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DOE OSTI2024

Estimating Carbon Dioxide Emissions in Two California Cities Using Bayesian Inversion and Satellite Measurements

NASA's Orbiting Carbon Observatories (OCO-2 and OCO-3) provide measurements of column-averaged carbon dioxide concentrations (XCO 2 ) with sufficient spatial resolution and precision to constrain bottom-up estimates of CO 2 fluxes at regional scales. We use Bayesian inversion methods assimilating satellite retrievals to improve estimates of CO 2 fluxes in the South Coast Air Basin (SoCAB) which surrounds Los Angeles, and in the San Francisco Bay Area Air Basin (SFBA). We study 2020 to understand the impact of the COVID-19 lockdowns and an active wildfire season. Our results indicated that a 50% (30%) reduction in CO 2 emissions relative to 2015 during the COVID-19 lockdown period was consistent with OCO measurements for SFBA (SoCAB). We find that posterior wildfire emissions differed significantly from the prior at the scale of individual wildfires, though with large uncertainties, and that wildfire emissions in SFBA are significant, attributing 72% of the region's CO 2 emissions during August 2020 to wildfires.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Dual-Comb Spectroscopy to Tethered Balloon System (DCS to TBS) Field Campaign Report

There is a critical need for the ability to detect, quantify, and attribute sources and sinks of greenhouse gases (GHGs) with high temporal and spatial resolution. Current methods are typically based on continuously operating near-surface point sensors or intermittent aerial/satellite observations. Dual optical frequency comb spectroscopy (“dual-comb spectroscopy”; DCS) is a laser-based technique that can bridge this sensing gap by providing high-precision, high-accuracy, and high-temporal-resolution detection and quantification of trace gases over kilometer-length scales, connecting local and regional observations. A single DCS system can also provide spatial resolution for source/sink attribution with the addition of a few minor optical elements, enabling cross-validation of other observations (e.g., Total Carbon Column Observing Network [TCCON], Orbiting Carbon Observatory [OCO-2/3], Greenhouse Gases Observing Satellite [GOSAT-1/2][).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2010

3D Online Visualization and Synergy of NASA A-Train Data Using Google Earth

This poster presentation reviews the use of Google Earth to assist in three dimensional online visualization of NASA Earth science and geospatial data. The NASA A-Train satellite constellation is a succession of seven sun-synchronous orbit satellites: (1) OCO-2 (Orbiting Carbon Observatory) (will launch in Feb. 2013), (2) GCOM-W1 (Global Change Observation Mission), (3) Aqua, (4) CloudSat, (5) CALIPSO (Cloud-Aerosol Lidar & Infrared Pathfinder Satellite Observations), (6) Glory, (7) Aura. The A-Train makes possible synergy of information from multiple resources, so more information about earth condition is obtained from the combined observations than would be possible from the sum of the observations taken independently

Chen, Aijun↗

NASA NTRS2011

Low-Cost Miniaturized Laser Heterodyne Radiometer for Highly Sensitive Detection of CO2 and CH4 in the Atmospheric Column

We present a new passive ground-network instrument capable of measuring carbon dioxide (CO2) at 1.57 microns and methane (CH4) at 1.62 microns -- key for validation of OCO-2, ASCENDS, OCO-3, and GOSAT. Designed to piggy-back on an AERONET sun tracker (AERONET is a global network of more than 450 aerosol sensing instruments), this instrument could be rapidly deployed into the established AERONET network of ground sensors. Because aerosols induce a radiative effect that influences terrestrial carbon exchange, this simultaneous measure of aerosols and carbon cycle gases offers a uniquely comprehensive approach. This instrument is a variation of a laser heterodyne radiometer (LHR) that leverages recent advances in telecommunications lasers to miniaturize the instrument (the current version fits in a carry-on suitcase). In this technique, sunlight that has undergone absorption by the trace gas is mixed with laser light at a frequency matched to a trace gas absorption feature in the infrared (IR). Mixing results in a beat signal in the RF (radio frequency) region that can be related to the atmospheric concentration. By dividing this RF signal into a filter bank, concentrations at different altitudes can be resolved. For a one second integration, we estimate column sensitivities of 0.1 ppmv for CO2, and <1 ppbv for CH4.

Wilson, Emily L.↗

NASA NTRS2011

Next Generation Grating Spectrometer Sounders for LEO and GEO

AIRS and MODIS are widely used for weather, climate, composition, carbon cycle, cross-calibration, and applications. The community asking for new capability in the 2020 timeframe, capabilities desired: (1) Hyperspectral UV to LWIR, High Spatial ?1km IFOV (2) Maximize Synergies of Solar Reflected and IR. Synergies with OCO-2. We expect more users and applications of next gen LEO IR Sounder than GEO. These include: weather, climate, GHG monitoring, aviation, disaster response. There is a new direction for imagers and sounders: (1) Separate Vis/NIR/SWIR from MWIR/LWIR instruments reduces technology risk and complexity. (2) Expect Costs to be lower than CrIS & VIIRS Some additional ideas to reduce costs include: (1) minimum set of requirements (2) mini-grating spectrometers. supports constellation for higher revisit (3) new technology to reduce instrument size (large format fpa's) (4) hosted payloads

Hyperspectral↗

NASA NTRS2012

NASA's Carbon Monitoring System Flux-Pilot Project: A Multi-Component Analysis System for Carbon-Cycle Research and Monitoring

The importance of greenhouse gas increases for climate motivates NASA s observing strategy for CO2 from space, including the forthcoming Orbiting Carbon Observatory (OCO-2) mission. Carbon cycle monitoring, including attribution of atmospheric concentrations to regional emissions and uptake, requires a robust modeling and analysis infrastructure to optimally extract information from the observations. NASA's Carbon-Monitoring System Flux-Pilot Project (FPP) is a prototype for such analysis, combining a set of unique tools to facilitate analysis of atmospheric CO2 along with fluxes between the atmosphere and the terrestrial biosphere or ocean. NASA's analysis system is unique, in that it combines information and expertise from the land, oceanic, and atmospheric branches of the carbon cycle and includes some estimates of uncertainty. Numerous existing space-based missions provide information of relevance to the carbon cycle. This study describes the components of the FPP framework, assessing the realism of computed fluxes, thus providing the basis for research and monitoring applications. Fluxes are computed using data-constrained terrestrial biosphere models and physical ocean models, driven by atmospheric observations and assimilating ocean-color information. Use of two estimates provides a measure of uncertainty in the fluxes. Along with inventories of other emissions, these data-derived fluxes are used in transport models to assess their consistency with atmospheric CO2 observations. Closure is achieved by using a four-dimensional data assimilation (inverse) approach that adjusts the terrestrial biosphere fluxes to make them consistent with the atmospheric CO2 observations. Results will be shown, illustrating the year-to-year variations in land biospheric and oceanic fluxes computed in the FPP. The signals of these surface-flux variations on atmospheric CO2 will be isolated using forward modeling tools, which also incorporate estimates of transport error. The results will be discussed in the context of interannual variability of observed atmospheric CO2 distributions.

Pawson, S.↗

NASA NTRS2013

Scatter-Reducing Sounding Filtration Using a Genetic Algorithm and Mean Monthly Standard Deviation

Retrieval algorithms like that used by the Orbiting Carbon Observatory (OCO)-2 mission generate massive quantities of data of varying quality and reliability. A computationally efficient, simple method of labeling problematic datapoints or predicting soundings that will fail is required for basic operation, given that only 6% of the retrieved data may be operationally processed. This method automatically obtains a filter designed to reduce scatter based on a small number of input features. Most machine-learning filter construction algorithms attempt to predict error in the CO2 value. By using a surrogate goal of Mean Monthly STDEV, the goal is to reduce the retrieved CO2 scatter rather than solving the harder problem of reducing CO2 error. This lends itself to improved interpretability and performance. This software reduces the scatter of retrieved CO2 values globally based on a minimum number of input features. It can be used as a prefilter to reduce the number of soundings requested, or as a post-filter to label data quality. The use of the MMS (Mean Monthly Standard deviation) provides a much cleaner, clearer filter than the standard ABS(CO2-truth) metrics previously employed by competitor methods. The software's main strength lies in a clearer (i.e., fewer features required) filter that more efficiently reduces scatter in retrieved CO2 rather than focusing on the more complex (and easily removed) bias issues.

Mandrake, Lukas↗

NASA NTRS2012

An Examination of Coarse Sun Sensor Contingencies in Attitude Determination and the Sun Vector Calculation

Satellite pointing is vital to the success of a mission. One element of that entails describing the position of the sun relative to the frame of the satellite. Coarse Sun Sensors (CSS) are typically used to provide the information to calculate the sun's position in Safe Modes or contingency operations. In the OCO-2 configuration there are 13 CSS total, which provide redundant 4 celestial coverage. Failures of the individual CSS elements can introduce holes in the celestial coverage resulting in potential loss of sun knowledge. These failures must be analyzed to determine if the contingency plan is sufficient to assure mission success. First the static case was looked at and determined that at a maximum, 3 CSS failures can be sustained on the body and 1 on the array without causing coverage holes. Also array sensors are more important to mission success. The Sun Vector calculation has been transcribed to MATLAB code and failure scenarios are being examined to determine the maximum error given a set of failure scenarios. This activity indicated that if there is a loss of the sun, the sun-searching algorithm could be modified to use XZ rotation as that is guaranteed to find it whereas the design using the YZ rotation misses the sun if it is at the + or - Y orientation.

celestial bodies↗

NASA NTRS2015

Lidar Observations of Atmospheric CO2 Column During 2014 Summer Flight Campaigns

Advanced knowledge in atmospheric CO2 is critical in reducing large uncertainties in predictions of the Earth' future climate. Thus, Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) from space was recommended by the U.S. National Research Council to NASA. As part of the preparation for the ASCENDS mission, NASA Langley Research Center (LaRC) and Exelis, Inc. have been collaborating in development and demonstration of the Intensity-Modulated Continuous-Wave (IM-CW) lidar approach for measuring atmospheric CO2 column from space. Airborne laser absorption lidars such as the Multi-Functional Fiber Laser Lidar (MFLL) and ASCENDS CarbonHawk Experiment Simulator (ACES) operating in the 1.57 micron CO2 absorption band have been developed and tested to obtain precise atmospheric CO2 column measurements using integrated path differential absorption technique and to evaluate the potential of the space ASCENDS mission. This presentation reports the results of our lidar atmospheric CO2 column measurements from 2014 summer flight campaign. Analysis shows that for the 27 Aug OCO-2 under flight over northern California forest regions, significant variations of CO2 column approximately 2 ppm) in the lower troposphere have been observed, which may be a challenge for space measurements owing to complicated topographic condition, heterogeneity of surface reflection and difference in vegetation evapotranspiration. Compared to the observed 2011 summer CO2 drawdown (about 8 ppm) over mid-west, 2014 summer drawdown in the same region measured was much weak (approximately 3 ppm). The observed drawdown difference could be the results of the changes in both meteorological states and the phases of growing seasons. Individual lidar CO2 column measurements of 0.1-s integration were within 1-2 ppm of the CO2 estimates obtained from on-board in-situ sensors. For weak surface reflection conditions such as ocean surfaces, the 1- s integrated signal-to-noise ratio (SNR) of lidar measurements at 11 km altitude reached 376, which was equivalent to a 10-s CO2 error 0.33 ppm. For the entire processed 2014 summer flight campaign data, the mean differences between lidar remote sensed and in-situ estimated CO2 values were about -0.013 ppm. These results indicate that current laser absorption lidar approach could meet space measurement requirements for CO2 science goals.

Lin, Bing↗

NASA NTRS2015

Airborne Measurements in Support of the NASA Atmospheric Carbon and Transport - America (ACT-America) Mission

NASA announced the research opportunity Earth Venture Suborbital -2 (EVS-2) mission in support of the NASA's science strategic goals and objectives in 2013. Penn State University, NASA Langley Research Center (LaRC), and other academic institutions, government agencies, and industrial companies together formulated and proposed the Atmospheric Carbon and Transport -America (ACT -America) suborbital mission, which was subsequently selected for implementation. The airborne measurements that are part of ACT-America will provide a unique set of remote and in-situ measurements of CO2 over North America at spatial and temporal scales not previously available to the science community and this will greatly enhance our understanding of the carbon cycle. ACT -America will consist of five airborne campaigns, covering all four seasons, to measure regional atmospheric carbon distributions and to evaluate the accuracy of atmospheric transport models used to assess carbon sinks and sources under fair and stormy weather conditions. This coordinated mission will measure atmospheric carbon in the three most important regions of the continental US carbon balance: Northeast, Midwest, and South. Data will be collected using 2 airborne platforms (NASA Wallops' C-130 and NASA Langley's B-200) with both in-situ and lidar instruments, along with instrumented ground towers and under flights of the Orbiting Carbon Observatory (OCO-2) satellite. This presentation provides an overview of the ACT-America instruments, with particular emphasis on the airborne CO2and backscatter lidars, and the, rationale, approach, and anticipated results from this mission.

Meadows, Byron↗

NASA NTRS2015

Aerosol Properties From Combined Oxygen A Band Radiances and Lidar

We have developed a new aerosol retrieval technique based on combing high-resolution A band spectra with lidar profiles. Our goal is the development of a technique to retrieve aerosol absorption, one of the critical parameters affecting the global radiation budget and one which is currently poorly constrained by satellite measurements. Our approach relies on two key factors: 1) the use of high spectral resolution (17,000:1) measurements which resolve the A-band line structure, and 2) the use of co-located lidar profile measurements to constrain the vertical distribution of scatterers in the forward model. The algorithm has been developed to be applied to observations from the CALIPSO and OCO-2 satellites, flying in formation as part of the A-train constellation. We describe the approach and present simulated retrievals to illustrate performance potential.

Winker, Dave↗

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

B33C-0612: Evaluation of Simulated Biospheric Carbon Dioxide Fluxes and Atmospheric Concentrations Using Global in Situ Observations

Atmospheric mixing ratios of carbon dioxide (CO2) are largely controlled by anthropogenic emission sources and biospheric sources/sinks. Global biospheric fluxes of CO2 are controlled by complex processes facilitating the exchange of carbon between terrestrial ecosystems and the atmosphere. These processes which play a key role in these terrestrial ecosystem-atmosphere carbon exchanges are currently not fully understood, resulting in large uncertainties in the quantification of biospheric CO2 fluxes. Current models with these inherent deficiencies have difficulties simulating the global carbon cycle with high accuracy. We are developing a new modeling platform, GEOS-Chem-CASA by integrating the year-specific NASA-CASA (National Aeronautics and Space Administration - Carnegie Ames Stanford Approach) biosphere model with the GEOS-Chem (Goddard Earth Observation System-Chemistry) chemical transport model to improve the simulation of atmosphere-terrestrial ecosystem carbon exchange. We use NASA-CASA to explicitly represent the exchange of CO2 between terrestrial ecosystem and atmosphere by replacing the baseline GEOS-Chem land net CO2 flux and forest biomass burning CO2 emissions. We will present the estimation and evaluation of these "bottom-up" land CO2 fluxes, simulated atmospheric mixing ratios, and forest disturbance changes over the last decade. In addition, we will present our initial comparison of atmospheric column-mean dry air mole fraction of CO2 predicted by the model and those retrieved from NASA's OCO-2 (Orbiting Carbon Observatory-2) satellite instrument and model-predicted surface CO2 mixing ratios with global in situ observations. This evaluation is the first step necessary for our future work planned to constrain the estimates of biospheric carbon fluxes through "top-down" inverse modeling, which will improve our understanding of the processes controlling atmosphere-terrestrial ecosystem greenhouse gas exchanges, especially over regions which lack in situ observations.

in situ observations↗

NASA NTRS2012