| NSF Org: |
AGS Division of Atmospheric and Geospace Sciences |
| Recipient: |
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| Initial Amendment Date: | August 26, 2014 |
| Latest Amendment Date: | September 20, 2018 |
| Award Number: | 1457852 |
| Award Instrument: | Cooperative Agreement |
| Program Manager: |
Sarah Ruth
sruth@nsf.gov (703)292-7594 AGS Division of Atmospheric and Geospace Sciences GEO Directorate for Geosciences |
| Start Date: | August 15, 2014 |
| End Date: | September 30, 2018 (Estimated) |
| Total Intended Award Amount: | $200,326.00 |
| Total Awarded Amount to Date: | $315,436.00 |
| Funds Obligated to Date: |
FY 2015 = $115,110.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
3090 CENTER GREEN DR BOULDER CO US 80301-2252 (303)497-1000 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
BOULDER CO US 80301-2252 |
| 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): | |
| Primary Program Source: |
01001516RB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): | |
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.050 |
ABSTRACT
This proposal supports the National Center for Atmospheric Research to assist the Army Research Laboratory (ARL)in preparing a written description of the observation-nudging code data assimilation in the community version of the Advanced Research Weather (ARW) Research Forecast model. The document might suggest changes to the available configurations of the ARW. In addition, NCAR will collaborate with ARL on a studey of the relative performances of the observation nudging in the community ARW and the observation nudging in the 4DWX system developed by NCAR Research Aviation Laboratory. The result will be a written at least one journal article. Furthermore, a white paper will be produced conveying best practices for applying the ARW for simulations at gride intervals less than 1.0km.
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.
This project outcomes report represents projects funded by Army Research Laboratory (ARL) and awarded through the National Science Foundation (NSF) Cooperative Agreement to the University Corporation for Atmospheric Research (UCAR) for the National Center for Atmospheric Research (NCAR).
The NCAR (Weather Research and Forecasting (WRF) Model-based RTFDDA-VLES (real-time four-dimensional data assimilation and very large eddy simulation) model was employed to study a case of summer monsoon convection and a case of late spring severe wind at White Sands Missile Range (WSMR), New Mexico. Both convection and high winds affect the planning and execution of missile tests at WSMR, and both are influenced by local complex terrain, including the steep San Andres Mountains to the west and the broader Sacramento Mountains to the east. It is of great interest to study the capability of numerical weather prediction models for simulating such weather at very high resolutions, with which fine-scale terrain can be reasonably represented. For the two case studies, 4-D data assimilation was active on the three coarsest grids. Preliminary modeling results show that smaller grid intervals increase the model's ability to simulate the fine-scale structure and intensity of moist convection and high wind. Sensitivity experiments reveal the impact of different sub-grid filters (diffusion schemes), terrain smoothing, and surface momentum fluxes and heat fluxes.
The research efforts led to the development of better documentation for the observation-nudging code for data assimilation in the community version of the WRF model. Project outcomes included output from the WRF Model, algorithms for filtering observations and model data, and an informal report on the developed algorithms for diagnosing turbulence at unmanned aircraft system scales. These tools improve the use and visualization of forecasts by end users.
Results from the project have been disseminated through journal articles, conference presentations, and technology transfer of computer code.
Last Modified: 12/11/2018
Modified by: Antonio J Busalacchi
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