College Park, Maryland, USA Expansion of all-sky microwave radiance assimilation to ATMS in the GSI and other progress at NCEP Yanqiu Zhu, George Gayno, Paul van Delst, Emily Liu, Jim Purser, Xiujuan Su IMSG @ NOAA/NCEP/EMC College Park, Maryland, USA Acknowledgements to EMC colleagues, including: Ruiyu Sun, Jongil Han, John Derber, Brad Ferrier, Fanglin Yang, Andrew Collard, Dave Groff, Runhua Yang, Jun Wang 97th AMS Annual Meeting, Jan. 22-26, 2017, Seattle, WA 1 1

Current status of all-sky radiance assimilation in the GSI at NCEP The capability for all-sky microwave radiance assimilation in the GSI analysis system has been developed at NCEP, and the assimilation of cloudy radiances from AMSU-A microwave radiometers for ocean FOVs became operational in the Global Forecast System (GFS) on May 12, 2016 (Zhu et al. 2016, MWR). The assimilation of cloudy AMSU-A radiances in the GFS improves temperature and relative humidity fields at 850hPa, and reduces positive biases in stratocumulus cloud amounts coincident with eastern boundary ocean currents. 2 2

Experiments (for May 2016 implementation) of all-sky microwave radiance assimilation in the GFS 3D EnVar T670/T254 ECMWF RH at 850 hPa Relative to GFS clear-sky analyses, results indicate that GFS all-sky temperature and relative humidity analyses are in better agreement with ECMWF analyses. ClrSky-ECMWF AllSky-ECMWF Anomaly Correlation at 500 hPa ECMWF T at 850hPa ClrSky-ECMWF AllSky-ECMWF 3

Configuration of all-sky radiance assimilation in the GSI Cloud control variable(s): Normalized cloud water is currently used in the GFS Flexibility to choose either normalized cloud water or individual hydrometeors (cloud liquid water, cloud ice, snow, rain, graupel, hail) Selection of control/state variables & use of Jacobians is specified in “anavinfo”. Background error covariance: static + ensemble contributions. Observation error: symmetric observation error method (Geer and Bauer 2011) and situation-dependent observation error inflation. Bias correction: only a selected data sample, where clouds retrieved from the observation and those from the first guess are consistent, are used to derive the bias correction coefficients. Quality control: surface emissivity sensitivity check, cloud effect check, precipitation screen (only the radiances affected by non-precipitating clouds are assimilated so far), gross error check. Current moist physics schemes used in the GFS Cloud microphysics parameterization (Zhao and Carr 1997, Sundqvist et al. 1989, Moorthi et al. 2001) Deep and shallow cumulus convection parameterizations (Han and Pan 2011) 4

Currently ongoing work Expansion of all-sky radiance assimilation to ATMS Addition of fractional cloud coverage capabilities (Geer et al. 2009) in the CRTM (P. v. Delst and E. Liu) Code generalization for all-sky radiance assimilation to facilitate the expansion of the all-sky approach to additional sensors – expected to be in the GSI trunk in Feb. 2017 Application of new variational quality control scheme (VQC, Purser 2011) to radiance data Treatment of subgrid convective clouds in the GSI; Impact on OmF, background error covariance, analysis, and forecast. (currently, convective clouds, precipitation and snow information are not available for use in the GSI because they are not included in model output) 5

Expansion of all-sky radiance assimilation to ATMS All-sky ATMS radiance assimilation will follow the quality control procedures of all-sky AMSUA radiance assimilation. Cloud affected radiances over ice, snow, and mixed surfaces are not assimilated. In Addition, Extend ATOVS and AVHRR Pre-processing Package (AAPP, NWP SAF/EUMETSAT) to perform spatial averaging for all ATMS channels. This facilitates application of a common beamwidth for all ATMS channels in calculating Field of View (FOV) and cloud amount/detection Tie surface properties to the size/shape of FOV Additional quality control procedures are applied to remove large OmFs along coastlines and cryosphere boundaries, and to screen precipitating clouds using MHS-like channels ATMS specific all-sky observation error assignment Cycling experiments are underway 6 6

Relative antenna power decreasing to 1% of the maximum at the FOV edge Spatial averaging and FOV calculation ATMS beam widths are 5.2 degrees for channels 1-2, 2.2 degrees for channels 3-16, 1.1 degrees for channels 17-22. For clear-sky ATMS assimilation, AAPP spatial averaging is only applied to channels 1-16 to convert the beamwidths to 3.3 degrees For all-sky ATMS assimilation, the FOV calculation is performed after applying AAPP spatial averaging to all channels (3.3 degree FOVs). Relative antenna power decreasing to 50% of the maximum at the FOV edge Uniform antenna power Relative antenna power decreasing to 1% of the maximum at the FOV edge 7

Calculation of FOV surface type: fwater % Dark grey location (fwater%>0.9): water; Colored location: mixed surface type w/o FOV calculation FOV calc. (decreasing to 50%) FOV calc. (decreasing to 1%) Applying FOV surface type in QC to remove large OmFs over mixed surface: antenna power (Kleespies 2009) decreasing to 1% excludes large OmFs around coastline 8 OmF over water (ATMS Ch 2) 8

Additional quality control for MHS-like channels Scattering = cloud_effect(Ch16) – cloud_effect(Ch17) where cloud_effect = cloudy_Tb – clear-sky_Tb Cloud_effect over ocean If (|scattering| > 20.0) then channels 1-7 and 16-22 are excluded Ch.16 Ch.17 Quality control mark (orange color represents the scattering check) 9

Fractional Cloud Coverage GFS layer cloud fraction is diagnosed as a function of temperature, relative humidity, and cloud amount (Randal and Xu 1996) New development in CRTM: Four cloud overlap schemes (maximum, random, maximum-random, and hydrometeor weighted average) Two-column radiance calculation Impact of fractional cloud cover on AMSU-A brightness temperatures (BT): Hydrometeor weighted average total cloud cover is used for AMSU-A The impact on BT is larger in high frequency channel BT difference can be as large as 30K and more in rainy and snowy regions 23.8 GHz 89 GHz 10 Courtesy of E. Liu

Application of VQC to radiance data for non-Gaussian distribution of measurement errors Original VQC applied to conventional data only (Su and Purser) A new probability model for representing realistic measurement errors (Purser 2011), which generalizes the "logistic" distribution, corrects the defective characteristics of traditional nonlinear quality control by ensuring that the negative-log-posterior distribution preserves the property of convexity possessed by the negative-log- prior, and is therefore free of multiple minima. A linear combination of Gaussian and flat distributions (Purser, Lorenc, Andersson and Jarvinen) This non-Gaussian distribution may lead to multiple-minima in the cost function. It is important to have a preliminary analysis that is as good as possible at the start of VQC. VQC is activated after a number of iterations. 11

Why we need VQC for radiance data Channel 1 Channel 2 Channel 3 Black dots: OmF log(histogram) Channel 4 Channel 7 Channel 8 AMSU-A NOAA18 (20150601-20150720, m=0) Parameters are estimated using entropy fitting Gaussian distribution: f(x)=exp [-(x-m)2/(2s2)] Logistic distribution: f(x)=sech2 [(x-m)/(2s)] Channels 1-5 and 15 resemble logistic distribution; Channels 7-10’s patterns are quite Gaussian. Generalized logistic distribution: f(x) = exp(abx)*sech2b [(x-m)/(sqrt(2b)*s)], As b increases, the generalized logistic distribution becomes more Gaussian. 12

Future work and plan Adapt all-sky radiance assimilation in the FV3 framework as we are transitioning to FV3 and FV3 physics Examine and re-tune the all-sky radiance assimilation with individual hydrometeors as cloud control variables should they later become the prognostic variables in the forecast model As further refinements of all-sky assimilation continue, expand all- sky radiance assimilation to additional instruments All-sky radiance assimilation over land Further consider and test the application of different cloud control variables 13

Backup slides 14

Expansion of all-sky radiance assimilation to ATMS From Fuzhong Weng 15 15

f(x) = exp(abx)*sech2b [(x-m)/(sqrt(2b)*s)] Generalized Logistic distribution f(x) = exp(abx)*sech2b [(x-m)/(sqrt(2b)*s)] Channel 1 Channel 2 Channel 3 Channel 4 Channel 7 Channel 8 (AMSU-A NOAA18 a=0,m=0) As b increases, the generalized logistic distribution becomes more Gaussian. 16