Presentation is loading. Please wait.

Presentation is loading. Please wait.

11 th JCSDA Science Workshop on Satellite Data Assimilation Latest Developments on the Assimilation of Cloud-Affected Satellite Observations Tom Auligné.

Published byBertina Allison Modified over 8 years ago

Similar presentations

Presentation on theme: "11 th JCSDA Science Workshop on Satellite Data Assimilation Latest Developments on the Assimilation of Cloud-Affected Satellite Observations Tom Auligné."— Presentation transcript:

1 11 th JCSDA Science Workshop on Satellite Data Assimilation Latest Developments on the Assimilation of Cloud-Affected Satellite Observations Tom Auligné National Center for Atmospheric Research Acknowledgments: Gael Descombes, Greg Thompson, Francois Vandenberghe, Dongmei Xu (NCAR) Thomas Nehrkorn, Brian Woods (AER) Funding provided by the Air Force Weather Agency 1

2 Introduction Approaches for the use of cloud-affected radiances Nowcasting COP + advection 3D cloud fraction + WRF dynamical transport Oklahoma U. / GSI-RR cloud analysis Expansion of analysis control variable Total water + linearized physics Microphysical variables 2

3 Introduction Approaches for the use of cloud-affected radiances in NWP Nowcasting COP + advection 3D cloud fraction + WRF dynamical transport Oklahoma U. / GSI-RR cloud analysis Expansion of analysis control variable Total water + linearized physics Microphysical variables (WRF Data Assimilation) 3

4 Outline Control Variable Transform Multivariate Background Error Covariances Hybrid Ensemble/Variational Displacement Analysis Processing of Cloudy Satellite Observations Experimental Demonstration 4

5 Outline Control Variable Transform Multivariate Background Error Covariances Hybrid Ensemble/Variational Displacement Analysis Processing of Cloudy Satellite Observations Experimental Demonstration 5

6 Pseudo Single Observation Test (PSOT) Multivariate covariances for qc, qr, qi, qsn Binning option: dynamical cloud mask Vertical and Horizontal autocorrelations via Recursive Filters 3D Variance 6

7 Outline Control Variable Transform Multivariate Background Error Covariances Hybrid Ensemble/Variational Displacement Analysis Processing of Cloudy Satellite Observations Experimental Demonstration 7

8 NEW ALGORITHM: update of the ensemble perturbations inside the Variational analysis through Lanczos minimization (without external EnKF) NEW ALGORITHM: update of the ensemble perturbations inside the Variational analysis through Lanczos minimization (without external EnKF) Ensemble covariance included in 3D/4DVar through state augmentation (Lorenc 2003, Wang et al. 2008, Fairbairn et al., 2012) Ensemble/Variational Integrated Localized (EVIL) with 8

9 Outline Control Variable Transform Multivariate Background Error Covariances Hybrid Ensemble/Variational Displacement Analysis Processing of Cloudy Satellite Observations Experimental Demonstration 9

10 Forecast Calibration & Alignment Hurricane Katrina OSSE Synthetic observations (TCPW) Balanced displacement Nehrkorn et al., MWR 2013 (submitted) 10

11 Outline Control Variable Transform Multivariate Background Error Covariances Hybrid Ensemble/Variational Displacement Analysis Processing of Cloudy Satellite Observations Experimental Demonstration 11

12 Unified processing for Radiances and COP Retrievals VarBC: Variational Bias Correction (unchanged predictors) Revisited QC: extended Gross and First-Guess check (to conserve cloudy data) Huber Norm: robust definition of observation error Land Surface: T skin introduced as a sink variable Field of View: advanced interpolation scheme CRTM Jacobians: rescaled base state (floor and ceiling values for cloud parameters) Middle Loop: Multiple re-linearizations of obs. operator Unified Processing of Cloud-affected Satellite data 12

13 Observation Background Radiance (GOES imgr channel 4) AIRS, IASI, AMSU-A, AMSU-B, MHS, HIRS, SSMI/S BUFR files from NCEP MODIS HDF files (Terra & Aqua) from NASA GOES Sndr ASCII files (E & W) from Uwisc. GOES Imgr NetCDF files from NCAR/RAL Cloud-Affected Radiances 13

14 Observation Background Log 10 (COD) (GOES imgr) Log 10 (COD) (GOES imgr) GOES Imgr NetCDF files from Uwisc. MODIS NetCDF files from AFWA with (Benedetti and Janiskova, MWR 2008) Cloud Optical Properties 14

15 Unified processing for Radiances and COP Retrievals VarBC: Variational Bias Correction (unchanged predictors) Revisited QC: extended Gross and First-Guess check (to conserve cloudy data) Huber Norm: robust definition of observation error Land Surface: T skin introduced as a sink variable Field of View: advanced interpolation scheme CRTM Jacobians: rescaled base state (floor and ceiling values for cloud parameters) Middle Loop: Multiple re-linearizations of obs. operator Unified Processing of Cloud-affected Satellite data 15

16 Unified processing for Radiances and COP Retrievals VarBC: Variational Bias Correction (unchanged predictors) Revisited QC: extended Gross and First-Guess check (to conserve cloudy data) Huber Norm: robust definition of observation error Land Surface: T skin introduced as a sink variable Field of View: advanced interpolation scheme CRTM Jacobians: rescaled base state (floor and ceiling values for cloud parameters) Middle Loop: Multiple re-linearizations of obs. operator Unified Processing of Cloud-affected Satellite data 16

17 Unified processing for Radiances and COP Retrievals VarBC: Variational Bias Correction (unchanged predictors) Revisited QC: extended Gross and First-Guess check (to conserve cloudy data) Huber Norm: robust definition of observation error Land Surface: T skin introduced as a sink variable Field of View: advanced interpolation scheme CRTM Jacobians: rescaled base state (floor and ceiling values for cloud parameters) Middle Loop: Multiple re-linearizations of obs. operator Unified Processing of Cloud-affected Satellite data 17

18 Huber Norm: robust observation errors Figure from Fisher (2008) Innovations Normalized Obs Weight Distribution of innovations 18

19 Unified processing for Radiances and COP Retrievals VarBC: Variational Bias Correction (unchanged predictors) Revisited QC: extended Gross and First-Guess check (to conserve cloudy data) Huber Norm: robust definition of observation error Land Surface: T skin introduced as a sink variable Field of View: advanced interpolation scheme CRTM Jacobians: rescaled base state (floor and ceiling values for cloud parameters) Middle Loop: Multiple re-linearizations of obs. operator Unified Processing of Cloud-affected Satellite data 19

20 Unified processing for Radiances and COP Retrievals VarBC: Variational Bias Correction (unchanged predictors) Revisited QC: extended Gross and First-Guess check (to conserve cloudy data) Huber Norm: robust definition of observation error Land Surface: T skin introduced as a sink variable Field of View: advanced interpolation scheme CRTM Jacobians: rescaled base state (floor and ceiling values for cloud parameters) Middle Loop: Multiple re-linearizations of obs. operator Unified Processing of Cloud-affected Satellite data 20

21 Satellite Field of View (FoV) interpolation Calculate polygons (ellipses) List model grid points inside ellipse Use average input for CRTM Currently testing for AIRS and IASI Calculate polygons (ellipses) List model grid points inside ellipse Use average input for CRTM Currently testing for AIRS and IASI 21

22 Unified processing for Radiances and COP Retrievals VarBC: Variational Bias Correction (unchanged predictors) Revisited QC: extended Gross and First-Guess check (to conserve cloudy data) Huber Norm: robust definition of observation error Land Surface: T skin introduced as a sink variable Field of View: advanced interpolation scheme CRTM Jacobians: rescaled base state (floor and ceiling values for cloud parameters) Middle Loop: Multiple re-linearizations of obs. operator Unified Processing of Cloud-affected Satellite data 22

23 Unified processing for Radiances and COP Retrievals VarBC: Variational Bias Correction (unchanged predictors) Revisited QC: extended Gross and First-Guess check (to conserve cloudy data) Huber Norm: robust definition of observation error Land Surface: T skin introduced as a sink variable Field of View: advanced interpolation scheme CRTM Jacobians: rescaled base state (floor and ceiling values for cloud parameters) Middle Loop: Multiple re-linearizations of obs. operator Unified Processing of Cloud-affected Satellite data 23

24 Cost Function First GuessSecond GuessThird Guess First Analysis Second Analysis AIRS Window Channel #787 3DVar Middle loop 24

25 First Guess Second Guess Third Guess Observation Update of q cloud, q ice in WRF Observations AIRS Window Channel #787 25

26 Outline Control Variable Transform Multivariate Background Error Covariances Hybrid Ensemble/Variational Displacement Analysis Processing of Cloudy Satellite Observations Experimental Demonstration 26

27 WRF-ARW model, CONUS 15km, Thompson microphysics First Guess = Mean of 50-memb ens. from EnKF expt (Romine) Single analysis at 20012/06/03 (12UTC), 5 middle-loops GOES-Imager: Radiances (Thompson) & COD (Uwisc.) CTRL no DA COD Cloud Optical Depth (3DVar) FCACloud Optical Depth (Displacement) 3DVARRadiances (3DVar) EVIL Radiances (Hybrid) Experimental Framework 27

28 Incremental fit to obs Cloud Optical Depth 3DVar EVIL Radiances Obs 28

29 q cloud Level 10 Analysis increments CODFCA 3DVAREVIL 29

30 q cloud Level 10 Analysis increments CODFCA 3DVAREVIL 30

31 0.12° 0.25° 0.5° 1°2°3° Multi-scale verification 31

32 Multi-scale verification AnalysisForecast EVIL 3DVAR CTRL Impact of all-sky radiances on forecast up to 3-4h Best forecast with EVIL system Impact of all-sky radiances on forecast up to 3-4h Best forecast with EVIL system EVIL 3DVAR CTRL 32

33 Conclusion Expansion of analysis vector to clouds Multivariate, flow-dependent background errors Displacement pre-processing Updated processing of cloud-affected satellite data (bias correction, QC, interpolation, RTM, middle-loop) Sustained impact in short-term forecast More work required… 33

Download ppt "11 th JCSDA Science Workshop on Satellite Data Assimilation Latest Developments on the Assimilation of Cloud-Affected Satellite Observations Tom Auligné."

Similar presentations

Assimilation of T-TREC-retrieved wind data with WRF 3DVAR for the short-Term forecasting of Typhoon Meranti (2010) at landfall Xin Li 1, Yuan Wang 1, Jie.

Assimilation of T-TREC-retrieved wind data with WRF 3DVAR for the short-Term forecasting of Typhoon Meranti (2010) at landfall Xin Li 1, Yuan Wang 1, Jie.
© The Aerospace Corporation 2014 Observation Impact on WRF Model Forecast Accuracy over Southwest Asia Michael D. McAtee Environmental Satellite Systems.

© The Aerospace Corporation 2014 Observation Impact on WRF Model Forecast Accuracy over Southwest Asia Michael D. McAtee Environmental Satellite Systems.
Data Assimilation Andrew Collard. Overview Introduction to Atmospheric Data Assimilation Control Variables Observations Background Error Covariance Summary.

Data Assimilation Andrew Collard. Overview Introduction to Atmospheric Data Assimilation Control Variables Observations Background Error Covariance Summary.
Adaptation of the Gridpoint Statistical Interpolation (GSI) for hourly cycled application within the Rapid Refresh Ming Hu 1,2, Stan Benjamin 1, Steve.

Adaptation of the Gridpoint Statistical Interpolation (GSI) for hourly cycled application within the Rapid Refresh Ming Hu 1,2, Stan Benjamin 1, Steve.
2. Description of MIIDAPS 1. Introduction A Generalized Approach to Microwave Satellite Data Assimilation Quality Control and Preprocessing Here, we present.

2. Description of MIIDAPS 1. Introduction A Generalized Approach to Microwave Satellite Data Assimilation Quality Control and Preprocessing Here, we present.
1 Met Office, UK 2 Japan Meteorological Agency 3 Bureau of Meteorology, Australia Assimilation of data from AIRS for improved numerical weather prediction.

1 Met Office, UK 2 Japan Meteorological Agency 3 Bureau of Meteorology, Australia Assimilation of data from AIRS for improved numerical weather prediction.
Handling Cloud-Affected Infrared Radiances in the GSI Will McCarty GSFC/Global Modeling and Assimilation Office JCSDA Workshop 10 October 2012.

Handling Cloud-Affected Infrared Radiances in the GSI Will McCarty GSFC/Global Modeling and Assimilation Office JCSDA Workshop 10 October 2012.
Transitioning unique NASA data and research technologies to the NWS 1 Radiance Assimilation Activities at SPoRT Will McCarty SPoRT SAC Wednesday June 13,

Transitioning unique NASA data and research technologies to the NWS 1 Radiance Assimilation Activities at SPoRT Will McCarty SPoRT SAC Wednesday June 13,
Improving High Resolution Tropical Cyclone Prediction Using a Unified GSI-based Hybrid Ensemble-Variational Data Assimilation System for HWRF Xuguang Wang.

Improving High Resolution Tropical Cyclone Prediction Using a Unified GSI-based Hybrid Ensemble-Variational Data Assimilation System for HWRF Xuguang Wang.
Improved Simulations of Clouds and Precipitation Using WRF-GSI Zhengqing Ye and Zhijin Li NASA-JPL/UCLA June, 2011.

Improved Simulations of Clouds and Precipitation Using WRF-GSI Zhengqing Ye and Zhijin Li NASA-JPL/UCLA June, 2011.
5/22/201563rd Interdepartmental Hurricane Conference, March 2-5, 2009, St. Petersburg, FL Experiments of Hurricane Initialization with Airborne Doppler.

5/22/201563rd Interdepartmental Hurricane Conference, March 2-5, 2009, St. Petersburg, FL Experiments of Hurricane Initialization with Airborne Doppler.
Satellite SST Radiance Assimilation and SST Data Impacts James Cummings Naval Research Laboratory Monterey, CA 93943 2013 Sea Surface Temperature Science.

Satellite SST Radiance Assimilation and SST Data Impacts James Cummings Naval Research Laboratory Monterey, CA Sea Surface Temperature Science.
Jidong Gao and David Stensrud Some OSSEs on Assimilation of Radar Data with a Hybrid 3DVAR/EnKF Method.

Jidong Gao and David Stensrud Some OSSEs on Assimilation of Radar Data with a Hybrid 3DVAR/EnKF Method.
Hans Huang: Regional 4D-Var. July 30th, 2012 1 Regional 4D-Var Hans Huang NCAR Acknowledgements: NCAR/MMM/DAS USWRP, NSF-OPP, NCAR AFWA, KMA, CWB, CAA,

Hans Huang: Regional 4D-Var. July 30th, Regional 4D-Var Hans Huang NCAR Acknowledgements: NCAR/MMM/DAS USWRP, NSF-OPP, NCAR AFWA, KMA, CWB, CAA,
ECMWF CO 2 Data Assimilation at ECMWF Richard Engelen European Centre for Medium-Range Weather Forecasts Reading, United Kingdom Many thanks to Phil Watts,

ECMWF CO 2 Data Assimilation at ECMWF Richard Engelen European Centre for Medium-Range Weather Forecasts Reading, United Kingdom Many thanks to Phil Watts,
Progress in Cloudy Microwave Satellite Data Assimilation at NCEP Andrew Collard 1, Emily Liu 4, Yanqiu Zhu 1, John Derber 2, Daryl Kleist 3, Rahul Mahajan.

Progress in Cloudy Microwave Satellite Data Assimilation at NCEP Andrew Collard 1, Emily Liu 4, Yanqiu Zhu 1, John Derber 2, Daryl Kleist 3, Rahul Mahajan.
Background and Status of Q1FY16 Global Implementation

Background and Status of Q1FY16 Global Implementation
A comparison of hybrid ensemble transform Kalman filter(ETKF)-3DVAR and ensemble square root filter (EnSRF) analysis schemes Xuguang Wang NOAA/ESRL/PSD,

A comparison of hybrid ensemble transform Kalman filter(ETKF)-3DVAR and ensemble square root filter (EnSRF) analysis schemes Xuguang Wang NOAA/ESRL/PSD,
Numerical Weather Prediction Division The usage of the ATOVS data in the Korea Meteorological Administration (KMA) Sang-Won Joo Korea Meteorological Administration.

Numerical Weather Prediction Division The usage of the ATOVS data in the Korea Meteorological Administration (KMA) Sang-Won Joo Korea Meteorological Administration.
Comparison of hybrid ensemble/4D- Var and 4D-Var within the NAVDAS- AR data assimilation framework The 6th EnKF Workshop May 18th-22nd1 Presenter: David.

Comparison of hybrid ensemble/4D- Var and 4D-Var within the NAVDAS- AR data assimilation framework The 6th EnKF Workshop May 18th-22nd1 Presenter: David.

Similar presentations

Ads by Google