Investigations into
the improvement of
automated
precipitation type
observations at KNMI
Marijn de Haij
Wiel Wauben
KNMI
R&D Information and Observation Technology
TECO-2010, Helsinki | 31 August 2010
Contents
• The main issues
• Investigation of new sensors
• Conclusions and outlook
TECO-2010, Helsinki | 31 August 2010
TECO-2010, Helsinki | 31 August 2010
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
TECO-2010, Helsinki | 31 August 2010
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Comparison with human observer
Overlapping hourly observations at 6 KNMI stations in 2000-2002
Correction rules and averaging applied on 1-min sensor data
Precipitation
FD12P
Obs
yes
no
yes
16729
3600
no
4224 117657
Liquid
POD
FAR
CSI
HSS
BIAS
N
POD
FAR
CSI
HSS
BIAS
N
82%
20%
68%
78%
1.03
24553
Obs
yes
no
Freezing
FD12P
FD12P
yes
no
Obs
yes
15055
3650 yes
28
4261 119244 no
33
80%
22%
66%
76%
1.03
22966
POD
FAR
CSI
HSS
BIAS
N
Solid
no
Obs
28
yes
142121 no
50%
54%
31%
48%
1.09
89
POD
FAR
CSI
HSS
BIAS
N
FD12P
yes
808
181
no
466
140755
63%
18%
56%
71%
0.78
1455
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
TECO-2010, Helsinki | 31 August 2010
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
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Sensors under test
Ott Parsivel
Thies LPM
Optical disdrometer
Optical disdrometer
51cm2 sheet, 650nm
46cm2 sheet, 785nm
Extinction-> D,v
Extinction-> D,v
8 types:
9 types:
L,LR,R,LRS,S,SG,SP,A
P,L,LR,R,LRS,S,IP,SG,A
Lufft R2S
Vaisala WXT520
24 GHz Doppler radar
RAINCAP Ø94mm
Frequency shift-> v
Drop impact-> volume
4 types:
Distinction rain/hail:
R,LRS,S,A
R,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: evaluation
Hourly evaluation performed by meteorologist beside normal duties
Selection of events where disagreement with FD12P was indicated
Results (≠ skill scores):
Hourly observations
# obs.
# OK / N / NOK
LPM
141
56 / 0 / 85
Parsivel
141
FD12oper
141
10-min observations
# obs.
# OK / N / NOK
LPM
269
232 / 7 / 30
31 / 0 / 110
Parsivel
269
184 / 0 / 85
22 / 0 / 119
FD12oper
269
107 / 21 / 141
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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
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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
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Thanks for your attention!
See paper 3(2) for further details
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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
LPM Tel3: average normalized drop volume (surface plot)
-5.0
0.0
5.0
L01
14.0
L02
34.0
L03
54.0
L04
74.0
L05
94.0
L06
114.0
L07
134.0
L08
154.0
L09
174.0
L10
194.0
10.0
L11
214.0
position, distance along beam (mm)
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
distance across beam (mm)
-10.0
Sensor
Scale
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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
Sum
N/A
FD12P PWc
N/A
C
719
7494
5230 117657
2
25
310
1535
98
182
545
1722
12
2
11
20
5
64
P
42
353
3
46
20
106
6
LR
154
248
7
121
365
1694
19
4
2
3
107
22
3
1
6
1
2621
54
10
12463
20
2
32
10
1
3
8
16
2
2
6937 128751
15
6
629
5331
7
9.9%
14
22
L
282
1234
1
987
760
2014
1
Band0 89.5%
Band0*
47.3%
R
663
2233
253
465
940
7709
ZL
ZR
LRS
17
13
9
3
1
2
1
2
1
3
17
2
1
2
6
2
1
4
46
Band1 93.9%
41
Band1*
S
2
17
2
5
3
13
IP
10
47
3
3
2
4
1
SG
11
IC
4
26
5
35
442
1
30
4
16
9
2
2
209
587
47
160
78.2%
A
2
2
1
7
59
1
19
65
81
SP
4
65
0
0
2
Sum
9396
127111
296
3474
2374
13810
31
25
316
737
8
97
0
134
15
157824
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Amplitude  Diameter
Duration  Velocity
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Classification FD12P vs disdrometer
Vaisala FD12P
Optisch/DRD12 =
“grootte”/”waterinhoud”
+ temperatuur
+ max. deeltjesgrootte
Disdrometer (bv. Ott/Thies)
+ evt. temperatuur
TECO-2010, Helsinki | 31 August 2010
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Intermezzo scores
other method
event
yes
Probability of detection
POD = hit / (hit+miss)
yes
no
hit
miss
False alarm rate
FAR = false / (hit+false)
none
Critical succes index
CSI = hit / (hit+miss+false)
referen
ce
no
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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Overview
Precipitation type
PW
code
NWS
code
METAR
code
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