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Lee, Tsengdar J.

Publications and source records attributed to Lee, Tsengdar J..

Earth Observations from a New Generation of Geostationary Satellites

The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of widely used polar orbiting sensors such as EOS/MODIS. More importantly, they provide observations at 1-5-15 minute intervals, instead of twice a day from MODIS, offering unprecedented opportunities for monitoring large parts of the Earth. In addition to serving the needs of weather forecasting, these observations offer new and exciting opportunities in managing solar power, fighting wildfires, and tracking air pollution. Creation of actionable information in near real-time from these data streams is a challenge that is best addressed through collaborative efforts among the industry, academia and government agencies.

Nemani, Ramakrishna R.

Earth Observations from a New Generation of Geostationary Satellites

The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of widely used polar orbiting sensors such as EOS/MODIS. More importantly, they provide observations at 1-5-15 minute intervals, instead of twice a day from MODIS, offering unprecedented opportunities for monitoring large parts of the Earth. In addition to serving the needs of weather forecasting, these observations offer new and exciting opportunities in managing solar power, fighting wildfires, and tracking air pollution. Creation of actionable information in near real-time from these data streams is a challenge that is best addressed through collaborative efforts among the industry, academia and government agencies.

Nemani, Ramakrishna R.

NASA Earth eXchange (NEX) App Store

NASA Earth Exchange (NEX), and her public cloud version OpenNEX, have become platforms supporting scientific collaboration, knowledge sharing and research for the entire Earth science community. To date, a number of custom tools and capabilities have been integrated into the platforms. However, such integration has to undergo a case-by-case manual process thus lacks scalability. This timely project builds an App Store onto OpenNEX as a building block. Climate data analytics tools/programs can be easily uploaded, shared, organized, searched, and recommended like photos and videos on the YouTube. The foundation of our App Store is a provenance server, which not only records metadata but also execution history of climate data analytics apps including the input data and parameters, output data and products, who runs the app for which purpose, and how apps may be chained into workflows. Researchers can thus understand, reproduce, and repurpose existing apps and workflows. Machine learning approaches are applied to mine provenance to provide recommend-as-you-go services for Earth scientists, such as to recommend suitable apps and workflow snippets. A browser-based workflow tool is also provided for researchers to explore the provenance server and design value-added workflows. Scalability, sustainability, extensibility, usability, adaptability, security and privacy are considered in the App Store.

eXchange

Enabling Reanalysis Research Using the Collaborative Reanalysis Technical Environment (CREATE)

Modern atmospheric and oceanic reanalysis are valuable assets for atmospheric research and climate monitoring (Kalnay et al. 1996). Now that most reanalysis records are more than 36 years long, the data have become more useful for climate modeling research (Dole et al. 2008). For investigators who need to use multiple reanalysis, a common challenge is that the data are distributed at various sites and often in different formats. The NASA CREATE system provides access to the data in one location in a standard format (one variable per file and standardized metadata in the CMIP5 style; see Table 1 for a list of the key acronyms used in this paper). The collection includes monthly and 6-hourly data from the seven major atmospheric reanalysis: CFSR (Saha et al. 2010), ERA-Interim (Dee et al. 2011), MERRA (Rienecker et al. 2011), MERRA-2 (Gelaro et al. 2017), JRA-25 (Onogi et al. 2007), JRA-55 (Kobayashi et al. 2014), and 20CRv2c (Compo et al. 2011). An ancillary portion of CREATE includes eight ocean reanalysis: NCEP CFSR, CMCC C-GLORSv5 (Storto et al. 2016), ECMWF ORAS4 (Balmaseda et al.2013), ECMWF ORAP5.0 (Zuo et al. 2015), University of Hamburg GECCO2 (Köhl 2015), GFDL ECDA (Zhang et al. 2007), NOAA GODAS (Saha et al. 2010), and MOVE/MRI.COM-G2i (Toyoda et al. 2016). The ocean state variables were similarly reformatted but were then also regridded onto a common horizontal and vertical grid. This approach facilitated the calculation of an ensemble average and spread that is also published alongside the native gridded data. A third reanalysis product is a global hourly 0.5° land surface air temperature dataset constructed by Wang and Zeng (2013). All three datasets are distributed through the ESGF in a format consistent with the CMIP style described by Cinquini et al. (2014).

Potter, Gerald L.

Enabling Research Tools for Sustained Climate Assessment

The U.S. Global Change Research Program Sustained Assessment process benefits from long-term investments in Earth science research that enable the scientific community to conduct assessment-relevant science. To this end, NASA initiated several research programs over the past five years to support the Earth observation community in developing indicators, datasets, research products, and tools to support ongoing and future National Climate Assessments. These activities complement NASA's ongoing Earth science research programs. One aspect of the assessment portfolio funds four "enabling tools" projects at NASA research centers. Each tool leverages existing capacity within the center, but has developed tailored applications and products for National Climate Assessments. The four projects build on the capabilities of a global atmospheric reanalysis (MERRA-2), a continental U.S. land surface reanalysis (NCA-LDAS), the NASA Earth Exchange (NEX), and a Regional Climate Model Evaluation System (RCMES). Here, we provide a brief overview of each enabling tool, highlighting the ways in which it has advanced assessment science to date. We also discuss how the assessment community can access and utilize these tools for National Climate Assessments and other sustained assessment activities.

Research

Climate Analytics as a Service

Climate science is a big data domain that is experiencing unprecedented growth. In our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). CAaaS combines high-performance computing and data-proximal analytics with scalable data management, cloud computing virtualization, the notion of adaptive analytics, and a domain-harmonized API to improve the accessibility and usability of large collections of climate data. MERRA Analytic Services (MERRA/AS) provides an example of CAaaS. MERRA/AS enables MapReduce analytics over NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of key climate variables. The effectiveness of MERRA/AS has been demonstrated in several applications. In our experience, CAaaS is providing the agility required to meet our customers' increasing and changing data management and data analysis needs.

big data