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Investigations into the improvement of automated precipitation type observations at KNMI

Investigations into the improvement of automated precipitation type observations at KNMI. Marijn de Haij Wiel Wauben KNMI R&D Information and Observation Technology. Contents. The main issues Investigation of new sensors Conclusions and outlook. Precipitation type observation.

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Investigations into the improvement of automated precipitation type observations at KNMI

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  1. Investigations into the improvement of automated precipitation type observations at KNMI Marijn de Haij Wiel Wauben KNMI R&D Information and Observation Technology

  2. Contents The main issues Investigation of new sensors Conclusions and outlook

  3. Precipitation type observation Visual observations in SYNOP/METAR issued by KNMI fully automated using Vaisala FD12P scatterometers, with exception of 2 airports Combines optical (~size) and DRD12 detector (~water content) signals Differences 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

  4. Comparison with human observer Overlapping hourly observations at 6 KNMI stations in 2000-2002 Correction rules and averaging applied on 1-min sensor data Poor skill scores found for freezing and solid precipitation Additional rules based on RH, TA, MOR evaluated with reference set Further improvement not likely -> test with ‘new’ instruments

  5. 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 2010 Additional 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 tool Only precipitation type is used – wawa without intensity indication

  6. 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 Lufft R2S 24 GHz Doppler radar Frequency shift-> v 4 types: R,LRS,S,A Vaisala WXT520 RAINCAP Ø94mm Drop impact-> volume Distinction rain/hail: R,A

  7. 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

  8. Example 16 January 2010: wintry precipitation (2) First report LRS/S R2S 1615 PAR 1842 LPM 1845 FD12 1853

  9. 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

  10. Results: evaluation Hourly evaluation performed by meteorologist beside normal duties Selection of events where disagreement with FD12P was indicated Results (≠ skill scores):

  11. Results: general impression Technically OK for 18 months without maintenance Frequency distribution (10-min) LPM: UP due to spiders, some added value for hail and classification of 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

  12. Conclusions and outlook None of the automated systems has perfect performance Thies LPM is able to partially solve the issues encountered with the precipitation type observation by the FD12P Analysis of the improvement limited due to availability of reference Winter 2010-2011: Second test of LPM disdrometer at airports Schiphol and Rotterdam Entry of PW changes on a 1-minute basis by human observer Optimization of combination FD12P/LPM for precipitation type LPM issues that still need to be addressed: Contribution of false reports by spider(web)s Sensitivity/threshold Wind effect on the determination of the precipitation type

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

  14. Indoor check Setup of test for homogeneity and reproduceability of disdrometers Prior to field test and after 1 year Problem: accurate positioning of drops in the light sheet! Peristaltic pump Droplet plate Sensor Scale Good agreement with Thies factory calibration

  15. Contingency table 2000-2002

  16. Amplitude  Diameter Duration  Velocity

  17. Classification FD12P vs disdrometer Vaisala FD12P Disdrometer (bv. Ott/Thies) Optisch/DRD12 = “grootte”/”waterinhoud” + temperatuur + max. deeltjesgrootte + evt. temperatuur

  18. Intermezzo scores

  19. 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

  20. Overview

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