Anthony Illingworth, + Robin Hogan, Ewan OConnor, U of Reading, UK and the CloudNET team (F, D, NL, S, Su). Reading: 19 Feb 08 – Meeting with Met office. Evaluation of the representation of clouds in NWP using ground based radar and lidar: The Cloudnet Project.

Overview Cloud radar and lidar sites worldwide Cloud evaluation in NWP models over Europe as part of Cloudnet –Identifying targets in radar and lidar data (cloud droplets, ice particles, drizzle/rain, aerosol, insects etc) –Evaluation of cloud fraction –Liquid water content –Ice water content –Forecast evaluation using skill scores –First results from ARM sites.

The Cloudnet methodology Recently completed EU project; BAMS Article June 2007 Aim: to retrieve and evaluate the crucial cloud variables in forecast and climate models –Models: Met Office (4-km, 12-km and global), ECMWF, Météo-France, KNMI RACMO, Swedish RCA model, DWD –Variables: target classification, cloud fraction, liquid water content, ice water content, drizzle rate, mean drizzle drop size, ice effective radius, TKE dissipation rate –Sites: 4 Cloudnet sites in Europe, 6 ARM including 3 for mobile facility –Period: Several years near-continuous data from each site Crucial aspects –Common formats (including errors & data quality flags) allow all algorithms to be applied at all sites to evaluate all models –Evaluate for months and years: avoid unrepresentative case studies

Continuous cloud-observing sites Key cloud instruments at each site: –Radar, lidar and microwave radiometers S.Germany for COPS

Standard CloudNET observations (e.g. Chilbolton ) RadarLidar, gauge, radiometers But can the average user make sense of these measurements?

Example from US ARM site: Need to distinguish insects from cloud First step: target classification Ice Liquid Rain Aerosol Insects Combining radar, lidar and model allows the type of cloud (or other target) to be identified From this can calculate cloud fraction in each model gridbox

CHILBOLTON Observations Met Office Mesoscale Model ECMWF Global Model Meteo-France ARPEGE Model KNMI RACMO Model Swedish RCA model Cloud fraction

Cloud fraction in 7 models Mean & PDF for 2004 for Chilbolton, Paris and Cabauw Illingworth et al, BAMS, km –Uncertain above 7 km as must remove undetectable clouds in model –All models except DWD underestimate mid-level cloud; some have separate radiatively inactive snow (ECMWF, DWD); Met Office has combined ice and snow but still underestimates cloud fraction –Wide range of low cloud amounts in models –Not enough overcast boxes.

A change to Meteo-France cloud scheme But human obs. indicate model now underestimates mean cloud-cover! Compensation of errors: overlap scheme changed from random to maximum-random Compare cloud fraction to observations before and after April 2003 Note that cloud fraction and water content are entirely diagnostic beforeafter April 2003

ARM MOBILE 2007: MURGTAL, GERMANY. 140 days Cloud fraction Met Office Model GOOD, but amount of mid level cloud too low

Equitable threat score –ETS removes those hits that occurred by chance –1 = perfect forecast, 0 = random forecast Measure of the skill of forecasting cloud fraction>0.05 –Assesses the weather of the model not its climate –Persistence forecast is shown for comparison Lower skill in summer convective events Met Office global and mesoscale – equally good.

Liquid water content Cant use radar Z for LWC: often affected by drizzle –Simple alternative: lidar and radar provide cloud boundaries –Model temperature used to predict adiabatic LWC profile –Scale with LWP from microwave radiometers (often sub-adiabatic) Radar reflectivity Liquid water content Rain at ground: unreliable retrieval

Liquid water content LWC derived using the scaled adiabatic method –Lidar and radar provide cloud boundaries, adiabatic LWC profile then scaled to match liquid water path from microwave radiometers –Met Office mesoscale tends to underestimate supercooled water –SMHI too much liquid –ECMWF has far too great an occurrence of low LWC values 0-3 km

ECMWF: ARM: North Slope Of Alaska 2001 Model LWP far too low! ECMWF low level cloud present 60% of time, Observed <20%!!

Ice water from Z Observations Met Office Mesoscale Model ECMWF Global Model Meteo-France ARPEGE Model KNMI Regional Atmospheric Climate Model

Ice water content IWC estimated from radar reflectivity and temperature –Rain events excluded from comparison due to mm-wave attenuation –For IWC above rain, use cm-wave radar (e.g. Hogan et al., JAM, 2006) 3-7 km –ECMWF and Met Office within the observational errors at all heights –Encouraging: AMIP implied an error of a factor of 10! –DWD (pink) far too low -Be careful in interpretation: mean IWC dominated by occasional large values so PDF more relevant for radiative properties - DWD (pink) pdf best – apart from max bin – so mean value worst.

To Do List. Classification of skill as a function of ascent/descent at 300mb, 700mb and Surface Stability (Malcolm Brooks et al 20??) (Bony diagrams now very fashionable) Does the 4km model have better skill for clouds than the NAE 12km or the global? (What about the 1.5km)? Light drizzle falls nearly all the time from low level water clouds (should be more episodic) – also it reaches the ground where it should evaporate.

GLOBAL MODEL CLOUD FRACTION 2006

MESOSCALE MODEL ONE YEAR 2003 SPOT THE DIFFERENCE

OCCURRENCE OF DRIZZLE Yearly comparisons –Met Office GLOBAL MODEL - Broken Contours –OBSERVATIONS – GREY SCALE AND SOLID CONTOURS mm/hr

OCCURRENCE OF DRIZZLE Yearly comparisons –Met Office mesoscale model – DOTTED CONTOURS –OBSERVATOINS GREY SCALE AND SOLID CONTOURS.

Drizzle drop size –Met Office model uses explicit size distributions –Treats all precipitation as marsahll-Palmer rain –Drizzle drops too big – so they dont evaporate.

To Do List. Classification of skill as a function of ascent/descent at 300mb, 700mb and Surface Stability (Malcolm Brooks et al 20??) (Bony diagrams now very fashionable) Does the 4km model have better skill for clouds than the NAE 12km or the global? (What about the 1.5km)? Light drizzle falls nearly all the time from low level water clouds (should be more episodic) – also it reaches the ground where it should evaporate.

ARM 2006: Niamey - Ice Water Content ECMWF Met Office Observations June 2006 Weak convection But too often.

Niamey Ice Water Content Met Office Global model GOOD, but pdf of IWC too peaked For midlevel cloud.

Summary Cloud radar and lidar sites worldwide Cloud evaluation over Europe and ARM –Rapid feedback (2 months) on model performance when changes in in parameterisation schemes. –IWC and LWC profiles surprisingly good –BUT: –Not enough mid level cloud. –Problems with supercooled clouds. –North slope: ECMWF, lwp too low: 60% low cloud, observed 20%; –Niamey: ECMWF low cloud fraction too low but too frequently. –We would welcome participation of US models.

ARM SITES NOW BEING PROCESSED VIA CLOUDNET SYSTEM MANUS ARM SITE IN W PACIFIC. CLOUD FRACTION CEILOMETER ONLY: HIGH CIRRUS IS OBSERVED BY MPL LIDAR: NOT YET CORRECT IN CLOUDNET

TROPICAL CONVECTION: MANUS ARM SITE IN W PACIFIC. CLOUD FRACTION ECMWF MODEL - MODEL CONVECTION SCHEME TRIGGERS INTERMITTENTLY ALL THE TIME - GIVES V LOW CLOUD FRACTION IN TOO MANY BOXES. OBSERVED – HIGH CIRRUS NOT YET CORRECT IN CLOUDNET

Basics of radar and lidar Radar/lidar ratio provides information on particle size Detects cloud base Penetrates ice cloud Strong echo from liquid clouds Detects cloud top Radar: Z~D 6 Sensitive to large particles (ice, drizzle) Lidar: ~D 2 Sensitive to small particles (droplets, aerosol)

Cloud Parameterisation Operational models currently in each grid box typically two prognostic cloud variables: –Prognostic liquid water/vapour content –Prognostic ice water content (IWC) OR diagnose from T –Prognostic cloud fraction OR diagnosed from total water PDF Particle size is prescribed: –Cloud droplets - different for marine/continental –Ice particles – size decreases with temperature –Terminal velocity is a function of ice water content Sub-grid scale effects: –Overlap is assumed to be maximum-random –What about cloud inhomogeneity? How can we evaluate & hence improve model clouds?

Evaluation of Meteo-France Arpege total cloud cover using conventional synoptic observations. Changes to cloud scheme in seem to have made performance worse! More rms Error Worse Bias

CloudNET: monthly profiles of mean cloud fraction and pdf of values of cloud fraction v model Jan 2003 Jan 2005 Objective CloudNET analysis shows a remarkable improvement in model clouds.

Liquid water path: microwave radiometer+lidar –Brightness temperatures -> Liquid water path Use lidar to determine whether clear sky or not Adjust coefficients to account for instrument drift Removes offset for low LWP LWP - initial LWP - lidar corrected

Compare measured lwp to adiabatic lwp obtain dilution coefficient Dilution coefficient versus depth of cloud LIQUID WATER CONTENT (LWC) OF STRATOCUMULUS: Cloud base from lidar: Cloud top from radar. Use model temperatures to give adiabatic lwc. Scale adiabatic lwc profile to match lwp from radiometers

2004 – model performance for Sc ECMWF Met Office ECMWF: Mean LWC OK but occurs to often MET OFFICE pdf of LWP wrong occurrence ok