Development of Convective Initiation Algorithm for GEO ... · (1) Immature cloud pixel in unstable...
Transcript of Development of Convective Initiation Algorithm for GEO ... · (1) Immature cloud pixel in unstable...
Development of Convective Initiation
Algorithm for GEO-KOMPSAT-2A
2018. 4. 19.
Hye-In Park ([email protected])
National Meteorological Satellite Center
Satellite Development Team
2018 Convection Working Group Workshop
Contents
Introduction
Algorithm Description
Results
Summary & Future plan
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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.
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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)
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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
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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)
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Atmosphere instability index (AII) CCM flowchart
Use the concept of “Region Growing” clustering method
Step2: Clustering Immature Cells
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(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
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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
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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 ㎛
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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
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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)
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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.
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UM 925 hPa Synoptic chart at
00:00 UTC UM 850hPa Wind at 00:00 UTC Himawari-8/AHI channel 13
Results
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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
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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
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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
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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
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Thank you!