Citation: Hirt, Mirjam and Craig, George C. and Schäfer, Sophia and Savre, Julien and Heinze, Rieke: Cold pool driven convective initiation: using causal graph analysis to determine what convection permitting models are missing. 2020. Open Data LMU. 10.5282/ubm/data.178
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Other (Tar-archive containing netcdf data for Cold pool characteristics)
Cold_pool_data.tar 111MB |
DOI: 10.5282/ubm/data.178
This dataset is available unter the terms of the following Creative Commons LicenseCCO
Abstract
The data in this folder comprises all data necessary to produce the Figures presented in our paper (Hirt et al, 2020, in review, Quarterly Journal of the Royal Meteorological Society). Corresponding Jupyter notebooks, which were used to analyse and plot the data, are available at https://github.com/HirtM/cold_pool_driven_convection_initiation. The datasets are netcdf files and should contain all relevant metadata. cp_aggregates2*: These datasets contain different variables of cold pool objects. For each variable, several different statistics are available, e.g. the average/median/some percentile over the area of each cold pool object. Note that the data does not contain tracked cold pools. Any sequence of cold pool indices is hence meaningless. Each cold pool index does not only have information about its cold pool, but also its edges (see mask dimension). P_ci_* These datasets contain information on convection initiation within cold pool areas, cold pool edge areas or no cold pool areas. No single cold pool objects are identified here. prec_* As P_ci_*, but for precipitation. synoptic_conditions_variables.nc This dataset contains domain averaged (total domain, not cold pool objects) timeseries of selected variables. The selected variables were chosen in order to describe the synoptic and diurnal conditions of the days of interest. This dataset is used for the causal regression analysis. All the data here is derived from the ICON-LEM simulation conducted within HDCP2: http://hdcp2.eu/index.php?id=5013 Heinze, R., Dipankar, A., Carbajal Henken, C., Moseley, C., Sourdeval, O., Trömel, S., Xie, X., Adamidis, P., Ament, F., Baars, H., Barthlott, C., Behrendt, A., Blahak, U., Bley, S., Brdar, S., Brueck, M., Crewell, S., Deneke, H., Di Girolamo, P., Evaristo, R., Fischer, J., Frank, C., Friederichs, P., Göcke, T., Gorges, K., Hande, L., Hanke, M., Hansen, A., Hege, H.-C., Hoose, C., Jahns, T., Kalthoff, N., Klocke, D., Kneifel, S., Knippertz, P., Kuhn, A., van Laar, T., Macke, A., Maurer, V., Mayer, B., Meyer, C. I., Muppa, S. K., Neggers, R. A. J., Orlandi, E., Pantillon, F., Pospichal, B., Röber, N., Scheck, L., Seifert, A., Seifert, P., Senf, F., Siligam, P., Simmer, C., Steinke, S., Stevens, B., Wapler, K., Weniger, M., Wulfmeyer, V., Zängl, G., Zhang, D. and Quaas, J. (2016): Large-eddy simulations over Germany using ICON: A comprehensive evaluation. Q.J.R. Meteorol. Soc., doi:10.1002/qj.2947 M.Hirt, 9 Jan 2020
Uncontrolled Keywords
Atmospheric Science, Numerical weather prediction, Convective inititation, Cold pools, Linear causal analysis
Source
http://hdcp2.eu/index.php?id=5013
Item Type: | Data |
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Contact Person: | Hirt, Mirjam |
E-Mail of Contact: | m.hirt at physik.uni-muenchen.de |
URL of Contact: | https://www.wavestoweather.de/people/phd_students/hirt_mirjam/index.html |
Subjects: | Physics |
Dewey Decimal Classification: | 500 Natural sciences and mathematics 500 Natural sciences and mathematics > 530 Physics |
ID Code: | 178 |
Deposited By: | Mirjam Hirt |
Deposited On: | 14. Feb 2020 06:44 |
Last Modified: | 21. Mar 2023 12:40 |
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