# Presentation: 079967

## Climate Modeling Using Earth Observation Data to Improve Public Health Decisions

**PHAiRS Team**
**CCSP Workshop**
**Climate Science in Support of Decision Making**

**14-16 November, 2005**
**Arlington, VA**

## PI & Co-PI
Dr. S. Morain (UNM)
Dr. W. Sprigg (UA)
Project Scientists
A. Budge (UNM)
Dr. K. Benedict (UNM)
Dr. W. Hudspeth (UNM)
T. Budge (UNM)
Dr. D. Yin (UA)
Dr. B. Barbaris (UA)
S. Caskey (SNL)
Dr. D Holland (NASA-SSC)
Dr. J. Speer (TTUHSC)

**PI & Co-PI**
**Dr. S. Morain (UNM)**
**Dr. W. Sprigg (UA)**
**Project Scientists**
**A. Budge (UNM)**
**Dr. K. Benedict (UNM)**
**Dr. W. Hudspeth (UNM)**
**T. Budge (UNM)**
**Dr. D. Yin (UA)**
**Dr. B. Barbaris (UA)**
**S. Caskey (SNL)**
**Dr. D Holland (NASA-SSC)**
**Dr. J. Speer (TTUHSC)**

**Research Assistants**
**G. Sanchez (UNM)**
**B. Chandy (UA)**
**C. Cattrall**
**Public Health Partners**
**City of Lubbock Dept of Health**
**Pima County Dept of Environmental Quality**
**Arizona Dept of Health Services**
**NM Dept of Health**
**ARES Corporation**

**Notes:**
The Public Health Applications in Remote Sensing (PHAiRS) Team:
University of New Mexico, Earth Data Analysis Center: Dr. Stan Morain, PI; Amelia Budge, Project Coordinator; Dr. Karl Benedict, IT; Dr. William Hudspeth, DREAM Output and Web Mapping; Thomas Budge, EO Data Discovery and Access; Gary Sanchez, Research Assistant (EO Data Formatting and Assimilation)
University of Arizona, Department of Atmospheric Sciences: Dr. William Sprigg, Co-PI; Dr. Dazhong Yin, DREAM Modeler; Dr. Brian Barbaris, DREAM Verification, Validation & Performance; Beena Chandy, Research Assistant (DREAM Performance)
University of Arizona, Optical Sciences Center: Dr. Kurt Thome, EO Dust Detection; Christopher Catrall, Research Assistant (EO Dust Detection)
Sandia National Laboratories, Albuquerque, NM: Susan Caskey, RSVP decision support system development and testing
Texas Tech University, Health Science Center: Dr. James Speer, Public Health Data & Health Surveillance
National Aeronautics and Space Administration, Stennis Space Center: Dr. Donald Holland, EO Data Assessment and Assimilation
Stakeholder Partners:
Dr. Alan Zelicoff (MD), ARES Corporation: RSVP and SYRIS concept, development, and deployment;
??? Pima County, Department of Environmental Quality;
Ken Komatsu, Epidemiologist & Lea Trujillo, Syndromic Surveillance, AZ Department of Health;
Gary Simpson, Epidemiologist, NM Department of Health;
??? City of Albuquerque, NM, Department of Air Quality;
Tigi Ward, Director, City of Lubbock, TX, Department of Health

## Public Health Applications in Remote Sensing (PHAiRS)

- Focus on SW, dust storms, respiratory diseases, and syndromic surveillance
- 3 thrusts
- Assimilate EO data into DREAM as part of NCEP/Eta forecasting system
- Measure incremental improvements to DREAM outputs as inputs to RSVP/SYRIS
- Create collaborations with public health authorities to validate relationships between dust episodes and respiratory complaints

**Notes:**
Focus is on asthma as a rapidly growing chronic respiratory illness among children and elderlies, especially in the Northern Hemisphere; and on valley fever as an example of diseases spread by airborne pathogens.
DREAM is the Dust Regional Atmospheric Model that predicts entrainment of dust based on observed surface conditions and NCEP/Eta atmospheric measurements. The project wants to replace static surface parameters in DREAM with dynamic EO surface measurements to benchmark improvements in dust entrainment predictions.
Dust storms over the SW in December 2003 were used to baseline DREAM performance before EO Data assimilation by comparing model outputs with standard products from surface synoptic sites, METAR reports, and local air quality networks. To date, assimilated surface EO products have shown significant improvements in DREAM outputs; subsequent improvements are expected as the assimilation process is iterated and refined.
While DREAM outputs are being improved using EO data, the PHAiRS Team has engaged public health authorities in AZ, NM, and TX to guide the geospatial requirements in syndromic surveillance, and, where possible, to identify cause-and-effect relationships between dust and human health responses. These communities include developers of syndromic surveillance systems, state and local departments of health, and regional air quality authorities.

## Project Framework

**Project Framework**

**Notes:**
The approach for an integrated system solution for REASoN is based on an architecture developed by NASA’s Applications Division of the Science Mission Directorate.  It is frequently referred to as the “Mission-to-Models” chart.  In this construct, sensor measurements  are indicated in the lower left blue box.  The items listed represent specific sensors and products. These products represent outputs from Science Instrument Teams created by NASA.  Before being released for general use, products are calibrated and verified for their technical quality, and validated for conveying the information they claim to convey.  Each product is thoroughly documented as to algorithms and “pedigree,” though they often lack in having adequate metadata for non-science users.  Many of the parameters required to drive models are represented by one or more Mission products, so there is a challenge finding the best product for a given application.
The modeling component of the system solution is represented in the upper left blue box.  NASA has researched a large number of Earth system science models and is collaborating with sister agencies to identify those most likely to be enhanced by replacing traditional parameter data with EO measurements that are continuous in the spatial domain and more frequent in the temporal domain. For public health applications focused on chronic respiratory illnesses, NOAA’s NCEP/Eta model has been selected from the ESMF as an excellent candidate for ingesting Mission data.  “Dream” is an experimental component of NCEP/Eta designed specifically as a dust forecasting model.  Other important models for consideration should be useful for visualizing 2-D and 3-D plume dispersions and other environmental phenomena.
     Observations and predictions from the Missions-to-Models architecture feed into the designated decision support system (RSVP/SYRISS) and form the basis for a public health toolkit. Support systems most often are developed to aid stakeholders (purple box) in a decision-making environment.  Therefore, the expectation is that stakeholders and their primary federal agencies (CDC, NIH, etc.) will collaborate to define system requirements.  Once these have been articulated, the integrated system solution can be reverse-engineered back to Missions-to-Models to provide the needed information.

## New Mexico/Texas Dust Storm – Dec 2003

**New Mexico**

**Mexico**

**Texas**

**New Mexico/Texas Dust Storm – Dec 2003**

**Notes:**
On December 15-16, 2003 a severe dust storm originated in northern Mexico, eastern New Mexico and West Texas, spawned by a cold front accompanied by dry surface conditions.  This storm was observed from NASA’s Terra satellite by the Moderate Resolution Imaging Spectroradiometer (MODIS).  Wind patterns are evident in the pattern of clouds and dust. Ferric, siliceous, and calcareous dusts are all present. Larger population centers are shown as yellow dots. In addition to mineral dust, organic dust from cattle feed lots and cotton ginning also are susceptible to entrainment. These organic dusts probably have different but equally serious health consequences.

## DREAM’s Governing Equation

**Notes:**
Dream has two parts:
An atmospheric modeling system having 32 model layers extending from the Earth’s surface to 100 hPa in the vertical and ranging in the x,y dimensions from 0.1 degree to 1.0 degree lat./lon resolution.
A dust concentration module parameterizes both wet and dry deposition. Soil textures are specified by the NCEP/Eta model using Zobler seven textural classes @ 1° resolution and the UNCEP/GRIDDED FAO/UNESCO soil units @ 2 ́resolution. Other surface parameters include vegetation cover, soil moisture, surface atmospheric turbulence, and topography. One of the most important parameters for dust entrainment is surface roughness length (z0). 
The static datasets employed in the baseline DREAM were: FAO 2 x 2 minute soil types converted into texture classes; 10 minute resolution Olson World Ecosystem land cover; and 1-km2 USGS topography. These are the data sets targeted for replacement by higher resolution dynamic EO data.

## Observed Visibility vs Modeled Dust

**Observed Visibility vs Modeled Dust**
**Concentrations Dec. 15-16, 2003**

**Texas**
**Continuous Air Monitoring Stations**

**DREAM Baseline (no EO data included)**

**Notes:**
An animation of the December 15-16 dust storm in eastern New Mexico and west Texas was created from the TX Continuous Air Monitoring Stations (CAMS) data set. The data portray visibility in three categories (yellow < 7 miles; orange < 3 miles; and red < 1 mile). The origin of the dust storm is first measured by the western-most CAMS around 9 am CST and progresses rapidly toward the east and southeast.
The DREAM animation is derived from a much larger geographic domain than the CAMS because it requires much more atmospheric and surface data to model conditions. The output measurement is modeled dust concentrations, not visibility.
Both animations contain six “frames” representing the same times intervals. While far from perfect, DREAM is able to simulate the origin and progress of the dust storm. In addition, one must remember that the CAMS stations are limited in number (not synoptic).
Further comparisons of both the synoptic upper and surface wind speeds, directions, and temperatures compared to their modeled counterparts provide further validation as to the performance of DREAM.  

## Modeled vs Observed Synoptic Patterns

**DREAM Simulation**

**Observed Geopotential Height**

**Observed Temperature**

**Modeled vs Observed Synoptic Patterns**
**12Z 16 Dec 03**

**Notes:**
The modeled simulation seems to have fine agreement with both the observed upper-level trough in the geopotential height field and the observed temperature field. An essential criterion for system performance is to demonstrate conclusively that the atmospheric components provided by NCEP/Eta for DREAM are not significantly affected when surface components are replaced by EO data.  

## Comparison of DREAM Dust

**Comparison of DREAM Dust**
**Concentrations at 20Z 15 Dec 03**

**Static Surface Inputs**

**EO Surface Inputs**

**Notes:**
Using the baseline surface data inputs for DREAM, the PHAiRS Team created a snapshot of dust concentrations for 20Z on December 15, 2003. It then created a similar snapshot after higher resolution MOD-12 land cover data replaced the Olson World Ecosystem land cover data. Assimilation of EO data provides a more detailed visualization of surface conditions than the baseline model run. The intention is to refine the outputs even more with other EO measurements.  

## DREAM Performance

| Metrics | Wind
Speed (m/s) | Wind
Direction (°) | Temp.
(K) | Definition
(M: modeled; O: observed) |
| --- | --- | --- | --- | --- |
| Mean observed | 
5.53
 | 231.40 | 276.74 |  |
| Mean modeled | 4.65
4.37 | 226.60
230.38 | 275.56
277.48 |  |
| Mean
bias | -0.88
-1.16 | -4.80
-1.02 | -1.20
  0.72 |  |
| Mean
error | 1.97
2.03 | 51.76
47.85 | 4.09
2.67 |  |
| Agreement index | 0.74
0.75 | 0.74
0.76 | 0.71
0.95 |  |

**Blue values** = before EO Data Assimilation
**Red  values** =  after   EO Data Assimilation

**DREAM Performance**
**Before & After EO Data Assimilation**

**Notes:**
Surface wind speed, direction, and temperature change as weather systems pass through. The ability of DREAM to simulate these surface conditions is critical to its overall performance. The table compares both the observed and modeled surface wind speed, direction, and temperature before and after EO data assimilation. In this example the only EO data assimilated into the model were higher spatial resolution MODIS-15 land cover data, replacing the Olson World Ecosystem (OWE) land cover values. Values for surface wind speed and direction appear to be little different from the baseline model run, but the agreement index for surface temperature is significantly improved. The baseline model is therefore considered to simulate observed conditions fairly well; while the replacement of OWE with MOD-15 improves the simulation of surface temperature. Future EO data assimilations will replace 1x1-km USGS topography with SRTM re-sampled to 1x1-km, estimations of surface roughness length (z0) derived from MODIS using a look-up table, soil moisture derived from AMSR-E, and Fraction of Photosynthetically Active Radiation (FPAR) measurements as a means for determining dust source areas.

## Enhancing Decision Support Tools

**Notes:**
This slide demonstrates the functionality of the dust modeling client that can be viewed by public health authorities using their own decision support tool.
The user is viewing dust concentration changes as expressed by the EPA Air Quality Index categories (shown in the color key in the lower left of the image screen).
The client will provide a 48-hour forecast on a daily basis. The user can select any point within the analysis area, either by means of a mouse click on the map, or from a drop-down list of the larger cities and towns. The user can also select to view the forecast of one of four different particle size classes. In the example presented here, the user has selected the particle size 2 category (roughly equivalent to PM10), viewing a 48-hour ground-level forecast for Dodge City, Kansas. 
The graph to the right of the image screen will plot the dust concentration levels for the selected geographic location over the 48 hour time frame. Notice the red bar that moves across the graph. This marker corresponds to the specific frame of data being displayed as the animation progresses. 
Using the controls below the map area, the user can play the animation continuously, pause or stop the animation, or progress either forwards or backwards one frame (representing an hour of time) at a time. While not seen in this image, the user can save any given frame to a PDF file for subsequent printing.

## Relevance to CCSP

**<25**
**26-50**
**51-75**
**76-100**
**101-125**
**>125**

**Deaths per**
**100,000 adults**

**Premature Mortality Risk  Attributable to PM2.5**

**Locations of Emerging Infectious Diseases**

**Notes:**
Goal 3 of the Decisions Support Resources Development (Chapter 11) of the CCSP Strategic Plan is to develop and evaluate methods (scenario evaluations, integrated analyses, and alternative analytical approaches) to support climate change policymaking and to demonstrate the methods with case studies. PHAiRS is entering year 3 of a 5-year project that will in part meet this goal.
Increased concentrations of PM2.5 appear to be increasing premature mortality, mostly in the form of heart attacks. Similarly, asthma is rising sharply across the Northern Hemisphere, perhaps as a response to easterly-moving aerosol clouds that appear to be increasing in frequency and concentration. These clouds have been referred to as “a bathtub ring” circling the Northern Hemisphere.
Finally, emerging infectious diseases seem to be migrating away from the tropics, where they traditionally have been most pronounced, and into northeastern NA, western Europe and Japan. While health trends are based on statistical evidence; their linkages to climate change are still mostly anecdotal. Through collaborations with public health authorities, the PHAiRS team is working to establish medically sound environmental relationships that advance decision support of respiratory diseases.  Through continued assimilation of EO measurements into accepted weather and dust forecasting models, public health decisionmaking will be scientifically more reliable and timely for populations at risk.