Critical issues of ensemble data assimilation in application to GOES-R risk reduction program D. Zupanski 1, M. Zupanski 1, M. DeMaria 2, and L. Grasso.

Slides:



Advertisements
Similar presentations
TWO STEP EQUATIONS 1. SOLVE FOR X 2. DO THE ADDITION STEP FIRST
Advertisements

Ensemble Sensitivity Analysis Applied to Tropical Cyclones: Preliminary Results from Typhoon Nuri (2008) Rahul Mahajan & Greg Hakim University of Washington,
Operational Numerical Forecasting on Tropical Cyclones Yuqing Wang International Pacific Research Center and Department of Meteorology University of Hawaii.
1 What constrains spread growth in forecasts initialized from ensemble filters? Tom Hamill (& Jeff Whitaker) NOAA Earth System Research Lab Boulder, Colorado,
ECMWF Training Course 2005 slide 1 Forecast sensitivity to Observation Carla Cardinali.
Improvements in Statistical Tropical Cyclone Forecast Models: A Year 2 Joint Hurricane Testbed Project Update Mark DeMaria 1, Andrea Schumacher 2, John.
Université du Québec École de technologie supérieure Face Recognition in Video Using What- and-Where Fusion Neural Network Mamoudou Barry and Eric Granger.
SEASONAL sensitivity study on COBEL-ISBA LOCAL FORECAST SYSTEM for fog and low clouds at Paris CDG airport ROQUELAURE Stevie and BERGOT Thierry Météo-France.
Introduction to Data Assimilation Peter Jan van Leeuwen IMAU.
Ensemble data assimilation (EnsDA) activities of the GOES-R project Progress report Dusanka Zupanski CIRA/CSU GOES-R meeting 8 September 2004 Dusanka Zupanski,
P2.1 ENSEMBLE DATA ASSIMILATION: EXPERIMENTS USING NASA’S GEOS COLUMN PRECIPITATION MODEL D. Zupanski 1, A. Y. Hou 2, S. Zhang 2, M. Zupanski 1, C. D.
Dusanka Zupanski CIRA/Colorado State University Fort Collins, Colorado Model Error and Parameter Estimation Joint NCAR/MMM CSU/CIRA Data Assimilation Workshop.
1B.17 ASSESSING THE IMPACT OF OBSERVATIONS AND MODEL ERRORS IN THE ENSEMBLE DATA ASSIMILATION FRAMEWORK D. Zupanski 1, A. Y. Hou 2, S. Zhang 2, M. Zupanski.
Dusanka Zupanski CIRA/Colorado State University Fort Collins, Colorado A General Ensemble-Based Approach to Data Assimilation Model Error and Parameter.
Dusanka Zupanski CIRA/Colorado State University Fort Collins, Colorado Ensemble Kalman Filter Guest Lecture at AT 753: Atmospheric Water Cycle 21 April.
P1.8 QUANTIFYING AND REDUCING UNCERTAINTY BY EMPLOYING MODEL ERROR ESTIMATION METHODS Dusanka Zupanski Cooperative Institute for Research in the Atmosphere.
Model error estimation employing ensemble data assimilation Dusanka Zupanski and Milija Zupanski CIRA/Colorado State University, Fort Collins, CO, U.S.A.
Dusanka Zupanski CIRA/Colorado State University Fort Collins, Colorado Ensemble Data Assimilation and Prediction: Applications to Environmental Science.
MODEL ERROR ESTIMATION Cooperative Institute for Research in the Atmosphere Research Benefits to NOAA:This is a novel research approach, providing an optimal.
Whitecaps, sea-salt aerosols, and climate Magdalena D. Anguelova Physical Oceanography Dissertation Symposium College of Marine Studies, University of.
Addition 1’s to 20.
25 seconds left…...
1 A Data Assimilation System for Costal Ocean Real-Time Predictions Zhijin Li and Yi Chao Jet Propulsion Laboratory, California Institute of Technology.
ROMS User Workshop, October 2, 2007, Los Angeles
Introduction to data assimilation in meteorology Pierre Brousseau, Ludovic Auger ATMO 08,Alghero, september 2008.
Week 1.
PSSA Preparation.
Uncertainty Representation and Quantification in Precipitation Data Records Yudong Tian Collaborators: Ling Tang, Bob Adler, George Huffman, Xin Lin, Fang.
1 ECE 776 Project Information-theoretic Approaches for Sensor Selection and Placement in Sensor Networks for Target Localization and Tracking Renita Machado.
Probabilistic Reasoning over Time
Effects of model error on ensemble forecast using the EnKF Hiroshi Koyama 1 and Masahiro Watanabe 2 1 : Center for Climate System Research, University.
Ibrahim Hoteit KAUST, CSIM, May 2010 Should we be using Data Assimilation to Combine Seismic Imaging and Reservoir Modeling? Earth Sciences and Engineering.
Carbon Flux Bias Estimation at Regional Scale using Coupled MLEF-PCTM model Ravindra Lokupitiya Department of Atmospheric Science Colorado State University.
Ensemble Kalman Filter Methods
Experimenting with the LETKF in a dispersion model coupled with the Lorenz 96 model Author: Félix Carrasco, PhD Student at University of Buenos Aires,
Models for model error –Additive noise. What is Q(x 1, x 2, t 1, t 2 )? –Covariance inflation –Multiplicative noise? –Parameter uncertainty –“Structural”
Maximum Liklihood Ensemble Filter (MLEF) Dusanka Zupanski, Kevin Robert Gurney, Scott Denning, Milia Zupanski, Ravi Lokupitiya June, 2005 TransCom Meeting,
A comparison of hybrid ensemble transform Kalman filter(ETKF)-3DVAR and ensemble square root filter (EnSRF) analysis schemes Xuguang Wang NOAA/ESRL/PSD,
EnKF Overview and Theory
Observing Strategy and Observation Targeting for Tropical Cyclones Using Ensemble-Based Sensitivity Analysis and Data Assimilation Chen, Deng-Shun 3 Dec,
Computational Issues: An EnKF Perspective Jeff Whitaker NOAA Earth System Research Lab ENIAC 1948“Roadrunner” 2008.
1 ESTIMATING THE STATE OF LARGE SPATIOTEMPORALLY CHAOTIC SYSTEMS: WEATHER FORECASTING, ETC. Edward Ott University of Maryland Main Reference: E. OTT, B.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 MAP (Maximum A Posteriori) x is reduced state vector [SST(x), TCWV(w)]
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.
Dusanka Zupanski And Scott Denning Colorado State University Fort Collins, CO CMDL Workshop on Modeling and Data Analysis of Atmospheric CO.
1 GSI/ETKF Regional Hybrid Data Assimilation with MMM Hybrid Testbed Arthur P. Mizzi NCAR/MMM 2011 GSI Workshop June 29 – July 1, 2011.
DoD Center for Geosciences/Atmospheric Research at Colorado State University WSMR November 19-20, 2003 ARMY Research Lab and CIRA/CSU Collaboration on.
2004 SIAM Annual Meeting Minisymposium on Data Assimilation and Predictability for Atmospheric and Oceanographic Modeling July 15, 2004, Portland, Oregon.
14 th Annual WRF Users’ Workshop. June 24-28, 2013 Improved Initialization and Prediction of Clouds with Satellite Observations Tom Auligné Gael Descombes,
Applications of optimal control and EnKF to Flow Simulation and Modeling Florida State University, February, 2005, Tallahassee, Florida The Maximum.
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.
MODEL ERROR ESTIMATION EMPLOYING DATA ASSIMILATION METHODOLOGIES Dusanka Zupanski Cooperative Institute for Research in the Atmosphere Colorado State University.
MODEL ERROR ESTIMATION IN ENSEMBLE DATA ASSIMILATION FRAMEWORK Dusanka Zupanski Cooperative Institute for Research in the Atmosphere Colorado State University.
Development of an EnKF to estimate CO 2 fluxes from realistic distributions of X CO2 Liang Feng, Paul Palmer
A Sequential Hybrid 4DVAR System Implemented Using a Multi-Grid Technique Yuanfu Xie 1, Steven E. Koch 1, and Steve Albers 1,2 1 NOAA Earth System Research.
# # # # An Application of Maximum Likelihood Ensemble Filter (MLEF) to Carbon Problems Ravindra Lokupitiya 1, Scott Denning 1, Dusanka Zupanski 2, Kevin.
Data assimilation and forecasting the weather (!) Eugenia Kalnay and many friends University of Maryland.
DATA ASSIMILATION AND MODEL ERROR ESTIMATION Dusanka Zupanski Cooperative Institute for Research in the Atmosphere Colorado State University Fort Collins,
Local Predictability of the Performance of an Ensemble Forecast System Liz Satterfield and Istvan Szunyogh Texas A&M University, College Station, TX Third.
Implementation and Testing of 3DEnVAR and 4DEnVAR Algorithms within the ARPS Data Assimilation Framework Chengsi Liu, Ming Xue, and Rong Kong Center for.
Prepared by Dusanka Zupanski and …… Maximum Likelihood Ensemble Filter: application to carbon problems.
, Karina Apodaca, and Man Zhang Warn-on-Forecast and High-Impact Weather Workshop, February 6-7, 2013, National Weather Center, Norman, OK Utility of GOES-R.
A Random Subgrouping Scheme for Ensemble Kalman Filters Yun Liu Dept. of Atmospheric and Oceanic Science, University of Maryland Atmospheric and oceanic.
Ensemble forecasting/data assimilation and model error estimation algorithm Prepared by Dusanka Zupanski and Milija Zupanski CIRA/CSU Denning group meeting.
JCSDA Science Workshop on Satellite Data Assimilation June 5-7, 2013, NCWCP, College Park, MD Utility of GOES-R ABI and GLM instruments in regional data.
Assimilation of GPM satellite radiance in improving hurricane forecasting Zhaoxia Pu and ChauLam (Chris) Yu Department of Atmospheric Sciences University.
Data Assimilation Theory CTCD Data Assimilation Workshop Nov 2005
Information content in ensemble data assimilation
Developing 4D-En-Var: thoughts and progress
Hartmut Bösch and Sarah Dance
Presentation transcript:

Critical issues of ensemble data assimilation in application to GOES-R risk reduction program D. Zupanski 1, M. Zupanski 1, M. DeMaria 2, and L. Grasso 1 1 CIRA/Colorado State University, Fort Collins, CO 2 NOAA/NESDIS Fort Collins, CO Ninth Symposium on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS) 9-13 January 2005 San Diego, CA Dusanka Zupanski, CIRA/CSU Research partially supported by NOAA Grant NA17RJ1228

OUTLINE  Critical data assimilation issues related to GOES-R satellite mission  Ensemble based data assimilation methodology: Maximum Likelihood Ensemble Filter  Experimental results  Conclusions and future work Dusanka Zupanski, CIRA/CSU

Critical data assimilation issues of GOES-R and similar missions  Assimilate satellite observations with high special and temporal resolution  Employ state-of-the-art non-linear atmospheric models (without neglecting model errors)  Provide optimal estimate of the atmospheric state  Calculate uncertainty of the optimal estimate  Determine amount of new information given by the observations Dusanka Zupanski, CIRA/CSU What is the value added of having new observations (e.g., GOES-R, CloudSat, GPM) ?

METHODOLOGY Maximum Likelihood Ensemble Filter (MLEF) (Zupanski 2005; Zupanski and Zupanski 2005) Developed using ideas from  Variational data assimilation (3DVAR, 4DVAR)  Iterated Kalman Filters  Ensemble Transform Kalman Filter (ETKF, Bishop et al. 2001) MLEF is designed to provide optimal estimates of  model state variables  empirical parameters  model error (bias) MLEF also calculates uncertainties of all estimates (in terms of P a and P f ) Dusanka Zupanski, CIRA/CSU

MLEF APPROACH - non-linear forecast model Minimize cost function J - model state vector of dim Nstate >>Nens Dusanka Zupanski, CIRA/CSU Analysis error covariance Forecast error covariance - information matrix of dim Nens  Nens

EXPERIMENTAL DESIGN  Hurricane Lili case  35 1-h DA cycles: 13UTC 1 Oct 2002 – 00 UTC 3 Oct  CSU-RAMS non-hydrostatic model  30x20x21 grid points, 15 km grid distance (in the Gulf of Mexico)  Control variable: u,v,w,theta,Exner, r_total (dim=54000)  Model simulated observations with random noise (7200 obs per DA cycle)  Nens=50  Iterative minimization of J (1 iteration only) Dusanka Zupanski, CIRA/CSU

Experimental design (continued) 13 UTC Cycle 1 14 UTC00 UTC Cycle 2Cycle 35 1 Oct Oct Oct UTC 2 Oct 2002 Cycle 33 Dusanka Zupanski, CIRA/CSU

Experimental design (continued) Dusanka Zupanski, CIRA/CSU 1200 w obs 1200 Exner obs 1200 theta obs Sub-cycles 1-4 Sub-cycles 5-8 Sub-cycles 9-12 Sub-cycles Sub-cycles Sub-cycles u obs 1200 v obs 1200 r_total obs  Split cycle 33 into 24 sub-cycles  Calculate eigenvalues of (I+C) -1/2 in each sub-cycle (information content) Information content of each group of observations

RESULTS Dusanka Zupanski, CIRA/CSU Sub-cycles 1-4 u- obs groups System is “learning” about the truth via updating analysis error covariance.

RESULTS Dusanka Zupanski, CIRA/CSU Sub-cycles 5-8 v- obs groups Most information in sub-cycles 5 and 6.

RESULTS Dusanka Zupanski, CIRA/CSU Sub-cycles 9-12 w- obs groups Most information in sub-cycle 10.

RESULTS Dusanka Zupanski, CIRA/CSU Sub-cycles Exner- obs groups

RESULTS Dusanka Zupanski, CIRA/CSU Sub-cycles theta- obs groups

RESULTS Dusanka Zupanski, CIRA/CSU Sub-cycles theta- obs groups Sub-cycles with little information can be excluded  data selection.

CONCLUSIONS Dusanka Zupanski, CIRA/CSU  Ensemble based data assimilation methods, such as the MLEF, can be effectively used to quantify impact of each observation type.  The procedure is applicable to a forecast model of any complexity. Only eigenvalues of a small size matrix (Nens x Nens) need to be evaluated.  Data assimilation system has a capability to learn form observations. Value added of having new observations (e.g., GOES-R, CloudSat, GPM) can be quantified applying a similar procedure.

References Zupanski, M., 2005: The Maximum Likelihood Ensemble Filter. Theoretical aspects. Accepted in Mon. Wea. Rev. [Available at ftp://ftp.cira.colostate.edu/milija/papers/MLEF_MWR.pdf] Zupanski, D., and M. Zupanski, 2005: Model error estimation employing ensemble data assimilation approach. Submitted to Mon. Wea. Rev. [Available at ftp://ftp.cira.colostate.edu/Zupanski/manuscripts/MLEF_model_err.revised2.pdf]