slide 1 ecmwf training course - the global observing system - 05/2010 the global observing system...

ECMWF Training Course - The Global Observing System - 05/2010

The Global Observing System

Peter Bauer and colleagues

European Centre for Medium-Range Weather Forecasts


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


Delayed Ocean Analysis ~12 days

Real Time Ocean Analysis ~8 hours

Medium-Range Forecasts

(Deterministic and EPS)

Medium-Range Forecasts

(Deterministic and EPS)

Seasonal Forecasts

Seasonal Forecasts

Monthly Forecasts

Monthly Forecasts

Atmospheric model

Wave model

Ocean model

Atmospheric model

Wave model

ECMWF forecasting systems


Data assimilation system (4D-Var)

The observations are used to correct errors in the short forecast from the previous analysis time.

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

This is done by a careful 4-dimensional interpolation in space and time of the available observations; this operation takes as much computer power as the 10-day forecast.


Satellite observing system

Data types:

Data volume:


Example of conventional data coverage


LEO Sounders LEO Imagers

Scatterometers GEO imagers

Satellite Winds (AMVs)

GPS Radio Occultation

Example of 6-hourly satellite data coverage

9 April 2010 00 UTC


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!


Observation numbers per cycle

Average radiance data count per analysis from period 08/12/2008-28/02/2009:

EXP-HI EXP EXP-SV EXP-CLI EXP-RND


(Trémolet 2004)

T799L91

T95L91 T159L91T255L91

T799L91

Data Assimilation – Incremental 4D-Var


0x iM iHix iy

i

J

y

0xJ T

iMTiH

i

J

x

Control Variable / state vector

Forecastmodel

State at time i

Radiativetransfer

Radianceobservations

Wind and mass, humidity

Wind and mass, humidity,

Clear skyClear skyDynamics,moist physics

clouds and rain

Clear, cloud and rain including

scattering

Clear, cloud and rain

Transfer of information between radiances and control variables

Data Assimilation – Radiances


Example 1: Radiosonde profile of T H = spatial interpolation

Example 2: Clear-sky radiance observation H = spatial interpolation + clear-sky radiative transfer

Example 3: Cloud/rain radiance observation H = spatial interpolation + moist physical parameterizations+ multiple scattering radiative transfer

ModelSSM/I

What is the observation operator?

MVIRI Model




Data monitoring



Concluding remarks


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


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

(W. Bell)


Sensitivity of analysis increments to observations• 2007 GMAO/GSI system, 1.875o, 64 levels, 6-hour window;• J from analysis increments; August 2004.

temperature zonal windNorth-Pacific North Pacific

temperature zonal windUS US

satelliteconventionaltotal (Zhu & Gelaro 2008)

1,

2J x S x


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


Relative FC error reduction per system

Relative FC error reduction per observation

(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.


0 1 2 3 4 5 6 7 8 9

SYNOPAIREPDRIBUTEMPPILOTGOES-

Met-AMVSCATHIRS

AMSU-AAIRSIASI

GPS-ROSSMIMHS

AMSU-BMet-RadMet-Rad

MERISMTSAT-

GOES-RadO3

FEC %

black cntrl3 AMSU-A, 2 MHS vs 1 AMSU-A, 0 MHS

(C. Cardinali)

Advanced diagnostics – MW sounder denial

Forecast error reduction [%]


Advanced diagnostics – MW imager denial

(C. Cardinali)

Forecast error reduction [%]

No MW-imagersControl




Data monitoring



Concluding remarks


Time evolution of statistics over predefined areas/surfaces/flags

Data monitoring – time series

(M. Dahoui)


Time evolution of statistics for

several channels

Useful for quick and routine verifications

Can not be used for high spectral resolution

sounders

RTTOV version upgrade

Data monitoring – overview plots

(M. Dahoui)


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:


Data monitoring – automated warnings



Satellite data monitoringData monitoring – automated warnings





Data monitoring



Concluding remarks


New data availabilities•2010:

•Oceansat-2 (Scatterometer: surface wind vector)•DMSP F-18 SSMIS (MW T:, q-sounding, clouds and

precipitation)•SMOS (MW: soil moisture)•Megha Tropiques MADRAS/SAPHIR (MW: q-sounding,

clouds and precipitation)•FY-3A IRAS/MWTS/MWHS/MWRI (IR/MW: T, q-sounding,

clouds and precipitation) •GOSAT FTS (Advanced IR: T, q, trace gas sounding)

•2011:•NPP (Advanced IR: T, q-sounding)•ADM (Doppler-lidar: Atmospheric wind vector)

•2012 and beyond:•More advanced IR sounders in polar (Metop, NPOESS) and

geostationary orbits (MTG, GOES) for general sounding•More active instruments (wind, clouds, precipitation)


Cloudsat/CALIPSO data monitoring

(J.-J. Morcrette)


H-pol

80°S80°S

70°S 70°S

60°S60°S

50°S 50°S

40°S40°S

30°S 30°S

20°S20°S

10°S 10°S

0°0°

10°N 10°N

20°N20°N

30°N 30°N

40°N40°N

50°N 50°N

60°N60°N

70°N 70°N

80°N80°N

160°W

160°W 140°W

140°W 120°W

120°W 100°W

100°W 80°W

80°W 60°W

60°W 40°W

40°W 20°W

20°W 0°

0° 20°E

20°E 40°E

40°E 60°E

60°E 80°E

80°E 100°E

100°E 120°E

120°E 140°E

140°E 160°E

160°E

Points:94635Lon: lldegrees(lon@hdr)Lat: lldegrees(lat@hdr)Value: obsvalue@body-tbvalue@body

-50 - -40 -40 - -30 -30 - -20 -20 - -10 -10 - 0 0 - 1010 - 20 20 - 30 30 - 40 40 - 50

H-pol

22 January 2010 00 UTC; 1st background departure monitoring (no q/c)

Global monitoring:• Development of model forward operator (emissivity model)• Data pre-processing (HDF2BUFR → ODB/IFS)• Implementation of passive monitoring system, diagnostics, quality control

Data assimilation study:• Impact of SMOS constrained soil moisture

on medium-range forecasts

0

500

1000

1500

2000

2500

3000

3500

4000

4500

-200 -150 -100 -50 0 50 100 150 200

mean: 13.3 std: 51min: -230 max: 247

Total number of points: 94635DB column: obsvalue@body-tbvalue@body

0

1000

2000

3000

4000

5000

6000

7000

8000

-200 -150 -100 -50 0 50 100 150 200

mean: 1.96 std: 51.5min: -242 max: 249

Total number of points: 94882DB column: obsvalue@body-tbvalue@body

H-pol

V-pol

ECMWF usage of SMOS data


FG departure in m3/m3 (January 2010)

FG departure bias vs ASCAT incidence angle

Histograms of FG departures

(P. de Rosnay)

Soil moisture from ASCAT data


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




Data monitoring



Concluding remarks


Areas of instability: Eady indexEady-index as a proxy for baroclinic instability in the atmosphere

difference between seasons is rather strong; year-to-year variability has significant seasonal dependence as well.


Data coverage14/12/2008 00 UTC data density AMSU-A channel 9

EXP-HI:

EXP:

EXP-SV:

EXP-CLI:

EXP-RND:

01-07/01/2009 AverageSV

RND

CLI


JAS08 D08JF09

Forecast impact: z500 – D08JF09




Data monitoring



Concluding remarks


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.


Global mean bias corrections produced in ERA-Interim (MSU Channel 2):

Recorded warm-target temperatures, NOAA-14:(Grody et al. 2004)

• Variations in warm target are due to orbital drift

• VarBC is able to correct the resulting calibration errors

Reanalysis as inter-calibration tool

(D. Dee)




Data monitoring



Concluding remarks


Combining NWP with CTM models and data assimilation systems

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




Data monitoring



Concluding remarks


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:- early data access after launch:

(1) fast monitoring of data quality – feedback to space agencies,(2) early testing of data impact in NWP data assimilation systems.

- near real-time data access to maximize operational use. optimal return of investment by global user community (example: METOP).

• Currently most important NWP instruments at ECMWF:- advanced 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).


Concluding remarks

• Future challenges with respect to observations:- active instruments – radar, lidar (wind, aerosols, clouds, precipitation, water vapour),- advanced imagers – synthetic aperture radiometers (soil moisture).

• Future challenges with respect to data assimilation:- model resolution upgrades also affect data assimilation resolution,- more intelligent data thinning using ensemble methods (B) and forecast error growth metrics,- assimilation of cloud/precipitation-affected data will require revised control variable, background error statistics.

• Future upgrades to data monitoring:- more sophisticated data co-location tools to compare performance between data from different sensors,- more advanced automated warning system.

slide 1 ecmwf training course - the global observing system - 05/2010 the global observing system...

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