On the assimilation of hyperspectral infrared sounder radiances in cloudy skies

Transcription

1 On the assimilation of hyperspectral infrared sounder radiances in cloudy skies Jun Li 1, Pei Wang 1, Zhenglong Li 1, Jinlong Li 1 and Mitchell D. Goldberg 2 1 Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison 2 JPSS Program Office, NESDIS/NOAA 7 th Asia-Oceania Meteorological Satellite Users Conference October 2016 Songdo City, Korea Acknowledgement: This research is partly supported by NOAA JPSS PGRR

2 Outlines Challenges on the assimilation of hyperspectral IR radiances in cloudy skies Practical approaches on improving IR radiance assimilation in cloudy skies Summary and future work 2

3 Satellite Data Assimilation for Tropical storms (SDAT) ( Real-time SDAT ( wisc.edu/sdat) Research testbed for improving the utilization of GOES- R/JPSS data (Sounder, ABI, radiances, TPW, AMVs, Clouds) Research Path Assimilation at SSEC Using Radiances from LEOs, and LPW, AMVs from GEOs HIW Forecast Improvement with JPSS/GOES-R R2O Research Refining the Operational Path Operational NWP models: EMC GFS (Andrew Collard) EMC HWRF (Vijay Tallapragada) End Users (NHC and Local Forecasters) 3

4 Track Verification (2015 Hurricane Season) (from Mark DeMaria, NHC) SDAT track errors comparable with NHC s best operational models SDAT has large track skill compared with OCD5 4

5 Hurricane (2016) Matthew

6 Challenges on hyperspectral IR radiance assimilation in cloudy skies Both NWP and RTM have larger uncertainties in cloudy regions; There is a discontinuity in the temperature Jacobians at atmospheric layers when clouds exist; IR observations and NWP may be inconsistent on clouds (e.g., satellite observations have clouds but NWP does not and vice versa); Atmospheric parameters have a higher nonlinearity to the IR radiances in cloudy situations. Li et al. 2016, Journal of Meteorological Research 6

7 Some practical approaches on hyperspectral IR radiance assimilation in cloudy skies Direct IR radiance assimilation (challenging); Combined 1DVAR/3DVAR, or 1DVAR/4DVAR assimilation. Use 1DVAR for obtaining cloud-top pressure in the pre-processing, and 3DVAR (or 4DVAR) for assimilating clear channel radiances (not affected by clouds); Cloud-cleared radiances (CCRs) (need additional information for obtaining CCRs). 7

8 CrIS/VIIRS cloud clearing case demonstration Imager-based CC technique: Li et al. (2005) Clear Cloud Cloud impact removed! VIIRS cloud mask identifies partially cloudy FOVs (black circle) VIIRS radiances help quality control cloud cleared CrIS radiances Only three VIIRS bands (4.05, , and um) used (overlapped with CrIS) Cloud cleared radiances very close to VIIRS clear sky radiances 14 % of partially cloudy FOVs are successfully cloud cleared for Hurricane Joaquin CLR VIIRS: mean clear sky VIIRS radiances CC cloud cleared CrIS radiances converted to VIIRS spectrum 8

9 30 Oct 2015 Significantly increase clear observations by 48 % more! 9

10 AIRS(GSI clr) AIRS(MOD clr) AIRS(MOD cld-clr) Cloud-cleared radiances (CCRs): clear equivalent radiances from partly cloud cover FOV after cloud effect is removed using additional information. Currently three are types of CCRs: (1) Imager-based (2) Microwave-based (3) Background-based AIRS data locations at 18z 25, Oct 2012 Imager-based CCRs Wang et al JGR 10

11 Impact of assimilating CCRs (imager-based) on temperature forecasts RMSE against RAOBs AMSUA from NOAA-15, -18, Aqua and Metop-A Pressure (hpa) 24-hour 48-hour 72-hour T-RMSE (K) T-RMSE (K) T-RMSE (K) Wang et al (JGR) 11

12 Experimental Design Hurricane Joaquin (2015) Hurricane Joaquin (2015) Best track WRF-ARW v3.6.1: 12 km horizontal resolution (480*380), 52 vertical layers from surface to 10hPa GSI v3.3: 3D-Var Data Assimilation Method NAM background error covariance matrix Cycled bias correction Conventional Data (GTS) AMUS-A radiances onboard NOAA-15, NOAA- 18, NOAA-19, and Metop-A ATMS onboard Suomi-NPP CrIS radiances onboard Suomi-NPP Updated bias correction for each cycling, enhanced bias correction method in GSI Model domain Hurricane Joaquin (2015) Assimilation : Sep 30 06z to Oct 01 18z, 2015 Forecasts: Sep 30 05z to Oct 06 18z, 2015 Assimilation every 6 hour, 7 groups in statistics GOES μm 30 06z 6h 120h Forecast Data 30 06z 6h 120h Forecast Data 12

13 Hurricane Joaquin (2015) case. 13

14 Hurricane Joaquin (2015) best track Analyzing 120hr forecast from z Weighting function of CrIS Channel 130 How do CrIS cloud-cleared radiances (CCRs) improve track forecast? CrIS Original data assimilated at z of channel 130 CrIS CCR assimilated at z of channel

15 120 hr forecasting from z 500hPa CrIS Original CrIS CC Hurricane Joaquin is merged with the low pressure center over CONUS with assimilation of CrIS data (left). Hurricane Joaquin is separated from the low pressure center over CONUS with assimilation of CrIS CC data (right), which is verified with GOES Imager. 15

16 Summary and future work Summary Direct assimilation of hyperspectral IR radiances in cloudy skies is challenging; Alternative cloud-cleared radiance assimilating in cloudy skies showed promising in tropical cyclone forecast experiments with CIMSS SDAT as research testbed. Future work Conduct hyperspectral IR CCR assimilation experiments in operational HWRF; Compare VIIRS-based, ATMS-based and backgroundbased CrIS CCRs for assimilation. 16