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Case studies to demonstrate the value of satellite remotely sensed observations of atmospheric parameters are desirable to demonstrate the utility of the WMO/CEOS/CGMS Climate Architecture, released in 2013. This paper is an initial attempt to show the widespread utility of satellite observations to climate applications in preparation of a Global Framework for Climate Services.
Drought and flooding in Illinois have severe impacts on the communities and ecosystems of the state. Soil moisture is a valuable indicator of drought and flood vulnerability but can be difficult to measure since in situ monitoring is limited to discrete stations throughout the state. The team created a framework to compare in situ, modeled, and NASA satellite soil moisture measurements to increase the spatial coverage of soil moisture monitoring. The team partnered with the Illinois State Weather Survey, USDA Midwest Climate Hub, NOAA Regional Climate Services of the Central Region, NOAA National Integrated Drought Information System’s Midwest Drought Early Warning System, and the NOAA North Central River ForecastCenter. The team standardized and compared soil moisture data from NASA’s Soil Moisture Active Passive (SMAP) mission, modeled soil moisture outputs from NASA’s SPoRT Land Information System (SPoRT-LIS), and in situ measurements from the Illinois State Weather Survey’s Water and Atmospheric Resources Monitoring (WARM) program. Compared to the WARM data, the satellite and modeled data showed seasonally variable differences and bias. The difference was highest in the winter months and lowest in the late summer and early fall months for the SMAP and SPoRT-LIS data products. SPoRT-LIS produced lower seasonal variability and SMAP demonstrated higher correlation values and lower differences. These analyses suggest that both SMAP and SPoRT-LIS products offer unique strengths and limitations when used for soil moisture monitoring
Inclusion of ecosystem services (ES) information into national-scale development and climate adaptation planning has yet to become common practice, despite demand from decision makers. Identifying where ES originate and to whom the benefits flowunder current and future climate conditionsis especially critical in rapidly developing countries, where the risk of ES loss is high. Here, using Myanmar as a case study, we assess where and how ecosystems provide key benefits to the countrys people and infrastructure. We model the supply of and demand for sediment retention, dry-season baseflows, flood risk reduction and coastal storm protection from multiple beneficiaries. We find that locations currently providing the greatest amount of services are likely to remain important under the range of climate conditions considered, demonstrating their importance in planning for climate resilience. Overlap between priority areas for ES provision and biodiversity conservation is higher than expected by chance overall, but the areas important for multiple ES are underrepresented in currently designated protected areas and Key Biodiversity Areas. Our results are contributing to development planning in Myanmar, and our approach could be extended to other contexts where there is demand for national-scale natural capital information to shape development plans and policies
Exascale computing, big data, and cloud computing are driving the evolution of large-scale information systems toward a model of data-proximal analysis. In response, we are developing a concept of climate analytics as a service (CAaaS) that represents a convergence of data analytics and archive management. With this approach, high-performance compute-storage implemented as an analytic system is part of a dynamic archive comprising both static and computationally realized objects. It is a system whose capabilities are framed as behaviors over a static data collection, but where queries cause results to be created, not found and retrieved. Those results can be the product of a complex analysis, but, importantly, they also can be tailored responses to the simplest of requests. NASA's MERRA Analytic Service and associated Climate Data Services API provide a real-world example of climate analytics delivered as a service in this way. Our experiences reveal several advantages to this approach, not the least of which is orders-of-magnitude time reduction in the data assembly task common to many scientific workflows.
Successful climate change mitigation will involve not only technological innovation, but also innovation in how we understand the societal and individual behaviors that shape the demand for energy services. Traditionally, individual energy behaviors have been described as a function of utility optimization and behavioral economics, with price restructuring as the dominant policy lever. Previous research at the macro-level has identified economic activity, power generation and technology, and economic role as significant factors that shape energy use. However, most demand models lack basic contextual information on how dominant social phenomenon, the changing demographics of cities, and the sociocultural setting within which people operate, affect energy decisions and use patterns. Here we use high-quality Suomi-NPP VIIRS nighttime environmental products to: (1) observe aggregate human behavior through variations in energy service demand patterns during the Christmas and New Year's season and the Holy Month of Ramadan and (2) demonstrate that patterns in energy behaviors closely track sociocultural boundaries at the country, city, and district level. These findings indicate that energy decision making and demand is a sociocultural process as well as an economic process, often involving a combination of individual price-based incentives and societal-level factors. While nighttime satellite imagery has been used to map regional energy infrastructure distribution, tracking daily dynamic lighting demand at three major scales of urbanization is novel. This methodology can enrich research on the relative importance of drivers of energy demand and conservation behaviors at fine scales. Our initial results demonstrate the importance of seating energy demand frameworks in a social context.
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The Missouri River Basin is a major global breadbasket, containing large amounts of agricultural land. Recent devastating weather events have motivated regional organizations to dedicate efforts to drought and flood monitoring and early warning systems. The DEVELOP team partnered with the following organizations: Montana Climate Office, NOAA National Weather Service (NWS) Missouri Basin River Forecast Center, NOAA Regional Climate Services of the Central Region, NOAA Physical Sciences Laboratory, and the US Army Corps of Engineers' Missouri River Basin Water Management Division. The team collaborated with the partners in their efforts to monitor flood and drought by enhancing a composite moisture index (CMI) for the Missouri River Basin. The CMI leverages NASA Earth observations to derive snow cover data from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) mission and snow water equivalent and snowdepth datasets from the NOAA NWS National Operational Hydrologic Remote Sensing Center’s Snow Data Assimilation System (SNODAS). Building upon this framework, the team added groundwater storage data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On missions, soil moisture data from Soil Moisture Active Passive (SMAP), and United States Geological Survey in situ streamflow data to the CMI. To test the tool’s validity, the team compared the CMI results to historically extreme dry and extreme wet years, March 2017 and March 2019, respectively. The CMI accurately reflects both 2017 and 2019 climate conditions in the Missouri River Basin. The refined CMI enhances the understanding of antecedent soil moisture conditions and improves flood and drought forecasting in the Missouri River Basin before the growing season.
The Missouri River Basin provides irrigation water for a substantial part of the domestic agricultural sector within the United States. Drought events pose a significant threat to economic livelihoods of dependent individuals, industries, and ecosystems (e.g., farmers, local tribes, hydroelectric power, wildlife). In 2017 alone, the Missouri River Basin experienced a severe drought that resulted in a $2.6 billion loss to the U.S. Northern Plains. In response to such events, organizations throughout the basin, such as the Montana Climate Office, have dedicated efforts for drought monitoring and communicating relevant information to local stakeholders. In an effort to aid regional decision-making capabilities, the project team partnered with the Montana Climate Office, NOAA National Weather Service (NWS) Missouri Basin River Forecast Center, and NOAA Regional Climate Services, Central Region to create a monthly Composite Moisture Index (CMI) that relies on NASA Earth observations from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and the Soil Moisture Active Passive (SMAP) mission. From these satellites, as well as the NOAA NWS National Operational Hydrologic Remote Sensing Center Snow Data Assimilation System (SNODAS), our team aggregated climate datasets including soil moisture, snow cover, snow depth, and snow water equivalent to compute a CMI that indicates regional moisture conditions during the winter months. The March CMI values produced over the Missouri River headwater subbasin strongly correlate (r = 0.75) with spring and early summer stream discharge, demonstrating the use of this metric to indicate moisture conditions for the snowmelt and growing seasons.
The Missouri River Basin provides irrigation water for a substantial part of the domestic agricultural sector within the United States. Drought events pose a significant threat to the economic livelihoods of dependent individuals, industries, and ecosystems (e.g. farmers, local tribes, hydroelectric power, wildlife). In 2017 alone, the Missouri River Basin experienced a severe drought that resulted in a $2.6 billion loss to the U.S. Northern Plains. In response to such events, organizations throughout the basin, such as the Montana Climate Office, have dedicated efforts for drought monitoring and communicating relevant information to local stakeholders. In an effort to aid regional decision-making capabilities, the project team partnered with the Montana Climate Office, NOAA National Weather Service (NWS) Missouri Basin River Forecast Center, and NOAA Regional Climate Services, Central Region to create a monthly Composite Moisture Index (CMI) that relies on NASA Earth observations from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and the Soil Moisture Active Passive (SMAP) mission. From these satellites, as well as the NOAA NWS National Operational Hydrologic Remote Sensing Center’s Snow Data Assimilation System (SNODAS), our team aggregated climate datasets including soil moisture, snow cover, snow depth, and snow water equivalent to compute a CMI that indicates regional moisture conditions during the winter months. The March CMI values produced over the Missouri River headwater subbasin strongly correlate (r = 0.75) with spring and early summer stream discharge, demonstrating the use of this metric to indicate moisture conditions for the snowmelt and growing seasons.
The NASA Center for Climate Simulation (NCCS) offers integrated supercomputing, visualization, and data interaction technologies to enhance NASA's weather and climate prediction capabilities. It serves hundreds of users at NASA Goddard Space Flight Center, as well as other NASA centers, laboratories, and universities across the US. Over the past year, NCCS has continued expanding its data-centric computing environment to meet the increasingly data-intensive challenges of climate science. We doubled our Discover supercomputer's peak performance to more than 800 teraflops by adding 7,680 Intel Xeon Sandy Bridge processor-cores and most recently 240 Intel Xeon Phi Many Integrated Core (MIG) co-processors. A supercomputing-class analysis system named Dali gives users rapid access to their data on Discover and high-performance software including the Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT), with interfaces from user desktops and a 17- by 6-foot visualization wall. NCCS also is exploring highly efficient climate data services and management with a new MapReduce/Hadoop cluster while augmenting its data distribution to the science community. Using NCCS resources, NASA completed its modeling contributions to the Intergovernmental Panel on Climate Change (IPCG) Fifth Assessment Report this summer as part of the ongoing Coupled Modellntercomparison Project Phase 5 (CMIP5). Ensembles of simulations run on Discover reached back to the year 1000 to test model accuracy and projected climate change through the year 2300 based on four different scenarios of greenhouse gases, aerosols, and land use. The data resulting from several thousand IPCC/CMIP5 simulations, as well as a variety of other simulation, reanalysis, and observationdatasets, are available to scientists and decision makers through an enhanced NCCS Earth System Grid Federation Gateway. Worldwide downloads have totaled over 110 terabytes of data.
With its unique and trusted earth observations, NASA is a critical source in informing decisions that will help achieve the U.S. goal of Net-Zero Greenhouse Gas (GHG) Emissions by 2050. NASA’s Prediction of Worldwide Energy Resource (POWER) project facilitates the use of NASA Earth Science data holdings within the energy, agricultural, and building heating/cooling design industries. POWER packages solar and meteorological data from several NASA projects in a user friendly GIS-enabled web services system (https://power.larc.nasa.gov). As part of the development of new data products to support the energy and building heating/cooling design communities, we estimate the changes in energy required to heat and cool buildings in the future climate at 14 different NASA site locations spread throughout the continental United States, as projected by CMIP6 climate models under different emissions scenarios. Bias-corrected downscaled time series of meteorological variables are taken from NASA Earth Exchange (NEX) Global Daily Downscaled Projections (GDDP-CMIP6) downscaled climate model data. The spread between the different model projections is accounted for by analyzing both the ensemble average of 22 CMIP6 models and 6 representative models with different climate sensitivities and different interannual variability. Changes in energy use are estimated in two ways. First, changes in the total annual heating and cooling degree days (HDD and CDD, respectively) are calculated relative to the current climate. This is done at all 14 sites. Second, the downscaled time series are used as inputs into RETScreen(R), a clean energy management decision tool, to give an estimate of heating/cooling energy use for a typical office building. We use this estimation method with model data at Langley Research Center. In the next 50 years, the annual total of HDD (CDD) is projected to decrease by 8-38% (increase by 5-28%) at all sites, with the increase in CDD typically a larger magnitude the decrease in HDD. For a typical small office building at Langley Research Center, the amount of energy needed to cool increases by 33-54% and the amount of energy to heat decreases by 29-40%. POWER is working to develop long term climate data services based on these results to include in future data products to provide to users.
1 A Breakthrough in Rotorcraft Prediction Accuracy Using Detached Eddy Simulation; 2 Adjoint-Based Design for Complex Aerospace Configurations; 3 Simulating Hypersonic Turbulent Combustion for Future Aircraft; 4 From a Roar to a Whisper: Making Modern Aircraft Quieter; 5 Modeling of Extended Formation Flight on High-Performance Computers; 6 Supersonic Retropropulsion for Mars Entry; 7 Validating Water Spray Simulation Models for the SLS Launch Environment; 8 Simulating Moving Valves for Space Launch System Liquid Engines; 9 Innovative Simulations for Modeling the SLS Solid Rocket Booster Ignition; 10 Solid Rocket Booster Ignition Overpressure Simulations for the Space Launch System; 11 CFD Simulations to Support the Next Generation of Launch Pads; 12 Modeling and Simulation Support for NASA's Next-Generation Space Launch System; 13 Simulating Planetary Entry Environments for Space Exploration Vehicles; 14 NASA Center for Climate Simulation Highlights; 15 Ultrascale Climate Data Visualization and Analysis; 16 NASA Climate Simulations and Observations for the IPCC and Beyond; 17 Next-Generation Climate Data Services: MERRA Analytics; 18 Recent Advances in High-Resolution Global Atmospheric Modeling; 19 Causes and Consequences of Turbulence in the Earths Protective Shield; 20 NASA Earth Exchange (NEX): A Collaborative Supercomputing Platform; 21 Powering Deep Space Missions: Thermoelectric Properties of Complex Materials; 22 Meeting NASA's High-End Computing Goals Through Innovation; 23 Continuous Enhancements to the Pleiades Supercomputer for Maximum Uptime; 24 Live Demonstrations of 100-Gbps File Transfers Across LANs and WANs; 25 Untangling the Computing Landscape for Climate Simulations; 26 Simulating Galaxies and the Universe; 27 The Mysterious Origin of Stellar Masses; 28 Hot-Plasma Geysers on the Sun; 29 Turbulent Life of Kepler Stars; 30 Modeling Weather on the Sun; 31 Weather on Mars: The Meteorology of Gale Crater; 32 Enhancing Performance of NASAs High-End Computing Applications; 33 Designing Curiosity's Perfect Landing on Mars; 34 The Search Continues: Kepler's Quest for Habitable Earth-Sized Planets.
The biodiversity, ecosystem services and climate variability of the Antarctic continent, and the Southern Ocean are major components of the whole Earth system. Antarctic ecosystems are driven more strongly by the physical environment than many other marine and terrestrial ecosystems. As a consequence, to understand ecological functioning, cross-disciplinary studies are especially important in Antarctic research. The conceptual study presented here is based on a workshop initiated by the Research Programme Antarctic Thresholds - Ecosystem Resilience and Adaption of the Scientific Committee on Antarctic Research, which focused on challenges in identifying and applying cross-disciplinary approaches in the Antarctic. Novel ideas, and first steps in their implementation, were clustered into eight themes, ranging from scale problems, risk maps, organism and ecosystem responses to multiple environmental changes, to evolutionary processes. Scaling models and data across different spatial and temporal scales were identified as an overarching challenge. Approaches to bridge gaps in the research programmes included multi-disciplinary monitoring, linking biomolecular findings and simulated physical environments, as well as integrative ecological modelling. New strategies in academic education are proposed. The results of advanced cross-disciplinary approaches can contribute significantly to our knowledge of ecosystem functioning, the consequences of climate change, and to global assessments that ultimately benefit humankind.
The comprehensive and innovative evaluation of climate models with newly available global observations is critically needed for the improvement of climate model current-state representation and future-state predictability. A climate model diagnostic evaluation process requires physics-based multi-variable analyses that typically involve large-volume and heterogeneous datasets, making them both computation- and data-intensive. With an exploratory nature of climate data analyses and an explosive growth of datasets and service tools, scientists are struggling to keep track of their datasets, tools, and execution/study history, let alone sharing them with others. In response, we have developed a cloud-enabled, provenance-supported, web-service system called Climate Model Diagnostic Analyzer (CMDA). CMDA enables the physics-based, multivariable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. At the same time, CMDA provides a crowd-sourcing space where scientists can organize their work efficiently and share their work with others. CMDA is empowered by many current state-of-the-art software packages in web service, provenance, and semantic search.
Climate science and applications require access to vast amounts of archived high quality data, software tools and services for data manipulation and information extraction. These on the other hand require gaining detailed understanding of the data's internal structure and physical implementation to data reduction, combination and data product production. This time-consuming task must be undertaken before the core investigation can begin and is an especially difficult challenge when science objectives require users to deal with large multi-sensor data sets of different formats, structures, and resolutions. In order to address these issues the Goddard Space Flight Center (GSFC) Earth Sciences (GES), Data and Information Service Center (DISC) Distributed Active Archive Center (DAAC) has made great progress in facilitating science and applications research by developing innovative tools and data services applied to the Earth sciences atmospheric and climate data. The GES/DISC/DAAC has successfully implemented and maintained a long-term climate satellite data archive and developed tools and services to a variety of atmospheric science missions including AIRS, AVHRR, MODIS, SeaWiFS, SORCE, TOMS, TOVS, TRMM, and UARS and Aura instruments providing researchers with excellent opportunities to acquire accurate and continuous atmospheric measurements. Since the number of climate science products from these various missions is steadily increasing as a result of more sophisticated sensors and new science algorithms, the main challenge for data centers like the GES/DISC/DAAC is to guide users through the variety of data sets and products, provide tools to visualize and reduce the volume of the data and secure uninterrupted and reliable access to data and related products. This presentation will describe the effort at the GES/DISC/DAAC to build a bridge between multi-sensor data and the effective scientific use of the data, with an emphasis on the heritage satellite observations and science products for climate applications. The intent is to inform users of the existence of this large collection of data and products; suggest starting points for cross-platform science projects and data mining activities and provide data services and tools information. More information about the GES/DISC/DAAC satellite data and products, tools, and services can be found at http://daac.gsfc.nasa.gov.
The Atmospheric Infrared Sounder (AIRS) instrument suite is designed to observe and characterize the entire atmospheric column from the surface to the top of the atmosphere in terms of surface emissivity and temperature, atmospheric temperature and humidity profiles, cloud amount and height, and the spectral outgoing infrared radiation on a global scale. The AIRS Data Support Team at the GES DISC provides data support to assist others in understanding, retrieving and extracting information from the AIRS/AMSU/HSB data products. Because a number of years has passed since its operation started, the amount of data has reached a certain level of maturity where we can address the climate change study utilizing AIRS data, In this presentation we will list various service we provide and to demonstrate how to utilize/apply the existing service to long-term and short-term variability study.
The National Climate Assessment-Land Data Assimilation System (NCA-LDAS) is an Integrated Terrestrial Water Analysis, and is one of NASAs contributions to the NCA of the United States. The NCA-LDAS has undergone extensive development, including multi-variate assimilation of remotely-sensed water states and anomalies as well as evaluation and verification studies, led by the Goddard Space Flight Centers Hydrological Sciences Laboratory (HSL). The resulting NCA-LDAS data have recently been released to the general public and include those from the Noah land-surface model (LSM) version 3.3 (Noah-3.3) and the Catchment LSM version Fortuna-2.5 (CLSM-F2.5). Standard LSM output variables including soil moistures temperatures, surface fluxes, snow cover depth, groundwater, and runoff are provided, as well as streamflow using a river routing system. The NCA-LDAS data are archived at and distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). The data can be accessed via HTTP, OPeNDAP, Mirador search and download, and NASA Earth data Search. To further facilitate access and use, the NCA-LDAS data are integrated into the NASA Giovanni, for quick visualization and analysis, and into the Data Rods system, for retrieval of time series of long time periods. The temporal and spatial resolutions of the NCA-LDAS data are, respectively, daily-averages and 0.125x0.125 degree, covering North America (25N 53N; 125W 67W) and the period January 1979 to December 2015. The data files are in self-describing, machine-independent, CF-compliant netCDF-4 format.
NASA's climate reanalysis datasets from the Modern Era Retrospective-analysis for Research and Applications, Version 2 (MERRA-2) contains numerous long-term atmosphere, land, and ocean data products from 1980-present. MERRA-2 datasets, such as precipitation, soil moisture, and temperature, have been used widely to study extreme events. The native archived MERRA-2 data files are day-file (hourly time interval) and month-file, containing up to 125 parameters in one file. Due to the large number of data files and volumes, it is challenging for users, especially the applications research community, to handle the original hourly data files for long time periods to analyze extreme events. In this presentation, we review MERRA-2 data for studies of extreme conditions, and demonstrate analytic services at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). One of the current operational services, 'subsetter', allows users to download only specific data of interest, i.e. data selected by parameter, region, and time period. New services are under development that will provide more 'on-the-fly' statistical calculations when downloading data; improve efficiency when accessing long time-series data. We will provide additional "How-to" resources that include step-by-step instructions on data access and usage. We have tested restructuring of day-files in an optimized data cube, which has significantly improved system performance for accessing long time-series. Overall performance is associated with cube size and structure, data compression method, and how the data are accessed. The optimized data cube structure will enable better online analytic services for statistical analysis and extreme events mining. To demonstrate the service, we use an extreme drought associated with the anomalous 2016 monsoon over southern Asia. This prototype time-series service may be augmented in the cloud infrastructure in the future.