1. Handling Cloud-Affected Infrared Radiances in the GSI Will McCarty GSFC/Global Modeling and Assimilation Office JCSDA Workshop 10 October 2012

2. Introduction In GMAO forward processing, infrared radiances are assimilated from IASI, AIRS, and HIRS Heritage “multi”-spectral sounders like HIRS (~ 18 channels) and the GOES Sounder are being phased out – The US HIRS instruments replaced by CrIS from NPP onward (hyperspectral – 1297 ch total, 399 for DA) – The final European HIRS launched on MetOp-B. MetOp-C will only fly IASI (hyperspectral – 8461 ch, 616 for DA) – No Sounder in US GEO beginning w/ GOES-R – Hyperspectral sounding potentially in GEO in a number of future longitudes

3. 3 Number of observations considered for assimilation Number of observations used for assimilation Observation volume January 1977 to present Reduction of observations heavily due to presence of clouds in observations Observations processed per 6h 1979 − 2011 Observations used per 6h 1979 − 2011 AIRS IASI AIRS IASI After thinning, QC Before thinning, QC

4. How are Clouds Handled in GSI Cloud screening is a two-step process 1.Retrieve a cloud height This is done via a minimum residual method (Eyre and Menzel 1989) 2.Compare cloud height against transmittance profile If layer-to-top of atmosphere transmittance of a channel at the retrieved cloud height is greater than 2% reject the channel For channels most-sensitive to the surface, this rejects ~80% of these data.

5. Further Exploiting IR Data To further exploit IR data, the next step is to include some characterization of clouds in the analysis

6. Clouds in the Infrared Quick lession in radiative transfer: – All forward radiative transfer in the GSI is done via the CRTM (in Europe, they use RTTOVS) as the observation operator – In a very basic sense, consider CLEAR-SKY radiative transfer equation as: Clear IR Measurement = Surface +  (Atmospheric Layers)

7. Clouds in the Infrared If you consider the signature of a very, very dense cloud in the IR, we can make some assumptions and then define CLOUDY SKY radiative transfer equation as: Cloudy IR Measurement = Cloud Top +  (Atmospheric Layers above cloud)..this is known as a blackbody assumption, as the cloud top is considered “black”

8. Clear IR Measurement = Surface +  (Atmospheric Layers) Cloudy IR Measurement = Cloud Top +  (Atmospheric Layers above cloud) Retrieved Cloud Height

9. In the cloud height retrieval, a cloud fraction, N, is also solved Under the graybody assumption, the partially cloudy observation can then be considered for a single, fractional cloud as: In the GSI, we can then restructure the H operator to include the Cloud Height and Cloud fraction to allow for a partially cloudy forward operator (and also partially cloudy Jacobians) Clouds in the Infrared Partially Cloudy IR Measurement = N * Cloudy IR Measurement + (1 – N) * Clear IR Measurement

10. 10 Obs minus Forecast (clear)Obs minus Forecast (cloudy) Considering the O-Fs versus cloud fraction, it is seen that the O-Fs are closer, but the cold bias is, as expected, amplified for higher (colder) clouds The accuracy of the calculated cloudy radiance is fundamentally dependent on the accurate retrieval of cloud height and fraction

11. Cloudy Infrared Radiance Assimilation within the GSI Jacobians are adjusted to move sensitivity from below cloud to cloud surface Single footprint assimilation shows that the system is drawing to the retrieved cloud top Magnitude is inflated due to low observation errors. Error in CTP will result in an erroneous O-F, which then can negatively impact the analysis To compensate, CTP is allowed to vary in the minimization as a control variable 11 Uncontaminated Including Cloud Cloud Top

12. Observation-Centered Control Variables Current GSI implementations consider control variable only in terms of grids (2D & 3D) and channel-by-channel bias predictors Bias prediction coefficients are of the dimension [5,number of channels] –each satellite channel on each instrument has its own set of predictiors (i.e. MetOp_AMSU-A channel 8 will have the same set of five coefficients across every footprint globally Observation-Centered control variables –consider a control variable at a footprint location over all channels measured at that point –Dimension dynamic -> any number of observation- centered control variables can be appended to the control vector 12

13. Observation-Centered Control Variables Once developed, the functionality was expanded to CTP –Cloud Fraction still considered constant and set as the retrieved value Jacobians –In addition to modified  T B /  T(p),  T B /  q v (p), etc., the minimization now incorporates the CTP Jacobian,  T B /  p cld. –  T B /  p cld can be directly differentiated from the radiative transfer equation (i.e. the appendix of Li et al. 2001) Background error for CTP 13

14. Background error for CTP Background error for CTP (  B CTP ) was considered first in a single-footprint case: –Initial CTP – 624 hPa –Initial N – 0.968 Consider behavior of three values of  B CTP compared to clear-sky observations only and a static CTP (no variational CTP) –  B CTP = 50 hPa, 10 hPa, and 5 hPa 14

15. Background error for CTP 15 Clear Cloudy Static CTP Cloudy varCTP

16. Background error for CTP 16 Clear Cloudy Static CTP Cloudy varCTP

17. Background error for CTP 17 Clear Cloudy Static CTP Cloudy varCTP

18. Background error for CTP Variational CTP acts as a “sink”, as a function of  B CTP –As the bkg error is increases, the cloud signal is absorbed into the CTP variable –the solution approaches clear-only result –As bkg error is decreased, result approaches static CTP Expected as CTP is tightly constrained to retrieved guess This is only for a single footprint. How does the analysis respond to a full suite of observations –Since only CTP is varying, only consider cloudy IR if 1.0 > N > 0.9 -> higher confidence in cloud height for opaque clouds 18

19. CTP Increments  B CTP = 5 hPa 19

20. CTP Increments  B CTP = 50 hPa 20

21. Background error for CTP The CTP control variable increment variance is a function of: –Latitude –Height Convergence is degraded as a function of background error –A 5 hPa error aloft is much different in terms of cloud temperature than near the sfc –Also seen at ECMWF in similar McNally 2009 implementation A simplified error model, as a f(CTP) implemented, ranging from 5 hPa aloft to 13 hPa below – convergence on par w/ fixed error 21

22. Differences of Analyses 22 Std. Dev. of (A(5) – A(FullOS)) The Variance of the difference between a CTL analysis (all obs) and a Cloudy AIRS analysis (CTL + AIRS Cld) at 0000 UTC, 850hPa Satellite Track of Aqua satellite evident

23. 23 The Variance of the difference between a CTL analysis (all obs) and a Cloudy AIRS analysis (CTL + AIRS Cld) at 0000 UTC, 850hPa Satellite Track of Aqua satellite evident Differences of Analyses Std. Dev. of (A(5) – A(FullOS))

24. Differences of Analyses 24 Std. Dev. of (A(5) – A(FullOS)) In regions of expected persistent cloudiness, there are more changes to the analysis (month- averaged CTP, top, and cloud fraction, bottom)

25. Future Efforts This work is still ongoing Cycling runs w/ advanced CTP bkg error model are under investigation There are a number of issues to consider: –Inclusion of a 2 nd outer loop showed bias w.r.t. CTP solution --- GSI expanded to interpolate between levels –GSI calls setuprad each outer loop, and this work is thus re-initialized w/ a second retrieved CTP CTP is not re-linearized about the solution from the first outer loop –CTP was only considered for 15-11 μm 25

26. Future Efforts Expansion of observation-centered predictors to other studies –ECMWF has had this infrastructure, and it’s my thought that this has a lot of possibilities for future work –A logical potential expansion of this study is to cloud fraction –With various assumptions, this capability can be expanded to SST, sfc emissivity, Cloud-Water Path, etc. –With a little cleaning up, it will be as simple as setting an initial value, background error, and Jacobians of any predictor 26

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28. Differences of Analyses 28 Std. Dev. of (A(50) – A(FullOS))Std. Dev. of (A(5) – A(FullOS)) Changes away from tracks for  B CTP = 50 hPa are a result of decreased convergence in the minimization