1. Cloudy Radiance Assimilation in the NCEP Global Forecast System 1 1 IMSG@NOAA/NCEP/EMC 2 SRG@NOAA/NCEP/EMC 3 NOAA/NCEP/EMC 4 ESSIC, University of Maryland, 5 University of Maryland, 6 CIMSS/University of Wisconsin Andrew Collard 1, Yanqiu Zhu 1, Emily H. C. Liu 2, Li Bi 1, Haixia Liu 1, Xiaoyan Zhang 4, Jim Jung 6, Rahul Mahajan 1, Catherine Thomas 1, Daryl Kleist 5, David Groff 1, Paul Van Delst 1, Ruiyu Sun 1, Russ Treadon 3, John Derber 3 Presentation for the 3 rd Joint JCSDA-ECMWF Workshop on Assimilating Satellite Observations of Clouds and Precipitation into NWP Models JCSDA/ECMWF Cloudy Radiance Workshop

2. How to deal with clouds in data assimilation Avoid them Correct for them Model them – But don’t feed back to analysis – And feed back to the analysis We are trying all of these. 2 JCSDA/ECMWF Cloudy Radiance Workshop

3. How to deal with clouds in data assimilation Avoid them – Simply clear-sky assimilation Correct for them Model them – But don’t feed back to analysis – And feed back to the analysis 3 JCSDA/ECMWF Cloudy Radiance Workshop

4. How to deal with clouds in data assimilation Avoid them Correct for them Model them – But don’t feed back to analysis Retrieve a simplified cloud to use in the RT calculation – We have been looking at this in the context of McNally cloud retrievals, initially for SEVIRI. – And feed back to the analysis 4 JCSDA/ECMWF Cloudy Radiance Workshop

5. How to deal with clouds in data assimilation Avoid them Correct for them – Cloud Cleared Radiances Model them – But don’t feed back to analysis – And feed back to the analysis 5 JCSDA/ECMWF Cloudy Radiance Workshop

6. Cloud Cleared Radiances: Motivation JCSDA/ECMWF Cloudy Radiance Workshop6 Assimilation experiments with AIRS Cloud-Cleared Radiance product Unfortunately quality degraded after problems with the AMSU-A on Aqua – first guess is important. Control +AIRS CCR

7. Cloud-Clearing Methodology 7 Assume: R clr and in the 2 adjacent FOVs are same After eliminating the from above 2 equations, we can get: Extend to multiple cloud layers and more adjacent pixels: α2α2 α1α1 R clr surface JCSDA/ECMWF Cloudy Radiance Workshop FOV1: R 1 = (1-α 1 ).Rclr + α 1.R cld FOV2: R 2 = (1-α 2 ).Rclr + α 2.R cld R clr R cld R ccr = R 1 + η.(R 1 -R 2 ), where η = α 1 /(α 2 - α 1 ) and α 1 ≠α 1 R ccr = R 1 + η 1.(R 1 -R 2 ) +η 2.(R 1 -R 3 ) + ……. + η k.(R 1 -R k ), η 1, η 2 … are cloud-clearing parameters which depend on the α only. They can be estimated using a set of cloud-sounding channels to solve an over-constrained least-squares problem.

8. Flow Chart of Variational Cloud-Clearing Model first guess CRTM Observed radiances at adjacent pixels from cloud sounding channels Observed radiances at adjacent pixels from all channels GSI inner loop Rclr @ pivot pixel A and R ccr @ pivot pixel The cloud-clearing parameters are estimated and updated inline together with other meteorological variables within the variational framework and reconstructed Rccr is assimilated with all other available observations. 8JCSDA/ECMWF Cloudy Radiance Workshop

9. 9 CCR close to the clr-sky obs CCR does not depends on the ges Validate CCR with adjacent clear-sky observations

10. Cloud Cleared Radiances: Impact 10 JCSDA/ECMWF Cloudy Radiance Workshop Control Cloud Cleared CrIS Small, not statistically Significant positive impact SH T500 AC SH T250 AC

11. How to deal with clouds in data assimilation Avoid them Correct for them Model them – But don’t feed back to analysis – And feed back to the analysis A cloud analysis for the microwave will go operational in the Q3FY16 global model upgrade. See poster by Yanqiu Zhu. Infrared is in progress with a focus on improving radiative transfer quality. 11 JCSDA/ECMWF Cloudy Radiance Workshop

12. All-Sky Radiance Assimilation Issues Quality of the forecast model/background Quality of the Radiative Transfer – See talk by Paul van Delst and poster by Emily Liu Specification of Background and Observation Errors Quality Control Bias Correction Linearity Balance 12 JCSDA/ECMWF Cloudy Radiance Workshop

13. All-Sky Radiance Assimilation Issues Quality of the forecast model/background Quality of the Radiative Transfer – See talk by Paul van Delst and poster by Emily Liu Specification of Background and Observation Errors Quality Control Bias Correction Linearity Balance 13 JCSDA/ECMWF Cloudy Radiance Workshop

14. Maps of Mean First-Guess Departure … 14 JCSDA/ECMWF Cloudy Radiance Workshop … can help identify where the model or the radiative transfer are introducing biases.

15. Standard Deviation of First Guess Departures 15 JCSDA/ECMWF Cloudy Radiance Workshop The standard deviation of first guess departures over the same period can give some indication of the magnitude of the background standard deviation. Of course, in addition to the background error, this also includes contribution from instrument noise, representivity error and forward model error.

16. For the infrared, cloud fraction matters 16 JCSDA/ECMWF Cloudy Radiance Workshop Clear Sky Overcast Cloud Cloud Fraction from model Histogram of O-B for IASI Channel 1090 (917.25 cm -1 )

17. All-Sky Radiance Assimilation Issues Quality of the forecast model/background Quality of the Radiative Transfer – See talk by Paul van Delst and poster by Emily Liu Specification of Background and Observation Errors Quality Control Bias Correction Linearity Balance 17 JCSDA/ECMWF Cloudy Radiance Workshop

18. Symmetric Observation Error Assignment for AMSU-A under All-sky Condition  Observation error is assigned as a function of the symmetric cloud amount (Geer et al. 2011)  Gross check ±3 of the normalized FG departure (accept Gaussian part of the samples) Before QC After QC Error Model Non-precipitating Samples Normalized by std. dev. of the OMF in each symmetric CLWP bin Gaussian Un-normalized Normalized Obs. error used in the analysis 18

19. Background Error Specification 19 JCSDA/ECMWF Cloudy Radiance Workshop The 4D Ens Var system specifies background error through a combination of the 80-member ensemble (which prescribes 87.5% of the solution) and a static term (12.5%) The ensembles are run at the same resolution of the analysis (T574) while the deterministic model runs at T1534. For cloud water the static term standard deviation is set to 5% of the amount of cloud in the deterministic model. This helps ensure increments arising from the static term are balanced.

20. Resolution Dependence of Ensemble Spread 20 JCSDA/ECMWF Cloudy Radiance Workshop Cloud Temp T574 T256 Cloud spread has high dependency on resolution, Temperature and RH (not shown) has less.

21. All-Sky Radiance Assimilation Issues Quality of the forecast model/background Quality of the Radiative Transfer – See talk by Paul van Delst and poster by Emily Liu Specification of Background and Observation Errors Quality Control Bias Correction Linearity Balance 21 JCSDA/ECMWF Cloudy Radiance Workshop

22. All-sky Radiance Bias Correction for AMSU-A  Based on cloud liquid water (clw, Grody et al. 2001) calculated from radiance observation (O) and first guess (F), different cloud information: 1) O:clear vs. F:clear 2) O:clear vs. F:cloudy 3) O:cloudy vs. F:clear 4) O:cloudy vs. F:cloudy  Bias correction coefficients are obtained using only a selected data sample with consistent cloud info between the first guess and the observation  Use latest bias coef. available to bias correct the data with mismatched cloud info 22 Normalized OmF w/ BC using the all-sky strategywithout using the all-sky strategy

23. AMSU-A Cloudy Radiance Assimilation Impact 23 JCSDA/ECMWF Cloudy Radiance Workshop Clear Sky All Sky Clear Sky All Sky 500 hPa Height Anomaly Correlation Small positive impact from cloudy assimilation

24. Conclusions At NOAA/NCEP/EMC we are working on the cloudy radiance problem in a variety of areas. For the infrared we are looking at three different strategies to use cloudy observations: –Cloud Cleared Radiances –Single layer grey cloud retrievals –Full cloudy radiance assimilation For the microwave we are about to go operational with all-sky assimilation of AMSU-A in early 2016. 24 JCSDA/ECMWF Cloudy Radiance Workshop