Evaluation of Satellite-Derived Air-Sea Flux Products Using Dropsonde Data Gary A. Wick 1 and Darren L. Jackson 2 1 NOAA ESRL, Physical Sciences Division 2 CIRES, University of Colorado

HS3 Science Meeting, April 30, 2014Wick and Jackson Motivation Desire data-based fields of turbulent air-sea heat flux components Several products exist but validation still limited –OAFlux, HOAPS, J-OFURO –Validation typically from ship and buoy data Performance in extreme environments largely unknown Cione cite need for specific humidity in hurricane vicinity Goal: Improve uncertainty estimates

Example Latent Flux Fields Wick and JacksonHS3 Science Meeting, April 30, February, August, 2005 WHOI OAFLUX SAT Only

Satellite-Derived Flux Approach Turbulent flux calculation –Bulk aerodynamic formula: Q E =  C E L v u(q s -q a ) –Requires transfer coefficient and mean atmospheric variables –Greatest variable uncertainty in near-surface air temperature and specific humidity Near-surface air temperature and specific humidity –Regression based approach using microwave imager and sounder data –Trained on high-quality ship data –Validated against ships and buoys –Problems expected where significant precipitation Wick and JacksonHS3 Science Meeting, April 30, 2014

Multi-Sensor Retrievals of q a and T a Most past qa retrievals based solely on SSM/I and indirect relationship between surface values and total column water vapor Jackson et al. (2006) achieved improved accuracy through inclusion of sounder data Profile information from microwave sounder data helps remove variability in total column measurements not associated with the surface Can the combination of microwave sounder and imager data provide improved estimates? Figure courtesy P. Zuidema

Satellite-Derived Flux Approach Turbulent flux calculation –Bulk aerodynamic formula: Q E =  C E L v u(q s -q a ) –Requires transfer coefficient and mean atmospheric variables –Greatest variable uncertainty in near-surface air temperature and specific humidity Near-surface air temperature and specific humidity –Regression based approach using microwave imager and sounder data –Trained on high-quality ship data –Validated against ships and buoys –Problems expected where significant precipitation Wick and JacksonHS3 Science Meeting, April 30, 2014

Regression Results Qa = x T x T x T 19V x T 37V x T 22V Ta = x SST x T x T 53.6

ICOADS Validation AMSU/SSMI RMS = 1.55 g/kg AMSU/SSMI BIAS = g/kg Jackson et al [2006] RMS = g/kg Jackson et al. [2006] BIAS = g/kg Qa Ta

Coverage Wick and JacksonHS3 Science Meeting, April 30, February, August, 2005

Collocation Approach 2011 Global Hawk dropsondes and NCAR hurricane database (G-IV and P-3) for Matched dropsonde splash location with 3-hr, 0.25 degree satellite grids of Ta and qa Allow 1 cell separation (< 50 km) and < 6 hr time difference Extracted single dropsonde observation between 8-12 m above surface F17 SSMIS March 10, 2011 Wick and JacksonHS3 Science Meeting, April 30, 2014

Overall Results XXX HS3 Science Meeting, April 30, 2014Wick and Jackson

Tropical Storm Results by Aircraft XXX HS3 Science Meeting, April 30, 2014Wick and Jackson

Water Vapor Dependence XXX HS3 Science Meeting, April 30, 2014Wick and Jackson < 4 cm > 5 cm

Wind Speed Dependence XXX HS3 Science Meeting, April 30, 2014Wick and Jackson < 7 m/s > 7 m/s

Conclusions Dropsondes a valuable validation source for satellite- derived flux data –Significant new data in extreme environments Agreement in T a /q a outside of hurricane environment exceeds that seen from ship/buoy data Significantly increased scatter as approach tropical storm environment –Relative impact largest on specific humidity –Biases increased in moist environment Many more observations to be considered Wick and JacksonHS3 Science Meeting, April 30, 2014