1. Rutherford Appleton Laboratory OEM retrievals with IASI, AMSU and MHS PM1 Telecon 9 April 2014 R.Siddans, D. Gerber (RAL),

2. Agenda 15:00 Review KO minutes / actions 15:10 Task 1: summary of literature review 15:25 Task 1: Analysis of AMSU+MHS observation errors based on FM simulations 15:40 Task 2: Summary of Task 2 & 3 results including Comparison of RAL and Eum ODV results Comparison of IR and MWIR retrievals over land and sea 16:30 Plans for remaining tasks 16:45 Discussion date for next meeting 17:30 close

3. Actions from KO

4. Task 1: Literature Review Overview of literature presented on AMSU data processing Different methods used to analyse the measurements (and errors) Presentation of the most significant results Conclusions for our own study

5. Different Data Processing Methods Linear Regression Algorithms A “heuristic” relation between scene brightness temperate and humidity for select channels is exploited. No error treatment, so less useful as a source of information. Physical Methods (i.e. OEM) Finding the most likely state within the boundaries of measurement errors and climatological variability. Requires a solid assessment of all errors, hence good source of information. Neural Networks “Black-box” handling of measurement/instrument errors in the training of the network, so no explicit error quantification.

6. Summary of Literature and Relevant Findings

7. Overview of AMSU Random Errors from Literature

8. Comparison of MetOffice Sy vs. Literature 65432106543210

9. Overview of AMSU Systematic Errors from Literature

10. Some Specific Findings Atkinson 2001: There was a 40K bias of some AMSU-B channels pre September 1999 (data transmitters). Wu 2001: RTTOV statistics compared to observations indicate random errors (and biases) far larger than pure NEBT. Chou 2004: Standard deviation of error is different for off-nadir views than for nadir view. The sign of the difference is channel dependent! Olsen 2008: Channel 4 (post Aug 2007 NEBT increase) no information on surface or atmosphere – use for cloud flagging only. Mitra 2010: Temperature anomaly in channel 7 (They exploit it to detect cyclones). Generally NEBT was higher at the start (Atkinson 2001) and higher towards the end (MacKaque 2001, 2003).

11. Some Specific Findings Atkinson 2001: Slight gain drop and NEBT increase in Chs.18 & 20. Thermal oscillation of Ch.16 in early 1999, also Temp anomaly of Ch.17. Li 2000, Rosenkranz 2001: Critical dependence on first-guess profile (iterative pre-selection). Geomagentic field correction to Ch.14. Eyre 1998: Retrieval more affected by correlations in background error covariance matrix than observation error.

12. Conclusions NEBT values in literature roughly consistent. Increased numbers (in some channels) for later publications. Some channels require bias correction (corrected in latest version of Lv1b data). Some channels have intermittent problems (abnormal bias or NEBT, so select dates accordingly) Most recent data of NEBT consistent with Met Office “diagnosed error”. All records of total measurement error from NWP analysis consistent with Met Office “operational error”.

13. Testimng RAL implentation: RAL vs Eumetsat RT simulations

14. RAL vs Eumetsat Initial Cost function

15. Estimation of AMSU+MHS errors: Simulations from PWLR

16. Observation – simulations (PWLR)

17. Observation - simulations

19. Observation – simulations (IASI)_

20. Observation – simulations (after bias correction and retrieval)

21. Observation – simulations (x-track dependence, from PWLR)

22. Observation – simulations (x-track dependence, from IASI retrieval)

23. Observation – simulations after MW bias correction

24. Observation coveariance derived from MW residuals from IASI retrieval

25. Task 2 & 3 Retrievals run over both sea (T2) and land (T3) All 3 days (17 April, 17 July, 17 October 2013) IR-only retrievals compared to Eumetsat ODV Differences small cf noise and mainly related to different convergence approach, which affects scenes for which final cost high (deserts, sea ice) MWIR retrieval run with 2 options for Sy Correlated (as previous slide) Uncorrelated (same diagonals) Linear simulations also performed for 4 sample scenes to assess information content Additional case of 0.2K NEBT (uncorrelated) Approximate perfect knowledge of MWIR emissivity

30. Summary of DOFS

31. Summary from Linear Simulations Using the derived observation errors, IASI+MHS add 2 degrees of freedom to temperature and about half a degree of freedom to water vapour. Effects on ozone are negligible. Neglecting off-diagonals reduces DOFS on temperature and water vapour by about 0.1 (a small effect). For temperature, the improvements are related mainly to the stratosphere though some improvement is also noticeable in the troposphere, esp over the ocean (where the assumed measurement covariance is relatively low). For water vapour improvements are mainly related to the upper troposphere, and penetrate to relatively low altitudes in the mid-latitudes. Assuming 0.2 K NEBT errors to apply to all channels adds an additional degree of freedom to temperature and an additional half a degree of freedom to water vapour, in some cases considerably sharpening the near- surface averaging kernel.

32. Assessment of full Retrieval Based on comparing retrieval to analysis (ANA), Eumetsat retrieval (ODV), PWLR and analysis smoother by averaging kernel (ANA_AK): x’ = a + A ( t - a ) Where a is the a priori profile from the PWLR, t is the supposed "true", A is the retrieval averaging kernel Profiles smoothed/sampled to grid more closely matching expected vertical resolution (than 101 level RTTOV grid): Temperature: 0, 1, 2, 3, 4, 6, 8, 10, 12, 14, 17, 20, 24, 30, 35,40,50 km. Water vapour: 0, 1, 2, 3,4, 6, 8, 10, 12, 14, 17,20 km Ozone: 0, 6, 12, 18, 24, 30, 40 km. The grid is defined relative to the surface pressure / z*.

33. Mid-lat land full retrieval: Measurements and residuals: IR only

34. Mid-lat land full retrieval: Measurements and residuals: MWIR

35. Mid-lat land full retrieval: Profile comparisons: IR only

36. Mid-lat land full retrieval: Profile comparisons: MWIR

37. Mid-lat ocean full retrieval: Measurements and residuals: IR only

38. Mid-lat land full retrieval: Measurements and residuals: MWIR

39. Mid-lat ocean full retrieval: Profile comparisons: IR only

40. Mid-lat ocean full retrieval: Profile comparisons: MWIR

41. Cost function + Number of iterations: IR only

42. Cost function + Number of iterations: MWIR

43. IR vs MWIR: Temperature

44. IR vs MWIR: Water vapour

45. Latitude dependence: MWIR

46. Latitude dependence: IR only

47. View dependence: MWIR only

48. View dependence:IR only

49. IR only

50. MWIR

51. Summary from full retrievals Differences between (RAL) retrievals and (Eumetsat) ODV are generally very small, particularly compared to the estimated retrieval error Remaining differences probably due to convergence approach Ice surface remain problematic in MW due to the difficulty defining the surface emissivity. For now we focus on results at latitudes tropical and mid-latitudes (60 S to 60 N). Desert surfaces problematic in IR – may be affecting derivation of MW bias correction + error covariance over land (?) Including AMSU+MHS generally reduces estimated errors (as in linear simulations), but slightly degrades comparison with analysis The apparent degradation in performance in terms of agreement with analysis, accounting for kernels, is largely independent of viewing angle, latitude, and whether observations are over land or sea. Including or not off-diagonals in the AMSU+MHS observation covariance has a minor effect Benefit of AMSU+MHS in OE will be more important in cloudy scenes

52. Task 2: OEM (MWIR/Metop-B) over ocean, clear-sky Implement the IASI product processing facility (PPF) settings (as provided by Eumetsat) in our IASI retrieval scheme and verify that the scheme produces consistent results. Apply this scheme to the Eumetsat selected days of IASI and AMSU/MHS cloud-free data over ocean, to generate results for IR only and MW+IR (MWIR). These will be evaluated using the diagnostics: PWLR, OEM(IR), OEM(MWIR) cf reference profiles (ECMWF analysis) vertical profiles of bias, standard deviation histograms and scatter plots for selected pressure levels maps of departures The water-vapour shall analysed in mixing and relative humidity. DOFS, AKs, fit residuals of OEM(IR) cf OEM(MWIR) Eumetsat masking to be used, though could refer to our own IASI cloud flagging if cloud-related issues suspected Compile and discuss results in DFR delivered prior to PM1

53. Task 2+3: Next steps Current MW obs covariance over land affected by degradation in window channel obs-sim std.dev. From IASI retrieval compared to PWLR Repeat analysis using sims based on PWLR and/or analysis Check if current apparent degradation using MW in comparison to ANA_AK is due to improvement in sensitivity or “real” degradation in performance (compare IR-only also to ANA_AK for MWIR) Check if use of other cloud flags change stats (currently using cloud fraction < 0.01) Other suggestions ?

54. Task 4: OEM (MWIR/Metop-B) over land, clear-sky, with variable emissivity Extend state vector to include land emissivity State represented by principle components Already implemented in RAL IASI Ozone scheme based on UW/CIMS principle components Will consider if RAL channel selection has advantages for retrieving emissivity (was based on info content for emissivity) Potential for correlations between MW and IR emissivities to be investigated Repeat analysis of Task 2 to assess results with emissivity fitted Results included in DFR produced before PM2

55. Task 5:OEM (MWIR/Metop-B) in partially or fully cloudy IFOVs Two retrieval configurations will be assessed, building on the optimum retrieval configuration following Task 4: 1.IASI L2 cloud information (provided by Eumetsat) is used to identify cloud and thereby select a sub-set of IR channels assumed insensitive to a given cloud, using the approach of McNally and Watts. Cloud contaminated channels ignored by “inflating” Sy. 2.Cloud is retrieved, represented (in IASI) as a black body with given area fraction and pressure (using RTTOVs cloud modelling). Adapted retrieval applied over land and sea Analysis of task 2 repeated to assess scheme Land and sea separately As function of cloud fraction, pressure

56. Task 6: Retrievals with one or more missing AMSU channels Results will be analysed with a view to drawing conclusions re performance of Metop-A vs Metop-B and for the combination with/without channel 7 channels 3,7 and 8. DFR updated prior to PM3

57. Task 7: Reporting and consolidation of data sets Final report delivered (consolidated version of DFR produced at end of each task) Organise and document the processed output files (hdf5 or netcdf?) Organise and document the analysis outputs (hdf5?) Statistics, AKs, DOFSs etc

58. CFIs   

59. Schedule

60. EventLocationDeliverablesPlanned Date Kick-Off Meeting Teleconferenec e 9 th December 2013 PM 1TeleconferenceReport on Tasks 1-2 1 st April 2014 PM 2EUMETSATReport on Tasks 3-4 24 th June 2014 PM 3TeleconferenceReport on Tasks 5-6 11 th November 2014 Final Presentation EUMETSATFinal Report, Datasets and Presentation 9 December 2014 Meetings / deliverables

61. IR only

62. MWIR