7 ESIP/Google SOC projects approach 200 messages, deep learning & hurricane forecasts, and ESIP Summer Meeting!Also in the update, University of Houston researchers’ Lab funding improves hurricane forecasts, earthquake challenge, and citizen snow observations.

ESIP Lab Update: March 2019 

Highlights from your favorite virtual Earth science tech lab.
View this email in your Browser | esipfed.org/lab

In this email: 

  1. Google Summer of Code Update – SO much activity
  2. Lab Funded Project Highlight: Deep Learning for Hurricane Prediction
  3. Challenge Highlight: Earthquake forecasting
  4. Augmenting snowpack modeling with citizen science observations

Greetings all –

Spring is nearly here and I’m happy to say we have opened registration and are accepting your workshop and session proposals for the 2019 ESIP Summer Meeting in Tacoma, WA! 

Also, a quick job alert from one of our members. Battelle and the NSF are ramping up to the next phase of the NEON program. To do so, Battelle needs to hire more than 200 temporary field technicians who typically work from May to September alongside full-time researchers collecting samples and data of flora, fauna, water and air through a series of field-based sampling and collection methods. Interested? Find out more here.

I hope everyone is enjoying the extra sunshine after work!

Annie Burgess
Director, ESIP Lab

P.S. Check out upcoming ESIP telecons here.

Google Summer of Code Projects

Holy moly, there is a lot of activity going on with the ESIP Google Summer of Code projects! In just two weeks we’ve had nearly 200 student-lead queries about our posted projects. Mentors from six different ESIP-member organizations have been busy getting these eager students from around the world set-up to potentially work on their proposed Google Summer of Code projects. 

Lab-Funded Projects Highlight: Deep Learning for Hurricane Prediction 

Update from Yunsoo Choi and Ebrahim Eslami, University of Houston, on their 2018 ESIP Lab Project
 
We proposed a novel, artificial intelligence-driven approach using a deep learning algorithm, to predict hurricanes. We developed a hybrid three-step (direction, distance traveled, and intensity) deep-learning based hurricane forecasting model using the output of dynamical hurricane models, remote sensing data, and observation network. The preliminarily results of our model for 19 tropical storms in 2017 showed statistical advantages (~8% and ~30% improvement in track and intensity forecast biases respectively) over the National Hurricane Center official forecasts for 24-hours ahead forecasts. For predicting more than one-day ahead, we are currently implementing a post-processing analysis by ensemble Kalman filter to mitigate the bias of our deep learning ensemble model. Based on the new model, we are proposing the long-term hourly forecasting of hurricane’s intensity, path, precipitation for the entire 2017. This proposal will be featured in NASA Advanced Information Systems Technology (AIST) program. We also submitted an abstract to the 1st Workshop on Leveraging AI in the Exploitation of Satellite Earth Observations & Numerical Weather Prediction, hosted by NOAA, in late April. We are also working to create a framework for an operational hurricane forecasting system across the United States.
Want to know more? Contact: ychoi23 [at] central.uh.edu

Challenge Highlight: Can you predict laboratory earthquakes?

Forecasting earthquakes is one of the most important problems in Earth science because of their devastating consequences. Current scientific studies related to earthquake forecasting focus on three key points: when the event will occur, where it will occur, and how large it will be.

In this competition, you will address when the earthquake will take place. Specifically, you’ll predict the time remaining before laboratory earthquakes occur from real-time seismic data.

This challenge is hosted by Los Alamos National Laboratory – learn more here.

Augmenting snowpack modeling with citizen science observations

Lab Fellow Ben Roberts-Pierel highlights one of his favorite data exploration tools

In the snow hydrology world, as with many fields, there are limited in situ stations to monitor snowpack in many of the world’s mountain regions. Water content in snow is measured by snow water equivalent (SWE), a product of snow density and depth. However, with a lack of in situ data and extreme heterogeneity in mountain regions, there are many times a paucity of resources for forcing or validating models to predict SWE and forecast runoff. One way that this deficit is being addressed is through citizen science.

The use of citizen science data has long occupied a hazy and at times controversial area in scientific research, but with projects like the Globe Observer Program and others, NASA has shown they are fully behind the idea. One group that is working on addressing this issue is Community Snow Observations, a group of snow hydrologists and researchers, funded by NASA’s Citizen Science for Earth Systems project. They have teamed up with outdoor professionals, winter recreators and a host of other organizations interested in understanding cryospheric change and water availability to gather snow depth data from across the Northern Hemisphere. Snow depth is a crucial yet easily measured data point, particularly given that many people traveling in the mountains are already carrying a simple depth gauge in the form of an avalanche probe. Researchers, including colleagues here at Oregon State, are then using these data in existing models. The inclusion of these additional depth data has resulted in marked improvements to their outputs.

GitHub

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Website

https://www.esipfed.org/esip-lab

Questions/comments? Reply directly to this note or email us at lab@esipfed.org

Keep up on all the action on Slack! If you are not already on the ESIP Slack team: here is your INVITATION.  If you are on Slack, are you using the Slack App? It’s a much better GUI. Grab it HERE

ESIP is funded with support from NASA, NOAA, and the USGS. 

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