Copy
View this email in your browser
Share
Tweet
Forward
+1
Share

Week 5 Newsletter: July 2-6, 2018

RECCS is a 9-week summer student research program for Colorado community college students funded by the National Science Foundation and coordinated by CIRES Education Outreach and the Boulder Creek Critical Zone Observatory.

For recent RECCS happenings, check out the RECCS blog.

RECCS Student Researcher Spotlight

This week our first featured RECCS student researcher is James Butts. He is mentored by Juliana Dias and John Albers of the NOAA/ESRL Physical Sciences Division Atmosphere-Ocean Process Team based in Boulder, Colorado.
James Butts

Research Project:  North American Sub-seasonal Climate Prediction Using Big Data

Watch James' elevator speech

James currently lives in Greeley, Colorado, but grew up in the small town of Byers in eastern Colorado. He enjoys multiple hobbies, such as gaming, computer technologies, reading, running cross country, biking, and swimming.  In addition, he has always enjoyed math and science courses.

James earned his Associates of Science in Biology in Spring 2017, but continued at Northeastern Junior College (NJC) in Sterling, Colorado for a third year to finish up some extra classes. This upcoming Fall 2018 semester, James plans to transfer to the University of Northern Colorado (UNC) in Greeley, Colorado. At UNC, he will pursue a bachelor's degree in both Biology and Software Engineering. What he plans to do after graduating is unknown, but he may attempt to become an Optometrist or a Microbiologist. 

What's the Weather Forecast Weeks from Now?

James Butts and his mentor John Albers (above) are investigating the forecasting skill for seasonal and subseasonal weather forecasts.

Current technology allows experts to provide reasonably accurate weather predictions out to about 2 weeks. Beyond 2 weeks, the chaotic nature of atmospheric variability makes forecasting exceedingly difficult. However, we are also able to provide reasonably accurate forecasts for time periods beyond about 3 months. Forecasts between these two ranges are referred to as subseasonal to seasonal (S2S) predictions. Predictions in this range are very limited and mostly unreliable, but are “highly sought after by the energy, water management, agriculture and emergence sectors” (Mariotti et. al., 2018, p.1).

James will investigate the S2S predictions by comparing global modeled data from the
European Centre for Medium-Range Weather Forecasts (ECMWF) to the observed (JRA-55 reanalysis) data from the Japan Meteorological Agency.


Citation: Mariotti, A., Ruit, P.M., and Rixen, M., 2018. Progress in subseasonal to seasonal prediction through a joint weather and climate community effort, npj Climate and Atmospheric Science1, 1. https://doi.org/10.1038/s41612-018-0014-z
James uses MATLAB to input the weekly global modeled ECMWF data and observed JRA-55 weather data to determine the forecast skill. Variables that he will analyze include surface temperature and mean sea level pressure (shown in image on the right).

Once James subtracts the weekly climatology data (also provided by the Japan Meteorological Agency) from both the modeled and observed data, he can then determine the mean square error and the anomaly correlation to quantify the forecast skill.  Finally, he can plot these values which shows him areas around the world where the forecast performed well or did not perform well.

Communication Workshop Update

Happy 4th of July! No science communication workshop this past week. 

RECCS Student Researcher Spotlight

This week our second featured RECCS student researcher is Kelly Sullivan. She is mentored by Sam Califf of the National Centers for Environmental Information (NCEI) at NOAA in Boulder, Colorado.
Kelly Sullivan

Research Project:  Quantifying the Error in Magnetic Field Models Near Geostationary Orbit of the GOES-16 Satellite

Watch Kelly's elevator speech

Having moved around with the Air Force for most of her life, Kelly is a nomad. She has developed a passion for travel and experiencing new cultures but is still seeking a place to call home. Currently residing in Colorado Springs, she enjoys a good dose of nature by hiking the Front Range with her chocolate lab, Guinness. She just received her Associate of Arts at Pikes Peak Community College (PPCC) at the end of the Spring 2018 semester and plans to continue her studies there as well as her role working to spearhead the first STEM club at PPCC. From there, she is considering a degree in Environmental Design or Urban Planning at one of the University of Colorado campuses. 

Kelly has always enjoyed the fusion of what is creative with what is logical and hopes to bring that creative thinking into a career in sustainable architecture and green building. She has a passion for protecting and preserving the environment and in finding ways to reduce our human impact through sustainable development. Her philosophy is: "Tread lightly, live thoughtfully, and always have empathy."

How can we Improve Models of the Earth's Magnetic Field?

Kelly Sullivan and her mentor Sam Califf (above) are working together on a research project to improve the accuracy of the magnetic field models near the geostationary orbit of the GOES-16 satellite.

Kelly will be analyzing GOES-16 data to eliminate discrepancies between the two magnetometers (the inboard and the outboard) on the satellite to identify why and where the error is occurring; she will use that information and compare it to measurements from other nearby satellites. Minimizing this error between the magnetometers will create a better magnetic field model of the Earth, which will help provide more reliable data to many sectors such as GPS navigation, electric power transmission, radio communication, and space operations.

Kelly uses MATLAB to create plots to illustrate the magnetic field values of the model compared to the observations as shown above. This plot shows the Be magnetic field component in units of nanotesla or nT (magnetic flux density) versus the Coordinated Universal Time (UTC) time over 24 hours. The Tsyganenko Magnetic Field Model (TS04), the dotted orange line, is a commonly used dynamical model of geomagnetic fields based on data from 37 major events between 1996 and 2000 (Tsyganenko & Sitnov, 2005). The observational data are collected from the two magnetometers found aboard the GOES-16 satellite. The inbound (IB) and outbound (OB) magnetometer values are shown as the solid red line and the solid blue line.

This plot of the diurnal cycle illustrates small nT values during the time of full sunlight (around 1800 UTC) when the magnetic field is compressed and then conversely the nT values are large when the magnetometer is not facing the full sun (around 00 UTC).  In addition to plots similar to this, Kelly will be performing statistical analysis to determine the error between the magnetic field models and magnetic field observations.

Citation: Tsyganenko, N.A. and Sitnov, M.I., 2005. Modeling the dynamics of the inner magnetosphere during strong geomagnetic storms. Journal of Geophysical Research, 110, A03208. https://doi.org/10.1029/2004JA010798.

Student Tips

  • Meet with your mentor to review your research project progress to date and discuss your final work plans.
  • Remember, the RECCS staff are here for you and your mentor—so, reach out for support.

Student Deadlines

  • Practice white board speech for the next workshop (7/11).
  • Create and add to a draft of your References section as you go.

Mentor Tips

  • RECCS has reached the halfway point! Now is a good time to meet with your student researcher to assess their data collection to date, confirm plans for analyzing their data, and review their timeline for completing the program deliverables.
  • The final week of RECCS is dedicated to the students attending a poster session on 8/2 and the research project presentations on 8/3.
Copyright © 2018 CIRES Education Outreach, All rights reserved.


renee.curry@colorado.edu
 

RECCS is funded by the National Science Foundation Grant Award Number 1461281


unsubscribe from this list   
update subscription
preferences
 

Email Marketing Powered by Mailchimp