Researchers create tool to better geographic projections in atmospheric modelingPosted on March 19, 2018
Open-source code developed by a Penn State graduate could improve weather forecasting and a range of other research endeavors that rely on pairing atmospheric models with satellite imagery.
Yanni Cao, who earned her master’s degree in geography in 2016, developed the code while a member of Penn State’s Geoinformatics and Earth Observation laboratory (GEOlab) as a way to fix errors created when satellite data is combined with the Weather Research and Forecasting (WRF) model. The work was done in collaboration with her adviser, Guido Cervone, head of GEOLab, associate professor of geoinformatics and associate director of the Institute for Computational and Data Sciences, and the National Center for Atmospheric Research (NCAR).
To streamline calculations, the commonly used WRF model — like most — assumes the Earth is a perfect sphere. Yet satellites capture a more realistic oblate spheroid shape of the Earth, as planets are bulged due to gravity and rotation. Inaccuracies in calculations are caused by shifting locations of models from a perfect sphere to the observational data on Earth that flattens near the poles and bulges around the equator.
“The basic idea is that the Earth is not a perfect sphere,” said Cao. “In order to make easy computations, most, if not all, weather models see the Earth as a perfect sphere. However, most of the remote sensing systems see the Earth as a spheroid. There is a difference between the two. When you use spheroid geographic coordinate systems with a spherical model, a mismatch is created.”
This is widely known to cause calculation errors, Cao said, but those errors are more pronounced when models are set to run with high spatial resolution. For example, location errors of up to 13 miles were introduced into the WRF model when it was run at a .6-mile grid size, causing errors in temperature and other weather variables, particularly near mountains and bodies of water.
To analyze these errors and develop a methodology that fixes these issues, Cao analyzed an area of the U.S. that is about 17,000 square miles. She used the WRF model under three scenarios: low resolution satellite data, high resolution satellite data, and then WRF-corrected data using the code she created. Cao selected an area in the northeast United States because it contained large elevation gradients and diverse land-use patterns such as forests, urban areas and wetlands.
She used the data to quantify how pairing WRF models with differing satellite resolutions has an impact on projecting meteorological variables such as temperature, wind direction, wind speed and atmospheric mixing ratios.
“While some of these errors can be small, they still introduce bias into the model output,” Cao said. “For very high resolutions simulations, these biases are compounded and can lead to significant errors in the model results.”
Her results show that the mismatch resulted in errors in the model results for each variable.
Cao used this methodology to improve the accuracy of models for methane emissions and now works as a data scientist detecting methane leaks for Picarro, a private company based in Silicon Valley.
Cervone added that the code will be beneficial in a range of research areas. “This research fits well with questions being investigated at NCAR, which is why researchers there were so interested in advancing this new tool,” said Cervone. “And it will only prove to be even more useful as high resolution satellite imaging becomes more commonplace.”
The research was published in Geoscientific Model Development and was partially funded by the Department of Energy and the Office of Naval Research.
This article originally appeared in Penn State News. Read the original.
- SMH! Brains trained on e-devices may struggle to understand scientific info
- Multi-institutional team to use AI to evaluate social, behavioral science claims
- NSF invests in cyberinfrastructure institute to harness cosmic data
- Center for Immersive Experiences set to debut, serving researchers and students
- Distant Suns, Distant Worlds
- CyberScience Seminar: Researcher to discuss how AI can help people avoid adverse drug interactions
- AI could offer warnings about serious side effects of drug-drug interactions
- Taking RTKI drugs during radiotherapy may not aid survival, worsens side effects
- Cost-effective cloud research computing options now available for researchers
- Costs of natural disasters are increasing at the high end
- Model helps choose wind farm locations, predicts output
- Virus may jump species through ‘rock-and-roll’ motion with receptors
- Researchers seek to revolutionize catalyst design with machine learning
- Resilient Resumes team places third in Nittany AI Challenge
- ‘AI in Action’: Machine learning may help scientists explore deep sleep
- Clickbait Secrets Exposed! Humans and AI team up to improve clickbait detection
- Focusing computational power for more accurate, efficient weather forecasts
- How many Earth-like planets are around sun-like stars?
- Professor receives NSF grant to model cell disorder in heart
- Whole genome sequencing may help officials get a handle on disease outbreaks
- New tool could reduce security analysts’ workloads by automating data triage
- Careful analysis of volcano’s plumbing system may give tips on pending eruptions
- Reducing farm greenhouse gas emissions may plant the seed for a cooler planet
- Using artificial intelligence to detect discrimination
- Four ways scholars say we can cut the chances of nasty satellite data surprises
- Game theory shows why stigmatization may not make sense in modern society
- Older adults can serve communities as engines of everyday innovation
- Pig-Pen effect: Mixing skin oil and ozone can produce a personal pollution cloud
- Researchers find genes that could help create more resilient chickens
- Despite dire predictions, levels of social support remain steady in the U.S.
- For many, friends and family, not doctors, serve as a gateway to opioid misuse
- New algorithm may help people store more pictures, share videos faster
- Head named for Ken and Mary Alice Lindquist Department of Nuclear Engineering
- Scientific evidence boosts action for activists, decreases action for scientists
- People explore options, then selectively represent good options to make difficult decisions
- Map reveals that lynching extended far beyond the deep South
- Gravitational forces in protoplanetary disks push super-Earths close to stars
- Supercomputer cluster donation helps turn high school class into climate science research lab
- Believing machines can out-do people may fuel acceptance of self-driving cars
- People more likely to trust machines than humans with their private info
- IBM donates system to Penn State to advance AI research
- ICS Seed Grants to power projects that use AI, machine learning for common good
- Penn State Berks team advances to MVP Phase of Nittany AI Challenge
- Creepy computers or people partners? Working to make AI that enhances humanity
- Sky is clearing for using AI to probe weather variability
- ‘AI will see you now’: Panel to discuss the AI revolution in health and medicine
- Privacy law scholars must address potential for nasty satellite data surprises
- Researchers take aim at hackers trying to attack high-value AI models
- Girls, economically disadvantaged less likely to get parental urging to study computers
- Seed grants awarded to projects using Twitter data
- Researchers find features that shape mechanical force during protein synthesis
- A peek at living room decor suggests how decorations vary around the world
- Interactive websites may cause antismoking messages to backfire
- Changing how government assesses risk may ease fallout from extreme financial events
- Algorithm aims to alert consumers before they use illicit online pharmacies
- Using cues and actions to help people get along with artificial intelligence
- Multi-university NSF grant to boost research computing expertise