Cloudy GOES-Imager assimilation with hydrometeor control variable Byoung-Joo JUNG NCAR/MMM Student presentation at JCSDA Summer Colloquium on Satellite.

Slides:

Advertisements

Similar presentations

© The Aerospace Corporation 2014 Observation Impact on WRF Model Forecast Accuracy over Southwest Asia Michael D. McAtee Environmental Satellite Systems.

Advertisements

2. Description of MIIDAPS 1. Introduction A Generalized Approach to Microwave Satellite Data Assimilation Quality Control and Preprocessing Here, we present.

Handling Cloud-Affected Infrared Radiances in the GSI Will McCarty GSFC/Global Modeling and Assimilation Office JCSDA Workshop 10 October 2012.

Design and Validation of Observing System Simulation Experiments (OSSEs) Ronald Errico Goddard Earth Sciences Technology and Research Center at Morgan.

Enhancements of Radiance Bias Correction in NCEP’s Data Assimilation Systems Yanqiu Zhu Contributions from: John Derber, Andrew Collard, Dick Dee, Russ.

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.

Satellite SST Radiance Assimilation and SST Data Impacts James Cummings Naval Research Laboratory Monterey, CA Sea Surface Temperature Science.

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.

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.

UKmet February Hybrid Ensemble-Variational Data Assimilation Development A partnership to develop and implement a hybrid 3D-VAR system –Joint venture.

© The Aerospace Corporation 2014 Observation Impact on Mesoscale Model Forecast Accuracy over Southwest Asia Dr. Michael D. McAtee Environmental Satellite.

Operational Global Model Plans John Derber. Timeline July 25, 2013: Completion of phase 1 WCOSS transition August 20, 2013: GDAS/GFS model/analysis upgrade.

Towards Utilizing All-Sky Microwave Radiance Data in GEOS-5 Atmospheric Data Assimilation System Development of Observing System Simulation Experiments.

Ensemble-variational sea ice data assimilation Anna Shlyaeva, Mark Buehner, Alain Caya, Data Assimilation and Satellite Meteorology Research Jean-Francois.

Korea Institute of Atmospheric Prediction Systems (KIAPS) ( 재 ) 한국형수치예보모델개발사업단 Observation impact in East Asia and western North Pacific regions using.

EnKF Overview and Theory

MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD Quality Control-Consistent algorithm for all sensors to determine.

Eidgenössisches Departement des Innern EDI Bundesamt für Meteorologie und Klimatologie MeteoSchweiz Statistical Characteristics of High- Resolution COSMO.

3D/4D-Var Methods Liang Xu (NRL) JCSDA Summer Colloquium on Satellite DA 1 3D-Var/4D-Var Solution Methods Liang Xu Naval Research Laboratory, Monterey,

1 NCEP data assimilation systems status and plans John C. Derber Environmental Modeling Center NCEP/NWS/NOAA With input from: Many others.

GSI developments and plans at NCAR/MMM Tom Auligné Aimé Fournier, Hans Huang, Andy Jones, Hui-Chuan Lin, Zhiquan Liu, Yann Michel, Arthur Mizzi, Thomas.

Data assimilation and observing systems strategies Pierre Gauthier Data Assimilation and Satellite Meteorology Division Meteorological Service of Canada.

GSI EXPERIMENTS FOR RUA REFLECTIVITY/CLOUD AND CONVENTIONAL OBSERVATIONS Ming Hu, Stan Benjamin, Steve Weygandt, David Dowell, Terra Ladwig, and Curtis.

NCODA Variational Ocean Data Assimilation System (NCODA v3.5) James Cummings Naval Research Laboratory, Monterey, CA GODAE Ocean View / CLIVAR GSOP Workshop.

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

14 th Annual WRF Users’ Workshop. June 24-28, 2013 Improved Initialization and Prediction of Clouds with Satellite Observations Tom Auligné Gael Descombes,

Variational Assimilation of MODIS AOD using GSI and WRF/Chem Zhiquan Liu NCAR/NESL/MMM Quanhua (Mark) Liu (JCSDA), Hui-Chuan Lin (NCAR),

MODEL ERROR ESTIMATION EMPLOYING DATA ASSIMILATION METHODOLOGIES Dusanka Zupanski Cooperative Institute for Research in the Atmosphere Colorado State University.

Towards retrieving 3-D cloud fractions using Infrared Radiances from multiple sensors Dongmei Xu JCSDA summer colloquium, July August

Challenges and practical applications of data assimilation in numerical weather prediction Data Assimilation Education Forum Part I: Overview of Data Assimilation.

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,

Status of improving the use of MODIS, AVHRR, and VIIRS polar winds in the GDAS/GFS David Santek, Brett Hoover, Sharon Nebuda, James Jung Cooperative Institute.

Introduction of temperature observation of radio-sonde in place of geopotential height to the global three dimensional variational data assimilation system.

MCS Introduction Where? Observed reflectivity at 3km from

Implementation and Testing of 3DEnVAR and 4DEnVAR Algorithms within the ARPS Data Assimilation Framework Chengsi Liu, Ming Xue, and Rong Kong Center for.

I 5.11 Validation of the GMAO OSSE Prototype Runhua Yang 1,2 and Ronald Errico 1,3 1 Global Modeling and Assimilation office, GSFC, NASA 2 Science Systems.

1 Observation Impact Using a Variational Adjoint System PI: Dr. James Cummings, Code Co-PIs: Dr. Hans.

The Hyperspectral Environmental Suite (HES) and Advanced Baseline Imager (ABI) will be flown on the next generation of NOAA Geostationary Operational Environmental.

MIIDAPS Application to GSI for QC and Dynamic Emissivity in Passive Microwave Data Assimilation.

Considerations for the Physical Inversion of Cloudy Radiometric Satellite Observations.

Current modification gridinfo.f90Add new subroutine: getgridinfo_reg --- calculate the pressure in each sigma level and the virture temperature gridio.f90Add.

© Crown copyright Met Office Assimilating cloud affected infrared radiances at the Met Office Ed Pavelin and Roger Saunders, Met Office, Exeter.

A step toward operational use of AMSR-E horizontal polarized radiance in JMA global data assimilation system Masahiro Kazumori Numerical Prediction Division.

Impact of Blended MW-IR SST Analyses on NAVY Numerical Weather Prediction and Atmospheric Data Assimilation James Cummings, James Goerss, Nancy Baker Naval.

Incrementing moisture fields with satellite observations

Cloudy Radiance Assimilation in the NCEP Global Forecast System NOAA/NCEP/EMC 4 ESSIC, University of Maryland,

HYCOM/NCODA Variational Ocean Data Assimilation System James Cummings Naval Research Laboratory, Monterey, CA GODAE Ocean View III Meeting November.

Assimilation of radar observations in mesoscale models using approximate background error covariance matrices (2006 Madison Flood Case) 1.

Infrared Sounding Data in the GMAO Data Assimilation System JCSDA Infrared Sounding Working Group (ISWG) 30 January 2009.

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.

Status on Cloudy Radiance Data Assimilation in NCEP GSI 1 Min-Jeong Kim JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim 2.

Mesoscale Assimilation of Rain-Affected Observations Clark Amerault National Research Council Postdoctoral Associate - Naval Research Laboratory, Monterey,

Assessing ACCESS-G Global Data Assimilation Using Adjoint-Based Forecast Sensitivity to Observations Jin LEE 1 and Paul GREGORY 2 Centre for Australian.

© Crown copyright Met Office Assimilating infra-red sounder data over land John Eyre for Ed Pavelin Met Office, UK Acknowledgements: Brett Candy DAOS-WG,

Assimilation of GPM satellite radiance in improving hurricane forecasting Zhaoxia Pu and ChauLam (Chris) Yu Department of Atmospheric Sciences University.

June 20, 2005Workshop on Chemical data assimilation and data needs Data Assimilation Methods Experience from operational meteorological assimilation John.

The GSI Capability to Assimilate TRMM and GPM Hydrometeor Retrievals in HWRF Ting-Chi Wu a, Milija Zupanski a, Louie Grasso a, Paula Brown b, Chris Kummerow.

ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course Mar 2016.

Microwave Assimilation in Tropical Cyclones

Japan Meteorological Agency / Meteorological Research Institute

Summer 2014 Group Meeting August 14, 2014 Scott Sieron

5 July 2018 Nikki Privé R.M. Errico

Infrared Satellite Data Assimilation at NCAR

Assimilation of Global Positioning System Radio Occultation Observations Using an Ensemble Filter in Atmospheric Prediction Models Hui Liu, Jefferey Anderson,

Impact of Assimilating AMSU-A Radiances on forecasts of 2008 Atlantic TCs Initialized with a limited-area EnKF Zhiquan Liu, Craig Schwartz, Chris Snyder,

Presentation transcript:

Cloudy GOES-Imager assimilation with hydrometeor control variable Byoung-Joo JUNG NCAR/MMM Student presentation at JCSDA Summer Colloquium on Satellite Data Assimilation, July 27 – August

Background Ph.D. in Atmospheric Sciences, Yonsei University, Korea Adjoint sensitivity analysis Singular Vector for adaptive strategy in T-PARC field campaign (Tropical cyclone) Evaluation of observation impact with WRF 3DVAR, EnSRF, and FSO Researcher at Korea Institute of Atmospheric Prediction Systems TLM/ADM coding for global spectral-element dycore Representer-based variational system Vertical localization of radiance obs. in LETKF system

Channel 4 Channel 6 ObservationBackgroundInnovation (OmB)

GSI DA system with WRF background on CONUS domain CRTM with qc, qi, qr, qs, qg from model forecast 2 hydrometeors (qc, qi) added as control variable Static background error statistics for hydrometeors (from GEN_BE v2.0) Conventional obs + GOES-13/15 Imager channel 4 (10.7 um) & channel 6 (13.3 um) Relax first-guess check criteria Skip cloud screening in QC Non-Gaussian distribution for obs. error Multiple outer loop (re-linearization)  nonlinearity Approach *High spatial/temporal resolution

- Less weight for large innovation - Enlarge the obs. Error - Affect the conditioning itself Huber norm Gaussian Huber Iteratively Re-weighted Least Square (IRLS)

Channel 4 Channel 6 Error normalized innov. Huber via IRLS Gaussian OmB Histogram

Innovation (OmB)OmA(B/A) vs. O Cost Function Gradient Norm

Bias in hydrometeors is more pronounced. ₋Require much re-linearization for the cases O/B~[cloudy/clear or clear/cloudy] ₋Feature calibration and alignment (FCA) can help to reduce displacement error. How to determine the Huber parameter? (or other error model?) ₋Critical for balanced conditioning Hybrid approach can help the multi-variate relation between meteorological fields & cloud variables. ₋How effective will be the alpha-CV of ensembles on this? What can be the proper predictor for “cloudy” bias correction? Proper control variable for hydrometeors Accurate surface emissivity, cloud properties for various instruments in CRTM Inter-channel correlation can be affected by cloud. How to sustain the hydrometeor increments in the forecast? How to validate the impact of cloudy radiance assimilation? Many many questions