1. NCAR GIS Program : Bridging Gaps
Jennifer Boehnert
GIS Coordinator
NCAR

2. Scope of work
Animation

3. NCAR GIS Program
Spatially explicit research in interactions between Climate, Environment and Society Interactions requires integration of social, Earth system sciences (including Atmospheric Sciences) and GIS
Integration of these disciplines presents an interesting research challenge: we need to find appropriate linkages between different theoretical and methodological concepts, study objects, ways of thinking about data, semantics, scales and uncertainty.
GIS approach provides opportunity to focus on the space (as a synthesizing medium) and to focus on and resolve differences between different data models.
.Goals of GIS Initiative:
Conduct research integrating atmospheric, the Earth system and social sciences through spatial analysis and interoperability of georeferenced information;
Support the use of GIS as both an analysis, and an infrastructure tool in atmospheric research;
Improve usable science and knowledge sharing between science groups, educators and stakeholders.

4. Goals and program elements
Conduct research integrating the Earth system and social sciences through spatial analysis and interoperability of georeferenced information;
Support the use of GIS as both an analysis, and an infrastructure tool in atmospheric research;
Improve usable science and knowledge sharing between science groups, educators and stakeholders.
Education,
Training &
User
Support
Research
Enabled by
By GIS
Data
Integration
&
Distribution
Research
In GIS
Technology
Building
Community

5. Atmospheric Data Model
2006
Atmospheric Data Model
2005
Working dialog between ESRI and weather and climate community:
Five workshops in
The goal is seamless integration of atmospheric and oceanographic data:
Observations
Products
Infrastructure
Collaborators:
NCAR, Unidata/UCAR, NWS. NOAA NCDC, University of
Oklahoma, Pacific Marine Environment Lab, National Marine Fisheries Service, NASA Jet Propulsion Laboratory, ESRI, George Mason University
ESRI provides links to Case Studies, Design Templates, and Tools at
The ADM is generally reviewed annually at the ESRI International User Conference; for the latest presentation see:
Wilhelmi, Boenhert, Shipley, Kopp, Domenico and Breman (2006) Weather and Climate Data Model Technical Workshop (70 slides), ESRI User Conference, San Diego, CA - presented 10 Aug 06.
2004
Team Atmosphere (150 members)
Data Modeling Group:

6. Satellite Images - Raster Atmospheric Boundaries
The Thematic Layers
Observations
Satellite Images - Raster
Radar - Polygons
Surface - Points
Mobile Obs – Points
Infra
Human Elements
Earth Surface
Products
Weather Events
Atmospheric Boundaries
Climate Variables
Numerical Models
ESRI provides links to Case Studies, Design Templates, and Tools at
The ADM is generally reviewed annually at the ESRI International User Conference; for the latest presentation see:
Wilhelmi, Boenhert, Shipley, Kopp, Domenico and Breman (2006) Weather and Climate Data Model Technical Workshop (70 slides), ESRI User Conference, San Diego, CA - presented 10 Aug 06.

7. NetCDF in ArcGIS Adopting NetCDF CF data format by ESRI GIS software
Since 2006 release of ArcGIS 9.2 NetCDF CF format can be read in GIS
Opens atmospheric and oceanographic data to millions of GIS users

8. CCSM: Community Climate System Model

9. Climate change on the map
Distributing CCSM IPCC projections (monthly averages) in a GIS format
GIS – a tool of many stakeholders (land and resource management)
Step by step data access

10. Who is using CCSM projections?
Biomass potential
Salmon conservation
Resource management
Climate change education
Vegetation ecology
Water resources
Agriculture
Vulnerability of population and ecosystems
Human health
Energy
~2500 users from 108 countries
Users: Research; Education; Government; GIS; Environmental; Military and defense;
Industry; Regional planning and economic development; Native American Tribes, Other…

11. Statistical downscaling of CCSM runs
From 155 km to 4.5 km grid cell
Monthly temperature and precipitation projections for the continental U.S.
Statistical downscaling method using PRISM (Parameter-elevation Regressions on Independent Slopes Model), dataset for derivation of a regression coefficients
20C3M 1 Jan 1896 Tas
The downscaling process starts with the PRISM data from a group in Oregon. "... PRISM (Parameter-elevation Regressions on Independent Slopes Model) climate mapping system, developed by Dr. Christopher Daly, PRISM Group director. PRISM is a unique knowledge-based system that uses point measurements of precipitation, temperature, and other climatic factors to produce continuous, digital grid estimates of monthly, yearly, and event-based climatic parameters." The ones I use are at about 4.5km resolution. The PRISM data only exist 'over land', so the oceans are right out ) I fit a spline to the GCM data (about 800 points) and predict at all the (1405*621 = ) PRISM locations. 2) I adjust the predictions by applying a linear model (slope and intercept) derived from the PRISM data. Each prediction location has a unique model for EACH month (but all januaries have the same model).  Put another way, there are *12 linear models. The PRISM data exist as monthly means for 111 years now ( ), so to determine the model coefficients, I aggregated the PRISM data to the GCM grid and used that as the 'observations' for the regression. As such, the GCM data need to exist on the PRISM grid in order to apply the linear model. This is accomplished by using a thin-plate spline to fit a 2D model with the 779 GCM data and using it to predict on the PRISM grid. I have not (yet) tried a cross-validation approach of withholding some of the 111 years of PRISM data and using the resulting model to predict. We then could compare the predictions and the withheld PRISM data to get some indication of prediction uncertainty. There is some hope that the process could be applied real-time and thereby greatly reduce the amount of storage needed.
celsius

12. New Products Anomaly – change Annual and seasonal averages
Present – 2030
Present – 2050
Present – 2099
PPT –total precipitation
TAS – air temperature
Annual and seasonal averages
Available by fall

13. Extreme Weather Application
Develop an interactive web mapping application which integrates weather forecast models with socio-economic and infrastructure data
Making weather models more useful and understandable for non-meteorologists
Communicate the impacts from extreme weather events

14. Visualization
Saffir-Simpson Hurricane Wind Scale
The Saffir-Simpson Hurricane Wind Scale is a 1 to 5 categorization based on the hurricane’s intensity at the indicated time. The scale provides examples of the type of damages and impacts in the United States associated with winds of the indicated intensity. In general, damages rise by about a factor of four for every category increase. The maximum sustained surface wind speed (peak 1-minute wind at 10 m [33 ft]) is the determining factor in the scale. The scale does not address the potential for such other hurricane-related impacts, as storm surge, rainfall-induced floods, and tornadoes. These wind-caused impacts are to apply to the worst winds reaching the coast and the damage would be less elsewhere. It should also be noted that the general wind-caused damage descriptions are to some degree dependent upon the local building codes in effect and how well and how long they have been enforced. Hurricane wind damage is also dependent upon such other factors as duration of high winds, change of wind direction, amount of accompanying rainfall, and age of structures.
Dynamically adds netCDF WRF Hurricane model through ArcGIS Server
Using ArcGIS Online free map as background
Symbolized based on hurricane wind scale (Saffir-Simpson Wind Scale) and accumulated rain.

15. Population Impacts
Find all people who will be effected by winds greater than 70 mph from 12:00 – 5:00 AM Sept 13th.

16. GIS Program
THANK YOU