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wget and curl are the two standard tools that are available on most Linux and macOS computers. wget contains a feature for downloading a list of files:
wget -x -nH -i 'https://opendata.physik.lmu.de/gmjQaVOkkmbQCqC/?list'curl is missing a feature like that, but the same functionality can be created by combining curl and xargs:
curl 'https://opendata.physik.lmu.de/gmjQaVOkkmbQCqC/?list' | xargs -I URL -n1 bash -c 'curl --create-dirs -o ${1:31} ${1}' -- URLConvective permitting WRF simulations with 5 microphysics schemes over 30 days
Author: Gregor Köcher (LMU - MIM)
Contact: gregor.koecher@physik.uni-muenchen.de
This dataset contains Weather Research and Forecasting (WRF) model output with five different cloud microphysics schemes for 30 convective precipitation days in 2019 and 2020. A key feature is the large number of simulation days at high horizontal grid spacing (400 m) which allows for a statistical comparison of the influence of microphysics schemes on the prediction of convection. The simulation setups include three domains. An outer europe domain with 10 km horizontal grid spacing and two two-way nested domains: Germany (2 km) and Munich (400 m). Each simulation starts at 18 UTC on the previous day and runs 30 hours. Initial and lateral boundary conditions were provided by GFS reanalysis data at 0.25° grid spacing (https://rda.ucar.edu/datasets/d084001/). Five different microphysics schemes were used and are listed in the table below.
| Name | WRF ID | Publication |
|---|---|---|
| Thompson 2-mom | 8 | Thompson et al. (2008) |
| Morrison 2-mom | 10 | Morrison et al. (2009) |
| Thompson aerosol aware | 28 | Thompson and Eidhammer (2014) |
| Fast spectral bin (FSBM) | 30 | Shpund et al. (2019) |
| Predicted Particle Properties (P3) | 50 | Morrison and Milbrandt (2015) |
The dataset is comprised of the following directories/files:
- wrfout: Complete WRF output hourly available
- clouds: Selected variables available in 5-min resolution
- wrfmp: Spectral bin data of the FSBM scheme
- namelist.input: Example namelist used to run the WRF simulations
In each of the folders, the data is organized in directories according to date (year/month/day) and the microphysics scheme (WRF_ID). See table above for the WRF-IDs. The domain is part of the filename: d01 for the outer europe domain, d02 for the German nest, and d03 for the inner Munich nest.
Complete WRF output
The complete WRF output was saved hourly and is available in the wrfout directory. This output includes all standard WRF variables. The data is available for alle three domains.
Cloud radar variables
Variables that are required for a radar forward operator were saved at 5-min resolution in the clouds directory. This includes mixing ratios and number concentrations of hydrometeor classes, as well as temperature, air density and wind information. This data is available only for the inner nest (Munich domain).
Spectral bin data
One of the employed microphysics schemes is a spectral bin scheme (MP30: FSBM). This scheme explicitly resolves the particle size distribution with a number of size bins. These bins are available in the wrfmp directory. This data is only available for the inner nest (Munich domain.)
namelist.input
All settings used for the simulations can be found in the namelist.input file. This namelist is an example for the simulations with the Morrison scheme (WRF-ID: 10). The other simulations used exactly the same settings, except for the choice of the microphysics scheme.
Example plot
Example to plot snow mixing ratio of the Morrison 2-mom scheme (WRF-ID: 10) on the inner Munich domain at 13 UTC on 1st of July 2019:
import xarray as xr
import matplotlib.pyplot as plt
ds = xr.open_dataset('wrfout/icepolcka/2019/07/01/MP10/wrfout_d03_2019-07-01_130000.nc')
ds['QSNOW'].isel(bottom_top=22).plot()
plt.show()
References
- Morrison, H., Thompson, G., and Tatarskii, V.: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one-and two-moment schemes, Mon. Weather Rev., 137, 991–1007, 2009.
- Morrison, H. and Milbrandt, J. A.: Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part I: Scheme description and idealized tests, J. Atmos. Sci., 72, 287–311, 2015.
- Shpund, J., Khain, A., Lynn, B., Fan, J., Han, B., Ryzhkov, A., Snyder, J., Dudhia, J., and Gill, D.: Simulating a Mesoscale Convective System Using WRF With a New Spectral Bin Microphysics: 1: Hail vs Graupel, J. Geophys. Res.-Atmos., 124, 14072–14101, 2019.
- Thompson, G. and Eidhammer, T.: A study of aerosol impacts on clouds and precipitation development in a large winter cyclone, J. Atmos. Sci., 71, 3636–3658, 2014.
- Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization, Mon. Weather Rev., 136, 5095–5115, 2008.
