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CMA Applications of Radiative Transfer Model in Product Generation and Sensor Monitoring
Qifeng Lu, Fuzhong WengNSMC, CMA, [email protected]
Chunqiang Wu, Juyang Hu, Min Min, Wengguang Bai, Ling Sun, Fangli Dou, Yong Zhang, Yang Guo, Dawei An, Chengli Qi, Shengli Wu, Miao Zhang, Hui Liu, Xiaoqing Li
Lin Chen, Yuan Li, Liqing Hu
Outline
2
Background
Model Development
Calibration and validation
Sensor Monitoring
Product generation and validation
Outline
3
Background
Model Development
Calibration and validation
Sensor Monitoring
Product generation and validation
引自:Gerald van der Grijn (2004), Met-OP Training Course: Use and Interpretation of ECMWF Products.
4
Satellite Measurement and Calibration
BRDF、Emmisivity()
ATM
Absorb, scatter, reflect
_ _(1 )( )atm isatm ii iRR
_atm iR _satm iR s
bi i iR
( )i iB T ( )i i s iB T
Radiative Transfer Eq:
O
可见定标器
碲镉汞探测器
主镜次镜
折镜
分离镜 硅光探测器
定标平面镜
黑体定标透镜
第一中继透镜
第二中继透镜
滤光片
output
Count(DN)
计数
值(D
N)
入瞳辐射 (R)
g 斜率
b 截距
DN = g*R + b
Calibration
Problem of Space Sensors:
(1)The calibration system is not good enough;(2)No absolute calibration system, no reference;(3)Not good enough with the stability;
Attenuation of NOAA reflect Channels in 3 years
Attenuation of FY-1 reflect Channels in 5 years 5
Calibration: to Improve Satellite Data quality
Reffence
Before Correction
After Correction 6
The RTMs are widely used at NSMC
Surf Info
ATM Info
Sat Info
Observation
Radiance
Count
Forward andAD Model
ForwardJacobian Cal/Val
Monitor
Product Gen
Product Val
Others
SatSensorSat
Sensor
Outline
8
Background
Model Development
Calibration and validation
Sensor Monitoring
Product generation and validation
Model Development
• Models used at NSMCRTTOV, CRTM, ARMS, LBLRTM, MonoRTM, 6S, ModTran, FYRTM…
• Models in developing at NSMCFast Model for High Spectral RadianceFor lunar spectral irradiancesFor Cloud scattering
Case1 Rayleigh (Rayleigh only, NNUM=4, SZA=60, VZA=0.0, SAZ=0, VAZ=180, PS=950, PLOC=0.9, SIGMA=0.05, ALBEDO=0.1, AOD=0.0, US STANDARD)
Compared with VLIDORT、SCIATRAN
Near Infrared Spectral Calculation with Polarization
Simulation of TOA lunar spectral irradiances
Figure 1. Simulated TOA lunar spectral irradiances at lunar phase angles =0°, 45°, 90°, and 135° and NPP/VIIRS‐DNB SRF (black solid line) (a), and its corresponding normalized weighting values of DNB (b).
Periodical Characteristics of Lunar Irradiance
Min, et al, 2017. JGR
Modified 2.25μm Channel in Cloud remote sensing
Wang, et al., 2018. IEEE Transactions on Geoscience and Remote Sensing
Why are the Adjoint Model Important? • For physical retrievals
Sensor Physical retrieval
Brightness Temperature
Air Temp
Humidity
pSurface Temp
Radiance Flux
… …
AdjointModel
pInstrument paras
The potentials of Adjoint Model
Forward Model
ATM ProfilesInput Output
Radiance
Tangent linear Model
ATM Profiles Disturb
Input Output Radiance Disturb
AdjointModel
Radiance Disturb
Input Output ATM Profiles Disturb
Temperature Jacobians for VASSFY3C VASS FY3B VASSNA18 ATOVS
US Standard,IR EMIS=0.98,MW EMIS=0.68
qR
ln
ozR
ln
Jacobians for FY‐4 GIIRSFYRTM/FY4 GIIRS RTTOV/IASI
US Standard,IR EMIS=0.98
Outline
17
Background
Model Development
Calibration and validation
Sensor Monitoring
Product generation and validation
Cal/Val: FY‐3D GAS Spectrum in TVAC Test
HIRAS OBS compared with LBLRTM simulation (LW)
Clear pixels determined from MERSI cloudmask
MERSI‐II CloudMask
Frequency biases relative to LBLRTM will be derived; ILS & laser parameters may be adjusted
Cal/Val: To estimate FY‐3D HIRAS Spectral Calibration Coefs
5 10 15 20 25 30 35 40-20
-15
-10
-5
0
5
10
15
20
Sample
Spec
tral B
ias/
ppm
LW Spectral Bias
FOV-1FOV-2FOV-3FOV-4
Stability
412 nm Magnitude of Polarization correction
412 nm O-B412 nm Polarization Effect
Polarization effect estimation
The polarization effect of FY-3 MERSI could be estimated using the simulation of FY-3 MERSI under the clear sky open sea pixels
Cal/Val: Correction of Polarization effect for MERSI
FY-3A MERSI
0 500 1000 1500 2000 25000
20
40
60
EV-SV
Ref
Fact
or(%
)
FY-3B MERSI
B3:1370<=DSL<1390 Y=-1.77e-007*X2+0.0272*X R2=0.99 M=-0.017 S=0.723 N=67
DunhuangCQinghaihuCDunhuangLibya1Libya4Arabia2LanaiAlgeria5SonoraAlgeria3Mauritania2
RTM + Campaign OBS for Cal/Val
Old New
Surface Lambert BRDF
Scatter Scalar Vector
Trans Band model Mid-Res
Uncertainty
6% 3~5%
Sea Color
aerosol
Before After
Cal/Val: campaign for FY‐3c/d MWHSSurface ATM Profiles Surface OBS
RTTOV Forward Model
MWHS II Simulation
MWHS II L0
MWHS II SDR
On board Precision
过境普洱中心时刻北京时
卫星编号仪器天顶角(度)
2018/3/3 14:08:40.6 FENGYUN 3D 46.88
2018/3/3 23:06:56.5 FENGYUN 3C 15.1 2018/3/4 11:35:13.3 FENGYUN 3C 13.58 2018/3/5 11:16:20.6 FENGYUN 3C 22.65 2018/3/5 15:10:09.4 FENGYUN 3D 50.87
2018/3/6 14:51:58.5 FENGYUN 3D 25.582018/3/7 14:33:04.3 FENGYUN 3D 9.3 2018/3/8 3:01:21.0 FENGYUN 3D 10.49
2018/3/10 11:22:00.3 FENGYUN 3C 11.36 2018/3/12 14:38:37.1 FENGYUN 3D 1.81 2018/3/13 3:06:54.0 FENGYUN 3D 1.012018/3/14 22:59:22.4 FENGYUN 3C 1.44 2018/3/15 11:27:39.7 FENGYUN 3C 0.67
Campaign Validation flaw
Sounding Profiles
亮温
差(
K)
Blue: FY‐3C MWHSGreen: FY‐3D MWHS Yellow: Specification
Outline
23
Background
Model Development
Calibration and validation
Sensor Monitoring
Product generation and validation
Flow Chart of RTM SAT‐Simulator
RTMs:CRTM, RTTOV, 6S etc
NWP:FNL、ERA 5、 CRA‐40 etc
Clear Sky only
Monitoring instrumental performance using simulations
118.750.3 GHz
Web: : http://satellite.nsmc.org.cn
MWTS channel 6: 54.94GHz
北京华云星地通科技有限公司 26
定制统计:
More details for the abnormal diagnosis
Statistics of MWRI O‐B
The improved OMB for channels from UKMO
O‐B of MWTS Sounding
Channel, the Surface
Information exists.
T=Tj+k(T1‐Tj);j=5,6,7,8
Surface Info in MWTS Sounding Channels
The attenuation effect could be detected and corrected
121 3 5 7 9111 3 5 7 9111 3 5 7 9111 3 5 7 9111 3 5 7 9111 3 5 7 9111 3 5 70.8
0.9
1
1.1
2010 2011 2012 2013 2014 2015 2016 2017
Date
Norm
alized
Res
pons
e
FY-3B MERSI
121 3 5 7 9111 3 5 7 9111 3 5 7 9111 3 5 7 9111 3 5 7 9111 3 5 7 9111 3 5 70.6
0.7
0.8
0.9
1
1.1
2010 2011 2012 2013 2014 2015 2016 2017
Date
Norm
alized
Res
pons
eFY-3B MERSI
121 3 5 7 911 1 3 5 7 9111 3 5 7 911 1 3 5 7 9111 3 5 7 911 1 3 5 7 911 10.7
0.8
0.9
1
1.1
Norm
alized
Res
pons
e
2010 2011 2012 2013 2014 2015 2016
121 3 5 7 9111 3 5 7 9111 3 5 7 9111 3 5 7 9111 3 5 7 9111 3 5 7 9111 3 5 70.7
0.8
0.9
1
1.1
2010 2011 2012 2013 2014 2015 2016 2017
Date
Norm
alized
Res
pons
e
FY-3B MERSI
B03B04B13B14B15B16
B01B02B08B09B10B11B12B20
B17B19
B06B07
Monitoring of Reflected Channels of Imager
Outline
32
Background
Model Development
Calibration and validation
Sensor Monitoring
Product generation and validation
RTMs in Product Generation
Simulation – Look Up Tables FWD and AD/Jacobian Matrix in physical Retrievals
Retrieval
Radiative Transfer Model
Look‐Up TablesJ(xa ) min
xJ(x) x near xb
J(x) 12
(x xb )T B1(x xb ) 12
(H (x) yobs )T (O F)1(H (x) yobs )
x analysis variablexa final analysisxb backgroundB background error covariance
yobs observationsO observation error covarianceH observation operatorF forward model error covariance
Super Typhoon Maria
34Slide courtesy of Fuzhong Weng
Thermal Structure from ATMS and CMWS
35
ATMS CMWS‐20 CMWS‐28
CMWS=MWTS+MWHS Slide courtesy of Fuzhong Weng
Typhoon Maria(玛利亚) and Mangkhut(山竹) Precipitation from FY‐3 MWTS and MWHS
36
Precipitation from CMWS-28 Precipitation from CMORPH
FY3-CMWS-28 is combined from MWTS and MWHSCMORPH stands for NOAA Climate Prediction Center Morphing Technique
Slide courtesy of Fuzhong Weng
Product Development:Infrared Total Precipitable Water Inversion
Input Data(cloudmask,T639,satellite observations, observed
geometric parameters)
RTTOV Simulation(two split‐window radiance, transmittance)
Total Precipitable Water calculation
(coefficient calculation, TPW retrieval)
Data preprocessing(NWP data spatio‐temporal interpolation、surface emissivity
estimation)
20140107-day
TPW:mm
-50510152025303540455055606570
20140107-night
New-3bands-20150401-0030
TPW:mm
Fillvalue
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
FY‐3C VIRR TPW
FY‐2F TPW
• Algorithm:
• D‐Matrix coefficient training, based on atmospheric profile sample library and rapid radiation transfer model
Product Development : Sea surface wind speed and cloud water retrieval algorithm
Semi‐Statistical‐Physical Model
0 5 10 150
5
10
15
TAO浮标海面10米风速/m·s-1
MWRI反演海面10米风速/m·s-1
(a)
0 5 10 150
5
10
15
TAO浮标海面10米风速/m·s-1
MWRI反演海面10米风速/m·s-1
( b)
Semi Statistics and Physical Alg Statistics Alg
Product Development :Wind Profile Radar(simulator and products)
The WindRAD observation system is greatly different from the scattero‐meter systems domestic and abroad: fan‐beam, rotating scanning
WindField
GMF
Generate satellite and beam center positions by STK
Compute sample positions
Compute node positions
Resampling for each node(ViewNum Neff IncAng AzAng
SNR′)
Generate true sig0 for each view
Compute noised sig0(Kp Kg sig0′)
Output:Wind field solution in Wind vector cell (WVC)
Simulator:
100oE 110oE 120oE 130oE 140oE 0o
8oN
16oN
24oN
32oN
2km
6km
18km
Typhoon wind field simulated by WRF‐ARW Inversion products of simulator
2019/5/1 40
With the improved instrumental performance (NE∆T), and traceable radiometric measurements, FY series are becoming one of the important components of global observation to support the wide application.
Any progress of RTMS for satellite instruments are expected and welcomed
Way forward
Thank You!谢谢!