1. 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 Motivation/background Global Hawk observations Validation of Ta/Qa data Outline

2. ESA, October 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 Goal: Validate flux inputs and products in more extreme environments and improve uncertainty estimates

3. 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 JacksonESA, October 2014

4. 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

5. 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 JacksonESA, October 2014

6. Ta and Qa Retrievals Qa Retrievals –Multi-channel SSM/I regression retrievals (Schluessel, Schulz, Bentamy) –Multi-channel regression retrieval (Jackson and Wick) combining imager (SSM/I) and sounder (AMSU) observations –Neural network retrievals (SSM/I – Roberts, AMSU – Shi) –Retrieval errors range from 0.8 – 1.3 g/kg for these methods Ta Retrievals –Multi-channel, multi-satellite, regression retrieval (Jackson and Wick) –Neural network retrievals (SSM/I – Roberts, AMSU – Shi) –SST is a key parameter used in all Ta retrievals. –Retrieval errors range from 1.0 – 1.5 o C Flux products –Need ~0.5 g/kg and 1.0 o C accuracy to achieve 10 Wm -2 flux accuracy ESA, October 2014Wick and Jackson

7. Multi-Satellite Regression Method Construct grids of satellite brightness temperatures over 3 hour time periods. Training data sets includes 2580 collocated satellite observations with ESRL and NOAA research vessels Use forward multiple linear regression approach for both Qa and Ta. Qa = F (T 2 52.8, T 52.8, T 19v, T 22v, T 37v ) Ta = F (T 52.8, T 53.6, T 19v, T 22v, T 37v ) Quadratic 52.8 GHz term better represents non-linear relationship between lower tropospheric temperature and Qa. Validation accomplished with ICOADS and SAMOS ship observations ESA, October 2014Wick and Jackson

8. Gary Wick Robbie Hood, Program Director Endurance: 24-26 hours Cruise Altitude: ~18.3 km Cruise Speed: 620 km/hr

9. Relevant Global Hawk Missions NASA Genesis and Rapid Intensification Processes (2010) First overflights of hurricanes Microwave sounder, Doppler radar, lightning sensor NOAA Winter Storm Pacific and Atmospheric Rivers (2011) Dropsondes and microwave sounder First operational dropsonde deployment, 177 sondes total NASA Hurricane and Severe Storm Sentinel (2011 – 2014) Remote deployment of 2 Global Hawk aircraft Dropsondes, infrared sounder, cloud lidar Doppler radar, microwave sounder, surface winds 3 field seasons in Atlantic basin NOAA Sensing Hazards with Operational Unmanned Technology (2014 - 2017) Tropical storms and Pacific high-impact weather Dropsondes, microwave sounder, Doppler radar? ESA, October 2014Wick and Jackson

10. Global Hawk Flights in HS3 HS3 Science Meeting, April 30, 2014Wick and Jackson

11. Winter Storms and Pacific Atmospheric Rivers WISPAR Feb-Mar 2011, NASA Dryden Flight Research Center, Edwards, CA WISPAR flights were designed to: Demonstrate the NOAA/NCAR GH dropsonde system for NOAA operations and research Evaluate the capabilities of the GH for operational observations of atmospheric rivers (ARs), winter storms, and remote Arctic atmosphere 177 soundings performed on 3 high-altitude long-endurance science flight ESA, October 2014Wick and Jackson

12. Global Hawk Dropsonde System Developed through partnership between NOAA, NCAR, and NASA Uses new Global Hawk sonde: smaller and lighter than standard dropsondes System has 88-sonde and 8- channel capacity (track 8 sondes simultaneously) Sustained deployment rate of every 2.5 min (~25 km) Capability to drop up to 8 sondes every 1.5 min Automated telemetry frequency selection Real-time data processing and transmission ESA, October 2014Wick and Jackson

13. Sonde Specifications Size: 4.5 cm dia. X 30.5 cm length Mass: ~167 g Fall rate: ~11 m/s at surface Sensors based on Vaisala RS-92 radiosonde sensor module Temperature: +60° to -90 ° C, 0.01 ° C resolution Humidity: 0 to 100%, 0.1% resolution Pressure: 1080 to 3 mb, 0.01 mb resolution 2 Hz update rate Winds based on OEM GPS receiver and position 4 Hz update rate Stable cone parachute design Remote control of power on/off and sonde release Designed for extreme environmental conditions ESA, October 2014Wick and Jackson

14. Collocation Approach 2011 and 2013 Global Hawk dropsondes and NCAR hurricane database (G-IV and P-3) for 2010- 2011 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 JacksonESA, October 2014

15. Global Hawk Results Ta bias at lower temperatures and rms difference similar to regression results Qa bias greatest for satellite retrieval at the highest Qa observations Qa rms difference significantly affected by bias and reduces to 0.8 g/kg with a line fit Moist bias consistent with WHOI study comparing Qa data with buoy data and OAFlux ESA, October 2014Wick and Jackson

16. Results from All Aircraft G-IV and P-3 data for tropical storm environment Qa bias similar to Global Hawk sondes but scatter increased Ta bias increases with addition of NOAA data Potential for greater number of “in storm” observations from P-3 aircraft to influence increased scatter ESA, October 2014Wick and Jackson

17. Tropical Storm Results by Aircraft XXX ESA, October 2014Wick and Jackson G-IV P-3

18. Tropical Storm Humberto ESA, October 2014Wick and Jackson Global Hawk dropsonde pattern shown with satellite overlay of Qa observations Two sets of satellite observations 12 hours apart corresponding to drop locations marked in white

19. Tropical Storm Humberto ESA, October 2014Wick and Jackson Ta time series shows satellite and OAFlux capture variations in temperature field near tropical storm Qa variability during late period captured well. Bias seen in both satellite and OAFlux product during earlier period.

20. 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 encouraging Significantly increased scatter as approach tropical storm environment –Qa moist (dry) bias in high (low) IWV environment. Wet bias also seen in SAMOS ship and OAFlux buoy results –Ta bias smaller for all conditions Satellite Ta and Qa variations in tropical storm environment agree well with dropsonde observations Wick and JacksonESA, October 2014

21. Water Vapor Dependence Qa bias related to integrated water vapor (IWV). Satellite Qa is too dry for low IWV and too wet for high IWV Ta bias smaller but may be too cold at low IWV Should be able to correct bias since satellite observations can also are used to retrieve IWV ESA, October 2014Wick and Jackson

22. Ongoing Retrieval Developments Increased resolution of AMSU observations from 1.0 o to 0.5 o resolution. – 1.0 o resolution used to accommodate lower resolution at scan limb – New 0.5 o grid uses higher resolution at nadir views and interpolates at limb. Intercalibrated satellite data. – CSU FCDR SSM/I and SSMIS data – NOAA/NESDIS FCDR AMSU-A data – Will provide more stability Ta and Qa products and mitigate artificial trends and bias in the data set. A new validation source using dropsondes.