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Robin HoganEwan O’ConnorDamian Wilson
Malcolm Brooks
Evaluation statistics of cloud fraction and
water content
Overview• Cloudnet level 3 data• A solution to the problem of evaluating high cloud?• Summary of errors in model cloud fraction and water
content climatologies over Europe– ECMWF model– KNMI Regional atmospheric climate model (RACMO)– Met Office mesoscale and global– SMHI Rossby Centre atmospheric model (RCA)– Meteo France ARPEGE model– DWD Lokal Modell
• Forecast skill
Cloudnet level 3 data• Level 3 files summarise the comparison of a
observations and model over a certain period:– Long-term mean of a quantity versus height– Separation into “freq. of occurrence” and “amount when
present”– PDFs in height ranges 0-3 km, 3-7 km, 7-12 km and 12-18 km– Skill scores versus height for different thresholds
• Separate level-3 files/quicklooks are produced for– Each variable: cloud fraction, LWC, IWC, high cloud fraction– Each site: 4 European, 4 ARM (so far)– Each model: 7 so far, plus persistence/climatology forecasts– Each month and each year– Different forecast lead times (Met Office meso and DWD only)– In principle: different model resolutions / parameterisations
• Over 5000 files so far!
Observations
Met Office
Mesoscale Model
ECMWF
Global Model
Meteo-France
ARPEGE Model
KNMI
RACMO Model
Swedish RCA model
Cloud fraction
What can we do about high cloud?
• All models see more cirrus than observed– We use the known radar sensitivity to remove clouds from
model that we would not expect to detect (affecting heights > 7 km)
– Does not usually remove enough cloud to bring into agreement
• Are all models wrong?– Or does radar miss more IWC than it thinks due to small
particles?
ARM Nauru 8 Nov 2003
Night-time
Radar35 GHz MMCR
LidarMerged ceilometer and micropulse lidar
October 2003: Normal processing
No periods when rain rate > 8 mm/h
Large difference between observations and ECMWF model, whether model is modified for radar sensitivity or not
…only periods of high lidar sensitivity
Consider only night-time and periods when lidar is unobscured by liquid cloud, rain or melting ice
Liquid clouds removed from comparison
Cloud fraction OK but peak 2 km too high
One month later
Model grossly overestimates high cloud fraction
To evaluate high clouds in models: need a high sensitivity lidar and appropriate sampling of data (both model and observations)
ECMWF cloud
fraction• Cabauw 2002:
– Amount when present is good
– Mean cloud fraction and frequency of occurrence too high in the boundary layer
– Need to treat snow as cloud in the model
• Chilbolton 2004 (and all mid-latitude sites 2003-2005):– Boundary layer
cloud fraction much more accurate
– Still need to treat snow as cloud
ECMWF water content• Mean LWC and IWC accurate to
observational uncertainties• Freq. of occurrence too high;
amount when present too low• Inconsistent with cloud frac.?• PDF shows occurrence of low
values is too high
Chilbolton 2004: LWC
Chilbolton 2004: IWC
RACMO• Cloud fraction errors similar to
ECMWF before 2003• Water content errors (mean,
frequency of occurrency) much as ECMWF
• Lower IWC in high cirrus
Met Office mesoscalecloud fraction
• Mean amount when present too low through most of atmosphere
• Largely due to inability of model to simulate 100% cloud fraction, as shown by the PDFs
• Error in high cloud needs to be checked using high sensitivity lidar
Cabauw 2004
Met Office global cloud
fraction• Observations show greater
frequency of cloud with increased gridbox size; opposite in model
• PDF error unchanged
Cabauw 2004
Met Office mesoscale water content
• Liquid occurrence very good
• Boundary layer perhaps too low
• Mean LWC underestimated above 3 km
• Similar to previous result found for occurrence of supercooled layers
Chilbolton 2004: LWC Chilbolton 2004: IWC
• Mean IWC very good
• Frequency of ice cloud occurrence too high above 3 km
• PDFs much better than ECMWF!
Met Office global water content• Mean LWC
similar but frequency of occurrence much lower
• IWC generally higher
Chilbolton 2004: LWC Chilbolton 2004: IWC
SMHI Rossby Centre model• Amount when present
reasonable but frequency of occurrence and overall mean much too high
• Similar picture for LWC/IWC: mean overestimated due to cloud too often
Palaiseau 2004
Meteo France
cloud fraction
• Before Apr ‘03– Amount when
present far too low
– High values rarely predicted
Cabauw 2002
• After Apr ‘03– Amount when
present very good (better than Met Office & ECMWF)
– Mean cloud fraction much better
– Amazingly, worse agreement with synoptic obs of cloud cover!
Cabauw 2004
Meteo Fr. water content
• Boundary-layer LWC too low• Frequency of supercooled liquid
much too high– Need to change the T-dependent
ice/liquid ratio
• PDF of LWC and IWC too narrow• Mean IWC too low in mid-levels
Chilbolton 2004: IWC
Chilbolton 2004: LWC
DWD cloud
fraction• Cloud fraction
generally very good – But frequency
of occurrence always overestimated by 20-30%
• PDFs particularly well simulated
Chilbolton 2004
DWD water
content
• Frequency of liquid cloud occurrence too high
• LWC in supercooled clouds too high
• Frequency of ice cloud occurrence OK
• Mean IWC and mean amount when present (in-cloud IWC) are both underestimated below 7 km
Chilbolton 2004
Equitable threat score• Measure of skill of forecasting
cloud fraction>0.05• Persistence and climatology
shown for comparison• Lower skill in summer
convective events
Skill versus lead time• Unsurprisingly UK model most accurate in UK,
German model most accurate in Germany!
• Typically 500-mb geopotential height used in operational forecast verification
• Cloud fraction a more challenging test: more rapid loss of skill with time