utilization of satellite data at the met office (uk)

© Crown copyright Met Office

Utilization of satellite data at the Met Office (UK)

Simon J. KeoghWMO ET-SUP, Geneva, 27-30 May 2013.

Contents

• Numerical Weather Prediction

• Imagery for Forecasting Applications

• Ocean Forecasting Applications

• Climate Service Applications

• Summary


NUMERICAL WEATHER PREDICTION (NWP)


33km ensemble

1.5km modelUp to 36hr f/c3-hourly update

4.4km modelUp to 120hr f/c

2.2km ensembleUp to 36hr f/c6-hourly update

Production NWP (end FY2013-14)

12km NAE18km MOGREPS-R

17km modelUp to 144hr f/c6-hourly update

33km ensembleUp to 3day f/c6-hourly update

60km coupled modelUp to 6 monthsDaily lagged ensemble

Up to 120hr f/c6-hourly update

Dec 12• MetOp-B GRAS

Jan 13• Correlated observation errors for IASI � reduced obs error variances• Variable obs errors for ATOVS (depending on scan angle, surface type, …) • OSCAT winds - from scatterometer on India's Oceansat-2• Radio occultation observations from new satellite, CNOFS

Recent upgrades in the use of satellite data in NWP


• Radio occultation observations from new satellite, CNOFS• Revised radio occultation observation errors• Revised AMV thinning• Clear-sky radiances from GOES-15• MetOp-B ATOVS (NB: We started receiving NRT Metop-B AMSU/MHS data on 1st Nov

2012 following launch only a few weeks earlier on 17th Sept 2012!)

Feb 13• MetOp-B IASI

April 13• MetOp-B ASCAT (The final MetOp-B instrument to be utilised for us)• Suomi NPP ATMS & CrIS• MODIS Aerosol Optical Depth

Forecast impacts in component tests:full observing system plus ATMS and CrIS

Verification versus OBS

Verificationversus ANALYSIS

+ATMS

+CrIS


Consistently +ve impacts from ATMS and CrIS, various configurations tested

+CrIS

+ATMS +CrIS

~+0.4

Satellite data used in NWP (1)May 2013

Observation type Satellites NWP models *

AMSU/MHS radiances 4 NOAA + Metop G, R

HIRS clear radiances 2 NOAA + Metop G, R

IASI and AIRS clear+cloudy radiances Metop + Aqua G, R

ATMS & CrIS radiances Suomi NPP G

SSMIS radiances 1 DMSP G, R

Geo imager clear IR radiances MSG, GOES, MTSAT2 G, R, UK

GPS RO bending angles 5 COSMIC, Metop/GRAS, GRACE-A, TerraSAR-X, CNOFS

G, R

GPS ZTDs ~350 European stations G, R, UK

* G=Global, R=regional=N.Atlantic+Europe, UK=UK area

Satellite data used in NWP (2)May 2013

Observation type Satellites NWP models *

AMVs – geo 5 geo satellites G, R, UK

AMVs – MODIS and AVHRR Aqua, Terra, NOAA, Metop G, R

Scatterometers: sea-surface winds Metop/ASCAT, Oceansat-2/OSCAT

G, R, UKOceansat-2/OSCAT

MW imager sea-surface winds: Windsat Coriolis G, R

SEVIRI cloud height/amount MSG R, UK

SSTs: AVHRR, AMSR-E… NOAA, Metop, Aqua G, R, UK

Soil moisture: ASCAT Metop G, R, UK

Sea ice: SSM/I, SSMIS DMSP G, R

Snow cover various G, R

* G=Global, R=regional=N.Atlantic+Europe, UK=UK area

IMAGERY FOR WEATHER FORECASTING APPLICATIONS


Recent updates to our imagery for forecasters

Existing products include:

• Cloud top pressure/height, Fog, Ice, Water Vapour, IR, Visible from SEVIRI, MODIS, AVHRR and imagers on MTSAT and GOES

• Lightning imagery

New imagery products for forecasters:• Volcanic ash products via optimal estimation

• Ash cloud-top pressure, ash column amount, particle size• Imagery from VIIRS on Suomi-NPP• Imagery from FY-3 series satellites


Utilization of imagery

• Internal website (Showcase) to show our new imagery developments. This is the start of the “imagery lifecycle”

• Forecaster Desktop (SWIFT) allows forecasters to • Forecaster Desktop (SWIFT) allows forecasters to overlay imagery on other types of geospatial information (e.g. Forecast data, conventional observations, road/rail maps etc). Only the most useful satellite imagery will be imported into this tool due to the need to manage data volumes.


Dust RGB Ash(-top)

heightImproved detection

1D-Var scheme developed to retrieve quantitative volcanic ash information from SEVIRI dataUses channels at 10.8 µm, 12.0 µm and 13.4 µm


Ash size

Ash column loading

Francis, P., M. Cooke and R. Saunders (2012), Journal of Geophysical Research, 117.

Lightning Imagery


Cloud data is from MSG satellite. Lightning data is from our ATDnet network

Indian Ocean imagery

• Currently using the ageing Meteosat-7

• Very important for the generation of Atmospheric Motion Vectors

• Useful for general • Useful for general forecasting

• Also valuable for collection of DCP data from buoys etc

• We could do with an additional (more modern) satellite in this location with this capability


VIIRS imagery


Modelled imagery from UK 1.5kmusing fast RT model

Model Simulated T+0

Meteosat IR Valid time 4/4/13 12UTCObservationsSimulated T+12


Used by forecasters to check fidelity of model fields

OCEAN FORECASTING APPLICATIONS


SST gradients, Gulf Stream, March 2012

• Used by ECMWF, DWD & Meteo-France for NWP

• Uses OSI-SAF ice analyses • Satellite bias correction was

based on AATSR; now uses MetOp AVHRR; will use SLSTR

OSTIA - Operational Sea-surface Temperature and sea-Ice Analysis

© Crown copyright Met Office© Crown copyright Met Office

Old system

New system

Observation type

RMS

Old New

Drifters 0.52 0.37

AATSR 0.45 0.37

Argo 0.47 0.43

SLSTR • New version implemented on

17 Jan 2013

Obs-background stats, March 2012

FOAM (Forecasting Ocean Assimilation Model)

• Our operational ocean forecasting system is called FOAM (Forecasting Ocean Assimilation Model). Running operationally since 1997.

• FOAM is run for the deep ocean (globally and basin-scale) and for shelf-seas. Described in Storkey et al. 2010, and O’Dea et al. 2012. The current operational FOAM system consists of:

• Ocean model (NEMO).

• Sea Ice model (LIM2).

• Data assimilation (Analysis Correction scheme);

• Quality control

• Flux processing

• Main users of the ocean information include:

• Royal Navy, MyOcean users (a large European project to provide operational ocean forecast data), Ship routing, oil spill, search and rescue.

• Currently working on developing coupled use within NWP systems (as the lower boundary condition).

• Initial conditions for the GloSea system generated since Autumn 2012.

IntroductionOcean surface observations

10 days of satellite altimeter sea surface height ( SSH)(Jason 1, Jason 2, ENVISAT)

1 day of satellite and in-situ sea surface temperat ure (SST) (AATSR, AMSRE, AVHRR, drifters, ships, …)

0 1

1 day of satellite sea-ice concentration (SSMI, from EUMETSAT OSI-SAF)

-0.6 0.6 -2 32

0 1

GlobColour

Data Assimilation of Ocean Colour in an Operational Framework

1st Jan 2003

log 10(chlorophyll) errorNorth Atlantic

(mg/

m3 )

)

Error reduction

ESA CCI OC

-2 -1.5 -1 0-0.5 0.5log10(mg/m3)

RM

S e

rror

(lo

g 10(

mg/

m

Error reduction

Control GlobColour assim CCI assim 2003

The assimilation of OC data (regardless of its source) into global FOAM-HadOCC ORCA1 has a significant beneficial impact on the modelled chlorophyll, as the significant reduction in the rms error shows

CLIMATE APPLICATIONS


Satellite data in climate

•Sea Surface Temperature: ATSR, ATSR2, AATSR, AVHRR

•Radiation Budget: CERES, ISCCP-FD, ERBE, GERB

•Clouds/precipitation: ISCCP, CloudSat, CALIPSO, SSM/I, AMSR-E, TMI, MODIS, MISR, TRMM


•Land/ocean surface: MODIS, SEVIRI, QuikSCAT

•Cryosphere: SSMI, ENVISAT, CryoSat

•Other: GPS-RO

•Future

•EarthCARE, ESA-CCI.

Use not only driven by quality of products, but also by accessibility, documentation and format.

Model development: is the model improving? Look at SW radiation.

• Development of new Hadley Centre climate model

• Shortwave

Model 1-ISCCP Model 2-ISCCP


Model 1-CERES

• Shortwave radiation at TOA

• ISCCP � model improves

• CERES � model gets worse

Model 2-CERES

GCOS ECVs

Atmosphere

Surface Air temperature; Precipitation, Pressure, Surface radn budget, Wind

Upper Air Clouds, Wind, Earth Radn Budget

Upper air temp, water vapour

Composition Carbon dioxide, methane & GHGs

Ozone, Aerosol propertiesOzone, Aerosol properties

Ocean

Surface SST, Sea-level, Sea-ice, Ocean colour

Sea state, Salinity, CO 2 partial pressure

Sub-surface Temperature, Salinity, Current, Nutrients, Carbon, Ocean Tracers, Phytoplankton

Terrestrial Glaciers & Ice caps, Land cover, Fire disturbance, FaPAR, LAI, Albedo, Biomass,Lake levels, Snow cover, Soil moistu re, Water use, Ground water, River discharge, Permafrost, Sea sonally frozen ground, Ice Sheets

What ECVs are missing?

Atmosphere

Surface Air temperature , Precipitation, Pressure ,Surface radn budget, Wind

Upper Air Clouds, Wind, Earth Radn Budget

Upper air temp, water vapour

Composition Carbon dioxide, methane & GHGs

Ozone, Aerosol propertiesOzone, Aerosol properties

Ocean

Surface SST, Sea-level, Sea-ice, Ocean colour

Sea state, Salinity , CO2 partial pressure

Sub-surface Temperature, Salinity, Current, Nutrients, Carbon, Ocean Tracers, Phytoplankton

Terrestrial Glaciers & Ice caps, Land cover, Fire disturbance, FaPAR, LAI, Albedo, Biomass,Lake levels , Snow cover, Soil moisture, Water use, Ground water, River discharge, Permafrost, Seasonally frozen ground , Ice Sheets

SUMMARY

• Met Office is making active use of satellite data for NWP, Forecasting, Ocean Forecasting and Climate Applications

• MetOp-B and Suomi NPP are now assimilated operationally. Met-Op B ATOVS data were available from EUMETSAT so quickly in near real time that they were assimilated within only 120 days. It would be great if other satellite operators only 120 days. It would be great if other satellite operators could follow this example.

• It would be useful to have an additional geostationary satellite over the Indian Ocean which produces good quality AMVs and offers DCP communications services

• Use of products is not only driven by quality of products, but also by accessibility, documentation, format and ease of integration with existing systems (e.g. Forecaster desktops).



Thank you! Questions?

utilization of satellite data at the met office (uk)

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