© Crown copyright Met Office Report to 21st NAEDEX Meeting Roger Saunders, Met Office, Exeter.

Slides:



Advertisements
Similar presentations
© Crown copyright 2007 Impact studies with satellite observations at the Met Office John Eyre and Steve English Met Office, UK 4th WMO Workshop on "The.
Advertisements

ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) The Global Observing System Overview of data sources Data coverage Data.
© 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.
1 Met Office, UK 2 Japan Meteorological Agency 3 Bureau of Meteorology, Australia Assimilation of data from AIRS for improved numerical weather prediction.
EUMETSAT04 04/2004 © Crown copyright Use of EARS in Global and Regional NWP Models at the Met Office Brett Candy, Steve English, Roger Saunders and Amy.
1 00/XXXX © Crown copyright Use of radar data in modelling at the Met Office (UK) Bruce Macpherson Mesoscale Assimilation, NWP Met Office EWGLAM / COST-717.
© Crown copyright Met Office Impact experiments using the Met Office global and regional model Presented by Richard Dumelow to the WMO workshop, Geneva,
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.
© Crown copyright Met Office Instrumentation planned for MetOp-SG Bill Bell Satellite Radiance Assimilation Group Met Office.
1 ATOVS and SSM/I assimilation at the Met Office Stephen English, Dave Jones, Andrew Smith, Fiona Hilton and Keith Whyte.
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.
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.
Status of operational NWP system and satellite data utilization at JMA APSDEU-8 Montreal, Canada, October 10-12, 2007 Masahiro KAZUMORI Numerical Prediction.
Recent activities on utilization of microwave imager data in the JMA NWP system - Preparation for AMSR2 data assimilation - Masahiro Kazumori Japan Meteorological.
Francesca Marcucci, Lucio Torrisi with the contribution of Valeria Montesarchio, ISMAR-CNR CNMCA, National Meteorological Center,Italy First experiments.
Operational Global Model Plans John Derber. Timeline July 25, 2013: Completion of phase 1 WCOSS transition August 20, 2013: GDAS/GFS model/analysis upgrade.
1 Tropical cyclone (TC) trajectory and storm precipitation forecast improvement using SFOV AIRS soundings Jun Tim Schmit &, Hui Liu #, Jinlong Li.
Météo-France status Operational changes since the 22 th North America / Europe Data Exchange meeting and short term plans Jean-François MAHFOUF (and many.
Impact study with observations assimilated over North America and the North Pacific Ocean at MSC Stéphane Laroche and Réal Sarrazin Environment Canada.
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.
ECMWF NAEDEX 2012 – ECMWF Status Report – Stephen Engilsh ECMWF Status Report Stephen English ECMWF.
Lessons on Satellite Meteorology Part VII: Metop Introduction to Metop Instruments The sounders with focus on IASI The GRAS instrument The ASCAT scatterometer.
Presentation to N.America-Europe Data Exchange
© Crown copyright Met Office Plans for Met Office contribution to SMOS+STORM Evolution James Cotton & Pete Francis, Satellite Applications, Met Office,
Régis Borde Polar Winds EUMETRAIN Polar satellite week 2012 Régis Borde
Land Surface Analysis SAF: Contributions to NWP Isabel F. Trigo.
ECMWF WMO Workshop19-21 May 2008: ECMWF OSEs Slide 1 A summary of OSE and OSSE activities at ECMWF. Erik Andersson, Graeme Kelly, Jean-Noël Thépaut, Gabor.
Using satellite data to understand uncertainties in reanalyses: UERRA Richard Renshaw, Peter Jermey with thanks to Jörg Trentmann, Jennifer Lenhardt, Andrea.
Scatterometers at KNMI; Towards Increased Resolution Hans Bonekamp Marcos Portabella Isabel.
Weather forecasting by computer Michael Revell NIWA
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.
© Crown copyright Met Office Data Assimilation Developments at the Met Office Recent operational changes, and plans Andrew Lorenc, DAOS, Montreal, August.
Evaluation of impact of satellite radiance data within the hybrid variational/EnKF Rapid Refresh data assimilation system Haidao Lin Steve Weygandt Ming.
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.
Current and Future Use of Satellite Data in NWP at Environment Canada Satellite Direct Readout Conference 2011 Miami, USA David Bradley, Gilles Verner,
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.
WP 3: DATA ASSIMILATION SMHI/FMI Status report 3rd CARPE DIEM meeting, University of Essex, Colchester, 9-10 January 2003 Structure SMHI/FMI plans for.
Impact of FORMOSAT-3 GPS Data Assimilation on WRF model during 2007 Mei-yu season in Taiwan Shyuan-Ru Miaw, Pay-Liam Lin Department of Atmospheric Sciences.
1 NCEP Current Data Usage and Future Plans Dr. Bradley Ballish NCEP/NCO/PMB Presentation to JAG/ODAA October 2008 at OFCM “Where America’s Climate and.
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,
STATUS REPORT 19 th North America/Europe Data Exchange Meeting NOAA Silver Spring, MD May 3-5, 2006 Paul Poli (CNRM/GMAP) replacing Bruno Lacroix (DPrévi/COMPAS)
ECMWF reanalysis using GPS RO data Sean Healy Shinya Kobayashi, Saki Uppala, Mark Ringer and Mike Rennie.
NCAR April 1 st 2003 Mesoscale and Microscale Meteorology Data Assimilation in AMPS Dale Barker S. Rizvi, and M. Duda MMM Division, NCAR
Page 1 Developments in regional DA Oct 2007 © Crown copyright 2007 Mark Naylor, Bruce Macpherson, Richard Renshaw, Gareth Dow Data Assimilation and Ensembles,
IV WMO Impact Workshop 2008Alexander Cress Global impact studies at the Deutscher Wetterdienst (DWD) Alexander Cress, Reinhold Hess Detlef Pingel Andreas.
© Crown copyright Met Office Assimilating cloud affected infrared radiances at the Met Office Ed Pavelin and Roger Saunders, Met Office, Exeter.
Impact of Blended MW-IR SST Analyses on NAVY Numerical Weather Prediction and Atmospheric Data Assimilation James Cummings, James Goerss, Nancy Baker Naval.
MODIS Winds Assimilation Impact Study with the CMC Operational Forecast System Réal Sarrazin Data Assimilation and Quality Control Canadian Meteorological.
1 3D-Var assimilation of CHAMP measurements at the Met Office Sean Healy, Adrian Jupp and Christian Marquardt.
© Crown copyright Met Office Report to 22 nd NAEDEX Meeting Roger Saunders + many others, Met Office, Exeter.
The Joint Center for Satellite Data Assimilation: Science Workshop 2006 John Le Marshall Director, JCSDA.
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.
Use of high resolution global SST data in operational analysis and assimilation systems at the UK Met Office. Matt Martin, John Stark,
Station lists and bias corrections Jemma Davie, Colin Parrett, Richard Renshaw, Peter Jermey © Crown Copyright 2012 Source: Met Office© Crown copyright.
© Crown copyright Met Office Assimilating infra-red sounder data over land John Eyre for Ed Pavelin Met Office, UK Acknowledgements: Brett Candy DAOS-WG,
Recent Developments in assimilation of ATOVS at JMA 1.Introduction 2.1DVar preprocessor 3.Simple test for 3DVar radiance assimilation 4.Cycle experiments.
Coordination Group for Meteorological Satellites - CGMS Korea Meteorological Administration, May 2015 Satellite Data Application in KMA’s NWP Systems Presented.
Slide 1 Investigations on alternative interpretations of AMVs Kirsti Salonen and Niels Bormann 12 th International Winds Workshop, 19 th June 2014.
National Meteorological Satellite Center
Plans for Met Office contribution to SMOS+STORM Evolution
Winds in the Polar Regions from MODIS: Atmospheric Considerations
Introduction to ACCESS NWP
Stéphane Laroche Judy St-James Iriola Mati Réal Sarrazin
NWP Modelling systems at NCMRWF
Steps towards evaluating the cost-benefit of observing systems
James Cotton, Mary Forsythe IWW14, Jeju City, South Korea.
Why use NWP for GSICS? It is crucial for climate and very desirable for NWP that we understand the characteristics of satellite radiance biases Simultaneous.
Introduction to ACCESS NWP
Presentation transcript:

© Crown copyright Met Office Report to 21st NAEDEX Meeting Roger Saunders, Met Office, Exeter

© Crown copyright Met Office Met Office Operational Models : 2008  Global ~40km ~50Level  North Atlantic/Europe 12km ~50level  UK 4km(1.5km) ~50Level  Re-locatable Defence and Civilian  Ensemble (global & regional) at half horizontal resolution 25 members each  Data assimilation 4DVar, 6 hour window for global and regional models

© Crown copyright Met Office Planned NWP model changes ModelNow Global40km 25km 18km 50 levels70 levels 100 levels N At Eur12km8km 5km 50 levels70 levels 100 levels UK4km1.5km ~0.8km 50 levels70 levels 100 levels Specialist 0.25km 100 levels

© Crown copyright Met Office Supercomputing - NEC SX6, SX8 [2004-March 2009] IBM Power 5 & Power 6 [Nov > See Theoretical peak power of 16 billion calculations per second per processor

© Crown copyright Met Office IBM Power 5 & 6 Supercomputer Phased upgrade during x initial speed-up Phase 2 in 2011, further 3x increase on initial. Funded on ‘UK PLC’ (not Met Office) cost-benefits Funding from Met Office, Govt stakeholders and NERC contributions 1.2 MWatt 125 TFLOP (23 fold inc)

© Crown copyright Met Office How we get the data Global US data Local reception EUMETCASTGlobal Networks

© Crown copyright Met Office Observations assimilated Observation groupObservation Sub-groupItems usedDaily extracted% used in assimilation Ground-based vertical profiles TEMP PILOT PROFILER T, V, RH processed to model layer average As TEMP, but V only ,93, Satellite-based vertical profiles METOP-A NOAA-15/16/18, Aqua AIRS, IASI, HIRS, AMSU-A/B, MHS, DMSP-SSMIS Radio-occultation COSMIC, Champ, Grace, GRAS Radiances directly assimilated with channel selection dependent on surface instrument and cloudiness. Profiles of refractive index ATOVS:4,000,000 IASI: 324,00 AIRS:324,000 COSMIC: 1600 GRAS: 600 CHAMP+Grace: Aircraft Manual AIREPS Automated AMDARS T, V as reported with duplicate checking and blacklist ,000 17, 16 28, 2 Satellite atmospheric motion vectors GOES 11,12 BUFR Meteosat 7, 9 BUFR MTSAT BUFR MODIS, AVHRR polar AMVs High resolution IR winds IR, VIS and WV winds AVHRR AVHRR 8 Satellite-based surface winds DMSP-SSM/I-13 Seawinds ERS-2 scatt, METOP ASCAT In-house 1DVAR wind-speed retrieval NESDIS retrieval of ambiguous winds. Ambiguity removal in 4DVAR. 3,000,000 1,800,000 ERS-2 not incuded 1,500, Ground-based surface Land SYNOP SHIP Fixed Buoy Drifting BUOY GPS IWV Pressure only (processed to model surface),V,T,RH P,V,T,RH P,V,T P Total column water ,87,87,86 93,94,94,94 87,85, Cloud/Rain observations METEOSAT-9 SEVIRI and UK rain radar network Nimrod – MOPS cloud in NAE rain cloud 100

© Crown copyright Met Office Satellite delays May 2007 Aug 2008 Aqua degraded

© Crown copyright Met Office AIRS delays Mean Mode Min

© Crown copyright Met Office Recent changes to usage of data METOP ASCAT surface winds assimilated from Nov 07 METOP IASI radiances (~138 channels) assimilated Nov 07 AVHRR polar winds assimilated May 08 METOP GRAS (GPS RO) assimilated from July 08 Modified AMV observation errors to take account errors of height assignment New OSTIA SST and sea-ice analysis operational SST analysis uses, AVHRR, AATSR, ASMR-E, TMI, in situ Sea ice is based on OSI SAF product

© Crown copyright Met Office Regional ATOVS Retransmission System (RARS) forecast impact Forecast benefit of timely ATOVS data 2 experiments: All ATOVS: data assimilated regardless of arrival time RARS: ATOVS global + fast delivery data from RARS stations NHSH

© Crown copyright Met Office 500 hPa height. RMS difference between analyses with all ATOVS and operationally-available ATOVS ATOVS data missing cut-off would benefit N Pacific and S Hem. Regional ATOVS Retransmission System (RARS) forecast impact

© Crown copyright Met Office European METOP payload Launched Oct 2006 AMSU-A Assimilated January 2007 HIRS/MHS assimilated March 2007 New Sensors IASI ASCAT assimilated Nov 2007 GRAS GOME Under development

© Crown copyright Met Office Polar winds are derived in the overlap region (shown in white) between three successive orbits, by tracking clouds or WV features. Picture from Dave Santek AVHRR polar winds - introduction MODISAVHRR PlatformsTerra, AquaNOAA Metop ChannelIR, WVIR only Available since (NOAA platforms) EUMETSAT plan to produce winds from Metop AVHRR soon. Main disadvantage of AVHRR for wind generation is lack of WV channel.

© Crown copyright Met Office NOAA-18 IRTerra IR Similar quality to MODIS winds (slightly worse at high level in SH). Collocated observations compare well. AVHRR polar winds – comparisons with MODIS

© Crown copyright Met Office Started assimilating NOAA AVHRR winds operationally at the Met Office on 20 May Provides some coverage improvement. AVHRR polar winds – data coverage

© Crown copyright Met Office Impact fairly neutral (as expected when assimilated on top of MODIS polar winds). Main benefit at longer range in extra-tropics. good bad 26 day trial Verification versus analyses AVHRR polar winds – forecast impact

© Crown copyright Met Office MetOp IASI Red – Used (Sea/Land, Clear/MWcloud) Yellow – Used (Sea/Clear only) Blue – Used (1D-Var preprocessor only) Cyan – Rejected Green / Lime – Rejected water vapour channels Channel selection

© Crown copyright Met Office Flight B290 – comparison of observation with LBL simulation and operational O-B statistics for 10-40N

© Crown copyright Met Office Comparison of Met Office and ECMWF profiles for this observation location

© Crown copyright Met Office IASI impact Dec 07 vs Jun 07 June: +1.2 v Obs +0.8 v Anl December: v Obs v Anl December plots “upside down”!

© Crown copyright Met Office 24 May – 24 June 2007 Preferred configuration include water vapour channels obs errors in 4D-Var: 0.5K / 1K / 4K Met Office global NWP index v obs, v analysis +1.0 overall Compare with AIRS for same period v obs, v analysis overall normally see more impact from AIRS MetOp IASI impact trial results

© Crown copyright Met Office IASI Relative to other instruments Verified against observations IASI impact very similar to one AMSU/MHS Compare more channels with coverage in cloudy areas AIRS impact about half of IASI (agrees with other trials) Probably due to observation weighting Cloudy AIRS trial brings impact up to similar level as IASI or AMSU AMSU/MHS and HIRS are MetOp only

© Crown copyright Met Office Cloudy AIRS radiances In current assimilation of AIRS and IASI, cloud-affected obs are rejected only a small proportion of observations retained Moving towards assimilation of cloud- affected radiances simple cloudy RT models allow careful use of channels peaking above cloud Cloud top Weighting functions of channels peaking above cloud

© Crown copyright Met Office Cloudy AIRS radiances Impact of assimilating AIRS in cloudy areas twice as many observations assimilated observations assimilated in meteorologically active areas +1.0 points on Met Office global NWP index equivalent to doubling overall impact of AIRS NWP index change with cloudy AIRS assimilation

© Crown copyright Met Office Trial of new SEAWINDS product The new product results in improved tropical winds, particularly in ocean areas where there is significant rainfall.

© Crown copyright Met Office New SEAWINDS product

© Crown copyright Met Office WindSat wind vectors QuikScatWindSat WindSat-specific quality control developed In particular, low wind speeds rejected due to low information content Ambiguous wind vectors assimilated in similar manner to Quikscat

© Crown copyright Met Office Windsat wind vectors analysis increments and forecast impact QuikScat WindSat relative forecast impact 1-month trial, Aug 2005

© Crown copyright Met Office GPS radio occultation Met Office operational use Sep 2006First assimilation of CHAMP and GRACE-A (GFZ) refractivities Nov 2006 CHAMP and GRACE-A withdrawn – GFZ qc problems May COSMIC satellites assimilated Nov  6 COSMIC satellites Apr 2008 Increase vertical range: 4-27 km  0-40 km Jul 2008Reintroduced CHAMP and GRACE-A Jul 2008Added METOP GRAS

© Crown copyright Met Office Typical coverage during 6 hour period Coverage during a 6 hr cycle

© Crown copyright Met Office COSMIC radio occultation data forecast temperature v sondes S.Hem., Dec 2006, 6 COSMIC v no GPS-RO 24h temperature forecast 200 hPa temperature Mean error RMS error bias rms K 0 42K042K h K

© Crown copyright Met Office Groundbased GPS coverage Observations from E-GVAP near real- time GPS network very high time resolution - often several per hour - potentially useful in 4D-Var At the Met Office: assimilating ZTD into regional (12 km) and UK (4 km) models assimilating one per hour in 4D-Var small positive impacts on cloud, surface temperature, visibility and precipitation operational since March 2007

© Crown copyright Met Office Using IMS data to implement a snow analysis The NESDIS IMS NH snow cover product has been used at the Met Office in combination with snow amount information from the global model background to create a daily snow analysis. Fractional cover on model grid derived from IMS snow cover Presence of snow compared with model first guess Snow removed from or added to model snow field where there is disagreement as to presence of snow Snow amount to add determined using empirical relationship between fractional cover and snow water equivalent S = ( - ln (1 - f c ) ) / D S = snow-water equivalent (1 mm snow-water equivalent ≈ 1 kgm-2 snow areal density) D = masking depth of vegetation fc = fractional snow cover Assimilation trials yield neutral impacts on forecast skill Improvements are seen in analysed snow presence, verified against ground stations Some evidence of improvements in surface/low level T and RH, especially where snow is predominantly removed

© Crown copyright Met Office Snow analysis during assimilation trials

© Crown copyright Met Office 1/12/06 Problems due to IMS data time lag

© Crown copyright Met Office Work in progress….. Add more IASI channels (incl water vapour channels) Assimilation of MSG clear sky radiances and cloud information Extend use of SSMIS to window/wv channels NESDIS snow cover becomes operational Assimilation of WINDSAT winds Longer term…. AMSR-E precipitation Scatterometer soil moisture data assimilated ADM doppler lidar winds preparations underway NPP

© Crown copyright Met Office Questions and answers