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SSAI Aids Struggling Parents During the Pandemic by Providing Family Activities Based on NASA Science
In response to the COVID-19 pandemic, SSAI's GLOBE Observer Team brought NASA Earth science into the homes of families through a novel series of activity videos, a new web activity page, and a family science guide.
SSAI Successfully Supports the Framework for Live User-Invoked Data (FLUID)
The SSAI-led FLUID team was recognized for contributing to the success of seven NASA field campaigns - by working long hours and weekends to efficiently accommodate real-time support, last minute requests, and complex custom services.
SSAI Developed an Innovative IT Solution to Enable COVID Contact Tracing at Customer Facilities
In response to the pandemic, SSAI coordinated with government civil servants to develop an automated solution called PASS - Project Access Selection System - to efficiently manage and monitor movements of personnel at NASA Langley Research Center.
SSAI Scientist Simulates Arctic Clouds Using a Multi-scale Modeling Framework
Arctic clouds are a pivotal part of global climate; yet, due to a series of challenges associated with obtaining accurate Arctic cloud data, they are often poorly simulated in most global climate models. SSAI scientist Dr. Zhujun Li took a new approach to climate modeling by using a multi-scale modeling framework (MMF).
SSAI Builds a City Government Website, Creating a Modern, Fully Responsive Experience that Addresses the Customer's Needs
The SSAI web and mobile applications team successfully redesigned the City of Burbank, California’s website to meet the needs of the various city departments; improve the aesthetics, organization, and useability; and bring it in line with the latest website technology
SSAI Leverages Machine Learning to Obtain Accurate Satellite Retrievals
Interference from clouds, aerosols, and strong water surface reflection known as sunglint are major obstacle for obtaining accurate satellite retrievals. Thus, the SSAI OMI team set out to develop new techniques to mitigate this challenge using machine learning.
SSAI Computer Scientist Maura Tokey uses Machine Learning to Predict the Effects of Global Warming on Crop Yield
SSAI computer scientist Maura Tokay set out to predict the crop yield for corn, wheat, and soybeans using meteorological data in conjunction with machine learning predictive models in order to determine the importance of each weather variable on crop yield outcome.