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.

Slides:



Advertisements
Similar presentations
Numerical Weather Prediction (Met DA) The Analysis of Satellite Data lecture 2 Tony McNally ECMWF.
Advertisements

Numerical Weather Prediction Readiness for NPP And JPSS Data Assimilation Experiments for CrIS and ATMS Kevin Garrett 1, Sid Boukabara 2, James Jung 3,
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.
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,
Slide 1 IPWG, Beijing, October 2008 Slide 1 Assimilation of rain and cloud-affected microwave radiances at ECMWF Alan Geer, Peter Bauer, Philippe.
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,
1 Impact study of AMSR-E radiances in NCEP Global Data Assimilation System Masahiro Kazumori (1) Q. Liu (2), R. Treadon (1), J. C. Derber (1), F. Weng.
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.
Recent Progress on High Impact Weather Forecast with GOES ‐ R and Advanced IR Soundings Jun Li 1, Jinlong Li 1, Jing Zheng 1, Tim Schmit 2, and Hui Liu.
Sean P.F. Casey 1,2,3,4, Lars Peter Riishojgaard 2,3, Michiko Masutani 3,5, Jack Woollen 3,5, Tong Zhu 3,4 and Robert Atlas 6 1 Cooperative Institute for.
ECMWF – 1© European Centre for Medium-Range Weather Forecasts Developments in the use of AMSU-A, ATMS and HIRS data at ECMWF Heather Lawrence, first-year.
Motivation Many GOES products are not directly used in NWP but may help in diagnosing problems in forecasted fields. One example is the GOES cloud classification.
Recent activities on utilization of microwave imager data in the JMA NWP system - Preparation for AMSR2 data assimilation - Masahiro Kazumori Japan Meteorological.
Towards Utilizing All-Sky Microwave Radiance Data in GEOS-5 Atmospheric Data Assimilation System Development of Observing System Simulation Experiments.
NASA/GMAO Activities in Support of JCSDA S. Akella, A. da Silva, C. Draper, R. Errico, D. Holdaway, R. Mahajan, N. Prive, B. Putman, R. Riechle, M. Sienkiewicz,
Assimilation of GOES Hourly and Meteosat winds in the NCEP Global Forecast System (GFS) Assimilation of GOES Hourly and Meteosat winds in the NCEP Global.
MIIDAPS Status – 13 th JCSDA Technical Review and Science Workshop, College Park, MD Quality Control-Consistent algorithm for all sensors to determine.
Five techniques for liquid water cloud detection and analysis using AMSU NameBrief description Data inputs Weng1= NESDIS day one method (Weng and Grody)
Slide 1 EUMETSAT Fellow Day, 9 March 2015 Observation Errors for AMSU-A and a first look at the FY-3C MWHS-2 instrument Heather Lawrence, second-year EUMETSAT.
On Improving GFS Forecast Skills in the Southern Hemisphere: Ideas and Preliminary Results Fanglin Yang Andrew Collard, Russ Treadon, John Derber NCEP-EMC.
Simulation of Observation Simulation of Conventional Observations Jack Woollen (NCEP/EMC) Considerations Data distribution depends on atmospheric conditions.
A NON-RAINING 1DVAR RETRIEVAL FOR GMI DAVID DUNCAN JCSDA COLLOQUIUM 7/30/15.
Advanced Baseline Imager (ABI) will be flown on the next generation of NOAA Geostationary Operational Environmental Satellite (GOES)-R platform. The sensor.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 POES Microwave Products Presented.
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),
Global Observing System Simulation Experiments (Global OSSEs) How It Works Nature Run 13-month uninterrupted forecast produces alternative atmosphere.
P1.85 DEVELOPMENT OF SIMULATED GOES PRODUCTS FOR GFS AND NAM Hui-Ya Chuang and Brad Ferrier Environmental Modeling Center, NCEP, Washington DC Introduction.
11 Background Error Daryl T. Kleist* National Monsoon Mission Scoping Workshop IITM, Pune, India April 2011.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Infrared Temperature and.
1 Scott Zaccheo AER, Inc ASCENDS End-to-End System Performance Assessment: Analysis of Atmospheric State Vector Variability.
1 Hyperspectral Infrared Water Vapor Radiance Assimilation James Jung Cooperative Institute for Meteorological Satellite Studies Lars Peter Riishojgaard.
Hyperspectral Infrared Alone Cloudy Sounding Algorithm Development Objective and Summary To prepare for the synergistic use of data from the high-temporal.
Slide 1 VAISALA Award Lecture Characterising the FY-3A Microwave Temperature Sounder Using the ECMWF Model Qifeng Lu, William Bell, Peter Bauer, Niels.
AIRS Radiance and Geophysical Products: Methodology and Validation Mitch Goldberg, Larry McMillin NOAA/NESDIS Walter Wolf, Lihang Zhou, Yanni Qu and M.
Application of COSMIC refractivity in Improving Tropical Analyses and Forecasts H. Liu, J. Anderson, B. Kuo, C. Snyder, and Y. Chen NCAR IMAGe/COSMIC/MMM.
A Generalized Approach to Microwave Satellite Data Assimilation Quality Control and Preprocessing Kevin Garrett 1 and Sid Boukabara 2 11 th JCSDA Science.
Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration Recent Advances towards the Assimilation.
AIRS, HIRS and CSBT Experiments Plus Other FY05/06 Results by James A. Jung Tom H. Zapotocny and John Le Marshall Cooperative Institute for Meteorological.
The Hyperspectral Environmental Suite (HES) and Advanced Baseline Imager (ABI) will be flown on the next generation of NOAA Geostationary Operational Environmental.
Challenges and Strategies for Combined Active/Passive Precipitation Retrievals S. Joseph Munchak 1, W. S. Olson 1,2, M. Grecu 1,3 1: NASA Goddard Space.
MIIDAPS Application to GSI for QC and Dynamic Emissivity in Passive Microwave Data Assimilation.
, 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.
Considerations for the Physical Inversion of Cloudy Radiometric Satellite Observations.
Cloudy GOES-Imager assimilation with hydrometeor control variable Byoung-Joo JUNG NCAR/MMM Student presentation at JCSDA Summer Colloquium on Satellite.
Recent development of all-sky radiance assimilation at JMA Kozo Okamoto, Masahiro Kazumori Japan Meteorological Agency (JMA) The 3 rd Joint JCSDA-ECMWF.
Applications of ATMS/AMSU Humidity Sounders for Hurricane Study Xiaolei Zou 1, Qi Shi 1, Zhengkun Qin 1 and Fuzhong Weng 2 1 Department of Earth, Ocean.
A step toward operational use of AMSR-E horizontal polarized radiance in JMA global data assimilation system Masahiro Kazumori Numerical Prediction Division.
Cloudy Radiance Assimilation in the NCEP Global Forecast System NOAA/NCEP/EMC 4 ESSIC, University of Maryland,
A New Ocean Suite Algorithm for AMSR2 David I. Duncan September 16 th, 2015 AMSR Science Team Meeting Huntsville, AL.
Overview of Community Radiative Transfer Model (CRTM) Fuzhong Weng, Yong Chen and Min-Jeong Kim NOAA/NESDIS/Center for Satellite Applications and Research.
Infrared Sounding Data in the GMAO Data Assimilation System JCSDA Infrared Sounding Working Group (ISWG) 30 January 2009.
NCEP Assessment of ATMS Radiances Andrew Collard 1, John Derber 2 and Russ Treadon 2 1 IMSG at NOAA/NCEP/EMC 2 NOAA/NCEP/EMC 1NPP ATMS SDR Product Review13th.
1 Moisture Profile Retrievals from Satellite Microwave Sounders for Weather Analysis Over Land and Ocean John M. Forsythe, Stanley Q. Kidder*, Andrew S.
3 rd JCSDA Science Workshop Assimilation of Infrared and Microwave Window Channels over Land Benjamin Ruston and Nancy L. Baker In collaboration with:
© 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
College Park, Maryland, USA
Initialization of Numerical Forecast Models with Satellite data
Radiometer channel optimization for precipitation remote sensing
Assimilation of Global Positioning System Radio Occultation Observations Using an Ensemble Filter in Atmospheric Prediction Models Hui Liu, Jefferey Anderson,
The impact of ocean surface and atmosphere on TOA mircowave radiance
Presentation transcript:

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 Fuzhong Weng, 3 Emily Liu, 4 Will McCarty, 3 Yanqiu Zhu, 3 John Derber, and 3 Andrew Collard 1 Cooperative Institute for the Research in Atmosphere (CIRA), Colorado State Univ. 2 NOAA/NESDIS Center for Satellite Data and Application (STAR) 3 NOAA/NWS National Centers for Environmental Prediction (NCEP) 4 NASA/GSFC Global Modeling and Assimilation Office (GMAO)

Outline 1. Motivation 2. Progress made to date 3. Assessments from preliminary results 4. Current Issues 5. Summary 6. Future Work

Motivation & Objective JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim Cloud or precipitation indicates that some dynamically important weather is occurring. Subsequent forecasts are often sensitive to initial conditions in regions with cloud and precipitation occurrence. ( clwp < kg/m 2 ) ( QC passed in GSI) Hurricane Igor (09/19/2010)

Motivation & Objective Analysis increments from current operational GSI We might be able to improve cloud analysis by assimilating cloudy radiance data. ZERO increments for clouds in the current GSI JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim

Which Instruments ? AMSU-A cloudy radiance data are being tested. Microwave imagers such as AMSR-E and TMI will be tested afterwards. Cloud type? Cloudy radiance data in the region with non-precipitating clouds over the ocean are currently included in addition to the data already assimilated in the operational GSI system. Global analysis or regional analysis? Cloudy radiance data assimilation are being tested only for global GSI analysis for now. Similar methodologies will be tested in NCEP WRF NMM and HWRF cloud analyses in the future. Framework AMSU-A LandOcean latitude < -60 Slatitude > -60 S Clear sky radiance DA (Operational GSI) All sky (clear+cloudy) radiance DA (New GSI) to avoid sea ice contamination JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim

Quick View of the Progress to Date Operational GSI (Clear Sky radiance DA) New GSI (All-sky radiance DA) (1) Forward operator & First guess fields Do not include cloudInclude clouds for Tb and jacobians (2) AMSU-A radiance Observations Clear sky over land and ocean + Cloudy sky over the ocean (3) Observation errorStatistics based in clear sky conditions Statistics based in clear and cloudy sky conditions (4) Control variableT, q, ozone profiles, sfc P, u, v Not including cloud water T, q, ozone profiles, sfc P, u, v + Cloud water profile (5) Background error covarianceT, q, ozone profiles, sfc P, u, v Using NMC method T, q, ozone profiles, sfc P, u, v + cloud water Using NMC method (6) Quality control Screen out cloudy data Gross check: Keep cloudy data unless cloud liquid water path > 0.5kg/m 2 Gross check: JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim

Progress (1) : Set-up Observation Operator CH GHz CH GHz AMSU-A Observed Tb First-Guess Tb CH GHz CH GHz Much warmer than FG Scattering signal in observations.  Precipitation + ice clouds JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim

8 Water Vapor Liquid Cloud Ice Cloud CRTM Computed Jacobians Progress (2) : Including Tb Sensitivity to CW JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim

Progress (3): Understanding O-F behaviors in cloudy conditions Retrieved Cloud Liquid Water Path (CLWP) Non-Precipitating Cloudy Conditions Obs Tbs with CLWP > 0.5kg/m2 screened out

All-Sky GSI clear sky for all surface + nonprecipitating cloudy sky over the ocean Original GSI clear sky over the ocean Histogram: Observed Tbs - First guess Tbs Progress (3): Understanding O-F behaviors in cloudy conditions JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim

Obs: Cloudy sky Model: Clear Obs: Clear sky Model: Cloudy Obs error function of Obs cloud Large obs error (Small weight) Small obs error (Large weight) Dry model atmosphere Obs error function of Model cloud Small obs error (Large weight) Large obs error (Small weight) Moisten model atmosphere Method learned from Geer et al. ECMWF Progress (4) : New Observation Errors function of observed cloud or model cloud ? JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim

Mean of Tb Departure JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim Progress (4) : New Observation Errors JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim All statistics from data of 7 days

8K8K13K 6.5K 9.5K Standard Deviation of Tb Departure JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim Progress (4) : New Observation Errors JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim All statistics from data of 7 days

For clear and non-precipitating cloudy sky over the ocean CLWP = 0.5*(obs clwp + model clwp) A i = clear sky obs error for each channel(i) B i = cloudy sky obs error for each channel(i) If(CLWP.lt. 0.05) then Obs_error i = A i else if (CLWP.ge and..lt. 0.25) then Obs_error i = A i + (CLD-0.05)*(B i -A i )/( ) else if (CLWP.ge and. lt. 0.5) then Obs_error i = A i + (CLD-0.25)*(A i - B i )/( ) else Obs_error i = B i endif JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim Progress (4) : New Observation Errors JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim

Progress (5): New Quality Control Methods 1. Screening cloud affected AMSU-A data Criteria:(1) scattering index (using ch1, 2, and 15 + ch6 Tb departure) (2) retrieved clwp + ch4 Tb departure  New: Screening retrieved averaged CLWP > 0.5kg/m 2 over the ocean 2. Topography effect: for observations at Zsfc > 2km, observation errors get increased. 4. Transmittance at the top of the model less than 1 : inflating observation error 3. Too much sensitivity to sfc temperature/sfc emissivity: inflating observation error 4. New gross check for all-sky(clear and cloudy) radiance data  ich : New observation error (I.e. function of averaged CLWP) JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim

- Cloud water mixing ratio at each layer is additional control variable - Background error covariance for clouds are from NMC method. Large standard deviations in the region of convections near tropics and midlatitude frontal systems Not much horizontal correlation for cloud Larger correlation in vertical than in horizontal Progress (6): Adding Cloud Control Variable & Cloud Background Error Covariance for Radiance DA JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim Daryl Kleist (EMC) has set up for the current BK error covariance using NMC method. Will McCarty (NASA/GMAO) will improve BK error covariance for cloud related control variables. JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim latitude

Assessment: Cloud Analysis Increments Clouds have been actively assimilated. Cloud analysis fields got improved and closer to the observations (retrieved clouds). Retrieved CLWP (observations)First-guess CLWP Analysis CLWP CLWP Analysis Increment

Assessment : FG Tb departures vs. Analysis Tb departures AMSU-A Something going on with channel 15.. O-F O-A O-F O-A Final iteration First iteration

Assessment : MHS FG departures vs. Analysis departures Mean of Tb differences JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim All-sky Clear-sky All-sky Clear-sky

clear sky + nonprecipitating cloudy sky over the ocean JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim Current Issue (1) : Bias Correction AMSU-A O-F vs. O-A O-A Tb departure distribution : titled toward negative..

Observation errors depend on retrieved cloud liquid water paths and no retrieval algorithms can be perfect. Separating precipitating clouds and non-precipitating clouds cannot be perfect. Current Issue (2) : Retrieval Algorithm for CLWP titled toward negative.. JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim

Cloud water error statistics show “non-Gaussian” distribution. Current Issue (3): Non-Gaussian BK Error Distribution Approach I Single control variable: total moisture, q tot (=q+q c )) UK Met Office has been using q tot as a single moisture control variable Emily Liu has been working to set up with q tot in GSI. (Next talk) Approach 2 Keep two separate control variables for water vapor and clouds However, develop a different form of cloud water related control variable of which error distribution is more Gaussian than q c ECMWF’s current efforts are toward this approach. This will be tested for comparisons with Approach 1.

Summary JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim 1. There has been great progress in assimilating AMSU-A cloudy radiance data in NCEP Global Data Assimilation System (GDAS). 2. New observation errors and quality control methods, which are applicable for clear and cloudy sky conditions, have been developed. 3. For now, cloud water mixing ratio for each layer is used as a control variable and background error covariance from NMC method has been used. 4. Assessments based on statistics for AMSU-A and MHS sounders demonstrated that more data in cloud sensitive channels are being assimilated by including cloudy radiance data and brightness temperature departures got reduced for those channels. 5. Preliminary results from case studies show that cloud fields are now being actively assimilated and cloud analysis fields get much closer to the retrieved values. Assessments for other fields and forecast skill scores are in progress.

Improving bias corrections by testing different predictors. Adding observations in precipitating cloudy conditions to the current cloudy radiance data assimilation experiments Testing impacts on GFS model forecasts and HWRF model forecasts skill scores. Comparisons of static background error covariance with ensemble background error covariance Ongoing Work & Future Plans JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim