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performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with National Aeronautics and Space Administration. Airborne lidar imagery was obtained by the NASA Jet Propulsion Laboratory Airborne Snow Observatory, funded by NASA’s Terrestrial Hydrology Program. We especially thank Vaisala Inc., who took quick action to replace a stolen radiosonde unit just prior to the start of OLYMPEX. REFERENCES Chapman , D. , and K. A. Browning , 1997 : Radar observations of
performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with National Aeronautics and Space Administration. Airborne lidar imagery was obtained by the NASA Jet Propulsion Laboratory Airborne Snow Observatory, funded by NASA’s Terrestrial Hydrology Program. We especially thank Vaisala Inc., who took quick action to replace a stolen radiosonde unit just prior to the start of OLYMPEX. REFERENCES Chapman , D. , and K. A. Browning , 1997 : Radar observations of
useful for biomass measurements, but commonly SAR and lidar data are used in combination (e.g., Asner et al. 2012 ; Mitchell et al. 2017 ). EO-based AGB estimates need ancillary data, e.g., ground data and close-range remote sensing sources such as terrestrial and airborne lidar data for the calibration and validation of the satellite observations ( Herold et al. 2019 ). Large uncertainties in global estimates of water stored in biomass result from various measurement errors and generalization
useful for biomass measurements, but commonly SAR and lidar data are used in combination (e.g., Asner et al. 2012 ; Mitchell et al. 2017 ). EO-based AGB estimates need ancillary data, e.g., ground data and close-range remote sensing sources such as terrestrial and airborne lidar data for the calibration and validation of the satellite observations ( Herold et al. 2019 ). Large uncertainties in global estimates of water stored in biomass result from various measurement errors and generalization
. (2010) for observations analyzed in a similar region to where GCPEx took place [Alliance Icing Research Study (AIRS) in Toronto, Ontario, Canada, and the Canadian CloudSat / Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) Validation Programme (C3VP) near Barrie]. Figure 2 shows PDFs of D m , σ m , and μ derived from the in situ observations for all of the flights that took place during GCPEx. The properties have different distributions depending on temperature
. (2010) for observations analyzed in a similar region to where GCPEx took place [Alliance Icing Research Study (AIRS) in Toronto, Ontario, Canada, and the Canadian CloudSat / Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) Validation Programme (C3VP) near Barrie]. Figure 2 shows PDFs of D m , σ m , and μ derived from the in situ observations for all of the flights that took place during GCPEx. The properties have different distributions depending on temperature
regions, and are fraught with problems like undercatch and wind-blown snow biases ( Fassnacht 2004 ). This measurement gap can be bridged by spaceborne active and passive microwave (PMW) sensors that are tailored to detect and quantify snowfall thanks to their ability to probe within clouds ( Levizzani et al. 2011 ; Skofronick-Jackson et al. 2017 ). Two spaceborne radars paved the way toward ground-breaking vertically resolved observations of falling snow over much of the globe: the CloudSat Cloud
regions, and are fraught with problems like undercatch and wind-blown snow biases ( Fassnacht 2004 ). This measurement gap can be bridged by spaceborne active and passive microwave (PMW) sensors that are tailored to detect and quantify snowfall thanks to their ability to probe within clouds ( Levizzani et al. 2011 ; Skofronick-Jackson et al. 2017 ). Two spaceborne radars paved the way toward ground-breaking vertically resolved observations of falling snow over much of the globe: the CloudSat Cloud
Blankenship (2012) , and Ortega et al. (2016) . TRMM observations have already shed light on where the most intense thunderstorms occur and what their microwave radiometer and Ku-band radar footprints are ( Zipser et al. 2006 ). Because of the high single-scattering albedo of ice particles, passive microwave radiometers feature large brightness temperature depressions corresponding to large amounts of ice ( Cecil 2011 ; Cecil and Blankenship 2012 ). The most extreme storm in the TRMM dataset was
Blankenship (2012) , and Ortega et al. (2016) . TRMM observations have already shed light on where the most intense thunderstorms occur and what their microwave radiometer and Ku-band radar footprints are ( Zipser et al. 2006 ). Because of the high single-scattering albedo of ice particles, passive microwave radiometers feature large brightness temperature depressions corresponding to large amounts of ice ( Cecil 2011 ; Cecil and Blankenship 2012 ). The most extreme storm in the TRMM dataset was
