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Observing Convection with FY-4A Satellite Qin Danyu [email protected] Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie National Satellite Meteorological Center (NSMC), CMA The CWG Workshop Meeting, Ljubljana, Slovenia, 17-19 April 2018

Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu [email protected] Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

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Page 1: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Observing Convection with FY-4A Satellite

Qin Danyu

[email protected]

Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

National Satellite Meteorological Center (NSMC), CMA

The CWG Workshop Meeting, Ljubljana, Slovenia, 17-19 April 2018

Page 2: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

GIIRS: Geo. Interferometric Infrared Sounder

AGRI: Advanced Geosynchronous Radiation Imager

LMI: Lightning Mapping Imager

SEP: Space Environment Package

FY-4A: New Era of China GEO Satellite

• The FY-4A satellite was launched successfully on 11 Dec 2016

• It’s position is now at 104.7°E

• Data have been broadcasted by CMACast since 12 Mar 2018.

Page 3: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Spectral

Coverage

Spectral

Band (µm)

Spatial

Resolution (Km) Sensitivity

Main

Applications

VIS/NIR

0.45~0.49 1 S/N≥90 (ρ=100%) Aerosol

0.55~0.75 0.5~1 S/N≥200 (ρ=100%) Fog, Clouds

0.75~0.90 1 S/N≥5(ρ=1%)@0.5Km Vegetation

1.36~1.39 2

S/N≥200 (ρ=100%)

Cirrus

1.58~1.64 2 Cloud,Snow

2.10~2.35 2~4 Cirrus,Aerosol

Middle-

wave IR

3.50~4.00 2 NEΔT≤0.7K(300K) Fire

3.50~4.00 4 NEΔT≤0.2K(300K) Land surface

5.80~6.70 4 NEΔT≤0.3K(260K) WV

6.90~7.30 4 NEΔT≤0.3K(260K) WV

Long-wave

Infrared

8.00~9.00 4 NEΔT≤0.2K(300K) WV,Clouds

10.3~11.3 4 NEΔT≤0.2K(300K) SST

11.5~12.5 4 NEΔT≤0.2K(300K) SST

13.2~13.8 4 NEΔT≤0.5K(300K) Clouds,WV

Spectral Parameters

(Normal mode)

Range Resolution Channels

LWIR: 700-1130 cm-1 0.8 538

S/MIR:1650-2250 cm-1 1.6 375

VIS : 0.55- 0.75 μm

Spatial Resolution LWIR/MWIR : 16 Km SSP

VIS : 2 Km SSP

Operational Mode China area 5000 5000 Km2

Mesoscale area 1000 1000 Km2

Temporal Resolution China area <1 hr

Mesoscale area <½ hr

Sensitivity

(mW/m2srcm2)

LWIR: 0.5-1.1 S/MIR: 0.1-0.14

VIS: S/N>200(ρ=100% )

Calibration accuracy 1.5 K (3σ) radiation

Calibration accuracy 10 ppm (3σ) spectrum

Quantization Bits 13 bits

Spatial resolution about 7.8Km at SSP

Sensor size 400300 2

Wave-length at center 777.4nm

Band-width 1nm±0.1nm

Detection efficiency >90%

False-alarm ratio <10%

Dynamic range >100

SNR >6

Frequency of frames 2ms

Quantization 12 bits

Measurement Error 10%

Characteristics of Payloads (Specification & Main Usage)

AGRI’s Main Usage:

Acquire multiple band, high

temporal resolution, high

radiation accuracy images

of Earth’s surface,

atmosphere and cloud

GIIRS’s Main Usage:

Acquire atmospheric

temperature and humidity

profile structures under

clear condition

LMI’s Main Usage:

Acquire lightning distribution

maps for a certain coverage

AGRI

GIIRS

LMI

Page 4: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Three steps of FY-4A IOT :

1st stage :20161226-20170630,testing mainly for satellite function and performance,

spatial segment of FY-4A is turned over to end users

2nd stage:20170630-20170930,testing mainly for L1 products

3rd stage: 20171001-20171230,testing mainly for L2+ products .

Now operational used

Stages of in-orbit testing (IOT) for FY-4A

Page 5: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

AGRI

Page 6: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Cyclone in Australia Haze in the Bay

of Bengal

Vortex in the South Pole Area

Cellular Clouds

in the South Pole Area

Tropical Cyclone

(local area)

Tibet and its surrounding areas

Tropical Cyclone

(wide area) Frontal Cloud across mid China Snow Cover Monitoring

In north China

Page 7: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

卡努 KHANUN(1720)

Page 8: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

2017.07.06

Page 9: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Binary typhoons

TALIM (1718)

and DOKSURI(1719)

Page 10: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

2017.06.24

The Meiyu frontal cloud system over Yangzi river basin, it is a stationary frontal system, usually can last a few days.

Page 11: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Clear Sky Masks Cloud Type Cloud Optical Depth Cloud Liquid Water Path Cloud Ice Water Path Cloud Particle Size Distribution Cloud Phase

Cloud Top Temperature Cloud Top Height Cloud Top Pressure

FY-4A L2+ product examples

Aerosol Detection Rainfall Rate/QPE Atmospheric Motion Vector Downward Long wave

Radiation:Surface

Upward Long

wave Radiation:

Surface

Reflected

Shortwave Radiation

Land Surface (Skin)

Temperature Sea Surface (Skin)

Temperature Land Surface Emissivity Land Surface Emissivity

Page 12: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

FY-4A Convection Product

Page 13: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

For the Rapid Developing Convection product,

What we did during the IOT

Solar zenith angle correction for VIS albedo

Multi channel thresholds tuning base on real data

Optimize the convection detection by testing their

size expanding rate, colder pixels increase rate ...

Combine to use partial filter and overlap method for

multi targets tracing according to the combined

observation mode of FY-4A

Page 14: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

BJT 08,19 09 10 11 12,13 14 15,16 17,18

Param(k) 1.4 1.3 1.0 0.8 0.6 0.7 0.8 1.0

The k values vs Beijing time(BJT)

Ac=A*(sec θ)k

A. Sun zenith angle correction

Without sun zenith angle correction With sun zenith angle correction With sun zenith angle correction but k=1

Page 15: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

B7 B8

B2 B1 B3 B4

B5 B6

B9 B10 B11 B12

B13 B14

Tuning include:

BTD=6.2µ-10.8µ

BTD=6.2µ-7.1µ

BTD=8.5µ-10.8µ

BTD= (8.5µ-10.8µ)-(10.8µ-

12.0µ)

BTD=12.0µ-10.8µ

BTD=13.5µ-10.8µ

B. Multi channel thresholds tuning base on real data

Page 16: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

C. Choose one of the two

tracing methods

according to the

combined observation

mode

Baseline observation every hour one

FD(15min)

Every 3 hours , two more FD observation(15

min),Deriving AMV

During 17-19 (BJT), AGRI is suspended to

ensure its safety.

All the other time RRS (5min*9=45min)

15min FD 5min RRS AGRI combined observation

Page 17: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Convective Initiation(pink) and Possible Convective Initiation(blue)

Page 18: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Summary for the FY-4A convection product

The FY-4A convection product can provide convective initiation and

rapid convection growing information to end-user, it is good used for

nowcasting

The FY-4A convective initiation results have false alarms, many CI

detections do not produce severe weather

To distinguish the convective cloud from non convective frontal cloud

system is still a challenge, but this is quite important during summer

rain season in China

Page 19: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

1. GIIRS:BT animation of different layers in troposphere for China area

He

igh

t (k

m)

Courtesy of Han Wei

Page 20: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Skew T-lnP diagram over Fangshan, Beijing

during 00:00 UTC-12:00 UTC (08:00-20:00 LST)

02 Aug 2017

Convective Available Potential Energy (CAPE) map during

00:00 UTC-12:00 UTC (08:00-20:00 LST) 02 Aug 2017 (grey

to white areas represent cloud observed by FengYun-4 satellite;

the asterisk denotes the location of Fangshan, Beijing)

Fangshan, Beijing:24h precipitation 111.9mm

Page 21: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Horizontal distribution of a) K index; b)

Showalter index; and c) Lift index at 09:00

UTC (17:00 LST) (2hrs before the occurrence

of the rainstorm over Fangshan) (grey to white

areas represent clouds observed by FengYun-4

satellite)

(a) (b)

(c)

Page 22: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

2.LMI:Dynamic Distribution of Lighting

A typical thunderstorm occurred in West Australia during 13 February, 2017

Page 23: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Lightning Event detected from FY-4A LMI

LMI lightning events about 3 hours, is displayed

over the LMI background image in June 5, 2017.

Red color indicates lightning events. The

brightest storm system is located in the south of

the Yangtze River.

Distribution of number of lightning events,

group and flash about 3 hours with 0.5º× 0.5º

resolution in June 5, 2017.

Brighter colors indicate more lightning events,

group and flash was recorded.

event

group

flash

Page 24: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Severe Weather,June 28th to 29th,2017

2017.06.28 (UTC) 2017.06.29 (UTC)

LMI lightning events distribution,is displayed over the AGRI 11 μm channel image in

June 28th to 29th,2017.

Red color indicates lightning events, moving with convective storms in the southern China.

Page 25: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

AGRI AGRI+LMI AGRI+LMI+GIIRS AGRI+LMI+GIIRS+ NWP AGRI+LMI+GIIRS+ NWP+Radar or GPM…

Question:

How to combine use these new data to better identify the convective activities?

How to get added value information of severe and high impact weather, more

easily and more quickly?.

Page 26: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Future Plan

To use machine learning and deep learning to develop new satellite convection

products in next few years.

It needs well organization. A joint working group will be established with NSMC

scientists, AI scientists and forecasters to develop new algorithms and products

Machine learning and deep learning such new technology will introduce to FY-

4B/C satellites to generate better products and better applications.

Page 27: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Motivation

Focus on those RDC connected to severe storm or high impact weather .

Three modules are included in this system :

RDC Training module ( Sensitivity analysis of interest fields constructed by FY-

4/AGRI、H8/AHI channels & thresholding training for RDC);

RDC Project Running module ;

RDC product validation module。

A. New Rapid Developing Convective Clusters (RDC) Algorithm-RDC v2

RDC Training module

(In prepare)

Page 28: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

B. Machine Learning for predicting Convective Storm and QPE of FY-4

Data

• NASA GPM IMERG 0.1°*0.1 ° grid data in a half hour resolution

(Truth for training)

• FY-4A/AGRI or Himawari-8/AHI FullDisk infrared band

measurements (TBB) FY-4A/AGRI uses 6 infrared bands or

Himawari-8/AHI uses 9 infrared bands observations for training the

model

• Numerical Weather Prediction (NWP) data (GFS 0.5°*0.5 ° /Grapes

0.25°*0.25 ° )

• Surface ancillary data (i.e., elevation, surface type)

NWP data

Global Forecast System

Page 29: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Methodology

• Track Convection Cells

• Co-locate GPM data, FY-4A/AGRI or Himawari-8/AHI data, and NWP data

• Extract some useful samples from matched dataset

• Train classification and regression models for predicting Convective Storm and QPE

based on Machine Learning

• Predict Convective Storm and QPE using real-time FY-4A/AGRI or Himawari-8/AHI

and NWP data and models

Page 30: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Rank of predicted factors

rank name score 1 dtb62max 0.10849001

2 ch13 var min 0.102537347

3 dtb73max 0.094444007

4 dtb70max 0.088137094

5 dtb96max 0.080524218

6 ch16-ch13max 0.07700988

7 area 0.063007372

8 dtb96mean 0.029540668

9 ch13 var mean 0.026863466

10 ch14-ch15min 0.01872382

11 dtb86min 0.014983453

12 dtb12min 0.011430704

13 dtb12max 0.010817736

14 dtb86max 0.009642071

15 dtb11min 0.009274285

16 ch11-ch14max 0.008871846

17 dtb11max 0.008870689

18 ch16-ch13 min 0.008594803

19 dtb62mean 0.008255241

20 dtb70mean 0.008029407

21 dtb73mean 0.006768807

22 div850max 0.005789622

23 ch13 10per warm mean 0.005747327

24 thtse925min 0.005490885

25 dtb73min 0.005393001

Random Forest

n_estimators=100

max_depth=10

max_features=10

Training sample

date: April-October,2016

total 389315 convection cloud system

Page 31: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Case ——07/29/2016

Observation at 1200 UTC,precipitation 16 mm/h

RF,0700 UTC LR,0900 UTC NB,1030 UTC

Page 32: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Courtesy of Min Min

Result TBB at 11μm

Ground rainfall

observation

Machine Learning for Predicting Convective Storm and QPE by FY-4 Data

prediction

GPM IMERG observation

Page 33: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Summary

Every day the forecaster has to face torrent of data from satellite, surface,

balloon, radar, lightning…. How to use these data to forecast the severe

weather is a challenge. How to pick out the valuable information from big data

automatically, more quickly and more easy to use is also an other challenge.

We have to look for new approach to solve these problems, and machine

learning and deep learning technique show great potential benefit for

applications, we need prepare for that.

Page 34: Observing Convection with FY-4A Satellite · 2018-06-05 · Observing Convection with FY-4A Satellite Qin Danyu qindy@cma.gov.cn Sun Fenglin, Shou Yixuan, Wu Chunqiang, Cao Dongjie

Thanks for your attention!