1. SHENAIR University of Virginia Research Projects Status Report August 7, 2006 Robert Davis, Stephen Gawtry, David Knight, David Hondula, Temple Lee, Luke Sitka, Jerry Stenger

2. University of Virginia Project Components Back-trajectory climatology - HYSPLIT Model, ECMWF initialization Back-trajectory climatology - HYSPLIT Model, ECMWF initialization wind/pressure field wind/pressure field Air mass climatology - According to Spatial Synoptic Classification (Sheridan, 2002) Air mass climatology - According to Spatial Synoptic Classification (Sheridan, 2002) Air Quality and Asthma Alert System - Hospital admission data & pollen data Air Quality and Asthma Alert System - Hospital admission data & pollen data

3. Hourly Weather Stations Virginia Charlottesville (CHO) : 1973 – 2006 (74.1%) Charlottesville (CHO) : 1973 – 2006 (74.1%) Roanoke (ROA) : 1948–2005 (98.3%) Roanoke (ROA) : 1948–2005 (98.3%) Richmond (RIC) : 1948 – 2005 (98.4%) Richmond (RIC) : 1948 – 2005 (98.4%) Shenandoah Valley (SHD) : 1973 – 2006 (11.5%) Shenandoah Valley (SHD) : 1973 – 2006 (11.5%) West Virginia Beckley (BKW) : 1963 – 2005 (95.1%) Beckley (BKW) : 1963 – 2005 (95.1%) Huntington (HTS) : 1961 – 2005 (95.5%) Huntington (HTS) : 1961 – 2005 (95.5%) Charleston (CRW) : 1949 – 2005 (98.3%) Charleston (CRW) : 1949 – 2005 (98.3%) Lynchburg (LYH) : 1948 – 2006 (60.8%) Lynchburg (LYH) : 1948 – 2006 (60.8%) Martinsburg (MRB) : 1949 – 2006 (81.2%) Martinsburg (MRB) : 1949 – 2006 (81.2%) Washington, D.C. Dulles Airport (IAD) : 1962 – 2005 (95.8%) Dulles Airport (IAD) : 1962 – 2005 (95.8%)

4. Shenandoah Valley Region Hourly Weather Stations

6. Back-Trajectory Model HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) Version 4.8 HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) Version 4.8 Downloaded from: http://www.arl.noaa.gov/ready/hysplit4.html Downloaded from: http://www.arl.noaa.gov/ready/hysplit4.html http://www.arl.noaa.gov/ready/hysplit4.html Single or multiple (space or time) simultaneous trajectories of parcels Single or multiple (space or time) simultaneous trajectories of parcels 3D particle dispersion 3D particle dispersion Computations forward or backward in time Computations forward or backward in time Newer version includes improved advection algorithms Newer version includes improved advection algorithms

7. Back-Trajectory Data Input ECMWF initialization wind/pressure field ECMWF initialization wind/pressure field ETA 80 km resolution ETA 80 km resolution 1/1/1997 - 12/31/2005 1/1/1997 - 12/31/2005 8 Shenandoah Valley first-order stations: VA : Lynchburg, Richmond, Roanoke, Wash. Dulles 8 Shenandoah Valley first-order stations: VA : Lynchburg, Richmond, Roanoke, Wash. Dulles WV: Beckley, Charleston, Huntington, Martinsburg

8. Back-Trajectory Data Output 315,552 total back-trajectories from: - 8 stations - 9 total years (1997-2005) - Twice daily time steps at 6Z and 18Z - 6 levels (10 hPa levels from surface) 315,552 total back-trajectories from: - 8 stations - 9 total years (1997-2005) - Twice daily time steps at 6Z and 18Z - 6 levels (10 hPa levels from surface) Tracks trajectory latitude, longitude, and elevation over 72 hours Tracks trajectory latitude, longitude, and elevation over 72 hours 12-hour time steps 12-hour time steps

9. Back-Trajectory Example

10. Proposed Analysis of Back- Trajectories Compass direction Compass direction Examined for seasonality, long-term trends, and spatial variability Examined for seasonality, long-term trends, and spatial variability

11. Cluster Analysis Desire to group back- trajectories according to direction, transport length, elevation, point of origin, etc. Desire to group back- trajectories according to direction, transport length, elevation, point of origin, etc. Clusters examined for seasonality, long-term trends, and spatial variability Clusters examined for seasonality, long-term trends, and spatial variability

12. Probabilities Likelihood trajectory passed through given regions Likelihood trajectory passed through given regions Examined for seasonality, long- term trends, and spatial variability Examined for seasonality, long- term trends, and spatial variability Source: Kordziel, N., 2005

13. Development of Air Mass Climatology Spatial Synoptic Classification (SCC) Spatial Synoptic Classification (SCC) Developed by Sheridan et al., 1996 and 2006 Developed by Sheridan et al., 1996 and 2006 “Weather types” “Weather types”

14. Air Mass Types 1 - Dry Moderate (DM) - Often found in east/central - Often found in east/central U.S. U.S. - Mild conditions - Mild conditions - Air warmed/dried over Rockies - Air warmed/dried over Rockies 2 - Dry Polar (DP) - A.k.a. Continental Polar - A.k.a. Continental Polar - Clear, cool conditions - Clear, cool conditions - Usually advected from Canada - Usually advected from Canada 3 - Dry Tropical (DT) - Hottest, driest conditions - Hottest, driest conditions - Advected from SW U.S. or - Advected from SW U.S. or Mexico Mexico Santa Anna/Chinook winds Santa Anna/Chinook winds 4 - Moist Moderate (MM) - Cloudy, moist mild - Cloudy, moist mild - Frontal overruning - Frontal overruning - Typically appears south of MP - Typically appears south of MP 5 - Moist Polar (MP) - Cloudy, humid, cool conditions - Cloudy, humid, cool conditions - Air transported inland from - Air transported inland from cool ocean cool ocean - Frontal overruning well to - Frontal overruning well to south south 6 - Moist Tropical(MT) - Warm, very humid - Warm, very humid - Warm sector of frontal cyclones - Warm sector of frontal cyclones - Gulf return-flow of highs in - Gulf return-flow of highs in eastern U.S. eastern U.S. 7 - Transition (T) - Air masses changing on - Air masses changing on given day given day

15. MT/MT+/MT++ MT weather often associated with elevated health risks MT weather often associated with elevated health risks MT+ and MT++ being extreme MT days MT+ and MT++ being extreme MT days MT+ represents top 25% of MT days (Sheridan et al., 1996 and 2002) MT+ represents top 25% of MT days (Sheridan et al., 1996 and 2002)

16. Time Series Analysis Gaps can be documented/undocumented Gaps can be documented/undocumented Gaps arise from inhomogeneities: - Change in station location, instumentation, data acquisition, etc. - Actual climate shifts Gaps arise from inhomogeneities: - Change in station location, instumentation, data acquisition, etc. - Actual climate shifts Employ Menne and Williams (2005) Likelihood Ratio Test Employ Menne and Williams (2005) Likelihood Ratio Test - Ratio test (to detect shift in mean) - Ratio test (to detect shift in mean) - Multi-phase regression - Multi-phase regression - Detection of multiple changepoints in a time series - Detection of multiple changepoints in a time series

17. Sample Air Mass Results DP frequencies: IAD Summer MT frequencies: IAD Summer

20. Asthma Data Solucient, healthcare information provider Solucient, healthcare information provider Patient-level data Patient-level data UB-92 form filled out by patients during payment for treatment UB-92 form filled out by patients during payment for treatment Daily admissions to all hospitals in Shenandoah Valley Daily admissions to all hospitals in Shenandoah Valley 1/1/01 – 12/31/05 1/1/01 – 12/31/05

21. Pollen Data Surveillance Data, Inc. (SDI) - Aerobiological environmental Surveillance Data, Inc. (SDI) - Aerobiological environmental consulting firm (1980) - Supplies “Pollen.com” consulting firm (1980) - Supplies “Pollen.com” Contacted three specialized Ph.D’s Contacted three specialized Ph.D’s

22. Next Steps Back-trajectories: Back-trajectories: -Perform cluster analysis to develop -Perform cluster analysis to develop climatology climatology Air mass analysis: Air mass analysis: -Apply Menne and Williams (2005) to -Apply Menne and Williams (2005) to examine datasets for trends and examine datasets for trends and changepoints changepoints Asthma analysis : Asthma analysis : -Begin sorting and examining asthma dataset -Find adequate pollen data -Begin sorting and examining asthma dataset -Find adequate pollen data