SMAP Wind Speeds

The issue with the observed degradation in the SMAP wind speed BETA Version starting in May 2017 has been tracked down and traced back to an error in the sea ice mask, which affected the sensor calibration leading to erroneous results.  The problem has been resolved.  All SMAP wind speeds have been reprocessed back to April 1, 2017.  On July, 25, 2017 regular processing and distribution of the SMAP wind speed BETA Version has resumed. 

SMAP sea surface wind speeds (SSWS) are now available from Remote Sensing Systems.  We use the L-band radiometer to retrieve surface wind speeds at a height of 10 meters above the sea surface.  The near-polar orbit of SMAP allows for complete global coverage of the oceans in 3 days with a repeat cycle of 8 days. The wind speed data product is a new data set, still under development, which is best used for studying higher wind speeds.  We produce a daily map consisting of ascending and descending orbital segments.   The development of the SMAP wind retrieval algorithm is still ongoing, but we have released these SMAP wind speeds to share with scientists for evaluation and to utilize SMAP’s unique capability to measure wind speeds in intense tropical and extratropical storms. The figure below illustrates this capability for three very intense cyclones: Winston (Fiji islands, February 2016), Patricia (Mexico, October 2015), and Fantala (Seychelles, April 2016).  A detailed description of the extreme winds observed during the entire lifetime of the storm Winston is described in the RSS blog  SMAP winds in Winston

SMAP Sea Surface Wind Speed Data Processing

L1B SMAP antenna temperature data files are obtained from the NASA Snow and Ice Data Center [Piepmeier et al., 2016]. RSS has developed a SMAP sea surface wind speed retrieval algorithm that ingests the L1B radio frequency interference (RFI)-filtered antenna temperatures (TA) together with spacecraft location, velocity, attitude and time of observation. As a first step in data processing, optimum interpolation (OI) [Poe, 1990] is used to resample the L1B TA data onto a fixed 0.25 deg Earth grid (now considered a Level 2A file). The resampling is done separately for the forward and backward instrument looks. The OI approximation maintains the noise and spatial resolution (47 km x 39 km) of the original data. The RSS SMAP wind speed retrieval algorithm then uses the resample data to produce calibrated SMAP Level 2C ocean surface brightness temperatures (TB) and surface wind speed values.

For details about the SMAP instrument calibration and TA to TB transformation see the SMAP Salinity Description Page

SMAP Geophysical Model Function and Wind Speed Retrieval Algorithm

The SMAP sea surface wind retrievals are based on the Geophysical Model Function (GMF) for the SMAP wind-induced emissivity.  The emissivity has been derived from match-ups between SMAP TB and collocated WindSat [Wentz et al., 2013] wind speeds.  Between speeds of 18 and 30 m/s the GMF for both vertical (V-pol) and horizontal (H-pol) polarization grows linearly with wind speed.  We then linearly extrapolate the GMF above 30 m/s. The SMAP GMF is consistent with that previously derived from Aquarius [Meissner et al., 2014]. 

The wind speed retrieval algorithm requires ancillary input data for sea surface salinity (HYCOM) and sea surface temperature (NOAA OI SST) to compute the brightness temperature of a flat ocean surface [Meissner and Wentz, 2004; 2012].  This computer brightness temperature is then subtracted from the measured surface brightness temperature, and the difference is matched to the GMF for the wind-induced emissivity using a Maximum Likelihood Estimator (MLE). 

We use both vertical and horizontal polarizations in the sea surface wind speed retrievals. 

Performance and Suggested Use of SMAP Wind Speeds

We have completed an evaluation of the performance of the SMAP retrieved wind speeds by comparing them with wind speeds from the collocated WindSat winds.  This comparison shows a global RMS of about 1.5 m/s for the SMAP – WindSat wind speed difference when the SMAP for and aft observations in one Earth cell are averaged together.  This means that the global accuracy of the SMAP wind speeds matches approximately that of most numerical weather prediction models, such as from NCEP or ECMWF, but that the performance is not as good as that for most other active and passive microwave satellite sensors (SSMI, TMI, GMI, WindSat, AMSR, QuikSCAT, ASCAT, RapidScat), which typically have a RMS of 1.0 m/s or better. 

The big strength of the SMAP winds lies in the retrieval of high wind speeds and thus winds in storms.  The capability of L-band radiometers to measure winds in storms has been demonstrated by the SMOS mission [Reul et al., 2012].  The SMAP wind induced emissivity signal grows approximately linearly between 18 and 70 m/s and thus maintains measurement sensitivity over a wide range of wind speeds.  In addition, SMAP sea surface wind retrievals are little or not affected by rain.  SMAP has a distinct advantage over many other satellite active (ASCAT, QuikScat, RapidSCAT) and passive sensors (SSMI, TMI, GMI, WindSat, AMSR), whose wind-induced signals either saturate in very high winds or degrade in the presence of rain.

We recommend using the RSS SMAP wind data primarily for speeds above 12 m/s

We have validated the SMAP winds in storms by collocating SMAP winds with those from the Stepped Frequency Microwave Radiometer SFMR [Uhlhorn et al., 2007; Black and Uhlhorn, 2014] for 10 tropical cyclones in 2015. The agreement between these two instruments for winds greater than 30 m/s is very good, with a bias of 0.64 m/s and a standard deviation of ~3 m/s. In this small sample only Ignacio and Patricia had winds above 55 m/s. 

Description of SMAP Wind Speed Product

The SMAP sea surface wind speed data now available at RSS are the Version 00.1 (a BETA release - experimental release only).  These Level 3 daily files are distributed in netCDF format, with CF1.6 compliant metadata. 

Each data file consists of two 0.25° gridded daily maps for each parameter, one containing ascending (18:00 local equatorial crossing time) orbit segments and the other containing descending (06:00 local equatorial crossing) orbit segments.  Each data file contains only those observations for which the UTC observation time falls within that UTC day.  The time of the observation is recorded as minute-of-day in the files. 

We do not average the observations from different orbit segments.  If observations from different orbits fall within the same grid cell, the observation from the latest orbit is used and all other observations are overwritten.

If a grid cells contains observations from both the forward look and the aft look of the same orbit, then the forward and aft observations are averaged together as these have occurred very close in time.

We do not use observations for which either the gain-weighted land or sea fractions in the antenna field of view exceed 0.002.   The wind speeds are at 10-meter height above the sea surface and are considered an equivalent neutral wind.

The table below outlines the contents of each file:

Array Product
Scale Offset Valid Data Range Missing Data Value Reason for No Data
minute minute of day (UTC) 1.0 0.0 0.0 to 1440 sec -9999 no data in grid cell, land or ice in cell

SMAP wind speed

0.01 0.0 0.0 to 100.0 m/s     (at 10 meter ht)  -9999 no data in grid cell, land or ice in cell

The daily files are stored in separate subdirectories. Given that YYYY is the four digit year, MM is the month number, and DD is the day-of-month, the file names have the following naming convention:

Product Directory Path File Name
Daily L3/v00.1/daily/YYYY/

The center of the first cell of the 1440 column and 720 row map is at 0.125 E longitude and -89.875 latitude. The center of the second cell is 0.375 E longitude, -89.875 latitude. Latitude and longitude variables are included in the netCDF files. 

Missing Data

Missing data exist within a file. There are gaps between orbit segments.  There are also gaps caused by data from NASA being unavailable due to instrument issues. Invalid data values in the wind speed and minute maps are set to -9999.  In any given map, missing values also occur in locations of land, ice, and coastal areas since winds can only be obtained over the oceans.  

Large data gaps other than the between orbit spaces are generally due to missing data upstream from our processing facility, such as the instrument being turned off or data files not provided to us. Occasionally, there are delays in obtaining and/or processing data, however, beyond several weeks, it is unlikely that missing data will become available.  For information on a current outages, please check the RSS web page announcements.

SMAP Wind Speed Data Access

Browse Images

Each daily wind speed is plotted for user examination in our image browse environment. The scale for each browse image is located below the map for reference. To access the images, click the link on the upper left.

When visually browsing the wind speed imagery, the navigation may skip dates with no data, or you may see a blank map stating that no data are available for that time. If this occurs and you are sure there should be data for that date, please contact RSS Support. NetCDF data files for dates with completely missing data are not produced and will be absent from our FTP server.

Wind Speed Product Read Routines

Any third party tool/software capable of reading netCDF files can be used, such as Panoply.


Known Issues

This is an experimental product.  Please take that into consideration when using for scientific study.

The SMAP wind speed retrieval algorithm uses ocean surface salinity form the HYCOM model as ancillary input field. The HYCOM salinity filed is largely based on measurments by drifters from the ARGO network.  Though the HYCOM ancillary field is in general of high quality (with an RMS error of about 0.25 psu) there are some instances in which the HYCOM salinity does not accurately represent the salinity within the upper few centimeters of the surface that is seen by the SMAP L-band radiometer.  Important examples are:

1. Freshwater outflows from large rivers (Amazon, Congo, Ganges, Mississipi, ...). ARGO drifters cannot get close to the shelf and therefore the salinity values from the HYCOM model can be inaccurate, because of the lack of input data.  SMAP wind speed retrievals in those areas can have spurious biases and should be used with great care.

2. Freshening by heavy rain in low winds can cause stratification within the upper ocean layer.  The ARGO drifters measure salinity at 5 meter depth and therefore the HYCOM model, which is based on ARGO, can be too salty compared with the SMAP measurement.  The rain freshening effect is not a problem when measuring winds in storms, because at high wind speeds the upper ocean layer is well mixed [Boutin et al., 2015].  



Boutin, J., and Coauthors, 2015: Satellite and In Situ Salinity: Understanding near-surface stratification and sub-footprint variability. Bull. Amer. Meteor. Soc., in press, open access version:, doi: 10.1175/BAMS-D-15-00032.1.

Klotz, B., and E. Uhlhorn, 2014: Improved Stepped Frequency Microwave Radiometer Tropical Cyclone Surface Winds in Heavy Precipitation, Journal of Atmospheric and Oceanic Technology, 31, 2392 – 2408, doi: 10.1175/JTECH-D-14-00028.1.

Meissner, T., and F. Wentz, 2004: The complex dielectric constant of pure and sea water from microwave satellite observations, IEEE Transactions on Geoscience and Remote Sensing,42(9), 1836 -1849.

Meissner, T., and F. Wentz, 2006: Ocean Retrievals for WindSat: Radiative transfer model, algorithm, validation, talk given at the 9th Specialist Meeting on Microwave Radiometry and Remote Sensing Applications, San Juan, Puerto Rico, USA, IEEE Catalog no. 06EX1174C, doi: 10.1109/MICRAD.2006.1677074.

Meissner, T., and F. Wentz, 2012: The emissivity of the ocean surface between 6 - 90 GHz over a large range of wind speeds and Earth incidence angles, IEEE Transactions on Geoscience and Remote Sensing, 50(8), 3004 – 3026.

Meissner, T., F. Wentz, and L. Ricciardulli, 2014: The emission and scattering of L-band microwave radiation from rough ocean surfaces and wind speed measurements from the Aquarius sensor, Journal of Geophysical Research, 119, doi: 10.1002/2014JC009837.

Meissner, T., L. Ricciardulli, and F. Wentz, 2016: Capability of the SMAP Mission to Measure Ocean Surface Winds in Storms, Bulletin of the American Meteorological Society, in preparation.

Poe, G. A., 1990: Optimum interpolation of imaging microwave radiometer data, IEEE Transactions on Geoscience and Remote Sensing, 28(5), 800–810.

Piepmeier, J. R., P. Mohammed, J. Peng, E. J. Kim, G. De Amici, and C. Ruf. 2016. SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures, Version 3. [RFI-filtered antenna temperatures]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi:

Reul, N., J. Tenerelli, B. Chapron, D. Vandemark, Y. Quilfen, and Y. Kerr, 2012: SMOS satellite L-band radiometer: A new capability for ocean surface remote sensing in hurricanes, Journal of Geophysical Research, 117, C02006, doi: 10.1029/2011JC007474.

Uhlhorn, E., J. Franklin, M. Goodberlet, J. Carswell, and A. Goldstein, 2007: Hurricane surface wind measurements from an operational stepped frequency microwave radiometer, Monthly Weather Review, 135(9), 3070 – 3085, doi:

Wentz,F., L. Ricciardulli, C. Gentemann, T. Meissner, K. Hilburn,J. Scott, 2013: Remote Sensing Systems Coriolis WindSat Daily Environmental Suite on 0.25deg grid, Version 7.0.1 LF Wind Speeds. Remote Sensing Systems, Santa Rosa, CA. Available online at


How to Cite These Data

Continued production of these data products require support from NASA.  We need you to be sure to cite these data when used in your publications so that we can demonstrate the value of these products to the scientific community.  Please include the following statement in the acknowledgement section of your paper:

"SMAP sea surface wind data are produced by Remote Sensing Systems and sponsored by NASA Earth Science funding. Data are available at "

An official data citation for use in journal publications is given below.  Select or insert the appropriate information in brackets.

Meissner, T., L. Ricciardulli, and F. Wentz, 2016: Remote Sensing Systems SMAP daily Sea Surface Winds Speeds on 0.25 deg grid, Version 00.1. (BETA). Remote Sensing Systems, Santa Rosa, CA. Available online at