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
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
July 2003
Period of Activity: July 1, 2003 – June 30, 2004
____________________________
Dr. Thomas H. Vonder Haar, P.I.
Director, CIRA
(970) 491-8448
__________________________
Mary Atella
Senior Research Administrator
Office of Sponsored Programs
(970) 491-2083
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 instruments with
comparable spatial and spectral resolution. A three-year science study is proposed to use
numerical simulations and existing in situ and satellite data to better understand the
capabilities of these advanced instruments. This science study will help to reduce the time
needed to fully utilize GOES-R as soon as possible after launch.
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 three mesoscale
atmospheric phenomena that evolve on time scales faster than that which can be sampled
from polar orbiting satellites. Our study will focus on applications of GOES-R to tropical
cyclones (with emphasis on intensity prediction), severe weather and mesoscale aspects
of winter weather, including lake-effect snowfall. The approach is to use high-resolution
numerical simulations coupled with radiative transfer models to determine sampling
requirements, provide simulated data for development of retrieval algorithms and data
assimilation strategies. In addition, we will assemble a multi-platform database of
currently available earth-based and satellite (operational and experimental) observations
for several cases of each type of weather system (tropical cyclone, severe weather and
winter weather) to provide a sub-set of the type of data that will available from GOES-R.
This database will be used for preliminary product development, to assess the utility of
GOES-R instruments, and for training activities.
2. Atmospheric Phenomena
The Regional and Mesoscale Meteorology Team (RAMMT) located at CIRA has a
long history of using satellite observations for research on severe weather, tropical
cyclones, and mesoscale aspects of mid-latitude cyclones. GOES-R observations will be
most applicable to these same atmospheric phenomena, due to the consistency between
their physical time scales and the temporal sampling capability of the satellite.
Severe weather evolution is especially sensitive to the thermodynamic and dynamic
environment surround the storms, and GOES-R will provide a unique opportunity to
examine this sensitivity. Convection that develops into severe storms is often initiated by
existing air mass boundaries on a variety of spatial scales, which can be monitored in the
visible, near IR, and IR frequencies, since these areas are usually only partially cloudy
before the convection develops. Thus, the HES may be especially useful to measure these
structures, and to provide quantitative input for numerical forecast models. In addition,
severe convection sometimes has unique cloud top structures such as over-shooting tops,
and “enhanced-V” signatures that are often visible before severe weather is detected at
the surface, or by Doppler radar.
The skill of tropical cyclone intensity forecasts is considerably less than the skill of
track forecasts. Track forecasts primarily requires an accurate prediction of the synopticscale wind surrounding storm, but an intensity forecast requires an accurate estimate of
the thermodynamic and dynamic properties of the storm environment, and the mesoscale
processes with the storm itself. The GOES-R HES has tremendous potential for
measuring the thermodynamic structure of the storm environment, and the potential to
diagnose the inner structure for storms that have relatively cloud free eyes.
Although wintertime precipitation is often associated with synoptic-scale midlatitude cyclones, there can be extreme local variations in snowfall amounts due to
interaction with coastal fronts, gravity waves, and surface features such as the land/sea
interface and large inland lakes. These local variations have fairly short time scales, and
are often sensitive to the thermodynamic environments upstream from the precipitation.
GOES-R data has the potential to improve the forecasts of local variations in winter
precipitation through thermodynamic diagnosis from the HES, and the development of
now-cast products from the ABI.
3. Case Study Database
Although observations similar to those from GOES-R might not become available
prior to the GOES-R launch (depending on the outcome of the GIFTS program), “slices”
of similar data are now or will become available well before GOES-R, from a variety of
experimental and operational satellites and in-situ platforms. We propose to develop a
multi-platform database for each of the three atmospheric phenomena described above.
As a starting point, we will consider the following three cases:
1. Hurricane Lili (Sept. 21-Oct. 5, 2002). Lili formed in the Caribbean, rapidly
intensified in the Gulf of Mexico, and then rapidly weakened just before making landfall
along the Louisiana coast.
2. The May 7-10, 2003 Severe Weather Outbreak. During this four-day period, 1463
severe weather reports were logged by the NCEP Storm Prediction Center, including 223
tornados. This outbreak affected a large area from the front range of the Rockies through
the Midwest.
3. The February 12-14, 2003 Lake-Effect Snow Storm “Newton”. A narrow band of
snow formed downwind of Lake Ontario and dropped about 50 inches of snow centered
on Oswego County, NY. A less intense snow band formed east of Lake Erie. This event
was nicknamed “Newton” by the Buffalo NWS office, who informally names the most
intense Lake-Effect snowstorms.
The pathway to HES includes the current GOES sounder, AIRS (2002), IASI (~2005),
CrIS (~2006) and possibly GIFTS (2008?). For the initial case studies, current GOES
sounder and AIRS data will be collected. The Hyperion data from EO-1 might also
provide useful information concerning moisture variability. Although this data has a very
narrow swath, it might also be useful for some limited areas. For imager applications, the
data from GOES, AVHRR, AMSU, and MODIS will be collected initially. MSG data
will also be collected for future tropical cyclone cases in the eastern Atlantic. It will also
be possible to obtain TRMM data for the tropical cyclone studies. As the project evolves,
data from NPOESS Prepatory Project (NPP) and other satellites will be included. In
addition, in-situ data will be collected for ground truth and validation, including
specialized in-situ observations such as hurricane aircraft reconnaissance and
Kansas/Oklahoma ARM data when possible. The conventional rawinsondes, surface
observations, etc will also be obtained for the case studies.
All of the this data will be converted to standard formats and used for science studies,
advanced product development, input for numerical modeling studies, and training. A
web site will be available for viewing the combined datasets. The longer-range goal of
the project is to transition from case study to real time data displays, organized by
atmospheric phenomena. For example, a “hurricane” site would provide multi-platform
observations with common map backgrounds, enhancement curves, etc, to provide users
with advanced looks at what will be available with GOES-R.
4. Numerical Modeling Studies
Perhaps the most difficult aspect of GOES-R to simulate using existing and pre-GOES-R
satellite observations is the temporal resolution. For this reason, a numerical simulation
component will be included in our science study. In the initial three-year research
program, will we use numerical models in three ways, as follows.
1. Physical Process Studies - High resolution mesoscale models initialized will real data
will be used to simulate each of the atmospheric phenomena described in section 2. The
environments of simulated severe weather, tropical cyclones, and snow bands can be
studied in detail to gain a better understanding of their temporal and spatial variability.
For example, how rapidly does the near environment of a tornadic supercell or the
temperature profile in the eye of a rapidly intensifying tropical cyclone vary with time,
and how sensitive is the model evolution to this variation? These types of studies can
provide justification of the need for thermodynamic sampling at a greater frequency than
is possible from polar-orbiting platforms.
2. Simulated Satellite Observations - The numerical model output can be used as input to
radiative transfer models to generate simulated satellite data. This simulated data can be
used for advanced product development, and for input to satellite retrieval algorithms and
data assimilation studies. This approach was particularly successful for the AIRS
program, where simulated radiances were generated from the NCEP global model fields
and radiative transfer models more than a year before the launch of the satellite.
3. Data Assimilation Studies – The simulated satellite data from the numerical model in
combination with their data assimilation systems can be used to estimate the impact of
various data types. For example, the initial condition from the model can be sub-sampled,
and then used as input to a data assimilation system. It is then possible to address
questions such as “How much would the forecast be improved if high resolution
temperature profiles were available every ½ hour instead of every 4 hours, in a severe
weather environment?” In addition, different data assimilation techniques can be tested to
help determine optimal methods for using observations in various forms, and with
variable accuracy and resolution. We will consider ensemble Kalman filtering
assimilation techniques, which are being considered as the next generation method at
NCEP.
For the numerical modeling studies, we propose to start with the Colorado State
University Regional Atmospheric Modeling System (RAMS), which also includes an
advanced data assimilation system. The RAMS model includes a comprehensive
land/surface model, advanced microphysical parameterizations, and moving nested grids
for hurricane simulations. This model is probably about five years more advanced than
current operational mesoscale models used in NCEP operations. However, as the project
progresses, we plan to make a transition to the Weather Research and Forecast (WRF)
model. This model will likely become the next generation regional model at
NOAA/NCEP, and also the next generation mesoscale model for the much of the
research community. In addition, we plan to closely coordinate with the Joint Center for
Satellite Data Assimilation (JCSDA), especially with regard to radiative transfer
modeling. The JCSDA is developing a community radiative transfer modeling system,
which we will incorporate into our research.
5. Project Personnel
Project Oversight will be provided by Prof. Thomas Vonder Haar and Ken Eis
(CIRA/CSU) and Dr. Mark DeMaria (NESDIS/ORA).
The numerical modeling studies will primarily be performed by Drs. Milija Zupanski,
Dusanka Zupanski, Louis Grasso, M. Sengupta with oversight from Mark DeMaria.
Milija and Dusanka are CIRA employees who formerly worked for the NCEP
Environmental Modeling Center, and are experts 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. A ¾ time CIRA
computer programmer and ¾ time meteorologist will also be supported to assist with the
database development, as well as with analysis of the numerical modeling output.
The project also will include a PhD student to focus on a specific aspect of the research,
depending upon their 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.
6. Proposed Budget
The proposed budget for the first year of the project is $350 K. A detailed breakdown of
the costs is provided in the attached spreadsheet. It is anticipated that the second and third
year budgets will be lower (one the order of 300 K per year, depending on the availability
of funds), because the first year includes $68 K for procurement of hardware for a mass
storage device to house the extensive satellite database and numerical model output.
Continuation proposals will be written for year 2 and year 3, at the discretion of
NESDIS/OSD.
7. Project Coordination and Long Range Planning
It is anticipated that this research will be part of a larger GOES-R Risk Reduction
program that will be coordinated with all of NESDIS/ORA. It is also anticipated that
science activities will continue beyond the first three years proposed here. The
continuation research will evolve towards making real time multi-platform datasets
available for training and to familiarize the user community with potential GOES-R
products. The numerical modeling studies will be generalized to assimilate real data (in
addition to the simulated data proposed above), and to coordinate this effort with the ongoing activities of the JCSDA. Prior to the launch of GOES-R, real-time simulated data
should be produced on a routine basis in combination with a mesoscale model forecast at
NCEP.
8. Progress Reports
Semi-annual progress reports will be provided to NESDIS/OSD management.
9. 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. One trip to Madison is requested to attend the next NOAA Hyperspectral
Satellite Data Workshop, planned for May of 2004.
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.
V. SUPPLIES
Storage media will be required to transport and back-up data and numerical model output
associated with this project.
VI. TUITION
It is anticipated that one graduate student will be supported as part of this research
project.
VII. EQUIPMENT
The hyperspectral data required for this project are extremely voluminous. Funds are
included for a high-speed random access mass storage device to house these large
satellite datasets.