class: center, middle, inverse, title-slide .title[ # Geostationary Operational Environmental Satellite ] .subtitle[ ## Remote Sensor Presentation ] .author[ ### Josiah Tan ] .date[ ### 2023-01-28 ] --- ## 1. Sensor of Choice This presentation is on the Geostationary Operational Environmental Satellite, operated by United States' National Oceanic and Atmospheric Administration (NOAA). Mainly used to capture **atmospheric** data (Jensen 2015) such as: - Clouds - Precipitation - Water Vapor This allows for better: - fire track and intensity estimation - detection of low cloud/fog - tropical cyclone track and intensity forecasts - monitoring of smoke and dust - air quality warnings and alerts - transportation safety and aviation route planning - monitoring of atmospheric river events --- ## 2. Background History and purpose (Wikipedia, 2023) The first GOES satellite, GOES-1, was launched in October 1975. 2nd generation of GOES: 1994-2001 3rd generation of GOES: 2006- 4th generation of GOES: 2016- The GOES System operates in **geostationary orbit** 35,790km above Earth. It continuously views the continental United States, Pacific and Atlantic Oceans, Central and South America, and southern Canada. The evolution of atmospheric phenomena can be followed, ensuring real-time coverage of meteorological events such as severe local storms and tropical cyclones. The importance of this capability was proven during hurricanes Hugo (1989) and Andrew (1992). --- ## 3. Technical Specifications 1 The latest GOES satellite (GOES-17) of the 4th series was launched in 2018. (NOAA/NASA 2023) It provides multi-spectral imaging for weather forecasts and meteorological and environmental research Resolutions - Spectral Resolution (16 bands) - 2 Visible Bands - 4 Near IR Bands - 10 other Infrared Bands - Temporal resolution - Imaging of entire western hemisphere every 5-15 minutes - Imaging of continental US every 5 minutes - Spatial resolution - Resolution of up to 500m for band 2, 2km for NIR/IR. --- ## 4. Technical Specifications 2 (NESDIS 2023) **Sensors:** Geostationary Lightning Mapper (GLM) - Used for measuring lightning - Monitors the 777.4-nm channel in the NIR constantly - Has 1 1372 x 1300 pixel CCD, with 8-14km spatial resolution and a frame interval of 2 milliseconds Solar Ultraviolet Imager (SUVI) to observe coronal holes and solar flares Extreme Ultraviolet and X-ray Irradiance Sensors (EXIS) for monitoring solar irradiance in the upper atmosphere **These combine to give warning of events strong enough to cause radio blackouts, and are also used for space weather predictions** Space Environment In-Situ Suite (SEISS) for monitoring proton, electron and heavy ion fluxes in geostationary orbit --- ## 5. Applications of data collected Data can be accessed and downloaded for public and private uses (NCEI 2017) Practical uses: - North American weather monitoring and forecasting operations - by the National Weather Service (NWS) and the Meteorological Service of Canada - The NOAA Hazard Mapping System Fire and Smoke Product (HMS) provides fire locations from MODIS and smoke plume extent from GOES and MODIS, and is used frequently by first responders and forecasters (Duncan et al. 2014) Academic uses: - Understand land, atmosphere, ocean, and climate dynamics --- ## 6. Examples of studies using GOES data Method to correct surface effects and retrieve aerosol optical depth using visible reflectance measurements proposed by Knapp et al. (2005), which can be used for aviation, air pollution and health applications. Finding that frequency of extreme fire events in the US had increased from 1995-2011, with an observed increase of extreme weather conditions (Zhang et al. 2014). Detection of biomass burning in South America (Prins and Menzel 1992) which can be used to track forest fires and illegal forest clearance. --- ## 7. Possible Applications GOES-R Geostationary Lightning Mapper (GLM) can be combined with AI to predict where lightning might be observed next (Uni of Wisconsin's CIMSS) (Symonds 2021) Can be combined with Machine Learning to combine satellite imagery from other sensors to model various useful scenarios and build a global atlas (Rolf et al. 2021) --- ## References - Duncan, B. N., A. I. Prados, L. N. Lamsal, Y. Liu, D. G. Streets, P. Gupta, E. Hilsenrath, R. A. Kahn, J. E. Nielsen, A. J. Beyersdorf, S. P. Burton, A. M. Fiore, J. Fishman, D. K. Henze, C. A. Hostetler, N. A. Krotkov, P. Lee, M. Lin, S. Pawson, G. Pfister, K. E. Pickering, R. B. Pierce, Y. Yoshida, L. D. Ziemba (2014) 'Satellite data of atmospheric pollution for U.S. air quality applications: Examples of applications, summary of data end-user resources, answers to FAQs, and common mistakes to avoid', Atmospheric Environment, 94, 647-662. - Jensen, J.R. (2015) Introductory Digital Image Processing. 4th edn. Pearson Higher Education US (A Remote Sensing Perspective). - Knapp, K. R., R. Frouin, S. Kondragunta and A. Prados (2005) 'Toward aerosol optical depth retrievals over land from GOES visible radiances: determining surface reflectance', International Journal of Remote Sensing, 26(18), 4097-4116. - National Centers for Environmental Information (NCEI) (2017) ‘GOES-R, Building Better Predictors’ Available at: https://www.ncei.noaa.gov/news/goes-r-building-better-predictors (Accessed: 24 January 2023). - NESDIS (2023) ‘GOES-R Series Spacecraft & Instruments’. Available at: https://www.nesdis.noaa.gov/current-satellite-missions/currently-flying/geostationary-satellites/goes-r-series-spacecraft (Accessed: 24 January 2023). - NOAA/ NASA (2023) ‘Geostationary Operational Environmental Satellites - R Series | NOAA/NASA’ (2023). Available at: https://www.goes-r.gov/ (Accessed: 24 January 2023). --- ## References 2 - Prins, E. M., and W. P. Menzel (1992) 'Geostationary satellite detection of biomass burning in South America', International Journal of Remote Sensing, 13(15), 2783– 99. - Rolf, E., J. Proctor, T. Carleton, I. Bolliger, V. Shankar, M. Ishihara, B. Recht, S. Hsiang (2021) 'A generalizable and accessible approach to machine learning with global satellite imagery', Nature Communications, 12, 4392 (2021). Available at: https://doi.org/10.1038/s41467-021-24638-z. - Symonds, D (2021) 'GOES-R satellite imagery combined with AI to predict future lightning strikes', Meteorological Technology International. Available at: https://www.meteorologicaltechnologyinternational.com/news/lightning-detection/goes-r-satellite-imagery-combined-with-ai-to-predict-future-lightning-strikes.html. - Wikipedia (2023) ‘Geostationary Operational Environmental Satellite’. Available at: https://en.wikipedia.org/w/index.php?title=Geostationary_Operational_Environmental_Satellite&oldid=1131422345 (Accessed: 24 January 2023). - Zhang, X., S. Kondragunta and D. P. Roy (2014) 'Interannual variation in biomass burning and fire seasonality derived from geostationary satellite data across the contiguous United States from 1995 to 2011', JGR Biogeosciences, 119 (6), 1147-62. --- ``` ``` --- --- # Thanks! Slides created via the R packages: [**xaringan**](https://github.com/yihui/xaringan)<br> [gadenbuie/xaringanthemer](https://github.com/gadenbuie/xaringanthemer) The chakra comes from [remark.js](https://remarkjs.com), [**knitr**](http://yihui.name/knitr), and [R Markdown](https://rmarkdown.rstudio.com).