All-sky observations: errors, biases, representativeness and gaussianity

Transcription

1 All-sky observations: errors, biases, representativeness and gaussianity Alan Geer, Peter Bauer, Philippe Lopez Thanks to: Bill Bell, Niels Bormann, Anne Foullioux, Jan Haseler, Tony McNally Slide 1 ECMWF-JCSDA workshop, June 2010 Slide 1

2 Assimilation of cloud and precipitation affected microwave radiances at ECMWF Microwave imagers, e.g. SSM/I, SSMIS, TMI, AMSR-E - Radiances are sensitive to humidity, cloud, precipitation, and the ocean surface 1D+4D-Var of cloud and precipitation-affected microwave imagers from June 2005 All-sky assimilation of radiances directly into 4D-Var from March All-sky = clear, cloudy and precipitating conditions together (no cloudclearing) - Cloud and precipitation are part of the 4D-Var minimisation - Increased weight of observations for summer 2010 (revised observation errors and quality control) Slide 2 All-sky 4D-Var microwave sounder (AMSU-A) radiances in testing ECMWF-JCSDA workshop, June 2010 Slide 2

3 Introduction Adding cloud / precip observations to an operational system needs: - Neutral or improved medium-range forecast scores - Improved fits to other observations in analysis and first guess - (Fast computational speed) To achieve this: - Appropriate background and observation errors - Gaussian error statistics - Linearity of models (Philippe Lopez s talk) - Quality control - Representativeness of observations and model - Bias correction Slide 3 ECMWF-JCSDA workshop, June 2010 Slide 3

4 Observation errors and cloud sampling Slide 4 ECMWF-JCSDA workshop, June 2010 Slide 4

5 Sampling Observation (Obs) First guess (FG) Obs - FG Slide 5 ECMWF-JCSDA workshop, June 2010

6 All-sky SSM/I first guess departures as a function of observed cloud Mean channel 19v departures [K] as a function of mean of observed and forecast cloud as a function of forecast cloud Cloud amount derived from 37GHz radiances Slide 6 ECMWF-JCSDA workshop, June 2010 Slide 6

7 Symmetry in all-sky assimilation Any property in a data assimilation system that varies as a function of cloud or rain may lead to asymmetric sampling errors Bias correction as a function of observed cloud - Never enough model cloud when cloud is observed Observation error as a function of observed cloud amount - Will lock in the sampling bias Symmetric cloud / rain predictors: - Mean of observed and first guess cloud - Max of observed and first guess cloud - Constant error more appropriate for AMSU-A and rain radar Slide 7 ECMWF-JCSDA workshop, June 2010 Slide 7

8 Error standard deviations in an ideal world Observation Background Obs FG (mean absolute error) Slide 8 ECMWF-JCSDA workshop, June 2010

9 All-sky SSM/I first guess departures Std. dev. channel 19v departures [K] as a function of forecast cloud as a function of observed cloud as a function of mean of observed and forecast cloud Cloud amount derived from 37GHz radiances Slide 9 ECMWF-JCSDA workshop, June 2010 Slide 9

10 Symmetric model for all-sky observation error Based on variance of actual FG departures FG Departure variance [K] Cloudy obs error Cloudy FG error Clear FG error Clear FG error Mean (obs,fg) 37GHz cloud [0-1] Slide 10 ECMWF-JCSDA workshop, June 2010 Slide 10

11 Symmetric model for all-sky observation error FG Departure variance [K] Cloudy obs error Cloudy FG error Clear FG error Clear FG error Mean (obs,fg) 37GHz cloud [0-1] Slide 11 ECMWF-JCSDA workshop, June 2010 Slide 11

12 Useful properties of symmetric errors Slide 12 ECMWF-JCSDA workshop, June 2010 Slide 12

13 All-sky departures: not gaussian? SSM/I Channel 37v departure Gaussian Slide 13 ECMWF-JCSDA workshop, June 2010 Slide 13

14 All-sky departures: actually quite gaussian normalised 37v departure Gaussian d SYM Slide 14 ECMWF-JCSDA workshop, June 2010 Slide 14

15 All-sky 4D-Var departures: QC SSM/I 37v departure Normalised Slide 15 ECMWF-JCSDA workshop, June 2010 Slide 15

16 Representativity Slide 16 ECMWF-JCSDA workshop, June 2010 Slide 16

17 9 GHz 37 GHz 10 to 1 superobbing Taking closest obs to model gridpoint (50 by 50km sampling) Slide 17 Raw AMSR-E data: 10km by 9km sampling ECMWF-JCSDA workshop, June 2010

18 Number (log scale) AMSRE Raw Superob First guess SSM/I Raw First guess Clear, dry, cold 37v brightness temperature Slide 18 [K] Rainy ECMWF-JCSDA workshop, June 2010 Slide 18

19 Model representativity: saved by effective resolution of cloud Hires obs Hires model Lores obs subsampled model Lores obs superobbed model Slide 19 ECMWF-JCSDA workshop, June 2010 Slide 19

20 Error inflation with colocation distance model vs. observation Std. dev. channel 19v departures [K] Slide 20 ECMWF-JCSDA workshop, June 2010 Slide 20

21 Representativity - summary High-res PDFs (e.g. of precipitation or brightness temperature) are very different to lo-res PDFs Subsampling (or use of single observations) is wrong High-res observations lo-res model - Must spatially average ( superob ) observations to appropriate model scale. High-res model lo-res observation - Must spatially average ( superob ) model to appropriate observation scale - But in practice, model cloud and FG error scales are much coarser than nominal resolution So it s ~OK to subsample. Model vs. observation colocation distance not too important (at least over km) Sub-grid cloud/precip variability Slide 21 - Well-known issue for moist physics and observation operators ECMWF-JCSDA workshop, June 2010 Slide 21

22 Biases between model and cloud / precipitation affected observations Slide 22 ECMWF-JCSDA workshop, June 2010 Slide 22

23 Biases: fronts SSM/I Channel 19v Slide 23 ECMWF-JCSDA workshop, June 2010 Slide 23

24 Biases: fronts Slide 24 ECMWF-JCSDA workshop, June 2010 Slide 24

25 Biases: fronts Slide 25 ECMWF-JCSDA workshop, June 2010 Slide 25

26 Biases: fronts Slide 26 ECMWF-JCSDA workshop, June 2010 Slide 26

27 Biases: fronts Slide 27 ECMWF-JCSDA workshop, June 2010 Slide 27

28 Biases: fronts Slide 28 ECMWF-JCSDA workshop, June 2010 Slide 28

29 Biases: fronts Slide 29 ECMWF-JCSDA workshop, June 2010 Slide 29

30 Biases: fronts Slide 30 ECMWF-JCSDA workshop, June 2010 Slide 30

31 Biases: fronts Slide 31 ECMWF-JCSDA workshop, June 2010 Slide 31

32 Biases: fronts Slide 32 ECMWF-JCSDA workshop, June 2010 Slide 32

33 Biases: fronts Slide 33 ECMWF-JCSDA workshop, June 2010 Slide 33

34 Biases: fronts Slide 34 ECMWF-JCSDA workshop, June 2010 Slide 34

35 Biases: fronts Slide 35 ECMWF-JCSDA workshop, June 2010 Slide 35

36 Biases: fronts Slide 36 ECMWF-JCSDA workshop, June 2010 Slide 36

37 Slide 37 ECMWF-JCSDA workshop, June 2010 Slide 37

38 PDF of brightness temperature: Channel 19v Cold sector bias South Atlantic, August 2009 Not enough WV, cloud or precip in model Observations First guess Too much WV, cloud or precip in model Slide 38 ECMWF-JCSDA workshop, June 2010 Slide 38

39 Bias correction as a function of cloud as a function of observed cloud as a function of mean of observed and forecast cloud Cloud amount derived from 37GHz radiances as a function of forecast cloud Slide 39 ECMWF-JCSDA workshop, June 2010 Slide 39

40 Difficulties with adaptive bias correction Signal to noise: biases of ~2K against standard deviations of 15K - mean cloud predictor not targeted enough? Biases can be determined by a few observations at the extreme cloudy end Interactions with quality control Slide 40 ECMWF-JCSDA workshop, June 2010 Slide 40

41 Difficulties with adaptive bias correction Signal to noise: - biases of ~2K against standard deviations of 15K Biases can be determined by a few observations at the extreme cloudy end - Vulnerable to interactions with quality control Mean cloud predictor is not well targeted - But no success with more precise approaches either e.g., tropics vs. midlatitude separation Slide 41 ECMWF-JCSDA workshop, June 2010 Slide 41

42 Screening criteria for bad biases FG departures [K] Slide 42 Cold sector bias indicator [0-1] ECMWF-JCSDA workshop, June 2010 Slide 42

43 Biases fixed: cloud overlap in the RTTOV-SCATT radiative transfer model Original overlap Revised overlap SSM/I Channel 19v mean departure [K] 20 independent column reference Slide 43 ECMWF-JCSDA workshop, June 2010 Slide 43

44 Error tuning experiments Slide 44 ECMWF-JCSDA workshop, June 2010 Slide 44

45 Symmetric model for all-sky observation error FG Departure variance [K] Cloudy obs error Cloudy FG error Clear FG error Clear FG error 37GHz cloud [0-1] Slide 45 ECMWF-JCSDA workshop, June 2010 Slide 45

46 Symmetric model for all-sky observation error FG Departure variance [K] Cloudy obs error Cloudy FG error Clear FG error Clear FG error 37GHz cloud [0-1] Slide 46 ECMWF-JCSDA workshop, June 2010 Slide 46

47 All-sky observation error tuning VarQC off AMSU-B FG departure std. dev. [K] VarQC on Slide 47 Increasing all-sky observation weight ECMWF-JCSDA workshop, June 2010 Slide 47

48 All-sky observation error in practice FG Departure variance [K] Cloudy obs error Clear FG error Clear FG error 37GHz cloud [0-1] Slide 48 ECMWF-JCSDA workshop, June 2010 Slide 48

49 All-sky observation error after tuning experiments Channel 19v in cloudy areas: - FG departure standard deviation: 15 K - Observation error: K In practice, ALL cloudy error is assigned as observation error. Why? - ECMWF system does not correctly represent background error covariances in cloudy areas? - Error correlations not considered see Niels Bormann s talk. - Forecast model bias Slide 49 ECMWF-JCSDA workshop, June 2010 Slide 49

50 Status Observation errors Stopgap solution - Need to be symmetric (i.e. not causing sampling biases) - Symmetric approach for all-sky assimilation - Observation error being used to account for forecast model error! Quality control - OK - Threshold checks using symmetric model for FG departures - VarQC Gaussianity and linearity OK for now Representativity Saved by very broad scales of model cloud Model biases The real problem - E.g. fronts, cold sectors - Correlated errors Slide 50 ECMWF-JCSDA workshop, June 2010 Slide 50

51 Recommendations Background errors - Need to represent broad areas of uncertainty around fronts and clouds - Ensemble methods should help Bias correction - Predictors must be symmetric - Refine current methods (e.g. better VarBC predictors) - New methods to represent cloud and precipitation biases? Model biases - Screen out observations that disagree with the model - Improve the models - Weak constraint 4D-Var - Parameter estimation Slide 51 ECMWF-JCSDA workshop, June 2010 Slide 51