Progress Towards the Assimilation of Cloud Affected Infrared Radiances at the GMAO

1. Progress Towards the Assimilation of Cloud Affected Infrared Radiances at the GMAO Will McCarty NASA Goddard Space Flight CenterGlobal Modeling and Assimilation Office 12th JCSDA Technical Review & Science Workshop 22 May 2014

2. Cloud Height Estimation Errors Only Infrared radiances unaffected by clouds are assimilated • cloud-free (all channels) • cloudy, but unaffected (only channels sensing above cloud) • Nearly opaque, using a graybody assumption in the forward operator QC determines the cloud height and screens channels whose weighting functions are sensitive to the retrieved cloud height In the GSI data assimilation algorithm, a minimum residual method (Eyre and Menzel 1989) is used to determine the cloud height • Assumptions in this method are fundamental sources of error: • Single layer clouds; Infinitesimally thin, black clouds; treated as fractionally gray

3. Forecast Impact of Cloud Contamination For clear sky, the misassessment of cloud height can result in the cloud signals being erroneously projected on the mass (T & q) fields For cloud-affected observations, the misassessment of cloud height vertically can erroneously project the cloud top temperature signal at the wrong vertical level The cloud height retrievals were determined to need further refinement. Initially, a verification effort of the retrieval of cloud top pressure (CTP) from AIRS was compared against MODIS v5 retrieved CTP (CO2 Slicing) and CALIPSO lidar-measured CTP

4. AIRS CTP vs. MODIS High CTP Comparing: AIRS Cld Top Pressure (CTP) - ~15 km MODIS MYD06 CTP - 5 km, coincident 3x3 - highest CTP reported Source of Contamination 1 Jul-30 Sept 2013 Over-Conservative

5. AIRS CTP vs. MODIS High CTP Comparing: AIRS Cld Top Pressure (CTP) - ~15 km MODIS MYD06 CTP - 5 km, coincident 3x3 - highest CTP reported 1 Jul-30 Sept 2013

6. AIRS CTP vs. MODIS High CTP Comparing: AIRS Cld Top Pressure (CTP) - ~15 km MODIS MYD06 CTP - 5 km, coincident 3x3 - highest CTP reported Considering observations as function of cloud fraction shows biases in confident (opaque) and uncertain (variant) areas of cloudiness 1 Jul-30 Sept 2013

7. AIRS v. MODIS CTP by Fraction MODIS CTP uncertain> 700 hPa and in low fraction Conservative in areas of low fraction Apparent bias in areas of high fraction More low fraction observations by design 0.25 < N < 0.5 #: 112239 0.0 < N < 0.25 #: 279311 0.75 < N < 1.0 #: 138513 0.5 < N < 0.75 #: 85490

8. AIRS CTP vs. CALIPSO Highest CTP Compared against L2 CALIPSO-CALIOP determined CTP (1 km, V3-30) • Similar trends, but reduction in lower-troposphere contamination (600-800 hPa) • Includes poles, but separation was considered • - No distinct polar signals Likely sampling issues (spatial and temporal) 1 Jul 2013 – 28 Feb 2014

9. Forecast Impact of Cloud Contamination To quantify these cloud assessment errors as to how they impact the analysis, two metrics investigated: • Observation Departures (bias corrected) • Observed minus Forecasted Brightness Temperature (O-F) • Adjoint-Based Observation Impacts • A 24 hour forecast error projected onto the observations of its initial analysis using the adjoints of the forecast model and assimilation system • The measure is a moist energy norm (u, v, T, ps , qv → J/kg) • This method is run routinely in GMAO ops at 0000 UTC • A negative value equates a reduction in error, so NEGATIVE = GOOD

10. Impact of AIRS AIRS is generally larger than any single AMSU-A Per radiance, AIRS is significantly less than AMSU-A • Assumed as redundancy, but can cloud signals get past QC?

11. Channel Sensitivity 4μm T T High → Low → Window Water Vapor AIRS Jacobians for temperature and water vapor The top color bar will serve as reference for the remaining plots

12. Impact of AIRS by Channel Tropospheric observations show consisted normalized impact, but more observations aloft (unaffected by clouds) lead toward greater total impact 4μm H2Ov T Hi → Lo → Win

13. AIRS CTP vs. MODIS High CTP Low Clouds 1000-700 hPa Mid-Level Clouds 700-440 hPa High Clouds 440-100 hPa Clouds classified into (roughly) ISSCP height classifications as a function of AIRS Cloud Top Pressure

14. Observation Impact – High Clouds 4μm T T High→Low→Window T High→Low→Window T High→Low→Window Water Vapor 4μm T 4μm T H2Ov H2Ov • Negative O-F signal apparent where MODIS/AIRS disagree most towards contamination • Small count relative to total, but show inconsistent impact per observation in this region

15. Observation Impact – High Clouds T High→Low→Window T High→Low→Window 4μm T 4μm T H2Ov H2Ov • High clouds show that even w/ an AIRS low bias (vertically), impact is generally show observations reduce error • Channels less effected by these high clouds (< 50) show more neutral impact – effect of the metric

16. Observation Impact – Mid Clouds 4μm T T High→Low→Window T High→Low→Window T High→Low→Window Water Vapor 4μm T 4μm T H2Ov H2Ov • Cloud signals apparent in lower tropospheric channels • There is an opposite signal in the window (and low-level H2Ov) channels • Low count – consistent signal. Needs further investigation. Unusual for window channel observations deemed ‘cloudy’ at the mid-level to be assimilated

17. Observation Impact – Mid Clouds 4μm T T High→Low→Window T High→Low→Window T High→Low→Window Water Vapor 4μm T 4μm T H2Ov H2Ov • Mid-Level clouds show, again, generally improved impact, though notes of cloud contamination are evident in lower tropospheric channels • Magnitude of negative impact more pronounced than in high clouds

18. Observation Impact – Low Clouds 4μm T T High→Low→Window T High→Low→Window T High→Low→Window Water Vapor 4μm T 4μm T H2Ov H2Ov • Cloud signal again apparent • Positive O-F bias in areas of agreement – potentially a feedback of cloud contamination having an effect on bias correction • Impact per observation strongly negative in areas of cloud contamination

19. Observation Impact – Low Clouds 4μm T T High→Low→Window T High→Low→Window T High→Low→Window Water Vapor 4μm T 4μm T H2Ov H2Ov • Misrepresentation of low clouds relative to MODIS show clear increase in error for channels sensitive to low clouds • There is a real chance that thin cirrus over small clouds can be a source of error • Magnitude of total degradation due to clouds becomes much more significant w.r.t. overall positive impact

20. AIRS ‘Clear’/MODIS Cloudy Observations • Cloud signal again apparent, and largest magnitude of total degradation • Warm O-F signal near sfc again MODIS CTP (hPa) H2Ov 4μm T T High→ Low → Window

21. AIRS ‘Clear’/MODIS Cloudy Observations MODIS CTP (hPa) • Per observation, the degradationis again apparent H2Ov H2Ov 4μm T 4μm T T High→ Low → Window T High→ Low → Window

22. Heterogeneity in AIRS Observations • Observations with notable subscale variability generally degrade, and have a negative O-F signal. • This is using MODIS, which has many of the same limitations as AIRS. Impact per Ob MODIS CTP (Low – High, hPa) O-F H2Ov 4μm T T High → Low → Window

23. Conclusions/Future Work • Incorporation of CALIPSO datasets was an attempt to overcome issues with MODIS data • Even though different algorithms, MYD06 (CO2 Slicing/IR Window) and GSI (MinRes) algorithms build on same assumptions • CALIPSO + GSI Thinning = very limited sample size • An assimilation study incorporating MODIS CTPs to eliminate potential sources of contamination • In addition to impact assessment, it would help to quantify the effect that cloud contamination has on variational bias correction (B. Zavodsky) • Incorporation of NCEP updates to CTP retrieval in radiance space • Ultimately, this helps quantify an issue that has been acknowledged • Various studies have shown improvement by incorporating collocated imagery in QC • Tom Auligne has done some work with multilevel cloud retrievals in the GSI • This work provides a level of verification necessary to continue efforts to assimilate cloud-affected IR observations

24. Unrelated – CrIS Impacts vs. Apodization