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Using Simulated OCO Measurements for Assessing Terrestrial Carbon Pools in the Southern United States. PI: Nicolas H. Younan Surya S. Durbha, Fengxiang Han, Roger L. King, Jian Chen, Zhiling Long GeoResources Institute (GRI) Institute for Clean Energy Technology Mississippi State University.

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Using Simulated OCO Measurements for Assessing Terrestrial Carbon Poolsin the Southern United States

PI: Nicolas H. Younan

Surya S. Durbha, Fengxiang Han, Roger L. King, Jian Chen, Zhiling Long

GeoResources Institute (GRI)

Institute for Clean Energy Technology

Mississippi State University

orbiting carbon observatory oco
Orbiting Carbon Observatory (OCO)
  • First global, space-based measurements of atmospheric carbon dioxide (CO2) with the precision, resolution, and coverage needed to characterize CO2 sources and sinks on regional scales.
  • Uncertainties in the atmospheric CO2 balance could be reduced substantially if data from the existing ground based CO2 network were augmented by spatially resolved, global, measurements of the column integrated dry air mole fraction (X CO2 ) with precisions of ~1 ppm (0.3% of 370 ppm (Crisp et al 2004)

Source:http://oco.jpl.nasa.gov/images/ground_track-br.jpg

NASA RPC- 10 Jul 2007

scope of the research
Scope of the Research
  • This research is focused on the assessment of terrestrial carbon pools in the southeast and south central United States.
  • In particular, this investigation intends to leverage upon:
    • Multiple NASA sensors
    • The terrestrial ecosystem model (CASA) and
    • Transport model GISS: GCM Model E
  • Undertake a Rapid Prototyping (RPC) experiment to address the need to quantify the carbon exchange over different ecosystems.
  • Test how well data from OCO observations and CO2 measurement networks constrain CO2 fluxes at model-grid resolution.

NASA RPC- 10 Jul 2007

science questions
Science Questions
  • Proposed RPC experiment seeks to address the following questions:
    • What information about carbon exchange can be obtained from OCO high-precision column measurements of CO2?
    • How can we integrate top-down OCO measurements with ground based measurements, atmospheric and terrestrial ecosystem models to quantify carbon exchange over different ecosystems?
    • What are the current annual rates of terrestrial carbon sequestration in each state of the Southeast and South-central U.S.?
    • What is the current baseline in the region for possible carbon trading?
    • What is the potential for enhancing terrestrial carbon sequestration?

NASA RPC- 10 Jul 2007

currently funded doe project for leverage
Currently funded DOE project for leverage
  • What are the current annual rates of terrestrial carbon sequestration in each state of the region?
  • What's the overall contribution of terrestrial carbon sequestration in each state of the region to mitigating its total greenhouse gas emission?
  • What's the current baseline for possible carbon trading in the region?
  • What's the potential of further enhancing terrestrial carbon sequestration in the region? And
  • What are the overall economic impacts of current and potential terrestrial carbon sequestration on the region?

NASA RPC- 10 Jul 2007

findings
Findings
  • Current annual terrestrial carbon sequestration (soil, forest, crop, pasture and house/furniture) in the region can offset 40% of the total annual greenhouse gas emission.
  • Through proper policies and best management, about 10.1% of the total greenhouse gas in the region can be further offset by terrestrial sequestration.
  • Terrestrial carbon sequestration proves to be the most cost-effective option for sequestering carbon in the region.

Han, F.X., J. Lindner, and C. Wang. 2007. Making carbon sequestration a paying proposition. Naturwissenschaften 94: 170-182. DOI 10.1007/s00114-006-0170-6.

Han, F.X., M. J. Plodinec, Y. Su, D.L. Monts, and Z. Li. 2007. Terrestrial carbon pools in southeast and south-central United States. Climatic Change. DOI 10.1007/s10584-007-9244-5.

Han F.X., Z.P. Li, J. Lindner, Y. Su, D. L. Monts, R. King, B. Xing, and J.M. Plodinec. 2007. Role of soils and soil management for mitigating greenhouse effect. In B. Xing, F. Wu (eds) Natural Organic Matter and Its Significance in the Environment. The Science Press, Beijing and Brill Academic Publisher, Leiden, Boston and Tokyo.

NASA RPC- 10 Jul 2007

rapid prototyping using simulated data sets
Rapid Prototyping Using Simulated Data Sets
  • RPC using:
    • Simulated Orbiting Carbon Observatory (OCO) obtained through the Observing System Simulation Experiment (OSSE)
  • Perform various sensitivity studies and understand their suitability.
  • NASA Carbon Query and Estimation Tool (CQUEST) is the target DSS.

NASA RPC- 10 Jul 2007

rapid prototyping concept rpc
Rapid Prototyping Concept (RPC)

NASA RPC- 10 Jul 2007

rpc experimental design

Fossil Fuels

  • OCO, Networks
  • Winds, cloud mass fluxes, model Parameters
  • Assimilation of aircraft measurements, satellite data (precipitable water, surface winds)

Meteorology

(e.g. GOES data analysis)

  • Forward Transport Model

Transport Model (GISS GCM Model E)

[CO2] OBS

  • Vegetation Indices
  • Biome type
  • Soil properties
  • Weather Reanalysis

1 year spinup

(2002)

Land Surface Model (CASA)

  • Terrestrial CO2 surface flux

Inversion

  • 1 year spinup
  • Monthly
RPC Experimental Design

NASA RPC- 10 Jul 2007

rpc experimental design13

Fossil Fuels

  • OCO, Networks
  • Winds, cloud mass fluxes, model Parameters
  • Assimilation of aircraft measurements, satellite data (precipitable water, surface winds)

Meteorology

(e.g. GOES data analysis)

  • Forward Transport Model

Transport Model (GISS GCM Model E)

[CO2] OBS

1 year spinup

(2002)

Inversion

RPC Experimental Design
  • Vegetation Indices
  • Biome type
  • Soil properties
  • Weather Reanalysis

Land Surface Model (CASA)

  • Terrestrial CO2 surface flux
  • 1 year spinup
  • Monthly

NASA RPC- 10 Jul 2007

evaluating the suitability of vegetation parameters
Evaluating the Suitability of Vegetation Parameters
  • Evaluate the usefulness of multi-angle measurements from MISR data sets to assess the model predictions in the event of using NDVI and LAI observation from multi-angle data sets.

Durbha, S.S., R.L. King, and N.H. Younan, Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer, Remote Sensing of Environment Special Issue: Multi-angle Imaging SpectroRadiomenter (MISR), Volume 107, Issues 1-2, pp. 348-361, March 2007.

NASA RPC- 10 Jul 2007

casa model tasks
CASA Model Tasks
  • Sensitivity analysis of how much NPP increase is required to sustain the regional terrestrial carbon sink of the study area.
  • Net Ecosystem Productivity (NEP) defined as Net Primary Production (NPP) minus the heterotrophic soil respiration predictions would be used to infer variability in regional scale carbon fluxes and to better understand patterns over terrestrial carbon sinks.
  • The CASA model estimates of carbon products would be calibrated with field-based measurements of
      • Crop production,
      • Forest ecosystem fluxes, and
      • Inventory estimates of carbon pool sizes at multiple locations in south eastern and south central United States.

NASA RPC- 10 Jul 2007

rpc experimental design16

Assimilation of aircraft measurements, satellite data (precipitable water, surface winds)

Meteorology

(e.g. GOES data analysis)

  • Vegetation Indices
  • Biome type
  • Soil properties
  • Weather Reanalysis

Land Surface Model (CASA)

  • 1 year spinup
  • Monthly
RPC Experimental Design
  • Fossil Fuels
  • OCO, Networks
  • Winds, cloud mass fluxes, model Parameters
  • Forward Transport Model

Transport Model (GISS GCM Model E)

[CO2] OBS

1 year spinup

(2002)

  • Terrestrial CO2 surface flux

Inversion

NASA RPC- 10 Jul 2007

oco data assimilation problem formulation
OCO Data Assimilation: Problem Formulation
  • Compare predictions from atmospheric transport model (e.g. GISS Model E) and measurements of atmospheric carbon abundances from OCO and at observation sites distributed over the regions of interest.
  • Spatial pattern of the observed and predicted differences can be used to infer the spatial distribution of sources and sinks of carbon dioxide by seeking a distribution of fluxes that in a least squares sense minimizes the difference between the model predictions and observation, as well as any prior information used to constrain the problem.

NASA RPC- 10 Jul 2007

oco data assimilation techniques and strategies

Cost function to minimize

(Baker et al., 2006)

OCO Data Assimilation: Techniques and Strategies
  • Commonly employed technique to estimate carbon fluxes is Bayesian synthesis inversion.
  • A cost function is formulated that has two terms:
      • One involving the observations and one involving a prior estimate of the fluxes.
      • Resulting flux estimates are constrained both by observations and prior estimates.

NASA RPC- 10 Jul 2007

oco data assimilation techniques and strategies19
OCO Data Assimilation: Techniques and Strategies
  • Improved Kalman Smoother for atmospheric inversion.
    • Produces estimates of fluxes at a particular time using observations from that time step as well as observations from subsequent times.
    • Normal Kalman filter would use only past observations to estimate fluxes at a particular time step
  • Ensemble Kalman filters allows for application on large problem.
  • Adjoint-based descent methods for variational data assimilation
  • We are exploring the possibility of developing a Support Vector Regression-based technique for this purpose

NASA RPC- 10 Jul 2007

significant issues
Significant Issues
  • OCO simulated data acquisition problems
  • Any leads would be highly appreciated!

NASA RPC- 10 Jul 2007

summary oco data assimilation for assessing terrestrial carbon pools in the southern us
Summary: OCO Data Assimilation for Assessing Terrestrial Carbon Pools in the Southern US
  • High-density data (e.g. OCO) should allow us to address a variety of science and policy questions that have remained previously unanswered.
  • Resolving surface fluxes to the regional biome level will help to quantify the relative importance of the key driving processes.
  • Resolving them to regional levels helps in the carbon management and verification of carbon credits, compliance, etc.

NASA RPC- 10 Jul 2007

slide23

Questions?

NASA RPC- 10 Jul 2007

oco simulated data evaluation
OCO Simulated Data Evaluation
  • In situ observations from global surface sites (NOAA/CMDL, AMERIFLUX) would be used to calculate the trends in the seasonal cycle
  • Test whether the true flux distribution can be retrieved using the OCO observations and independent CO2 flux distribution.
  • Combine the CASA model with a global transport model (GISS) to identify and relate the amplitude/seasonal cycle of biospheric CO2 from OCO observations
    • Helps to understand and develop methods to reduce uncertainty in regional CO2 flux estimates.

NASA RPC- 10 Jul 2007

specific tasks
Specific Tasks
  • The input drivers to the NASA-CASA model consists of parameters derived from climatic, site, vegetation, soils and resolution (e.g. daily, monthly). The following parameters are required for model initialization.
    • Monthly data of temperature, precipitation, PAR, NDVI Soil type/soil water capacity, Vegetation type, CO2
  • The various parameters from different sources would be studied for their suitability.
  • in situ based measurements would be assessed for their inclusion into the model input.
  • Combine the CASA model with a global transport model (GISS Model E) to identify the changes in the terrestrial biosphere that are consistent with the observed increases in the amplitude of the seasonal cycle of atmospheric CO2.

NASA RPC- 10 Jul 2007

oco data assimilation techniques and strategies26
OCO Data Assimilation: Techniques and Strategies
  • No single model or set of observations can quantify the dynamics of terrestrial Carbon exchanges, and describe the governing processes.
  • Recent attempts are to develop coherent methods treating both data and models as sources of information.
  • Core problem is “How to combine and weight the various information sources?”

NASA RPC- 10 Jul 2007

models and data
Models and Data

NASA RPC- 10 Jul 2007