Evaluation of Satellite-Derived Air-Sea Flux Products Using Dropsonde Data

Gary A. Wick1 and Darren L. Jackson2

1NOAA ESRL, Physical Sciences Division2CIRES, University of Colorado

• Motivation/background• Global Hawk observations• Validation of Ta/Qa data

Outline

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

Satellite-Derived Flux Approach

• Turbulent flux calculation– Bulk aerodynamic formula: QE=CELvu(qs-qa)– 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 Jackson ESA, October 2014

Multi-Sensor Retrievals of qa and Ta

• 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: QE=CELvu(qs-qa)– 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 Jackson ESA, October 2014

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 – 1.5oC

• Flux products– Need ~0.5 g/kg and 1.0oC accuracy to achieve 10 Wm-2 flux

accuracy

ESA, October 2014Wick and Jackson

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 (T252.8, T52.8, T19v, T22v, T37v)

• Ta = F (T52.8, T53.6, T19v, T22v, T37v)• 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

Gary Wick

Robbie Hood, Program Director

Endurance: 24-26 hoursCruise Altitude: ~18.3 kmCruise Speed: 620 km/hr

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?

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Global Hawk Flights in HS3

HS3 Science Meeting, April 30, 2014Wick and Jackson

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

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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

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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

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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 Jackson ESA, October 2014

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

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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

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Tropical Storm Results by Aircraft

• XXX

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G-IV P-3

Tropical Storm Humberto

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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

Tropical Storm Humberto

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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.

Conclusions

• Dropsondes a valuable validation source for satellite-derived flux data– Significant new data in extreme environments

• Agreement in Ta/Qa 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 Jackson ESA, October 2014

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

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Ongoing Retrieval Developments

• Increased resolution of AMSU observations from 1.0o to 0.5o resolution. – 1.0o resolution used to accommodate lower resolution at scan limb

– New 0.5o 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.