The 44th Northeastern Storm Conference was held in Saratoga Springs, NY, on March 8-10, 2019. The Northeastern Storm Conference is an excellent opportunity for students, professors, scientists, and other professionals to share and learn about the latest weather and climate research.
This year’s Friday evening speaker was Keith Carson, ’05.
NVU-Lyndon Atmospheric Sciences students and faculty attended the American Geophysical Union (AGU) Fall Meeting this week in Washington, D.C. This year’s centennial event attracted more than 27,000 geoscientists from around the world. In addition to learning about cutting-edge geoscience research from leading experts, the Lyndon ATM group authored five accepted abstracts.
On Monday, Dr. Hanrahan presented a poster on recent curricular changes to the Atmospheric Sciences program. She and her coauthor, Dr. Shafer, discussed the integration of climate change in the core Atmospheric Sciences curriculum and student outreach activities through The Climate Consensus. On the same day, Dr. Siuta presented on work he completed with Dr.Shafer and current student, Francis Tarasiewicz, that evaluates model forecasts of ice and wet snow events in the northeastern U.S.
On Tuesday, students Jessica Langlois and Lauren Cornell presented their results from a summer internship with Dr. Hanrahan, on the impact of Great Lakes’ water temperature increases on simulated downwind precipitation. This work was completed in collaboration with researchers at Dartmouth College and the National Center for Atmospheric Research (NCAR). On Wednesday, student Celia Fisher presented on meteorological drivers of rapid wildfire growth in Alaska’s Boreal Forest. This work was completed during a summer internship at the National Weather Service in Alaska as part of her her Hollings Undergraduate Scholarship experience.
On Friday, Dr. Preston discussed simulations of chemical transport by Typhoon Mireille, work which was completed in collaboration with researchers at Florida State University and NCAR.
On Tuesday, we hosted a gathering for Atmospheric Sciences students and alumni. We enjoyed learning about alumni accomplishments and hearing you reminisce about your time at Lyndon!
Forecasts from Numerical Weather Prediction (NWP) models provide one of the primary tools meteorologists use to produce weather forecasts. Historically, running NWP models has required vast computing resources to complete weather forecasts in a timely fashion. Until recently, running such NWP simulations quickly at a high enough resolution to capture mesoscale features (such as tight temperature gradients, mountain/valley flows, and mesoscale precipitation banding features within midlatitude cyclones) required the purchase of a supercomputing cluster. However, the rise of cloud computing technologies has removed that barrier. Now, companies like Google, Amazon, and Microsoft provide the required computing resources at a per use cost to the public and academic communities.
This fall, Dr. Siuta’s junior-level Analysis and Forecasting 1 class won a Google Cloud for Education grant, which allowed them to use Google’s Cloud Platform to run their own NWP model simulations using the state-of-the-art Weather Research and Forecasting (WRF) model. Dr. Siuta had previously co-authored an article on the Viability of Cloud Computing for Real-Time Numerical Weather Prediction in the journal Weather and Forecasting.
Students learned the basic components of a NWP model, weather model limitations, and how adjusting model physics can lead to different forecast outcomes through simulating a high-impact weather event of their choice. Students ran cases covering Superstorm Sandy (October 29-30, 2012), the February 8-10, 2013 nor’easter, the March 7-9, 2018 nor’easter, the March 1-3, 2018 nor’easter, the January 4-6, 2018 nor’easter, and the October 2017 New England wind storm. Unidata’s Integrated Data Viewer was used to visualize the WRF output.
The grant from Google was sufficient to cover the simultaneous use of 336 virtual computing cores, so that students could run each of these cases with model resolutions matching the standards of today’s national model centers — down to the 3-5 km scale over the entire northeastern US.
Key findings by the students are summarized below.
Acknowledgements: We thank the Google Cloud Platform for providing the funds through the GCP Education Grants program for our class.
Case Study 1: October 29-30, 2017 wind storm WRF model sea-level pressure (left panel) and wind speed (right panel) output for the 29-30 October 2017 wind storm. This case study was chosen by Alex DaSilva and Nick Ferrando-Boucher, who varied the WRF planetary boundary layer scheme to see the effect on the strength of the low-pressure center and magnitude of the wind speeds in Vermont. They found that a non-local-mixing boundary-layer scheme provided a better forecast than a local-mixing only boundary-layer scheme for the wind speeds observed at Northern Vermont University-Lyndon during the event.
Case Study 2: March 7-9, 2018 Nor’easter A comparison of WRF model forecast snow depth (using 10:1 ratio) to that of observations in southern New England for the 7-9 March 2018 Nor’easter (Quinn). This case study was run by Sarah-Ellen Calise and Lauren Cornell, who varied WRF cloud microphysics schemes to see the impact each scheme had on snowfall forecasts. D01 are forecasts for a 15-km outer nest while D02 are forecasts for a 5-km inner nest. Sarah-Ellen and Lauren found that in areas closer to the coast (e.g., Providence, RI), the microphysics scheme had a substantial impact on the location of the rain/snow line and overall snow amounts in the area.
Case Study 3: Superstorm Sandy (October 27-30, 2012) Jonathan Hutchinson, Taylor Leitch, and Lillie Farrell varied the planetary boundary layer schemes in the WRF model to see the impact on the development of Superstorm Sandy. Shown here is one of their WRF simulations predicting landfall on October 28 along the coast of New Jersey as Sandy is undergoing a transition from a tropical to extra-tropical system. The group found ~10-mb sea-level pressure difference between their runs at Atlantic City, NJ caused by differences in forecast track. Landfall varied between Sandy Hook and Cape May, NJ depending on the planetary boundary layer scheme that was used.
Case Study 4: February 8-10, 2013 Nor’easter Students John Drugan, Radek Przygodzki, and Alex Doone ran the February 2013 nor’easter, which resulted in heavy, wet snow and 700,000 people losing power in parts of New England. John, Radek, and Alex varied cloud microphysics scheme and determined that the microphysics choice had a distinct impact on the location of a mesoscale precipitation band forecast to occur near the eastern tip of Long Island. Depending on the scheme, this band shifted to the east or west, leading to the highest forecast storm totals shifter slightly towards (right panel) or away (left panel) from population centers.
Case Study 5: January 4-6, 2018 Nor’easter Kelsey Emery and Dan Carneiro simulated the January 2018 nor’easter because they were personally affected by the storm, which prevented them making it to the national American Meteorological Society meeting due to disrupted flights. The storm hammered southern New England with 1-2 feet of snow and winds gusting over 50 mph. Shown below are Dan and Kelsey’s WRF simulations of the low tracking off the Massachusetts coast.
Case Study 6: March 1-3, 2018 Nor’easter A comparison of model forecast soundings produced by varying the boundary layer choice during the March 1-3, 2018 nor’easter. This nor’easter left close to 2 million people without power in the northeastern US due to wet snow and high winds. The far right graphic shows the corresponding observed sounding at Albany, NY taken 0000 UTC 3 March 2018. Rosemary Webb and Sarah Sickles found that varying the boundary layer choice affected the ability of the model to depict the vertical profile of the atmosphere at Albany, NY.
Dr. Geoffrey Stano is part of NASA’s Short-term Prediction Research and Transition Center (SPoRT). He has been involved with the GOES-R Proving Ground since 2009, and in 2016 began serving as the Satellite Liaison for the Geostationary Lightning Mapper (GLM). His role has been to support the National Weather Service in preparing for the GLM. This has been done through webinars, training sessions, and the development of training modules for the forecasters. This seminar will provide a short background on the NASA SPoRT program as well as the GLM instrument. The remainder of the presentation will focus on real-world applications of the GLM data as it is being integrated into the National Weather Service.
Dr. Geoffrey Stano
Dr. Stano has focused on operational applications research, specifically with lightning observations, for the past 15 years. This has included work with ground-based lightning mapping arrays to the first of its kind Geostationary Lightning Mapper aboard the new GOES-R series. In addition to his role as a lightning expert and trainer with the NASA SPoRT center, he currently serves as the chair for the American Meteorological Society’s Atmospheric Electricity Scientific and Technological Activities Commission.
Atmospheric rivers are relatively long, narrow areas of moisture transport. They’re responsible for approximately 90% of meridional water vapor transport, and also for many high impact rainfall events. This presentation will provide an overview of the structure, climatology, and impacts of atmospheric river events. Predictability and forecast tools will also be discussed.
Dr. Greg West is a Research Associate in the Weather Forecast Research Team at the University of British Columbia in Vancouver, BC. Research projects he is involved with center around improving weather forecasts for clean energy production for the province’s primary electric utility, BC Hydro. This primarily includes improvement of probabilistic forecast post-processing via machine learning methods, evaluation of forecasts, and creation of innovative new forecast tools such as situational awareness forecast indices.
Dr. Stephanie Spera’s research seeks to understand landscape-level human-environment feedbacks with regard to social, economic, and environmental drivers and consequences. She is currently a Postdoctoral Researcher at Dartmouth College. She analyzes large datasets, develops algorithms, and integrates spatial data to characterize landscapes and landscape-scale dynamics. Broadly, she asks, ‘How do we ensure that we manage our landscapes sustainably?’ She is interested in how and to what extent humans are modifying the landscape; what is driving changes in land cover; how these changes are affecting the environment; and how humans are, in turn, responding to these changes. She uses different methods and tools, like remote-sensing, regional climate models, spatial statistics, and GIS, to help answer these questions. Her most recent work focuses on answering these questions focusing on agricultural land-use change and regional climate dynamics in the Brazilian Cerrado, a biodiversity hotspot and agricultural breadbasket.
Have you ever wanted to be an aviation meteorologist?
In this seminar, Erin Rinehart talks about her journey to becoming the primary night shift meteorologist at Southwest Airlines in Dallas, Texas. She attended Baylor University where she earned a B.A. in Earth Science. After college Erin joined the U.S. Air Force as a weather forecaster where she served for eight years. Following military service, she enrolled in the M.S. in Applied Meteorology program at Plymouth State University. After a short time working as an aviation forecaster contracted with American Airlines, she moved to Southwest Airlines. In this talk, Erin also discusses various aviation meteorology products used by both the military and civilian airlines, along with her various shift duties at Southwest Airlines. She also touches on some of the differences between airline aviation forecasts and the media forecasts created for the general public.
Last month, Atmospheric Sciences students Nick Ferrando, Jonathan Hutchinson, Jessica Langlois, Evan Levine, and Francis Tarasiewicz, from the AMS Club and The Climate Consensus group, teamed up to host a table at the VSAC Northeast Kingdom STEM (Science, Technology, Engineering, and Mathematics) Fair at Miller’s Run School in Sheffield. They taught 7th and 8th graders about weather and climate, and how weather and climate relate to one another. Interactive activities included demonstrations of ice albedo, atmospheric moisture content, and the effects of climate change. Jonathan Hutchinson said, “Being able to go out into the community and seeing the students’ amazement and developing interests in the different science sectors was a heartwarming experience for all of us who participated.” Nearly 400 students attended this two-day event, whose goal was to inspire young people to consider pursuing careers in STEM fields.
How do water temperature changes on the Great Lakes affect modeled downwind precipitation? Two Atmospheric Sciences students, Jess Langlois and Lauren Cornell, just completed their summer internship to answer that question. Over a seven-week period, they worked with Dr. Hanrahan to analyze simulated rainfall data obtained from the Weather Research and Forecasting (WRF) model. Several simulations were completed to assess the sensitivity of precipitation in New England to changing water temperatures of the Great Lakes. Their work will inform the configuration of the regional climate model which will be used to downscale future global climate model simulations under human-caused climate change. As members of the BREE (Basin Resilience to Extreme Events) Climate Modeling Team, Jess and Lauren presented their work at Plymouth State University and Dartmouth College. They also plan to present results at the American Geophysical Union Fall Meeting this upcoming December.
Jess and Lauren also practiced presenting their research to faculty and staff at Northern Vermont University-Lyndon:
Integration of renewable wind energy sources into the electric transmission grid has created new challenges for energy planners. Winds are highly variable and power generated by wind fluctuates with wind speed. Energy planners need to be able to anticipate these fluctuations to keep electricity supplied to the grid in sync with demand. Numerical weather prediction is key to effectively integrate wind resources, which supplies future estimates of wind power generation. Weather forecasts are used several hours to several days in advance to manage which generation sources will be used to supply power to the grid, which resources can be freed up to perform maintenance, and how much electricity is available to be traded on the market.
Weather forecasts, however, are imperfect due to incomplete initial conditions, assumptions in model physics, and the poor representation of terrain and complex flows. In the past, small reductions in forecast error have been shown to result in substantial annual savings to electric grid operators through optimized grid planning strategies. Dr. Siuta’s recent publication explains one method to achieve reduced forecast error through use of ensembles. He used a 36-member mesoscale ensemble forecast system run by the University of British Columbia to show that, on average, reductions in forecast error by using an ensemble mean forecast over a single-model forecast is between 10 and 20% through a seven-day forecast horizon. Additionally, the ensemble mean provided a minimum of an additional two days of forecast skill over the ensemble member forecasts when compared to a reference climatology forecast (see figure). In the same work, Dr. Siuta also shows a method to produce calibrated probabilistic forecasts and the effect of an ensemble on reducing forecast uncertainty.