Investigations into the improvement of automated precipitation type observations at KNMI

TECO-2010, Helsinki | 31 August 2010

Investigations into the improvement of automated precipitation type observations at KNMI

Marijn de HaijWiel Wauben

KNMIR&D Information and Observation Technology


Contents

• The main issues

• Investigation of new sensors

• Conclusions and outlook


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Precipitation type observationVisual observations in SYNOP/METAR issued by KNMI fully automated using Vaisala FD12P scatterometers, with exception of 2 airportsCombines optical (~size) and DRD12 detector (~water content) signalsDifferences with human observer analyzed and reported to users (e.g. Wauben, 2002)

Most important issues:- Discrimination of mixed/solid precipitation- Classification of light precipitation events- Detection of hail- Precipitation detection in fog (MOR<400m)

FD12P De Bilt Test


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Comparison with human observerOverlapping hourly observations at 6 KNMI stations in 2000-2002Correction rules and averaging applied on 1-min sensor data

Poor skill scores found for freezing and solid precipitationAdditional rules based on RH, TA, MOR evaluated with reference setFurther improvement not likely -> test with ‘new’ instruments

Precipitation Liquid Freezing SolidFD12P FD12P FD12P FD12P

Obs yes no Obs yes no Obs yes no Obs yes noyes 16729 3600 yes 15055 3650 yes 28 28 yes 808 466no 4224 117657 no 4261 119244 no 33 142121 no 181 140755

POD 82% POD 80% POD 50% POD 63%FAR 20% FAR 22% FAR 54% FAR 18%CSI 68% CSI 66% CSI 31% CSI 56%HSS 78% HSS 76% HSS 48% HSS 71%BIAS 1.03 BIAS 1.03 BIAS 1.09 BIAS 0.78N 24553 N 22966 N 89 N 1455


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Investigation of new sensors (2008-2010)KNMI selected four commercially available sensors:

- with the potential to improve the observation (combined w/ FD12P)- which are suitable for use at AWS at an affordable price tag

Ott Parsivel, Thies LPM, Lufft R2S, Vaisala WXT520

Setup:Field test in De Bilt September 2008-March 2010Additional data: FD12P (2x), rain gauge, wind, PTU, …Assessment of possibilities for indoor check

Reference:Evaluation by data validation specialists (10-min) and meteorologists (hourly) in a web toolOnly precipitation type is used – wawa without intensity indication


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Sensors under test

Ott Parsivel

Optical disdrometer

51cm2 sheet, 650nm

Extinction-> D,v

8 types: L,LR,R,LRS,S,SG,SP,A

Thies LPM

Optical disdrometer

46cm2 sheet, 785nm

Extinction-> D,v

9 types: P,L,LR,R,LRS,S,IP,SG,A

Vaisala WXT520

RAINCAP Ø94mm

Drop impact-> volume

Distinction rain/hail: R,A

Lufft R2S

24 GHz Doppler radar

Frequency shift-> v

4 types: R,LRS,S,A


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Example 16 January 2010: wintry precipitation

Transition from liquid to solid precipitation around 19UT

Captured well by disdrometers, 2 FD12P sensors show difference

R2S: mixture reported due to temperature threshold 4˚C

Meteorologist confirms light drizzle detections of LPM


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Example 16 January 2010: wintry precipitation (2)

First report LRS/S

R2S 1615

PAR 1842

LPM 1845

FD12 1853


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Example 15 December 2008: dense fog

Dense fog event identified in the evening (MOR<200 m), just above 0˚C

Both FD12Ps report snow and snow grains at max. 0.03 mm/h

Other sensors do not report precipitation, as confirmed by meteorologist


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Results: evaluationHourly evaluation performed by meteorologist beside normal duties

Selection of events where disagreement with FD12P was indicatedResults (≠ skill scores):

Hourly observations

# obs. # OK / N / NOK

LPM 141 56 / 0 / 85

Parsivel 141 31 / 0 / 110

FD12oper 141 22 / 0 / 119

10-min observations

# obs. # OK / N / NOK

LPM 269 232 / 7 / 30

Parsivel 269 184 / 0 / 85

FD12oper 269 107 / 21 / 141


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Results: general impressionTechnically OK for 18 months without maintenanceFrequency distribution (10-min)

LPM: UP due to spiders, some

added value for hail and classificationof light events, no detection in fog

Parsivel: high FAR for hail types,

insensitive to L/SG, solid “spider”reports (no T included)

WXT520: no hail events reported,

although 3 confirmed cases

R2S: high FAR for LRS, insect

detections, threshold D≥0.3mm


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Conclusions and outlookNone of the automated systems has perfect performanceThies LPM is able to partially solve the issues encountered with the precipitation type observation by the FD12PAnalysis of the improvement limited due to availability of reference

Winter 2010-2011:Second test of LPM disdrometer at airports Schiphol and RotterdamEntry of PW changes on a 1-minute basis by human observerOptimization of combination FD12P/LPM for precipitation type

LPM issues that still need to be addressed:Contribution of false reports by spider(web)sSensitivity/thresholdWind effect on the determination of the precipitation type


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Thanks for your attention!See paper 3(2) for further details


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Indoor check

Peristaltic pump

Scale

Sensor

Droplet plate

Setup of test for homogeneity and reproduceability of disdrometersPrior to field test and after 1 yearProblem: accurate positioning of drops in the light sheet!

L0114.0

L0234.0

L0354.0

L0474.0

L0594.0

L06114.0

L07134.0

L08154.0

L09174.0

L10194.0

L11214.0

10.0

5.0

0.0

-5.0

-10.0

position, distance along beam (mm)

dist

ance

acr

oss

beam

(m

m)

LPM Tel3: average normalized drop volume (surface plot)

0.00-0.20 0.20-0.40 0.40-0.60 0.60-0.80 0.80-1.00

Good agreement with Thies factory calibration


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Contingency table 2000-2002

Observer N/A C P L LR R ZL ZR LRS S IP SG IC SP A SumN/A 719 7494 42 282 154 663 17 9 2 10 4 9396C 5230 117657 353 1234 248 2233 13 3 17 47 11 65 127111P 2 25 3 1 7 253 2 3 296L 310 1535 46 987 121 465 1 1 5 3 3474

LR 98 182 20 760 365 940 2 2 3 2 2374R 545 1722 106 2014 1694 7709 1 13 4 2 13810ZL 12 6 1 6 3 1 2 31ZR 2 3 2 17 1 25

LRS 11 20 14 32 19 107 2 65 35 4 7 316S 5 64 22 10 4 22 1 1 81 442 26 59 737IP 1 3 2 1 1 8SG 7 20 2 3 2 1 4 5 30 4 19 97IC 0SP 8 16 15 6 6 54 16 9 2 2 134A 2 2 1 10 15

Sum 6937 128751 629 5331 2621 12463 46 41 209 587 47 160 0 0 2 157824

N/A 9.9% Band0 89.5% Band0* 47.3% Band1 93.9% Band1* 78.2%

FD12P PWc


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Amplitude Diameter

Duration Velocity


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Classification FD12P vs disdrometer

Optisch/DRD12 = “grootte”/”waterinhoud”+ temperatuur+ max. deeltjesgrootte

Vaisala FD12P Disdrometer (bv. Ott/Thies)

+ evt. temperatuur


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Intermezzo scores

event

other method

yes no

reference

yes hit miss

no false none

Probability of detectionPOD = hit / (hit+miss)

False alarm rateFAR = false / (hit+false)

Critical succes indexCSI = hit / (hit+miss+false)


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Example 26 May 2009: hail event

Parsivel and LPM report hail between 0215 and 0225UT

Temperature drops 5˚C, radar summer hail chance >90%But unfortunately no evaluation

Other sensors report heavy rain, including both FD12Ps


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OverviewPrecipitation type PW

codeNWScode

METARcode

No precipitation 00 C -

Unknown precipitation 40 P UP

Drizzle 50 L DZ

Freezing drizzle 55 ZL FZDZ

Drizzle and rain 57 LR DZRA

Rain 60 R RA

Freezing rain 65 ZR FZRA

Drizzle/rain and snow 67 LRS RASN

Snow 70 S SN

Ice pellets 75 IP PL

Snow grains 77 SG SG

Ice crystals 78 IC IC

Snow pellets 87 SP GS

Hail 89 A GR

Investigations into the improvement of automated precipitation type observations at KNMI

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