Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.240
IF5.240
IF 5-year value: 5.768
IF 5-year
5.768
CiteScore value: 8.9
CiteScore
8.9
SNIP value: 1.713
SNIP1.713
IPP value: 5.53
IPP5.53
SJR value: 3.18
SJR3.18
Scimago H <br class='widget-line-break'>index value: 71
Scimago H
index
71
h5-index value: 51
h5-index51
Download
Short summary
We describe a new computer program that is able produce maps of carbon dioxide or other quantities based on data collected by satellites that orbit the Earth. When working with such data there is often too much data in one area and none in another. The program is able to describe the fields even when data is not available. To be able to do so, new computational methods were developed. The program is also able to describe how uncertain the estimated carbon dioxide or other fields are.
GMD | Articles | Volume 13, issue 7
Geosci. Model Dev., 13, 3439–3463, 2020
https://doi.org/10.5194/gmd-13-3439-2020
Geosci. Model Dev., 13, 3439–3463, 2020
https://doi.org/10.5194/gmd-13-3439-2020

Model description paper 31 Jul 2020

Model description paper | 31 Jul 2020

Efficient multi-scale Gaussian process regression for massive remote sensing data with satGP v0.1.2

Jouni Susiluoto et al.

Related authors

Efficient Bayesian inference for large chaotic dynamical systems
Sebastian Springer, Heikki Haario, Jouni Susiluoto, Aleksandr Bibov, Andrew Davis, and Youssef Marzouk
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-350,https://doi.org/10.5194/gmd-2020-350, 2020
Preprint under review for GMD
Short summary
Parameter calibration and stomatal conductance formulation comparison for boreal forests with adaptive population importance sampler in the land surface model JSBACH
Jarmo Mäkelä, Jürgen Knauer, Mika Aurela, Andrew Black, Martin Heimann, Hideki Kobayashi, Annalea Lohila, Ivan Mammarella, Hank Margolis, Tiina Markkanen, Jouni Susiluoto, Tea Thum, Toni Viskari, Sönke Zaehle, and Tuula Aalto
Geosci. Model Dev., 12, 4075–4098, https://doi.org/10.5194/gmd-12-4075-2019,https://doi.org/10.5194/gmd-12-4075-2019, 2019
Short summary
Calibrating the sqHIMMELI v1.0 wetland methane emission model with hierarchical modeling and adaptive MCMC
Jouni Susiluoto, Maarit Raivonen, Leif Backman, Marko Laine, Jarmo Makela, Olli Peltola, Timo Vesala, and Tuula Aalto
Geosci. Model Dev., 11, 1199–1228, https://doi.org/10.5194/gmd-11-1199-2018,https://doi.org/10.5194/gmd-11-1199-2018, 2018
Short summary
HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands
Maarit Raivonen, Sampo Smolander, Leif Backman, Jouni Susiluoto, Tuula Aalto, Tiina Markkanen, Jarmo Mäkelä, Janne Rinne, Olli Peltola, Mika Aurela, Annalea Lohila, Marin Tomasic, Xuefei Li, Tuula Larmola, Sari Juutinen, Eeva-Stiina Tuittila, Martin Heimann, Sanna Sevanto, Thomas Kleinen, Victor Brovkin, and Timo Vesala
Geosci. Model Dev., 10, 4665–4691, https://doi.org/10.5194/gmd-10-4665-2017,https://doi.org/10.5194/gmd-10-4665-2017, 2017
Short summary
Constraining ecosystem model with adaptive Metropolis algorithm using boreal forest site eddy covariance measurements
Jarmo Mäkelä, Jouni Susiluoto, Tiina Markkanen, Mika Aurela, Heikki Järvinen, Ivan Mammarella, Stefan Hagemann, and Tuula Aalto
Nonlin. Processes Geophys., 23, 447–465, https://doi.org/10.5194/npg-23-447-2016,https://doi.org/10.5194/npg-23-447-2016, 2016
Short summary

Related subject area

Numerical Methods
An N-dimensional Fortran interpolation programme (NterGeo.v2020a) for geophysics sciences – application to a back-trajectory programme (Backplumes.v2020r1) using CHIMERE or WRF outputs
Bertrand Bessagnet, Laurent Menut, and Maxime Beauchamp
Geosci. Model Dev., 14, 91–106, https://doi.org/10.5194/gmd-14-91-2021,https://doi.org/10.5194/gmd-14-91-2021, 2021
Short summary
A framework to evaluate IMEX schemes for atmospheric models
Oksana Guba, Mark A. Taylor, Andrew M. Bradley, Peter A. Bosler, and Andrew Steyer
Geosci. Model Dev., 13, 6467–6480, https://doi.org/10.5194/gmd-13-6467-2020,https://doi.org/10.5194/gmd-13-6467-2020, 2020
Inequality-constrained free-surface evolution in a full Stokes ice flow model (evolve_glacier v1.1)
Anna Wirbel and Alexander Helmut Jarosch
Geosci. Model Dev., 13, 6425–6445, https://doi.org/10.5194/gmd-13-6425-2020,https://doi.org/10.5194/gmd-13-6425-2020, 2020
Short summary
A fast and efficient MATLAB-based MPM solver: fMPMM-solver v1.1
Emmanuel Wyser, Yury Alkhimenkov, Michel Jaboyedoff, and Yury Y. Podladchikov
Geosci. Model Dev., 13, 6265–6284, https://doi.org/10.5194/gmd-13-6265-2020,https://doi.org/10.5194/gmd-13-6265-2020, 2020
Short summary
Necessary conditions for algorithmic tuning of weather prediction models using OpenIFS as an example
Lauri Tuppi, Pirkka Ollinaho, Madeleine Ekblom, Vladimir Shemyakin, and Heikki Järvinen
Geosci. Model Dev., 13, 5799–5812, https://doi.org/10.5194/gmd-13-5799-2020,https://doi.org/10.5194/gmd-13-5799-2020, 2020
Short summary

Cited articles

Ambikasaran, S., Foreman-Mackey, D., Greengard, L., Hogg, D. W., and O'Neil, M.: Fast Direct Methods for Gaussian Processes, IEEE T. Pattern Anal., 38, 252–265, https://doi.org/10.1109/TPAMI.2015.2448083, 2016. a
Bertaux, J., Hauchecorne, A., Dalaudier, F., Cot, C., Kyrölä, E., Fussen, D., Tamminen, J., Leppelmeier, G., Sofieva, V., Hassinen, S., Fanton d'Andon, O., Barrot, G., Mangin, A., Theodore, B., Guirlet, M., Korablev, O., Snoeij, P., Koopman, R., and Fraisse, R.: First results on GOMOS/ENVISAT, Adv. Space Res., 33, 1029–1035, https://doi.org/10.1016/j.asr.2003.09.037, 2004. a
Bertaux, J. L., Kyrölä, E., Fussen, D., Hauchecorne, A., Dalaudier, F., Sofieva, V., Tamminen, J., Vanhellemont, F., Fanton d'Andon, O., Barrot, G., Mangin, A., Blanot, L., Lebrun, J. C., Pérot, K., Fehr, T., Saavedra, L., Leppelmeier, G. W., and Fraisse, R.: Global ozone monitoring by occultation of stars: an overview of GOMOS measurements on ENVISAT, Atmos. Chem. Phys., 10, 12091–12148, https://doi.org/10.5194/acp-10-12091-2010, 2010. a
Chiles, J.-P. and Delfiner, P.: Geostatistics, Wiley, 2012. a
Cressie, N.: Mission CO2ntrol: A Statistical Scientist's Role in Remote Sensing of Atmospheric Carbon Dioxide, J. Am. Stat. Assoc., 113, 152–168, https://doi.org/10.1080/01621459.2017.1419136, 2018. a
Publications Copernicus
Download
Short summary
We describe a new computer program that is able produce maps of carbon dioxide or other quantities based on data collected by satellites that orbit the Earth. When working with such data there is often too much data in one area and none in another. The program is able to describe the fields even when data is not available. To be able to do so, new computational methods were developed. The program is also able to describe how uncertain the estimated carbon dioxide or other fields are.
Citation