This GOMBERT_readme20240618.txt file was generated on 20240618 by KAYLEE ALEXANDER ------------------- GENERAL INFORMATION ------------------- 1. Title of Dataset Pacific Low Cloud Occurrence Variability Associated with ENSO as Observed by CloudSat and CALIPSO 2. Author Information Principal Investigator Contact Information Name: Peter M. Gombert Institution: University of Utah, Department of Atmospheric Sciences Address: 135 South 1460 East, RM 819, William Browning Building, Salt Lake City, Utah 84112, USA Email: u1113223@utah.edu ORCID: Associate or Co-investigator Contact Information Name: Gerald G. (Jay) Mace Institution: University of Utah, Department of Atmospheric Sciences Address: 135 South 1460 East, RM 819, William Browning Building, Salt Lake City, Utah 84112, USA Email: jay.mace@utah.edu ORCID: 0000-0001-73338-7726 Associate or Co-investigator Contact Information Name: Courtenay Strong Institution: University of Utah, Department of Atmospheric Sciences Address: 135 South 1460 East, RM 819, William Browning Building, Salt Lake City, Utah 84112, USA Email: court.strong@utah.edu ORCID: 0000-0001-5866-4377 3. Date of data collection (single date, range, approximate date) 2023-06-01 to 2024-06-01 (collection) 2007-2018 (source) 4. Geographic location of data collection (where was data collected?): Salt Lake City, Salt Lake County, Utah, USA 5. Information about funding sources that supported the collection of the data: NASA Jet Propulsion Laboratory award number 1536019 NASA Science Mission Directorate Award 80NSSC19K1251 -------------------------- SHARING/ACCESS INFORMATION -------------------------- 1. Licenses/restrictions placed on the data: CC BY NC - Allows others to use and share your data non-commercially and with attribution. 2. Links to publications that cite or use the data: NA 3. Links to other publicly accessible locations of the data: NA 4. Links/relationships to ancillary data sets: NA 5. Was data derived from another source? Yes Mace, G. G., and Q. Zhang (2014), The CloudSat radar-lidar geometrical profile product (RL-GeoProf): Updates, improvements, and selected results, J. Geophys. Res. Atmos., 119, doi:10.1002/2013JD021374. Available at: https://www.cloudsat.cira.colostate.edu/data-products/2b-geoprof-lidar. Global Modeling and Assimilation Office (GMAO) (2015), MERRA-2 tavg3_3d_asm_Nv: 3d,3-Hourly,Time-Averaged,Model-Level,Assimilation,Assimilated Meteorological Fields V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed:20230601, 10.5067/SUOQESM06LPK. Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., Takahashi, K., 2015. The JRA-55 Reanalysis: general specifications and basic characteristics. J. Meteor. Soc. Jpn. 93, 5-48. DOI: 10.2151/jmsj.2015-001 Available at: https://psl.noaa.gov/enso/mei/;NASA/LARC/SD/ASDC, 2023. CERES Energy Balanced and Filled (EBAF) TOA and Surface Monthly means data in netCDF Edition 4.2. Available at: https://doi.org/10.5067/TERRA-AQUA-NOAA20/CERES/EBAF_L3B004.2. 6. Recommended citation for the data: Peter M. Gombert, Gerald G. Mace, and Courtenay Strong. 2024. "Pacific Low Cloud Occurrence Variability Associated with ENSO as Observed by CloudSat and CALIPSO." The Hive: University of Utah Research Data Repository. https://doi.org/10.7278/S5d-64j3-1n2n. --------------------- DATA & FILE OVERVIEW --------------------- 1. File List A. Filename: figure_builder.py Short description: Python code to create the figures in the paper. B. Filename: go_stats.py Short description: Python code to create the statistical analysis. C. Filename: ceres_mm_2000_2022.nc4 Short description: CERES top of atmosphere radiances. D. Filename: djf_mast.nc4 Short description: Merra2 Reanalysis monthly mean values for December, January, and February. E. Filename: jja_mast.nc4 Short description: Merra2 Reanalysis monthly mean values for June, July, and August. 2. Relationship between files: Files A and B are python code files used to create the results for the paper from the datasets listed in section 5 of "SHARING/ACCESS INFORMATION". Files C, D, and E are the climatology files created by Files A, B. 3. Additional related data collected that was not included in the current data package: NA 4. Are there multiple versions of the dataset? No -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Description of methods used for collection/generation of data: We derive cloud occurrence with observations from the recently concluded CloudSat and CALIPSO (hereafter CC) missions. These data sets are highly complementary in that CALIPSO observed optically thin clouds, the tops of optically thick layers, and clouds below the blind zone of CloudSat (<1 km above the surface). As a 94 GHz radar, the CloudSat cloud profiling radar was able to documenthydrometeor layers that fully attenuated the CALIPSO lidar. During much of the CC mission,(2006-2023), the spacecraft were flown in a tight formation that allowed the merging of their data streams as documented in the Radar-Lidar Geometrical Profile (RL Geoprof) product. The RL Geoprof data are recorded in a gridded data set described in Mace et al. (2009) and Mace (2010). Monthly cloud occurrence counts were initially recorded in a 1° latitude x 1° longitude (1°x1°) grid in terms of the base and top heights of hydrometeor layers and their layer thicknesses. We aggregated to a 2°x2° grid to lessen sampling noise caused by the coarse nature of the CC data set. We defined a low cloud in the CC data as a hydrometeor layer with a cloud base < 2km above the surface and a cloud vertical thickness <1500 m. 2. Methods for processing the data: We used CC data between 2007 and 2018 in this study excluding the period between April 2011 and May 2012 when CloudSat was inoperable after a severe battery anomaly. Following May 2012, only daytime data were used while prior to April 2011 both day and night data were used. After 2018, the two spacecraft were only rarely aligned as the aging spacecraft were experiencing challenges with formation flying. Thus, we use 11 full years of CC data. The large-scale meteorology was obtained from the Modern-Era Retrospective analysis for Research and Applications 2 (MERRA2) product. To establish robust statistics between ENSO and the cloud controlling factors, we used MERRA2 data from 1980 though 2016 in our analysis. We used the Multivariate El Niño/Southern Oscillation (ENSO) index known as MEI.v2. 3. Instrument- or software-specific information needed to interpret the data: Use netcdf4 to convert and read the files. Install the following modules for python: os, dumpy, scipy, pandas, matplotlib, datetime, pyhdf, netCDF4, carroty, warnings. Run the python program figure_builder.py 4. Standards and calibration information, if appropriate: NA 5. Environmental/experimental conditions: NA; these are datasets collected by NASA satellites. 6. Describe any quality-assurance procedures performed on the data: NA; the datasets are collected and quality-assured by NASA 7. People involved with sample collection, processing, analysis and/or submission: NA ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: figure_builder.py ----------------------------------------- 1. Number of variables: 7 2. Number of cases/rows: 365 3. Python code to create the figures in the paper. 4. Missing data codes: 9999 Missing Data 5. Specialized formats of other abbreviations used: NA ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: go_stats.py ----------------------------------------- 1. Number of variables: 10 2. Number of cases/rows: 365 3. Python code to create the statistical analysis. 4. Missing data codes: 9999 Missing Data 5. Specialized formats of other abbreviations used: NA ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: ceres_mm_2000_2022.nc4 ----------------------------------------- 1. Number of variables: 4 2. Number of cases/rows: 12 3. Variable List A. Name: toa_sw_all_mon Description: Top of The Atmosphere Shortwave Flux, All-Sky conditions, Monthly Means W m-2 B. Name: toa_lw_all_mon Description: Top of The Atmosphere Longwave Flux, All-Sky conditions, Monthly Means W m-2 C. Name: toa_net_all_mon Description: Top of The Atmosphere Net Flux, All-Sky conditions, Monthly Means W m-2 C. Name: toa_net_clr_c_mon Description: Top of The Atmosphere Net Flux, Clear-Sky (for cloud-free areas of region) conditions, Monthly Means W m-2 4. Missing data codes: -999 Missing Data 5. Specialized formats of other abbreviations used: NA ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: djf_mast.nc4 ----------------------------------------- 1. Number of variables: 3 2. Number of cases/rows: 3 3. Variable List A. Name: lat Description: Latitude Degrees B. Name: lon Description: Longitude Degrees C. Name: sst Description: Sea Surface Temperature Kelvin 4. Missing data codes: -9999 Missing 5. Specialized formats of other abbreviations used: NA ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: jja_mast.nc4 ----------------------------------------- 1. Number of variables: 3 2. Number of cases/rows: 3 3. Variable List A. Name: lat Description: Latitude Degrees B. Name: lon Description: Longitude Degrees C. Name: sst Description: Sea Surface Temperature Kelvin 4. Missing data codes: -9999 Missing 5. Specialized formats of other abbreviations used: NA