key 13: the future observing system in the utls

COST/ESF School: UTLS, Cargese, 3-15 October 2005

Key 13: The future observing system in the UTLS

Author: W.A. Lahoz

Data Assimilation Research Centre, University of Reading RG6 6BB, UK


Among the maxims on Lord Naoshige’s wall there was this one: “Matters of concern should be treated lightly.” Master Ittei commented, “Matters of small concern should be treated seriously.” Among one’s affairs there should not be more than two or three matters of what one could call great concern. If these are deliberated upon during ordinary times, they can be understood. Thinking about things previously and then handling them lightly when the time comes is what this is all about. To face an event and solve it lightly is difficult if you are not resolved beforehand, and there will always be uncertainty in hitting your mark. However, if the foundation is laid previously, you can think of the saying, “Matters of great concern should be treated lightly,” as your own basis for action.

Prepare well…

Hagakure, The Book of the Samurai


•The importance of the UTLS region:

NWP; climate; monitoring; understanding atmosphere (obs, models)

•What information we require from the UTLS:

Geophysical parameters, coverage, data transmission & resolution

•How can we provide this information:

What is the current global observing system and how it should evolve

•How can DA help to provide this information & quantify value of global observing system components?

Topics:


The importance of the UTLS region


Observation types used by Met Office for NWP

UTLS


Courtesy IGACO

UTLS


Importance of UTLS

Radiative-dynamics-chemistry feedbacks associated with strat O3

& relevant to studies of climate change & attribution (WMO 1999)

Important role UTLS water vapour plays in atmos radiative budget (SPARC 2000)

Need realistic representation of the STE & between tropics & extra-tropics in strat -> key role in the distribution of strat O3 (WMO 1999) -> radiative budget

ALSO: Quantitative evidence knowledge of the strat state may help predict the tropospheric state at time-scales of 10-45 days (Charlton et al. 2003) -> strat—trop connections


Importance of water vapour

Radiation: Dominant GHG in atmosphere

Radiative forcing from water vapour

Dynamics: Diagnostic of atmospheric circulation

Transport & distribution of tracers

Chemistry: Source of OH; PSCs; HOx cycles

Ozone loss via PSCs & HOx


Recommendations from SPARC assessment on UT/S H2O

Quantify & understand differences between sensors: - importance of high resolution in situ data for trop/strat transport

Strong validation programmes: - previous lack in UT

Continuity of measurements to determine long-term changes especially stratospheric H2O (what is the trend?)

Monitor UTH to determine long-term variations. - Need complementary observations

Process studies of UTH & convection. - Joint measurements of H2O, cloud microphysical properties & tracers with signature of “age of air”

More observations in tropical tropopause region (15-20 km) (in situ & remote sensing) needed to improve understanding of STE

Monitor stratospheric H2O (CH4 measurements desirable). Overlap of future satellites with current instruments

Theoretical work to understand observations


Integrated Global Atmospheric Chemistry Observations (IGACO) / Integrated Global Observing Strategy (IGOS)-> identified four grand challenges in atmospheric chemistry:

• Tropospheric air quality: O3, CO,…

• Oxidation efficiency of the atmosphere: O3, CO,

• Stratospheric chemistry and ozone depletion: O3, H2O,…

• Chemistry-climate interactions: CO2, O3, H2O,…

Increased recognition of importance of chemistry

Atmospheric chemistry:

Role of UTLS


Importance of ozone

Recognition of key role of stratospheric O3 in determining temperature distribution & circulation of atmosphere ->

Incorporation of photochemical schemes of varying complexities into climate models:

Coupled climate/chemistry models (e.g. Austin 2002)

CTMs for study of ozone loss (e.g. Khattatov et al. 2003)

Cariolle scheme in NWP systems (ECMWF; Struthers et al. 2002)

SPARC CCMVal initiative: evaluate Chemistry-climate models


• Paucity of observations of key species (H2O, O3): time and space; coverage

• Model shortcomings: parametrizations, e.g., convection

• Coupling dynamics/radiation/chemistry: how to couple? how to include aerosols?

• Many processes require high temporal & spatial resolution: observations & models; higher resolution DA (balance?)

• Lack of global observations of stratospheric winds in the current operational meteorological system

• We have no good current estimates of state of the tropical stratosphere

Challenges in UTLS


What information we require from the UTLS


Courtesy IGACO 2004Chemical variables

Dynamical (and other) variables


Based on IGACO

Group 1: O3, H2O, CO2, CO, NO2, BrO, ClO, HCl, N2O, CFCs, ClONO2 & aerosol optical properties.

•Reasonably comprehensive set of global observations for both troposphere & stratosphere using sparse number of LEOs, g-based networks & aircraft measurements.

•Good atmospheric modelling capabilities.

•Good network of g-based & satellite observations that only require maintenance & some gaps to be filled. Routine aircraft observations but not yet comprehensive enough.

•DA in good shape.

Observation requirements


Courtesy IGACO 2004

Target/threshold

(1) Hours (NWP);(2) days-weeks (O3 loss,…);(3) months (climate

research)


Based on IGACO

Group 2: CH4, HCHO, VOCs, SO2, HNO3, OClO, NO, CH3Br, the halons, and j(NO2) and j(O1D).

•All current satellites are in experimental “demonstration” mode & only have limited lifetime.

•Some g-based in situ measurements.

•Except for CH4, global network sparse.

•Next 10 years need to be spent developing instrumentation & putting monitoring infrastructure in place.

Observation requirements


Courtesy IGACO 2004 **: in situ measurements


Courtesy IGACO 2004 Aerosol requirements


How can we provide this information


Ground-based data


Geostationary satellite orbitcourtesy NASDA: GEO

High temporal resolution->

Diurnal variabilityNow-casting

Quasi-polar satellite orbits courtesy www.planetearthsci.com: LEO

High spatial resolution & global coverage->

NRT information for initializing NWP models


Recent developments to take account of

Satellite data (Research)

• NASA: EOS-Terra, EOS-Aqua, EOS-Aura

• ESA: ERS-2, Envisat, GMES Sentinels (esp. 4-5)

• NASDA: ADEOS-1,-2, GOSAT

• ESA/CSA: ODIN

Future satellite data (Operational): e.g. METOP, MSG

Synergy between research & operational satellite data


Study & monitoring of atmospheric composition & transcontinental pollution, a minimum set of requirements can be identified:

• Provision of height-resolved observations of key parameters in the stratosphere and UTLS: O3 and H2O.

• Provision of tropospheric column observations of key parameters: O3, CO2, CO, CH4.

• Provision of information appropriate for estimating sources and sinks of key parameters: CO2, CO, CH4.

• Provision of dynamical information: pressure, temperature, winds.

• High benefit/cost ratio for observation platforms.

Some key data requirements


• Difficult to find observing platforms that satisfy all these minimum data requirements. GEOs; LEOs.

• Importance of synergy with other missions (operational & research). A

synergy similar to A-train would enhance the platforms considered and could make them more attractive.

• Combine with in situ networks. High spatial & temporal resolution + global

• Need to evaluate in a quantitative way. A recommendation would be OSSEs; they are already used by ESA to evaluate future missions. Role of DA. See DA 12

• Multi-disciplinary task: involve all actors in mission (instrument teams, modellers, theoreticians…)

Considerations for GOS


1. Limb/nadir geometries-> stratosphere/troposphere

2. Different instruments/species/frequencies (ozone, water vapour) -> cal-val/robustness/extend domain

3. Model/observations evaluation (using DA) ->cal-val

4. Dynamics/chemistry (partition effects; improved assimilation; unobserved species)

5. Operational/research (chemistry feedbacks; use all data)

6. Geostationary/polar satellites (use all data)

7. In situ + satellite (good resolution + global coverage)

Synergies:


Operational/research synergy:

Already happening at a number of met agencies

ECMWF: operational use of GOME total ozone data (April 2002 – June 2003), MIPAS data (Sep 2003 – April 2004) for ozone and SCIAMACHY total ozone data (Sep 2004 - )

Met Office (with U. Reading/DARC): assimilation of research satellite data with operational data, ozone + temperature (UARS MLS & GOME + operational; MIPAS + operational)

Météo-France (with CERFACS): development of a coupled NWP/chemistry assimilation system

Also BIRA-IASB/MSC


Likely outcomes from operational/research data synergy:

Operational use of research satellite data: ozone (already assimilated at ECMWF), stratospheric H2O

Limb/nadir synergy: combine advantages from each geometry

Satellite constellations: operational/research satellites

Assimilation of limb radiances by research/operational groups.

Development of fast & accurate RT models.Progress more advanced for IR radiances than UV/Vis

Chemical forecasting & tropospheric pollution forecasting

Coupled dynamics/chemistry DA systems (GCM/CTM)


Relatively good horizontal resolution Relatively poor vertical resolution

Relatively poor horizontal resolution

Relatively good vertical resolution

Combine the advantages of these geometries

-> synergy

Used by met agencies

Used by research groups

Courtesy NATO ASI 2003


Example of limb/nadir synergy:

UARS MLS

ERS-2 GOME

Courtesy UARS MLS web-site & ESA web-site


The Earth Observing System AM Constellation

Landsat-7EO-1 SAC-C

Terra27 min

12 min1 min


How can data assimilation help?


• Information on Earth System (observations - Truth) discrete in space & time

• Further progress: quantification -> observational “information gaps” need to be filled in (see DA 11)

• Models (understanding) of how information varies between discrete set of observations

• Observations and models have errors

Information


• Filling in “information” gaps requires observational & model information:

• How can we combine in an objective way, information from observations with information from a model of evolving system, taking account of errors in observations and model?

• Framework of data assimilation encompasses multiple techniques from estimation & control theories that can be used to address this question (NATO ASI 2003).

• DA tells us how to use an objective model to interpolate in space & time information from observations, taking due account of observation & model errors


1. Observations (truth): satellite, ground-based, aircraft, sondes,…

2. Models (understanding): GCMs, CTMs, coupled GCM/CTM

3. Errors: observations (random, bias, representativeness)

4. Errors: models (“background”: B, “model”: Q)

5. Algorithms: variational (3d- & 4d-var), sequential (KF & variants), ensembles

6. Assimilation cycle: quality control, initialization, analysis, forecast

Ingredients of DA:


Need to take account of recent atmospheric model developments(also increases in computing power):

•Increases in resolution:

horizontal: T511 at ECMWF; vertical in UTLS

•Top of atmospheric models extended upwards

•Improve forecasting & long-term capability

•Extend range of validity of forecasts; novel geophysical parameters

•More consistent & realistic climate models

•Confront & evaluate forecast & climate models (done at NWP centres)

•ALSO many obstacles to be removed (e.g. access to large EO archives & metadata, common formats)


& of recent developments in DA:

GCM:

1. incorporation of “novel” atmospheric species (ozone)

2. extensions of simple photochemical parametrizations (Cariolle)

3. incorporation of novel observation geometries (limb)

4. improvements in error characterization of model

5. radiance assimilation

CTM:

1. extension of models to include novel chemical species (e.g. CFCs)

2. improvements in heterogeneous chemistry

3. incorporation of aerosols (troposphere & stratosphere)

4. improvements in error characterization of model

5. radiance assimilation


• NWP: UV-forecasting; air quality

• Radiance assimilation code: temperature, ozone

• Monitoring

• Constraints on other chemical species

• Test chemical theories

• Tracer information

Specific example: Why ozone DA?


BUT: Challenges in DA:

• Bias models/DA systems -> inappropriate increments?

• Assimilation of water vapour in stratosphere/tropopause region

• Assimilation of “novel” geophysical parameters (e.g. ozone, stratospheric winds) into NWP systems

• Synergy from measurement geometries

• Coupled dynamics/chemistry in data assimilation

• Limb radiance assimilation

• Assimilation of novel photochemical species

(e.g. CFC-11, CFC-12, ClONO2)

• Aerosol assimilation (stratosphere & troposphere)

• Tropospheric chemistry

• Novel retrieval methods (e.g. tomography)

• Data management


Biases in DA?

Position of parcels after 50 days; parcels launched from the tropics; Schoeberl et al. 2003

CTM forced by a DA system CTM forced by a GCM


What does UTLS GOS require?

•Global, ht-resolved meas of several key chemical species: H2O, O3:High vertical resolution: ~1 km or better; horizontal resolution ~100 km

•In situ measurements of several key chemical species: H2O, O3:V. high vertical & horizontal resolution (~100’s metres) (BUT not global)

•Global tropospheric columns of several key species: CO, CH4, CO2, O3

•Global, ht-resolved meas of wind:High vertical resolution: ~2 km or better; 2 wind components (unless use DA)

•Continuity of measurements

-> Radiative budget; dynamics information; chemical distributions

-> Sources & sinks of pollution/transcontinental transportObservational requirements for CO2: Houweling et al. (2004)

-> Dynamics & transport

-> Heritage; monitoring


DA has an important role to play in setting up GOS for UTLS

Benefits: Climate studies – better models & simulations Monitoring – better observations (quality, coverage) NWP – better use of observations, better models OSSEs – quantification of future observations (see DA 12) - note methodology & caveats

-> impact on society: health, compliance with treaties, information for policy makers,…

BUT: models and observations are important ingredients!

Note CAPACITY study: http://www.knmi.nl/capacity/workshop.html Looked at development of operational atmospheric chemistry missions

Conclusions


850 KM

3 5 8 0 0 K m SUBSATELLITE POINT

GOMS (Russian Federation)

76E

MSG

(EUMETSAT) 63 E

MTSAT (J apan)

140E

FY-2 (China)

105E

GOES-E (USA) 75W

NPOESS (USA)

GOES-W (USA) 135W

G E O S T

A T I O N A R Y

O R B I T

Global Earth Observing system for 2008-2010:An artist’s view – need a scientific view!

key 13: the future observing system in the utls

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