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The Global Observing System

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Presentation on theme: "The Global Observing System"— Presentation transcript:

1 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

2 Role of observations Observations limit error growth and make forecasting possible…. 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. SEVIRI 6.2 µm RMS error (m) Time (hours)

3 The state space MASS (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

4 Composition Mass Moisture Wind Ozone sondes Air quality stations
Soil moisture Rain gauge Radiosonde Synop Ship Aircraft Buoys Profilers Wind

5 Data sources: Conventional
Instrument Parameters Height SYNOP SHIP METAR temperature, dew-point temperature, wind Land: 2m, ships: 25m BUOYS temperature, pressure, wind 2m TEMP TEMPSHIP DROPSONDES temperature, humidity, pressure, wind Profiles PROFILERS wind Aircraft temperature, pressure wind Flight level data

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

7 Composition Mass Moisture Wind IR = InfraRed MW = MicroWave
Ultraviolet sensors Sub-mm, and near IR plus Visible (e.g. Lidar) Polar IR + MW sounders Mass Radar and GPS total path delay Moisture Radio occultation Geo IR Sounder Geo IR and Polar MW Imagers Feature tracking in imagery (e.g. cloud track winds), scatterometers and doppler winds IR = InfraRed MW = MicroWave Wind

8 Metop

9 Metop

10 Example of conventional data coverage
Aircraft – AMDAR (note also have Airep and ACARs) Buoy Surface (synop) - ship Balloon profiles e.g. radiosondes

11 Example of 6-hourly satellite data coverage
LEO Sounders LEO Imagers Scatterometers GEO imagers Satellite Winds (AMVs) GPS Radio Occultation 30 March UTC


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

14 User requirements and satellite data: OSCAR
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)

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).

16 2009 Experiments Enza Di Tomaso* and Niels Bormann
MetOp-A AM Early AM PM NOAA-17 T i m e NOAA-16 NOAA-15 NOAA-18 NOAA-19 Aqua

17 FY3 orbit: what is the optimal orbit configuration?
“NOAA-15 experiment” * MetOp-A * NOAA * NOAA-15 “two-satellite experiment” * MetOp-A * NOAA-18 “NOAA-19 experiment” * MetOp-A * NOAA * NOAA-19

18 Are 3 satellites better than 2?
3.5 months 107 cases CY36R1 T511 “no-MW sounder experiment” GOOD “two-”, “three-”, “all-satellite experiment” Are 3 satellites better than 2? YES Both the assimilations of NOAA-15 and NOAA-19 data have a clearly positive forecast impact in the Southern Hemisphere compared to the use of two satellites only two-satellite RMSE – no-Mw sounder RMSE three-satellite RMSE – no-Mw sounder RMSE all-satellite RMSE – no-Mw sounder RMSE

19 RMS difference forecast – analysis for NOAA-15 and NOAA-19 experiments
Do orbital positions matter? YES NOAA-19 experiment GOOD NOAA-15 experiment When averaged over the extra-Tropics the impact for the forecast of the geopotential of “NOAA-15 experiment” versus “NOAA-19 experiment” is neutral to slightly positive RMS difference forecast – analysis for NOAA-15 and NOAA-19 experiments

20 2012 Experiments Tony McNally
MetOp-A NOAA-17 AM Early AM PM NOAA-16 NOAA-15 NOAA-18 NOAA-19 Aqua NPP

21 2012 experiments Baseline 1: microwave only (NPP + METOP-A) Baseline 2: microwave + infrared (NPP + METOP-A)

22 3 months 90 cases CY38R1 T511 Microwave + infrared baseline Microwave only baseline pm better NH NH Early am better SH SH

23 Preparing for future missions e.g. Aeolus and EarthCARE
1D-Var Assimilation of Cloudsat Radar Reflectivities (dBZ) 15 – 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 Observation Analysis 23

24 New requirements in GOS for atmospheric composition
Combining NWP with CTM models and data assimilation systems New requirements in GOS for atmospheric composition

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.

26 Data monitoring – automated warnings
Selected statistics are checked against an expected range. E.g., global mean bias correction for GOES-12 (in blue): -alert Soft limits (mean ± 5 stdev being checked, calculated from past statistics over a period of 20 days, ending 2 days earlier) Hard limits (fixed) alert: (M. Dahoui & N. Bormann)

27 Data monitoring – automated warnings

28 Data monitoring – automated warnings
Satellite data monitoring Data monitoring – automated warnings

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.

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

31 Backup slides Detailed list of instruments for NWP and atmospheric composition (not shown but included for information)

32 Sun-Synchronous Polar Satellites
Instrument Early morning orbit Morning orbit Afternoon orbit High spectral resolution IR sounder IASI Aqua AIRS NPP CrIS Microwave T sounder F16, 17 SSMIS Metop AMSU-A FY3A MWTS DMSP F18 SSMIS Meteor-M N1 MTVZA NOAA-15, 18, 19 AMSU-A Aqua AMSU-A FY3B MWTS, NPP ATMS Microwave Q sounder + imagers Metop MHS FY3A MWHS NOAA-18, 19 MHS FY3B MWHS, NPP ATMS Broadband IR sounder Metop HIRS FY3A IRAS NOAA-19 HIRS FY3B IRAS IR Imagers Metop AVHRR Meteor-M N1 MSU-MR Aqua+Terra MODIS NOAA-15, 16, 18, 19 AVHRR Composition (ozone etc). NOAA-17 SBUV NOAA-18, 19 SBUV ENVISAT GOMOS AURA OMI, MLS ENVISAT SCIAMACHY GOSAT

33 High inclination (> 60°) Low inclination (<60°)
Sun-Synchronous Polar Satellites (2) Instrument Early morning orbit Morning orbit Afternoon orbit Scatterometer Metop ASCAT Coriolis Windsat Oceansat OSCAT Radar CloudSat Lidar Calipso Visible reflectance Parasol L-band imagery SMOS SAC-D/Aquarius Non Sun-Synchronous Observations Instrument High inclination (> 60°) Low inclination (<60°) Radio occultation GRAS, GRACE-A, COSMIC, TerraSarX C-NOFS, (SAC-C), ROSA MW Imagers TRMM TMI Meghatropics SAFIRE MADRAS Radar Altimeter ENVISAT RA JASON Cryosat

34 Data sources: Geostationary Satellites
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

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