An Assessment of HIRS Surface Air Temperature with USCRN Data

The surface air temperature retrievals from the High Resolution Infrared Radiation Sounder (HIRS) are evaluated by using observations from the U.S. Climate Reference Network (USCRN) for the period of 2006 to 2013. One year of the USCRN data is also used as ground truth in calibrating retrieval biases. The final retrieval results show that mean biases of HIRS retrievals from comparisons to all surface stations for each year are mostly in the range of ±0.2 °C, and the root mean square difference (RMSD) values are 3.2–3.5 °C. Results for biases of individual stations are mostly within ±2 °C. In average, RMSDs are smaller over the eastern U.S. than over the western U.S., smaller at nighttime than at daytime, and smaller at lower elevations. The comparison patterns are consistent from year to year and for different satellites, showing the potential of HIRS data for long-term studies.

[1]  Edzer Pebesma,et al.  Spatio‐temporal interpolation of daily temperatures for global land areas at 1 km resolution , 2014 .

[2]  Martha C. Anderson,et al.  Validation of GOES-Based Insolation Estimates Using Data from the U.S. Climate Reference Network , 2005 .

[3]  W. Rossow,et al.  Cloud Detection Using Satellite Measurements of Infrared and Visible Radiances for ISCCP , 1993 .

[4]  Jeffrey R. Key,et al.  Characteristics of Satellite-Derived Clear-Sky Atmospheric Temperature Inversion Strength in the Arctic, 1980–96 , 2006 .

[5]  Lei Shi,et al.  An Evaluation of HIRS Near-Surface Air Temperature Product in the Arctic with SHEBA Data , 2016 .

[6]  K. Moffett,et al.  Remote Sens , 2015 .

[7]  Venkat Lakshmi,et al.  Land surface air temperature mapping using TOVS and AVHRR , 2001 .

[8]  T. Karl,et al.  A Comparative Study of ASOS and USCRN Temperature Measurements , 2005 .

[9]  Jesse,et al.  U.S. Climate Reference Network after One Decade of Operations: Status and Assessment , 2013 .

[10]  Lei Shi,et al.  Scene Radiance–Dependent Intersatellite Biases of HIRS Longwave Channels , 2008 .

[11]  M. Matricardi,et al.  An improved fast radiative transfer model for assimilation of satellite radiance observations , 1999 .

[12]  Eric F. Wood,et al.  Creating consistent datasets by combining remotely-sensed data and land surface model estimates through Bayesian uncertainty post-processing: The case of Land Surface Temperature from HIRS , 2015 .

[13]  Darren L. Jackson,et al.  Detection and Correction of Diurnal Sampling Bias in HIRS/2 Brightness Temperatures , 2007 .

[14]  Elizabeth Good,et al.  Daily minimum and maximum surface air temperatures from geostationary satellite data , 2015 .

[15]  Nuno Carvalhais,et al.  Estimating air surface temperature in Portugal using MODIS LST data , 2012 .

[16]  Andi Walther,et al.  The Pathfinder Atmospheres–Extended AVHRR Climate Dataset , 2014 .

[17]  M. Schildhauer,et al.  Using multi‐timescale methods and satellite‐derived land surface temperature for the interpolation of daily maximum air temperature in Oregon , 2015 .

[18]  K. G. Hubbard,et al.  Air Temperature Comparison between the MMTS and the USCRN Temperature Systems , 2004 .

[19]  Lei Shi Intersatellite Differences of HIRS Longwave Channels Between NOAA-14 and NOAA-15 and Between NOAA-17 and METOP-A , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Xuan Feng,et al.  Validation of the Surface Air Temperature Products Retrieved From the Atmospheric Infrared Sounder Over China , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Lei Shi,et al.  Algorithm Development of Temperature and Humidity Profile Retrievals for Long-Term HIRS Observations , 2016, Remote. Sens..

[22]  Jared Rennie,et al.  The international surface temperature initiative global land surface databank: monthly temperature data release description and methods , 2014 .

[23]  John Kochendorfer,et al.  U.S. Climate Reference Network Soil Moisture and Temperature Observations , 2013 .