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“High Resolution CONUS Reanalysis and Pest Emergence Prediction”

“High Resolution CONUS Reanalysis and Pest Emergence Prediction”. Andrew Monaghan National Center for Atmospheric Research, Boulder, Colorado, USA 19 May, 2014. Outline. Brief description of some recent modeling projects. Overview of proposed high-resolution CONUS reanalysis

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“High Resolution CONUS Reanalysis and Pest Emergence Prediction”

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  1. “High Resolution CONUS Reanalysis and Pest Emergence Prediction” Andrew Monaghan National Center for Atmospheric Research, Boulder, Colorado, USA 19 May, 2014

  2. Outline • Brief description of some recent modeling projects. • Overview of proposed high-resolution CONUS reanalysis • Overview of ongoing mosquito/dengue risk mapping project. 2

  3. Recent Modeling Projects 3

  4. CFDDA: A global 20-y 40-km meso-scale reanalysis with Newtonian nudging Rife et al. 2010; Monaghan et al., 2010 4

  5. Uganda: 2-km hybrid statistical-dynamical downscaling Monaghan et al. 2012 5

  6. (1) More efficient dynamical-downscaling of seasonal and decadal climate projections Low-res GCM run High-res WRF run Pinto et al. 2013 6

  7. (2) More efficient dynamical-downscaling of seasonal and decadal climate projections Vanvyve et al., submitted 7

  8. Bias-corrected climate simulations 8

  9. A high-resolution reanalysis over CONUS 9

  10. Overview • Currently proposed to DOE SciDAC program. • Ultimate Goal: 1-km, 35-year hourly reanalysis over U.S. • Extremely challenging computationally Annual Precipitation, mm/day, 1986-2005 10

  11. Details • Model Domain • Initial and Lateral boundaries from ERA-Interim Reanalysis • 4800 x 3100 grid points, 51 vertical levels • Use 37,200 cores on ORNL’s Titan machine, 118.8 M core-hours • 19 PB of model output. This will be condensed to < 1 PB. • Employ NCAR’s Climate-FDDA modeling technology • Continuously assimilates surface and radiosonde observations, aircraft reports, wind profiles, NEXRAD winds, satellite winds. • Refine CFDDA for super high resolution • Focus on atmospheric boundary layer and precipitation: both are partially-resolved at 1-km scales • Emphasis on Validation • Go beyond conventional statistics and employ new and advanced feature-based verification approaches 11

  12. Toward Pest Emergence Prediction 12

  13. Dengue Fever • Dengue Fever and Dengue Hemorrhagic Fever are caused by dengue viruses transmitted by Aedes mosquitoes • Annually, ~400 million people contract dengue worldwide • ~1 million of those people develop severe dengue hemorrhagic fever • No approved vaccine available • Increasing number and severity of cases in the Americas. 13

  14. Estimated Distribution of Dengue in Mexico, Present Day Study Area Dengue endemic regions (blue) Mexico City Source: DengueMap – a CDC-HealthMap collaboration) 14

  15. Framework for AedesaegyptiStudy 15

  16. Aedes/Dengue Risk Mapping System Courtesy Paul Bieringer, NCAR, STAR, LLC 16

  17. WRF Reanalysis: Year 2013, hourly, 3-km Courtesy Paul Bieringer, NCAR, STAR, LLC 17

  18. Image Processing Algorithm to Estimate Container Habitat Quantities/Locations Courtesy Paul Bieringer, NCAR, STAR, LLC 18

  19. Energy Balance Modeling in Breeding Containers The heat storage (i.e., change in temperature) in the water container is equal to the balance of energy to/from the container SW: Shortwave radiation LW: Longwave radiation H: Sensible heat L: Latent heat G: Ground heat C: Conduction from container surfaces S: Heat storage Steinhoff and Monaghan (2013) 19

  20. Skeeter Buster: All Life Stages:Temperature-Dependent Development • This is based on an enzyme kinetics model, where development is based on a single rate-controlling enzyme that is denatured at high or low temperatures • Parameters determined from iteratively fitting observed data to nonlinear regression function, using initial values from literature. This equation used for all life stages, with different parameters • Development is cumulative to a threshold 20

  21. Ae. aegyptiDevelopment and Temperature Mexico City Average Wet Season Temperature 21 Focks et al. 1993; Tun-Lin et al. 2000; Kearney et al. 2009

  22. Results: Female Ae. aegyptiabundance, 2013 JAN JAN MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Courtesy Paul Bieringer, NCAR, STAR, LLC 22

  23. Summary • Our work employs dynamical downscaling as a tool to investigate problems in climate change, renewable energy, human health and other sectors. • Our recent focus has been on: • Developing more economical ways to dynamically downscale • Linking dynamically downscaled meteorological fields with physically-based downstream models to address weather-sensitive applications. 23

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