CarboEurope workshop, Viterbo 2004Markus Reichstein Gap-filling algorithm Assumptions: NEE = NEE(R, T, VPD, t) +  NEE(R, T, VPD, t)  NEE(R+  R, T+ 

Slides:



Advertisements
Similar presentations
Time-Series Analysis of Astronomical Data
Advertisements

Modeling Recruitment in Stock Synthesis
Why gap filling isn’t always easy Andrew Richardson University of New Hampshire Jena Gap Filling Workshop September 2006.
TEMPORAL VARIABILITY AND DRIVERS OF NET ECOSYSTEM PRODUCTION OF A TURKEY OAK (QUERCUS CERRIS L.) FOREST IN ITALY UNDER COPPICE MANAGEMENT Luca Belelli.
From
Pinpointing the security boundary in high-dimensional spaces using importance sampling Simon Tindemans, Ioannis Konstantelos, Goran Strbac Risk and Reliability.
Brasil-flux eddy covariance CO2 flux measurements: Uncertainty analysis, u* threshold and GEP calculation Natalia Restrepo-Coupe, Xubin Zeng, Rafael Rosolem,
Applications of GRACE data to estimation of the water budget of large U.S. river basins Huilin Gao, Qiuhong Tang, Fengge Su, Dennis P. Lettenmaier Dept.
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Prentice Hall Trapezoidal Rule Section 5.5.
Uncertainty in eddy covariance datasets Dario Papale, Markus Reichstein, Antje Moffat, Ankur Desai, many others..
Gap filling using a Bayesian-regularized neural network B.H. Braswell University of New Hampshire.
Andrea Colombari and Andrea Fusiello, Member, IEEE.
A methodology for translating positional error into measures of attribute error, and combining the two error sources Yohay Carmel 1, Curtis Flather 2 and.
Choosing an Order for Joins Sean Gilpin ID: 119 CS 257 Section 1.
Cross-spectral analysis on Net Ecosystem Exchange: Dominant timescale and correlations among key ecosystem variables over the Ameriflux Harvard forest.
4 th COPS Workshop, Hohenheim, 25 – 26 September 2006 Modeling and assimilation efforts at IPM in preparation of COPS Hans-Stefan Bauer, Matthias Grzeschik,
Energy, Energy, Energy  Worldwide efforts to reduce energy consumption  People can conserve. Large percentage savings possible, but each individual has.
A Dynamic Messenger Problem Jan Fábry University of Economics Prague
Reducing uncertainty in NEE estimates from flux measurements D. Hollinger, L. Mahrt, J. Sun, and G.G. Katul Ameriflux Meeting, Boulder CO., October 20,
Raw data Hz HH data submitted for synthesis Flux calculation, raw data filtering Additional filtering for footprint or instrument malfunctioning.
Titel Gap Filling of CO 2 Fluxes of Frequently Cut Grassland Christof Ammann Agroscope ART Federal Research Station, Zürich Gap Filling Comparison Workshop,
Serial Correlation Serial correlation is a problem associated with time-series data. It occurs when the errors of the regression model are correlated with.
Using a Global Flux Network—FLUXNET— to Study the Breathing of the Terrestrial Biosphere Dennis Baldocchi ESPM/Ecosystem Science Div. University of California,
Unit of Biosystem Physics 1. O BJECTIVES To analyze impacts of grazing on carbon dioxide (CO 2 ) fluxes (F) exchanged by a meadow. 2. E XPERIMENTAL SITE.
New FLUXDATA collection Aim of the presentation: get feedbacks and suggestions from you in the next days.
Filling Mars Human Exploration Strategic Knowledge Gaps with Next Generation Meteorological Instrumentation. S. Rafkin, Southwest Research Institute
FLUXNET: Measuring CO 2 and Water Vapor Fluxes Across a Global Network Dennis Baldocchi ESPM/Ecosystem Science Div. University of California, Berkeley.
Christian Beer, CE-IP Crete 2006 Mean annual GPP of Europe derived from its water balance Christian Beer 1, Markus Reichstein 1, Philippe Ciais 2, Graham.
Spatial Statistics Jonathan Bossenbroek, PhD Dept of Env. Sciences Lake Erie Center University of Toledo.
Long term weather and flux data: treatment of discontinuous data. Bart Kruijt, Wilma Jans, Cor Jacobs, Eddy Moors Loobos.
A detailed look at the MOD16 ET algorithm Natalie Schultz Heat budget group meeting 7/11/13.
Hung X. Nguyen and Matthew Roughan The University of Adelaide, Australia SAIL: Statistically Accurate Internet Loss Measurements.
1 Welcome to the Gap Filling Comparison Workshop September 18-20, 2006 Antje Moffat.
Gap filling of eddy fluxes with artificial neural networks
Intro. ANN & Fuzzy Systems Lecture 26 Modeling (1): Time Series Prediction.
New tool for CO 2 flux partitioning with soil chamber flux implementation as a solution for site in topographically complex terrain Šigut, L., Mammarella,
You have NEE Now what? Ankur Desai, U Wisconsin-Madison.
On the use of eddy-covariance and optical remote sensing data for biogeochemical modelling Markus Reichstein, Dario Papale Biogeochemical Model-Data-Integration.
Chapter 5 The Decomposition Method Components of a Time Series Trend - Tr t Seasonal - Sn t Cyclical - Cl t Irregular -  t.
Gap-filling workshop, Jena 09/2006Markus Reichstein Gap-filling: What, why, how? - an Introduction Gap-filling Comparison Workshop, September 18-20, 2006.
     Giorgios de Milatos, Demis Baldocchialopoulos and Lorenzos Missonis.
Chris Chu Iowa State University Yiu-Chung Wong Rio Design Automation
Biases in land surface models Yingping Wang CSIRO Marine and Atmospheric Research.
1 Monte-Carlo Planning: Policy Improvement Alan Fern.
Edinburgh, June 2008Markus Reichstein Critical issues when using flux data for reducing Land Surfcace Model uncertainties – towards full uncertainty accounting?
Using AmeriFlux Observations in the NACP Site-level Interim Synthesis Kevin Schaefer NACP Site Synthesis Team Flux Tower PIs Modeling Teams.
MS Elí Rafael Pérez Ruiz Research Assistant Laboratorio de Percepción Remota y Sistemas de Información Geográfica Instituto Tecnológico.
Towards a robust, generalizable non-linear regression gap filling algorithm (NLR_EM) Ankur R Desai – National Center for Atmospheric Research (NCAR) Boulder,
LHC Collimation Working Group – 20 February 2012 Collimator Setup Software in 2012 G. Valentino R. W. Assmann, S. Redaelli and N. Sammut.
Applications of eddy covariance measurements, Part 1: Lecture on Analyzing and Interpreting CO 2 Flux Measurements Dennis Baldocchi ESPM/Ecosystem Science.
1 CHAPTER 6 FORECASTING WITH MOVING AVERAGE (MA) MODELS González-Rivera: Forecasting for Economics and Business, Copyright © 2013 Pearson Education, Inc.
Success and Failure of Implementing Data-driven Upscaling Using Flux Networks and Remote Sensing Jingfeng Xiao Complex Systems Research Center, University.
Gap Filling Comparison Workshop, September 18-20, 2006, Jena, Germany Corinna Rebmann Olaf Kolle Max-Planck-Institute for Biogeochemistry Jena, Germany.
Locations. Soil Temperature Dataset Observations Data is – Correlated in time and space – Evolving over time (seasons) – Gappy (Due to failures) – Faulty.
July 23, BSA, a Fast and Accurate Spike Train Encoding Scheme Benjamin Schrauwen.
The CarboeuropeIP Ecosystem Component Database: data processing and availability Dario Papale, Markus Reichstein.
Population Marginal Means Two factor model with replication Two factor model with replication.
Results from the Reflex experiment Mathew Williams, Andrew Fox and the Reflex team.
Sandra Castro and Gary Wick.  Does direct regression of satellite infrared brightness temperatures to observed in situ skin temperatures result in.
Learning to Align: a Statistical Approach
Algorithm Theoretical Basis Document GlobAlbedo Aerosol Retrieval
CarboEurope Open Science Conference
أنماط الإدارة المدرسية وتفويض السلطة الدكتور أشرف الصايغ
Lecture 30: Final Review Wednesday, December 6, 2000.
Ашық сабақ 7 сынып Файлдар мен қапшықтар Сабақтың тақырыбы:
Windows басқару элементтері
ITER FIRST WALL HEAT FLUX CONTROL DEVELOPMENT
Қош келдіңіздер!.
Информатика пән мұғалімі : Аитова Карима.
Types of Errors And Error Analysis.
Presentation transcript:

CarboEurope workshop, Viterbo 2004Markus Reichstein Gap-filling algorithm Assumptions: NEE = NEE(R, T, VPD, t) +  NEE(R, T, VPD, t)  NEE(R+  R, T+  VPD+  VPD, t+  t) The smaller  t and the more environmental constraints available the better Lag days Autokorrelation 01020

CarboEurope workshop, Viterbo 2004Markus Reichstein Gap-filling algorithm General type of approach same as Falge et al. (2001) Differences: –Dynamic averaging window size (as small as possible  better exploitation of temporal autocorrelation) –„Moving“ look-up table (  value to be filled always in the center of the class) –Combination of MDV and LUT methods

CarboEurope workshop, Viterbo 2004Markus Reichstein MDS method +Exploits meteorological drivers as much as available +Localized (exploits autocorrelation) +Fills all gaps +Gives tentative quality index +Yields error estimates for the flux +Easy to understand +Fast -Heuristic -Exhibits discontinuities with large gaps

Rg available with |dt| ≤ 7 days |dt| ≤ 1 hour |dt| ≤ 1 day (& same hour of day)Rg, T, VPD available with |dt| ≤ 21, 28,..., 140 daysRg available with |dt| ≤ 14, 21,..., 140 days |dt| ≤ 7, 14,... days Rg, T, VPD available with |dt| ≤ 14 days No Yes Fill with average of available values: f_met = 1; f_win=|dt|; f_qc=1; fqc_ok=1 Rg, T, VPD available with |dt| ≤ 7 days No Quality-controlled half-hourly data (storage, ustar,...) NEE present ? Yes f_met=0, f_qc=0; fqc_ok=1 Don‘t fill: f_met = 1; f_win=|dt|; f_qc=1; fqc_ok=1 Yes f_met = 3; f_win=|dt|; f_qc=1; fqc_ok=1 Yes f_met = 2; f_win=|dt|; f_qc=2; fqc_ok=0 Yes f_met = 3; f_win=|dt|; f_qc=2; fqc_ok=0 Yes f_met = 2; f_win=|dt|; f_qc=2 or 3; fqc_ok=0 Yes f_met = 1; f_win=|dt|; f_qc=2 or 3; fqc_ok=0 Yes f_met = 3; f_win=|dt|; f_qc= 3; fqc_ok=0 Yes