scholarly journals Data reduction for inverse modeling: an adaptive approach v1.0

2021 ◽  
Vol 14 (7) ◽  
pp. 4683-4696
Author(s):  
Xiaoling Liu ◽  
August L. Weinbren ◽  
He Chang ◽  
Jovan M. Tadić ◽  
Marikate E. Mountain ◽  
...  
Keyword(s):  
Case Studies ◽  
Data Reduction ◽  
Inverse Modeling ◽  
Co2 Flux ◽  
Real Data ◽  
Single Observation ◽  
Adaptive Approach ◽  
Sources And Sinks ◽  
Guiding Principle ◽  
Inverse Models

Abstract. The number of greenhouse gas (GHG) observing satellites has greatly expanded in recent years, and these new datasets provide an unprecedented constraint on global GHG sources and sinks. However, a continuing challenge for inverse models that are used to estimate these sources and sinks is the sheer number of satellite observations, sometimes in the millions per day. These massive datasets often make it prohibitive to implement inverse modeling calculations and/or assimilate the observations using many types of atmospheric models. Although these satellite datasets are very large, the information content of any single observation is often modest and non-exclusive due to redundancy with neighboring observations and due to measurement noise. In this study, we develop an adaptive approach to reduce the size of satellite datasets using geostatistics. A guiding principle is to reduce the data more in regions with little variability in the observations and less in regions with high variability. We subsequently tune and evaluate the approach using synthetic and real data case studies for North America from NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite. The proposed approach to data reduction yields more accurate CO2 flux estimates than the commonly used method of binning and averaging the satellite data. We further develop a metric for choosing a level of data reduction; we can reduce the satellite dataset to an average of one observation per ∼ 80–140 km for the specific case studies here without substantially compromising the flux estimate, but we find that reducing the data further quickly degrades the accuracy of the estimated fluxes. Overall, the approach developed here could be applied to a range of inverse problems that use very large trace gas datasets.

scholarly journals Data reduction for inverse modeling: an adaptive approach v1.0

2020 ◽  
Author(s):  
Xiaoling Liu ◽  
August L. Weinbren ◽  
He Chang ◽  
Jovan Tadić ◽  
Marikate E. Mountain ◽  
...  
Keyword(s):  
Case Studies ◽  
Data Reduction ◽  
Inverse Modeling ◽  
Co2 Flux ◽  
Real Data ◽  
Single Observation ◽  
Adaptive Approach ◽  
Sources And Sinks ◽  
Guiding Principle ◽  
Inverse Models

Abstract. The number of greenhouse gas (GHG) observing satellites has greatly expanded in recent years, and these new datasets provide an unprecedented constraint on global GHG sources and sinks. However, a continuing challenge for inverse models that are used to estimate these sources and sinks is the sheer number of satellite observations, sometimes in the millions per day. These massive datasets often make it prohibitive to implement inverse modeling calculations and/or assimilate the observations using many types of atmospheric models. Although these satellite datasets are very large, the information content of any single observation is often modest and non-exclusive due to redundancy with neighboring observations and due to measurement noise. In this study, we develop an adaptive approach to reduce the size of satellite datasets using geostatistics. A guiding principle is to reduce the data more in regions with little variability in the observations and less in regions with high variability. We subsequently tune and evaluate the approach using synthetic and real data case studies for North America from NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite. The proposed approach to data reduction yields more accurate CO2 flux estimates than the commonly-used method of binning and averaging the satellite data. We further develop a metric for choosing a level of data reduction; we can reduce the satellite dataset to an average of one observation per ~80–140 km for the specific case studies here without substantially compromising the flux estimate, but we find that reducing the data further quickly degrades the accuracy of the estimated fluxes. Overall, the approach developed here could be applied to a range of inverse problems that use very large trace gas datasets.

scholarly journals Supplementary material to "Data reduction for inverse modeling: an adaptive approach v1.0"

2020 ◽  
Author(s):  
Xiaoling Liu ◽  
August L. Weinbren ◽  
He Chang ◽  
Jovan Tadić ◽  
Marikate E. Mountain ◽  
...  
Keyword(s):  
Data Reduction ◽  
Inverse Modeling ◽  
Adaptive Approach ◽  
Supplementary Material

scholarly journals Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO<sub>2</sub> data assimilation

2013 ◽  
Vol 13 (23) ◽  
pp. 11643-11660 ◽  
Author(s):  
A. Chatterjee ◽  
A. M. Michalak
Keyword(s):  
Kalman Filter ◽  
Inverse Modeling ◽  
Ensemble Kalman Filter ◽  
Co2 Flux ◽  
Estimation Problem ◽  
Square Root ◽  
Flux Estimation ◽  
Source Sink ◽  
Operational Constraints ◽  
Square Root Filter

Abstract. Data assimilation (DA) approaches, including variational and the ensemble Kalman filter methods, provide a computationally efficient framework for solving the CO2 source–sink estimation problem. Unlike DA applications for weather prediction and constituent assimilation, however, the advantages and disadvantages of DA approaches for CO2 flux estimation have not been extensively explored. In this study, we compare and assess estimates from two advanced DA approaches (an ensemble square root filter and a variational technique) using a batch inverse modeling setup as a benchmark, within the context of a simple one-dimensional advection–diffusion prototypical inverse problem that has been designed to capture the nuances of a real CO2 flux estimation problem. Experiments are designed to identify the impact of the observational density, heterogeneity, and uncertainty, as well as operational constraints (i.e., ensemble size, number of descent iterations) on the DA estimates relative to the estimates from a batch inverse modeling scheme. No dynamical model is explicitly specified for the DA approaches to keep the problem setup analogous to a typical real CO2 flux estimation problem. Results demonstrate that the performance of the DA approaches depends on a complex interplay between the measurement network and the operational constraints. Overall, the variational approach (contingent on the availability of an adjoint transport model) more reliably captures the large-scale source–sink patterns. Conversely, the ensemble square root filter provides more realistic uncertainty estimates. Selection of one approach over the other must therefore be guided by the carbon science questions being asked and the operational constraints under which the approaches are being applied.

scholarly journals The impact of improved satellite retrievals on estimates of biospheric carbon balance

2020 ◽  
Vol 20 (1) ◽  
pp. 323-331 ◽  
Author(s):  
Scot M. Miller ◽  
Anna M. Michalak
Keyword(s):  
Carbon Balance ◽  
Near Infrared ◽  
Co2 Flux ◽  
Retrieval Algorithm ◽  
Co2 Uptake ◽  
Near Infrared Radiation ◽  
Sources And Sinks ◽  
Satellite Retrievals ◽  
The Impact ◽  
Insight Into

Abstract. The Orbiting Carbon Observatory 2 (OCO-2) is NASA's first satellite dedicated to monitoring CO2 from space and could provide novel insight into CO2 fluxes across the globe. However, one continuing challenge is the development of a robust retrieval algorithm: an estimate of atmospheric CO2 from satellite observations of near-infrared radiation. The OCO-2 retrievals have undergone multiple updates since the satellite's launch, and the retrieval algorithm is now on its ninth version. Some of these retrieval updates, particularly version 8, led to marked changes in the CO2 observations, changes of 0.5 ppm or more. In this study, we evaluate the extent to which current OCO-2 observations can constrain monthly CO2 sources and sinks from the biosphere, and we particularly focus on how this constraint has evolved with improvements to the OCO-2 retrieval algorithm. We find that improvements in the CO2 retrieval are having a potentially transformative effect on satellite-based estimates of the global biospheric carbon balance. The version 7 OCO-2 retrievals formed the basis of early inverse modeling studies using OCO-2 data; these observations are best equipped to constrain the biospheric carbon balance across only continental or hemispheric regions. By contrast, newer versions of the retrieval algorithm yield a far more detailed constraint, and we are able to constrain CO2 budgets for seven global biome-based regions, particularly during the Northern Hemisphere summer when biospheric CO2 uptake is greatest. Improvements to the OCO-2 observations have had the largest impact on glint-mode observations, and we also find the largest improvements in the terrestrial CO2 flux constraint when we include both nadir and glint data.

scholarly journals Surrogate‐Based Bayesian Inverse Modeling of the Hydrological System: An Adaptive Approach Considering Surrogate Approximation Error

2020 ◽  
Vol 56 (1) ◽  
Author(s):  
Jiangjiang Zhang ◽  
Qiang Zheng ◽  
Dingjiang Chen ◽  
Laosheng Wu ◽  
Lingzao Zeng
Keyword(s):  
Inverse Modeling ◽  
Approximation Error ◽  
Adaptive Approach ◽  
Hydrological System

scholarly journals Observation of vertical profiles of NO, O3, and VOCs to estimate their sources and sinks by inverse modeling in a Japanese larch forest

2020 ◽  
Vol 76 (1) ◽  
pp. 1-10
Author(s):  
Ryuichi WADA ◽  
Masahito UEYAMA ◽  
Akira TANI ◽  
Tomoki MOCHIZUKI ◽  
Yuzo MIYAZAKI ◽  
...  
Keyword(s):  
Inverse Modeling ◽  
Japanese Larch ◽  
Larch Forest ◽  
Vertical Profiles ◽  
Sources And Sinks

scholarly journals Précis of Neuroconstructivism: How the Brain Constructs Cognition

2008 ◽  
Vol 31 (3) ◽  
pp. 321-331 ◽  
Author(s):  
Sylvain Sirois ◽  
Michael Spratling ◽  
Michael S. C. Thomas ◽  
Gert Westermann ◽  
Denis Mareschal ◽  
...  
Keyword(s):  
Cognitive Development ◽  
Case Studies ◽  
Brain Regions ◽  
Object Representations ◽  
Phonological Development ◽  
Contextual Influences ◽  
Guiding Principle ◽  
Levels Of Organization ◽  
Atypical Development ◽  
The Brain

AbstractNeuroconstructivism: How the Brain Constructs Cognition proposes a unifying framework for the study of cognitive development that brings together (1) constructivism (which views development as the progressive elaboration of increasingly complex structures), (2) cognitive neuroscience (which aims to understand the neural mechanisms underlying behavior), and (3) computational modeling (which proposes formal and explicit specifications of information processing). The guiding principle of our approach is context dependence, within and (in contrast to Marr [1982]) between levels of organization. We propose that three mechanisms guide the emergence of representations: competition, cooperation, and chronotopy; which themselves allow for two central processes: proactivity and progressive specialization. We suggest that the main outcome of development is partial representations, distributed across distinct functional circuits. This framework is derived by examining development at the level of single neurons, brain systems, and whole organisms. We use the terms encellment, embrainment, and embodiment to describe the higher-level contextual influences that act at each of these levels of organization. To illustrate these mechanisms in operation we provide case studies in early visual perception, infant habituation, phonological development, and object representations in infancy. Three further case studies are concerned with interactions between levels of explanation: social development, atypical development and within that, developmental dyslexia. We conclude that cognitive development arises from a dynamic, contextual change in embodied neural structures leading to partial representations across multiple brain regions and timescales, in response to proactively specified physical and social environment.

scholarly journals Error correlation between CO<sub>2</sub> and CO as constraint for CO<sub>2</sub> flux inversions using satellite data

2009 ◽  
Vol 9 (3) ◽  
pp. 11783-11810
Author(s):  
H. Wang ◽  
D. J. Jacob ◽  
M. Kopacz ◽  
D. B. A. Jones ◽  
P. Suntharalingam ◽  
...  
Keyword(s):  
Inverse Modeling ◽  
Large Scale ◽  
Transport Model ◽  
Meteorological Data ◽  
Co2 Flux ◽  
Surface Fluxes ◽  
Satellite Observations ◽  
Carbon Surface ◽  
Chemical Transport Model ◽  
Error Correlation

Abstract. Inverse modeling of CO2 satellite observations to better quantify carbon surface fluxes requires a forward model such as a chemical transport model (CTM) to relate the fluxes to the observed column concentrations. Model transport error is an important source of observational error. We investigate the potential of using CO satellite observations as additional constraints in a joint CO2–CO inversion to improve CO2 flux estimates, by exploiting the CTM transport error correlations between CO2 and CO. We estimate the error correlation globally and for different seasons by a paired-model method (comparing CTM simulations of CO2 and CO columns using different assimilated meteorological data sets for the same meteorological year) and a paired-forecast method (comparing 48- vs. 24-h CTM forecasts of CO2 and CO columns for the same forecast time). We find strong positive and negative error correlations (r2>0.5) between CO2 and CO columns over much of the world throughout the year, and strong consistency between different methods to estimate the error correlation. Application of the averaging kernels used in the retrieval for thermal IR CO measurements weakens the correlation coefficients by 15% on average (mostly due to variability in the averaging kernels) but preserves the large-scale correlation structure. Results from a testbed inverse modeling application show that CO2–CO error correlations can indeed significantly reduce uncertainty on surface carbon fluxes in a joint CO2–CO inversion vs. a CO2–only inversion.

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