A geostatistical approach to surface flux estimation of atmospheric trace gases

By Anna M. Michalak, L. Bruhwiler, P. Tans. 2004

190 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/b9d927ba41735cadfc264a1a27ba7244a84ceb24

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. D. J. Jacob
2. O. Saunier
3. A. Mathieu
4. A. Zammit‐Mangion
5. David Crisp
6. J. Whitaker
7. Yuk L. Yung
8. Matthew Sainsbury-Dale

In Environmental Engineering

1. Marc Bocquet
2. A. Zammit‐Mangion
3. Matthew Sainsbury-Dale
4. Milija Zupanski
5. J. Whitaker
6. Yuk L. Yung
7. Anna M. Michalak
8. Noel Cressie

In Global and Planetary Change

1. David Crisp
2. Milija Zupanski
3. Marc Bocquet
4. Paul O. Wennberg
5. Dusanka Zupanski
6. D. J. Jacob
7. Charles E. Miller
8. Phil DeCola

Citations in the corpus

• An ensemble data assimilation system to estimate CO2 surface fluxes from atmospheric trace gas observations (2005, background): “Vectors xi can easily be created as unconditional realizations of matrix P, for instance through a Cholesky decomposition (see Michalak et al. [2004] for an example).”
• Precision requirements for space-based XCO2 data (2007, methodology): “The distance was calculated using the great circle distance between points on the surface of the earth [e.g., Michalak et al., 2004]: h xi; xj ¼ r cos 1 sinfi sinfj þ cosfi cosfj cos #i #j ð2Þ where the coordinates xi = (fi, Ji) are the latitude and longitude, respectively, of the sample locations,…”
• Estimation of Errors in the Inverse Modeling of Accidental Release of Atmospheric Pollutant: Application to the Reconstruction of the Cesium-137 and Iodine-131 Source Terms from the Fukushima Daiichi Power Plant (2012, methodology): “, 2007] or the maximum likelihood principle [Michalak et al., 2004].”
• Basis-Function Models in Spatial Statistics (2021, background): “These sources and sinks are more-or-less uncertain, and their estimation requires spatial-statistical methods (e.g., Michalak et al., 2004, ZammitMangion et al., 2021a).”
• Carbon flux bias estimation employing Maximum Likelihood Ensemble Filter (MLEF) (2007, methodology): “Since then, the problem of model bias estimation was further investigated under various data assimilation methods, including Kalman filter, variational, geostatistical and ensemble-based methods [e.g., Bennett et al., 1993; Dee, 1995; Reichle et al., 2002; Nichols, 2003; Michalak et al., 2004; Tsyru”