This site is the home of the Pheno Analysis project. Pheno Analysis employs sequential ensemble data assimilation by use of the Ensemble Kalman Filter (EnKF) to constrain empirical phenology parameters and prognostic phenology model states with quality screened Fraction of Photosyntetically Active Radiation absorbed by plants (FPAR) and Leaf Area Index (LAI) observations from NASA’s MODIS sensor on board TERRA satellite. The aim of Pheno Analysis is to reduce the uncertainty in globally applicable phenology models in order to make them more realistic for their application within global climate model simulations, especially for those including long term vegetation dynamics and predictions of land surface biogeochemistry coupled to the global carbon and water cycle.
A global-scale FPAR and LAI reanalysis dataset has been generated for the period 1960-2009 and is ready for use. Please see below in the download section!
Reto Stöckli started this project as part of his postdoctoral employment at Colorado State University, Department for Atmospheric Science with Prof. A. Scott Denning's BioCycle research group. The project was funded through NASA's Energy and Water Cycle Study (NEWS) and the resulting model code is now fully available here under the GNU General Public License Version 3 to the research community for use in climate analysis and climate modeling studies.
The author of the Pheno Analysis project has a new job position with MeteoSwiss in Zürich, Switzerland. Due to his changed research focus he would like to motivate researchers to make use of the Pheno Analysis framework and make good science out of it!
Please download here the current stable release of Pheno Analysis including a sample set of meteorological driver and satellite assimilation datasets for both local- and regional-scale data assimilation.
Please download here the current 50 year long (1960-2009) global daily FPAR and LAI reanalysis dataset.
- The local-scale mode of Pheno Analysis is documented in a peer-reviewed research article: R. Stöckli, T. Rutishauser, D. Dragoni, J. O. Keefe, P. E. Thornton, M. Jolly, L. Lu, and A. S. Denning. Remote sensing data assimilation for a prognostic phenology model. J. Geophys. Res. - Biogeosciences, 113 (G04021), doi: 10.1029/ 2008JG000781 PDF
- The regional- and global-scale mode of Pheno Analysis is documented in a peer-reviewed research article: R. Stöckli, T. Rutishauser, I. Baker, M. Liniger, and A. S. Denning (in press), A global reanalysis of vegetation phenology, J. Geophys. Res. - Biogeosciences, 116 (G03020), doi:10.1029/2010JG001545 PDF
- A User Manual and sample scripts with four local-scale and one regional-scale experiments is part of the download source code package (see above).
The regional-scale mode of Pheno Analysis is still in development with the following scientific and technical aims:
- implement a more advanced data assimilation method by Milja and Dusanka Zupanski called Mean Likelihood Ensemble Filter (MLEF)
- optimize the ensemble solver for MPI architectures for better load balancing
- use parameters from the global data assimilation for predicting phenology in land surface models like SiB, CLM or JULES
- constrain parameters for other phenology models (e.g. BIOME-BGC, TRIFFID, IBIS)
- couple Pheno Analysis to a NWP model to concurrently predict phenology and weather
- solve the current issue with the too high reduction of variance in global data assimilation experiments
- find out how quickly light will become the main limitation of spring green up with raising spring temperatures
Fellow researchers using the Pheno Analysis framework are happily welcome to address these questions in their own research!
- Reto Stöckli, Blue Marble Research, Bern, Switzerland (website: http://www.bluemarble.ch)
- Scott Denning, Biocycle Research Group, Department of Atmospheric Science, Colorado State University, Fort Collins CO, USA (website: http://biocycle.atmos.colostate.edu)
The NASA Energy and Water Cycle Study (NEWS) grant No. NNG06CG42G was the main funding source of this software. Computing resources were mainly provided by sub-contract 2207-06-016 issued by Science System and Application Inc. through NASA contract NAS5-02041. The MODIS Science Team and the MODIS Science Data Support Team provided the MOD15A2 and the MOD12Q1 data. Meteorological predictor data have been provided by the site PI’s and their teams participating the AmeriFlux and LBA projects as part of FLUXNET: Mike Goulden (Santarem Km83), Steven Wofsy (BOREAS NSA Old Black Spruce), Hans Peter Schmid (Morgan Monroe State Forest), Brian Amiro (BOREAS NSA Old Black Spruce) and Dennis Baldocchi (Tonzi Ranch). The first author is grateful to Arif Albayrak (NASA/GSFC GMAO) for his advice and comments on the ensemble data assimilation methodology.