The Precipitating Clouds Product of the Nowcasting SAF Anke Thoss, Ralf Bennartz*, Adam Dybbroe
description
Transcript of The Precipitating Clouds Product of the Nowcasting SAF Anke Thoss, Ralf Bennartz*, Adam Dybbroe
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 1
SA
FN
WC
The Precipitating Clouds Product
of the Nowcasting SAF
Anke Thoss, Ralf Bennartz*, Adam Dybbroe
*University of Wisconsin, USA
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 2
SA
FN
WC
Outline
Introduction
Method overview •AVHRR•AMSU•combining AMSU and AVHRR•algorithm performance
Case Studies
Summary and outlook
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 3
SA
FN
WC Goal:Algorithm for Nowcasting applications
fast absolute accuracy not of primary importance applicable over land and sea, day and night use satellite data directly received at weather service
NOAA /(EPS): IR-VIS-MWMSG: IR-VIS
considerable uncertainties in both VIS/IR as well as scattering based MW precipitation retrieval
likelihood estimates in intensity classes more appropriate
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 4
SA
FN
WC
Four classes of precipitation intensity from
co-located radar data
Rain rate
Class 0: Precipitation-free 0.0 - 0.1 mm/h
Class 1: Very light precipitation 0.1 - 0.5 mm/h
Class 2: Light/moderate precipitation 0.5 - 5.0 mm/h
Class 3: Intensive precipitation 5.0 - ... mm/h
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 5
SA
FN
WC
The data set
• Eight months of NOAA-16 AMSU-A/B (Feb -Aug 2001, 867 overpaths) for AMSU algorithm development
• 12 months (June 99 - May 00) for AVHRR algorithm development.
• Co-located BALTEX-radar Data Centre radar data for the entire Baltic region, up to 30 radars, gauge adjusted
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 6
SA
FN
WC
AMSU-A/B
• cross track scanning microwave radiometer• spectral range 23-190 GHz, channels used:
23 GHz, 89GHz, 150GHz• 3.3 degree resolution AMSU-A (23-89GHz) • 1.1 degree resolution AMSU-B (89-190GHz)
AVHRR•channels used: 0.6 m, 1.6 m 3.7 m, 11 m and 12 m•1km resolution at nadir ( 0.054 degree )
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 7
SA
FN
WC
AVHRR Algorithm development:
•Based on Cloud type output
•Correlation of spectral features with precipitation investigated •Special attention to cloud microphysics (day/night algorithms)
•Precipitation Index PI constructed as linear combination of spectral features
•Algorithms cloud type specific
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 8
SA
FN
WC
Which Cloud types are potentially raining?
all cloudfree types P(rain) < 2.6%
medium level P(rain) = 21.2%
very low clouds P(rain) = 2.1%low clouds P(rain) = 5.5%
high opaque P(rain) = 38.9% very high opaque P(rain) = 47.0% Ci very thin P(rain) = 4.9%
Ci thin P(rain) = 8.4% Ci thick P(rain) = 11.1% Ci over lower clouds P(rain) = 16.5%
fractional clouds P(rain) = 3.5%
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 9
SA
FN
WC
Correlation of Spectral features with rain
Correlation with class, all potentially raining cloudtypes
T11 -0.24Tsurf - T11 0.26T11-T11 -0.16R0.6 0.18R3.7 -0.18ln(R0.6/R3.7) 0.26R0.6/R0.6 0.42
3.7m day algorithm, all 0.351.6m day algorithm, all 0.44night algorithm, all 0.30
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 10
SA
FN
WC
Precipitation Index
Example 3.7 day algorithm, all cloud types:
PI=35+0.644(Tsurf-T11)+5.99(ln(R0.7/R3.7))-3.93(T11-T12)
Example 1.6 day algorithm, all cloud types: PI = 65 -15*abs(4.45-R0.6 /R1.6)+0.495*R0.6-0.915(T11-T12) +0*Tsurf+0*T11
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 11
SA
FN
WC
Probability distribution, all raining Cloudtypes
1.6 Day algorithm
Night algorithm 3.7 Day algorithm
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 12
SA
FN
WC
NOAA-15 overpass 27 May 2000 17:22 UT
AVHRR Cloud type
AVHRR-RGB
CH 3,4,5
AVHRR-RGB
CH 1,2,4
unprocessedcloud free landcloud free seasnow (land)snow/ice (sea)very low cloudsvery low cloudslow cloudslow cloudsmedium cloudsmedium cloudshigh opaque high opaquevery high opaquevery high opaqueCi, very thinCi, thinCi, thickCi over lower cloudfractional cloudunclassified
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 13
SA
FN
WC
NOAA-15 overpass 27 May 2000 17:22 UT
AVHRR -night
Precipitation classification RGB:
•blue: intensive
•green: moderate
•red: light
AVHRR-RGB
CH 3,4,5
BALTRAD
radar composite
AVHRR -day/night
Precipitation classification RGB:
•blue: intensive
•green: moderate
•red: light
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 14
SA
FN
WC
Passive microwave precipitation signal
• Most directly linked to surface precipitation
• Over cold (water) surfaces only
• Works over both land and water surfaces
• More indirect
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 15
SA
FN
WC
The scattering index
Has been found to be a linear measure for precipitation intensity
Predict brightness temperature T * in absence of scattering from low frequencies (functional relation is found by inverse radiative transfer modelling or global brightness temperature statistics)
observedhighfreqlowfreq TTTSI ,* )(
Take T * and subtract the observed high frequency brightness temperature
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 16
SA
FN
WC
AMSU-A water or coast, AMSU-B land:SI = T89-CORR -T150
AMSU-A land (and AMSU-B land):SI = T23-CORR -T150
AMSU-B water:SI = T89-CORR -T150
CORRTT lowfreq *
For our algorithm:
CORR corrects for scan position effects and statistical offset for non scattering situations
for SI water CORR is adjusted dynamically
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 17
SA
FN
WC
Linear dependence of MW Tb’s on land fraction within FOV
coastal estimates can be computedas a linear combination of land and sea estimate according to land fraction:
SIcoast = (1-Nland)*SIsea + Nland*SIland
important to properly convolve a high resolution LSM to the AMSU FOV
AMSU-A
Nland
T23
important to properly convolve AMSU-B to AMSU-A
for algorithm development: convolve radar to AMSU-B!
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 18
SA
FN
WC
150 GHz versus 89 GHz scattering index over land, Results from NOAA15
(23GHz as low frequency channel)
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 19
SA
FN
WC Results hardclustering NOAA16
AMSU SEA %c0 %c1 %c2 %c3 radar c0 70 27 3 0 radar c1 15 53 31 1 radar c2 4 24 55 17 radar c3 3 6 27 64
AMSU Land %c0 %c1 %c2 %c3 radar c0 69 26 4 1 radar c1 16 49 24 11 radar c2 4 31 33 32 radar c3 10 5 14 71
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 20
SA
FN
WC
NOAA-15 overpass 27 May 2000 17:22 UT
AMSU-RGB
89,150,183±7 GHz
Precipitation classification RGB:
•blue: intensive
•green: moderate
•red: lighPt
AVHRR-RGB
CH 3,4,5BALTRAD
radar composite
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 21
SA
FN
WC
AVHRR AMSU
+ high spatial resolution - low spatial resolution
+ convective cells, even small - small convective cells ones can be well identified sometimes missed
- no strong coupling between + stronger coupling between spectral signature and rain rain and scattering signature
- area of potential rain + rain areas better delineated overestimated generally low likelihood
- intensity and likelihood not + more independent intensity really decoupled and likelihood information
- sometimes spurious light rain
- not applicable over snow and ice
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 22
SA
FN
WC
Combining AVHRR and AMSUAVHRR mainly used for QC of AMSU:
*thresholidng with a 5% likelihood from AVHRR has the effect that about 2.5% of the rain according to radar estimates for
potentially raining clouds are missed.
over snow and sea ice use AVHRR only (to be implemented )
if total precipitation likelihood from AVHRR > 5%*, replace precipitation estimate with AMSU estimate
(if available)
for AVHRR pixels containing a potentially raining cloud type compute precipitation likelihood
run cloud type analysis
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 23
SA
FN
WC
NOAA-15 overpass 27 May 2000 17:22 UT
AMSU/AVHRR
Precipitation classification RGB:
•blue: intensive
•green: moderate
•red: light
AVHRR-RGB
CH 3,4,5
BALTRAD
radar composite
AVHRR -day/night
Precipitation classification RGB:
•blue: intensive
•green: moderate
•red: light
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 24
SA
FN
WC
Algorithm performance
Different algorithms - different characteristics to compare different algorithms: hardclustering performed with monthly varying, algorithm dependent thresholds. If P(rr) threshold, assign to rain class with greatest likelihood, otherwise assign to no-rain.
Total rain thresholds used:
month 1 2 3 4 5 6 7 8 9 10 11 12day3.7/ night 30 30 40 40 50 50 50 40 30 30 30 30night 30 30 30 30 30 30 30 30 30 30 30 30AMSU 30 40 40 50 50 50 50 40 50 50 40 40
Thresholds selected according to average monthly likelihood per class
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 25
SA
FN
WC
Flexible clustering, potentially raining cloud types one year data set
AVHRR day/night %c0 %c1 %c2 %c3 radar c0 57 2 35 6 radar c1 36 2 51 11 radar c2 18 2 59 21 radar c3 8 0 44 48
AVHRR night %c0 %c1 %c2 %c3 radar c0 71 1 28 0 radar c1 46 3 51 0 radar c2 28 2 69 1 radar c3 13 4 77 6
AMSU/AVHRR %c0 %c1 %c2 %c3 radar c0 70 19 8 3 radar c1 46 36 13 5 radar c2 27 38 23 12 radar c3 10 24 29 37
All year (120 scenes), every 30th pixel
AMSU only Coastal %c0 %c1 %c2 %c3 radar c0 70 26 4 0 radar c1 24 42 29 5 radar c2 9 26 44 21 radar c3 5 8 26 61
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 26
SA
FN
WC
0102030405060708090
Jan
mar
may ju
lse
pnov
day/night
night
amsu
Flexible clustering: correctly identified class0 (no rain)
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 27
SA
FN
WC
0
20
40
60
80
100
120
Jan
mar
may ju
lse
pnov
day/night
night
amsu
Flexible clustering: class2 (0.5-5mm/h classified as rain)
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 28
SA
FN
WC
0
20
40
60
80
100
120
Jan
mar
may ju
lse
pnov
day/night
night
amsu
Flexible clustering: class3 (>5mm/h classified as rain)
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 29
SA
FN
WC
Flexible clustering, potentially raining cloud types April-May 2001
AVHRR day 1.6, rainthresh 20% %c0 %c1 %c2 %c3 radar c0 74 15 11 0 radar c1 45 34 21 0 radar c2 27 42 31 0 radar c3 16 43 41 0
AVHRR night, rainthresh 30% %c0 %c1 %c2 %c3 radar c0 63 1 36 0 radar c1 54 1 45 0 radar c2 39 1 60 0 radar c3 21 0 78 1
AMSU/AVHRR, rainthresh 20% %c0 %c1 %c2 %c3 radar c0 68 24 6 2 radar c1 43 42 11 4 radar c2 25 42 22 11 radar c3 15 32 29 24
every 10th Pixel
SCORES RAIN THRESH POD FAR HK HSS night 30% 0.53 0.63 0.16 0.11day 1.6 20% 0.63 0.50 0.38 0.35 AMSU/AVHRR 20% 0.65 0.55 0.33 0.29AMSU/AVHRR 50% 0.52 0.52 0.29 0.27AMSU sea 0.89 0.83 0.47 0.17AMSU land 0.88 0.75 0.57 0.27
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 30
SA
FN
WC
Total rainlikelihood
10%20%30%40%50%
60-80%90-100%
NOAA16, 2001-04-05, 11:30UTC
upper: total precipitation likelihood,left:IR, middle:VIS,right:AMSU
lower left: AMSU likelihood RGBRed:intensivegreen: light/moderateblue:very light
lower right: BRDC radar composite
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 31
SA
FN
WC
Total rainlikelihood
10%20%30%40%50%
60-80%90-100%
NOAA16, 2001-04-23, 11:45UTC
upper: total precipitation likelihood,left:IR, middle:VIS,right:AMSU
lower left: AMSU likelihood RGBRed:intensivegreen: light/moderateblue:very light
lower right: BRDC radar composite
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 32
SA
FN
WC
Total rainlikelihood
10%20%30%40%50%
60-80%90-100%
NOAA16, 2001-05-19, 10:45UTC
upper: total precipitation likelihood,left:IR, middle:VIS,right:AMSU
lower left: AMSU likelihood RGBRed:intensivegreen: light/moderateblue:very light
lower right: BRDC radar composite
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 33
SA
FN
WC
upper: total precipitation likelihood,left:IR, middle:VIS,right:AMSU
lower left: AMSU likelihood RGBRed:intensivegreen: light/moderateblue:very light
lower right: INM radar composite
NOAA16, 2001-05-21, 14:000UTC
10%20%30%40%50%
60-80%90-100%
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 34
SA
FN
WC
Algorithm Performance - Summary
AMSU highest potential to delineate intensity classes. Underestimates intensity when estimates are translated to pixel level (Scale!)
all algorithms miss a lot of precipitation events in winter, but AMSU Alg. was recently improved on this point in summer generally acceptable performance, but area extend of precipitation overestimated. AVHRR 3.7 day algorithm can delineate moderate to strong precipitation,but assigns too many no rain cases high precipitation likelihood in summer
AVHRR 1.6 day algorithm can delineate precipitation areas quite well, but can not delineate intensity. Seasonal andangular dependence needs to be investigated.
IPW
G,
Madri
d
23
-27
Septe
mber
20
02
PC 35
SA
FN
WC
Outlook
Develop combined 1.6, 3.9 m algorithm for MSG
Refine coupling of VIS/IR/MW
Calibrate MSG estimates with MW estimates?
Check stability of 1.6um algorithm