Use of high resolution global SST data in operational analysis and assimilation systems at the UK Met Office. Matt Martin, John Stark, Craig Donlon

Contents Description of the OSTIA system: Overview of the OSTIA system Bias correction Use in NWP OSTIA: Operational Sea surface Temperature and sea Ice Analysis A global, high resolution SST sea ice analysis system. Use of SST in FOAM open ocean forecasting system: Overview of FOAM Previous operational use of SST Impact of assimilating GHRSST data in FOAM. FOAM: Forecasting Ocean Assimilation Model A nested set of high resolution OGCMs with data assimilation

Operational SST & Sea Ice Analysis Daily 1/20° (~5.6km) global SST analysis. –Analysis of the ‘foundation’ SST [pre- dawn or below the diurnal warm layer]. Blend of data sources, using satellite (microwave & IR) and in situ data. –Using many GHRSST data products. Now running daily, operationally (since mid December 2006). Using optimal interpolation, persistence based. Uses sea ice analysis performed by EUMETSAT OSI-SAF (met.no / DMI). Data sent to GDAC and LTSRF. Sample analysis for 19 Apr 2007

Daily SST Basic Architecture Quality Control -Background check -Diurnal warming flagging Satellite Bias Estimation -Find match-ups -O.I. estimate. O.I. Analysis -Using 2 background error scales -Spatially varying backgrd. errors Data Sources Anomaly Persistence Forecast With relaxation to climatology Met. D.B.

Quality Control and Preprocessing Reject all observations with non-zero reject flag in GHRSST data. Reject ‘daytime’ observations with ‘low’ wind speed to reduce diurnally warmed data. –Daytime : whether the sun is above the horizon. –Low wind speed : < 6 m/s. Adjust skin temperature measurements to ‘bulk’ (includes AATSR) Perform a background check against previous analysis (Bayesian) –Uses background error and observation error to estimate probability of gross error (PGE). –If PGE is large the observation is rejected. Assigns an error estimate using the error value supplied in GHRSST product. The bias value supplied with the data is subtracted from observation value. Ingestion of moored and drifting buoy & ship data via GTS. –Assigns error estimates to data based on type and station code. Background errors added from static 2D fields.

OSTIA : Source Data Sensor (Platform) TypeResolutionData SourceCoverageSubsa- mpling AMSR-E (Aqua) Microwave~25km (swath) Remote Sensing Systems (ssmi.com). L2P Format. Global (~1 million/day) 2222 TMI (TRMM)Microwave~25km (swath) Remote Sensing Systems (ssmi.com). L2P Format. Tropics (~0.5 million/day) None AATSR (Envisat)Infra-red~1km (swath)Medspiration RDAC, L2P Format Global (~2 million/day) 3333 AVHRR -LAC (NOAA 17 & 18) Infra-red~1/10° (Grid) Medspiration RDAC, L2P Format (NAR) & NAVOCEANO-JPL North Atlantic (grid) (~0.5 million/day) 3333 AVHRR -GAC (NOAA 17 & 18) Infra-red~1km (Swath) NAVOCEANO-JPL(~2 million/day)None SEVIRI (MSG1)Infra-red0.1° (Gridded)Medspiration RDAC, L2P Format Atlantic sector (~2 million/day) None In-SituShips, drifting and moored buoys. In-situMet Office MetDB (GTS) Global (~25,000 /day) None Sea IceSSMI, Gridded10km, Gridded. OSI-SAF (Met.no)Global. None.

Satellite Bias correction method Sample data from 18 April Find matchups (<25km, 12 hours) Create bias analysis, and remove bias from observations for use in SST analysis.

Verification of bias correction: Mediterranean. Shows observation minus background (previous day). ~250 in situ obs / day. Mean : and -0.03

Verification Comparison against buoys & ships using the previous days analysis (obs minus background) shows global RMS errors are less than 0.6K and the bias is less than 0.1K RMS Mean

Use of OSTIA in NWP Begun small trials using OSTIA SST in Met Office NWP models. Case study using a 5-day forecast of Hurricane Rita (20 – 25 September ’05) shows promise as the low pressure centre is shifted toward the observed track using OSTIA rather than NWP SSTs. Interest from ECMWF as potential users. Observed Track Surface Pressure NWP (fill) OSTIA (contours ) Pressure Difference (OSTIA – NWP)

Real-time data, including Argo, altimeter SSH, SST and sea-ice concentration Obs QC & processing Analysis Forecast to T+120 NWP 6 hourly fluxes Automatic verification T+24 forecast used in QC Product dissemination The FOAM system FOAM – Forecasting Ocean Assimilation Model 1º Global1/3º N. Atlantic and Arctic1/9º North Atlantic

SST in FOAM Current use of SST in operational FOAM –Until recently, used 2.5˚ AVHRR gridded product from NESDIS. –Will move to use GHRSST data. –(As an interim measure, the current operational system assimilates 50km/100km resolution product.) FOAM uses the same analysis scheme as OSTIA –OI type analysis – but currently no bias correction for SST. –Applies the 2-D surface analysis to all model depths within the mixed layer –Uses Incremental Analysis Updates to update the fields during the forecast.

SST in FOAM – impact of GHRSST data Two runs of FOAM up to 1/9˚ north Atlantic model from 26 January 2005 to 5 May 2005, assimilating: 1.2.5˚ AVHRR product (control) 2.all the GHRSST data assimilated by OSTIA (GHRSST) Both runs also assimilated all other data types (SSH, T and S profiles, in situ SST)

Impact of GHRSST in FOAM 1/9˚ - comparison with MODIS data Reduction in RMS errors in all areas for nighttime and daytime Significant nighttime bias may be due to poor cloud clearing in the night time data – no visible channels available to determine the cloud.

Impact of GHRSST in FOAM 1/9˚ - comparison with profile data Temperature Salinity GHRSST data reduces the RMS errors over the top 600m of ocean, with little change in the bias Near-surface salinity errors increased, particularly in NW Atlantic Reason for this is unclear at present – impact on stability?

Summary OSTIA is a new high resolution SST analysis building on the outputs from GHRSST-PP: –OI analysis with bias correction and persistence based forecast. –Trials into the use of OSTIA in the NWP system at the Met Office are underway. –Case studies into the impact of OSTIA on hurricane prediction in the Met Office NWP system show improvement. –Plans for reanalysis using Pathfinder (AVHRR and (A)ATSR) back to 1985 – will be available in Impact of high resolution SST data on FOAM analyses: –More high resolution information in analysis, particularly useful in areas of high variability such as the Gulf Stream –Overall reduction in RMS errors in SST –Reduction in RMS error in temperature in top 600m of ocean –Slight increase in salinity errors needs to be investigated –Plan to implement use of GHRSST data in the operational FOAM system by 2008.

Questions?

AATSR data (swath) ~1km Observation types : Footprint. Developed observation operators to represent full range of observation footprints. Simple ‘top hat’ weighting used for AMSR-E / TMI. Analysis Grid AMSR-E / TMI data (swath) Seviri data (gridded) X marks centre of observation ~25-50km~11km (1/10°) AVHRR-GAC data (swath) ~9km ~5.6km (0.05°)

Dynamic Bias Correction. -2K-1K0K+1K+2K The bias correction is updated daily for each satellite observation type. Showing AVHRR, AMSR-E, SEVIRI & TMI bias correction for March – April 2007