Development of Convective Initiation

Algorithm for GEO-KOMPSAT-2A

2018. 4. 19.

Hye-In Park (bhi1216@korea.kr)

National Meteorological Satellite Center

Satellite Development Team

2018 Convection Working Group Workshop

Contents

Introduction

Algorithm Description

Results

Summary & Future plan

2

Introduction

KMA plans to launch the next Korean geostationary meteorological

satellite GEO-KOMPSAT-2A (GK-2A) in Nov. 2018

GK-2A for the next generation Meteorological Imager and Space Weather monitoring

GK-2B for the Ocean Color(GOCI2) and Atmospheric Trace Gas(GEMS) monitoring

Meteorological

Sensor

Space weather

Sensor

Ground Segment Data Processing System

NMSC/KMA

Ocean /

Environmental Sensor

Geo-KOMPSAT-2A Geo-KOMPSAT-2B

2012 ~ 2018

(7 years development)

Geo-KOMPSAT-2A Programs

AMI (Advanced Meteorological Imager)

Center wavelength (μm)

AMI (Resolution) ABI AHI

1 blue 0.47 (1km) 0.47 0.46

2 green 0.511 (1km) 0.51

3 red 0.64 (0.5km) 0.64 0.64

4 0.856 (1km) 0.865 0.86

5 1.38 (2km) 1.378

6 1.61 (2km) 1.61 1.6

2.25 2.3

7 3.830 (2km) 3.90 3.9

8 6.241 (2km) 6.185 6.2

9 6.952 (2km) 6.95 7.0

10 7.344 (2km) 7.34 7.3

11 8.592 (2km) 8.50 8.6

12 9.625 (2km) 9.61 9.6

13 10.403 (2lkm) 10.35 10.4

14 11.212 (2km) 11.2 11.2

15 12.364 (2km) 12.3 12.3

16 13.31 (2km) 13.3 13.3

vs. AHI : addition : 1.38 m (NIR), subtraction 2.3 m (NIR)

- 1.38 m : favorable for cirrus cloud detection, cloud type and amount

- 2.3 m : favorable for Land/cloud Properties

KSEM(Korea Space wEather Monitor)

• PD : Particle Detector

• MG : Magnetometer

• CM : Charging Monitor

<Space weather Sensor>

Payloads for Geo-KOMPSAT-2A

52 Meteorological Products

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 Water

Land Surface Temperature Cloud Amount Volcanic Ash Detection and

Height Tropopause Folding Turbulence

Surface Emissivity Cloud Optical Depth Visibility Total Ozone

Surface Albedo Cloud Effective Radius Radiances SO2 Detection

Fire Detection Cloud Liquid Water Path Downward SW Radiation (SFC) Convective Initiation

Vegetation Index Cloud Ice Water Path Reflected SW Radiation (TOA) Overshooting Top Detection

Vegetation Green Fraction Cloud Layer/Height Absorbed SW Radiation (SFC) Aircraft Icing

Snow Depth Rainfall Rate Upward LW Radiation (TOA)

Ocean Current Rainfall Potential Downward LW Radiation (SFC)

Probability of Rainfall Upward LW Radiation (SFC)

23 Primary Products & 29 Secondary Products

Algorithm Description

Convective Initiation (CI)

Convective clouds cause severe weather such as

lightning, hail, gusty wind, and floods.

However, It is difficult to predict by radar or NWP

model because it is developed rapidly in a short period

of time(minute to ~1 hour/mesoscale).

The Convective Initiation(CI) algorithm detects

convective clouds which would rapidly develop to

bring severe weather within 2 hours.

8

Name Geographic Coverage Horizontal Resolution Refresh Time

Convective

Initiation

(CI)

ELA 2 km 2 minute

Processing time Output Measurements Accuracy

100 second

(To be corrected)

CI Score (High, middle,

low likelihood CI)

POD 70%

FAR 40%

Input Data

Himawari-8 AHI L1B data

- Water vapor channel 8 (6.2 𝜇m)

- Infrared channel 11, 13, 14, 15,

16 (8.6, 10.4, 11.2, 12.3, 13.3

𝜇m)

- Spatial resolution : 2km (FD)

- Refresh Rate : 10 minute

Atmospheric instability index

using NWP data

- CAPE (Convective Available

Potential Energy)

- LI (Lifted Index)

- SSI (Showalter Stability Index)

- KI (K-Index)

9

GK-2A/AMI Himawari-8/AHI

Band Wavelength

(㎛)

Resolution

(㎞) Band

Wavelength

(㎛)

Resolution

(㎞)

1 0.47 1 1 0.47 1

2 0.51 1 2 0.51 1

3 0.64 0.5 3 0.64 0.5

4 0.86 1 4 0.86 1

5 1.38 2

6 1.61 2 5 1.6 2

6 2.3 2

7 3.9 2 7 3.9 2

8 6.19 2 8 6.23 2

9 6.95 2 9 6.94 2

10 7.34 2 10 7.34 2

11 8.5 2 11 8.59 2

12 9.61 2 12 9.63 2

13 10.4 2 13 10.40 2

14 11.2 2 14 11.24 2

15 12.3 2 15 12.38 2

16 13.3 2 16 13.28 2

GK-2A CI algorithm

Flowchart of the CI algorithm

10

Step 1 : Immature cloud

detection in unstable

atmosphere

(Convective cloud mask; CCM)

Step 2 : The clustering of

immature cloud pixels

(Region growing method)

Step 3 : Determining CI object

(Interest Field Test)

Mecikalski and Bedka, 2006;

Izumi et al., 2011;

Walker et al., 2012

Immature cloud detection in unstable atmosphere

Step1: Convective Cloud Mask (CCM)

11

Atmosphere instability index (AII) CCM flowchart

Use the concept of “Region Growing” clustering method

Step2: Clustering Immature Cells

12

(1) Immature cloud pixel in unstable atmosphere defined by CCM.

(2) The difference between the maximum and minimum cloud temperature in a cloud object

should be less than 30 K.

(3) Maximum pixel number within a cloud object is “500 (~60 X 60 km2): meso- & -

scales”.

Step3: Determining CI object

Cloud object tracking

13

Interest field tests

The representative values of the cloud

object is the average of lower

temperature than 25 percentile of

BT(at 10.4 um) within an object.

Test to determine the likelihood of CI object

Spectral and temporal differencing test

Trend Tests = Current core BT

– Past core BT

Purpose of tests BT / BTD Tests Score

1

Cloud

microphysical

states

Cloud height 10.4 ㎛

1 2 Cloud height 6.2 - 10.4 ㎛

3 Cloud top glaciation 8.5 - 11.2 ㎛

4 Cloud height 13.3 – 10.4 ㎛

5

Time-trends

in cloud

microphysical

states

Weak updraft

strength

10.4 ㎛ 10-min trend 1

6 6.2 - 10.4 ㎛ 10-min trend 1

7 13.3 – 10.4 ㎛ 10-min trend 1

8

Strong updraft

strength

10.4 ㎛ 10-min trend 1

6.2 - 10.4 ㎛ 10-min trend 1 9

10 13.3 – 10.4 ㎛ 10-min trend 1

Step3: Determining CI object

Time trend in cloud microphysical states (Updraft strength)

14

BT[ch13] BT[ch08-ch13] BT[ch16-ch13]

Minimum -26.38 -3.08 -4.20

Maximum 2.61 16.35 4.70

25th percentile -5.98 0.74 0.31

75th percentile -0.87 4.51 1.40

Mean -4.18 2.89 0.84

Median -3.56 2.89 0.90

• The threshold value of weak updraft strength was obtained

using the 25th or 75th percentile value, and the strong

updraft strength was used median value (33 CI events)

(a) (b) (c)

(d)

Cloud growth

Results

Results

The Algorithm Test over Korean Peninsular on 2nd Aug. 2017

- Convective clouds occurred from 04:00 to 07:00 UTC.

16

UM 925 hPa Synoptic chart at

00:00 UTC UM 850hPa Wind at 00:00 UTC Himawari-8/AHI channel 13

Results

17

The Algorithm Test over Korean Peninsular on 2nd Aug. 2017

- Results of CCM and Clustering

03:40 UTC 04:20 UTC 04:40 UTC 05:00 UTC

03:40 UTC 04:20 UTC 04:40 UTC 05:00 UTC

Thick/

cold Cumulus Thin

Cirrus/

Clear

Cumulus/

Unstable

Results

18

The Algorithm Test over Korean Peninsular on 2nd Aug. 2017

- CI Forecast (03:40 UTC) Radar ≥ 35 dBZ (04:00 UTC) Lightning (05:00 UTC)

06:00 UTC

2,3 Weak updraft strength

4,5 Medium updraft strength

6,7 Strong updraft strength

Lightning

RADAR CAPPI 1.5km data

03:40 UTC 04:20 UTC

04:40 UTC 05:00 UTC

A

A

B

B

Results

19

03:40 UTC 04:20 UTC 04:40 UTC 05:00 UTC

05:20 UTC 05:40 UTC 06:00 UTC 06:30 UTC

B

The Algorithm Test over Korean Peninsular on 2nd Aug. 2017

- CI Forecast (04:20 UTC) Radar ≥ 35 dBZ (05:10 UTC) Lightning (05:40 UTC)

Validation method

• Radar CAPPI (Constant Altitude PPI) 1.5km data

• 20 minute ~2 hour after CI detection to perform validation for areas that occur

≥ 35 dBZ radar reflectivity. (approximately 5 mm/hr)

Results

20

27 Jun. 2017 24 Jul. 2017 2 Aug. 2017 25 Aug. 2017 19 Sep. 2017

POD 0.99 0.86 0.93 0.93 0.77

FAR 0.50 0.60 0.58 0.57 0.47

CI

Detection

10 minute

~ 2 hours

※ Radar data time interval : 10 minute

Validation data

Contingency

table

Was CI Forecast ?

Yes No

Did CI

Occur ?

Yes Hit Miss

No False Alarms Correct Negatives RADAR CAPPI 1.5km data

Summary & Future plan

Summary

The CI algorithm of GK-2A was developed using Himawari-8/AHI as a proxy

data. This is well detected the cloud object 2 hours before the occurrence of

lightning and radar.

CI Forecast results are similar to the radar echo area.

But false alarm ratio of this algorithm seems to be high. It is necessary to

improve the algorithm to reduce false alarm rate.

Future Plans

Improvement of interest field test and threshold values.

Improve and optimize cloud clustering and tracking algorithm.

Analysis of results using Himawari-8/AHI rapid scan data every 2 minutes.

Summary & Future plan

22

Thank you!