Jung hee KimS lli l i i i iSatellite Analysis Division

National Meteorological Satellite CenterKorea Meteorological AdministrationKorea Meteorological Administration

jhkim27@korea.kr

1 Introduction1. Introduction

2 Operational nowcasting applications2. Operational nowcasting applications 2.1 COMS Rainfall rate(RI)2.2 NWC SAF(CRR)2.3 NWC SAF(RDT)2.4 COMS RGB Convective Cloud Product2 5 COMS RGB W t & Cl d P d t2.5 COMS RGB Water vapor & Cloud Product

3 Summary & Future plan3. Summary & Future plan

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3

LST

AMV

CLD

UTH

FOG

SSI

SST

CSR

TPW

INSCTT/CTH

RI

INS

CLA

AOD AI

OLR

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* CMDPS : COMS Meteorological Data Processing System

Outline of Nowcasting and Very Short-Range Forecast

COMS Infrared image

COMSGround based observation

COMS Products

COMS

NWP dataMicrowave satellite data…

Operational nowcasting applications : 1. Cloud analysis2. Precipitation analysis3 Yellow dust/fog analysis

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3. Yellow dust/fog analysis4. Convective cloud analysis5. Severe weather analysis

COMS Tb RR+ = COMS RICOMS TbIR RRSSM/I+ = COMS RI

E i i l l ti b t COMS Tb d RR Empirical relations between COMS TbIR and RRSSM/I

Continuous observation can be possible for High-Impact Weather monitoring

05 < RI < 35 mm/h (depending on SSM/I range)

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0.5 < RI < 35 mm/h (depending on SSM/I range)

2013. 08.29 01UTC 2013. 08.29 02UTC 2013. 08.29 03UTC

~80mm/h ~50mm/h ~30mm/h

2013. 08.29 01UTC 2013. 08.29 02UTC 2013. 08.29 03UTC

/~13mm/h

~25mm/h ~25mm/h13mm/h

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Useful for warning of heavy rain beforehand when the heavy rainfallsystem is approaching from the sea

CRR v.2009+Spanish 2D CM

MTSAT+radar+UM(United Model)

v.2009 CRR v.2009+Korean 2D CM

COMS+radar+UM(United Model)

v.2011_kma CRR v.2012+Korean new 2D CM

COMS+radar+UM(United Model)

v.2012_kma

MTSAT+radar+UM(United Model)

Spanish 2D CM

: summer 2004-2006

for Spanish region

COMS+radar+UM(United Model)

Korean 2D CM

: rainy days 2011.04~2011.10

for Korean region

COMS+radar+UM(United Model)

Korean new 2D CM: (LUT Update)

: summer(MAY~SEP) 2011~2012

for Korean region

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p g g g

* LUT : Look Up Table* CM : Calibration matrices

06:00 UTC 6 Aug. 2013* AWS : Automated Weather Station

~50mm/h ~50mm

v.2009 v.2011_kma v.2012_kma

1 / 1 / /~10mm/h ~10mm/h ~20mm/h

V i M [ /h] RMSE [ /h] POD [%] FAR [%]Version Mean [mm/h] RMSE [mm/h] POD [%] FAR [%]

v.2009 4.1 47.2 67.9 46.6

v.2011_KMA 5.7 23.0 71.1 66.9

v 2012 KMA 12 0 43 9 67 9 46 6

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v.2012_KMA 12.0 43.9 67.9 46.6

Input data

Main channel: Tb

Input data

Main channel: Tb

v.2009_kma v.2012_kma

- Main channel: TbIR1(10.8)

- Option channel: TbWV(6.7), TbIR2(12.0)

- Other level 2 data: CTP, CTH, CT

sub data

- Main channel: TbIR1(10.8)

- Option channel: TbWV(6.7), TbIR2(12.0)

- Other level 2 data: CTP, CTH, CT

sub datasub data

- Lightning data

sub data

- Lightning data

- NWP indices (UM:United Model)

K-Index Showalter index

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Lift Index Convective Mask

00:00 UTC 29 Aug. 2013

v.2009_kma v.2012_kma: Trigger stage of convective

: Mature stage of convective

: Direction of convective

: Trajectories of convective

: Lightning observation data

~60mm~60mm/h

Version POD [%] Lead time [min]Contingency

TableLightning ○ Lightning ⅹ

v.2009_KMA 100 0

v.2012_KMA 82 +45

Convective(RDT)

Hits(H) False Alarms(F)

Non-Convective(RDT)

Misses(M)Correct

Rejections(C)

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Color Channel ( ) Threshold (K)

Case 1) 2013 24th Typhoon ‘DANAS’[2013.10.06 10:45UTC]

Color Channel (㎛) Threshold (K)

Red IR12.0 – IR10.8 -4 ~ 2

Green WV6.75 – IR10.8 -20 ~ 15

Blue IR10 8 210 ~ 300Blue IR10.8 210 ~ 300

RGB Convective Cloud Product COMS IR Image

RGB Convective Cloud Product

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Developing

convective cloud

Weakening

convective cloud

Low and

medium cloud

High cloud Land or Sea

Case 3) 17 Jul. 2014 heavy rain due to seasonal rain front

AWS 3h (15~18UTC) accumulated Radar 17UTC

~100mm~50mm/h

~10mm

~15mm/h

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5U C 6U C U C

Case 3) 17 Jul. 2014 heavy rain due to seasonal rain front

Radar 17UTC15UTC 16UTC 17UTC

15UTC 16UTC 17UTC

~10mm

15UTC 6U C

RGB Convective Cloud Product

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Developing

convective cloud

Weakening

convective cloud

Low and

medium cloud

High cloud Land or Sea

Color Channel (㎛)

Red VIS0.67(day) or SWIR3.7(night)

Green WV6 75 IR10 8Green WV6.75 – IR10.8

Blue IR10.8

Surface of the earth and low clouds are nearly dark color in WV images RGB & l d d id h d i f i f

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RGB water vapor & cloud product can provide the merged information for water vaporand cloud at the same time

Case 1) 31 Jul. 2014 intensive rainfall

AWS 02UTC AWS 06UTC

RGB Water vapor & Cloud Product

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p

Moist region Dry region High cloud or

Developing convective cloud

Low cloud

1. Results of quality monitoring of nowcasting products

RI, CRR – generally underestimate the radar maxima

- precipitation areas and intensities in CRR are closer to the ground rain gauge data

through version update

RDT – convective clouds are not detected in v.2012_kma for the light rain region

2. Plan for quality verification and control of nowcasting products

- v.2009_kma is showing better performance for tracking convective cell and lead time

Develop algorithmQuality Monitoring

by case study

Long-term

Qualityalgorithm

improvement

2. Plan for quality verification and control of nowcasting products

by case studyverification

improvement

Present stage

3. Utilize data of Himawari and the next COMS (Geo-KOMSAT-2A)

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AMI(Advanced Meteorological Imager) : 16 spectral bands

GeoGeo--KOMPSATKOMPSAT--2A AMI2A AMI COMS MI

ChannelBand name

wavelength

( )

resolution

(km)SNR

NEdT(K)(240/300K)

Radiometric Accuracy

Wavelength(µm)

Resolution(km)

(µm)(km)

1 VIS0.4 0.47 1 250 5%

2 VIS0.5 0.51 1 250 5%

3 VIS0 6 0 64 0 5 120 5% 0 675 13 VIS0.6 0.64 0.5 120 5% 0.675 1

4 VIS0.8 0.856 1 210 5%

5 NIR1.3 1.378 2 300 5%

6 NIR1 6 1 61 2 300 5%6 NIR1.6 1.61 2 300 5%

7 IR3.8 3.9 2 3/0.2 1K 3.75 4

8 IR6.3 6.185 2 0.4/0.1 1K

9 IR6.9 6.95 2 0.37/0.1 1K 6.75 4

10 IR7.3 7.34 2 0.35/0.12 1K

11 IR8.7 8.5 2 0.27/0.1 1K

12 IR9.6 9.61 2 0.35/0.15 1K

13 IR10 5 10 35 2 0 4/0 2 1K 10 8 4

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13 IR10.5 10.35 2 0.4/0.2 1K 10.8 4

14 IR11.2 11.2 2 0.19/0.1 1K

15 IR12.3 12.3 2 0.35/0.2 1.1K 12.0 4

16 IR13.3 13.3 2 0.48/0.3 1.1K

Scene & Surface Analysis (13)

Cloud & Precipitation (14) Aerosol & Radiation (14)Atmospheric condition &

Aviation (11)

Cloud detection Cloud Top Temperature Aerosol Detection Atmospheric Motion Vector

Snow Cover Cloud Top Pressure Aerosol Optical Depth Vertical Temperature Profile

Sea Ice Cover Cloud Top Height Asian Dust Detection Vertical Moisture Profile

Fog Cloud Type Asian Dust Optical Depth Stability Index

Sea Surface Temperature Cloud Phase Aerosol Particle Size Total Precipitable WaterSea Surface Temperature Cloud Phase Aerosol Particle Size Total Precipitable Water

Land Surface Temperature Cloud AmountVolcanic Ash Detection and Height

Tropopause Folding Turbulence

Surface Emissivity Cloud Optical Depth Visibility Total OzoneSurface Emissivity Cloud Optical Depth Visibility Total Ozone

Surface Albedo Cloud Effective Radius Radiances SO2 Detection

Fire Detection Cloud Liquid Water PathDownward SW Radiation (SFC)

Convective Initiation(SFC)

Vegetation Index Cloud Ice Water PathReflected SW Radiation (TOA)

Overshooting Top Detection

Vegetation Green Fraction Cloud Layer/HeightAbsorbed SW Radiation

Aircraft IcingVegetation Green Fraction Cloud Layer/Height(SFC)

Aircraft Icing

Snow Depth Rainfall Rate Upward LW Radiation (TOA)

Downward LW RadiationCurrent Rainfall Potential

Downward LW Radiation (SFC)

Probability of Rainfall Upward LW Radiation (SFC)

Thank youyhttp://nmsc.kma.go.kr

BACK-UP

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Y /M i MS O ARI vs. AWS

Year/Mon Bias RMSE POD FAR

2012/06 0.332 1.288 91.6 93.2

2012/07 -0 035 2 748 83 5 85 82012/07 -0.035 2.748 83.5 85.8

2012/08 -0.053 3.329 89.4 80.6

2013/06 0.132 1.352 75.4 93.4/

2013/07 -0.052 3.047 89.2 84.9

2013/08 0.002 2.201 88.8 84.4

2014/06 0.168 1.418 72.6 94.1

2014/07 0.051 1.916 75.2 89.6

2014/08 -0.034 2.550 77.5 84.0

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Case 2) 00:00 UTC 29 Aug. 2013

~60mm~60mm/h

v.2009 v.2011_kma v.2012_kma

1 / 1 /~13mm/h ~18mm/h ~15mm/h

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Quality monitoring of CRR case study

Validation of CRR with AWS(Automatic Weather System) Validation of CRR with AWS(Automatic Weather System)

- Temporal collocation

: [Accumulated precipitation for 15 minutes from satellite observation ⅹ4]

vs CRR (mm/h)vs. CRR (mm/h)

- Spatial collocation

: [Mean 3 ⅹ 3 pixel boxes value centered on each the ground observationpoints] vs. AWSpoints] vs. AWS

AWS

Version Mean RMSE POD FAR

Case 1

v.2009 4.1 47.2 67.9 46.6

v 2011 KMA 5 7 23 0 71 1 66 9AWS Case 1 v.2011_KMA 5.7 23.0 71.1 66.9

v.2012_KMA 12.0 43.9 67.9 46.6

Case 2

v.2009 3.7 17.3 79.7 23.4

v 2011 KMA 3 8 6 7 88 9 40 3

Satellite data

Case 2 v.2011_KMA 3.8 6.7 88.9 40.3

v.2012_KMA 4.0 22.3 79.3 22.7

improvementsCase 1 precipitation areas and intensities closer to the AWS ones

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- Case 1 : precipitation areas and intensities closer to the AWS ones

- Case 2: precipitation areas closer to the AWS ones but intensities are not beenmuch improved