1. Page 1 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 DA 11: Research satellites and data assimilation Author: W.A. Lahoz Data Assimilation Research Centre, University of Reading RG6 6BB, UK

2. Page 2 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Characteristics of research satellite data: Viewing geometry, coverage and observation types (e.g. chemical species) Motivation for assimilation of research satellite data: High vertical resolution, global coverage, novel data types and benefits of assimilating a novel data type such as ozone; synergies Efforts, with examples, at assimilating research satellite data: NWP centres (GCM and CTM models), research into data assimilation (GCM and CTM models) Use of data assimilation techniques to evaluate observations and models Topics:

3. Page 3 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Characteristics of research satellite data

4. Page 4 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 1.Resolution: temporal & spatial 2.Frequency: temporal & spatial 3.Wavelength of measurement: what region of EM spectrum 4.Radiometric noise: signal/noise (S/N) ratio 5.Coverage: global/local 6.Geometry: nadir/limb/occultation 7.Level of data: 0: photons; 1: radiances; 2: geophysical parameters 8.Errors: random, systematic – biases, “representativeness” 9.Platform: sondes, aircraft, satellites – resolution 10.Influences on time/space evolution: dynamics: temperature, winds, ozone; chemistry: ozone, ClO. Features of observations

5. Page 5 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Observations are our representation of the “Truth” Photons (L0 data) Radiances (L1 data) Geophysical parameters (L2 data) algorithms e.g. profiles (ozone), total column (ozone) What instrument receives Scientists normally work with L2 data NWP Centres work a lot with L1 data

6. Page 6 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Random: Assumed Gaussian; it is reduced by taking averages: Is it Gaussian? How do we check? What could be non-Gaussian? -> bi-modal distributions, e.g., precipitation Systematic (bias): Can vary temporally & spatially. If fixed and known, it should be removed Representativeness: Occurs when information is represented at a scale different from the source of the information (e.g. representation of sonde data in a GCM grid). More important for small-scale observations. X X ? Observation errors

7. Page 7 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Real resolution of observations could be coarser than that implied by the apparent resolution/frequency of the observations -> correlations (horizontal/vertical) between observations Correlations taken into account in the observation errors covariance: characterizes observation errors -> data assimilation (DA): - diagonal errors (e.g. for obs): no correlations - non-diagonal errors (e.g. for model, “background”): correlations between variables (could be different from obs) - estimation of “background” error an important issue in DA Stratosphere: horizontal correlations ~larger than for troposphere: Flow dominated by smaller wavenumbers in stratosphere Resolution of observations

8. Page 8 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 GOES-8: ~1 km Hurricane Erin 09/09/01 ~1530 Z Courtesy James Purdom

9. Page 9 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Hurricane Erin 09/09/01 ~1530 Z MODIS: ~250m Courtesy James Purdom

10. Page 10 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 L2:  Easier to assimilate than L1 -> historically L2 data has tended to be assimilated before L1  Recent ideas from Rodgers (“information content”) -> alleviate problems associated with L2 data: a priori information L1:  less “contaminated” (e.g. by a priori information)  Errors less correlated than for L2 data  Tendency to assimilate radiances: - nadir radiances already assimilated by met agencies; - limb radiances are much harder to assimilate  Improvements in analyses & forecast skill at NWP centres Level of data L1/L2:

11. Page 11 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Three ways: Passive technologies: sense LW radiation emitted by atmosphere, SW reflected by atmosphere. - imaging: optically thin -> information on Earth surface - sounding: optically thick -> information on atmosphere Active technologies: emit radiation & measure how much scattered/reflected back GPS: measure phase delay of signal as it is refracted in atmosphere BUT: other ways of sounding the atmosphere… Sounding the atmosphere using satellites

12. Page 12 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Conventional: surface, sondes (local coverage; high spatial & temporal resolution) – temperature, humidity, winds – “Synoptic” Aircraft: local coverage; high spatial & temporal resolution – temperature, humidity, winds – “Asynoptic” Satellites: “Asynoptic” Operational satellites: ATOVS, Satwinds, SSMI (nadir; global coverage; low spatial & temporal resolution) – temperature, humidity, winds AND: Recent interest in research satellites (nadir & limb): e.g. SCIAMACHY total ozone assimilated at ECMWF Research satellites now part of Global Observing System Observation types used by the Met Office

13. Page 13 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Operational: Used by NWP centres – objective is to improve weather forecast -> measure dynamical quantities (temperature & humidity) Continuity/heritage (NOAA-17…) Near-real-time (NRT): typically within 3 hours Research: Used for research – objective is to study key issues (ozone hole; climate change…) -> measure a range of quantities, both dynamical & chemical (ozone, …) Often one-off (continuity for Envisat?) Not NRT: fastest delivery 1-2 days (useless for NWP) Operational vs Research satellites

14. Page 14 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 ATOVS Global coverage ©Crown copyright, Met Office 16/10/02: Aircraft Local coverage Observations used by Met Office

15. Page 15 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Types of satellite observations: orbits 1.Geostationary (fixed point over the equator): 60N-60S Only one orbit: 35,800 km; ¼ Earth’s surface 2.Polar: quasi-global (e.g. 600 km Hubble, 225-250 km Shuttle ) 3.Sun-synchronous (fixed equator crossing time) 4.Non sunsynchronous (variable equator crossing time) Satellite orbits

16. Page 16 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 NOAA-15 NOAA-16 NOAA-17 Goes-W Met-7 Goes-W GMS(Goes-9) Polar orbiters LEOs Geostationary satellites GEOs Courtesy J-N Thépaut ECMWF

17. Page 17 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 1.Sun-synchronous satellites (e.g. Envisat, Eos Aura):  Instruments look away from the sun: no manoeuvre to prevent the sun damaging the instruments  Cannot observe the diurnal cycle at a particular place: e.g. diurnal cycle of NO, NO 2 2. Non sunsynchronous satellites (e.g. UARS):  Can observe the diurnal cycle at a particular place  Have to do manoeuvres to prevent the sun damaging the instruments -> North look/South look for UARS MLS Diurnal cycle & orbit

18. Page 18 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 ECMWF satellite data Courtesy J.-N. Thépaut Now 29Cyr2 (since Jun 2005)

19. Page 19 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 ECMWF satellite data Courtesy J.-N. Thépaut

20. Page 20 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 ECMWF satellite data Courtesy J.-N. Thépaut

21. Page 21 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Motivation for assimilation of research satellite data

22. Page 22 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 NASA UARS (CLAES, HALOE, HRDI, ISAMS, MLS): met data (temp, winds, water vapour); chemical species (ozone, ClO). Science: JAS 1994, Cal-val: JGR 1996 EP TOMS: ozone column EOS Terra: land, water & ice (ASTER); radiation (CERES); radiation & biosphere parameters (MISR); biological & physical processes on land & ocean (MODIS); CO & CH 4 in troposphere (MOPITT) EOS Aqua: clouds, radiation & precipitation (AMSR/E); clouds, radiation, aerosol & biosphere parameters (MODIS); temp & humidity (AMSU, AIRS, HSB); radiation (CERES) EOS Aura: atmospheric chemistry (Eos MLS); temp & constituents (HIRDLS); tropospheric ozone (TES); ozone column & UV (OMI) What do research satellites measure (observe)?

23. Page 23 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 ESA: ERS-2: ozone column & ozone profiles (GOME) Envisat: temperature, ozone, water vapour & other constituents using limb, nadir & occultation geometries (MIPAS, SCIAMACHY, GOMOS); aerosol (AATSR, MERIS), sea surface temperature (AATSR); ocean colour (MERIS); land & ocean imagery (ASAR); land, ice & ocean monitoring (RA-2); water vapour column & land parameters (MWR); cryosphere & land parameters (DORIS); RA-2 calibration (LRR) Synergy: combine nadir (SCIAMACHY) with limb (MIPAS, SCIAMACHY) -> Use of DA to evaluate Envisat & study chemical distributions ESA/CSA: ODIN (OSIRIS & SMR): ozone & NO2 (columns & profiles)

24. Page 24 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 NASDA-JAXA: ADEOS: ADEOS-TOMS (ozone column), ILAS (temperature, ozone, water vapour & other constituents) TRMM (with NASA): tropical rain -> global change and environmental policy ADEOS-II: water column, precipitation & ocean and ice parameters (AMSR), land, ice and ocean parameters (GLI), ocean winds (SeaWINDS), radiation parameters (POLDER), temperature, ozone & other constituents (ILAS-II)

25. Page 25 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Research satellite observations by frequency: Different parts of spectrum sensitive to different species & different atmospheric levels - can sample the depth of the atmosphere 1.Infrared (IR): ISAMS (UARS), MIPAS (Envisat), HIRDLS (Eos Aura) 2.Visible (Vis): GOME (ERS-2), SCIAMACHY (Envisat) 3.Ultraviolet (UV): GOME (ERS-2), GOMOS & SCIAMACHY (Envisat) 4.Microwave: MLS (UARS), Eos MLS (Eos Aura) Variety-> opportunity to evaluate observations (e.g. UARS, Envisat) EM spectrum properties: e.g. microwaves less affected by clouds EM spectrum & observations

26. Page 26 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005  Novel data types: ozone (for NWP) -> radiative transfer (nadir radiances); wind information; UV forecasts  Limb/nadir synergy -> information on tropospheric ozone ; relatively high vertical resolution of limb sounders  Information to help make chemical forecasts: tropospheric ozone -> air quality  Research satellite of today -> operational satellite of tomorrow Interest in research satellites by the NWP agencies make them more attractive to the EO community Differences between research and operational satellites becoming blurred: e.g. use of Envisat data Benefits from research satellites

27. Page 27 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Is the Earth’s ozone layer recovering? Is air quality getting worse? How is Earth’s climate changing? Questions that Eos Aura & Envisat could help answer

28. Page 28 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Examples

29. Page 29 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005  GCM: dynamics with “simple” chemistry (ozone sources & sinks; Cariolle + “cold tracer”)  interaction between dynamics/chemistry/radiation;  use of operational observations DARC/MetO (3d-var), ECMWF (4d-var) Used for: Ozone forecasts & re-analyses (ECMWF) Ozone studies (DARC/MetO) Observation & model evaluation (ECMWF, DARC/MetO) What models to use in DA?

30. Page 30 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 The SH polar vortex split of Sep 2002: ozone at one level MIPAS ozone DARC analyses Blue: low ozone; Red: high ozone; 10 hPa Courtesy Alan Geer

31. Page 31 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005  CTM: range of chemical models (Cariolle + “cold tracer” -> sophisticated photochemistry) forced by off-line winds/temperature  No interaction between dynamics/chemistry/radiation Cariolle: KNMI (KF), GMAO (PSAS) Sophisticated photochemistry: NCAR (KF), BIRA-IASB (4d-var) Used for: Testing methodologies (NCAR) Ozone forecasts (KNMI, GMAO) Studies of chemical distributions (BIRA-IASB): unobserved species from observed species; information: data-rich -> data-poor regions (reconstruct diurnal cycle, e.g., Lary) Observation/model evaluation (KNMI, GMAO, BIRA-IASB)

32. Page 32 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Ozone total column, GOME KNMI/ESA http://www.knmi.nl/gome_fd Ozone hole area wrt 220 DU in SH KNMI http://www.temis.nl 2002 2003 2005

33. Page 33 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Assimilated datasets from Envisat 24 Sep 12UTC: Ozone distribution BIRA-IASB MIPAS analyses: Red (high), blue (low) Ozone evolution 31.5 hPa; 22 Sep -> 25 Sep ACRI GOMOS analyses Red (high), blue (low)

34. Page 34 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005  Coupled GCM/CTM: The aim is to have the advantages of GCM & CTM approaches. How to couple? Météo-France/CERFACS, MSC/BIRA-IASB Still at an early stage but potentially promising See also EC GEMS proposal: http://www.ecmwf.int/research/EU_projects/GEMS/index.html

35. Page 35 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Use of data assimilation: evaluate models and observations

36. Page 36 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 O-A time series: stability of analysis & “spin-up ” Ozone analysis vs MLS ozone Operational data + ozone & temperature profiles (MLS) + ozone column (GOME) Struthers et al. JGR 2002 46.4 hPa 21.6 hPa 10 hPa 4.64 hPa “Spin-up” Stability (desirable property) 12/04/97 30/04/97 Cal-val: MLS+GOME

37. Page 37 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Stats for 14-28 Sep 2002 O-F O: MIPAS ozone obs From: Geer et al., QJ 2005 Accepted Rejected StDev Bias Skewness Kurtosis - 3 Cal-val MIPAS

38. Page 38 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Motivation: excessive vertical transport in the “old dynamics” MetO Model Forecast without chem. - analysis No chem, no ozone assim, but dynamical DA - analysis Difference in ozone between experiment and analyses after 7 days, 24 th Sept 2002 /ppmm Forecast with chem. - analysis Model evaluation Experiment – Analysis: E-A Dynamics -> relatively low ozone bias

39. Page 39 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 TOMS column ozone example – 12Z 8 th Nov 2003 TOMS L3 daily gridded product Analyses interpolated to TOMS grid 12Z analyses only -> +/- 12 hour colocation time window Above 5hPa no MOCAGE; BASCOE profile used above. Independent data Courtesy Alan Geer

40. Page 40 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Global mean total column differences DARC spinup Courtesy Alan Geer

41. Page 41 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Summary of all HALOE comparisons – mean Jul-Nov 2003 Courtesy Alan Geer

42. Page 42 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Intercomparison summary: Analyses; Jul-Nov 2003 Overall, comparison vs independent data indicates that in the stratosphere outside region of the vortex, analyses are broadly consistent & agree with independent data to within 10-20%. Analyses show largest inconsistencies in following regions: Mesosphere & stratopause Region of the “ozone hole” Tropical tropopause Troposphere Problem areas being investigated: http://darc.nerc.ac.uk/asset Issues: quality of Envisat data; transport scheme; chemistry scheme

43. Page 43 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 Satellite data have been v. successfully exploited by new DA schemes (4d-var at ECMWF). DA schemes -> introducing additional satellite data that is well characterized improves system. Combined availability of new & accurate satellite observations & improvements in models -> improved extraction of information content from these new observations using DA techniques. Proliferation of new satellite instruments -> choices on what datasets to use will have to be made (also data selection/thinning). Massive investment in data handling (metadata, data management, efficient data dissemination) & monitoring (data evaluation) needed. Important that a dialogue is maintained between the data suppliers (space agencies & NWP agencies) & end-users. End-to-end approach. Use of satellite data: operational/research

44. Page 44 COST/ESF School: UTLS, Cargese, 3 – 15 October 2005 1.Operational use of research satellite data (limb geometry -> better vertical resolution; global information on ozone) -> operational use of total column ozone from SCIAMACHY by ECMWF; stratospheric H 2 O under study 2.Assimilation of novel species: aerosols -> many from research satellites 3.Assimilation of limb radiances by operational centres and research institutions -> fast and accurate radiative models (progress more advanced in the IR) -> research satellites with limb geometry 4.Chemical forecasts -> tropospheric pollution (which model?) -> research satellites to penetrate into troposphere (IASI?); combine with in situ data (observation networks) The future