Pass#
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
January 2006
Period of Activity: July 31, 2006 – June 30, 2007
Amount Requested: $347,000
____________________________
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, completed in FY05, was 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 was 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).
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. This proposal is
the first year of phase 2.
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 from phase 1 are provided
2
in section 2, followed by plans for year 1 of phase 2 (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 Phase 1
The emphasis of the first phase 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://rammb.cira.colostate.edu/intranet/GOES-R_IPO/prevgoes_r_reports.html
3. Current Year (FY06) Plans
In the first year of phase two, the case study data collection activities will continue
and prototype product development will continue using the simulated and synthetic
(model generated) GOES-R data. The activities can be divided into the following seven
research topics.
Task 1. Mesoscale Weather Database
The mesoscale database will be supplemented with new cases. Data from AIRS,
AVHHR, MODIS, Meteosat Second Generation, and the current GOES satellites will be
included, along with in situ data such as rawindsondes and aircraft observations for
ground truth.
Task 2. Synthetic GOES-R data generation and analysis
New model simulations will be performed and the radiative transfer code will be refined.
The new simulations will include new tropical cyclone cases from the very active 2005
hurricane season, and another severe weather case. A method will also be developed to
simulate imagery wind fires for testing of GOES-R fire detection algorithms. The
emphasis on the radiative transfer code will be in the visible channels, and optimizing the
routines. The work will be coordinated with the NOAA GOES-R algorithm working
group, which is assembling proxy data sets for the first GOES-R product generation
system.
Task 3.Prototype product development for fog, smoke and volcanic ash analysis
A smart principal component imagery technique was developed to assist with optimal
channel selection. This method will be applied to simulated and synthetic GOES-R ABI
data to begin development of prototype products for fog, dust and volcanic ash detection.
In addition, AIRS data will be used as a proxy for the HES to develop products that use
spectral diagrams for qualitative feature identification.
3
Task 4: Tropical cyclone product development
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.
The analysis of AIRS temperature and moisture retrievals will continue. An algorithm for
estimating the storm intensity from eye soundings will be developed, along with methods
for retrieving the storm wind field through the application of hydrostatic and dynamical
constraints. The utility and accuracy of the hyperspectral soundings for tropical cyclone
analysis will be determined by comparing the AIRS retrievals to in situ observations in
storm environments from the NOAA Gulfstream Jet. The AIRS data is a proxy for the
HES.
A lightning data set in tropical cyclone environments was obtained in phase 1. The
dataset will be analyzed for utility in tropical cyclone intensity forecasting.
Task 5: Severe weather product development
A cloud top structure climatology and related algorithm for severe weather nowcasting is
under development using existing GOES data. This study will be continued by assessing
the potential value of GOES-R data. The potential for using lightning data for this
application will also be investigated.
The severe weather simulations will also be examined in greater detail to determine the
applicability of HES retrievals for short-term forecasting.
Task 6: Information content analysis using Maximum Likelihood Ensemble Kalman Filter
(MLEF) data assimilation
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 determination of the information content of the GOES-R data that the
MLEF technique provides. This work will be coordinated with the Joint Center for
Satellite Data Assimilation (JCSDA).
Task 7: Training Activities
Interaction with on-going 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
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 completed and used a case study approach to simulate subsets of GOES-R
observations with existing satellites, and created synthetic GOES-R observations using
numerical cloud model simulations coupled with radiative transfer code. The emphasis
was to demonstrate the utility of GOES-R and begin development of prototype mesoscale
nowcast and forecast products. Advanced data assimilation techniques were developed to
help assess the information content of satellite observations for mesoscale weather
forecasting. Techniques such as principal component imagery were applied to real and
synthetic imagery for the mesoscale product development.
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
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
5
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.
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 student participation. The inclusion of students will help
cultivate future scientists that have familiarity with the GOES-R program.
6. Proposed Budget
The proposed budget for the current year of the project is $347 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
7. 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.
8. 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).
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 computer charges provide computer and data support associated with this project.
The hourly rate is determined by CIRA and depends on the actual cost of the CIRA
computer operations.
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
7
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.
8