4dvar- instructions. Atmospheric Transport Model Parameterized Chemical Transport Model (PCTM; Kawa, et al, 2005) –Driven by reanalyzed met fields from.

Slides:



Advertisements
Similar presentations
Assimilation of radar data - research plan
Advertisements

GEMS Kick- off MPI -Hamburg CTM - IFS interfaces GEMS- GRG Review of meeting in January and more recent thoughts Johannes Flemming.
© Crown copyright Met Office Implementing a diurnal model at the Met Office James While, Matthew Martin.
Mercator Ocean activity
Polly Smith, Alison Fowler, Amos Lawless School of Mathematical and Physical Sciences, University of Reading Exploring coupled data assimilation using.
Data assimilation for validation of climate modeling systems Pierre Gauthier Department of Earth and Atmospheric Sciences Université du Québec à Montréal.
Yoichi Ishikawa 1, Toshiyuki Awaji 1,2, Teiji In 3, Satoshi Nakada 2, Tsuyoshi Wakamatsu 1, Yoshimasa Hiyoshi 1, Yuji Sasaki 1 1 DrC, JAMSTEC 2 Kyoto University.
ECMWF CO 2 Data Assimilation at ECMWF Richard Engelen European Centre for Medium-Range Weather Forecasts Reading, United Kingdom Many thanks to Phil Watts,
Data assimilation of trace gases in a regional chemical transport model: the impact on model forecasts E. Emili 1, O. Pannekoucke 1,2, E. Jaumouillé 2,
Microwindow Selection for the MIPAS Reduced Resolution Mode INTRODUCTION Microwindows are the small subsets of the complete MIPAS spectrum which are used.
Application of Satellite Data in the Data Assimilation Experiments off Oregon Peng Yu in collaboration with Alexander Kurapov, Gary Egbert, John S. Allen,
The comparison of TransCom continuous experimental results at upper troposphere Takashi MAKI, Hidekazu MATSUEDA and TransCom Continuous modelers.
Evaluating Spatial, Temporal, and Clear-Sky Errors in Satellite CO 2 Measurements Katherine Corbin, A. Scott Denning, Ian Baker, Aaron Wang, Lixin Lu TransCom.
A Variational Carbon Data Assimilation System (CDAS-4DVar)
Advanced data assimilation methods with evolving forecast error covariance Four-dimensional variational analysis (4D-Var) Shu-Chih Yang (with EK)
Page 1 1 of 21, 28th Review of Atmospheric Transmission Models, 6/14/2006 A Two Orders of Scattering Approach to Account for Polarization in Near Infrared.
The prototype carbon inverse problem: estimation of regional CO 2 sources and sinks from global atmospheric [CO 2 ] measurements David Baker NCAR / Terrestrial.
1 NGGPS Dynamic Core Requirements Workshop NCEP Future Global Model Requirements and Discussion Mark Iredell, Global Modeling and EMC August 4, 2014.
Differences in Model Transport of CO2. Cloud Contamination ✦ Radar indicates precipitation along fronts ✦ Coincidentally, this is where much of interesting.
Exploring strategies for coupled 4D-Var data assimilation using an idealised atmosphere-ocean model Polly Smith, Alison Fowler & Amos Lawless School of.
Numerical Weather Prediction Division The usage of the ATOVS data in the Korea Meteorological Administration (KMA) Sang-Won Joo Korea Meteorological Administration.
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.
Photo by Dave Fratello. Focus To evaluate CAM5/CARMA at 1x1 degree resolution with aircraft observations. - Improve cirrus cloud representation in the.
Meteorological Observatory Lindenberg – Richard Assmann Observatory The GCOS Reference Upper Air Network.
Coupled Model Data Assimilation: Building an idealised coupled system Polly Smith, Amos Lawless, Alison Fowler* School of Mathematical and Physical Sciences,
Your compuBase online services Module 5: Extract Data.
J.-F. Müller and T. Stavrakou IASB-BIRA Avenue Circulaire 3, 1180 Brussels AGU Fall meeting, Dec Multi-year emission inversion for.
Coupled Model Data Assimilation: Building an idealised coupled system Polly Smith, Amos Lawless, Alison Fowler* School of Mathematical and Physical Sciences,
3DVAR Retrieval of 3D Moisture Field from Slant- path Water Vapor Observations of a High-resolution Hypothetical GPS Network Haixia Liu and Ming Xue Center.
Interfaces EO data with Atmospheric and Land Surface Model: Progress report Liang Feng, Paul Palmer.
CS 320 Assignment 1 Rewriting the MISC Osystem class to support loading machine language programs at addresses other than 0 1.
Assimilating chemical compound with a regional chemical model Chu-Chun Chang 1, Shu-Chih Yang 1, Mao-Chang Liang 2, ShuWei Hsu 1, Yu-Heng Tseng 3 and Ji-Sung.
Soil moisture generation at ECMWF Gisela Seuffert and Pedro Viterbo European Centre for Medium Range Weather Forecasts ELDAS Interim Data Co-ordination.
Global Observing System Simulation Experiments (Global OSSEs) How It Works Nature Run 13-month uninterrupted forecast produces alternative atmosphere.
Sensitivity Analysis of Mesoscale Forecasts from Large Ensembles of Randomly and Non-Randomly Perturbed Model Runs William Martin November 10, 2005.
Research Vignette: The TransCom3 Time-Dependent Global CO 2 Flux Inversion … and More David F. Baker NCAR 12 July 2007 David F. Baker NCAR 12 July 2007.
Development of an EnKF to estimate CO 2 fluxes from realistic distributions of X CO2 Liang Feng, Paul Palmer
HP Project & Portfolio Management Entering Actual Hours for Project work July 11, 2007 CIMpleBS.com/HP-PPM/Training-Time Sheets.pps By Dan Gallagher See.
FastOpt Quantitative Design of Observational Networks M. Scholze, R. Giering, T. Kaminski, E. Koffi P. Rayner, and M. Voßbeck Future GHG observation WS,
Page 1© Crown copyright 2004 SRNWP Lead Centre Report on Data Assimilation 2005 for EWGLAM/SRNWP Annual Meeting October 2005, Ljubljana, Slovenia.
SPEAKERS: Gabriele Pfister, Scientist III, National Center for Atmospheric Research (NCAR) Brad Pierce, Physical Scientist, NOAA Salient Questions: 1.What.
ICDC7, Boulder September 2005 Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard.
LMDZ Single Column Model + what is it ? + why is it interesting ? + List of 1D cases + how to install and run it ? M-P Lefebvre and LMDZ team.
Prepared by Dusanka Zupanski and …… Maximum Likelihood Ensemble Filter: application to carbon problems.
A Brief Introduction to CRU, GHCN, NCEP2, CAM3.5 Yi-Chih Huang.
Error correlation between CO 2 and CO as a constraint for CO 2 flux inversion using satellite data from different instrument configurations Helen Wang.
Slide 1 NEMOVAR-LEFE Workshop 22/ Slide 1 Current status of NEMOVAR Kristian Mogensen.
A New Ocean Suite Algorithm for AMSR2 David I. Duncan September 16 th, 2015 AMSR Science Team Meeting Huntsville, AL.
CPS 100e 5.1 Inheritance and Interfaces l Inheritance models an "is-a" relationship  A dog is a mammal, an ArrayList is a List, a square is a shape, …
Météo-France / CNRM – T. Bergot 1) Methodology 2) The assimilation procedures at local scale 3) Results for the winter season Improved Site-Specific.
1 Satellite Winds Superobbing Howard Berger Mary Forsythe John Eyre Sean Healy Image Courtesy of UW - CIMSS Hurricane Opal October 1995.
Global vs mesoscale ATOVS assimilation at the Met Office Global Large obs error (4 K) NESDIS 1B radiances NOAA-15 & 16 HIRS and AMSU thinned to 154 km.
NASA, CGMS-44, 7 June 2016 Coordination Group for Meteorological Satellites - CGMS SURFACE PRESSURE MEASUREMENTS FROM THE ORBITING CARBON OBSERVATORY-2.
Retrieving sources of fine aerosols from MODIS/AERONET observations by inverting GOCART model INVERSION: Oleg Dubovik 1 Tatyana Lapyonok 1 Tatyana Lapyonok.
Comparison of adjoint and analytical approaches for solving atmospheric chemistry inverse problems Monika Kopacz 1, Daniel J. Jacob 1, Daven Henze 2, Colette.
Carbon Cycle Data Assimilation with a Variational Approach (“4-D Var”) David Baker CGD/TSS with Scott Doney, Dave Schimel, Britt Stephens, and Roger Dargaville.
Matt Vaughan Class Project ATM 621
The Lodore Falls Hotel, Borrowdale
ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course
Application Of KF-Convection Scheme In 3-D Chemical Transport Model
Adjoint modeling and applications
ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course
A New Scheme for Chaotic-Attractor-Theory Oriented Data Assimilation
Monika Kopacz, Daniel Jacob, Jenny Fisher, Meghan Purdy
File IO and Strings CIS 40 – Introduction to Programming in Python
PI: Steven Pawson (GMAO) Atmosphere:
ECMWF activities: Seasonal and sub-seasonal time scales
Carbon Model-Data Fusion
Directions of Inquiry Given a fixed atmospheric CO2 concentration assimilation scheme, what is the optimal network expansion? Given the wide array of available.
Hartmut Bösch and Sarah Dance
Presentation transcript:

4dvar- instructions

Atmospheric Transport Model Parameterized Chemical Transport Model (PCTM; Kawa, et al, 2005) –Driven by reanalyzed met fields from NASA/Goddard’s GEOS-DAS3 scheme –Lin-Rood finite volume advection scheme –Vertical mixing: diffusion plus a simple cloud convection scheme –Exact adjoint for linear advection case Basic resolution 2  x 2.5 , 25  layers,  t  30 min, with ability to reduce resolution to –4  x 5 ,  t  60 min –6  x 10 ,  t  120 min <  we’ll use this in the example –12  x 15 ,  t  180 min Measurements binned at  t resolution

Data Assimilation Experiments Monday: Test performance of 4DVar method in a simulation framework, with dense data (6°x10°, lowest level of model, every  t, 1 ppm (1  ) error) –Case 1 -- no data noise added, no prior –Case 2 -- w/ data noise added, no prior –Case 3 -- w/ data noise added, w/ prior –Case 4 -- no data noise added, w/ prior Tuesday: with Case 3 above, do OSSEs for –Case 5 -- dense data, but OCO column average –Case 6 -- OCO ground track and column average –Case 7 -- extended version of current network –Case 8 -- current in situ network Possibilities for projects: examine the importance of –Data coverage and accuracy vs. targeted flux resolution –Prior error pattern and correlation structures –Measurement correlations in time/space –Errors in the setup assumptions (“mistuning”) –Effect of biases in the measurements

How to run the 4D-Var code Home directory: /project/projectdirs/m598/dfb/4DVar_Example/scripts/case1/BFGS Work directories: /scratch/scratchdirs/dfb/case1/work_fwd & /scratch/scratchdirs/dfb/case1/work_adj Submit batch job by typing ‘llsubmit runBFGS2_LL’ while in /project/projectdirs/m598/dfb/4DVar_Example/scripts/case1/BFGS/ This executes the main driver script, found in BFGSdriver4d.F, in same directory, which controls setting up all the files and running FWD and ADJ inside the minimization loop The scripts that execute the FWD and ADJ runs of the model are found in …/4DVar_Example/scripts/case1/, named run.co2.fvdas_bf_fwd(adj)_trupri997_hourly Check progress of job by typing ‘llqs’ Jobs currently set up to do a 1 year-long run (360 days), solving for the fluxes in 5-day long chunks, at 6.4  x10  resolution, with  t =2 hours

How to monitor job while running In /scratch/scratchdirs/dfb/case1/work_fwd/costfuncval_history/temp –Column 1 -- measurement part of cost function –Column 2 -- flux prior part of cost function –Column 4 -- total cost function value –Column weighted mismatch from true flux –Column unweighted mismatch from true flux Columns 4, 10, and 11 ought to be decreasing as the run proceeds Columns X and Y give the iteration count and 1-D search count

How to view detailed results A results file in netCDF format written to: /scratch/scratchdirs/dfb/case1/work_fwd/estim_truth.nc sftp this to davinci.nersc.gov (rename it, so that you don’t overwrite another group’s file) Pull up an X-window to davinci and ssh -X davinci.nersc.gov On davinci, ‘module load ncview’ Then ‘ncview estim_truth.nc’ Click on a field to look at it Hint: set ‘Range’ to +/- 2e-8 for most fields

Other code details The code for the FWD and ADJ model is in../4DVar_Example/src_fwd_varres and src_adj_varres Measurement files are located in /scratch/scratchdirs/dfb/case1/meas Two files controlling the tightness of the prior and whether or not noise is added to the measurements are /scratch/scratchdirs/dfb/case1/work_fwd/ferror and /scratch/scratchdirs/dfb/case1/work_fwd/measnoise_on.dat

Monday’s Experiment 2-hourly measurements in the lowest model level at 6.4  x 10 , 1 ppm error (1  ) Iterate 30 descent steps, 1-year-long run, starts 1/1 4 cases –Case 1 -- No measurement noise added, no prior –Case 2 -- Add measurement noise added, no prior –Case 3 -- Add noise, and apply a prior –Case 4 -- No noise, but apply a prior Designed to test the method and understand the impact of data errors and the usefulness of the prior Case 3 is the most realistic and will be used to do OSSEs for several possible future networks for Tuesday’s problem set

Tuesday’s Experiment Use Case 3 from above to test more-realistic measurement networks: –Current in situ network –Extended version of current network –OCO satellite –Hourly 6.4  x 10  column measurements Essentially an “OSSE” (observing system simulation experiment) -- tells you how well your instrument should do in constraining the fluxes. Only gives the random part of the error, not biases

OCO Groundtrack, Jan 1st (Boxes at 6  x 10  ) Across 1 day 5 days 2 days