scholarly journals Global River Radar Altimetry Time Series (GRRATS): new river elevation earth science data records for the hydrologic community

2020 ◽  
Vol 12 (1) ◽  
pp. 137-150 ◽  
Author(s):  
Stephen Coss ◽  
Michael Durand ◽  
Yuchan Yi ◽  
Yuanyuan Jia ◽  
Qi Guo ◽  
...  
Keyword(s):  
Time Series ◽  
Earth Science ◽  
Altimeter Data ◽  
Radar Altimeter ◽  
Science Data ◽  
Radar Altimetry ◽  
In Situ Data ◽  
Virtual Station ◽  
Earth Science Data

Abstract. The capabilities of radar altimetry to measure inland water bodies are well established, and several river altimetry datasets are available. Here we produced a globally distributed dataset, the Global River Radar Altimeter Time Series (GRRATS), using Envisat and Ocean Surface Topography Mission (OSTM)/Jason-2 radar altimeter data spanning the time period 2002–2016. We developed a method that runs unsupervised, without requiring parameterization at the measurement location, dubbed virtual station (VS) level, and applied it to all altimeter crossings of ocean-draining rivers with widths >900 m (>34 % of the global drainage area). We evaluated every VS, either quantitatively for VS locations where in situ gages are available or qualitatively using a grade system. We processed nearly 1.5 million altimeter measurements from 1478 VSs. After quality control, the final product contained 810 403 measurements distributed over 932 VSs located on 39 rivers. Available in situ data allowed quantitative evaluation of 389 VSs on 12 rivers. The median standard deviation of river elevation error is 0.93 m, Nash–Sutcliffe efficiency is 0.75, and correlation coefficient is 0.9. GRRATS is a consistent, well-documented dataset with a user-friendly data visualization portal, freely available for use by the global scientific community. Data are available at https://doi.org/10.5067/PSGRA-SA2V1 (Coss et al., 2016).

scholarly journals Global River Radar Altimetry Time Series (GRRATS): New River Elevation Earth Science Data Records for the Hydrologic Community

2019 ◽  
Author(s):  
Stephen Coss ◽  
Michael Durand ◽  
Yuchan Yi ◽  
Yuanyuan Jia ◽  
Qi Guo ◽  
...  
Keyword(s):  
Time Series ◽  
Earth Science ◽  
Altimeter Data ◽  
Radar Altimeter ◽  
Science Data ◽  
Radar Altimetry ◽  
In Situ Data ◽  
Virtual Station ◽  
Earth Science Data

Abstract. The capabilities of radar altimetry to measure inland water bodies are well established and several river altimetry datasets are available. Here we produced a globally-distributed dataset, the Global River Radar Altimeter Time Series (GRRATS), using Envisat and Ocean Surface Topography Mission (OSTM)/Jason-2 radar altimeter data spanning the time period 2002–2016. We developed a method that runs unsupervised, without requiring parameterization at the measurement location, dubbed virtual station (VS) level and applied it to all altimeter crossings of ocean draining rivers with widths > 900 m (> 34 % of global drainage area). We evaluated every VS, either quantitatively for VS where in-situ gages are available, or qualitatively using a grade system. We processed nearly 1.5 million altimeter measurements from 1,478 VS. After quality control, the final product contained 810,403 measurements distributed over 932 VS located on 39 rivers. Available in-situ data allowed quantitative evaluation of 389 VS on 12 rivers. Median standard deviation of river elevation error is 0.93 m, Nash-Sutcliffe efficiency is 0.75, and correlation coefficient is 0.9. GRRATS is a consistent, well-documented dataset with a user-friendly data visualization portal, freely available for use by the global scientific community. Data are available at DOI 10.5067/PSGRA-SA2V1 (Durand et al., 2016).

Quantifying and attributing changes in tropospheric ozone from a combination of satellite measurements and models

2020 ◽  
Author(s):  
Jessica Neu ◽  
Kazuyuki Miyazaki ◽  
Kevin Bowman ◽  
Gregory Osterman
Keyword(s):  
Time Series ◽  
Tropospheric Ozone ◽  
Earth Science ◽  
Satellite Measurements ◽  
Science Data ◽  
The Past ◽  
Long Term Changes ◽  
Earth Science Data

<p>Given the importance of tropospheric ozone as a greenhouse gas and a hazardous pollutant that impacts human health and ecosystems, it is critical to quantify and understand long-term changes in its abundance.  Satellite records are beginning to approach the length needed to assess variability and trends in tropospheric ozone, yet an intercomparison of time series from different instruments shows substantial differences in the net change in ozone over the past decade.  We discuss our efforts to produce Earth Science Data Records of tropospheric ozone and quantify uncertainties and biases in these records.  We also discuss the role of changes in the magnitude and distribution of precursor emissions and in downward transport of ozone from the stratosphere in determining tropospheric ozone abundances over the past 15 years.</p>

scholarly journals Evaluation of MODIS and VIIRS cloud-gap-filled snow-cover products for production of an Earth science data record

2019 ◽  
Vol 23 (12) ◽  
pp. 5227-5241 ◽  
Author(s):  
Dorothy K. Hall ◽  
George A. Riggs ◽  
Nicolo E. DiGirolamo ◽  
Miguel O. Román
Keyword(s):  
Time Series ◽  
Snow Cover ◽  
Earth Science ◽  
Time Of Day ◽  
Science Data ◽  
Western Us ◽  
Data Record ◽  
Moderate Resolution ◽  
Terra Modis ◽  
Earth Science Data

Abstract. MODerate resolution Imaging Spectroradiometer (MODIS) cryosphere products have been available since 2000 – following the 1999 launch of the Terra MODIS and the 2002 launch of the Aqua MODIS – and include global snow-cover extent (SCE) (swath, daily, and 8 d composites) at 500 m and ∼5 km spatial resolutions. These products are used extensively in hydrological modeling and climate studies. Reprocessing of the complete snow-cover data record, from Collection 5 (C5) to Collection 6 (C6) and Collection 6.1 (C6.1), has provided improvements in the MODIS product suite. Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Collection 1 (C1) snow-cover products at a 375 m spatial resolution have been available since 2011 and are currently being reprocessed for Collection 2 (C2). Both the MODIS C6.1 and the VIIRS C2 products will be available for download from the National Snow and Ice Data Center beginning in early 2020 with the complete time series available in 2020. To address the need for a cloud-reduced or cloud-free daily SCE product for both MODIS and VIIRS, a daily cloud-gap-filled (CGF) snow-cover algorithm was developed for MODIS C6.1 and VIIRS C2 processing. MOD10A1F (Terra) and MYD10A1F (Aqua) are daily, 500 m resolution CGF SCE map products from MODIS. VNP10A1F is the daily, 375 m resolution CGF SCE map product from VIIRS. These CGF products include quality-assurance data such as cloud-persistence statistics showing the age of the observation in each pixel. The objective of this paper is to introduce the new MODIS and VIIRS standard CGF daily SCE products and to provide a preliminary evaluation of uncertainties in the gap-filling methodology so that the products can be used as the basis for a moderate-resolution Earth science data record (ESDR) of SCE. Time series of the MODIS and VIIRS CGF products have been developed and evaluated at selected study sites in the US and southern Canada. Observed differences, although small, are largely attributed to cloud masking and differences in the time of day of image acquisition. A nearly 3-month time-series comparison of Terra MODIS and S-NPP VIIRS CGF snow-cover maps for a large study area covering all or parts of 11 states in the western US and part of southwestern Canada reveals excellent correspondence between the Terra MODIS and S-NPP VIIRS products, with a mean difference of 11 070 km2, which is ∼0.45 % of the study area. According to our preliminary validation of the Terra and Aqua MODIS CGF SCE products in the western US study area, we found higher accuracy of the Terra product compared with the Aqua product. The MODIS CGF SCE data record beginning in 2000 has been extended into the VIIRS era, which should last at least through the early 2030s.

scholarly journals Earth Science Data Visualization Through Augmented Coloring Books

2018 ◽  
Author(s):  
Nihanth Cherukuru ◽  
Tim Scheitlin ◽  
Matt Rehme
Keyword(s):  
Data Visualization ◽  
Earth Science ◽  
Science Data ◽  
Earth Science Data

scholarly journals Toward a Collective Agenda on AI for Earth Science Data Analysis

2021 ◽  
Vol 9 (2) ◽  
pp. 88-104
Author(s):  
Devis Tuia ◽  
Ribana Roscher ◽  
Jan Dirk Wegner ◽  
Nathan Jacobs ◽  
Xiaoxiang Zhu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Earth Science ◽  
Science Data ◽  
Earth Science Data

scholarly journals Giovanni: The Bridge Between Data and Science

Eos ◽  
2017 ◽  
Author(s):  
Zhong Liu ◽  
James Acker
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Earth Science ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Science Data ◽  
Web Based ◽  
Sensing Data ◽  
Daunting Task ◽  
Earth Science Data

Using satellite remote sensing data sets can be a daunting task. Giovanni, a Web-based tool, facilitates access, visualization, and exploration for many of NASA’s Earth science data sets.

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