Diurnal Variation in Cloud Liquid Water Path Derived from Five Cross-Track Microwave Radiometers Onboard Polar-Orbiting Satellites

The Advanced Microwave Sounding Unit (AMSU)-A/Advanced Technology Microwave Sounder (ATMS) onboard the National Oceanic Atmospheric Administration (NOAA)-18/-19, MetOp-A/-B, and Suomi National Polar-orbiting Partnership satellites provide global observations of the cloud Liquid Water Path (LWP) almost 10 times a day. This study explores the possibility of capturing the diurnal cycle of the LWP. An inter-satellite cross-calibration is first carried out using a double-difference method. A remapping is then used to obtain the AMSU-A-like LWP to account for beam shape discrepancies between the ATMS and AMSU-A. We finally examine the diurnal cycle of the LWP over the Southeast Pacific Ocean using the ATMS and AMSU-A data from the five satellites mentioned above. Results show that the remapped ATMS results agree well with the AMSU-A results at the same local time over a stratocumulus region. LWP retrievals from multiple satellite cross-track microwave radiometers can well reproduce the diurnal variation characteristics of LWP in 2015 over the East Pacific Ocean, including the seasonal variation of the diurnal variation. This study presents the first step toward merging LWP data from all ATMS and AMSU-A radiometers and will be of interest to many researchers studying LWP-related weather and climate changes, especially considering the possible loss of higher-resolution microwave-frequency conical-scanning sensors in the coming years.

[1]  L. O'Neill,et al.  Satellite climatology of cloud liquid water path over the Southeast Pacific between 2002 and 2009 , 2011 .

[2]  Fuzhong Weng,et al.  30-Year atmospheric temperature record derived by one-dimensional variational data assimilation of MSU/AMSU-A observations , 2013, Climate Dynamics.

[3]  Ralf Bennartz,et al.  An Uncertainty Data Set for Passive Microwave Satellite Observations of Warm Cloud Liquid Water Path , 2018, Journal of geophysical research. Atmospheres : JGR.

[4]  Fuzhong Weng,et al.  Calibration of Suomi national polar‐orbiting partnership advanced technology microwave sounder , 2013 .

[5]  Hu Yang,et al.  Comparison of the Remapping Algorithms for the Advanced Technology Microwave Sounder (ATMS) , 2020, Remote. Sens..

[6]  Robert Wood,et al.  Diurnal cycle of liquid water path over the subtropical and tropical oceans , 2002 .

[7]  Fuzhong Weng,et al.  Improved Quantitative Precipitation Forecasts by MHS Radiance Data Assimilation with a Newly Added Cloud Detection Algorithm , 2013 .

[8]  Xiaolei Zou,et al.  Development and initial assessment of a new land index for microwave humidity sounder cloud detection , 2016, Journal of Meteorological Research.

[9]  Douglas L. Jones,et al.  Real-valued fast Fourier transform algorithms , 1987, IEEE Trans. Acoust. Speech Signal Process..

[10]  X. Zou,et al.  Effects of diurnal adjustment on biases and trends derived from inter-sensor calibrated AMSU-A data , 2018, Frontiers of Earth Science.

[11]  Patrick Minnis,et al.  Diurnal Variability of Regional Cloud and Clear-Sky Radiative Parameters Derived from GOES Data. Part I: Analysis Method , 1984 .

[12]  Xiaolei Zou,et al.  Diurnal Variation of Liquid Water Path Derived From Two Polar‐Orbiting FengYun‐3 MicroWave Radiation Imagers , 2018, Geophysical Research Letters.

[13]  John R. Christy,et al.  Global atmospheric temperature monitoring with satellite microwave measurements - Method and results 1979-84 , 1990 .

[14]  Runhua Yang,et al.  Expansion of the All-Sky Radiance Assimilation to ATMS at NCEP , 2019, Monthly Weather Review.

[15]  Fuzhong Weng,et al.  Impacts of assimilation of ATMS data in HWRF on track and intensity forecasts of 2012 four landfall hurricanes , 2013 .

[16]  R. Garreaud,et al.  The Diurnal Cycle in Circulation and Cloudiness over the Subtropical Southeast Pacific: A Modeling Study , 2004 .

[17]  Fuzhong Weng,et al.  Impacts from assimilation of one data stream of AMSU‐A and MHS radiances on quantitative precipitation forecasts , 2017 .

[18]  Christopher W. O'Dell,et al.  Cloud Liquid Water Path from Satellite-Based Passive Microwave Observations: A New Climatology over the Global Oceans , 2008 .

[19]  Hui Su,et al.  Application of active spaceborne remote sensing for understanding biases between passive cloud water path retrievals , 2014 .

[20]  Christopher W. O'Dell,et al.  The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP). , 2017, Journal of climate.

[21]  Patrick Minnis,et al.  First extended validation of satellite microwave liquid water path with ship‐based observations of marine low clouds , 2016 .

[22]  William Bell,et al.  An Assessment of Data from the Advanced Technology Microwave Sounder at the Met Office , 2015 .

[23]  Xiaolei Zou,et al.  Impacts of AMSU-A inter-sensor calibration and diurnal correction on satellite-derived linear and nonlinear decadal climate trends of atmospheric temperature , 2019, Climate Dynamics.