Introduction to Remote Sensing: Science for Society

Science for Society: Extending Earth Science Research Results into Decision Support Tool

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

App Store at OpenNEX: A Gateway to Help Find Apps over Big Data on the Cloud

In this on-going work, we report our efforts of building App Store at OpenNEX, which aims to provide a data analytics software search engine to help researchers find reusable software components to facilitate the development of their own algorithms

Big Data Challenges in Climate Science: Improving the Next-Generation Cyberinfrastructure

The knowledge we gain from research in climate science depends on the generation, dissemination, and analysis of high-quality data. This work comprises technical practice as well as social practice, both of which are distinguished by their massive scale and global reach. As a result, the amount of data involved in climate research is growing at an unprecedented rate. Climate model intercomparison (CMIP) experiments, the integration of observational data and climate reanalysis data with climate model outputs, as seen in the Obs4MIPs, Ana4MIPs, and CREATE-IP activities, and the collaborative work of the Intergovernmental Panel on Climate Change (IPCC) provide examples of the types of activities that increasingly require an improved cyberinfrastructure for dealing with large amounts of critical scientific data. This paper provides an overview of some of climate science's big data problems and the technical solutions being developed to advance data publication, climate analytics as a service, and interoperability within the Earth System Grid Federation (ESGF), the primary cyberinfrastructure currently supporting global climate research activities

Impact of vegetation on the atmospheric boundary layer and convective storms

Fall 1992.Also issued as author's dissertation (Ph.D.) -- Colorado State University, 1992.Includes bibliographical references.The impact of vegetation on atmospheric boundary layer and convective storms is examined through the construction and testing of a soil-vegetation-atmosphere transfer (SVAT) model. The Land Ecosystem-Atmosphere Feedback (LEAF) model is developed using an elevated canopy structure, an above-canopy aerodynamic resistance, two in-canopy aerodynamic resistances, and one stomatal conductance functions. The air temperature and humidity are assumed to be constant in the canopy whereas the wind and radiation follow a specified vertical profile. A simple dump-bucket method is used to parameterize the interception of precipitation and a multi-layer soil model is utilized to handle the vertical transfer of so water. Evaporation from soil and wet leaves and transpiration from dry leaves are evaluated separately. The soil water uptake is based on soil water potential rather than on the length of roots. Separate energy budgets for vegetation and for the soil are used in order to remove unnecessary assumptions on energy partition between the vegetation and the substrate. Primary parameters are LAI, maximum stomata! conductance, and albedo. Secondary parameters include displacement height and environmental controls on stomata! resistance function. Due to the complexity of the LEAF model, statistical methods are used to improve LEAF model performance. The Multi-response Randomized Block Permutation (MRBP) procedure is used to guide the choice of model parameter values. The Fourier Amplitude Sensitivity Test (FAST) is applied to better understand the model behavior in response to the changes in model parameters. Finally, LEAF is used to study the growth of boundary layer and the local thermal circulations generated by surface inhomogeneities. Results show the atmospheric boundary layer is substantially cooler and more moist over unstressed vegetation than over bare dry soil. Thermally forced circulation can result from the juxtaposition of two vegetation types due to different biophysical characteristics. Results from three-dimensional simulations show that the surface spatial heterogeneities made by vegetation play an important role in generating local convective storms.Sponsored by NSF ATM-8915265, and USGS 14-08-0001-A0929

Molecular Platinum Carbonyl Nanoclusters

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

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 (Khl 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)