1. Cloudy GOES-Imager assimilation with hydrometeor control variable Byoung-Joo JUNG NCAR/MMM Student presentation at JCSDA Summer Colloquium on Satellite Data Assimilation, July 27 – August 7 2015

2. Background Ph.D. in Atmospheric Sciences, Yonsei University, Korea Adjoint sensitivity analysis Singular Vector for adaptive strategy in T-PARC field campaign (Tropical cyclone) Evaluation of observation impact with WRF 3DVAR, EnSRF, and FSO Researcher at Korea Institute of Atmospheric Prediction Systems TLM/ADM coding for global spectral-element dycore Representer-based variational system Vertical localization of radiance obs. in LETKF system

3. Channel 4 Channel 6 ObservationBackgroundInnovation (OmB)

4. GSI DA system with WRF background on CONUS domain CRTM with qc, qi, qr, qs, qg from model forecast 2 hydrometeors (qc, qi) added as control variable Static background error statistics for hydrometeors (from GEN_BE v2.0) Conventional obs + GOES-13/15 Imager channel 4 (10.7 um) & channel 6 (13.3 um) Relax first-guess check criteria Skip cloud screening in QC Non-Gaussian distribution for obs. error Multiple outer loop (re-linearization)  nonlinearity Approach *High spatial/temporal resolution

5. - Less weight for large innovation - Enlarge the obs. Error - Affect the conditioning itself Huber norm Gaussian Huber Iteratively Re-weighted Least Square (IRLS)

6. Channel 4 Channel 6 Error normalized innov. Huber via IRLS Gaussian OmB Histogram

7. Innovation (OmB)OmA(B/A) vs. O Cost Function Gradient Norm

8. Bias in hydrometeors is more pronounced. ₋Require much re-linearization for the cases O/B~[cloudy/clear or clear/cloudy] ₋Feature calibration and alignment (FCA) can help to reduce displacement error. How to determine the Huber parameter? (or other error model?) ₋Critical for balanced conditioning Hybrid approach can help the multi-variate relation between meteorological fields & cloud variables. ₋How effective will be the alpha-CV of ensembles on this? What can be the proper predictor for “cloudy” bias correction? Proper control variable for hydrometeors Accurate surface emissivity, cloud properties for various instruments in CRTM Inter-channel correlation can be affected by cloud. How to sustain the hydrometeor increments in the forecast? How to validate the impact of cloudy radiance assimilation? Many many questions