1. © Crown copyright 2007 Impact studies with satellite observations at the Met Office John Eyre and Steve English Met Office, UK 4th WMO Workshop on "The impact of various observing systems on NWP; Geneva; 19-21 May 2008

2. © Crown copyright 2007 Impact studies with satellite observations at the Met Office Focus on results with implications for design of the GOS ATOVS – 1, 2, 3 satellites (reminder) Steve English ATOVS RARSBrett Candy ATOVS MetOpBrett Candy MetOp – IASI and ASCATFiona Hilton, Simon Keogh Cloudy AIRS radiancesEd Pavelin WindsatBrett Candy GPS-RO – COSMIC and othersMike Rennie AMVsMary Forsythe Ground-based GPSAdrian Jupp

3. © Crown copyright 2007 How many microwave sounders? Reduction in forecast rms error Impact Amount of data 0% 50% 100% 1 st ATOVS ~75% of impact at 45% of data coverage 2 nd ATOVS ~95% of impact at 85% of data coverage 3 rd ATOVS 100% of impact at 100% of data coverage Albach Workshop results: summary from 10 experiments

4. © Crown copyright 2007 How many microwave sounders? Conclusions First and second AMSU are very important to NWP Third satellite has positive impact overall, but its main role is robustness and mitigating data delays Complete global coverage is very important – more data improve forecasts if they fill gaps in data coverage Impact of 4 th satellite demonstrated with 4D-var … but it is still most important to fill the gaps!!

5. © Crown copyright 2007 Regional ATOVS Retransmission System (RARS) forecast impact Forecast benefit of timely ATOVS data 2 experiments: All ATOVS: data assimilated regardless of arrival time RARS: ATOVS global + fast delivery data from 14 RARS stations Baseline: operations with cut-off = 2h45 NHSH

6. © Crown copyright 2007 500 hPa height. RMS difference between analyses with all ATOVS and operationally-available ATOVS ATOVS data missing cut-off would benefit N Pacific and S Hem. Regional ATOVS Retransmission System (RARS) forecast impact

7. © Crown copyright 2007 Met Office global NWP index Skill score: S = 1 – r f 2 /r p 2 r f = rms forecast error r p = rms persistence error Weighted as table S mean N = (1 – S mean ) -½ Index = 100 x N / N 0 N 0 = value on 31 March 2000 June 2007 value = 130 1% reduction in r.m.s. error 1% increase in Index Weights Forecast period T+24T+48T+72T+96T+120 NH PMSL108644 H500642 W25012 TR W850532 W2506 SH PMSL54322 H500321 W2506

8. © Crown copyright 2007 MetOp ATOVS Switch from NOAA-15 to MetOp improved forecast skill: +0.6 on Met Office global NWP index Switch to operations was made on 17 January 2007 only 90 days after launch! Rapid access to new data is important to users

9. MetOp IASI Red – Used (Sea/Land, Clear/MWcloud) Yellow – Used (Sea/Clear only) Blue – Used (1D-Var preprocessor only) Cyan – Rejected Green / Lime – Rejected water vapour channels Channel selection

10. © Crown copyright 2007 24 May – 24 June 2007 Preferred configuration include water vapour channels obs errors in 4D-Var: 0.5K / 1K / 4K Met Office global NWP index +1.21 v obs, +0.80 v analysis +1.0 overall Compare with AIRS for same period +0.63 v obs, +0.12 v analysis +0.37 overall normally see more impact from AIRS MetOp IASI impact trial results

11. © Crown copyright 2007 H100,H50 H50 short range PMSL H500, Winds T100, T50 H500, Winds Down is Good! MetOp IASI Change in rms forecast error v observations

12. © Crown copyright 2007 IASI initial impact of +1.0 is top of: 3 x ATOVS on NOAA platforms + ATOVS on MetOp AIRS SSMIS Current use of data is cautious cloud-free fields of view over sea restricted channel set high observation errors Much more impact to come … MetOp IASI summary

13. © Crown copyright 2007 Cloudy AIRS radiances In current assimilation of AIRS and IASI, cloud-affected obs are rejected only a small proportion of observations retained Moving towards assimilation of cloud-affected radiances simple cloudy RT models allow careful use of channels peaking above cloud Cloud top Weighting functions of channels peaking above cloud

14. © Crown copyright 2007 Cloudy AIRS radiances Increased AIRS usage in cloudy regions

15. © Crown copyright 2007 Cloudy AIRS radiances Impact of assimilating AIRS in cloudy areas twice as many observations assimilated observations assimilated in meteorologically active areas +1.0 points on Met Office global NWP index equivalent to doubling overall impact of AIRS NWP index change with cloudy AIRS assimilation

16. © Crown copyright 2007 MetOp ASCAT C-band scatterometer Geophysical model function to transform wind vectors to backscatter coefficients Ambiguous wind vector retrievals - typically 180 degree ambiguity in wind direction

17. © Crown copyright 2007 MetOp ASCAT wind speed performance

18. © Crown copyright 2007 MetOp ASCAT Tropical Cyclone Gonu, June 2007 ASCAT + MSG IR C-band - far less rain-contaminated data than for Ku- band instruments

19. © Crown copyright 2007 MetOp ASCAT impact trial results 24 May – 24 June 2007 Met Office global NWP index: +0.35 v obs +0.35 v obs neutral v analysis in presence of QuikSCAT and ERS-2 data

20. © Crown copyright 2007 Scatterometers impact on Met Office global forecasts ASCAT is giving approx. same impact as Seawinds 2 global-coverage scatterometer missions provide significantly more benefit to NWP than one Met Office, Met R&D Technical Report 511, 2008 Trial compared with NO-SCAT control NWP index v obs NWP index v analysis All scatterometers+0.97-0.07 ASCAT only+0.61+0.29 QuikSCAT only+0.66-0.08

21. © Crown copyright 2007 WindSat wind vectors QuikScatWindSat WindSat-specific quality control developed In particular, low wind speeds rejected due to low information content Ambiguous wind vectors assimilated in similar manner to Quikscat

22. © Crown copyright 2007 Windsat wind vectors analysis increments and forecast impact QuikScat WindSat relative forecast impact 1-month trial, Aug 2005

23. © Crown copyright 2007 GPS radio occultation Met Office operational use Sep 20061st assimilation of CHAMP + GRACE-A (GFZ) refractivities Nov 2006 CHAMP and GRACE-A withdrawn – GFZ qc problems May 2007 4 COSMIC satellites assimilated Nov 2007 4 6 COSMIC satellites Apr 2008 Increase vertical range: 4-27 km 0-40 km Jul 2008?Plan to re-introduce CHAMP and GRACE-A

24. © Crown copyright 2007 COSMIC radio occultation data forecast temperature v sondes S.Hem., Dec 2006, 6 COSMIC v no GPS-RO 24h temperature forecast 200 hPa temperature Mean error RMS error bias rms 0.8 0.4 K 0 42K042K0 10 100 1000 0 48 96 120h 10 100 1000 0 1 2 3K

25. © Crown copyright 2007 Stratospheric bias is reduced by assimilating refractivities up to 40 km. Results shown in bending angle space Without RO Mean O-B St. Dev. O-B With RO COSMIC radio occultation data adding data up to 40 km

26. © Crown copyright 2007 Forecast rms % difference v radiosondes, June 07 betterworse 4 COSMICAll GPSRO Radio occultation: Increasing the number of occultations 23 May – 24 June 2007

27. © Crown copyright 2007 GPS radio occultation impact of increased vertical range Increased vertical range of refractivity assimilation: from 4-27 km to 0-40 km Small benefit: non-tropical RH low level winds temperature bias in lower stratosphere

28. © Crown copyright 2007 GPS radio occultation overall impact - refractivity assimilation Large impact in SH forecasts at all ranges for T, H and wind > 6 % improvement in rms error v sondes, for T100, T250 Useful improvements in Tropics in same fields: ~3% improvement in rms error for T50, T100 and T250. NH impacts small but positive Small improvements in RH Impact of 6 COSMIC on Met Office global NWP index: 1.3 v observations, 0.8 v analysis More impact: 4 6 8 satellites

29. AMV impacts Tested in 2 seasons: 12 Dec 05 – 11 Jan 06 4D-Var N216 L50 1.Control (operational observations, March 06) 2.All AMV data removed 3.All satellite data removed 4.AMVs added on no satellite baseline 12 Dec 07 – 12 Jan 08 4D-Var N216 L50 1.Control (operational observations, Nov 07) 2.All AMV data removed

30. Met Office Global NWP Index Measure of model forecasting skill Forecasts are verified by comparison with observations and analyses Calculated from a range of parameters (PMSL, H500, W850, W250), over different areas and forecast ranges. AMV impact Results from Dec 2005 1.5 18.8 8.6 Operational baseline No satellite baseline 1.AMV denial 2.No Satellite + AMV wind at 850 hPa v sondes Tropics NH SH AMVs improve forecasts, although impact is modest compared to ATOVS radiance data.

31. © Crown copyright 2007 TRNH 12 Dec 05 – 11 Jan 0612 Dec 07 – 12 Jan 08 Poor impact on TR PMSL Poor impact on TR height fields Overall similar pattern of impacts, but generally smaller in Dec 07 season. Possibly due to model and observation usage improvements (e.g. IASI, GPSRO), but may be partly seasonal variation. NWP index = -1.8 NWP index = -0.9 NHSH TR Mostly positive impact from AMVs (bars above line) AMV impact c omparing Dec 2005 with Dec 2007 Verification versus observations

32. © Crown copyright 2007 AMV impact MODIS polar winds T+48 500 hPa height forecast error, Dec 07 – Jan 08: difference between control and trial Control - No AMVs Improved RMS in H500 over NH polar region also seen in original MODIS polar wind experiments

33. © Crown copyright 2007 zenith total delay, ZTD Observations from E-GVAP near real-time GPS network very high time resolution - often several per hour - potentially useful in 4D-Var At the Met Office: assimilating ZTD into regional (12 km) and UK (4 km) models assimilating one per hour in 4D-Var small positive impacts on cloud, surface temperature, visibility and precipitation operational since March 2007 Ground-based GPS

34. © Crown copyright 2007 Impact studies with satellite observations at the Met Office: Conclusions Results with implications for design of the GOS: MW sounders in 3 well-space orbits are close to optimal ATOVS RARS – improved timeliness is beneficial for global NWP MetOp ATOVS – an excellent example of early availability MetOp IASI – substantial impact from cloud-free radiances … and more expected from cloudy radiances (IASI and AIRS) MetOp ASCAT – highest quality scatterometer; impact demonstrated Windsat – impact comparable to scatterometers (for global NWP) GPS-RO – good impact from 6 COSMIC; more impact from >6 AMVs – useful impact; qc and error characterisation problems remain Ground-based GPS – small positive impact in regional/UK models

35. © Crown copyright 2007 Questions?

36. © Crown copyright 2007 How many microwave sounders? Impact on analysis accuracy: change in fit to background Albach Workshop results

37. © Crown copyright 2007 IASI - Data Selection 1 pixel in 4 collocated-AVHRR Most Homogeneous field of view No data used over sea ice No data used where IR cloud tests failed Cost test (English et al. 1999) Compare IASI with AMSU (Cheng et al. 2006) Threshold on SD of 4 IASI pixels (Cheng et al. 2006)

38. © Crown copyright 2007 IASI - Channel selection for data storage 300 channels selected with information content method (Collard 2007, submitted to QJRMS) Choose successive channels which contain most information content for atmospheric profile Avoid adjacent channels to reduce correlated error (only use diagonal error covariance matrix in VAR) Avoid channels affected by trace gases we dont model Add 14 extra channels for monitoring to give 314 in total (cf AIRS: 324 channels)

39. © Crown copyright 2007 IASI - Channel selection for data processing Reject some problematic channels (inc highest peaking) Reduce number of water vapour channels used (will come back to this later) But note we are using water vapour channels! Left with 183 channels used in 1D-Var retrieval Reject low-peaking channels over land where AMSU detects cloud (by-product of surface type test) 138 used in 4D-Var where high-peaking channels are removed to avoid stratospheric ringing (cf AIRS: 63 channels)

40. © Crown copyright 2007 IASI - Data Processing We use RTTOV 7 kCARTA coefficients Observations are processed through a 1D-Var scheme before assimilation in 4D-Var Observation error (SD) of 0.5K 15μm CO 2 band (c.f. O-B fit of ~0.3K) 1K window channels (c.f. O-B fit of ~0.6K) 4K water vapour channels (c.f. O-B fit of ~1.4K – see later!)

41. © Crown copyright 2007 IASI - Assimilation trials Pre-operational testing via one-month trials 24 th May to 24 th June 2007 Processing very similar to existing ATOVS/AIRS processing Eight different configurations tested with Differing channel selections Different model resolutions (N216,N320; 50L,70L) Recalculated bias corrections Different observation errors It has also been tested with two different model physics packages

42. © Crown copyright 2007 IASI - Trial results Results fairly stable throughout trial period which was a difficult period for the Met Office Unified Model operationally Results proved robust to different trial configurations We measure trial performance using the NWP Index Combines 22 key variables of interest to our customers Weighted mean skill relative to persistence Measured using both observations and analyses as verification, and the two values averaged All trials showed positive impact

43. © Crown copyright 2007 MetOp IASI Change in rms forecast error v analyses T700, T500, T100 T250,T50 Heights Down is Good!

44. © Crown copyright 2007 IASI Improvement to model fields I wanted to show some nice plots of improvements to model fields… …but the changes are minor improvements across the board adding up to a good increase in the index overall. They dont show up in plots! Results are more robust to changes in configuration and model physics

45. © Crown copyright 2007 IASI – S.Hem. Height Profile T+24, Mean Forecast Error – Verification vs Sonde

46. © Crown copyright 2007 Tropics Relative Humidity 500hPa T+24 timeseries RMS Forecast Error – Verification vs Sonde Control IASI

47. © Crown copyright 2007 Improvement in fit to other satellite data NOAA-18 AMSU-A Channel 5 (750hPa) VAR RMS(ob-calc) trial-control [%] Initial FitFinal Fit

48. © Crown copyright 2007 Improvement in fit to other satellite data NOAA-16 AMSU-A Channel 14 (stratosphere) VAR RMS(ob-calc) trial-control [%] Initial FitFinal Fit

49. © Crown copyright 2007 Improvement in fit to other satellite data MetOp HIRS Channel 11 (water vapour) VAR RMS(ob-calc) trial-control [%] Initial FitFinal Fit

50. © Crown copyright 2007 MetOp ASCAT wind speed performance after Met Office quality control applied

51. © Crown copyright 2007 COSMIC-1 Global model biases Monitoring bending angle (O-B) statistics (using 6 hour forecast) shows a distinct S shape bias in the global model at higher than 50 hPa (~22km, around model level 35). Note ECMWF mean and standard deviation also shown for comparison.

52. © Crown copyright 2007 40 km upper range S shape bias can be reduced by refractivity assimilation up to 40 km. Note result on right in bending angle space Without RO Mean O-B St. Dev. O-B With RO