Www.cost.eu European Cooperation in Science and Technology COST is supported by the EU RTD Framework...

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European Cooperation in Science and Technology

COST is supported by the EU RTD Framework Programme

ESF provides the COST Office through an EC contract

European Ground-based Observations of Essential Variables for Climate and Operational Meteorology

“Remote sensing from the ground”

(EG-CLIMET: ES-0702 www.eg-climet.org 2008-2012)

Anthony J Illingworth, ChairDept of Meteorology, University of Reading, UK.(Vice chair - Dominique Ruffieux, MeteoSwiss)

WMO TECO conference, Helsinki, 30 Aug-1 Sep 2010

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NEW HIGH RESOLUTION (1km) EUROPEAN FORECAST MODELS

1. Within the next five years such models will be run every hour to provide specific and timely warnings of hazardous weather.

2. These models require high resolution tropospheric observations (with errors) for model evaluation and, ultimately, for data assimilation.

3. Need observations every hour or better, with horizontal resolution of a few km or less and vertical resolution of 100m or less.

4. Difficult from satellites: Clouds often obscure interesting weather. Large distance for active sensors (radar/lidar) Low Earth Orbit - Poor temporal sampling Geo-synchronous - Poor height resolution

5. The goal of COST ES 0702 is to provide specifications for a ground based network of remote sensing instruments to satisy this need.

www.cost.eu




COST Countries : 16

B, AT,

AU, BE,

CH, DE,

ES, FI,

FR, HU,

IT, IE,

NL, NO,

PL, PT,

SE.

Chair : U.K.

Representation of 13 National Weather Services

(i.e. not Ireland, Italy and Poland).

NOT TOO LATE

TOO JOIN!

Two plenary meetings per year

+ 4 to 5 STSMs per year - Short Term Scientific Missions one week

+ Special Working Group Meetings.

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THE CHALLENGE: FORECAST OF DEVELOPING CONVECTION For example, 1.5km model of UK Met Office: 000Z forecast.

Forecast of wind convergence and cloud development at 0800 and 0900Z

First shower forecast at 1000.Observed at 0915.

Most forecasts are not this good!

Need continuous high resolution observations of winds, humidity, clouds which can be assimilated into the model so that it keeps on track with reality.

HIGH RESOLUTION MODELS:

Each model grid box contains prognostic variables for:

Wind - u,v,w. Temp T Humidity - q

Cloud fraction. Average ice/liquid water content.


ESF provides the COST Office through an EC contract5

0800h T+8

• Height contours

• (black)

• Convergence (blue)

• Bands of cloud forming

• (white)

PERFORMANCE OF 1.5KM MODEL: INITIALISED 00Z



0900h T+9

• More extensive convergence

• More extensive cloud.

• T+10: 1000h first shower forecast.



0900h T+9

• Convergence (in red)• And first shower at 1000

10m winds



1000h – model and radar rainfall1.5km model run radar

mm/hr

•Forecast not usually so good.•Need to assimilate observations of wind, humidity, clouds… •To keep the model on track with reality.

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GROUND BASED REMOTE SENSING INSTRUMENTSRecent technological advances promises that these can now be:Inexpensive, reliable, low maintenance, operate 24/7 unmanned

1. Winds: Profiles from clear air radars at 64, 482, 1275MHz.

2. Temperature: Profiles from microwave radiometers.

3. Humidity: Vertical profiles from: GPS - ‘wet delay’ at the ground from GPS satellites Microwave radiometers. Raman lidars. Horizontal mapping using refractivity from precipitation (5cm) radars.

4. Clouds: Radars - mm wave (3mm or 8.6mm) Lidars. & Microwave radiometers.

5. Volcanic ash/aerosols: Lidars.

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WORKING GROUPS.a) INDIVIDUAL INSTRUMENTS. (WG1: Volker Lehmann, DWD). Specify performance of each instrument. Calibration, errors.

Cost/performance. Leading to ideal instrument specification.

b) INSTRUMENT SYNERGY (WG4: Ulrich Loehnert, U of Koln). What is the best combination of sensors to provide the best estimate of the geophysical variable (and its error) in the forecast model?

c) OPTIMUM OBSERVING NETWORK (WG3: Catherine Gaffard, Met Office, Martial Haeffelin, IPSL, France) What is the optimum density/complexity of the network?

d) DATA ASSIMILATION (WG2: Per Unden, SMHI). Data assimilation of observations to improve model initial state + to keep the model on track with reality => Combine model state – first guess (& errors) with new observations (& errors) improved initial state.



WIND PROFILERS

Profiles from clear air radars at 64, 482, 1275MHz.• Bragg back-scatter from clear air - need long dwell to get Doppler shift and

hence line of sight velocity.

• Dwell at vertical, then 15deg off vertical to the East (gives Easterly component of horizontal wind). 15deg off vertical to the West (gives Northerly component of vertical wind.)

Fairly well developed technology, many countries have wind profilers but still problems

1. STSM to identify and reject bird echoes - algorithm now incorporated in the operational software of Vaisala.

2. Approved STSM to look into how convection can affect horizontal winds.

3. Example of data assimilation - Swiss experiment simulating nuclear release.


ESF provides the COST Office through an EC contract12DWD 2010

Clutter: Bird echoes during nocturnal migration

1. STSM to identify bird echoes because of their intermittent return.

Power return over two minutes from gate at 1356m:

Varies as birds fly in and out of the beam.

Automatic recognition and rejection developed.

Now in standard Vaisala standard software.



Arrows are the direction of the retrieved horizontal winds in the lowest 3km.

Colours indicate the derived vertical winds in the convective boundary layer.

Lindenberg, May 06, 2007

Strong BL convection causes (erroneous ?) fluctuations in horizontal mean wind estimates

WHY? Is it because the convection is varying during the time for the profiler dwells in the three different directions?

2. Future STSM: How does convection affect the retrieval of horizontal winds.



00h forecast

Observation

Wind Speed

Analysis

12h forecastusing latestobservations

improvement

3. Effect of new wind observations on 12Z forecast.



TEMPERATURE

TEMPERATURE PROFILES FROM 50-58GHz MICROWAVE RADIOMETER

RESULTS OF STSM comparing temperatures from microwave radiometer with those from sonde ascent over one year (2008) in Switzerland.

MAX BIAS <1K

RMS ERROR 2K

Boundary layer:

Error <1K, Bias <0.5K



HUMIDITY

1. GPS: - talk at 1800 today by Franz Immler on LUAMI experiment. - special session on GPS at next COST meeting in Koln (16-18 Nov 2010).

2. RAMAN LIDAR - see later.

3. REFRACTIVITY: - MAPPING CLOSE TO THE SURFACE.

GROUND CLUTTER TARGETS WITHIN 30km of OPERATIONAL (5cm) RAIN RADARS

CLUTTER RIGOROUSLY REJECTED FROM RAIN ESTIMATES.

DOPPLER RADARS CAN MEASURE CHANGES IN THE WET DELAY FROM CLUTTER: SCAN EVERY 5 MINUTES, RESOLUTION 4Km.

PRE-OPERATIONAL TESTS IN UK AND FRANCE.

NEXT SLIDE SHOWS TEST RESULTS.



Coolwet

WarmDry

SEA BREEZE, CHILBOLTON - 13 JULY 2005: 0856-1616, SCAN EVERY 30MINS

COURTESY: John Nicol, U OF READING



CLOUDS: mm WAVE RADAR, LIDAR & RADIOMETERCLOUD RADAR:Backscatter varies as ND6 /4 Small - for better cloud sensitivitySees larger particles. Detects ice clouds - estimate ice water content. Water clouds - small droplets less easy to detect. Cannot see aerosol

LIDAR Backscatter varies as ND2 (projected surface area).Lidar sees what we see.Water clouds - v good target - but strong attenuation leads to extinction. Detects ice clouds - attenuation leads to extinction after km.Detects aerosols.

MICROWAVE RADIOMETERS (MWR).Combine 22 and 31GHz - total water vapour path + total liquid water path.Useful constraint for liquid water clouds identified by the radar and lidar. (Special WG meeting MWR retrievals and calibration. 19 Nov 2010 Koln).



Example of radar and lidar observations of clouds and aerosol:

Profiles every 30 seconds, resolution 30 or 60m

Two STSMs on target recognition from Radar/lidar synergy.

Detects cloud base

Penetrates ice cloud

Strong echo from liquid

clouds

Drizzling water clouds

Radar: Z~D6

Sensitive to large particles (ice, drizzle)

Lidar: ~D2

Sensitive to small particles

(droplets, aerosol)



CLOUDS: mm WAVE RADAR,

CLOUD RADAR:

94GHz new technology. Send out a short 1kW pulse. Expensive. Cost about 1M Euro.35GHZ pulsed magnetron - more sensitive - same cost.

Experiment with FM/CW radars in UK and F: Estimated cost < 100K Euro. - but range sidelobes are a problem for large signalsSpecial WG meeting 2 days October 2010 to discuss performance.



94 GHz FM/CW Radar Chilbolton (UK MetO), SIRTA (France)

Instrument problem? Range Side Lobes?

Spurious echoes from FM/CW cloud radar when vertical reflectivity gradient.

Special WG meeting Oct 2010 to discuss performance of FM/CW radars.



Görsdorf MOL/RAO COST ES 0702

Sensitivity comparison of cloud radars:Plot of the frequency of echo occurrence against height

FM/CW 100Keuro

-35dBZ at 1km height

Pulsed magnetron: 1MEuro

-65dBZ at 1km height

MAGNETRON 1000X MORE SENSITIVE



LIDAR: CLOUDS (and Volcanoes)1. Lidar “Ceilometers” are widespread - report cloud base at airports. New generation of ceilometers provide profiles of backscatter - see aerosols in boundary layer - estimate boundary layer depth. 2010 - STSM and Summer school on applications.Special one day WG meeting to discuss performance calibration and

target categorisation. 19 Nov 2010 Koln.

NEW GENERATION OF CHEAP (100K, NOT 1MEuro) UNMANNED LIDARS2. Doppler lidars at 1.5um - Doppler shift from aerosol targets. Provide

vertical winds in boundary layer. Identify depth of boundary layer depolarisation - identify jagged particles from volcanic ash.3. 355nm lidar - backscatter from molecules as well. - depolarisation - identify volcanic ash particles. COMING SOON?

4. Raman N2 - (see talk 12am today: Dinoev, MeteoSwiss) a) Direct measurement of optical depth aerosol/clouds. (2011?) b) Profiles of humidity. - still expensive, research only



European Meteorological Ceilometer Networks

Ceilometer networks in all (?) European countries.

Most systems only cloud base height.

WMO TECO 2008 conference, two studies on BLH retrieval from existing ceilometer networks:

Wauben et al. (KNMI)

Engelbart et al. (DWD)

UKMet

KNMI

FMI

DWD

Backscatter profiles

Denmark France IcelandNetherlands Sweden SwitzerlandGermany UK Finland

Manufacturer Model / Type Remarks DMIMétéo-France IMO KNMI SMHI MeteoSwissDWD UKMet FMI

Eliasson Engineering AB CBME80 xVaisala/Impulsphysic WHX05 Out of production x xVaisala CT25K Out of production x x x x x xVaisala/Impulsphysic LD40 Out of production x xVaisala CT12K Out of production x xVaisala CL31 x x x x xJenoptik CHM 15K x



Deutscher WetterdienstMeteorologisches Observatorium Hohenpeißenberg

(By Courtesy of Mr. H. Flentje from DWD)

Estimation of the Features of the Particles

Estimated Mass Concentr. of the Particles: 500-750 ( 300) µg/m³

VOLCANIC ASHFROM CEILOMETER.



HALO: 1.5m Doppler Lidar – aerosol & clouds.

Cu

aerosol

ice

convection

up

down

DOPPLER:

BACKSCATTER (+depol)



Courtesy of

Janet Barlow

Identification of the boundary layer by Doppler Lidar.

Identification the boundary layer by the vertical velocity variance.

Use of backscatter alone (top panel) does not show diurnal cycle.



VOLCANIC ASH: Doppler 1.5µm lidar: 16 April 2010VOLCANIC ASH: Doppler 1.5µm lidar: 16 April 2010

Descending volcanic ash? Mixes into

turbulent boundary layer

Spherical liquid droplets have very low depolarization

Ash is non-spherical so strongly depolarizing

Background aerosol particles in the boundary layer (0-1 km)



UV EZY Leosphere 355nm Lidar: Sees molecular, aerosol & clouds: 27 Aug 2007

MolecularReferenceGives opt depth

Mol +aerosolLiquid Cu

cirrus

S’cooled

Spherical

Aspherical

liquid

ice

liquid

DEPOL RATIO:SHAPE

BACKSCATTER



VOLCANIC ASH UV Leosphere 355nm lidar: 16 April 2010VOLCANIC ASH UV Leosphere 355nm lidar: 16 April 2010

Descending volcanic ash?

Mixes into turbulent boundary layer

Ash is non-spherical so strongly depolarizing

Background aerosol particles in the boundary layer (0-1 km)

Spherical hydrated aerosol with minimal depolarization



Summary Volcanic Ash. • Existing dense ceilometer networks can identify ash layers for confirming model

predictions.

• Existing ceilometers could have depol channel to aid ash identification.

• 355nm lidar backscatter + depol can identify ash.

• Detect molecular + aerosol backscatter (how do you separate them?)

• Reduction in molecular return - gives ash extinction.

• Lidar ratio (extinction/backscatter) of 50 helps to identify ash.

+

• Add Raman N2 channel - 1000 times less than elastic backscatter.

• 30 minute dwell - reduction in Raman N2 gives extinction profile.

• Tests with low cost Raman N2 channel to identify ash profiles

- very encouraging.


ESF provides the COST Office through an EC contract32© Crown copyright Met Office

WHAT IS IDEAL NETWORK NWP DENSITY? FUND: UK test Bed: Oct 2010 - 2012



INSRUMENT SPECIFICATION?RADAR: ICE CLOUD WATER CONTENT DECTECTABILITY

10Km ALTITUTUDE:

2 mg/m3 or 30 mg/m3

4 Km ALTITUTUDE:

0.05 mg/m3 or 1 mg/m3

PRICE

1M EURO 100KEURO



QUESTION• TO BE ASKED TO THE REPRESENTATIVE OF THE NATIONAL MET

SERVICES AT THE NEXT COST MEETING 16-18 NOV 2010 IN KOLN:

• DO YOU WANT AN EXPENSIVE RADAR WHICH WILL DETECT:

2mg/m3 of ice at 10km height?

or a denser network of radars detecting 30mg/m3?

• DO YOU WANT AN EXPENSIVE LIDAR WHICH WILL DETECT:

ice with an extinction coefficient of < 0.05/km at 10km?

or a denser network of ceilometers detecting extinction of just 0.5/km?

What does the audience think?

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