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NASA NTRS2016

Overview of Global Monitoring of Terrestrial Chlorophyll Fluorescence from Space

Despite the critical importance of photosynthesis for the Earth system, understanding how it is influenced by factors such as climate variability, disturbance history, and water or nutrient availability remains a challenge because of the complex interactions and the lack of GPP measurements at various temporal and spatial scales. Space observations of the sun-induced chlorophyll fluorescence (SIF) electromagnetic signal emitted by plants in the 650-850nm spectral range hold the promise of providing a new view of vegetation photosynthesis on a global basis. Global retrievals of SIF from space have recently been achieved from a number of spaceborne spectrometers originally intended for atmospheric research. Despite not having been designed for land applications, such instruments have turned out to provide the necessary spectral and radiometric sensitivity for SIF retrieval from space. The first global measurements of SIF were achieved in 2011 from spectra acquired by the Japanese GOSAT mission launched in 2009. The retrieval takes advantage of the high spectral resolution provided by GOSATs Fourier Transform Spectrometer (FTS) which allows the evaluation of the in-filling of solar Fraunhofer lines by SIF. Unfortunately, GOSAT only provides a sparse spatial sampling with individual soundings separated by several hundred kilometers. Complementary, the Global Ozone Monitoring Experiment-2 (GOME-2) instruments onboard MetOp-A and MetOp-B enable SIF retrievals since 2007 with a continuous and global spatial coverage. GOME-2 measures in the red and near-infrared (NIR) spectral regions with a spectral resolution of 0.5 nm and a pixel size of up to 40x40 km2. Most recently, another global and spatially continuous data set of SIF retrievals at 740 nm spanning the 2003-2012 time frame has been produced from ENVISATSCIAMACHY. This observational scenario has been completed by the first fluorescence data from the NASA-JPL OCO-2 mission (launched in July 2014) and the upcoming Copernicus' Sentinel 5-Precursor to be launched in early 2016. OCO-2 and TROPOMI offer the possibility of monitoring SIF globally with a 100-fold improvement in spatial and temporal resolution with respect to the current measurements from the GOSAT, GOME-2 and SCIAMACHY missions. In this contribution, we will provide an overview of existing global SIF data sets derived from space-based atmospheric spectrometers and will demonstrate the potential of such data to improve our knowledge of vegetation photosynthesis and gross primary production at the synoptic scale. We will show examples of ongoing research exploiting SIF data for an improved monitoring of photosynthetic activity in different ecosystems, including large crop belts worldwide, the Amazon rainforest and boreal evergreen forests.

vegetation agriculture satellite↗

NASA NTRS2015

Monitoring Atmospheric CO2 From Space: Challenge & Approach

Atmospheric CO2 is the key radiative forcing for the Earth's climate and may contribute a major part of the Earth's warming during the past 150 years. Advanced knowledge on the CO2 distributions and changes can lead considerable model improvements in predictions of the Earth's future climate. Large uncertainties in the predictions have been found for decades owing to limited CO2 observations. To obtain precise measurements of atmospheric CO2, certain challenges have to be overcome. For an example, global annual means of the CO2 are rather stable, but, have a very small increasing trend that is significant for multi-decadal long-term climate. At short time scales (a second to a few hours), regional and subcontinental gradients in the CO2 concentration are very small and only in an order of a few parts per million (ppm) compared to the mean atmospheric CO2 concentration of about 400 ppm, which requires atmospheric CO2 space monitoring systems with extremely high accuracy and precision (about 0.5 ppm or 0.125%) in spatiotemporal scales around 75 km and 10-s. It also requires a decadal-scale system stability. Furthermore, rapid changes in high latitude environments such as melting ice, snow and frozen soil, persistent thin cirrus clouds in Amazon and other tropical areas, and harsh weather conditions over Southern Ocean all increase difficulties in satellite atmospheric CO2 observations. Space lidar approaches using Integrated Path Differential Absorption (IPDA) technique are considered to be capable of obtaining precise CO2 measurements and, thus, have been proposed by various studies including the 2007 Decadal Survey (DS) of the U.S. National Research Council. This study considers to use the Intensity-Modulated Continuous-Wave (IM-CW) lidar to monitor global atmospheric CO2 distribution and variability from space. Development and demonstration of space lidar for atmospheric CO2 measurements have been made through joint adventure of NASA Langley Research Center and Exelis, Inc. As prototype space IPDA lidars, airborne laser absorption lidar systems operating in 1.57 CO2 absorption band have been developed and tested through lab, ground-based range, and flight campaigns. Very encouraging results have been obtained. The signal-to-noise ratio (SNR) for clear sky IPDA measurements of CO2 differential absorption optical depth (DAOD) for a 10-s integration over vegetated areas with about 10 km range was found to be as high as 1300, resulting in an error 0.077% or equivalent CO2 mixing ratio (XCO2) column precision of ~0.3 ppm. Precise range measurements using the IM-CW lidar approach were also achieved, and the uncertainties have been shown to be at sub meter level. Based on the airborne lidar development, space lidar and atmospheric CO2 observations are simulated. It shows that with the IM-CW approach, accurate atmospheric CO2 measurements can be achieved from space, and a space mission such as that proposed by the DS will meet science goals of atmospheric CO2 monitoring.

Lin, Bing↗

NASA NTRS2016

A Quantum Annealing Computer Team Addresses Climate Change Predictability

The near confluence of the successful launch of the Orbiting Carbon Observatory2 on July 2, 2014 and the acceptance on August 20, 2015 by Google, NASA Ames Research Center and USRA of a 1152 qubit D-Wave 2X Quantum Annealing Computer (QAC), offered an exceptional opportunity to explore the potential of this technology to address the scientific prediction of global annual carbon uptake by land surface processes. At UMBC,we have collected and processed 20 months of global Level 2 light CO2 data as well as fluorescence data. In addition we have collected ARM data at 2sites in the US and Ameriflux data at more than 20 stations. J. Dorband has developed and implemented a multi-hidden layer Boltzmann Machine (BM) algorithm on the QAC. Employing the BM, we are calculating CO2 fluxes by training collocated OCO-2 level 2 CO2 data with ARM ground station tower data to infer to infer measured CO2 flux data. We generate CO2 fluxes with a regression analysis using these BM derived weights on the level 2 CO2 data for three Ameriflux sites distinct from the ARM stations. P. Gentine has negotiated for the access of K34 Ameriflux data in the Amazon and is applying a neural net to infer the CO2 fluxes. N. Talik validated the accuracy of the BM performance on the QAC against a restricted BM implementation on the IBM Softlayer Cloud with the Nvidia co-processors utilizing the same data sets. G. Nearing and K. Harrison have extended the GSFC LIS model with the NCAR Noah photosynthetic parameterization and have run a 10 year global prediction of the net ecosystem exchange. C. Pellisier is preparing a BM implementation of the Kalman filter data assimilation of CO2 fluxes. At UMBC, R. Prouty is conducting OSSE experiments with the LISNoah model on the IBM iDataPlex to simulate the impact of CO2 fluxes to improve the prediction of global annual carbon uptake. J. LeMoigne and D. Simpson have developed a neural net image registration system that will be used for MODIS ENVI and will be converted to a BM algorithm implementation on the QAC. The first integer adder has been implemented on the D-Wave 2X by A. Shehab that will perform HAAR wavelets for image compression of MODIS scenes. Finally, based on the next generations of QACs, we are preparing a 5-year performance road map on the scalability of the current QAC algorithms.

Science Data Processing↗

NASA NTRS2016

Measurement and Modelling of Multicopter UAS Rotor Blades in Hover

Multicopters are becoming one of the more common and popular type of unmanned aircraft systems (UAS) which have both civilian and military applications. One example being the concept of drone deliveries proposed by the distribution company Amazon [1]. The electrical propulsion is considered to have both faster and easier deliveries and also environmental benefits compared to other vehicles that still use fossil fuel. Other examples include surveillance and just simple entertainment. The reason behind their success is often said to be due to their small size, relatively low cost, simple structure and finally simple usage. With an increase in the UAS market comes challenges in terms of security, as both people and other aircrafts could be harmed if not used correctly. Therefore further studies and regulations are needed to ensure that future use of drones, especially in the civilian and public sectors, are safe and efficient. Thorough research has been done on full scale, man or cargo transporting, helicopters so that most parts of flight and performance are fairly well understood. Yet not much of it have been verified for small multicopters. Until today many studies and research projects have been done on the control systems, navigation and aerodynamics of multicopters. Many of the methods used today for building multicopters involve a process of trial an error of what will work well together, and once that is accomplished some structural analysis of the multicopter bodies might be done to verify that the product will be strong enough and have a decent aerodynamic performance. However, not much has been done on the research of the rotor blades, especially in terms of structural stress analyses and ways to ensure that the commonly used parts are indeed safe and follow safety measures. Some producers claim that their propellers indeed have been tested, but again that usually tends towards simple fluid dynamic analyses and even simpler stress analyses. There is no real deflection measurement of said blades and all theory is today based on the theory developed for full scale helicopters. This report thus intends to highlight the problems that come with blade deflection theory and measurements for multicopters. This thesis starts with the introduction and problem formulation where the grounds for what the report contains are laid out. Then a literature review of the research within the area follows where previous and current research and methods are presented. In the background information and theory relevant to the area are presented. After that the methods and set up of the different experimental and computer simulation approaches are depicted. In the results section the obtained results are presented and compared. Lastly there is a discussion where the results and methods are evaluated and analyzed, followed by the most important conclusions.

Multicopter UAS Rotor Blades↗

NASA NTRS2016

Technical Challenges and Lessons from the Migration of the GLOBE Data and Information System to Utilize Cloud Computing Service

The Global Learning and Observation to Benefit the Environment (GLOBE) Data and Information System supports an international science and education program with capabilities to accept local environment observations, archive, display and visualize them along with global satellite observations. Since its inception twenty years ago, the Web and database system has been upgraded periodically to accommodate the changes in technology and the steady growth of GLOBEs education community and collection of observations. Recently, near the end-of-life of the system hardware, new commercial computer platform options were explored and a decision made to utilize Cloud services. Now the GLOBE DIS has been fully deployed and maintained using Amazon Cloud services for over two years now. This paper reviews the early risks, actual challenges, and some unexpected findings as a result of the GLOBE DIS migration. We describe the plans, cost drivers and estimates, highlight adjustments that were made and suggest improvements. We present the trade studies for provisioning, for load balancing, networks, processing, storage, as well as production, staging and backup systems. We outline the migration teams skills and required level of effort for transition, and resulting changes in the overall maintenance and operations activities. Examples include incremental adjustments to processing capacity and frequency of backups, and efforts previously expended on hardware maintenance that were refocused onto application-specific enhancements.

GLOBE↗

NASA NTRS2017

Technical Challenges and Lessons from the Migration of the GLOBE Data and Information System to Utilize Cloud Computing Service

GLOBE Data↗

NASA NTRS2016

GC31G-1182: Opennex, a Private-Public Partnership in Support of the National Climate Assessment

The NASA Earth Exchange (NEX) is a collaborative computing platform that has been developed with the objective of bringing scientists together with the software tools, massive global datasets, and supercomputing resources necessary to accelerate research in Earth systems science and global change. NEX is funded as an enabling tool for sustaining the national climate assessment. Over the past five years, researchers have used the NEX platform and produced a number of data sets highly relevant to the National Climate Assessment. These include high-resolution climate projections using different downscaling techniques and trends in historical climate from satellite data. To enable a broader community in exploiting the above datasets, the NEX team partnered with public cloud providers to create the OpenNEX platform. OpenNEX provides ready access to NEX data holdings on a number of public cloud platforms along with pertinent analysis tools and workflows in the form of Machine Images and Docker Containers, lectures and tutorials by experts. We will showcase some of the applications of OpenNEX data and tools by the community on Amazon Web Services, Google Cloud and the NEX Sandbox.

datasets↗

NASA NTRS2016

Using Docker Containers to Extend Reproducibility Architecture for the NASA Earth Exchange (NEX)

NASA Earth Exchange (NEX) is a data, supercomputing and knowledge collaboratory that houses NASA satellite, climate and ancillary data where a focused community can come together to address large-scale challenges in Earth sciences. As NEX has been growing into a petabyte-size platform for analysis, experiments and data production, it has been increasingly important to enable users to easily retrace their steps, identify what datasets were produced by which process chains, and give them ability to readily reproduce their results. This can be a tedious and difficult task even for a small project, but is almost impossible on large processing pipelines. We have developed an initial reproducibility and knowledge capture solution for the NEX, however, if users want to move the code to another system, whether it is their home institution cluster, laptop or the cloud, they have to find, build and install all the required dependencies that would run their code. This can be a very tedious and tricky process and is a big impediment to moving code to data and reproducibility outside the original system. The NEX team has tried to assist users who wanted to move their code into OpenNEX on Amazon cloud by creating custom virtual machines with all the software and dependencies installed, but this, while solving some of the issues, creates a new bottleneck that requires the NEX team to be involved with any new request, updates to virtual machines and general maintenance support. In this presentation, we will describe a solution that integrates NEX and Docker to bridge the gap in code-to-data migration. The core of the solution is saemi-automatic conversion of science codes, tools and services that are already tracked and described in the NEX provenance system, to Docker - an open-source Linux container software. Docker is available on most computer platforms, easy to install and capable of seamlessly creating and/or executing any application packaged in the appropriate format. We believe this is an important step towards seamless process deployment in heterogeneous environments that will enhance community access to NASA data and tools in a scalable way, promote software reuse, and improve reproducibility of scientific results.

earth exchange↗

NASA NTRS2016

Evaluation of the Sensitivity of the Amazonian Diurnal Cycle to Convective Intensity in Reanalyses

Model parameterizations of tropical deep convection are unable to reproduce the observed diurnal and spatial variability of convection in the Amazon, which contributes to climatological biases in the water cycle and energy budget. Convective intensity regimes are defined using percentiles of daily minimum 3-hourly averaged outgoing longwave radiation (OLR) from Clouds and the Earth's Radiant Energy System (CERES). This study compares the observed spatial variability of convective diurnal cycle statistics for each regime to MERRA-2 and ERA-Interim (ERA) reanalysis data sets. Composite diurnal cycle statistics are computed for daytime hours (06:00-21:00 local time) in the wet season (December-January-February). MERRA-2 matches observations more closely than ERA for domain averaged composite diurnal statistics-specifically precipitation. However, ERA reproduces mesoscale features of OLR and precipitation phase associated with topography and the propagation of the coastal squall line. Both reanalysis models are shown to underestimate extreme convection.

Itterly, Kyle F.↗

NASA NTRS2016

Scaling Estimates of Vegetation Structure in Amazonian Tropical Forests Using Multi-Angle MODIS Observations

Detailed knowledge of vegetation structure is required for accurate modelling of terrestrial ecosystems, but direct measurements of the three dimensional distribution of canopy elements, for instance from LiDAR, are not widely available. We investigate the potential for modelling vegetation roughness, a key parameter for climatological models, from directional scattering of visible and near-infrared (NIR) reflectance acquired from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). We compare our estimates across different tropical forest types to independent measures obtained from: (1) airborne laser scanning (ALS), (2) spaceborne Geoscience Laser Altimeter System (GLAS)/ICESat, and (3) the spaceborne SeaWinds/QSCAT. Our results showed linear correlation between MODIS-derived anisotropy to ALS-derived entropy (r(exp 2)= 0.54, RMSE= 0.11), even in high biomass regions. Significant relationships were also obtained between MODIS-derived anisotropy and GLAS-derived entropy(0.52 less than or equal to r(exp 2) less than or equal to 0.61; p less than 0.05), with similar slopes and offsets found throughout the season, and RMSE between 0.26 and 0.30 (units of entropy). The relationships between the MODIS-derived anisotropy and backscattering measurements (sigma(sup 0)) from SeaWinds/QuikSCAT presented an r(exp 2) of 0.59 and a RMSE of 0.11. We conclude that multi-angular MODIS observations are suitable to extrapolate measures of canopy entropy across different forest types, providing additional estimates of vegetation structure in the Amazon.

MODIS↗

NASA NTRS2017

Engineering a Live UHD Program from the International Space Station

The first-ever live downlink of Ultra-High Definition (UHD) video from the International Space Station (ISS) was the highlight of a “Super Session” at the National Association of Broadcasters (NAB) Show in April 2017. Ultra-High Definition is four times the resolution of “full HD” or “1080P” video. Also referred to as “4K”, the Ultra-High Definition video downlink from the ISS all the way to the Las Vegas Convention Center required considerable planning, pushed the limits of conventional video distribution from a space-craft, and was the first use of High Efficiency Video Coding (HEVC) from a space-craft. The live event at NAB will serve as a pathfinder for more routine downlinks of UHD as well as use of HEVC for conventional HD downlinks to save bandwidth. A similar demonstration was conducted in 2006 with the Discovery Channel to demonstrate the ability to stream HDTV from the ISS. This paper will describe the overall work flow and routing of the UHD video, how audio was synchronized even though the video and audio were received many seconds apart from each other, and how the demonstration paves the way for not only more efficient video distribution from the ISS, but also serves as a pathfinder for more complex video distribution from deep space. The paper will also describe how a “live” event was staged when the UHD video coming from the ISS had a latency of 10+ seconds. In addition, the paper will touch on the unique collaboration between the inherently governmental aspects of the ISS, commercial partners Amazon and Elemental, and the National Association of Broadcasters.

Grubbs, Rodney↗

NASA NTRS2017

Use of Schema on Read in Earth Science Data Archives

Traditionally, NASA Earth Science data archives have file-based storage using proprietary data file formats, such as HDF and HDF-EOS, which are optimized to support fast and efficient storage of spaceborne and model data as they are generated. The use of file-based storage essentially imposes an indexing strategy based on data dimensions. In most cases, NASA Earth Science data uses time as the primary index, leading to poor performance in accessing data in spatial dimensions. For example, producing a time series for a single spatial grid cell involves accessing a large number of data files. With exponential growth in data volume due to the ever-increasing spatial and temporal resolution of the data, using file-based archives poses significant performance and cost barriers to data discovery and access. Storing and disseminating data in proprietary data formats imposes an additional access barrier for users outside the mainstream research community. At the NASA Goddard Earth Sciences Data Information Services Center (GES DISC), we have evaluated applying the schema-on-read principle to data access and distribution. We used Apache Parquet to store geospatial data, and have exposed data through Amazon Web Services (AWS) Athena, AWS Simple Storage Service (S3), and Apache Spark. Using the schema-on-read approach allows customization of indexing spatially or temporally to suit the data access pattern. The storage of data in open formats such as Apache Parquet has widespread support in popular programming languages. A wide range of solutions for handling big data lowers the access barrier for all users. This presentation will discuss formats used for data storage, frameworks with This presentation will discuss formats used for data storage, frameworks with support for schema-on-read used for data access, and common use cases covering data usage patterns seen in a geospatial data archive.

cloud applications↗

NASA NTRS2017

AOD Distributions and Trends of Major Aerosol Species over a Selection of the World's Most Populated Cities Based on the 1st Version of NASA's MERRA Aerosol Reanalysis

NASA recently extended the Modern-Era Retrospective Analysis for Research and Application (MERRA) with an atmospheric aerosol reanalysis which includes five particulate species: sulfate, organic matter, black carbon, mineral dust and sea salt. The MERRA Aerosol Reanalysis (MERRAero) is an innovative tool to study air quality issues around the world for its global and constant coverage and its distinction of aerosol speciation expressed in the form of aerosol optical depth (AOD). The purpose of this manuscript is to apply MERRAero to the study of urban air pollution at the global scale by analyzing the AOD over a period of 13 years (2003-2015) and over a selection of 200 of the world's most populated cities in order to assess the impacts of urbanization, industrialization, air quality regulations and regional transport which affect urban aerosol load. Environmental regulations and the recent global economic recession have helped to decrease the AOD and sulfate aerosols in most cities in North America, Europe and Japan. Rapid industrialization in China over the last two decades resulted in Chinese cities having the highest AOD values in the world. China has nevertheless recently implemented emission control measures which are showing early signs of success in many cities of Southern China where AOD has decreased substantially over the last 13 years. The AOD over South American cities, which is dominated by carbonaceous aerosols, has also decreased over the last decade due to an increase in commodity prices which slowed deforestation activities in the Amazon rainforest. At the opposite, recent urbanization and industrialization in India and Bangladesh resulted in a strong increase of AOD, sulfate and carbonaceous aerosols in most cities of these two countries. The AOD over most cities in Northern Africa and Western Asia changed little over the last decade. Emissions of natural aerosols, which cities in these two regions tend to be mostly composed of, don't tend to fluctuate significantly on an annual basis.

Urban air pollution↗

NASA NTRS2017

A Pan-Tropical Cascade of Fire Driven by El Nino/Southern Oscillation

The El Nino/Southern Oscillation (ENSO) has a pronounced influence on year-to-year variations in climate. The response of fires to this forcing is complex and has not been evaluated systematically across different continents. Here we use satellite data to create a climatology of burned-area and fire emissions responses, drawing on six El Nino and six La Nina events during 1997-2016. On average, reductions in precipitation and terrestrial water storage increased fire emissions in pan-tropical forests by 133% during and following El Nino as compared with La Nina. Fires peaked in equatorial Asia early in the ENSO cycle when El Nino was strengthening (Aug-Oct), before moving to southeast Asia and northern South America (Jan-Apr), Central America (Mar-May) and the southern Amazon (Jul-Oct) during the following year. Large decreases in fire occurred across northern Australia during Sep-Oct of the second year from a reduced fuel availability. Satellite observations of aerosols and carbon monoxide provided independent confirmation of the spatiotemporal evolution of fire anomalies. The predictable cascade of fire across different tropical continents described here highlights an important time delay in the Earth system's response to precipitation redistribution. These observations help to explain why the growth rate of atmospheric CO2 increases during El Nino and may contribute to improved seasonal fire forecasts.

Chen, Yang↗

NASA NTRS2018

Geonex: A NASA-NOAA Collaboration for Producing Land Surface Products from Geostationary Sensors Using Cloud Computing

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 MODIS and VIIRS, useful for monitoring land surface conditions. The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort among scientists from NASA and NOAA exploring the feasibility of producing operational land surface products similar to those from MODIS/VIIRS. The team built a processing pipeline called GEONEX that is capable of converting raw geostationary data into routine products of Fires, surface reflectances, vegetation indices, LAI/FPAR, ET and GPP/NPP using algorithms adapted from both NASA/EOS and NOAA/GOES-R programs. The GEONEX pipeline has been deployed on Amazon Web Services cloud platform and it currently leverages near-realtime geostationary data hosted in AWS public datasets under a NOAA-AWS agreement.Initial analyses of various products from ABI/AHI sensors suggest that they are comparable to those from MODIS in representing the spatio-temporal dynamics of land conditions. Cloud computing offers a variety of options for deploying the GEONEX pipeline including choice CPUs, storage media, and automation. We estimate the cost of deploying GEONEX to be $400 - 750 a month for processing data (every 30 minutes) and producing products over the conterminous US. For products such as Fire, latency can be as little as 10 minutes from the time of data acquisition.

Geostationary↗

NASA NTRS2018

GEONEX: Webpage to Display NASA-NOAA Collaboration of Producing Land Surface Products from Geostationary Sensors

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 MODIS and VIIRS, useful for monitoring land surface conditions. The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort among scientists from NASA and NOAA exploring the feasibility of producing operational land surface products similar to those from MODIS/VIIRS. The team built a processing pipeline called GEONEX that is capable of converting raw geostationary data into routine products of Fires, surface reflectances, vegetation indices, LAI/FPAR, ET and GPP/NPP using algorithms adapted from both NASA/EOS and NOAA/GOES-R programs. The GEONEX pipeline has been deployed on Amazon Web Services cloud platform and it currently leverages near-realtime geostationary data hosted in AWS public datasets under a NOAA-AWS agreement.In order to better introduce the GEONEX products to the science community, we set up a simple webpage (www.geonex.org) to describe the background and the motivation of the project, the algorithms used in deriving the products, and user manuals to the data files. We will also update the status of the data processing, in particular the near-real-time products, on the website and provide links (in text or json files) to the latest datasets.

geostationary↗

NASA NTRS2018

Portable, Low-Cost, Column Carbon Dioxide and Methane Measurements for Validating Satellite Observations in Remote Locations

We present a low-cost (~$10K per instrument), portable solution to ground-based validation of satellite observations for difficult to reach locations with precisions of 1 ppm XCO2 and 10 ppb XCH4 for hourly data products. While Total Carbon Column Observing Network (TCCON) is the gold-standard for ground validation, there are locations where a ground column validation data point would be useful but conditions are not conducive to a permanent station. Examples include wetlands, thawing permafrost, the tropics, the Amazon, sub-Saharan Africa, as well as locations without a power grid or with geopolitical conflict. In addition, the low-cost and portability mean a geographical region can be studied in depth with multiple instruments. This passive, sun-pointing instrument is a miniaturized, laser heterodyne radiometer (mini-LHR) that has been under development by our team since 2009. It can be operated either in tandem with AERONET (a global network of 500 instruments that measure aerosol optical depth), or as a stand-alone instrument with a low-cost (~$3K), light-weight sun tracker. One of the main benefits of the mini-LHR is that it can quickly reach remote locations and provide a validation measurement even if there is limited or no infrastructure at the site. The instrument weighs ~10 lbs, fits into a backpack, and is powered by two folding solar panels and a battery pack. In clear conditions, the instrument can be set-up in under an hour. Portability means that mini-LHRs can be easily moved for side-by-side comparisons with other mini-LHRs and with TCCON which simplifies assessing instrument bias as well as accuracy. Like TCCON, the mini-LHR points directly at the sun with a narrow field-of-view and is its insensitivity to cloud and aerosol scattering that is common to nadir-pointing passive satellite approaches. Here we present a collection of sample data sets to demonstrate performance from locations that vary in climate, altitude, solar zenith angle, hours of sun per day, etc., as well as data from side-by-side TCCON comparisons. Retrievals of CO2 and CH4 were completed using the NASA/Goddard's Planetary Spectrum Generator (PSG) that incorporates meteorological inputs from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data set.

Wilson, Emily W.↗

NASA NTRS2018

Understanding Climate-Vegetation Interactions in Global Rainforests Through a GP-Tree Analysis

The tropical rainforests are the largest reserves of terrestrial carbon and, therefore, the future of these rainforests is a question that is of immense importance in the geoscience research community. With the recent severe Amazonian droughts in 2005 and 2010 and on-going drought in the Congo region for more than two decades, there is growing concern that these forests could succumb to precipitation reduction, causing extensive carbon release and feedback to the carbon cycle. However, there is no single ecosystem model that quantifies the relationship between vegetation health in these rainforests and climatic factors. Small scale studies have used statistical correlation measure and simple linear regression to model climate-vegetation interactions, but suffer from the lack of comprehensive data representation as well as simplistic assumptions about dependency of the target on the covariates. In this paper we use genetic programming (GP) based symbolic regression for discovering equations that govern the vegetation climate dynamics in the rainforests. Expecting micro-regions within the rainforests to have unique characteristics compared to the overall general characteristics, we use a modified regression-tree based hierarchical partitioning of the space to build individual models for each partition. The discovery of these equations reveal very interesting characteristics about the Amazon and the Congo rainforests. Our method GP-tree shows that the rainforests exhibit tremendous resiliency in the face of extreme climatic events by adapting to changing conditions.

genetic programming↗