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Success Stories

The following success stories highlight SCO activities and services. Click on the PDF icons to download the full story.

PRODUCTS & SERVICES

Climate Education Modules  

  • Survey results indicate that most extension agents have no meteorology related training.
  • These education modules address the need to help extension agents understand weather and climate.
  • Building on this success, modules were also built for K-12 educators to use in the classroom.

CRONOS Database  

  • There are many, disparate sources of climate data.
  • CRONOS archives over 30-thousand stations from over 9 different observing networks.
  • The database is accessed by clients throughout the nation and drives many climate-based decision support tools in North Carolina.

Data Services  

  • We serve the public, in part, by providing climate data.
  • Over 1800 requests were fulfilled from 2008-2010 to clients in every sector of our economy.

LSR Database  

  • There is a high demand for reports of lightning, hail, flooding and storm storm damage.
  • This database archives and maps storm reports throughout North Carolina and makes it easy to plot and analyze them.

NC-FIRST  

  • Emergency managers responding to severe weather emergencies such as hurricanes, tornadoes and winter storms, need to understand the meteorological basics.
  • This program provides an online count-level weather portal plus educational material that provides a basic understanding of various weather hazards.

Peanut Disease Advisories  

  • NC grows a lot of peanuts. But 2 diseases are devastating to the crop.
  • These climate-based advisories provide growers with guidance on disease pressures.
  • Use of these advisories contributes to $2-6 million annually in savings.

Reference Evapotranspiration and Open Water Evaporation Estimates  

  • Evapotranspiration and open water evaporation are essential to agricultural professionals and water resources managers, especially during times of drought.
  • Estimates of these parameters are available online to help quantify the amount of water lost to the atmosphere from these processes.

TIMS  

  • Turfgrass irrigation consumes a lot of water. Drought can make water resources scarce.
  • This product estimates the amount of water actually needed by turf based on recent climate.

Wind Rose  

  • A wind rose is helpful to visualize prevailing wind direction and speed over a period of time.
  • This tool is used by DAQ and others to automatically and quickly generate wind roses.

Winter Storm Database  

  • Winter weather can significantly impact North Carolina. Winter weather data can be hard to find.
  • This database makes winter storm data searchable by date and location.
  • It used by the media, forecasters, and researchers.


RESEARCH ACTIVITIES

ENSO and Crop Yield Relationships  

  • Weather and climate are driving forces for agricultural production
  • Evaluate ENSO's influence on corn yields, as it is the only predictable seasonal oscillation linked to shifts in southeastern U.S. temperature and precipitation
  • Pre-growing season values of Multivariate ENSO Index are more strongly correlated with summer corn yield than growing season values

ENSO's Influence on Corn Planting Dates  

  • ENSO affects precipitation patterns across the Southeast
  • Research ENSO's impacts on silting stage of corn growth
  • Reduced risk of drought stress during silking when planted:
    • As early as possible, or in June, of an El Niño year
    • In May of a La Niña year

Estimating Solar Radiation  

  • Solar radiation is a critical parameter in crop and ecological modeling
  • Evaluate two solar radiation estimation techniques -- Hargreaves and Samani; Stull
  • Hargreaves and Samani method (with a constant k coefficient of 0.18) performs best for NC

Modeling Soil Moisture  

  • Soil moisture observations are essential to the agricultural and environmental engineering communities
  • Evaluate a soil moisture estimation technique using the ARIMA model
  • Potentially use estimates for quality control of existing data and prediction of missing observations