======================================================== README ======================================================== Title: Data from: Antecedent Soil Moisture Conditions Determine Land-Atmosphere Coupling Drought Risk in the Northeastern United States [dataset]. Institution: Cornell University, Ithaca, NY, United States Principal Investigator: Marc J. Alessi (marc.alessi@colostate.edu) Associate Investigators: Dimitris A. Herrera, Colin P. Evans, Arthur T. DeGaetano, Toby R. Ault (toby.ault@cornell.edu) Keywords: drought, land-atmosphere coupling, WRF Language: English Description: This Readme file summarizes the netCDF and NumPy files used in Alessi et al. (2021): Antecedent Soil Moisture Conditions Determine Land-Atmosphere Coupling Drought Risk in the Northeastern United States. NetCDF (Network Common Data Form) is a common file format for sharing data in the Geosciences, and NumPy files are simply saved NumPy arrays. *NETCDF4 FILES* Each NetCDF file contains selected output data from the Weather Research and Forecasting Model (WRF) for seven summers: 2010-05-01 to 2010-08-31, 2011-05-01 to 2011-08-31, 2012-05-01 to 2012-08-31, 2013-05-01 to 2013-08-31, 2014-05-01 to 2014-08-31, 2015-05-01 to 2015-08-31, 2016-05-01 to 2016-08-31. The data is high-resolution (3 km grid) and covers a domain centered over western New York State with a bottom-left corner of 40.0265 N, 81.0698 W and a top-right corner of 45.1853 N and 74.0540 W. Selected output variables from these WRF simulations include latitude, longitude, precipitation, soil moisture, absorbed solar radiation at the surface, emitted longwave radiation from the surface, and latent and sensible heat fluxes from the surface. In total, there are 56 netCDF files: one for each year (7 years), each soil moisture scenario (4 scenarios), and each WRF parameterization combination (2 combinations). A soil moisture scenario represents a WRF simulation where all soil moisture values in the domain (except over water) were changed to a given scenario value. Four soil moisture scenarios are used in total: 0.05, 0.20, 0.35, and 0.50, each number representing the prescribed volumetric soil moisture for the domain. Two WRF physics parameterizations were used to create a WRF ensemble. The first combination uses the Mellor-Yamada Janjic Scheme for the planetary boundary layer parameterization, while the second combination uses the Yonsei University Scheme. NetCDF file variables: [0] FIRA (Time, south_north, west_east) Total net longwave radiation from surface Units: W/m^2 [1] FSA (Time, south_north, west_east) Total absorbed solar radiation Units: W/m^2 [2] HFX (Time, south_north, west_east) Upward heat flux at the surface Units: W/m^2 [3] LH (Time, south_north, east_west) Latent heat flux at the surface Units: W/m^2 [4] RAINC (Time, south_north, west_east) Accumulated total cumulus precipitation Units: millimeters [5] RAINNC (Time, south_north, west_east) Accumulated total grid scale precipitation Units: millimeters [6] SMOIS (Time, south_north, west_east) Soil Moisture Units: m^3/m^3 [7] XLAT (Time, south_north, west_east) Latitude, south is negative Units: degrees north [8] XLONG (Time, south_north, west_east) Longitude, west is negative Units: degrees south Time: 977 3-hourly time steps from May 1 00:00:00 GMT. south_north: 261 grid lines (latitude) from 40.0265 N to 45.1853 N. west_east: 261 grid lines (longitude) from 81.0698 W to 74.0540 W. Naming Convention: _.tar Each tar file contains 4 WRF output files, one for each soil moisture scenario given a certain year and ensemble member combination. One ensemble has combination 116114 and the other has combination 236114 representing the parameterization selection for: planetary boundary layer, cumulus (not important for convective resolving resolution), microphysics, outgoing longwave radiation, incoming solar radiation, and land surface scheme. *NUMPY FILES* There are three additional files that contain vital information for the project: lifting condensation level heights, atmospheric boundary layer heights, and percent atmospheric water content from evapotranspiration. These variables were calculated using 3D WRF output not provided. [0] LCL_heights.npy Lifting condensation level heights Dimensions: 7 years x 2 physics combinations x 4 soil moisture scenarios x 8 time of day. Units: meters Notes: Time of day is 1am, 4am, 7am, 10am, 1pm, 4pm, 7pm, and 10pm local time. Method: LCL height was calculated using wrf-python cape_2d (Ladwig, 2017). Input variables are pressure (hPa), temperature (K), water vapor mixing ratio, geopotential height (m), terrain, and surface pressure (hPa). [1] PBL_heights.npy Atmospheric boundary layer heights Dimensions: 7 years x 2 physics combinations x 4 soil moisture scenarios x 8 time of day. Units: meters Notes: Time of day is 1am, 4am, 7am, 10am, 1pm, 4pm, 7pm, and 10pm local time. Method: ABL height was calculated using wrf-python cape_2d (Ladwig, 2017). Input variables are pressure (hPa), temperature (K), water vapor mixing ratio, geopotential height (m), terrain, and surface pressure (hPa). [2] wat_vals.npy Fraction atmospheric water content from evapotranspiration. Dimensions: 7 years x 2 physics combinations x 4 soil moisture scenarios. Units: fraction (multiply by 100 to get percent) Method: Total precipitable water was calculated above a grid box near Ithaca, NY and averaged from 06:00am one day to 06:00am local time the following day. The percent of this water that originated from latent heat flux, or evapotranspiration, was found by dividing latent heat flux by the latent heat of vaporization of water, which gives the evaporation rate. The evaporation rate was summed from 0600am one day to 0600am the next day, and then divided by the total precipitable water. References: Ladwig, W. (2017). wrf-python (1.3.0) [software]. https://doi.org/10.5065/D6W094P1 Related Publication Alessi, M. J., Herrera, D. A., Evans, C. P., DeGaetano, A. T., & Ault, T. R. (2022). Soil moisture conditions determine land-atmosphere coupling and drought risk in the northeastern United States. Journal of Geophysical Research: Atmospheres, 127, e2021JD034740. https://doi.org/10.1029/2021JD034740 Licensing: Following the CC-BY License, these datasets can be freely used by the scientific community, and should be cited as follows: Alessi et al. (2021). Data from: Antecedent Soil Moisture Conditions Determine Land-Atmosphere Coupling Drought Risk in the Northeastern United States [dataset]. Cornell University Library eCommons Repository. ***********DOI URL************** Acknowledgements This study was partially supported by the National Science Foundation (NSF) CAREER Grant 1751535. Dr. Toby Ault was partially supported by the NSF Collaborative Proposal: MSB-FRA Grant 1702551. The European Centre for Medium-Range Weather Forecasts ERA-5 reanalysis data were retrieved through the Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory (https://doi.org/10.5065/BH6N-5N20). We would like to acknowledge high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. For further questions and clarifications, please contact Marc J. Alessi (marc.alessi@colostate.edu) or Toby Ault (toby.ault@cornell.edu).