Qin Danyu

qindy@cma.gov.cn

Thanks contributor Dr Sun Fenglin

National Satellite Meteorology Center （NSMC）China Meteorological Administration （CMA）

New approaches for Convective Initiation

Nowcasting based on the TV -L1 optical flow and

BP_Adaboost neural network algorithm

AOMSUC-10, 4-6 Dec 2019, Melbourne, Australia

Outline

1. Overview of Convective initiation(CI) by Satellite

2. Current FY-4A CI Product

3. New approaches for Convective Initiation

• TV -L1 optical flow

• BP_Adaboost neural network algorithm

4. Validation

5. Summary

This kind of convection activities usually

produce severe weathers such as heavy

rainfall, strong wind, hail, tornado etc.

and can make great damages and loses

even death.

China suffers from severe convective storms every year

@ 6minutes

◼Numerical Weather Prediction(NWP)◼In situ◼Radar◼Satellite

@ 5-15minutes

6hrs

For 0-2 hour nowcasting

Disadvantage of Radar :

• Depend on the elevation angle that some

time may probably lost low level information

of convections, like CI.

• Do not coverage all area, such as high

land(Tibet Plateau),deserts and ocean.

• Calibration problem

While GEO sat is excellent to monitor the CI and its environment,

especially for the new generation satellites such as FY-4 and H8,

because they have more powerful imager, ana ABI/AHI/AGRI.

1816 TD

FY-4 AGRI 0.64μm 5-minute animation

CI *Advantages：• Broad coverage

• High spatial

resolution

• Higher calibration

accuracy

• More stable

• More lead time

1. Overview of Convective initiation(CI) by Satellite

Motivation of Geo Sat CI AlgorithmDetect Convective Initiation using geostationary satellites to provide increased lead times for ANY event

Mecikalski and Bedka first

presented CI conception in

2006(MB06)

1. Overview of Convective initiation(CI) by Satellite

Motivation of Geo Sat CI AlgorithmDetect Convective Initiation using geostationary satellites to provide increased lead times for ANY event

Tracing

Convective targets

identification

CI interesting fields check

1

2

3

CI

Sensitive factors

CI

Evolution

ChannelNo Band

(μm)Spatial

resolution(km)Primary

application

Utilized in CI

Visible1 0.45-0.49 1 Aerosol no

2 0.55-0.75 0.5 Fog, cloud yes

Near-infrared

3 0.75-0.90 1 Vegetation no

4 1.36-1.39 2 Cirrus no

5 1.58-1.64 2 Cloud, snow

no

6 2.1-2.35 2 Cloud, aerosol

no

SW*-infrared

7 3.4-4.0(high)

2 Fire, land,

andsurface

No

8 3.4-4.0(low)

4 No

Water vapor9 5.8-6.7 4 WV* Yes

10 6.9-7.3 4 WV* Yes

LW*-Infrared

11 8.0-9.0 4 WV*, cloud Yes

12 10.3-11.3 4 SST*, cloud Yes

13 11.5-12.5 4 SST*, cloud Yes

14 13.2-13.8 4 Cloud Yes

FY-4A/AGRI specifications and channels used in CI algorithm

FY-4 CI/RDC Algorithm

1.Convective targets Identification

2.Multi Targets Trace3.Cloud Top Cooling rate

• Water shed method• IR and VIS thresholds • Multi channel tests

IR VIS

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 RRSAGRI combined observation

FY-4A

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

• 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

Alternative Approaches-False Alarm Reduction

In order to track rapid developing CIs, the first step is to calculate the cloud-

tracking motion derivation based on a classical TV-L1 optical flow(OF) method.

The OF fields represent the motion of pixels in two consecutive image frames under

the brightness constancy assumption. Some previous studies. Some previous studies

have developed a variation formulation to deal with the optical-flow problem. The

main equation of the TV-L1 OF method can be written as follows

𝒖 𝒙 ,𝒙∈𝜴

𝒎𝒊𝒏σ𝒙∈𝜴 𝛁𝒖𝟏 𝒙 + 𝛁𝒖𝟐 𝒙 + 𝝀 𝑰𝟎 𝒙 + 𝒖 𝒙 − 𝑰𝟏 𝒙

𝟐,

where 𝐼0 𝑥 and 𝐼1 𝑥 are the consecutive images sampled at time T and T+ΔT,

𝑢 𝑥 represents the motion fields desired to be reconstructed, Ω is the neighborhood

region of 𝑥, 𝜆 is the weight between the data fidelity term and the regularization

force.

Why use TV -L1 optical flow?

correlation coefficient

The smaller target has higher

possibility of false trace results.

The optical flow method can increase

correct percentage of tracing .

optical flow+overlap

overlap

Blue for previous time

Red for current time

Green bar for velocity estimated

Algorithm 1 The AdaBoost method

Input: Given sequence of 𝑁 labeled examples < 𝒙1, 𝑦1 , … , 𝒙𝑁, 𝑦𝑁 >

Distribution 𝐷 over the 𝑁 examples

Weak learning algorithm Weak Learn(BP Intelligence network)

Integer 𝑇 specifyinh number of iterations

Initialize the weight vector: 𝑤𝑖1 = 𝐷(𝑖) for 𝑖 = 1,… ,𝑁.

Do for 𝑡 = 1,… , 𝑇:

1.Set 𝒑𝑡 =𝑾𝑡

σ𝑖=1𝑁 𝑤𝑖

𝑡 (𝑾𝑡 =< 𝑤𝑖𝑡 >; 𝒑𝑡 =< 𝑝𝑖

𝑡 >, 𝑖 = 1, . . , 𝑁)

2.Call Weak Learn, providing it with the distribution 𝒑𝑡;get back a hypothesis

ℎ𝑡: 𝑋 → [0,1].

3.Calculate the error of ℎ𝑡: 𝜖𝑡 = σ𝑖=1𝑁 𝑝𝑖

𝑡|ℎ𝑡 𝒙𝑖 − 𝑦𝑖|.

4.Set 𝛽𝑡 = 𝜖𝑡/(1 − 𝜖𝑡).

5.Update the weights: 𝑤𝑖𝑡+1 = 𝑤𝑖

𝑡𝛽𝑡1−|ℎ𝑡 𝒙𝑖 −𝑦𝑖|

Output the hypothesis

ℎ𝑓 𝒙 = ቐ1 𝑖𝑓 σ𝑡=1

𝑇 (𝑙𝑜𝑔1

𝛽𝑡)ℎ𝑡 𝒙 ≥

1

2σ𝑡=1𝑇 (𝑙𝑜𝑔

1

𝛽𝑡)

0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

AdaBoost classification

BP classification

ML: BP+AdaBoost=BP_Adaboost

Objective is to choose CI from candidates

more accuracy

2018 16

MAY 09:42

CI 09:00

2018 16

MAY 09:30

Severe hail weather in RuDong, Jiangsu Province, eastern China, 2018

Radar 09:30

30min lead time

Testing && validation datasets

◆ A rough validation method（SATCAST v2 by Walker et al）

75km…

T0T0-

30Minutes

T0+Tk

…

T0+2hCI

Eastern

China

Southern

China

North-

Eastern

China

Qinghai-

Tibet

Plateau

Area

≥35dBZ

◆ A fine validation method（RDCMS）

Observed value

1 0

Forecast

value

1 TP（”hit”） FP（”false alarm”）

0 FN（”miss”） TN

Qinghai-

Tibet

Area

Eastern

China

North

Eastern

China

Southern

China

14~29

Jun. 8551 5137 3710 5732

05~12 Jul.5283 2762 1804 3440

08~24

Aug.5782 2115 1636 4195

10~19

Sept.3309 3401 1998 3754

09~25

Oct.5857 2589 1660 6001

Name Formula Range Optimum

POD TP/(TP+FN) [0,1] 1

FAR FP /(TP+ FP) [0,1] 0

CSI TP/(TP+FP+FN

)

[0,1] 1

Statistical metrics for RDCMS.

The meanings of TP, FN and FP are shown in the Table

Contingency table

Spatial and temporal distribution and total number

of CI forecast samples for the RDCMS validations

Testing && validation datasets

Evaluation-scores

• 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?.

Future Plan

• To use machine learning and deep learning to develop

new satellite convection products in next year.

• Machine learning and deep learning will introduce to

generate better products and better applications for FY-

4B/C satellites

• Link convective initiation with QPE.

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)

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

Rank of predicted factorsrank 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

Courtesy of Min Min

Result TBB at 11μmGround rainfall

observation

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

prediction

GPM IMERG observation

5. Summary

• Case studies show that the new approaches’ skills have been

improved as expected in four regions of China. In the southern China

region, the CI lead time is 17-40 mins, and the best probability of

detection (POD) is as high as 0.80, with the FAR lower than 0.34.

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

surface, balloon, radar, lightning etc. How to use these data to forecast

the severe weather is a challenge.

• How to pick out the valuable information from data automatically, more

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

• We have to look for new approaches to solve these problems, and

machine learning and deep learning technique show great potential

benefit for convection applications.