Impact of Research Satellite Observations in a Data ... · Impact of Research Satellite...

Page 13rd WMO Workshop on the Impact of Various Observing Systems in NWP 9-12 Mar 2004

Impact of Research Satellite Observations in a Data Assimilation System for the Troposphere-Stratosphere

Author: W.A. Lahoz and A. O’Neill

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

!Why use research satellite observations?

!How to use research satellite observations (evolution to coupled dynamics/chemistry systems)

!Impact studies (OSEs, OSSEs)

!Conclusions/Future


! Ozone:stratosphere/radiative transfer/UV forecasts/wind information

! Limb geometry: higher vertical resolution (e.g. UTLS)

! Nadir geometry: deep weighting functions

! Global coverage: troposphere & pollution

! Information on chemical species:chemical weather/pollution/UV forecasts

! Importance of stratosphere: more focus by research satellite data

Why use research satellite observations?


Relatively good horizontal resolutionRelatively poor vertical resolution

Relatively poor horizontal resolution Relatively good vertical resolution

Can we combine the advantages of these geometries? -> synergy

•MLS/GOME•MIPAS/SCIAMACHY from Envisat•Research/operational satellites

Used by the met agencies

Used by researchgroups

Courtesy NATO ASI 2003

Limb/nadir geometries


Data assimilation using:! CTMs (e.g. off-line data + research satellites):

- total column ozone forecasts from KNMI (GOME) & BIRA-IASB (MIPAS)- stratospheric ozone forecasts from BIRA-IASB (MIPAS)- tropospheric ozone forecasts by U. Köln (surface, sondes)

! GCMs (e.g. operational + research satellites):- ozone forecasts from ECMWF (MIPAS)- ozone/total column ozone analyses from DARC/Met Office (MLS/GOME, MIPAS)

! Coupled GCM/CTM: Météo-France

How to use research satellite observations:


Recent progress in data assimilation:

! GCM/CTM:

(1) inclusion of novel species (ozone – GCM; CFCs – CTM)

(2) extension of simple photochemical parametrizations (Cariolle in GCM)

(3) better heterogeneous chemistry & inclusion of aerosols (CTM)

(4) inclusion of new geometries – limb (GCM/CTM)

(5) better characterization of model errors (GCM/CTM)

(6) radiance assimilation (GCM/CTM)

Recent progress in data assimilation using GCMs & CTMs helpsdevelop coupled dynamics/chemistry data assimilation


! OSEs: adding novel data types (e.g. nadir/limb synergy; impact of novel geophysical parameters such as ozone, stratospheric H2O)

! OSSEs (mainly used by space agencies): evaluating proposed observation types from expensive EO missions (e.g. SWIFT stratospheric winds) - note use of “information content” at NWP centres

Impact studies:


! MLS limb / GOME nadir synergy (Struthers et al. JGR 2002)

-> better analyses

-> partition information between stratosphere & troposphere

-> feed into NWP development (background error covariances for ozone, B; H2O in EU-funded ASSET project: http://darc.nerc.ac.uk/asset)

-> apply to Envisat (MIPAS/SCIAMACHY)

OSEs:


RMS (O-A) -> BLUE: rms(0-A) must less than or equal than 1-σ (MLS ozone) −> “FIT THE OBSERVATIONS”*- -*: 1-σ observation error (MLS ozone) -: rms(O-A)Talagrand (2003), Struthers et al. JGR 2002

*

*

*

*

*

*

MLS GOME MLS+GOME

UARS MLS (limb) & GOME (nadir)

3 week average April 1997


RMS (O-A) -> BLUErms(0-A) must be less than or equal than 1-σ (GOMEColumn ozone)

Other tests, e.g., comparison with independent data, support these conclusions

**

**

**

MLS

GOME

MLS+GOME

Benefits of combiningnadir/limb geometries-> several applications


! Envisat MIPAS ozone & H2O (also temperature) (troposphere-stratosphere 3d-var: Swinbank et al. 2002)

-> analyses of key species

-> Envisat cal-val

-> strategy for assimilation of Envisat data / EO strategy (ASSET: http://darc.nerc.ac.uk/asset) : compare different approaches, e.g., representation of photochemistry, radiances vs retrievals

-> studies of ozone distribution (ASSET):different models & robustness

OSEs:


DA fills in the gaps – the SH polar vortex split of Sep 2002

MIPAS ozone

DARC analyses

Blue: low ozone; Red: high ozone; 10 hPaCourtesyAlan Geer


Statistics are a global mean at 10hPa

Ozone monitoring statistics at 10hPa in DARC assimilation

Analysis

O-B Residuals

Number of accepted observations

Observations

Background

Cal-val: MIPAS


Total column ozone, 24th Sep 2002

Dob

son

units

TOMS

GOME

Analysis

Bias (overestimate)in MIPAS analyses


Ozone evaluation - summary! MIPAS NRT ozone retrievals show large bias before 18th Sep 2002.

To be corrected in future assimilation runs.! MIPAS/model errors consistent with Gaussian statistics (not shown)! Analysed ozone generally consistent with independent data (sonde,

HALOE, GOME & TOMS total columns), but:" At 2hPa, model underestimates ozone (not shown)" Lower to mid stratosphere shows 0.3-0.7 ppmv too much ozone

(not shown) ⇒ a combination of MIPAS bias and Cariolle scheme bias?

" In the polar vortex, lower stratospheric ozone amount appears too high ⇒ GOME bias, MIPAS bias (cloud contamination?), chemistry scheme bias, excessive mixing with high ozone air?

-> evaluation of contributions from observations (MIPAS), advection scheme, Cariolle scheme & DA scheme to O3 analyses


S E

S

E

ATOVSimpact

TROPOSPHERE(ATOVS)

MIPASimpact

MIPAS H2O dataESA© 2002

ppmm

ECMWF H2O data

850 K

Large variability in H2O


Issues:• Large variability of water vapour (troposphere->stratosphere): what

control variable?• Ill-conditioned vertical transform of B matrix (currently weighted by

mass and standard deviation - max in boundary layer).• Excessive increments in lower stratosphere (e.g. 50 hPa), suspect due

to spurious correlations with lower levels.

-> Need to revisit calculation of B matrix! control variable

normalised RH – Hólm et al, ECMWF; pseudo RH – Dee, GMAO! treatment of tropopause

MO investigating performance of stratospheric H2O in “New Dynamics”

Stratospheric H2O


! SWIFT winds and ozone

-> Quantify impact and test significance

-> Need to evaluate (quantitatively) future expensive observing systems

Troposphere-stratosphere 3d-var: Lahoz et al. QJ 2004

OSSEs:


Note shortcomings of an OSSE:

! Expensive (cost ~ assimilation system) -> alleviate problem: “reduced OSSE” (e.g. profiles instead of radiances)Note: “reduced OSSE” generally only useful when observation of interest has relatively high impact (e.g. stratospheric winds)

! Difficult interpretation (model dependence) -> alleviate problem: conservative errors, several methods to investigate impact

! Incest -> alleviate problem: different models to construct “truth” & perform assimilation

Despite shortcomings, high cost of EO missions meansthat OSSEs often make sense to space agencies


http://swift.yorku.ca! Based on UARS WINDII principle (Doppler effect)! 2 wind components using 2 measurements at ~90o

! Thermal emission (mid-IR) of ozone (1133 cm-1)! Global measurements of wind & ozone profiles (~20-40 km)! N- & S–look (87°N-53°S, 53°N-87°S): non sun-synchronous orbit! Errors: conservative; random; representativeness (assumed small impact)

OSSE for proposed SWIFT instrument

SWIFT wind component error

0

10

20

30

40

50

60

0 5 10 15 20 25

error (m/s)

heig

ht (k

m)

SWIFT ozone error

10

20

30

40

50

0 5 10 15 20

error (as a percentage of the observation)

heig

ht (k

m)


1. Current observing system:

No operational observations of winds for levels above those of radiosondes (~10 hPa)

Note: indirect information on winds can be obtained from nadir soundings of temperature (thermal wind; but this breaks down in the tropics)

2. Science:

! Climatologies of tropical winds

! Transport studies (e.g. ozone fluxes)

! Use assimilation to obtain 4-d quality-controlled datasets for scientific studies (e.g. climate change and its attribution)

Why SWIFT?


! Establish basis for assimilating SWIFT observations (u, v; ozone)! Investigate scientific merits of SWIFT observations

Models used:! “Truth” (ECMWF directly, or forcing a CTM) : T! Assimilation system (Met Office) (cf. incest)

Simulated observations:Operational: Control, C {MetOP, MSG, sondes, balloons, aircraft, surface}

Temperature, winds, humidity, ozoneSWIFT; C+SWIFT = Perturbation, P

Ozone, winds (stratosphere, conservative errors)

Several assimilation experiments; analyses evaluated.Qualitative & quantitative tests

Design of SWIFT OSSE


Several tests -> robustness (cf. interpretation of the OSSE)

! Qualitative (histograms, monthly means)

! Quantitative (RMS statistics, significance tests)

Assumption: We can discount the bias between the ECMWF and Met Office because it is removed when comparing P-T and C-T

Evaluation of SWIFT impact


Quantitative test. Consider “Null hypothesis”:

“The calculated monthly means {P-T; C-T} are equal given the standard deviations 1-σ of the two datasets”

“2-sided” test at the 0.95 confidence level

1. Members of each dataset {P-T; C-T} are independent, & datasets are independent

2. Include effects of persistence & correlation between the datasets -> equivalent to reducing the degrees of freedom or increasing the variance of the datasets


Y=Abs(C-T) -Abs(P-T); Zonal-wind (m/s); January 2000; Shaded:95% C.L. & Y>0. Similar results for April 2000.

10 hPa 1 hPa

Significance tests Areas > 5%


SWIFT winds

! Significant impact in tropical stratosphere (except lowermost levels)! Can have significant impact in extra-tropics when:

! SWIFT observations available! Flow regime is variable (relatively fast changing)

! Have scientific merit in that they improve:! Information on tropical winds ! Wintertime variability

! Useful for forecasting & producing analyses to help study climate change & its attribution:better models, better initial conditions, model evaluation

SWIFT ozone

! Significant impact at 100 hPa & 10 hPa (regions of relatively high vertical gradient)

Conclusions


“Reduced OSSE”: Radiances used for AMSU-A, IASI at time of SWIFT launch:

1. Expectation is that impact in tropics will not change2. Impact in the extra-tropics may remain unchanged

(impact in flow regimes which change relatively fast)

Thermal wind relationship does not hold for 1, and is not accurate in 2

Higher horizontal resolution -> Less thinning of satellite data (AMSU-A,IASI)

! Would impact stratospheric wind analyses in the extra-tropics

! Conclusions in tropics should be robust

! Conclusions for ozone analyses (100 & 10 hPa) should not change

Caveats


! Increasing importance of research satellite data: route to coupled GCM-CTM assimilation/chemical weather/pollution forecasting

! Strategies for assimilation of EO data: synergies; OSEs

! Future use of EO data & EO strategies: Space agency policies

! NWP developments: error covariances; use of research satellites; keep up with evolution of Global Observing System

! Importance of evaluating future (expensive) observing systems: OSSEs one way

Future/conclusions:


Extra slides


MOCAGE

Data from ENVISATData from GOME…

ARPEGE

Conventional dataOzone from Envisat

Chem. analyses3D ozone

Analyses met.U,V,T,q,surface

Chemical data : L2, later L1

Coupled dynamics/chemistry system


Example of limb/nadir synergy:

UARS MLS

ERS-2 GOME

Courtesy UARS MLS web-site & ESA web-site


Global map of ozone column amount 21 April 1997 12 GMTMLS ozone profile + GOME column ozone

(Dobson units: red high, blue low)


Comparisons with HALOE ozoneHALOE - analysis

Coverage: approximately 50°S to 70°N

HALOE available

HALOE - UM matches

Bias (overestimate) in MIPAS analyses


Comparisons v ozone sondes

Sonde - analysis: global

1000

100

10

1

Pressure/ hPa

1000

100

10

1

Pressure/ hPa

-2 -1 0 1 2 -2 -1 0 1 2

latitude bands

ppmv ppmv

South pole

Bias in MIPASanalyses


Monitoring of GOMOS data

! Comparison area is30-60°N & 30-60°S

! GOMOS shows little bias compared to HALOE or sonde

GOMOS cal-val:


MIPAS ozone – sonde

MIPAS shows only a small bias (max ~0.3ppmv) in the lower stratosphere when compared with sonde ozone.

Coincidence criteria are 400km, 6 hours, for data through September 2002


September mean O-B for MIPAS ozone


! The space agencies (ESA, NASA, NASDA and CSA) invest a lot of money on missions (e.g. ESA’s Envisat has cost ~ 1BEuros)! It is important to evaluate beforehand the possible benefits

of future missions, especially those involving satellites

Techniques for evaluating future missions:

! A technique often used by the space agencies is the “Observing System Simulation Experiment” (OSSE)

! Information content (e.g. Prunet et al. QJ 98, IASI)! OSSEs tend not be used as much by the met agencies

! This is due to the shortcomings of OSSEs, that make them less attractive to the met agencies

Future EO missions


OSSE goal: evaluate if the difference P-T (measured objectively)is significantly smaller than the difference C-T

! Simulated atmosphere (“truth”; T) (using a model)! Simulated observations of instruments appropriate to the study,

including errors (using T)! Assimilation system (using a model)! Control experiment C (all observations except those under study)! Perturbation experiment P (all observations)

Structure of an OSSE


Several aspects investigated (robustness):

•Tropical winds (focus on zonal-winds) SHOW RESULTS

•Wintertime variability (focus on winds)

•Brewer-Dobson circulation

SWIFT & science:


Time series

Zonal-wind

10 hPa

Jan 2000

{P-T, C-T} <0

Shaded

Similar results

For April 2000

P-T

C-T

Impact of SWIFT winds

P-T smallerthan C-T


Impact of SWIFT winds on zonal-windTropics:Significant (0.95 C.L.) (50 hPa -> 1 hPa) (cf thermal wind)

Extra-tropics:Possible (50 hPa -> 1 hPa)Impact larger when SWIFT observations availableImpact larger for regimes of variable flow (autumn, winter, spring)(Indirect information from the nadir temperature sounders, direct

information from sondes, is not sufficient to describe the winds in these cases)

Little or no impact at 100 hPa (SWIFT errors relatively high; other sources of information)


850 KM

35 800 KmSUBSATELLITE POINT

GOMS (Russian Federation)

76E

MSG

(EUMETSAT) 63 E

MTSAT (Japan)

140E

FY-2 (China)

105E

GOES-E (USA) 75W

NPOESS (USA)

GOES-W (USA) 135W

GEOS

TATI

ONARY ORBIT

Global Earth Observing for the period 2008-2010

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