Cloud verification: a review of methodologies and recent developments

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1 Cloud verification: a review of methodologies and recent developments Anna Ghelli ECMWF Slide 1 Thanks to: Maike Ahlgrimm Martin Kohler, Richard Forbes Slide 1

2 Outline Cloud properties Data availability NWP model fields and observation: the matching game Standard scores and new ideas Example plots Active satellite profiling: new challenges Conclusions Slide 2 Slide 2

scores and new ideas Example plots Active satellite

3 Stratocumulus Lenticularis Los Lagos -- Chile Slide 3 Bernhard Mühr, Slide 3

4 Macrophysical properties Germany Bernhard Mühr, Cloud base height Cloud fraction Total cloud cover Cloud top height Slide 4 Stratocumulus stratiformis translucidus Slide 4

$de Cloud base height Cloud fraction Total cloud$

5 Microphysical properties Yellowstone, USA Bernhard Mühr, Cloud water ice content Cloud liquid water content Cloud droplet size Liquid water path Radar reflectivity Optical depth Slide 5 Stratocumulus stratiformis opacus cumulogenitus Slide 5

de Cloud water ice content Cloud liquid water content Cloud

6 What does the model produce? Total cloud cover High, low and medium clouds Temperature, humidity and cloudiness --> can be transformed into brightness temperature. Cloud fraction, Liquid Water Content, Ice Water Content (on Model Levels) Slide 6 Slide 6

humidity and cloudiness --> can be transformed into brightness

7 Observations -- what is available? Conventional observing systems: Satellite data: Geostationary SYNOPs RADAR Polar orbiting Active satellite profiling LIDAR Slide 7 Slide 7

8 Observations Conventional observing systems: Sparse and inhomogeneous coverage Decreasing in number Differences between manual and automated But: The data volumes are manageable Available at synoptic times Available in real time They measure the weather Satellite data: Large data volumes Need location and time matching Thinning algorithms are needed May not be available in real time But: Wide spatial coverage High spatial and temporal resolution Weather phenomena like fog can be assessed, not possible with conventional Slide 8 data Slide 8

data: Large data volumes Need location and time matching Thinning algorithms are needed May not be available in real time But: Wide

9 The matching game Need to address mismatch in spatial scales in model and obs (1 km) Need to address mismatch in time scales Approaches: Obs to model --> Average obs to model representative spatial scale Model to obs --> Statistically represent model sub-gridscale variability using a Monte-Carlo multi-independent column approach. Model gridbox cloud fraction Model generated sub-columns Compare CloudSat Obs Model Cloudy Obs Cloudy Cloud-free CloudSat Obs Obs averaged onto model gridscale Slide 9 Slide 9 Compare Model gridbox cloud fraction Richard Forbes, ECMWF

$Model gridbox cloud fraction Model generated sub-columns Compare CloudSat Obs Model Cloudy Obs Cloudy Cloud-free CloudSat Obs Obs averaged$

10 The matching game VOCALS field experiment off Chile GOES µm ECMWF 10.8µm Slide 10 Slide 10

11 The matching game SYNOPS Slide 11 Slide 11

12 Desirable properties Equitable --> random forecast scores zero Difficulty to hedge --> do not reward under or over predicting Independence of the frequency of occurrence --> can be used for rare events Dependence of forecast bias --> bias may influence the score and more? Slide 12 Slide 12

Independence of the frequency of occurrence --> can be used for rare

13 Scores and their properties Continuous scores: MAE and MAESS (Mean Absolute Error Skill Score) Bias (forecast - observation) Fractions Skill Score Contingency table-based scores: Heidke skill score Equitable Threat Score Odd Ratio Log Odd Ratio Extreme Dependence Score Symmetric Extreme Dependency Score (Hogan et al., QJ 2009) Range: - to 1, Perfect score = 1, No skill level = 0 Range: -1/3 to 1, Perfect score = 1, No skill level = 0 Range: 0 to, Perfect score =, No skill level = 1 Range: - to, Perfect score =, No skill level = 0 Range: -1 to 1, Perfect score = 1 Slide 13 Slide 13 Perfect score = 0 Range: 0 to 1, Perfect score = 1 Perfect score = 1

, QJ 2009) Range: - to 1, Perfect score = 1, No skill level = 0 Range: -1/3 to 1, Perfect score = 1, No skill level = 0 Range: 0 to, Perfect score =, No skill

14 Symmetric EDS EDS is easy to hedge predicting the event all the time EDS is not equitable SEDS= ln[(a+b)/n] + ln[(a+c)/n] ln (a/n) Slide 14 Hogan et all, QJ 2009 Slide 14

15 Timeseries of MAESS for total cloud clover (reference: persistence) Slide 15 Europe Slide 15

16 Timeseries of bias and stdv Slide 16 Slide 16

17 Time series of ETS Total Cloud Cover : Model vs SYNOP Slide 17 Slide 17

18 MODEL against SYNOPS Percentage correct 60 Canada PPM umosbin umos Finland Heidke Skill Score Slide hours PPM umosbin umos Slide 18

19 FC bias -- Winter Total Cloud Cover: 36h forecast versus SYNOP observation (high pressure days over central Europe) Slide 19 Slide 19

20 Observation to model trade cumulus clouds Low clouds over ocean have large radiative impact Low cloud fraction, but ubiquitous Slide 20 Maike Ahlgrimm, ECMWF Slide 20

$radiative impact Low cloud fraction, but$

21 Observation to model Can the model predict accurately this type of clouds? Probably not! Cloud characteristics: Ubiquitous Relatively small scale Verification strategy: Relax time and space constraints, i.e. I am not asking the model to forecast my cloud at the exact location and time. The new verification question is: Given an area and period of time, what is the frequency of Slide 21 occurrence of my event in the forecast and in the observation? Slide 21

22 TCu frequency of occurrence CALIPSO Slide 22 Model has TCu more frequently than observed (66% vs. 47%) Maike Ahlgrimm, ECMWF Slide 22

23 Observation to Model Cloud top height OBS ERA-I model clouds have higher cloud tops than observed Slide 23 Maike Ahlgrimm, ECMWF Slide 23

24 Observation to Model Ice Water Content Example cross section (Variational method: Delanoë and Hogan, 2009) 26/02/ Z Model ice water content (excluding precipitating snow). Ice water content derived from a 1DVAR retrieval of CloudSat/ CALIPSO/Aqua Eq Antarctica Eq Slide 24 Greenland Eq log 10 kg m -3 Slide 24

25 Observation to Model Ice Water Content (cloudsat/calipso) Slide 25 Richards Forbes (ECMWF) in collaboration with Delanoë and Hogan (Reading Univ., UK) Slide 25

26 MODEL to OBSERVATION Radar Reflectivity: Cross-section through a mid-latitude front Slide 26 Richard Forbes, ECMWF Slide 26

27 MODEL to OBSERVATION STATISTICS: Frequency of occurrence (Radar Reflectivity vs. Height) Tropics over ocean 30S to 30N for February 2007 Significantly Slide higher 27 occurrence of cloud in model Richard Forbes, ECMWF Slide 27

28 Conclusions.. or our challenges Observing systems. Data management issues. Model and observations: the matching game. 2D verification of clouds are well established, 3D evaluation of cloud properties is now possible with active satellite profiling. users --> involve them in any verification process. Slide 28 Slide 28

Studying cloud properties from space using sounder data: A preparatory study for INSAT-3D

Studying cloud properties from space using sounder data: A preparatory study for INSAT-3D Munn V. Shukla and P. K. Thapliyal Atmospheric Sciences Division Atmospheric and Oceanic Sciences Group Space Applications