Slide 1 ECMWF Training Course - The Global Observing System - 04/2012 The Global Observing System Stephen English and colleagues (with special thanks to

Slide 1

ECMWF Training Course - The Global Observing System - 04/2012

The Global Observing System

Stephen English and colleagues

(with special thanks to Peter Bauer )

European Centre for Medium-Range Weather Forecasts

Slide 2


NWP, conventional and satellite observations

General impact assessment of current observing system

Data monitoring

Future observations and observation usage

Special Applications: Climate & Chemistry

Concluding remarks

Slide 3




Data monitoring



Concluding remarks

Slide 4


Role of observations

Forecast lead time (days)

RM

S e

rror

(m

)

From C Lupu and E.Kallen

199020002010

Time (hours)

500hPa height, NH

SEVIRI 6.2 µm

Every 12 hours we assimilate 4 – 8,000,000 observations to correct the 100,000,000 variables that define the model’s virtual atmosphere.

Slide 5


From E. Kallen

Slide 6


Data sources: Conventional

Instrument Parameters Height

SYNOPSHIPMETAR

temperature, dew-point temperature, wind

Land: 2m, ships: 25m

BUOYS temperature, pressure, wind 2m

TEMPTEMPSHIPDROPSONDES

temperature, humidity,pressure, wind

Profiles

PROFILERS wind Profiles

Aircraft temperature, pressure wind

ProfilesFlight level data

Slide 7


Example of conventional data coverage

Aircraft – AMDAR

Synop - ship

Buoy

Temp

Slide 8


What types of satellites are used in NWP?

Advantages Disadvantages

GEO - large regional coverage - no global coverage by single satellite

- very high temporal resolution - moderate spatial resolution (VIS/IR)> short-range forecasting/nowcasting > 5-10 km for VIS/IR> feature-tracking (motion vectors) > much worse for MW> tracking of diurnal cycle (convection)

LEO - global coverage with single satellite - low temporal resolution

- high spatial resolution>best for NWP!

From P. Bauer

Slide 9


Sun-Synchronous Polar SatellitesInstrument Early morning

orbitMorning orbit Afternoon orbit

High spectral resolution IR sounder

IASI Aqua AIRSNPP CrIS

Microwave T sounder

F16, 17 SSMIS Metop AMSU-AFY3A MWTSDMSP F18 SSMISMeteor-M N1 MTVZA

NOAA-15, 18, 19 AMSU-A Aqua AMSU-AFY3B MWTS, NPP ATMS

Microwave Q sounder + imagers

F16, 17 SSMIS Metop MHSDMSP F18 SSMISFY3A MWHS

NOAA-18, 19 MHSFY3B MWHS, NPP ATMS

Broadband IR sounder

Metop HIRSFY3A IRAS

NOAA-19 HIRSFY3B IRAS

IR Imagers Metop AVHRRMeteor-M N1 MSU-MR

Aqua+Terra MODISNOAA-15, 16, 18, 19 AVHRR

Composition(ozone etc).

NOAA-17 SBUV NOAA-18, 19 SBUVENVISAT GOMOSAURA OMI, MLSENVISAT SCIAMACHYGOSAT

Slide 10


Instrument High inclination (> 60°) Low inclination (<60°)

Radio occultation

GRAS, GRACE-A, COSMIC, TerraSarXC-NOFS, (SAC-C), ROSA

MW Imagers TRMM TMIMeghatropics SAFIRE MADRAS

Radar Altimeter ENVISAT RAJASON Cryosat

Sun-Synchronous Polar Satellites (2)Instrument Early morning

orbitMorning orbit Afternoon orbit

Scatterometer Metop ASCATCoriolis Windsat

Oceansat OSCAT

Radar CloudSat

Lidar Calipso

Visible reflectance

Parasol

L-band imagery

SMOSSAC-D/Aquarius

Non Sun-Synchronous Observations

Slide 11


• Characterise the benefit of having ATOVS data from three evenly-spaced orbits

versus data from a less optimal coverage for NWP

MetOp-A

NOAA-18

NOAA-19 + NPP Aqua

NOAA-15

Ti

me

ECMWF support to EUMETSAT – LEO constellation

DMSP F16

DMSP F17

DMSP F18

FY-3B

FY-3A

ECWMF/EUMETSAT Bilateral Meeting 03/2012 SE

11

Coriolis

Slide 12


Product Status

SEVIRI Clear sky radiance Assimilated

SEVIRI All sky radiance Being tested for overcast radiances, and cloud-free radiances in the ASR dataset

SEVIRI total column ozone Monitored

SEVIRI AMVs IR, Vis, WV-cloudy AMVs assimilated

GOES AMVs

MTSAT AMVs

Data sources: Geostationary Satellites

Slide 13


LEO Sounders LEO Imagers

Scatterometers GEO imagers

Satellite Winds (AMVs)

GPS Radio Occultation

Example of 6-hourly satellite data coverage

30 March 2012 00 UTC

Slide 14


Profilers

RadiosondeSynopShip

AircraftBuoys

MoistureMass

Wind

Composition

Ozone sondesAir quality stations

Soil moistureRain gauge

Slide 15


GPSRO

Geo IR and Polar MW Imagers

AMVsScatterometersWind lidar

Geo IR Sounder

RadarGPS ZPD

PolarIR + MWsounders

MoistureMass

Wind

Composition

UV

Sub-mmVIS+NIRLidarLimb-sounders

Slide 16


Satellite data used by ECMWF

Slide 17


Slide 18


User requirements http://www.wmo-sat.info/db/

• Vision for the GOS in 2025 adopted June 2009• GOS user guide WMO-No. 488 (2007)• Manual of the GOS WMO-No. 544 (2003) (Update of satellite section being prepared for ET-SAT Geneva April 2012)

Slide 19




Data monitoring



Concluding remarks

Slide 20


Combined impact of all satellite data

EUCOS Observing System Experiments (OSEs):

• 2007 ECMWF forecasting system,• winter & summer season,• different baseline systems:

• no satellite data (NOSAT),• NOSAT + AMVs,• NOSAT + 1 AMSU-A,

• general impact of satellites,• impact of individual systems,• all conventional observations.

500 hPa geopotential height anomaly correlation

3/4 day

3 days

From P. Bauer

Slide 21


Impact of microwave sounder data in NWP: Met Office OSEs

2003 OSEs:2003 OSEs:• N-15,-16 and -17 AMSUN-15,-16 and -17 AMSU• N-16 & N-17 HIRSN-16 & N-17 HIRS• AMVsAMVs• Scatterometer windsScatterometer winds• SSM/I ocean surface wind speedSSM/I ocean surface wind speed• Conventional observationsConventional observations

2007 OSEs:2007 OSEs:• N-16, N-18, MetOp-2 AMSUN-16, N-18, MetOp-2 AMSU• SSMISSSMIS• AIRS & IASIAIRS & IASI• Scatterometer windsScatterometer winds• AMVsAMVs• SSM/I ocean surface wind speedSSM/I ocean surface wind speed• Conventional observationsConventional observations

(From W. Bell)

Slide 22


State atinitial time

NWPmodel

State at time i

Observationoperator

Observationsimulations

Advanced diagnostics

Observations

AD of forecastmodel

AD of observation

operator

Sensitivity of cost to change in state at time i

Cost function J

Sensitivity of cost to change at initial time

max. 12 hours

Data assimilation:

State atinitial time

NWPmodel

State at time i

AD of forecastmodel

max. 48 hours

Sensitivity of cost to change at initial time

Analysis

Cost function J

Forecast sensitivity:

State at analysis

time

Sensitivity of cost to

observations

From P. Bauer

Slide 23


Relative FC error reduction per system

Relative FC error reduction per observation

(From C. Cardinali)

Advanced diagnostics

The forecast sensitivity (Cardinali, 2009, QJRMS, 135, 239-250) denotes the sensitivity of a forecast error metric (dry energy norm at 24 or 48-hour range) to the observations. The forecast sensitivity is determined by the sensitivity of the forecast error to the initial state, the innovation vector, and the Kalman gain.

Slide 24




Data monitoring



Concluding remarks

Slide 25


Time evolution of statistics over predefined areas/surfaces/flags

Data monitoring – time series

(From M. Dahoui)

Slide 26


Selected statistics are checked against an expected range.

E.g., global mean bias correction for GOES-12 (in blue):

Soft limits (mean ± 5 stdev being checked, calculated from past statistics over a period of 20 days, ending 2 days earlier)

Hard limits (fixed)

Email-alert

Data monitoring – automated warnings

(M. Dahoui & N. Bormann)

http://www.ecmwf.int/products/forecasts/satellite_check/

Email alert:

Slide 27


Data monitoring – automated warnings

(From M. Dahoui & N. Bormann)

Slide 28


Satellite data monitoringData monitoring – automated warnings

(From M. Dahoui & N. Bormann)

Slide 29




Data monitoring



Concluding remarks

Slide 30


New data availabilities•Now

•SMOS, Suomi-NPP

•2013-2017•ADM (Doppler-lidar: Atmospheric wind vector)•SMAP (like SMOS but active + passive)•Earthcare (radar, lidar)•FY3 -> ATOVS quality

•2017-2020•Meteosat 3rd Generation •FY3 -> Metop quality

•2020+•EPS Second Generation

But don’t always focus on satellite data! RS90 radiosonde much better than older radiosondes....`advanced conventional observations’

Slide 31


REF_AT

62 N 60 N 58 N 56 N 54 N 52 N 50 N 48 N 46 N 44 N 42 N 40 N 38 N 36 N 34 N 32 N 30 NLat

100W 99 W 98 W 97 W 96 W 95 W 94W 93 W 92 W 91 W 90 W 89W 88W 87 WLon

1.1

1.9

3.0

4.3

5.9

7.6

9.3

10.9

12.5

Hei

gh

t (k

m)

-24 - -21 -21 - -18 -18 - -15 -15 - -12 -12 - -9 -9 - -6 -6 - -3 -3 - 0 0 - 3 3 - 6 6 - 9 9 - 12 12 - 15 15 - 18Observation

REF_AT



1.1

1.9

3.0

4.3

5.9

7.6

9.3

10.9

12.5

Hei

gh

t (k

m)

-24 - -21 -21 - -18 -18 - -15 -15 - -12 -12 - -9 -9 - -6 -6 - -3 -3 - 0 0 - 3 3 - 6 6 - 9 9 - 12 12 - 15 15 - 18

REF_AT



1.1

1.9

3.0

4.3

5.9

7.6

9.3

10.9

12.5

Hei

gh

t (k

m)

-24 - -21 -21 - -18 -18 - -15 -15 - -12 -12 - -9 -9 - -6 -6 - -3 -3 - 0 0 - 3 3 - 6 6 - 9 9 - 12 12 - 15 15 - 18

15 – 18

REF_AT

62N

60N

58N

56N

54N

52N

50N

48N

46N

44N

42N

40N

38N

36N

34N

32N

30N

Lat100

W99

W98

W97

W96

W95

W94

W93

W92

W91

W90

W89

W88

W87

WLon

1.1

1.9

3.0

4.3

5.9

7.6

9.3

10.9

12.5Height (km)

-24 - -21-21 - -18

-18 - -15-15 - -12

-12 - -9-9 - -6

-6 - -3-3 - 0

0 - 33 - 6

6 - 99 - 12

12 - 1515 - 18

-24 – -21

-21 – -18

-18 – -16

-16 – -12

-12 – -9

-9 – -6

-6 – -3

-3 – 0

0 – 3

3 – 6

6 – 9

9 – 12

12 – 15

Model First-Guess

Analysis

1D-Var Assimilation of Cloudsat Radar Reflectivities (dBZ)

EarthCARE

31

From S Di Michele

Slide 32


EarthCARE1D-Var Assimilation of Calipso lidar Backscatter Coefficients (km-1 sr-1)

Observation

Model First-Guess

Analysis

32

From S Di Michele

Slide 33


SMOS monitoring results

H-pol V-pol

• Monthly-average geographical mean evolution of the First-guess departures • Period Nov-2010 - August-2011

• fg departures in H-pol well correlated with snow covered areas, • Significant sources of RFI are still easy to spot with fg-departures,• In V-pol, observations are mainly overestimated.

From J. Munoz Sabater

Slide 34


ECMWF is responsible for the development of the level 2 processor and will exploit the data as soon as available.

Simulated DWL data adds value at all altitudes and well into longer-range forecasts.

S.Hem

0.0 0.5 1.0 1.51000

100

Zonal wind forecast error (m/s)

Pre

ssu

re (

hP

a)

Control+ADM

Control

Control-sondes

Active instruments: ESA’s ADMESA ADM AEOLUS Doppler Lidar for wind vector observation

From P Bauer

Slide 35


~210km~125km ~63km

~39km ~25km ~16km

Evolution of ECMWF forecast skill

From E Kallen

Slide 36




Data monitoring



Concluding remarks

Slide 37


Observations used in ERA-Interim:

The ERA-40 observing system:

VTPR

TOMS/ SBUV

HIRS/ MSU/ SSU Cloud motion winds

Buoy data

SSM/I ERS-1ERS-2

AMSU

METEOSAT reprocessed

cloud motion winds

Conventional surface and upper-air observationsNCAR/NCEP, ECMWF, JMA, US Navy, Twerle, GATE, FGGE, TOGA, TAO, COADS, …

Aircraft data

1957 2002

19731979

1982 1988

1973 19791987 1991

19951998• ERA-40 observations until August 2002

• ECMWF operational data after August 2002• Reprocessed altimeter wave-height data from ERS• Humidity information from SSM/I rain-affected radiance data• Reprocessed METEOSAT AMV wind data• Reprocessed ozone profiles from GOME• Reprocessed GPSRO data from CHAMP

ERA-Interim

1989

ECMWF Reanalysis• ERA-Interim is current ECMWF reanalysis project following ERA-

15 & 40.• 2006 model cycle, 4D-Var, variational bias-correction, more data

(rain assimilation, GPSRO); 1989-1998 period available, 1998-2005 period finished, real-time in 2009.

From P. Bauer

Slide 38


From E Kallen

Slide 39




Data monitoring



Concluding remarks

Slide 40


Combining NWP with CTM models and data assimilation systems

EC FP-6/7 projects GEMS/MACC (coordinated by ECMWF) towards GMES Atmospheric Service

From P Bauer

Slide 41


Satellite data on CO2 and CH4 for use in MACC

Comments: Post-EPS sounder and Sentinels 4/5 should come into the picture late in period or soon after. Fire products (METEOSAT, MODIS, …) are a common requirement.

From P Bauer

Slide 42


Satellite data on reactive gases for use in MACC

Comments: Post-EPS sounder and Sentinels 4/5 should come into the picture late in period or soon after. Fire products (METEOSAT, MODIS, …) are a common requirement. From P Bauer

Slide 43


Satellite data on aerosols for use in MACC

Comment: Fire products (METEOSAT, MODIS, …) are a common requirement.

From P Bauer

Slide 44




Data monitoring



Concluding remarks

Slide 45


Concluding remarks• At ECMWF, 95% of the actively assimilated data originates from

satellites (90% is assimilated as radiances and only 5% as derived products and 5% from conventional products).

• Impact experiments demonstrate the crucial role of conventional observations!

• Ingredients for successful data implementation:- pre-launch test data, well defined formats, testing of

telecommunications, provision of detailed instrument information.- early data access after launch and active “cal/val” role for NWP

centres- near real-time data access to maximize operational use.

optimal return of investment by global user community (e.g. Metop ATOVS was used operationally only 3 months after launch despite whole new ground segment!).

• Currently most important NWP instruments at ECMWF:- high spectral resolution infrared sounders (temperature, moisture),- microwave sounders and imagers (temperature, moisture, clouds, precipitation),- GPS transmitters/receivers (temperature),- IR imagers/sounders in geostationary orbits (moisture, clouds, wind),- scatterometers (near surface wind speed, wave height)- altimeters (height anomaly),- UV/VIS/IR spectrometers (trace gases, temperature).

Slide 46


Concluding remarks

• Future upgrades to data monitoring:- Coordination with data providers, building on experience within Europe e.g. Collaboration with China over FY3.- more effective automated warning system.

• Future challenges with respect to observations:- Active instruments – radar, lidar (wind, aerosols, clouds, precipitation, water vapour),- Advanced imagers – synthetic aperture radiometers (soil moisture).- Geostationary high spectral resolution sounders

• Future challenges with respect to design of the Global Observing System:- In the past over-reliance on US data. European data now very important. New partnerships (e.g. China) will become increasingly important- Coordination of multi-agency programmes- Prioritisation for high benefit : low cost missions versus “new science” missions- Knowing which observations will be needed in 10-20 years time when NWP will have advanced considerably- Balancing needs of NWP, Climate and nowcasting, alongside new requirements for environmental monitoring (composition and chemistry).

Documents

Slide 1 ECMWF Training Course - The Global Observing System - 04/2012 The Global Observing System Stephen English and colleagues (with special thanks to