Slide 1 ECMWF Training Course - The Global Observing System - 05/2010 The Global Observing System...
Transcript of Slide 1 ECMWF Training Course - The Global Observing System - 05/2010 The Global Observing System...
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Slide 1
ECMWF Training Course - The Global Observing System - 05/2010
The Global Observing System
Peter Bauer and colleagues
European Centre for Medium-Range Weather Forecasts
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Slide 2
ECMWF Training Course - The Global Observing System - 05/2010
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
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Slide 3
ECMWF Training Course - The Global Observing System - 05/2010
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
![Page 4: Slide 1 ECMWF Training Course - The Global Observing System - 05/2010 The Global Observing System Peter Bauer and colleagues European Centre for Medium-Range.](https://reader035.fdocuments.us/reader035/viewer/2022070305/55154bdf550346c77d8b6395/html5/thumbnails/4.jpg)
Slide 4
ECMWF Training Course - The Global Observing System - 05/2010
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
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Slide 5
ECMWF Training Course - The Global Observing System - 05/2010
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.
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Slide 6
ECMWF Training Course - The Global Observing System - 05/2010
Satellite observing system
Data types:
Data volume:
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Slide 8
ECMWF Training Course - The Global Observing System - 05/2010
Example of conventional data coverage
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Slide 10
ECMWF Training Course - The Global Observing System - 05/2010
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
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Slide 11
ECMWF Training Course - The Global Observing System - 05/2010
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!
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Slide 12
ECMWF Training Course - The Global Observing System - 05/2010
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
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Slide 13
ECMWF Training Course - The Global Observing System - 05/2010
(Trémolet 2004)
T799L91
T95L91 T159L91T255L91
T799L91
Data Assimilation – Incremental 4D-Var
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Slide 14
ECMWF Training Course - The Global Observing System - 05/2010
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
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Slide 15
ECMWF Training Course - The Global Observing System - 05/2010
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
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Slide 16
ECMWF Training Course - The Global Observing System - 05/2010
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
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Slide 17
ECMWF Training Course - The Global Observing System - 05/2010
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
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Slide 18
ECMWF Training Course - The Global Observing System - 05/2010
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)
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Slide 19
ECMWF Training Course - The Global Observing System - 05/2010
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
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Slide 20
ECMWF Training Course - The Global Observing System - 05/2010
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
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Slide 21
ECMWF Training Course - The Global Observing System - 05/2010
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.
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Slide 22
ECMWF Training Course - The Global Observing System - 05/2010
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 [%]
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Slide 23
ECMWF Training Course - The Global Observing System - 05/2010
Advanced diagnostics – MW imager denial
(C. Cardinali)
Forecast error reduction [%]
No MW-imagersControl
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Slide 24
ECMWF Training Course - The Global Observing System - 05/2010
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
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Slide 25
ECMWF Training Course - The Global Observing System - 05/2010
Time evolution of statistics over predefined areas/surfaces/flags
Data monitoring – time series
(M. Dahoui)
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Slide 26
ECMWF Training Course - The Global Observing System - 05/2010
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)
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Slide 27
ECMWF Training Course - The Global Observing System - 05/2010
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:
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Slide 28
ECMWF Training Course - The Global Observing System - 05/2010
Data monitoring – automated warnings
(M. Dahoui & N. Bormann)
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Slide 29
ECMWF Training Course - The Global Observing System - 05/2010
Satellite data monitoringData monitoring – automated warnings
(M. Dahoui & N. Bormann)
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Slide 30
ECMWF Training Course - The Global Observing System - 05/2010
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
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Slide 31
ECMWF Training Course - The Global Observing System - 05/2010
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)
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Slide 32
ECMWF Training Course - The Global Observing System - 05/2010
Cloudsat/CALIPSO data monitoring
(J.-J. Morcrette)
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Slide 33
ECMWF Training Course - The Global Observing System - 05/2010
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
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Slide 34
ECMWF Training Course - The Global Observing System - 05/2010
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
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Slide 35
ECMWF Training Course - The Global Observing System - 05/2010
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
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Slide 36
ECMWF Training Course - The Global Observing System - 05/2010
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
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Slide 37
ECMWF Training Course - The Global Observing System - 05/2010
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.
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Slide 38
ECMWF Training Course - The Global Observing System - 05/2010
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
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Slide 39
ECMWF Training Course - The Global Observing System - 05/2010
JAS08 D08JF09
Forecast impact: z500 – D08JF09
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Slide 40
ECMWF Training Course - The Global Observing System - 05/2010
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
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Slide 41
ECMWF Training Course - The Global Observing System - 05/2010
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.
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Slide 42
ECMWF Training Course - The Global Observing System - 05/2010
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)
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Slide 43
ECMWF Training Course - The Global Observing System - 05/2010
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
![Page 42: Slide 1 ECMWF Training Course - The Global Observing System - 05/2010 The Global Observing System Peter Bauer and colleagues European Centre for Medium-Range.](https://reader035.fdocuments.us/reader035/viewer/2022070305/55154bdf550346c77d8b6395/html5/thumbnails/42.jpg)
Slide 44
ECMWF Training Course - The Global Observing System - 05/2010
Combining NWP with CTM models and data assimilation systems
EC FP-6/7 projects GEMS/MACC (coordinated by ECMWF) towards GMES Atmospheric Service
![Page 43: Slide 1 ECMWF Training Course - The Global Observing System - 05/2010 The Global Observing System Peter Bauer and colleagues European Centre for Medium-Range.](https://reader035.fdocuments.us/reader035/viewer/2022070305/55154bdf550346c77d8b6395/html5/thumbnails/43.jpg)
Slide 48
ECMWF Training Course - The Global Observing System - 05/2010
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
![Page 44: Slide 1 ECMWF Training Course - The Global Observing System - 05/2010 The Global Observing System Peter Bauer and colleagues European Centre for Medium-Range.](https://reader035.fdocuments.us/reader035/viewer/2022070305/55154bdf550346c77d8b6395/html5/thumbnails/44.jpg)
Slide 49
ECMWF Training Course - The Global Observing System - 05/2010
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).
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Slide 50
ECMWF Training Course - The Global Observing System - 05/2010
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.