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.