1. Global data impact studies using the Canadian 4DEnVar system
L. Garand, S. Pellerin, S. Heilliette, S. MacPherson, and M. Roch
Environment and Climate Change Canada
6th WMO Workshop on the impact of various observing systems on NWP
Shanghai, China, May 2016

2. Contents 4DEnVar basic characteristics, assimilated data
Impact of inter-channel correlations, CrIS+ATMS
Impact of IASI A/B (international inter-comparison)
Selection (thinning) strategies
Serial correlations for GB-GPS
Assimilation of IR channel sensitive to land surface

3. 4DEnVar current features
Flow dependent background error covariance matrix from EnKF 256 members (50 % weight) + static NMC (50 %)
GEM model, top 0.1 hPa, 25 km res.
Variational analysis done at 50 km res. With background state from 25 km GEM grid
6-h continuous cycle
Incremental Analysis Update (IAU)
~3.3 M obs assimilated per 6-h
IR radiances from IASI A/B, AIRS, CrIS, GEORAD (CSR)
MW radiances from 8 sensors, including ATMS
Ref: Buehner et al, MWR 2015,

4. Daily data volumes (assimilated)
13 M/day
AMSU-A: NOAA , METOP A/B, AQUA; AIRS & IASI A/B: 142 channels each, CrIS: 103 channels.

5. Some questions for the optimal use of satellite radiances
Can we still measure the added value of new MW/IR sensors
on top of current ones?
Can a better characterization of errors (eg. Inter-channel correlation)
result in a measurable impact?
What are remaining obstacles for the successful assimilation
surface-sensitive radiances over land?
What is the appropriate selection strategy (thinning) of radiances?

6. Inter-channel error correlations estimated & implemented for all radiance sensors
Based on Desrozier’s method:
Ref: Heilliette et al., EUMETSAT 2015 Conf.

7. Implementation of 15 Dec 2015 New observational features
Inter-channel error correlation (IEC) for all radiances
Added CrIS: 103 channels
Added ATMS: 17 channels
Here impact using parallel run 20 Aug to 20 Oct of:
PAR as control versus:
PAR without IEC
PAR without CrIS+ATMS
PAR without IEC, CrIS and ATMS
Note: CrIS and ATMS were tested individually before, without IEC
Results indicated modest positive impact

8. Std diff (%) 850 hPa T vs Era-int. anal
Std diff (%) 850 hPa T vs Era-int. anal. (world) 123 forecasts , IEC: inter-channel error correlation ( to 10-20)
Slight pos. impact
of CrIS+ATMS
Marked pos. impact of IEC
early in forecast,
neutral after day 3
Combined impact larger
than individual impacts
Days 6-10
No CrIS
No ATMS
No IEC
No CrIS
No ATMS
No IEC

9. Std diff (%) 500/250 hPa T vs ERA Int. anal
Std diff (%) 500/250 hPa T vs ERA Int. anal. (world) 123 forecasts , IEC: inter-channel error correlation ( to 10-20)
500 hPa
250 hPa
No Cris
No ATMS
No IEC
No CrIS
Combined impact stronger than sum of individual impacts beyond day 5

10. T,q std dev vs ERA int. analyses (world)
No CrIS/ATMS No IEC No CrIS/ATMS/IEC
Temp.
(K)
Hum.
g/kg
IEC good impact early in forecast combined with CrIs/ATMS
Leads to dominantly positive impacts at all lead times

11. 24-h T, HU zonal stddev versus ERA-Interim analyses
Temp (K) HU (g/kg)
No CrIS
No ATMS
No IEC
Impact of CrIS+ATMS
significantly enhanced
with IEC
No CrIS
No ATMS
No IEC

12. Impact of IEC on monitoring statistics for water vapor channels (here: AIRS 1800 6.34 mic)
Bias
Std
Implementation of CrIs+ATMA+IEC (15 Dec)
Std (O-P) decreases ; std(O-A) increases.
Seems good to assimilate with less weight on obs.

13. Validation against RAOBS 72-h N-Hem 2015 08-20 to 10-20 EXP CNTL
No IEC No CrIS/ATMS No IEC/CrIS/ATMS
UU UV UU UV UU UV
GZ T GZ T GZ T
DPD DPD DPD
Modest, but consistent positive impact, especially for CrIS/ATMS

14. International IASI impact study
Control is same PAR run representing 15 Dec implementation including 4 hyperspectral IR (CrIS, IASI-A/B and AIRS), ATMS, and IEC
Experience denies IASI from Metop-A&B
Here we have results for 5 weeks cycle (12 weeks planned)

15. Validation against Era-Interim analysis (std)
TEMP HR NH
TRO SH
Positive
Mostly positive
Mixed

16. 500 hPa std difference (%) vs ERA-Interim
TEMP HR NH
TRO SH
Significant positive impact limited to NH for Temp and HR

17. However… Importance of verifying analysis, especially early in forecast: here SH 24-h results for IASI-denial
vs own analysis vs Era-Interim
Inter-comparison should consider both validating analyses

18. Validation against radiosondes, 72-h
NH SH
UU UV UU UV
GZ T GZ T
Modest impact compared to that of CrIS+ATMS, longer cycle needed

19. Questions on thinning/data selection
Currently at ECCC:
Uniform thinning at 150 km for all radiances
No temporal thinning, but thinning applies to individual 15 min bins
Selection of pixels have more channels usable for assimilation
Each instrument considered independently, justified by the fact that they
rarely overlap in the same temporal bin except in polar areas
We are curently revisiting our selection stategy
What is the ideal thinning for radiances?
Should it be situation dependent?
Should it be channel dependent?
Any objective means to optimize the thinning, based on specific
attributes of the observations such as QI or horizontal error correlation?

20. Reduced thinning for all radiances from 150 km to 125 km validation vs ERA interim analysesGZ NH
Temp
TRO SH
Mostlty positive impact
Mostly negative impact
Positive impact
Ref: Beaulne et al, Eumetsat Conf.2015

21. Thinning in polar areas (150 to 125 km)
SP NP
Better impact in NP than SP
Example of coverage for a 15 min
temporal bin showing overlap
In polar regions

22. Test: reducing thinning for AMSU-A from 150 km to 100 km validation vs ERA interim analyses
SH TRO NH
UU UV UU UV UU UV
GZ T GZ T GZ T
Again mostly positive impact except negative in Tropics

23. Way forward on thinning
More flexible data selection under development: by channel
associated with quality flags (TBD) rather then # of channels available at a given location.
consider together multiple instruments of same nature,
Consider estimates of horizontal error correlations for variations of thinning by channel, and possibly location.
STD (O-B) March 2016 AIRS #1852
(water vapor channel, 6.23 micron)
Variations due to model error, but also
linked to representativeness?

24. Test of Temporal error Correlations for GB-GPS ZTD Observations
A 1-month test cycle (summer 2014) was run with a version of EnVar code modified to handle GPS ZTD error correlations (i.e. non-diagonal R-matrix). Only the NOAA network 30-minute GB-GPS data were considered (12 observations per site for the 6-h assimilation window).
Control used GPS data assimilated every 2 hours (with no temporal error correlations), while experiment uses 30-min data with serial correlation.
Temporal error correlation for 30-min time bins
Derived from the Desrozier’s method. Coherent pattern found. Negative correlations set to zero.
Confirms limited correlations beyond 1.5h.
No significant impact is seen in the forecast verifications using radiosonde observations, analyses and GB-GPS observations.

25. R&D on assimilation of surface-sensitive IR radiances over land
Assimilation of AIRS and IASI surface sensitive channels extended
over land in clear situations under restrictive conditions:
Relatively flat terrain (~50 % of land masses)
Polar regions not considered
Surface emissivity > 0.90
150 km thinning (as all other radiances)
Assimilation over land
increases volume by 17%
for surface sensitive channels
Mean number of assimilated radiances
In 10 X 10 deg areas for AIRS ch 787

26. Key result from assimilating surface-sensitive IR radiances over land (AIRS+IASI)
T std vs ERA Interim T std vs own analysis
Good impact beyond day 2, but negative at low levels early in forecast.
Hypothesis: inconsistency between distinct upper air and surface analyses
Ref: Dutta et al., JAMC, 2016,

27. Sensitivity study : Adding Ts increments from assimilation of IR radiances over land to surface analysis
add Ts increment from atmospheric assimilation of IR data to surface temperature in ISBA
also add 0.5 Ts increment to root zone temperature in ISBA(in sectors where ISBA Ts increment is zero)
Better consistency between surface and upper air analyses is beneficial

28. Way forward on assimilation of surface-sensitive IR radiances over land
Seek consistency between upper air and surface analyses by assimilating
retrieved Ts in surface analysis (short of realizing full unification of both
analyses)
Use the same surface emissivity database (UW) in both analyses and in the
forecast model (i.e. derive broadband emissivity from the detailed
spectral database).
Further validation of cloud detection
Seek improvement of B matrix at low levels
Assimilate with finer horizontal thinning

29. Conclusions
Impact of adding CrIS+ATMS is clearly beneficial, although not spectacular
Impact of IEC is significant, notably early in forecast, and contributes significantly to improving the impact of CrIS+ ATMS
Impact of IASI A/B appears lower than that of CrIS+ATMS, and better in NH than SH
Serial correlations for GB-GPS found negligible beyond 1.5h. Assimilation at 30-min with correlation showed little impact.
Thinning lower than 150 km in tropics show negative results. Seeking objective guidance for local variations of thinning.
Assimilation of surface sensitive IR radiances over land is promising. Need for consistency between separated surface and upper air analyses identified as key recommendation. Second is consistency on emissivity definition.