MODIS Winds Assimilation Impact Study with the CMC Operational Forecast System Réal Sarrazin Data Assimilation and Quality Control Canadian Meteorological.

Slides:



Advertisements
Similar presentations
Slide 1 October 2011 Verification for polar regions  Scores computed for polewards of 65°  NB proposed for CBS is polewards of 60°  Verification at.
Advertisements

1 Met Office, UK 2 Japan Meteorological Agency 3 Bureau of Meteorology, Australia Assimilation of data from AIRS for improved numerical weather prediction.
1 ATOVS and SSM/I assimilation at the Met Office Stephen English, Dave Jones, Andrew Smith, Fiona Hilton and Keith Whyte.
Huang et al: MTG-IRS OSSEMMT, June MTG-IRS OSSE on regional scales Xiang-Yu Huang, Hongli Wang, Yongsheng Chen and Xin Zhang National Center.
The Effects of Grid Nudging on Polar WRF Forecasts in Antarctica Daniel F. Steinhoff 1 and David H. Bromwich 1 1 Polar Meteorology Group, Byrd Polar Research.
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.
Recent developments in data assimilation for global deterministic NWP: EnVar vs. 3D-Var and 4D-Var Mark Buehner 1, Josée Morneau 2 and Cecilien Charette.
Ensemble-variational sea ice data assimilation Anna Shlyaeva, Mark Buehner, Alain Caya, Data Assimilation and Satellite Meteorology Research Jean-Francois.
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.
Impact study with observations assimilated over North America and the North Pacific Ocean at MSC Stéphane Laroche and Réal Sarrazin Environment Canada.
Five techniques for liquid water cloud detection and analysis using AMSU NameBrief description Data inputs Weng1= NESDIS day one method (Weng and Grody)
On Improving GFS Forecast Skills in the Southern Hemisphere: Ideas and Preliminary Results Fanglin Yang Andrew Collard, Russ Treadon, John Derber NCEP-EMC.
Stephanie Guedj Florence Rabier Vincent Guidard Benjamin Ménétrier Observation error estimation in a convective-scale NWP system.
Data assimilation and observing systems strategies Pierre Gauthier Data Assimilation and Satellite Meteorology Division Meteorological Service of Canada.
30 November December International Workshop on Advancement of Typhoon Track Forecast Technique 11 Observing system experiments using the operational.
Régis Borde Polar Winds EUMETRAIN Polar satellite week 2012 Régis Borde
Global and regional OSEs at JMA Ko KOIZUMI Numerical Prediction Division Japan Meteorological Agency.
Research and development on satellite data assimilation at the Canadian Meteorological Center L. Garand, S. K. Dutta, S. Heilliette, M. Buehner, and S.
Polar Winds from Satellite Imagers and Sounders MODIS Winds Group: Jeff Key 1, David Santek 2, Christopher Velden 2, Lars Peter Riishojgaard 3, Paul Menzel.
Environment Canada Canadian Meteorological Centre Environnement Canada Centre météorologique canadien Meteorological Service of Canada Status Report Seoul,
1 Hyperspectral Infrared Water Vapor Radiance Assimilation James Jung Cooperative Institute for Meteorological Satellite Studies Lars Peter Riishojgaard.
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.
MODIS Polar Winds in ECMWF’s Data Assimilation System: Long-term Performance and Recent Case Studies Lueder von Bremen, Niels Bormann and Jean-Noël Thépaut.
MODIS Winds Applications to NWP/Data Assimilation studies; Fairbanks, 10/08/2003 Lars Peter Riishojgaard Yan-Qiu Zhu Global Modeling and Assimilation Office.
Introduction of temperature observation of radio-sonde in place of geopotential height to the global three dimensional variational data assimilation system.
Global Impact of Satellite Data on NWP Courtesy of Tom Zapotocny Model is GFS. Satellite data include HIRS, AMSU, Quickscat, and all geo winds.
1 Developing Assimilation Techniques For Atmospheric Motion Vectors Derived via a New Nested Tracking Algorithm Derived for the GOES-R Advanced Baseline.
Real-time Generation of Winds and Sea Ice Motion from MODIS Jeff Key 1, Dave Santek 2, Chris Velden 2 1 Office of Research and Applications, NOAA/NESDIS,
25 th EWGLAM/10 th SRNWP Lisbon, Portugal 6-9 October 2003 Use of satellite data at Météo-France Élisabeth Gérard Météo-France/CNRM/GMAP/OBS, Toulouse,
An Examination Of Interesting Properties Regarding A Physics Ensemble 2012 WRF Users’ Workshop Nick P. Bassill June 28 th, 2012.
Status of improving the use of MODIS and AVHRR polar winds in the GDAS/GFS David Santek, Brett Hoover, Sharon Nebuda, James Jung Cooperative Institute.
Preliminary results from assimilation of GPS radio occultation data in WRF using an ensemble filter H. Liu, J. Anderson, B. Kuo, C. Snyder, A. Caya IMAGe.
NCAR April 1 st 2003 Mesoscale and Microscale Meteorology Data Assimilation in AMPS Dale Barker S. Rizvi, and M. Duda MMM Division, NCAR
Impact of Blended MW-IR SST Analyses on NAVY Numerical Weather Prediction and Atmospheric Data Assimilation James Cummings, James Goerss, Nancy Baker Naval.
1 3D-Var assimilation of CHAMP measurements at the Met Office Sean Healy, Adrian Jupp and Christian Marquardt.
Satellite Data Assimilation Activities at CIMSS for FY2003 Robert M. Aune Advanced Satellite Products Team NOAA/NESDIS/ORA/ARAD Cooperative Institute for.
Towards Assimilation of GOES Hourly winds in the NCEP Global Forecast System (GFS) Xiujuan Su, Jaime Daniels, John Derber, Yangrong Lin, Andy Bailey, Wayne.
1 Satellite Winds Superobbing Howard Berger Mary Forsythe John Eyre Sean Healy Image Courtesy of UW - CIMSS Hurricane Opal October 1995.
Xiujuan Su 1, John Derber 2, Jaime Daniel 3,Andrew Collard 1 1: IMSG, 2: EMC/NWS/NOAA, 3.NESDIS Assimilation of GOES hourly shortwave and visible AMVs.
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.
OSEs with HIRLAM and HARMONIE for EUCOS Nils Gustafsson, SMHI Sigurdur Thorsteinsson, IMO John de Vries, KNMI Roger Randriamampianina, met.no.
Experience in numerical forecast verification in the Hydrometeorological Centre of Russia N. P. Shakina, E. N. Skriptunova, A. R. Ivanova Zürich 2005 COSMO.
Observing System Experiment of MTSAT-1R Rapid Scan AMV using the JMA operational NWP system from 2011 to 2013 Koji Yamashita Japan Meteorological Agency.
Assimilation experiments with CHAMP GPS radio occultation measurements By S. B. HEALY and J.-N. THÉPAUT European Centre for Medium-Range Weather Forecasts,
Atmospheric Motion Vectors - CIMSS winds and products (
- Current status of COMS AMV in KMA/NMSC E.J. CHA, H.K. JEONG, E.H. SOHN, S.J. RYU Satellite Analysis Division National Meteorological Satellite Center.
Patricia Pauley and Nancy Baker Naval Research Laboratory Monterey, California Superobbing Satellite-Derived Winds in the U.S. Navy Global Data Assimilation.
Coordination Group for Meteorological Satellites - CGMS Korea Meteorological Administration, May 2015 Satellite Data Application in KMA’s NWP Systems Presented.
Météo-France status report Hervé Roquet (DP/CMS) & Bruno Lacroix (DPrévi/COMPAS) Météo-France status report Hervé Roquet (DP/CMS) & Bruno Lacroix (DPrévi/COMPAS)
Slide 1 Investigations on alternative interpretations of AMVs Kirsti Salonen and Niels Bormann 12 th International Winds Workshop, 19 th June 2014.
1 MODIS winds assimilation experiments and impact studies to date at the Met Office Howard Berger, Mary Forsythe, Met Office, Bracknell/Exeter, UK UW-CIMSS.
Slide 1© ECMWF Extended climate reanalysis ERA-Interim replacement Coupled with ocean REPROCESSED AMVs FOR THE NEXT ECMWF GLOBAL REANALYSES C.Peubey, N.
Impact of AMDAR/RS Modelling at the SAWS
Validation of SAFNWC/MSG PGE09 High Resolution Winds
Reprocessing of Atmospheric Motion Vector for JRA-3Q at JMA/MSC
Use of satellite winds at Deutscher Wetterdienst (DWD)
Assimilation of GOES-R Atmospheric Motion Vectors
Stéphane Laroche Judy St-James Iriola Mati Réal Sarrazin
Studying AMV Errors With The NWP SAF Monitoring Web Site
James Cotton, Mary Forsythe IWW14, Jeju City, South Korea.
GOES-16 AMV data evaluation and algorithm assessment
Lidia Cucurull, NCEP/JCSDA
Item Taking into account radiosonde position in verification
Comparison of different combinations of ensemble-based and variational data assimilation approaches for deterministic NWP Mark Buehner Data Assimilation.
AMV impact studies at the Met Office
The Polar Wind Product Suite
Impact of aircraft data in the MSC forecast systems
MODIS Polar Winds Forecast Impact (3DVAR) Northern Hemisphere
VALIDATION OF DUAL-MODE METOP AMVs
Project Team: Mark Buehner Cecilien Charette Bin He Peter Houtekamer
Presentation transcript:

MODIS Winds Assimilation Impact Study with the CMC Operational Forecast System Réal Sarrazin Data Assimilation and Quality Control Canadian Meteorological Centre Meteorological Service of Canada Workshop on Short-to-Medium Range Regional NWP in the Arctic and Antarctic Fairbanks, Alaska, October 8-10, 2003 Environnement Canada Environment Canada Centre météorologique canadien Canadian Meteorological Centre

Observations

Satellite Winds Selection Procedure Geostationary Satellite winds: GOES-P / W / E, METEOSAT-7 / 5 Time window: within 90 minutes from analysis time Levels: VI below 700 hPa, WV above 400 hPa, IR all levels Wind speed: > 2.5 m/s Angle: < 55 deg. Land Mask: over ocean, over land south of 20°N and above 400 hPa quality indicator above threshold value: METEOSAT QI > 85, GOES-W / E: RFF 700 hPa extra-tropics tropics horizontal thinning: 1.5 X 1.5 deg. (priority: obs time, QI) Quality Control Background check done before the horizontal thinning during the analysis, Variational QC with asymetric condition for the AMVs

No SATWINDS experiments, 17 June 2002 to 31 July 2002 RMS of forecast Wind speed errors at 250 hPa e02cntrl: control, e02nosw: no AMVs, e02noto: no TOVS, e02nohu: no HUMSAT, e02nosat: no satellites

No SATWINDS experiments, 17 June 2002 to 31 July 2002 anomaly correlation GZ 500 hPa

MODIS Winds Assimilation Impact Trial Data obtained by ftp from CIMSS in near real time Assimilation Period: 18 July 2003 to 23 August 2003, same cut-off time as for the operational observations, T+6 at 06/18UTC, T+9 at 00/12UTC Evaluation Period: 5 weeks from 20 July to 23 August 2003, 6-day Forecast twice per day at 00 and 12 UTC, from the analyses of the assimialtion cycle. verification scores against radiosonde observations and against analyses.

RFF quality indicator versus “observation minus first guess” statistics for High level MODIS winds, infrared channel (using the control first-guess). Plotted are the RMSVD, average wind speed, wind speed bias and number of observations per 0.01 bin. The average model wind speed is slightly higher in the Arctic But the RMSVD values are lower than the Antarctic A NRMSVD gives higher values for the Antarctic WV winds (not shown) exhibit similar characteristics Period: 20 July – 08 August 2003 Arctic above, Antarctic below

The characteristics of the statistics are similar to those of GOES winds Including the shift of the distribution toward higher RFF values (and lower mean wind speed) for lower levels winds IR channel, statistics stratified in 3 layers, Arctic Region

QI quality indicator versus “observation minus first guess” statistics for High level MODIS winds, infrared channel (using the control first-guess). Plotted are the RMSVD, average wind speed, wind speed bias and number of observations per 0.01 bin. RMSVD values are almost constant, values for the Antarctic are higher Average wind speed increases with increasing QI values so NRMSVD increases Arctic above, Antarctic below, Period: 20 July – 08 August 2003

Satellite Winds Selection Procedure MODIS winds: Terra / Aqua Time window: within 90 minutes from analysis time Levels: IR above 700, WV above 550 hPa Wind speed: > 2.5 m/s Land Mask: over ocean, over land above 400 hPa quality indicator above threshold value: RFF < horizontal density thinning: average of ~180 km (priority: obs time, qi) Quality Control Background check done before the horizontal thinning during the analysis, Variational QC with asymetric condition for the AMVs (no observation height reassignment)

Example of AMVs distribution for one analysis, 28 august 12UTC

MODIS Winds trial, period: 20 July 2003 to 23 August hour Forecasts Verification against radiosondes, N of 60°N, Arctic RMS: solid lines Bias: dashed lines Control: blue lines MODIS: red lines There is a small negative impact (increased rms) on the errors of the forecasts in the Arctic UU: east-west wind component UV: wind speed GZ: geopotential heights TT: temperature

MODIS Winds trial, period: 20 July 2003 to 23 August hour Forecasts Verification against radiosondes, S of 60°S, Antarctic RMS: solid lines Bias: dashed lines Control: blue lines MODIS: red lines There is a negative impact on the errors of the forecasts in the Antarctic UU: east-west wind component UV: wind speed GZ: geopotential heights TT: temperature

MODIS Winds trial, period: 20 July 2003 to 23 August hour Forecasts Verification against radiosondes, Northern Extratropics RMS: solid lines Bias: dashed lines Control: blue lines MODIS: red lines There is little impact on the errors of the forecasts in the Northern Hemisphere UU: east-west wind component UV: wind speed GZ: geopotential heights TT: temperature

MODIS Winds trial, period: 20 July 2003 to 23 August hour Forecasts Verification against radiosondes, Southern Extratropics RMS: solid lines Bias: dashed lines Control: blue lines MODIS: red lines There is a small negative impact on the errors of the forecasts in the Southern Hemisphere UU: east-west wind component UV: wind speed GZ: geopotential heights TT: temperature

MODIS Winds trial, period: 20 July 2003 to 23 August 2003 Forecasts Verification against analyses, Anomaly correlation, N of 60°N and S of 60°S

MODIS Winds trial, period: 20 July 2003 to 23 August 2003 Forecasts Verification against analyses, Anomaly correlation, extratropics

MODIS Winds trial, period: 20 July 2003 to 23 August 2003 Forecasts Verification against analyses, Wind Speed errors RMS, N of 60°N and S of 60°S

Conclusion For this first relatively short trial, Verifications of the forecasts against radiosondes show a small negative impact from the MODIS winds, especially for the Antarctic Verifications of the forecasts against analyses show mixed results generally negative but with some positive impacts on winds speeds forecast quality at mid levels Longer trials are necessary before implementation