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