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 2007
Period of Activity: July1, 2007 – June 30, 2008
Amount Requested: $345,000
____________________________
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 the
Advanced Baseline Imager (ABI) with vastly improved spectral, spatial and temporal
resolution relative to the current GOES I-M series satellites. It will also include a
lightning mapper which, together with the ABI, offers the potential to significantly
improve the analysis and forecasts of tropical storms. 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 project originally included an advanced sounder (HES) that was cut from the
program. However, the ABI has enough channels for some limiting sounding capabilities,
and there is on-going discussion within the GOES program to add a sounder with more
capabilities than what would be possible with ABI, but less than what was planned for the
HES.
The first phase of this study, completed in FY05, was a three-year project to simulate
subsets of GOES-R observations using 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 began to make use of the knowledge gained in the first
phase to develop experimental products. In the first year of the second phase we began
with testing these products when possible using existing operational and experimental
satellites. Data assimilation experiments with real observations will continue to be
performed in the second phase. This aspect of the project is being closely coordinated
with the Joint Center for Satellite Data Assimilation (JCSDA). The second phase also
includes a training and outreach component on both the national and international levels.
This work is being 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 for the second year of phase two.
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.
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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 and from the
first year of phase 2 are provided in section 2, followed by plans for year 2 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 and Year 1 of Phase 2
The emphasis of the first phase was on the establishment of the dynamic web-based
case study database and on the set up of the numerical cloud model and radiative transfer
code. During the first year of phase two we successfully supplemented the case study
database with datasets covering Polar Low, Fog and Volcanic eruption events in 2004
and 2005, and Severe Weather and Tropical cyclone cases from 2005.
We also produced synthetic GOES-R ABI imagery for three different mesoscale weather
events, and we studied the application of various difference products from combinations
of the selected GOES-R channels. In addition, the GOES-13 NOAA Science Test was
conducted and we improved the method of covariance localization and data assimilation
results by introducing a new “distance” defined in terms of information measures, rather
than using a geographical distance. These and many more of the CIRA Team’s
achievements are summarized in the detailed quarterly reports which we provide
regularly to NOAA and which are available on-line at:
http://rammb.cira.colostate.edu/intranet/GOES-R_IPO/prevgoes_r_reports.html
3. Current Year (FY07) Plans
In the second year of phase two most of the research conducted during last year will
continue and many additional prototype products will be developed using simulated and
synthetic (model generated) GOES-R data. Our proposed activities can be divided into
the following seven research tasks:
Task 1. Mesoscale Weather Database
During the first year of phase two we added many more datasets to the case study
database. The database currently contains a wide range of data, including from GOES
Imager and Sounder data, Meteosat Second Generation data, MODIS AIRS Radiances,
AVHRR imagery, AIRS Soundings, RAMS Simulation, ETA Analyses, RADAR, GPS
Soundings, ARM Soundings and Rawindsondes. The inventory provides pertinent
information for those datasets and the information can be accessed quickly via the web:
http://shaula.cira.colostate.edu/goesr/inventory.asp.
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We will continue to archive new case study datasets during the second year of phase 2.
Primary focus will be on the simulation of existing cases in the now substantial database.
In addition we will conduct a major upgrade of the hardware hosting the database by
switching to a dual core Xeon server.
Summary of Task 1 Activities:
 Continue work on case study data collection database.
 Upgrade server hardware.
Task 2. Synthetic GOES-R data generation and analysis
Synthetic imagery for three mesoscale weather events (severe weather, lake effect snow,
hurricane) was produced during the first year of phase two. This work was conducted in
coordination with the Algorithm Working Group (AWG) activities at CIRA. In addition
the radiative transfer code was modified to include synthetic fire hotspots in the ABI
imagery.
During the second year of phase two we will produce synthetic imagery of additional
mesoscale study cases and new fire hot spots. The radiative impact of time dependent
smoke plumes to the fire hot-spots in the 3.9 µm imagery will be investigated. We will
also work on improving the brightness temperature fields by adding new features to the
production of the synthetic imagery. Once the coefficients for GOES-R channels 1-6
become available, synthetic imagery will be produced for the six channels from 0.46 µm
to 2 µm. We also intend to investigate detection methods and detection thresholds for fog
and dust.
Summary of Task 2 Activities:
 Create synthetic imagery of additional mesoscale study cases (winter event and a
tropical system).
 Produce additional fire hotspot simulations (in coordination with the AWG
activities at CIRA).
 Investigate the radiative impact of time dependent smoke plumes to the fire "hotspots" in the 3.9 µm imagery.
 Improve the brightness temperature fields by adding new features like:
o satellite-derived spectrally varying surface emissivity (instead of
constant emissivity value of 0.98).
o use of canopy temperatures as a lower boundary to the radiative transfer
models (instead of the temperature of the lowest forecast model level).
 Produce synthetic imagery for the six channels from 0.46 µm to 2 µm (once the
required coefficients become available).
 Investigate detection methods and detection thresholds for fog and dust.
Task 3. Prototype Hazard Products and Display
The focus of this task will be on the development of new products and the improvement
of existing products, particularly for atmospheric and surface-related phenomena. Initial
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emphasis had been placed on a daytime fog product and a blowing dust product. Other
possible applications include monitoring of volcanic ash and smoke from fires. Image
products to detect these events exist, but they can be improved with the additional
spectral coverage that will be available through GOES-R ABI bands. Development work
on these products will continue to focus on the ABI-equivalent bands from MODIS,
AIRS and MSG data. Each additional case study will be tested in real-time mode and
will be added to the case study database. New products will be made available to a wide
audience for testing as analysis and forecasting tools. First examples of the daytime fog
product and the blowing dust product have been shown in poster form at the last two
AMS Annual Meetings, and a publication featuring these products is currently being
reviewed for publication by AMS.
Summary of Task 3 Activities:
 New prototype hazard products for daytime fog, blowing dust, volcanic ash, and
smoke from fire will be developed.
 Testing of new/experimental products will be run in real-time mode utilizing
current MODIS imagery as well as geostationary MSG data (to provide high
temporal resolution).
 Each new case study will be added to the case study database.
 New product output will be sent to an experimental RAMSDIS-Online Website.
 New products can be viewed by a wide audience and tested as analysis and
forecasting tools.
 Begin preparation of algorithm theoretical basis documents (ATBD), as
appropriate, for the new fog and dust products.
Task 4: Tropical cyclone product development
GOES data are one of the cornerstones of tropical cyclone analysis. The GOES-R ABI
and lightning mapper have the potential to improve the analysis and forecasts of these
storms. Research in this area is focused on testing existing tropical cyclone intensity and
wind algorithms using proxy GOES-R data and the development of methods to exploit
new data from GOES-R for tropical cyclone forecasting. Listed below are the specific
areas of research for this task.
Summary of Task 4 Activities:
 Continue collection of MSG, MODIS and AVHRR data as proxy for the ABI.
 Investigate tropical cyclone intensity and surface wind estimation algorithms
using GOES-R proxy ABI data.
 Utilize MSG proxy data to investigate utility of new ABI channels for tropical
cyclone intensity forecasting, with emphasis on the ozone channel.
 Investigate MSG data for utility of tropical cyclone genesis forecasting, with
emphasis on the eastern Atlantic.
 Develop an ABI vertical shear analysis diagnostic product.
 Coordinate University of Alabama at Huntsville on lightning applications to
tropical cyclone intensity forecasting using ground-based and TRMM lightning
data.
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
Write ATBDs as appropriate for tropical cyclone applications.
Task 5: Severe weather and winter weather product development
Because of the rapid time scales, severe weather is one of the most important applications
of GOES-R. Mesoscale aspects of winter weather such as snow bands will also benefit
from the new data.
For the next phase of this research, the synthetic data will be analyzed (using Principle
Component Analysis, for example) to determine what combinations of spectral bands
provide the most information about certain thunderstorm features. For example, multichannel retrievals of cloud-top water droplet and ice crystal size may provide very useful
information about convective initiation and/or storm intensity. Output from the cloud
model can be used as "truth", so that we know precisely which clouds will develop into
thunderstorms and which thunderstorms are severe. We propose to use this method to
generate new GOES-R severe weather products and algorithms. In addition to the severe
weather applications, multi-channel retrievals of microphysical information from lake
effect snow bands may provide important information about snowfall rates, so a similar
method may be used with the 12 February 2003 simulated data.
Summary of Task 5 Activities:
 Analyze synthetic data to determine what combinations of spectral bands provide
the most information about certain thunderstorm features.
 Investigate whether multi-channel retrievals of microphysical information from
lake effect snow bands may provide important information about snowfall rates.
 Prepare ATBDs as product development becomes mature.
Task 6: Information content analysis using Maximum Likelihood Ensemble Kalman Filter
(MLEF) data assimilation
We propose to develop techniques to determine the information content of GOES-R data.
As described in the introduction, the advanced sounder originally planned for GOES-R
(the HES) was cut from the program. However, the ABI has enough channels for some
limiting sounding capabilities, and there is on-going discussion within the GOES
program to add a sounder with more capabilities than what would be possible with ABI,
but less than what was planned for the HES. For this task, the AIRS radiances will be
utilized. The full AIRS spectrum can be band-averaged to simulate the ABI or a reduced
capability advanced sounder. The information content analysis will then provide insight
into the improvements possible with variables levels of sounding capabilities on GOESR.
The AIRS information content analysis will utilize the NCEP operational mesoscale
model, the Weather Research and Forecasting Non-hydrostatic Mesoscale Model (WRFNMM). We will employ the Maximum Likelihood Ensemble Filter (MLEF) algorithm,
which was developed and tested in application to the Colorado State University Regional
Atmospheric Modeling System (RAMS) under this project in the previous years. For the
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initial tests we will use simulated AIRS radiances and gradually transition to using real
AIRS radiances, which are available at the NCEP operational database. We will evaluate
standard information content measures, such as degrees of freedom for signal and entropy
reduction and use these measures to select the geographical regions and channels with
most information to provide insight into the various versions of GOES-R sounding
capabilities that are being proposed. This work will be done in collaboration with Dr.
Zavisa Janjic (from UCAR/NCEP/EMC, official NCEP contact person for this task), who
is the principal developer of the WRF-NMM.
Summary of Task 6 Activities:
 Develop techniques to calculate information content of AIRS radiances
employing the NCEP operational mesoscale model (the WRF-NMM).
 Evaluate standard information content measures such as degrees of freedom for
signal and entropy reduction.
 Use these measures to select the geographical regions and channels with most
information.
Task 7: National and International Training Activities
We will continue to interact with our on-going training programs (VISIT, SHyMet,
WMO) in order to provide outreach on the capabilities of GOES-R.
Specifically we will provide two web based examples of products being developed for
GOES-R by the RAMM group. These will follow the format of a satellite interpretation
discussion.
Summary of Task 7 Activities:
 Continue to interact with our on-going training programs.
 Provide two web based examples of products being developed for GOES-R.
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.
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Phase 2: FY06-FY09:
In the first year of phase 2, we began to apply the results from the phase 1 case studies to
experimental product development when possible using existing experimental and
operational satellites. We will continue to apply these products to similar mesoscale
weather events as in the case studies, and we will continue to make experimental realtime results available via the Internet. In addition, the emphasis of the assimilation
experiments will shift from idealized experiments to those with real data. With the
beginning of phase 2 the MLEF assimilation system will have reached a mature stage and
we will begin to include the WRF-NMM model into the data assimilation system. In
addition we are planning to begin with prototype hazard product development of fog and
dust products.
Proposed Budget for Phase 2:
FY07 - 350 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 studies
will transition from the underlying CSU RAMS model to the Weather Research and
Forecast (WRF) model if similar horizontal resolution and physics will be available. This
move will ensure that the project will be even more closely coordinated with NCEP and
other operational forecast centers.
Proposed Budget for Phase 3:
FY10 – 450 K
FY11 – 500 K
FY12 – 500 K
5. Project Personnel
Project Oversight and management will be provided by Prof. Thomas Vonder Haar and
Dr. Renate Brummer (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.
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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 hourly 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 $345 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.
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
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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).
Base salary included in this proposal reflects the actual salaries approved by the
Governing Board of Colorado State University for the period July 1, 2006 through June
30, 2007. Any salary beyond this period is budgeted at a 4% increase over the prior
year’s annual base. The following estimated CSU fringe rates were applied to the
salaries based on the individual’s payroll classification:
FY 07-08
a. Faculty/Administrative Professional
22.8%
b. State Classified
25.4%
c. Non-Student Hourly
14.3%
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. Travel is budgeted based on the successful completion of the project and the
proposed dissemination of the research results. Per diem rates are applied when the
destination is to a location listed in the CSU per diem/city publication. Standard mileage
distances and rates are used for transportation to and from DIA from Fort Collins.
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
network/printing, consultation, materials, and data.
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.
V. MATERIALS and SUPPLIES
The project has massive data storage requirements. Funds are requested for a new
computer mass storage device to accommodate the collection of new case study data and
to store new numerical model simulations.
VII. INDIRECT COST RATE
The Indirect Rate of 25% charged on this proposal is the negotiated rate for CIRA’s
Cooperative Agreement from January 1, 2005-June 30, 2009. The rate is applied to
Modified Total Direct Costs (MTDC). MTDC is defined as Total Direct Costs less
Equipment, GRA Tuition, and Subcontracts > $25,000.
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