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, 10-13 May 2016

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

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, 2532-2559

Daily data volumes (assimilated) 13 M/day AMSU-A: NOAA 15-18-19, METOP A/B, AQUA; AIRS & IASI A/B: 142 channels each, CrIS: 103 channels.

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?

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

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. 2015 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

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 (2015 08-20 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

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 (2015 08-20 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

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

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

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.

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

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)

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

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

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

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

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?

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

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

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

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?

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.

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

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, 561-578

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

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

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.