Using generalized cross-validation to select parameters in inversions for regional carbon fluxes
نویسندگان
چکیده
منابع مشابه
Using generalized cross-validation to select parameters in inversions for regional carbon fluxes
[1] Estimating CO2 fluxes from the pattern of atmospheric CO2 concentrations with atmospheric transport models is an ill-posed inverse problem, whose solution is stabilized using prior information. Weights assigned to prior information and to CO2 concentrations at different locations are quantified by parameters that are not well known, and differences in the choice of these parameters contribu...
متن کاملBiases in regional carbon budgets from covariation of surface fluxes and weather in transport model inversions
Recent advances in atmospheric transport model inversions could significantly reduce uncertainties in land carbon uptake through the assimilation of CO2 concentration measurements at weekly and shorter timescales. The potential of these measurements for reducing biases in estimated land carbon sinks depends on the strength of covariation between surface fluxes and atmospheric transport at these...
متن کاملWavelet thresholding using generalized cross validation
De-noising algorithms based on wavelet thresholding replace small wavelet coeecients by zero and keep or shrink the coeecients with absolute value above the threshold. The optimal threshold minimizes the error of the result as compared to the unknown, exact data. To estimate this optimal threshold, we use Generalized Cross Validation. This procedure is fast and does not require an estimation fo...
متن کاملExplicit Solution to the Minimization Problem of Generalized Cross-Validation Criterion for Selecting Ridge Parameters in Generalized Ridge Regression
This paper considers optimization of the ridge parameters in generalized ridge regression (GRR) by minimizing a model selection criterion. GRR has a major advantage over ridge regression (RR) in that a solution to the minimization problem for one model selection criterion, i.e., Mallows’ Cp criterion, can be obtained explicitly with GRR, but such a solution for any model selection criteria, e.g...
متن کاملLarge-scale Inversion of Magnetic Data Using Golub-Kahan Bidiagonalization with Truncated Generalized Cross Validation for Regularization Parameter Estimation
In this paper a fast method for large-scale sparse inversion of magnetic data is considered. The L1-norm stabilizer is used to generate models with sharp and distinct interfaces. To deal with the non-linearity introduced by the L1-norm, a model-space iteratively reweighted least squares algorithm is used. The original model matrix is factorized using the Golub-Kahan bidiagonalization that proje...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Geophysical Research Letters
سال: 2004
ISSN: 0094-8276
DOI: 10.1029/2004gl020323