NASA SatCORPS Himawari Product Data

Authors:

William L. Smith (william.l.smith@nasa.gov)
Patrick Minnis

Data Set Overview:

This dataset contains cloud retrieval data from the Himawari satellite during SOCRATES. These data were produced 
by the NASA Langley’s Satellite ClOud and Radiation Property retrieval System (SatCORPS)  group.  The retrievals 
include broadband shortwave albedo, broadband longwave flux, cloud infrared emittance, cloud phase, cloud optical 
depth, effective particle radius or diameter, liquid or ice water path, cloud effective temperature, cloud top 
and bottom pressure, cloud effective pressure, cloud top and bottom height, cloud effective height, skin 
temperature, clear sky reflectance, infrared clear sky temperature, visible and near infrared reflectance, solar 
infrared temperature, infrared channel temperature, infrared mid-level water vapor, and the split-window channel 
temperature.  The data are in NetCDF format.

Data were collected around the SOCRATES (Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study) 
field phase from 28 December 2017 to 1 March 2018. The data cover the region from 0.5-71.5S and 170.5W-80.5E.

Data Collection and Processing

The methodology is described in the References included at the end of this document.

The Himawari-8 satellite underwent a short maintenance period during SOCRATES and during this period this data set was
developed using Himawari-9 data.  The Himawari-9 period is from 0300 UTC on 13 Febraury through 0700 UTC on 14 February 2018.

Data Format:

These data are in NetCDF (Network Common Data Form).  NetCDF was developed by Unidata.  At the time of this writing
NetCDF was documented here: https://www.unidata.ucar.edu/software/netcdf/docs/

Version Notes:

This is Version 2.2 of this dataset which was released 6 October 2020.  The dataset is at 2-km (nadir) resolution and 
available every 10-min during NSF/NCAR GV HIAPER aircraft flight times and half-hourly at other times.

The cloud properties in this version are identical to the previous version (2.0,2.1), however the radiative fluxes 
have been updated. This version uses updated methods for calculation of the TOA Broadband SW albedoes and LW fluxes.  
The LW Narrowband-to-Broadband (NB-BB) correlation is given by a method similar to the radiance-based approach in 
Doelling, D.R., et al 2016 (with modifications). LW fluxes were then normalized via application of a regional, 
scene-type based 5x5 degree monthly normalization to Edition 4 CERES SSF1deg Aqua LW fluxes, via a linear correction. 
The SW NB-BB correlation is given by a method described in Minnis, P., et al 2016. Derived BB SW albedoes were then 
normalized via a regional, scene-type based 5x5 degree monthly normalization to Edition 4 CERES SSF1deg Aqua SW 
fluxes. For the normalization, albedoes were converted to SW fluxes, and had a linear correction applied where 
SZA < 86 deg; final/corrected albedoes are constrained to 5% (minimum) to 95% (maximum)). “

Version 2.0 of this data set was released 10 October 2018. This version uses better inputs including NWP profile data, 
aerosol optical depths, etc that were unavailable during the real-time analysis. Additionally, the cloud mask and 
phase determination algorithms improved  leading to better cloud classification  and cloud micro-physical properties.
The dataset is at 2-km (nadir) resolution and available every 10-min during NSF/NCAR GV HIAPER aircraft flight times and 
half-hourly at other times.

References

Minnis, P., L. Nguyen, R. Palikonda, P. W. Heck, D. A. Spangenberg, D. R. Doelling, J. K. Ayers, W. L. Smith, Jr., 
M. M. Khaiyer, Q. Z. Trepte, L. A. Avey, F.-L. Chang, C. R. Yost, T. L. Chee, and S. Sun-Mack, 2008: Near-real time 
cloud retrievals from operational and research meteorological satellites. Proc. SPIE Remote Sens. Clouds Atmos. XIII, 
Cardiff, Wales, UK, 15-18 September, 7107-2, 8 pp., ISBN: 9780819473387.

Derivation Techniques and Algorithms

Minnis, P., Q. Z. Trepte, S. Sun-Mack, Y. Chen, D. R. Doelling, D. F. Young, D. A. Spangenberg, W. F. Miller, B. A. 
Wielicki, R. R. Brown, S. C. Gibson, and E. B. Geier, 2008: Cloud detection in non-polar regions for CERES using TRMM 
VIRS and Terra and Aqua MODIS data. IEEE Trans. Geosci. Remote Sens., 46, 3857-3884.

Minnis, P., S. Sun-Mack, D. F. Young, P. W. Heck, D. P. Garber, Y. Chen, D. A. Spangenberg, R. F. Arduini, Q. Z. Trepte, 
W. L. Smith, Jr., J. K. Ayers, S. C. Gibson, W. F. Miller, V. Chakrapani, Y. Takano, K.-N. Liou, Y. Xie, and P. Yang, 
2011: CERES Edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data, Part I: Algorithms. IEEE 
Trans. Geosci. Remote Sens., 49, 11, 4374-4400, doi:10.1109/TGRS.2011.2144601.

Minnis, P., G. Hong, S. Sun-Mack, W. L. Smith, Jr., Y. Chen, and S. Miller, 2016: Estimating nocturnal opaque ice cloud 
optical depth from MODIS multispectral infrared radiances using a neural network method. J. Geophys. Res., 121, 
doi:10.1002/2015JD024456.

Minnis, P., S. Sun-Mack, C. R. Yost, Y. Chen, W. L. Smith, Jr., F.-L. Chang, P. W. Heck, R. F. Arduini, Q. Z. Trepte, 
K. Ayers, K. Bedka, S. Bedka, R. R. Brown, E. Heckert, G. Hong, Z. Jin, R. Palikonda, R. Smith, B. Scarino, D. A. 
Spangenberg, P. Yang, Y. Xie, and Y. Yi, 2020: CERES MODIS cloud product retrievals for Edition 4, Part I: Algorithm 
changes to CERES MODIS. IEEE Trans. Geosci. Remote Sens., 58, doi:10.1109/TGRS.2020.3008866.

Trepte, Q. Z., P. Minnis, S. Sun-Mack, C. R. Yost, Y. Chen, Z. Jin, F.-L. Chang, W. L. Smith, Jr., K. M. Bedka, and 
T. L. Chee, 2019: Global cloud detection for CERES Edition 4 using Terra and Aqua MODIS data. IEEE Trans. Geosci. 
Remote Sens., 57, 9410-9449, doi:10.1109/TGRS.2019.2926620.