Restricted Access
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/2rvyDFh8t2W4QNX/?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/2rvyDFh8t2W4QNX/?list' | xargs -I URL -n1 bash -c 'curl --create-dirs -o ${1:31} ${1}' -- URLA gridded 30-Day dataset of convective precipitation from radar and WRF simulations with 5 microphysics schemes
Author: Gregor Köcher (LMU - MIM)
Contact: gregor.koecher@physik.uni-muenchen.de
This dataset contains 30 days of model and radar data of convective precipitation over the Munich area gridded to the same Cartesian grid with 400 m horizontal grid spacing. A key feature of this data set is easy statistical comparison of simulated convective precipitation with polarimetric radar observations. The original model data is based on Weather Research and Forecasting (WRF) simulations with 5 different microphysics schemes. This facilitates the evaluation of the impact of the cloud microphysics schemes on the prediction of convective precipitation. Synthetic polarimetric radar signals were created by employing the radar forward operator CR-SIM (Oue et al., 2020). The observational dataset was derived from polarimetric radar data from the C-band radar in Isen, operated by the German Meteorological Service. For fair comparison, the model and radar data were gridded to the same Cartesian grid by taking beam propagation effects into account. The 5 microphysics schemes that were used 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) |
Grid matching
Radar and Model data are available on different grids. For a fair evaluation these grids were matched. First, the model data was transformed to a spherical grid corresponding to the C-band radar data. This was done by employing the radar_filter from the Stony Brook University (https://you.stonybrook.edu/radar/research/radar-simulators/). The radar_filter considers beam propagation effects. This means, for the interpolation to a grid point in spherical coordinates, the input grid points are weighted depending on the distance to the radar volume center according to the radar beam shape. The data (model and radar) are then transformed to a common Cartesian grid by applying an inverse distance interpolation.
There are two subfolders, one for the radar observations (Obs) and one for the model simulations (WRF). The data is then organized into directories according to date (year/month/day) and in case of the model data according to the microphysics scheme ID (see table above for WRF-IDs).
Notable Variables:
| Variable | Dimensions | Description | Units |
|---|---|---|---|
| Zhh_corr | (height, y, x) | Horizontal reflectivity | dBZ |
| Zdr_corr | (height, y, x) | Differential reflectivity | dB |
| RHOhv_corr | (height, y, x) | Cross-correlation coefficient | Unitless |
| Kdp_corr | (height, y, x) | Specific differential phase | °/km |
For the model, simulated (differential) attenuation was substracted from the original radar forward operator output for these variables. The uncorrected radar signals (e.g., without attenuation effects) are also available for the model and do not have the suffix '_corr'.
Example plot
Example to plot synthetic radar reflectivity from the Morrison 2-mom (WRF-ID: 10) simulation at 13 UTC on 1st of July 2019:
import xarray as xr
import matplotlib.pyplot as plt
ds = xr.open_dataset('MODEL/MP10/2019/07/01/130000.nc')
ds['Zhh_corr'].isel(height=16).plot(vmin=0, vmax=50)
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.
- Oue, M., Tatarevic, A., Kollias, P., Wang, D., Yu, K., and Vogelmann, A. M.: The Cloud-resolving model Radar SIMulator (CR-SIM) Version 3.3: description and applications of a virtual observatory, Geosci. Model Dev., 13, 1975–1998, https://doi.org/10.5194/gmd-13-1975-2020, 2020.
- 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.
