US Soybean Rust Detection and Aerobiological Modeling November, 2004 Dan Borchert, Glenn Fowler and Roger Magarey (USDA-APHIS-PPQ-CPHST-PERAL) Daryl Jewett.

Slides:



Advertisements
Similar presentations
An atmosphere-ocean coupled regional climate model for the Mediterranean Alberto Elizalde Daniela Jacob Uwe Mikolajewicz Max Planck Institute for Meterology.
Advertisements

Weather Station Data Quality and Interpolation Issues in Modeling Joe Russo International Workshop on Plant Epidemiology Surveillance for the Pest Forecasting.
Некоторые результаты применения МС ТГУ-ИОА. Conditions of numerical simulations Research domain: 50x50km 2 nearby Tomsk Dates: January, June,
An Aerobiological Assessment of Soybean Rust Threat to North America Scott Isard (UI) & Roger Magarey, Bob Griffin (CPHST/APHIS) Joe Russo (ZedX) & Stuart.
North American Cereal Rust Workshop, St. Paul MN – April 2007 Early Detection and Rapid Response: Pest/pathogen modeling and early warning through the.
Global Weather Data for Pest Risk Mapping Center for Plant Health Science and Technology Source Dept. Agric. Vic. Source Plant Disease APS.
Climate modeling Current state of climate knowledge – What does the historical data (temperature, CO 2, etc) tell us – What are trends in the current observational.
All Sensors Note: Be sure you have already selected your station and time interval before choosing this product.
Warm Up 3/4/08 True or False: The seasons are caused by changes in Earth’s distance from the sun. False Does land or water heat more rapidly? Land heats.
Single Column Experiments with a Microwave Radiative Transfer Model Henning Wilker, Meteorological Institute of the University of Bonn (MIUB) Gisela Seuffert,
Thematic Maps Choropleth, Proportional/Graduated Symbol, Digital Image, Isoline/Isopleth and Dot Distribution Maps.
Estimate of Mercury Emission from Natural Sources in East Asia Suraj K. Shetty 1 *, Che-Jen Lin 1, David G. Streets 2, Carey Jang 3, Thomas C. Ho 1 and.
Climate and Biodiversity Chapter 5. Climate and Biodiversity How are climates determined? What is the climate’s affect on terrestrial and aquatic ecosystems?
The earth at night Source:
Modelling surface mass balance and water discharge of tropical glaciers The case study of three glaciers in La Cordillera Blanca of Perú Presented by:
NAPPFAST: A tool for risk analysis of exotic plant pests Roger Magarey Presented at the 3 rd Annual Meeting of Midwest Weather Working Group Charlotte.
Site-Specific Weather Data for Disease Forecasting: Reality or Pipe Dream? Bob Seem Cornell University New York State Agricultural Experiment Station Geneva,
Dan Borchert- Entomologist CPHST/PERAL Raleigh, NC.
1 Recent Advances in the Modeling of Airborne Substances George Pouliot Shan He Tom Pierce.
Upscaling disease risk estimates Karen Garrett Kansas State University.
A detailed look at the MOD16 ET algorithm Natalie Schultz Heat budget group meeting 7/11/13.
Roger Magarey CPHST/PERAL Raleigh, NC. Special thanks to Dan Borchert, Jessica Engle and Kathryn Echerd CPHST/PERAL Raleigh, NC.
Reducing Canada's vulnerability to climate change - ESS J28 Earth Science for National Action on Climate Change Canada Water Accounts AET estimates for.
1/26 APPLICATION OF THE URBAN VERSION OF MM5 FOR HOUSTON University Corporation for Atmospheric Research Sylvain Dupont Collaborators: Steve Burian, Jason.
Precipitation Hydrology (Spring 2013) Illinois State University Instructor: Eric Peterson.
Meteorological Data Analysis Urban, Regional Modeling and Analysis Section Division of Air Resources New York State Department of Environmental Conservation.
Modern Era Retrospective-analysis for Research and Applications: Introduction to NASA’s Modern Era Retrospective-analysis for Research and Applications:
Understanding hydrologic changes: application of the VIC model Vimal Mishra Assistant Professor Indian Institute of Technology (IIT), Gandhinagar
Chapter 4 Global Climates and Biomes.  Weather – the short term conditions of the atmosphere in a local area  Includes: temperature, humidity, clouds,
RegCM3 Lisa C. Sloan and Mark A. Snyder Climate Change and Impacts Laboratory Dept. of Earth and Planetary Sciences University of California, Santa Cruz.
Asian Soybean Rust Monitoring in 2005 and 2006 Dr. Layla Sconyers Dr. Robert Kemerait Dr. Philip Jost Dr. Dan Phillips Research Associate Extension Plant.
Poleward amplification of Northern Hemisphere weekly snowcover extent trends Stephen Déry & Ross Brown ENSC 454/654 – “Snow and Ice”
How Do Forests, Agriculture and Residential Neighborhoods Interact with Climate? Andrew Ouimette, Lucie Lepine, Mary Martin, Scott Ollinger Earth Systems.
DEVELOPMENT OF A NEW LETTUCE ICE FORECAST SYSTEM FOR YUMA COUNTY Paul Brown Mike Leuthold University of Arizona.
Evapotranspiration Eric Peterson GEO Hydrology.
Fig Decadal averages of the seasonal and annual mean anomalies for (a) temperature at Faraday/Vernadsky, (b) temperature at Marambio, and (c) SAM.
OBSERVATIONSMODELINGPROJECT SWAN (Simulating Waves Nearshore) ADCIRC (Advanced Circulation Model) BOM (Bergen Ocean Model) WRF-ARW (Weather Research.
Structure of the Earth’s Atmosphere * Chemical Composition * Vertical Layers * Coriolis Force * Hadley Cells.
Studying the Venus terminator thermal structure observed by SOIR/VEx with a 1D radiative transfer model A. Mahieux 1,2,3, J. T. Erwin 3, S. Chamberlain.
NUMERICAL STUDY OF THE MEDITERRANEAN OUTFLOW WITH A SIMPLIFIED TOPOGRAPHY Sergio Ramírez-Garrido, Jordi Solé, Antonio García-Olivares, Josep L. Pelegrí.
Copernicus Observations Requirements Workshop, Reading Requirements from agriculture applications Nadine Gobron On behalf Andrea Toreti & MARS colleagues.
1. The atmosphere 2 © Zanichelli editore 2015 Characteristics of the atmosphere 3 © Zanichelli editore 2015.
Air Sea Interaction Distribution of Solar Energy.
How Much Will the Climate Warm? Alex Hall and Xin Qu UCLA Department of Atmospheric and Oceanic Sciences UCLA Institute of the Environment Environmental.
The objective of the CRONUS-Earth Project is to simultaneously address the various uncertainties affecting the production and accumulation of in-situ cosmogenic.
Simulations of MAP IOPs with Lokal Modell: impact of nudging on forecast precipitation Francesco Boccanera, Andrea Montani ARPA – Servizio Idro-Meteorologico.
Wind-SST Coupling in the Coastal Upwelling --- An Empirical Numerical Simulation X. Jin, C. Dong, and J. C. McWilliams (IGPP/UCLA) D. B. Chelton (COAS/OSU)
Preliminary validation of computational procedures for a New Atmospheric Ionizing Radiation (AIR) model John M. Clem, Giovanni De Angelis, Paul Goldhagen.
Forecasting smoke and dust using HYSPLIT. Experimental testing phase began March 28, 2006 Run daily at NCEP using the 6Z cycle to produce a 24- hr analysis.
Synthesis of work on Budget of Water Vapor and Trace gases in Amazonia Transport and Impacts of Moisture, Aerosols and Trace Gases into and out of the.
2011 DSMC Workshop Workshop 2011 DSMC Workshop Workshop William McDoniel Modeling Gas and Dust Flow in Io’s Pele Plume William McDoniel D. B. Goldstein,
© Cambridge University Press 2015 McInerney et al Chapter 1 Understanding ecosystems.
Starter Which can lead to the depletion of the ozone layer on the earth? a) a)Coal-fired power plants b) b)Vehicle exhaust c) c)Burning compost d)
Coupling ROMS and CSIM in the Okhotsk Sea Rebecca Zanzig University of Washington November 7, 2006.
Model-based Climate Management during Winter Period in Mediterranean Greenhouses G. Dimokas and C. Kittas University of Thessaly, School of Agricultural.
Chapter 6 using weather data
Numerical Weather Forecast Model (governing equations)
Meso-scale Model's Results
Update on Soybean Rust Daren Mueller.
APHG U2 Population Part II: Population Density
North American Regional Climate Change Assessment Program
AVERAGE JANUARY TEMPERATURE (°F)
Fire Effects on Water September 27, 2006.
Estimating Ground-level NO2 Concentrations from OMI Observations
(PI: Peter Ojiambo) NCSU, Department of Plant Pathology August 8, 2014
How will the earth’s temperature change?
1 GFDL-NOAA, 2 Princeton University, 3 BSC, 4 Cerfacs, 5 UCAR
Distribution of Solar Energy
Scott A. Braun, 2002: Mon. Wea. Rev.,130,
Climate Climate Latitude
Presentation transcript:

US Soybean Rust Detection and Aerobiological Modeling November, 2004 Dan Borchert, Glenn Fowler and Roger Magarey (USDA-APHIS-PPQ-CPHST-PERAL) Daryl Jewett (USDA-APHIS) Annalisa Ariatti (UIUC) Scott Isard (PSU) Manuel Colunga and Stewart Gage (MSU) Glenn Hartman and Monte Miles (ARS and NSRL) Thomas Keever and Charlie Main (NCSU) Jeff Grimm, Aaron Hunt and Joe Russo (ZedX, Inc.)

Methods  The Integrated Aerobiology Modeling System (IAMS) was used to simulate daily soybean rust spore movement (Isard et al., 2004)  Viable spore deposition (logarithmic) is modeled from September 15 to 19, 2004 in association with Hurricane Ivan  Uncertainty is associated with spore source strength and the absolute quantity of spores

Aerobiological Model Assumptions  Source area (17,000 sq Km) was from soybean production areas in northwestern South America  Spores were released near midday from August 30 to September 9, 2004  25% of the source area was infested with soybean rust  6 million spores were released per day per heavily infected soybean plant with a planting density of 500,000 plants/ha  33% of these spores were released near midday  15% of the released spores were able to escape from the canopy  Mortality due to UVB radiation exposure in the air was proportional to cloud-adjusted surface total incoming solar radiation  Wet deposition of viable spores was proportional to the observed surface precipitation total

Computational Procedure  Model domain was divided into 14 km 2 grid cells  NWS reanalysis 2 dataset was used to calculate the most likely downwind direction for 6 pressure levels (altitudes) at 6 hr intervals  Spores were moved up or down among pressure levels in accordance to the vertical component of the wind  Mortality due to UV exposure and rainout of spores was calculated for each time step after downwind movement  Deposition of spores was accumulated for all days in the calculations and is given as the number of spores per hectare

US Planted Soybean Acreage per County US Planted Soybean Acreage per County

September 15, 2004

September 16, 2004

September 17, 2004

September 18, 2004

September 19, 2004

Planted Soybean Acreage per County

Kudzu Area per County

Soybean Rust Spore Deposition

Soybean Rust Spore Deposition and Planted Soybean Acreage per County

Soybean Rust Spore Deposition and Kudzu Area per County

Data Sources  Kudzu: Raw data Daryl Jewett (USDA-APHIS) unpublished data. Kudzu map Annalisa Ariatti and Scott Isard (PSU/UIUC).  Soybean Acreage: NASS, 2003; National Land Cover Data, 1992; Colunga, 2004  Spore Deposition: Isard, S., Main, C., Keever, T., Magarey, R., Redlin, S, and Russo, J. (2004) Weather-Based Assessment of Soybean Rust Threat to North America. Final Report to APHIS