On aggregation errors in atmospheric transport inversions

By T. Kaminski, Peter J. Rayner, Martin Heimann, I. Enting. 2001

275 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/0456cb7b1d7ba605f5d1ad01b936da732582cc64

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Citations in the corpus

A geostatistical approach to surface flux estimation of atmospheric trace gases (2004, background): “If measurements are sensitive to these prescribed flux patterns, the inferred total fluxes for given regions will not be representative of the actual overall fluxes for these regions [Kaminski et al., 2001; Peylin et al., 2002; Law et al., 2002; Rödenbeck et al., 2003].”
TransCom 3 inversion intercomparison: Impact of transport model errors on the interannual variability of regional CO2 fluxes, 1988–2003 (2006, methodology): “[62] Because the data error covariance matrix R reflects the errors in cobs cfwd, it must incorporate errors associated both with the measurement (instrument errors plus errors in the GLOBALVIEW data fitting procedure) and with the modeling of the measurement (including errors in the winds used, in ”
Satellite chartography of atmospheric methane from SCIAMACHY on board ENVISAT: 2. Evaluation based on inverse model simulations (2007, unknown): “minimize potential systematic errors due to the so-called aggregation error [Kaminski et al., 2001].”
Estimation of trace gas fluxes with objectively determined basis functions using reversible jump Markov Chain Monte Carlo (2016, methodology): “…of how many basis functions for which to solve can be seen as a balance between an under-determination of sources and so-called aggregation error, which is used to describe an error brought about by allowing the model too few degrees of freedom (Kaminski et al., 2001; Turner and Jacob, 2015).”
What can we learn from European continuous atmospheric CO 2 measurements to quantify regional fluxes – Part 2: Sensitivity of flux accuracy to inverse setup (2008, background): “In this case, the error reduction on estimated fluxes is larger but the so-called “aggregation error” (Kaminski et al., 2001; Peylin, 2001) linked to the use of incorrect prior patterns can generate estimates that strongly deviate from the truth.”
Balancing aggregation and smoothing errors in inverse models (2015, methodology): “…knowledge and not allowed to be optimized as part of the inversion The resulting error is called the aggregation error (Kaminski and Heimann, 2001; Kaminski et al., 2001; Schuh et al., 2009) Previous work by Bocquet (2009), Bocquet et al. (2011), Bocquet and Wu (2011), Wu et al. (2011), and…”
History of chemically and radiatively important atmospheric gases from the Advanced Global Atmospheric Gases Experiment (AGAGE) (2018, methodology): “…that is appropriate to effectively minimize “aggregation error” (i.e., an inflexibility in the space that could lead to errors in the prior distribution unduly influencing the outcome of the inversion; Kaminski et al., 2001), while maintaining an acceptable level of uncertainty reduction.”
Assimilating atmospheric data into a terrestrial biosphere model: A case study of the seasonal cycle (2002, background): “As pointed out by [Kaminski et al., 2001], this avoids a series of problems associated with trying to”
On the representation error in data assimilation (2018, unknown): “This comes with the price of representation error (Kaminski et al. , 2001).”
Comparative inverse analysis of satellite (MOPITT) and aircraft (TRACE-P) observations to estimate Asian sources of carbon monoxide (2004, background): “This forward model error includes contributions from transport and chemistry, as well as an ‘‘aggregation error’’ describing the errors on the assumed spatial and temporal variations of sources for individual components of the state vector [Kaminski et al., 2001].”
Impact of correlated observation errors on inverted CO2 surface fluxes from OCO measurements (2007, background): “... by misinterpreted features in the electromagnetic spectrum and bythe ancillary data usedin retrieval [see, e.g.,Chevallieret al., 2005, Figure 2]. Since the observations are interpreted in the inversion system with the help of an atmospheric transport model (that links the fluxes to the measurem”