Assessment of Geostationary Hyper-spectral Sounders in JEDI
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Assessment of Geostationary Hyper-spectral Sounders in JEDI
Wei Han1,2, Robert Knuteson3
1 JCSDA , 2 NWPC/CMA, 3 SSEC/UW
7th WMO workshop on the Impact of Various Observing System in NWP, Nov-Dec 2020
Outline
WHY Geo. Sounders ?
- Opportunities and challenges
An efficient and accurate algorithm ISSEC
- Iterative Spectral Shift Estimation and Correction (ISSEC)
- Application to SNOs and NWP simulation
GIIRS spectral bias characteristics
- Detector dependence
- Long term variation and Diurnal variation
Near Real Time monitoring of GIIRS at JCSDA
- GIIRS in JEDI
- http://nrt.jcsda.org/hofx/giirs-fy4a.html (Since September 1 2020)
Discussions
- Lessons learned
- Recommendation for future Geo. sounders
WHY Geo. Sounders: Opportunities and Challenges
Convective scale analysis and forecast
- Fast evolving weather
- Pre-convection monitoring
Targeted observing for high impact weather
- Hurricane
- Convection
- Strong precipitation
Large detector array challenges
- Calibration (spectral , radiometric)
- Bias correction
- Observation error estimation
Han W. et al, 2019: Assimilation of high temporal resolution GIIRS in 4D-Var , 2019 Joint Satellite Conference, Boston, Sept.28-Oct.4,2019.https://ams.confex.com/ams/JOINTSATMET/videogateway.cgi/id/505317?recordingid=505317
Targeted Observing : THORPEX(2005–14)
Majumdar, S.J., 2016: A Review of Targeted Observations.
Bull. Amer. Meteor. Soc., 97, 2287–2303.
Geo Sounder and Leo Sounder: Hurricane sounding
Geo Sounder: FY-4A GIIRS Leo Sounder: N20 CrIS
Typhoon Maria (2018)
Starting 00Z (UTC) 10 July 2018GIIRS provides observations every 15 minutes
Slide 7
Starting 00Z (UTC) 10 July 2018
GIIRS provides observations every 15 minutes
FY-4A GIIRS
Jul. 10th 00Z,2018
Typhoon MARIA
1650 chanels
FY-4A GIIRS humidity sounding(15min )
0
b
bqq
tqVV
Get wind information using 4D-Var by the water vapor “tracer effects”
There are 961 channels in water vapor band
3D winds informationthrough 4D-Var orretrieval
Impact of GIIRS high temporal observations on
Typhoon Maria forecasts (72-h)
Han W. et. al.,Assimilation of high temporal GIIRS radiance in GRAPES,
ITSC22, Québec, Canada, 31 October – 6 November 2019
Impact of GIIRS on Typhoon Maria Track forecasts
Time
Resolu
tion
00(%) 06(%) 12(%) 18(%) 24(%) 30(%) 36(%)Mean
Ratio
(%)
15min 0 59.84 55.17 57.52 59.29 35.36 35.15 43.19
30min 0 59.84 48.93 57.52 46.84 26.26 42.39 40.25
1hour 0 39.10 33.47 44.30 33.31 16.95 27.82 27.84
3hour 0 0 33.47 30.43 26.71 7.59 25.60 17.69
1
33
65
97
2
34
66
98
3
35
67
99
4
36
68
100
29
61
93
128
30
62
94
125
31
63
95
126
32 96
64
127
16KM 16KM 16KM 16KM 16KM 16KM 16KM
4.5333KM
8KM
16KM
North
South
West East
Detector Array:112kmX648km
FOV: 16km2 hour(15N-65N ,75E-135E)
Large detector array of FY-4A GIIRS: detector
dependent bias (spectral , radiometric,…)
FY-3D HIRAS:2X2
32X4
CrIS:3X3
Spectral bias of the off-axis FOV is an important issue
MTG IRS: 160X160648km×112km
Iterative Spectral Shift Estimation and Correction
using local quadratic approximation (ISSEC)
Resample calculation:𝐼𝑆𝑆𝐸𝐶
𝑆𝐸𝑄𝑈𝐸𝑁𝑇𝐼𝐴𝐿=
6
200≈
𝟏
𝟑𝟎
Han W. et. al., ISSEC: An efficient and accurate algorithm for HIS spectral
shift estimation and correction with application on FY-4A GIIRS, to be submitted.
ISSEC : a fast spectral shift estimation algorithm
lon=57.13,lat=14.87,detector=4 (lblrtm)
The spectral range of 740-760 cm-1 LBLRTM
Spectral bias estimation of GIIRS operational
L1 data using ISSEC (CrIS and GIIRS SNOs)
V3(11/07/2019)
SNOs are limited by the samples in time which could not to identify diurnal variation
Red Dots indicate Matchups between GIIRS (blue
dots) and CrIS. Green indicates GIIRS granules used.
Spectral bias long term trend of different detectors
Spectral bias estimation based on NWP(2020050406)
JEDI-FV3(CRTM) ECMWF(RTTOV12)
JEDI-FV3(RTTOV12) LBLRTM(ERA5)
M. Loveless, SSEC
C. Burrow, ECMWF
Uncertainty analysis of the spectral shift
estimation using RTTOV,CRTM,LBLRTM,SNOs
Spectral bias diurnal variation based on NWP
ECMWF-RTTOV
FV3-RTTOV
EC simulation for the period of May 05-08
FOV33 and 58
Diurnal variation of GIIRS spectral shift
00Z 12Z
06Z 21Z
Diurnal variation of GIIRS spectral shift
00Z 12Z 21Z
Spectral shift relative change
(difference to 00z) {{
Detector dependence of spectral variation std
14 47 79 112
Lessons learned from the GIIRS spectral shift diurnal variation:
• Off-axis detectors
• Satellite thermal state
• Spectral monitoring onboard satellite
• Spectral calibration
Near Real Time monitoring of GIIRS at JCSDA
http://nrt.jcsda.org/index.html
Global Ring of geo. sounders in global observing
system
Global OSSE of geo. sounders (Spectral, Temporal, FOV, Detector Array, …)
Target Observations to improve High Impact Weather (HIW)
(Credits: T. Kurino, JMA)
Discussions
Lessons learned from GIIRS
- Spectral calibration improvement
- Remain issues
Recommendations for future GEO. Sounders
- Global rings of geo. sounders (targeted obs. for HIW )
- Homogeneity of large detector array sensor
- Diurnal variation in Geo. Orbit
- Calibration challenges and solutions
Monitoring of spectral bias based on NWP
- Fast algorithm: ISSEC
- Diurnal variation
- Long term
- Fast RT model spectral bias evaluation (RTTOV, CRTM)
References
Yin, R.,W. Han, Z.Gao and D.Di. 2020, The evaluation of FY4A's Geostationary
Interferometric Infrared Sounder (GIIRS) longwave temperature sounding
channels using the GRAPES global 4D‐Var. Quarterly Journal of the Royal
Meteorological Society, 146, 1459–1476. https://doi.org/10.1002/qj.3746.
Yin R., W. Han, Z.Gao , G. Wang, 2019, A study on longwave infrared channel
selection based on estimates of background errors and observation errors in the
detection area of FY-4A. Acta Meteorologica Sinica, 77(5): 898-910,
doi:10.11676/qxxb2019.051
Di, D., J. Li, W. Han, W. Bai, C. Wu, and W. P. Menzel, 2018: Enhancing the fast
radiative transfer model for FengYun-4 GIIRS by using local training profiles,
Journal of Geophysical Research - Atmospheres,123, doi:10.1029/2018JD029089.
Yang, J., Z. Zhang, C. Wei, F. Lu, and Q. Guo, 2017: Introducing the New
Generation of Chinese Geostationary Weather Satellites, Fengyun-4. Bull. Amer.
Meteor. Soc., 98, 1637–1658, https://doi.org/10.1175/BAMS-D-16-0065.1.
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