Pass#50669
Renewal Proposal to
National Oceanic and Atmospheric Administration
National Environmental Satellite, Data, and Information Service
Office of Systems Development
Washington DC
Research and Development for GOES-R Risk Reduction for
Mesoscale Weather Analysis and Forecasting
Cooperative Institute for Research in the Atmosphere
Colorado State University
Foothills Campus
Fort Collins CO 80523-1375
Dr. Thomas H. Vonder Haar,
Principal Investigator
In partnership with
Dr. Mark DeMaria
Regional and Mesoscale Meteorology Team Leader
NESDIS Office of Research and Applications
Telephone: 970-491-8405
Email: Mark.DeMaria@noaa.gov
Debra Molenar
CIRA/Regional and Mesoscale Meteorology (RAMM) Team
Colorado State University
Telephone: 970-491-8447
Email: Molenar@cira.colostate.edu
March 2005
Period of Activity: July 1, 2005 – June 30, 2006
____________________________
Dr. Thomas H. Vonder Haar, P.I.
Director, CIRA
(970) 491-8448
__________________________
Anita Montgomery
Research Administrator
Office of Sponsored Programs
(970) 491-6586
1. Introduction
The next generation GOES satellites (beginning with GOES-R) will include an
Advanced Baseline Imager (ABI) and Hyperspectral Environmental Suite (HES) with
vastly improved spectral, spatial and temporal resolution relative to the current GOES IM series satellites. The GOES-R era will begin early in the next decade, and will be part
of a global observing system that includes polar orbiting satellites with comparable
spatial and spectral resolution instrumentation. However, GOES-R will have superior
temporal resolution. A continuing science study is proposed to use numerical simulations
and existing in situ and satellite data to better understand the capabilities of these
advanced instruments for mesoscale weather analysis and prediction. This science study
will help to reduce the time needed to fully utilize GOES-R as soon as possible after
launch.
The first phase of this study is a three-year project to simulate subsets of GOES-R
observations existing satellites and to create synthetic GOES-R observations using
numerical cloud model simulations coupled with radiative transfer code. A part of the
modeling project is to develop an advanced data assimilation method based upon the
ensemble Kalman filtering approach that can be used to assess the value of GOES-R data
for numerical weather prediction. This new method is referred to as the Maximum
Likelihood Ensemble Filter (MLEF). This proposal is for the third year of the first phase,
which began in July of 2003, and a transition to phase 2, which is described below.
The second phase of the study will make use of the knowledge gained in the first
phase to develop experimental products. The second phase will test these products when
possible using existing operational and experimental satellites. Data assimilation
experiments with real observations will also be performed in the second phase. This
aspect of the project will be closely coordinated with the Joint Center for Satellite Data
Assimilation (JCSDA). The second phase will also include a training and outreach
component on both the national and international levels. This work will be coordinated
with the Virtual Institute for Satellite Integration Training (VISIT) program, the
International Virtual Laboratory efforts of the World Meteorological Organization and
the Satellite Hydrology and Meteorology (SHyMet) Training Program.
In the third phase, preparation will begin on the development of Day-1 operational
products, and training activities will increase. Increased coordination with the JCSDA
will occur during phase three to identify methods to utilize GOES-R data in operational
mesoscale numerical models.
One of the advantages of GOES-R is the high temporal resolution that is possible
from geostationary orbit. The emphasis of our science study is on mesoscale atmospheric
phenomena that evolve on time scales faster than that which can be sampled from polar
orbiting satellites. These phenomena include tropical cyclones, severe weather and
mesoscale aspects of winter weather, including lake-effect snowfall, as well as the
detection of atmospheric hazards such as fog, dust and volcanic ash. This emphasis will
continue throughout the project. References to previous results are provided in section 2,
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followed by plans for year 3 (the current proposal). A research and budget outline of the
longer-term phases of the project is also provided, followed by a detailed budget for the
current year.
2. Summary of Results from Year 1 and 2
The emphasis of the first two years was on the establishment of the case study
database, and the set up of the numerical cloud model and radiative transfer code.
Detailed quarterly reports were provided to NOAA which are available from
http://www.cira.colostate.edu/ramm/KFIntranet/ProposalReports/CIRA_GOESR_FY05_1st_prog_rep.doc
and
http://www.cira.colostate.edu/ramm/KFIntranet/GOES-R_IPO/prevgoes_r_reports.html
3. Current Year (FY05) Plans
In the third year of phase one, the case study data collection activities will continue.
The radiative transfer code currently can generate synthetic imagery for 10 of the 16
channels proposed for the GOES-R Advanced Baseline Imager. Work will continue to
provide generalize this code to include additional channels.
The study of the potential improvements in Derived Product Imagery (DPI) from
GOES-R due to the increased spatial and temporal resolution will be written up for
publication.
Very promising results of using hyperspectral soundings to monitor tropical cyclone
environments and hurricane eyes were obtained in year 2. This work will be continued by
obtaining additional AIRS data cases to confirm the eye sounding results from hurricane
Isabel, and the environmental soundings from hurricanes Lili, Fabian and Isabel.
The development of the ensemble data assimilation methods will continue. The next
step is to apply the MLEF technique to a more realistic situation where the simulated
distribution is more similar to what will be available from GOES-R. The emphasis of this
work will be on the information content of the GOES-R data that the MLEF technique
provides.
A smart principal component imagery technique was developed to assist with
optimal channel selection. This method will be applied to simulated and synthetic GOESR data to begin development of prototype products for fog, dust and volcanic ash
detection.
A cloud top structure climatology and related algorithm for severe weather
nowcasting is under development using existing GOES data. This study will be continued
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by assessing the potential value of GOES-R data. The potential for using lightning data
for this application will also be investigated.
GOES infrared data has been used for decades to estimate tropical cyclone intensity
(the Dvorak method). A prototype algorithm for GOES-R will be developed from polar
orbiting data with comparable spatial resolution.
A transition from case studies to real time data will begin. Initial development of a
web site to display prototype products will begin.
Interaction with training programs (VISIT, SHyMet, WMO) will increase. Work will
continue on the ABI synthetic imagery tool to illustrate the sensitivity of imagery to
idealized inputs.
4. Longer Range Plans
As described in the Introduction, this project is part of a longer-range science plan for
GOES-R Risk Reduction for mesoscale weather. Brief summaries and proposed budgets
are provided below.
Phase 1: FY03-FY05:
Phase 1 is using a case study approach to simulate subsets of GOES-R observations with
existing satellites, and to create synthetic GOES-R observations using numerical cloud
model simulations coupled with radiative transfer code. The emphasis is to demonstrate
the utility of GOES-R and begin development of prototype mesoscale nowcast and
forecast products. Advanced data assimilation techniques are being developed to help
assess the information content of satellite observations for mesoscale weather forecasting.
Techniques such as principal component imagery are being applied to real and synthetic
imagery for the mesoscale product development. The budget for phase 1 is summarized
below. Coordination with on-going satellite training programs is being initiated.
Budget for Phase 1:
FY03 – 350 K (already funded)
FY04 – 197 K (already funded)
FY05 – 285 K (proposed)
Phase 2: FY06-FY09:
In phase 2, the results from the phase 1 case studies will be applied to experimental
product development when possible using existing experimental and operational
satellites. These products will be applied to similar mesoscale weather events as in the
case studies, and experimental real-time results will be made available via the Internet.
The numerical modeling studies will transition from the underlying CSU RAMS model to
the Weather Research and Forecast (WRF) model, to become more aligned with the
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efforts at operational forecast centers. In addition, the emphasis of the assimilation
experiments will shift from idealized experiments to those with real data. It is anticipated
that the MLEF assimilation system will be in a mature stage as phase 2 progresses. A
new aspect of phase 2 will be the increased emphasis on training material through
coordination with the Virtual Institute for Satellite Integration and the International
Virtual Laboratory in coordination with the WMO and the Regional Meteorological
Training Centers. Training material will be developed and delivered, sample simulated
data sets will be made available via the web, and experimental products using existing
satellite data will be provided.
Proposed Budget for Phase 2:
FY06 – 350 K
FY07 - 400 K
FY08 - 400 K
FY09 - 400 K
Phase 3 – FY10-FY12
In the third phase, preparations will begin for testing experimental GOES-R mesoscale
products based upon the results from phases 1 and 2. Education and training activities
will increase in preparation for the launch of GOES-R. The numerical modeling aspects
of the project will be even more closely coordinated with NCEP and other operational
forecast centers.
Proposed Budget for Phase 3:
FY10 – 500 K
FY11 – 600 K
FY12 – 600 K
5. Project Personnel
Project Oversight will be provided by Prof. Thomas Vonder Haar (CIRA/CSU) and Dr.
Mark DeMaria (NESDIS/ORA). Scientific guidance will also be provided by Dr. James
Purdom, a CIRA Senior Research Scientist, and former Director of ORA.
The numerical modeling studies will primarily be performed by Drs. Dusanka Zupanski,
Louis Grasso, M. Sengupta with oversight from Mark DeMaria. Dr. Dusanka is a CIRA
employee who formerly worked for the NCEP Environmental Modeling Center, and is an
expert on mesoscale modeling and data assimilation. Louis Grasso has extensive
experience with the RAMS model, and was a former student of Dr. Bill Cotton, the
original developer of RAMS. M. Sengupta will lead the radiative transfer modeling
activities. M. DeMaria is a NESDIS ORA employee with an extensive background in
tropical cyclone analysis and forecasting, and numerical weather prediction.
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The database development will be overseen by Debra Molenar and M. DeMaria. Debra
leads the RAMM Team computer infrastructure group and has considerable experience in
database management, satellite data processing and programming. Dave Watson, Kevin
Micke, and Hiro Gosden (CIRA research associates) will provide the computer support
for the project, and Bernadette Connell will coordinate the training aspects. Kathy Fryer
will provide administrative support.
Don Hillger will lead the information component analysis segment of the project, and is
the primary focal point for coordination with ORA to obtain AIRS temperature and
moisture retrievals.
The project also will include a graduate student to focus on a specific aspect of the
research, depending upon her interests, and a student hourly employee to assist with data
processing. The inclusion of students will help cultivate future scientists that have
familiarity with the GOES-R program.
5. Proposed Budget
The proposed budget for the current year of the project is $285 K. A detailed breakdown
of the costs is provided in the attached spreadsheet. Budget estimates for the out-years of
this proposal are included in section 4.
6. Project Coordination and Documentation
This research is part of a larger GOES-R Risk Reduction program that is being
coordinated with NESDIS/ORA and NESDIS/OSD. Quarterly progress reports will be
provided to OSD and ORA management, and research results will be presented at annual
activities reviews.
7. Budget Explanation
I. and II. PERSONNEL and FRINGE BENEFITS
Salaries and benefits are requested for the personnel that will be performing this research,
and providing administrative and computer support. In a basis consistent with our longstanding Memorandum of Understanding between NOAA and Colorado State University,
the enclosed budget specifically includes support for administrative and clerical
personnel (Fryer) directly associated with the technical and managerial administration of
GIMPAP. This support is “quid pro quo” for the reduced indirect cost rate agreed upon
in the long-standing subject memoranda. K. Fryer will provide communication and
collaboration support, assist in the acquisition and distribution of reference materials
relevant to the conception and execution of the project, technical editing of scientific
manuscripts, specialized reports and conference papers. All other budgeted personnel are
directly involved in the research which is identified in the Statement of Work (SOW).
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III. DOMESTIC TRAVEL
Funds are requested for two trips to Washington, DC for coordination with ORA
scientists. These trips are required to obtain the necessary radiative transfer modeling
capabilities, and training on some of the specialized experimental satellite data to be used
in this research. Trips to scientific conferences are also requested for presentation of
results.
IV. OTHER
1. The NT computer charges provide computer and data support associated with this
project. The NT hourly rate is determined by CIRA and depends on the actual cost of the
CIRA computer operations. CIRA charges Windows NT computer costs on an hourly
basis, based on log-on time collected electronically via infrastructure programs.
2. Much of this research is performed in a McIDAS programming environment. A
McIDAS Users Group (MUG) fee is necessary to use and update this software.
3. Funds are requested for publication of research results.
V. MATERIALS and SUPPLIES
The project requires has massive data storage requirements. Funds are requested to
accommodate the collection of new case study data and to store new numerical model
simulations.
VI. TUITION
It is anticipated that one graduate student will be supported as part of this research
project.
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