Assimilation of GPM satellite radiance in improving hurricane forecasting Zhaoxia Pu and ChauLam (Chris) Yu Department of Atmospheric Sciences University of Utah Vijay Tallapragada NCEP Environmental Modeling Center Jianjun Jin and William McCarty GMAO, NASA Goddard Space Flight Center JCSDA Technical Review and Science Workshop Moss Landing, CA May 31 – June 2, 2016 Acknowledgment: JCSDA Support

Outline Background Objective Current progress -- Assimilating GMI Level 1C data into HWRF with GSI ensemble-3DVAR hybrid data assimilation system  Hurricane Joaquin  Quality control  Bias correction  Forecast impact Summary and concluding remarks (On-going and future work)

Most of the lifetime of tropical cyclones (TCs) is over open oceans where conventional observations are sparse Satellite imagery become a very useful source of observations for improving hurricane research and forecasts Background

NASA Global Precipitation Mission Core Observatory (Hou et al. 2013) Launched in February 28, 2014 Aims to provide the next-generation global precipitation measurement Significant improvements in 1) Data coverage; 2) Resolution; 3) Number of channels Source (NASA) Background Source (NASA) Most of current GPM data assimilation efforts emphasize global models GPM vs. TRMM GPM Ch. 1-13

Objective Assimilating NASA GPM Microwave Imager (GMI) satellite radiance into NCEP operational Hurricane Weather Research and Forecasting (HWRF) model for improved hurricane prediction  Clear-sky radiance  Ch. 1-9 first; then Ch  All-sky

Data Assimilation for HWRF Vortex Initialization = vortex relocation + intensity correction + data assimilation Data Assimilation (GSI-based hybrid 3DVAR – ensemble system) B 1 : Static background error covariance; B 2 : Ensemble background error covariance. H: JCSDA Community Radiative Transfer Model (CRTM) (for satellite DA)

HWRF domains 18km/6km/2km

Hurricane Joaquin (2015) Courtesy Dr. Jim Doyle, NRL Investigation period: 24 Sep 2015 – 09 Oct 2015

A stand-alone CRTM is employed to simulate brightness temperature for each GMI channel  Make a close look at properties of each channel and their uncertainties Radiative Transfer Model - CRTM GMI Level 1C T b : Ch 9CRTM Simulated T b : Ch 9 Latitude Longitude Latitude Longitude 21 UTC 1 OCT 2015

A Quality Control (QC) scheme (Zhu et al. 2014) is applied to filter out cloud and precipitation affected radiance This QC scheme mainly consists of two steps: I. A threshold check on cloud liquid water II. A regression test on emissivity Quality Control After QC

Sources of biases in satellite measurements (not limited to)  Systematic errors in calibration processes  Errors in radiative transfer models  Scan angle dependent biases A collection of predictors is used to model the bias 1,2,3 where are the BC coefficients calculated by using a least-squares fit on a large sample of observations In GSI, the set of predictors include satellite scan angle, cloud liquid water, temperature lapse rate, and the square of the lapse rate Bias Correction (BC) 1: Zhu, et al. 2014; 2: Auligne, McNally and Dee, 2007; 3: Harris and Kelly, 2001

Channel 02 Biases: Large-scale vs. Regional-scale Large-scale data D02 collection Large-scale data D02 collection 2 nd OCT 00UTC Large-scale data Over Atlantic Ocean: 24 Sep – 8 Oct D02 collection HWRF ghost domain 2: 27 Sep – 4 Oct

Feeding BC coefficients into GSI Channel 6 00UTC 2 Oct 2015 Before BC After BC

Bias = Bias = Bias = Bias = Before BC After BC Ch 1 Ch 8 Before BC: Ch1 has cold bias Ch8 has warm bias After BC: Both channels have significant reduction in bias Bias Correction 00UTC 2 Oct 2015

Example: Observation Distribution 2 – 3 Oct 2015 Before BC After BC Channel 2 GMI T b Observational error (OmF)

Vortex initialization process in HWRF * Relocation * Intensity correction (turn off) Hurricane Joaquin (2015) Simulation window: 00 UTC 2 October – 06 UTC 7 October Experiments: 1. NoDA + Relocation (control) 2. GMI Assimilation (no BC) + Relocation 3. GMI Assimilation (with BC) + Relocation Forecast impact

GMI clear sky radiance was (successfully) assimilated into HWRF using GSI-based ensemble-3DVAR data assimilation system. Bias correction showed a evident reduction in observational biases in each channel. Case study using hurricane Joaquin (2015) showed that assimilation of GMI clear sky radiance has a positive impact on the track forecast. Summary and Concluding Remarks

On-going work More hurricane case studies. Integrated data assimilation with all other observations. Tests in quasi-operational environment. Future work Radiance observations are only assimilated in ghost domain 2 and they have little impact on the intensity forecast. Further improvement should be done. Cloud and precipitation affected radiances are crucial to hurricane forecast (all-sky data assimilation is necessary). Summary and Concluding Remarks (Cont.)

Thank you!