Reaching Broad Audiences from a Large Agency Setting

Abstract

NASA’s missions, engineering accomplishments, and scientific findings have inspired generations and advanced our understanding of the world we live in. NASA’s Earth science data are acquired by various sources, including satellites, aircraft, and field measurements. Captured data, their by-products, and their visual representations developed by research teams become available within few hours after satellite overpass or processing through a variety of NASA’s imaging, mapping services, and portals. Such online services as the Global Imagery Browse Services (GIBS)  [10], Worldview  [13], LANCE  [3], and LAADS DAAC  [35] are freely and openly available thanks to NASA’s Earth-Observing Satellite Data and Information Systems (EOSDIS)  [9]. These services provide access to products created over the last 30 years, support a broad range of users from the scientific community to the general public, and cover a multitude of applications such as basic and applied scientific research, natural hazard and disaster monitoring, and social and educational outreach. In order to illustrate the significance of the overall work, the visualization products, and the broad range of users, we present three case studies: NASA’s Black Marble Product Suite, the Global Imagery Browse Services (GIBS) and Worldview, and the scientific visualization production process to communicate results to the scientific community and the general public.

Appendices

Appendix A

Detailed descriptions of visualization productions.

 

Visualization 1::

Data-driven visualization of the El Niño phenomenon for the period 1982–2017. This production visualizes monthly sea surface temperature anomalies (SST) around the world from 1982 to 2017 and the Niño 3.4 index on a corresponding timeplot graph. The Niño 3.4 index represents average equatorial sea surface temperatures in the Pacific Ocean from about the International Date Line to the coast of South America. In the timeline, the major El Niño event years are highlighted. In these years, SST anomalies peaked for example during 1982–1983, 1997–1998, and 2015–2016. This visualization production creates visual associations between increases in sea surface temperature anomalies displayed on a flat map, Niño 3.4 indices in the timeplot, and the actual El Niño events. To learn more, please visit: https://svs.gsfc.nasa.gov/4695.

Visualization 2::

ENSO teleconnections in South East Asia Region during the El Niño event. The production starts in 2014 showing sea surface temperature (SST) anomaly data on a 3D globe. As time passes, in 2015 sea surface temperature anomalies in the equatorial Pacific Ocean (left) give rise to precipitation (center) and land surface temperatures (right) anomalies in Southeast Asia during the period 2015–2016. A multiplot on the bottom illustrates the interplay of Niño 3.4 index values and regional values for the South East Asia region of land surface temperature anomaly and precipitation anomaly. In the multiplot, the dengue outbreaks period is highlighted. Higher than normal land surface temperatures and therefore drier habitats drew mosquitoes into populated, urban areas containing the open water needed for laying eggs. As the temperature increased, mosquitoes had the urgency to bite more frequently but also reproduce and mature faster, resulting in an overall increase in population and mosquito bites, therefore the dengue outbreaks. To learn more please visit: https://svs.gsfc.nasa.gov/4697.

Visualization 3::

Visualize the correlations of precipitation, disease reports, and dengue outbreak period for the South East Asia region. The corresponding timeplot reveals the relationship between precipitation anomaly in Southeast Asia and dengue outbreaks. Drier than normal habitats draw mosquitoes into populated, urban areas containing the open water needed for laying eggs. Drier conditions induce higher than the normal temperature which have similar impacts as above. As time unfolds, dengue reports are mapped on to the region and the periods of dengue amplification are highlighted. To learn more, please visit: https://svs.gsfc.nasa.gov/4693.

Visualization 4::

Visualize the correlations of land surface temperature, disease reports, and dengue outbreaks period for the South East Asia region, using similar techniques with visualization 3 above to link the aftermaths of the drought persistence (lack of precipitation) to increased temperature on land, which increased mosquito vectors in the region. As time unfolds, dengue reports are mapped onto the region and the periods of dengue amplification are highlighted. To learn more, please visit: https://svs.gsfc.nasa.gov/4696.

 

Appendix B

Data sets used:

• Remote Sensing Data:

  • Sea Surface Temperature (SST) Anomaly, time series: 1982–2017. Global monthly SST data known as optimum interpolation (OI) SST version 2 data set produced by NOAA can be accessed from: https://www.ncdc.noaa.gov/oisst.

  • Land Surface Temperature (LST) Anomaly, time series: 2002–2017. Global monthly 0.05" LST MOD11C3 data set available at https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11c.

  • Precipitation Anomaly, time series: 2002–2017. Global Precipitation Climatology Project (GPCP) Global $1^{\circ }$ Monitoring Product, available at: ftp://ftp-anon.dwd.de/-pub/data/gpcc/html/-monitoring_download.html.

• Information Data:

  • Disease reports for South East Asia region. All global disease occurrences are georeferenced as sourced from https://www.promedmail.org/.

  • Niño 3.4 SST index. It can be obtained from the National Oceanic and Atmospheric Administration (NOAA)’s National Center for Climate Prediction online archives at: http://www.cpc.ncep.noaa.gov/data/indices/sstoi.indices.

  • Land Surface Temperature (LST) average for SE Asia Region (monthly time series: 2015–2016). Processed and provided by the Science Team for the visualization production.

  • Precipitation average for SE Asia Region (monthly time series: 2015–2016) Processed and provided by the Science Team for the visualization production.

  • Numbers of disease reports for SE Asia Region (monthly 2015–2016). Processed by Science Visualizer (Lead) from global disease occurrences provided from https://www.promedmail.org and approved by the Science Team.

• Cartographic Data: (developed internally at SVS)

  • Country outlines

  • Water mask (global)

  • South East Asia region mask

  • Latitude coordinates

  • Niño 3.4 region subset