Under the auspices of the World Climate Research Programme (WCRP) Global Energy and Water cycle EXperiment (GEWEX) Data and Assessment Panel (GDAP), the SeaFlux Project was created to investigate producing a high-resolution satellite-based dataset of surface turbulent fluxes over the global oceans. The most current release of the SeaFlux product is Version 1.0; this represents the initial release of turbulent surface heat fluxes, associated near-surface variables including a diurnally varying sea surface temperature

Bogdanoff, Alec S.

Clayson, Carol Anne

Roberts, J. Brent

NASA Technical Reports Server

 The SeaFlux Turbulent Flux Dataset 
Request to Users 
The SeaFlux dataset is developed and maintained through the international SeaFlux project. The 
SeaFlux producers kindly request that a citation for publications and/or presentations is provided to 
seaflux@whoi.edu to better gauge utilization of the SeaFlux product. Your cooperation will help in the 
future development of the SeaFlux product. Thank you. 
 
 
 
 
 
The SeaFlux Turbulent Flux Dataset Version 1.0 
Documentation 
Carol Anne Clayson1 
J. Brent Roberts2 
Alec S. Bogdanoff1,3 
1. Woods Hole Oceanographic Institution, Woods Hole, MA 
2. NASA Marshall Space Flight Center, Huntsville, AL 
3. Massachusetts Institute of Technology, Cambridge, MA 
 
Documentation Version 1.0 
February 3, 2012 
What’s New? 
Version 1.0 (February 3, 2012) – The SeaFlux Version 1.0 dataset is now publically available. Please 
visit www.seaflux.org for more information. Initial documentation release.  
https://ntrs.nasa.gov/search.jsp?R=20120004203 2019-08-30T19:41:28+00:00Z
 The SeaFlux Turbulent Flux Dataset 
 2 
About SeaFlux 
Under the auspices of the World Climate Research Programme (WCRP) Global Energy and Water 
cycle EXperiment (GEWEX) Data and Assessment Panel (GDAP), the SeaFlux Project was created to 
investigate producing a high-resolution satellite-based dataset of surface turbulent fluxes over the 
global oceans. The most current release of the SeaFlux product is Version 1.0; this represents the initial 
release of turbulent surface heat fluxes, associated near-surface variables including a diurnally varying 
sea surface temperature. The product is available at 0.25 x 0.25 degree spatial and 3-hourly temporal 
resolution covering the period 1998-2007. For more information, please visit www.seaflux.org. For any 
inquiries, please email us at seaflux@whoi.edu. 
Available Data 
• Latent Heat Flux - The latent heat flux (LHF) is proportional to the evaporation rate, dependent upon 
the air-sea humidity difference, wind speed, and sea surface roughness. Units: W/m2 
• Sensible Heat Flux - The sensible heat flux (SHF) is proportional to the temperature difference 
between the atmosphere and the sea surface, and dependent on the wind speed and sea surface 
roughness. Units: W/m2 
• Near-Surface Temperature – The near-surface temperature (Ta) is calculated using a neural network 
algorithm based on Roberts et al. (2010) and explained in more depth in the Definitions & Algorithms 
section. The near-surface temperature is valid at 10m.Units: °C 
• Near-Surface Specific Humidity – The near-surface specific humidity (Qa) is calculated using a neural 
network algorithm based on Roberts et al. (2010) and explained in more depth in the Definitions & 
Algorithms section. The near-surface temperature is valid at 10m. Units: g/kg 
• Wind Speed - Based on the Cross-Calibrated Multi-Platform (CCMP) Ocean Surface Wind 
Components and the neural network algorithm based on Roberts et al. (2010), the SeaFlux wind 
speed is an equivalent neutral wind valid at 10m. Units: m/s 
• Sea Surface Temperature - A diurnally varying sea surface temperature (DVSST) is used in the 
calculation of LHF and SHF. The DVSST is based on Bogdanoff and Clayson (2012) and further 
described in Clayson et al. (2012). Units: °C 
• Diurnal Sea Surface Temperature - The diurnal sea surface temperature (dSST) is the difference 
between the maximum and minimum sea surface temperature due to diurnal warming over a 24-hour 
period defined from 0000Z to 0000Z of the next day. The dSST is based on the Bogdanoff and 
Clayson (2012) parameterization, which is an updated version of the Webster, Clayson, and Curry 
(1996) parameterization. Units: °C 
Definitions & Algorithms  
A day is defined as 24 hours from 0000Z to 2400Z (or 0000Z of the next day). 
The algorithms used to create the swath-level near-surface temperature and near-surface specific 
humidity, as well as wind speeds under high cloud liquid water conditions, is based on the Neural 
Network methodology of Roberts et al. (2010). In this version of the SeaFlux dataset, the beta version 3 
Colorado State University (CSU) Inter-calibrated Brightness Temperatures (IBTs) from the SSM/I 
Sensors for the time period of 1998 through 2007 is used (http://rain.atmos.colostate.edu/FCDR). The 
length of this version of the IBT dataset limits the time coverage of this version of the dataset. The next 
 The SeaFlux Turbulent Flux Dataset 
 3 
SeaFlux version will encompass the entire SSM/I time period. The atmospheric variables are valid at 
10m. 
The diurnally varying sea surface temperature uses the Reynolds Optimally-Interpolated Version 2.0 
AVHRR-only sea surface temperature (Reynolds et al. 2007) as the foundation (or temperature at which 
there is no diurnal variability). The temperature in the upper-most ocean most resembles a foundation 
SST just before sunrise due to nighttime convective mixing. This temperature is used as the base 
temperature from which the daytime curve is applied, using the estimated daytime warming from 
Bogdanoff and Clayson (2012). In cases where there is 24-hours of sunlight, the foundation temperature 
is used at the time when the sun is lowest in the horizon.  
The fluxes are computed using a neural network emulation of the COARE 3.0 algorithm (Fairall et al. 
2003). 
Resolution & Coverage 
The SeaFlux product is three-hourly (averaged from 0000-0300Z, 0300-0600Z, 0600-0900Z, etc.). The 
SST however, is hourly, and the dSST is one value per day. All variables are currently available from 
January 1, 1998 through December 31, 2007. The product is currently analyzed on a 1/4-degree equal-
angle grid and covers the global, ice-free oceans. The DVSST product associated with SeaFlux is 1-
hourly with the same spatial coverage and resolution. The SST used in the surface flux computations is 
an average SST around the valid time of each 3 hourly period. For example, the 0130Z surface fluxes 
utilize the average SST from 1Z and 2Z for that day.  
Interpolation 
The interpolation technique of the SeaFlux dataset is a state-of-the-art Model Based Interpolation 
(MoBI) based on the time tendencies of NASA’s Modern Era Retrospective-analysis for Research and 
Applications (MERRA) dataset (Rienecker and coauthors 2011). The methodology is intended to take 
advantage of the dynamical time-tendency information available from the high-resolution reanalysis to 
estimate missing data. Note that the methodology only fills in missing data. Where satellite retrievals 
are available, they are kept in the product.  
Format/Reading the Data 
All SeaFlux data is in 2-byte integer binary format. Reads are currently available in Matlab, Fortran, 
and GrADS. Should you need help reading the data with alternate software or need any assistance, 
please email us at seaflux@whoi.edu. 
SeaFlux Accuracy  
In comparison with other retrieval algorithms, the neural network retrieval algorithm for near-surface 
variables has the smallest root mean square error for all variables, and smallest bias for the near-surface 
temperature and humidity. The root mean square error of near-surface humidity, near-surface 
temperature, and wind speed are 1.31 g/kg, 1.31°C, and 1.58 m/s, respectively. Further details of the 
accuracy of the neural network retrieval accuracy can be found in Roberts et al. (2010). Atlas et al. (2011) 
denote the RMS error on the CCMP satellite-based wind estimates of 0.5 m/s. The SSM/I only datasets 
have a greater RMS error of about 1 m/s. Uncertainty estimates are currently being calculated on the 
SeaFlux variables, and once complete, will be publicly available through the SeaFlux Data Repository.  
 The SeaFlux Turbulent Flux Dataset 
 4 
The figure below is a comparison of the SeaFlux wind speed, air temperature, and sea surface 
temperature for a winter season of buoy observations in the Gulf of Mexico. The biggest differences 
occur in the sea surface temperature and are mainly due to difference in the Reynolds SST, used as the 
foundation SST, and the buoy-based SST observation. It is promising to note the match up between 
the diurnal warming magnitudes, and timing and shape of the diurnal curves.  
0
5
10
15
20
W
ind
 S
pe
ed
 (m
/s)
1999 Winter Season
(SeaFlux:Red  Buoy42040:Blue)
5
10
15
20
25
30
Ai
r T
em
pe
ra
tu
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 (o
C)
Nov99 Dec99 Jan00 Feb00 Mar00 Apr0015
20
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Se
a 
Su
rfa
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 T
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 (o
C)
 
Future Development & Updates 
1. Greater temporal coverage – We hope to extend the length of the dataset over the entire time 
period of the SSM/I satellite mission with SeaFlux Version 2.0. 
2. Improved lag time algorithm – The lag time between solar noon and peak sea surface temperature 
is poorly understood. We are investigating the physics and parameterizing the lag time to create a 
better diurnally varying sea surface temperature.  
3. Improved land and ice mask – The SeaFlux project is working with its GEWEX counterparts to 
create a consistent land and variable ice mask across the GEWEX community. An updated release 
of the product will be available once the new ice flag is applied to the SeaFlux product. Please 
make sure to register on the website to get updates!  
4. Future versions will feature an equal area mode. The data will also be available in netCDF format. 
 
 The SeaFlux Turbulent Flux Dataset 
 5 
References 
Atlas, R., R. N. Hoffman, J. Ardizzone, S. M. Leidner, J. C. Jusem, D. K. Smith, D. Gombos, 2011: A 
Cross-calibrated, Multiplatform Ocean Surface Wind Velocity Product for Meteorological and 
Oceanographic Applications. Bull. Amer. Meteor. Soc., 92, 157–174. 
Bogdanoff, A. S. and C. A. Clayson, 2012: Estimation of sea surface diurnal warming. J. Phys. Oceanogr. 
(In preparation). 
Clayson, C. A., and A. S. Bogdanoff, 2012: The effect of diurnal sea surface temperature warming on 
climatological air-sea fluxes, J. Clim. (Submitted). 
Clayson, C. A., J. B. Roberts, and A. S. Bogdanoff, 2012: SeaFlux turbulent flux data set. Bull. Amer. 
Meteor. Soc. (In preparation). 
Fairall, C. W., E. F. Bradley, J. E. Hare, A. A. Grachev, and J. B. Edson, 2003: Bulk parameterization of 
air-sea fluxes: Updates and verification for the COARE algorithm. J. Climate, 16, 571-591. 
Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-
resolution-blended analyses for sea surface tempeature. J. Clim., 20, 5473-5496. 
Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for 
Research and Applications. J. Climate, 24, 3624–3648. 
Roberts, J. B., C. A. Clayson, F. R. Robertson, and D. L. Jackson, 2010: Predicting near-surface 
atmospheric variables from Special Sensor Microwave/Imager using neural networks with a first-
guess approach, J. Geophys. Res., 115, D19113, doi:10.1029/2009JD013099. 
Webster, P. J., C. A. Clayson and J. A. Curry, 1996: Clouds, radiation and the diurnal cycle of sea 
surface temperature in the tropical western Pacific Ocean. J. Clim., 9, 1712-1730. 
 
------------------------------------------------------------------------------------------------------------------------------ 
Contact Information General Questions: seaflux@whoi.edu 
 
 General Correspondence: 
   SeaFlux c/o Carol Anne Clayson - MS # 29 
   Woods Hole Oceanographic Institution 
   266 Woods Hole Road 
   Woods Hole, MA 02543-1050 
 
 Carol Anne Clayson cclayson@whoi.edu 
 J. Brent Roberts jason.b.roberts@nasa.gov 
 Alec S. Bogdanoff alecb@whoi.edu 
 
------------------------------------------------------------------------------------------------------------------------------ 
Revision History February 3, 2012 V1.0 ASB 
 October 22, 2011 D1.0 ASB 


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