Slide 1 ECMWF Training Course - The Global Observing System - 06/2013 The Global Observing System Stephen English With material kindly provided by Peter.

Slide 1

ECMWF Training Course - The Global Observing System - 06/2013

The Global Observing System

Stephen English

With material kindly provided by Peter Bauer, Cristina Lupu, Tony McNally, Mohamed Dahoui, Erland Kallen, Enza di Tomaso, Niels

Bormann, Sabatino di Michele and Richard Engelen

European Centre for Medium-Range Weather Forecasts

Slide 2


Role of observations

RM

S e

rror

(m

)

Time (hours)

SEVIRI 6.2 µm

Every 12 hours we assimilate ~7,000,000 observations to correct the 100,000,000 variables that define the model’s virtual atmosphere.

We monitor an additional 12,000,000.

Observations limit error growth and make forecasting possible….

Slide 3


The state spaceMASS (temperature, pressure…)Radiosondes, surface observations, satellite sounders, aircraft

MOISTURE (humidity, clouds, precipitation…)Radiosondes, surface observations, satellite sounders and imagers, aircraft, radar, lidar

DYNAMICS (wind, vorticity, convergence…)Radiosondes, surface observations, satellite imagers, satellite scatterometer/radar/lidar, aircraft

COMPOSITION (ozone, aerosol…)Ozone sondes, surface observations, satellite sounders

SURFACE (surface type, temperature, moisture, homogeneity…)Satellite active and passive systems, surface observations

Slide 4


Profilers

RadiosondeSynopShip

AircraftBuoys

MoistureMass

Wind

Composition

Ozone sondesAir quality stations

Soil moistureRain gauge

Slide 5


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 6


What types of satellites are used in NWP?

Advantages Disadvantages

GEO - Regional coverage No global coverage by single satellite

- Temporal coverage

LEO - Global coverage with single satellite

Slide 7


Radio occultation

Geo IR and Polar MW Imagers

Feature tracking in imagery (e.g. cloud track winds), scatterometers and doppler winds

Geo IR Sounder

Radar andGPS total path delay

PolarIR + MWsounders

MoistureMass

Wind

Composition

Ultraviolet sensors

Sub-mm,and near IR plusVisible (e.g. Lidar)

IR = InfraRedMW = MicroWave

Slide 8


Metop

Slide 9


Metop

Slide 10


Example of conventional data coverage

Aircraft – AMDAR (note also have Airep and ACARs)

Surface (synop) - ship

Buoy

Balloon profiles e.g. radiosondes

Slide 11


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 12


Slide 13


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

Slide 14


User requirements and satellite data: OSCAR www.wmo-sat.info

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

Slide 15


Using DA to help design the GOS

Examples questions we use Data Assimilation techniques to study:

•Would it be beneficial for the Chinese FY3 program to move to the “early morning orbit” with the Europeans occupying the “morning orbit” and the Americans the “afternoon orbit”?

•Preparation for future instruments such as lidar and radar (EarthCARE).

•Study using Ensemble of Data Assimilations to estimate the number of GPSRO soundings needed in future (discuss with Sean Healy if interested).

Slide 16


2009 ExperimentsEnza Di Tomaso* and Niels Bormann

MetOp-A

NOAA-18 NOAA-19 Aqua

NOAA-15

NOAA-16

NOAA-17

Ti

me

AM

Early AM

PM

Slide 17


FY3 orbit: what is the optimal orbit configuration?

“two-satellite experiment”* MetOp-A * NOAA-18

“NOAA-15 experiment”* MetOp-A * NOAA-18 * NOAA-15

“NOAA-19 experiment”* MetOp-A * NOAA-18 * NOAA-19

Slide 18


“no-MW sounder experiment”

GOOD

“two-”, “three-”, “all-satellite experiment”

GOOD

two-satellite RMSE – no-Mw sounder RMSE three-satellite RMSE – no-Mw sounder RMSE all-satellite RMSE – no-Mw sounder RMSE

Are 3 satellites better than 2?

YES

3.5 months 107 casesCY36R1 T511

Slide 19


RMS difference forecast – analysis for NOAA-15 and NOAA-19 experiments

NOAA-19 experiment

GOOD

NOAA-15 experiment

GOOD

Do orbital positions matter?

YES

Slide 21


2012 experiments

•Baseline 1: microwave only (NPP + METOP-A)

•Baseline 2: microwave + infrared (NPP + METOP-A)

Slide 22


NH NH

SH SH

Early am better

pm better

Microwave only baselineMicrowave + infrared baseline

3 months 90 casesCY38R1 T511

Slide 23


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)

Preparing for future missions e.g. Aeolus and EarthCARE

23

Slide 24


Combining NWP with CTM models and data assimilation systems

New requirements in GOS for atmospheric composition

Slide 25


Monitoring of observations

•Webpages•Automatic warnings•Collaboration between users and providers

•J = ½(y-H(x))TR-1(y-H(x)) + Jb

•At beginning and end of minimisation, with and without QC, plus bias corrections.

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

Slide 28


Satellite data monitoringData monitoring – automated warnings

Slide 29


Global Observing System is essential to weather forecasting

Technology driven….a more integrated approach now?

Mass is well observed.

Moisture – satellite observations are data rich but poorly exploited. Radar and lidar will become more important.

Dynamics – even wind observations are scarce.

Composition – NWP techniques have been successfully extended to environmental analysis and prediction but more observations are needed.

Surface – DA for surface fields is being attempted.

Slide 30


Thank you for your attention

Thanks again to Peter Bauer, Cristina Lupu, Tony McNally, Mohamed Dahoui, Erland Kallen, Enza di Tomaso, Niels Bormann, Sabatino di Michele and Richard Engelen

Slide 31


Backup slidesDetailed list of instruments for NWP and atmospheric composition

(not shown but included for information)

Slide 32


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 33


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 34


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 1 ECMWF Training Course - The Global Observing System - 06/2013 The Global Observing System Stephen English With material kindly provided by Peter.

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