Snowfall Detectability of NASA's CloudSat: the First Cross-Investigation of its 2c-Snow-Profile Product and National Multi-Sensor Mosaic QPE (NMQ) Snowfall Data

This study investigates snowfall detectability and snowfall rate estimation with NASA's CloudSat through the flrst evaluation of its newly released 2C-SNOW-PROFILE products using the National Mosaic and Multisensor QPE System (NMQ) snowfall products. The primary focus is on the detection and estimation of surface snowfall. The results show that the CloudSat product has good detectability of light snow (snow water equivalent less than 1mm/h) but degrades in moderate and heavy snow (heavier than 1mm/h). The analysis suggests that the new 2C-SNOW-PROFILE algorithm is insu-cient in correcting signal losses due to attenuation. Its underestimation is well correlated to snowfall intensity. Issues of sensitivity and data sampling with ground radars, which may afiect the interpretation of the results, are also discussed. This evaluation of the new 2C-SNOW-PROFILE algorithm provides guidance for applications of the product and identifles particular error sources that can be addressed in future versions of the CloudSat snowfall algorithm.

[1]  D. Legates,et al.  A climatology of snowfall‐temperature relationships in Canada , 1999 .

[2]  Guosheng Liu,et al.  Detecting snowfall over land by satellite high‐frequency microwave observations: The lack of scattering signature and a statistical approach , 2013 .

[3]  A. H. Auer The Rain versus Snow Threshold Temperatures , 1974 .

[4]  D. Kitzmiller,et al.  Evaluation of Radar Precipitation Estimates from the National Mosaic and Multisensor Quantitative Precipitation Estimation System and the WSR-88D Precipitation Processing System over the Conterminous United States , 2012 .

[5]  Kuolin Hsu,et al.  Intercomparison of High-Resolution Precipitation Products over Northwest Europe , 2012 .

[6]  Steven D. Miller,et al.  CloudSat Precipitation Profiling Algorithm—Model Description , 2010 .

[7]  Marco Tedesco,et al.  Identification of atmospheric influences on the estimation of snow water equivalent from AMSR-E measurements , 2007 .

[8]  Vincenzo Levizzani,et al.  Detection and Measurement of Snowfall from Space , 2011, Remote. Sens..

[9]  J. Qu,et al.  Satellite-based Applications on Climate Change , 2013 .

[10]  Jian Zhang,et al.  National mosaic and multi-sensor QPE (NMQ) system description, results, and future plans , 2011 .

[11]  David H. Staelin,et al.  Satellite Retrievals of Arctic and Equatorial Rain and Snowfall Rates Using Millimeter Wavelengths , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Gail Jackson Detection Thresholds of Falling Snow from Satellite-Borne Active and Passive Sensors , 2012 .

[13]  S. Joseph Munchak,et al.  Evaluation of precipitation detection over various surfaces from passive microwave imagers and sounders , 2013 .

[14]  Tristan L'Ecuyer,et al.  A Comparison of Precipitation Occurrence from the NCEP Stage IV QPE Product and theCloudSatCloud Profiling Radar , 2014 .

[15]  Yang Hong,et al.  Evaluation of Spatial Errors of Precipitation Rates and Types from TRMM Spaceborne Radar over the Southern CONUS , 2013 .

[16]  Robert F. Adler,et al.  Evaluation of TMPA satellite-based research and real-time rainfall estimates during six tropical-related heavy rainfall events over Louisiana, USA , 2009 .

[17]  Andrew S. Jones,et al.  Toward snowfall retrieval over land by combining satellite and in situ measurements , 2009 .

[18]  Yang Hong,et al.  Toward a Framework for Systematic Error Modeling of Spaceborne Precipitation Radar with NOAA/NSSL Ground Radar–Based National Mosaic QPE , 2012 .

[19]  Kazumasa Aonashi,et al.  Development of a snowfall retrieval algorithm at high microwave frequencies , 2006 .