IN22B:
Near Real Time/Low Latency Data for Earth Science and Space Weather Applications II
Submit an Abstract to this Session
IN22B:
Near Real Time/Low Latency Data for Earth Science and Space Weather Applications II
Near Real Time/Low Latency Data for Earth Science and Space Weather Applications II
Session ID#: 33662
Session Description:
Near real time/low latency data from satellite, airborne (including aircraft and uninhabited aerial vehicles-UAV), and surface sensors are transforming existing end-user applications and spawning new ones. These applications demonstrate the utility of timely data in diverse Earth and space science disciplines including weather prediction, river forecasting, natural and human-caused hazards, public health, agriculture, marine, early warning, and space weather applications. In addition to traditional computer analyses, the use of apps for smartphones and tablets presents an opportunity to improve and expand the timely usage of data products and services. This session seeks contributions that demonstrate the benefit of near real time/low latency scientific or social media data, discuss innovative real time analysis approaches, decrease data delivery latency, or identify gaps in current capabilities.
Primary Convener: Michael H Goodman, NASA Marshall Space Flight Center, Code ST10, Huntsville, AL, United States
Conveners: Gerald W Bawden, NASA Headquarters, Washington, DC, United States, Kevin J Murphy, NASA Headquarters, Washington, United States and James F Spann, NOAA National Environmental Satellite, Data, and Information Service, Space Weather Observations Office, Lanham, United States
Chairs: Gerald W Bawden, NASA Headquarters, Washington, DC, United States and Kevin J Murphy, NASA Goddard Space Flight Center, Greenbelt, MD, United States
OSPA Liaison: Michael H Goodman, NASA Headquarters, Washington, DC, United States
Cross-Listed:
- A - Atmospheric Sciences
- NH - Natural Hazards
- OS - Ocean Sciences
- SH - SPA-Solar and Heliospheric Physics
Index Terms:
1964 Real-time and responsive information delivery [INFORMATICS]
3360 Remote sensing [ATMOSPHERIC PROCESSES]
4337 Remote sensing and disasters [NATURAL HAZARDS]
7924 Forecasting [SPACE WEATHER]
Abstracts Submitted to this Session:
Deep Learning of Post-Wildfire Vegetation Loss using Bitemporal Synthetic Aperture Radar Images (300917)
See more of: Earth and Space Science Informatics
