: | $523,860.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
615 W 131ST ST NEW YORK NY US 10027-7922 (212)854-6851 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
500 West 120th Street New York NY US 10027-7922 |
| 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: |
01002526DB NSF RESEARCH & RELATED ACTIVIT 01002627DB NSF RESEARCH & RELATED ACTIVIT |
| 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
Numerical models that are used for weather forecasting and atmospheric research are extraordinarily complex, yet they still must make some generalized assumptions about atmospheric processes to be computationally efficient. The exchange of mass, energy, and momentum between the earth?s surface and atmosphere is evaluated by an overarching theory that works well for flat and homogeneous terrain, but less so for complex terrain and variable surfaces. In this project, the research team will develop and investigate a new machine-learning model to tackle the challenge of enabling accurate characterization of surface-atmosphere exchange. More than 70% of Earth?s land surface is in complex terrain, and improving on the ability of weather and climate models projections in these areas will be beneficial for weather forecasting, wildfire control, aviation, and military applications. Additionally, the project has several activities that are intended to provide students with the ability to explore the intersection between physics and machine learning.
The Monin-Obukhov Similarity Theory (MOST) has served as the primary method for evaluating the exchange of mass, energy, and momentum between the Earth's surface and the atmosphere in weather forecasting and climate projection models over the past decades. However, MOST has well-known deficiencies when applied to complex terrain environments. This project will enable the development of a physics-informed neural network (PINN) model that is expected to provide more accurate estimates of area-aggregate surface fluxes and enable a more straightforward and physically-justified assimilation of sparse observations for parameter estimation. The initial step in the project is the generation of a numerical database of microscale flow in complex terrain via a suite of process-resolving Large Eddy Simulations (LES). This database will then be used to train the physics-informed neural network for surface fluxes (PINN-FLUX). The final task in the project would be an assessment of PINN-FLUX?s ability to evaluate surface fluxes in complex terrain making use of sparse in-situ observations. The assessment task will be conducted using comparisons to modeling and observations.
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.
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