Assessment of future missions

Transcription

1 Assessment of future missions Author: W.A. Lahoz Data Assimilation Research Centre, University of Reading RG6 6BB, UK Page 1 Wealth of Envisat data: ESA GOMOS: limb Ozone Water vapour Temperature + more SCIAMACHY: Limb + nadir Ozono column NO 2 column Ozone vertical profiles + more MIPAS: limb Ozone, water vapour, CH 4 N 2 O, NO 2, HNO 3, + more Page 2 1

2 Future missions: The space agencies (ESA, NASA, NASDA y 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 Page 3 Technique for evaluating future missions: A technique often used by the space agencies is the Observing System Simulation Experiment (OSSE) 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 Page 4 2

3 Note: A technique often used by the met agencies to evaluate components of an existing observing system is the Observing System Experiment (OSE) An OSE studies the impact of one observation type by removing it from the system under study Page 5 Structure of an OSSE: Simulated atmosphere ( truth ; T) (using a model) Simlated 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) Evaluation of the impact of observations under study comparing the results of C and P vs T Page 6 3

4 The OSSE has as its goal to evaluate if the difference P-T (measured objectively) is significantly smaller than the difference C-T. Page 7 Shortcomings of an OSSE (examples): Expensive (cost comparableto that of an assimilation system) -> to alleviate the problem: reduced OSSE (e.g. use profiles instead of radiances) Note: The reduced OSSE is generally only useful when the observation of interest has a relatively high impact (e.g. stratospheric winds, where there are no other observations) Difficulty of interpretation (e.g. dependence on model) -> to alleviate the problem: conservative errors, use several methods to investigate impact Incest -> to alleviate the problem: use different models to construct the truth and perform the assimilation Page 8 4

5 Despite their shortcomings, the space agencies have no obvious alternatives to OSSEs to evaluate future missions and, often, the high cost of these missions means that OSSEs make sense Page 9 Example of an OSSE, evaluate SWIFT: SWIFT is a collaboration between ESA, CSA y NASDA Possibility of flying aboard NASDA s GOSAT (~2007) Based on the same principle as UARS WINDII (Doppler effect) 2 wind components using 2 measurements at ~90 o Thermal emission (mid-ir) of ozone (1133 cm -1 ) Technology difficult to implement Global measurements of wind and ozone profiles (~20-40 km) Page 10 5

6 ESA-ESRIN, Frascati, Rome, Italy 1st Envisat Data Assimilation Summer School 18th 29t h August Page 11 Why SWIFT? 1. Current observing system: There are no operational observations of winds for levels above those of the 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) 1st Envisat Data Assimilation Summer School 18th 29t h August 6 Page 12 18th 29th August 2003

7 Design of the SWIFT OSSE (see Lahoz et al. 2003): Establish the basis for assimilating SWIFT observations (winds u, v; ozone) Investigate the scientific merits of SWIFT observations Models used: Truth (ECMWF directly, or forcing a CTM) Assimilation system (Met Office) (cf. incest) Page 13 Simulated observations: 1-9: Operational: C; 10: SWIFT; 1-10: P 1. MetOp/IASI temperature, ozone and relative humidity 2. MetOp/AMSU-A - temperature 3. MetOp/GRAS - temperature 4. MetOp/GOME-2 - ozone 5. MSG/SEVIRI cloud-derived winds 6. MetOp/ASCAT winds using surface scatterometers 7. Radiosondes and balloons - winds, temperature and relative humidity 8. Aircraft/AIREP and AMDAR - height, winds and temperature 9. Surface, ships and buoys - pressure, temperature, relative humidity and winds 10. SWIFT ozone and winds Page 14 7

8 SWIFT: N - and S - observations (87 N-53 S, 53 N-87 S): sunsynchronous orbit 1 observation per minute (2 components) - winds 16-50km, every 2km approximately - ozone 16-44km, every 2km approximately Errors (conservative; random; representativeness error considered to be relatively unimportant): 60 SWIFT wind component error 50 SWIFT ozone error height (km) error (m/s) height (km) error (as a percentage of the observation) Page 15 Assimilation experiments: 1 Month runs: January 2000 & April 2000 (C and P): 17 days (1-17 January) SWIFT NH-look (53S-87N) 17 days (18 January 3 February) SWIFT SH-look (87S-53N) 17 days (28 March 13 April) SWIFT NH-look (53S-87N) 17 days (14-29 April) SWIFT SH-look (53S-87N) Page 16 8

9 Tests of the assimilation system: 1. Time series (O-A): spin-up, bias, self-consistency 2. Histograms (O-A, O-B): Gaussian errors, impact A vs B 3. Optimal analysis (RMS O-A): Consistency with the BLUE (Best Linear Unbiased Estimate) (Ideas of Talagrand; see Struthers et al. JGR 2002) Page 17 Test 1: P Example of spin-up 3 O-A zonal-wind SWIFT m/s January 16,12, 8.9 hpa Jan 13 Jan 25 Jan 3 Feb Page 18 9

10 Test 3: P Consistent with the BLUE RMS(O-A) Zonal-wind SWIFT m/s January σ Obs error Page 19 Test 3: P Not consistent with the BLUE RMS(O-A) ozone SWIFT ppmm January σ Obs error Page 20 10

11 Evaluation of the analyses: Winds: spin up in 2-4 days small biases O-B Gaussian optimal (consistent with the BLUE) Ozone similar, except: Suboptimal in the mid and upper stratosphere (not consistent with the BLUE) -> Impact of background error, bias between the models (CTM forced by ECMWF, and Met Office) Page 21 Evaluation of the impact: Several tests -> robustness (cf. interpretation of the OSSE) Qualitative (histograms, monthly means) Quantitative (RMS statistics, significance tests) We can discount the bias between the ECMWF and Met Office because it is removed when comparing P-T and C-T Page 22 11

12 Zonal-wind 2-31 January RMS(P-T) / RMS(C-T) Less than 0.8 NH/SH look Global 90S-60S 60S-30S 30S- 30N 30N- 60N 60N- 90N 100hPa hPa hPa hPa Page 23 Zonal-wind 2-17 January: RMS(P-T) / RMS(C-T) NH look Global 90S-60S 60S-30S 30S- 30N 30N- 60N 60N- 90N 100hPa hPa hPa hPa Page 24 12

13 Zonal-wind January: RMS(P-T) / RMS(C-T) SH look Global 90S-60S 60S-30S 30S- 30N 30N- 60N 60N- 90N 100hPa hPa hPa hPa Page 25 Meridional-wind 2-31 January: RMS(P-T) / RMS(C-T) NH/SH look Global 90S-60S 60S-30S 30S- 30N 30N- 60N 60N- 90N 100hPa hPa hPa hPa Page 26 13

14 ozone 2-31 January: RMS(P-T) / RMS(C-T) NH/SH look Global 90S-60S 60S-30S 30S- 30N 30N- 60N 60N- 90N 100hPa hPa hPa hPa Page 27 Null hypothesis: The calculated means are equal given the standard deviations 1-σ of the two datasets 2-sided test at the 0.95 confidence level (0.95 C.L.: data outside the region between 2.5% and 97.25% of the area) 1. Members of each dataset {P-T; C-T} are independent, and the datasets are independent 2. We include effects of persistence & correlation between the datasets (see Chapter 5 of Wilks) -> equivalent to reducing the degrees of freedom or increasing the variance of the datasets Page 28 14

15 Significance tests Y=Abs(C-T) -Abs(P-T) Zonal-wind (m/s) 10 hpa January 2000 Shaded: 95% C.L. & Y>0 Page 29 Significance tests Y=Abs(C-T) -Abs(P-T) Zonal-wind (m/s) 1 hpa January 2000 Shaded: 95% C.L. & Y>0 Page 30 15

16 Significance tests Y=Abs(C-T) -Abs(P-T) Zonal-wind (m/s) 10 hpa April 2000 Shaded: 95% C.L. & Y>0 Page 31 Significance tests Y=Abs(C-T) -Abs(P-T) Meridional-wind (m/s) 10 hpa January 2000 Shaded: 95% C.L. & Y>0 Page 32 16

17 Significance tests Y=Abs(C-T) -Abs(P-T) Ozone (ppmm) 10 hpa January 2000 Shaded: 95% C.L. & Y>0 Page 33 Impact of SWIFT winds on zonal-wind Tropics: Significant (0.95 C.L.) (50 hpa -> 1 hpa) (cf thermal wind) Extra-tropics: Possible (50 hpa -> 1 hpa) Impact larger when SWIFT observations available Impact 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) Page 34 17

18 Impact of SWIFT winds on meridional-wind May be significant (0.95 C.L.) at 10 hpa over various areas of the globe (but robustness is not clear) The impact at 100 hpa, 50 hpa & 1 hpa appears to be less significant than at 10 hpa Impact larger when SWIFT observations available Impact smaller than for zonal-wind (larger relative errors for meridional-wind; analysis increments tend to be non-divergent-> in zonal direction?) Page 35 Impact of SWIFT ozone Tropics: Significant (0.95 C.L.) at 10 hpa Extra-tropics: May be an impact at 10 hpa, but appears to be less significant Impact larger in regions of relatively high ozone vertical gradient (100 & 10 hpa vs 50 & 1 hpa) (importance of the relatively high vertical resolution of SWIFT) Page 36 18

19 SWIFT and science 3 aspects investigated (robustness): Tropical winds (focus on zonal-winds) Wintertime variability (focus on winds) Brewer-Dobson circulation Page 37 P-T is less than C-T P-T Monthly mean Zonal-wind January 2000 P<T shaded Page 38 19

20 C-T Monthly mean Zonal-wind January 2000 C<T shaded Page 39 P-T is less than C-T P-T Monthly mean Zonal-wind April 2000 P<T shaded Page 40 20

21 C-T Monthly mean Zonal-wind April 2000 C<T shaded Page 41 P-T is less than C-T P-T Time series Zonal-wind 10 hpa Jan 2000 P-T shaded Page 42 21

22 C-T Time series Zonal-wind 10 hpa January 2000 C<T shaded Page 43 P-T is less than C-T P-T Time series Zonal-wind 1 hpa January 2000 P<T shaded Page 44 22

23 C-T Time series Zonal-wind 1 hpa January 2000 C<T shaded Page 45 P-T is less than C-T P-T Wintertime variability 1-σ Zonal-wind (m/s) January 2000 Shaded P<T Page 46 23

24 C-T Wintertime variability 1-σ Zonal-wind (m/s) January 2000 Shaded C<T Page 47 Conclusions: SWIFT winds Significant in the stratosphere (except the lowermost levels) in the tropics Can have significant impact in the extra-tropics when: SWIFT observations are available The flow regime is variable (relatively fast changing) They have scientific merit in that they improve: Information on tropical winds Wintertime variability Useful for forecasting and producing analyses to help study climate change and its attribution: better models, better initial conditions, model evaluation Page 48 24

25 Conclusions: SWIFT ozone Significant impact at 100 hpa & 10 hpa (regions of relatively high vertical gradient) Page 49 Caveats: Reduced OSSE : Radiances to be used for AMSU-A, IASI (profiles for SWIFT) in ~2007: 1. Expectation is that impact in tropics will not change 2. 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 Page 50 25

26 Caveats: Higher horizontal resolution in ~2007: Less thinning of satellite data (AMSU-A,IASI) Would impact the stratospheric wind analyses in the extratropics Conclusions in the tropics should be robust Conclusions for ozone analyses (100 & 10 hpa) should not change Page 51 26