“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 Overview of ongoing mosquito/dengue risk mapping project. 2

Recent Modeling Projects 3

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

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

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

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

Bias-corrected climate simulations 8

A high-resolution reanalysis over CONUS 9

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

Details 11 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, 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

Toward Pest Emergence Prediction 12

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

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

Framework for Aedes aegypti Study 15

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

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

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

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

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

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

Results: Female Ae. aegypti abundance, JANAPRMARJAN MAYAUGJULJUN SEPDECNOVOCT Courtesy Paul Bieringer, NCAR, STAR, LLC

Summary 23 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.