Model output data
At present, MPTRAC offers seven options for model, referred to as "particle output", "grid output", "CSI output", "ensemble output", "profile output", "sample output", and "station output". By default, all output functions of MPTRAC create data files in an ASCII table format. This type of output is usually simple to understand and usable with many tools for data analysis and visualization. However, in the case of large-scale simulations, it is desirable to use more efficient file formats. Therefore, an option was implemented to perform file-I/O for binary or netCDF data for some types of output. Among these types are output files that facilitate interoperability between MPTRAC and the CLaMS model. Another option of output is to pipe the data directly from the model to a visualization tool such as the graphing utility gnuplot.
Particle data
The most comprehensive output of MPTRAC is the atmospheric output. Atmospheric output files can be generated at user-defined time intervals, which need to be integer multiples of the model time step. The atmospheric output files are the most comprehensive type of output because they contain the time, location, and the values of all user-defined quantities of each individual air parcel.
The atmospheric output file is set with the parameters ATM_TYPE and ATM_TYPE_OUT, where ATM_TYPE defines the file format for reading and ATM_TYPE_OUT defines the writing file format. However, if ATM_TYPE_OUT is not explicitly set in the control file, ATM_TYPE sets the file format for reading and writing.
| ATM_TYPE | output format |
|---|---|
| 0 | ASCII (default) |
| 1 | binary |
| 2 | netcdf |
| 3 | netcdf (CLaMS: trajectory and position file) |
| 4 | netcdf (CLaMS: position file) |
Gridded data
As the particle output can easily become too large for further analyses, in particular if many air parcels are involved, the output of gridded data was implemented. This output will be generated by integrating over the mass of all parcels in regular longitude × latitude × log-pressure height grid boxes. From the total mass per grid box and the air density, the column density and the volume mixing ratio of the tracer are calculated. Alternatively, if the volume mixing ratio per air parcel is specified, the mean volume mixing ratio per grid box is reported. In the vertical, it is possible to select only a single layer for the grid output, in order to obtain total column data. Similarly, by selecting only one grid box in longitude, it is possible to calculate zonal means.
CSI data
Another type of output that we used in several studies (Hoffmann et al., 2016; Heng et al., 2016) is the critical success index (CSI) output. This output is produced by analyzing model and observational data on a regular grid. The analysis is based on a 2×2 contingency table of model predictions and observations. Here, predictions and observations are counted as yes, if the model column density or the observed variable exceed user-defined thresholds. Otherwise, they would be counted as no. Next to the CSI, the counts allow us to calculate the probability of detection (POD) and the false alarm rate (FAR), which are additional skill scores that are often considered in model verification. More recently, the CSI output was extended to also include the equitable threat score (ETS), the linear and rank-order correlation coefficients, the bias, the root mean square (RMS) difference, and the mean absolute error. A more detailed discussion of the skill scores is provided by Wilks (2011).
Ensemble data
Another option to condense comprehensive particle data is provided by means of the ensemble output. This type of output requires a user-defined specific ensemble index value to be assigned to each air parcel. Instead of the individual air parcel data, the ensemble output will contain the mean positions as well as the means and standard deviations of the quantities selected for output for each set of air parcels having the same ensemble index. The ensemble output if of interest, if tracer dispersion from multiple point sources needs to be quantified by means of a single model run, for instance.
Profile data
The profile output of MPTRAC is similar to the grid output as it creates vertical profiles from the model data on a regular longitude × latitude horizontal grid. However, the vertical profiles contain not only volume mixing ratios of the species of interest but also profiles of pressure, temperature, water vapor, and ozone as inferred from the meteorological input data. This output is compiled with the intention to be used as input for a radiative transfer model, in order to simulate satellite observations for the given model output. In combination with real satellite observations, this output can be used for model validation but also as a basis for radiance data assimilation.
Sample data
The sample output of MPTRAC was implemented most recently. It allows the user to extract model information on a list of given locations and times, by calculating the column density and volume mixing ratio of all parcels located within a user-specified horizontal search radius and vertical height range. For large numbers of sampling locations and air parcels, this type of output can become rather time-consuming. It requires an efficient implementation and parallelization because it needs to be tested at each time step of the model whether an air parcel is located within a sampling volume or not. The numerical effort scales linearly with both the number of air parcels and the number of sampling volumes. The sample output was first applied in the study of Cai et al. (2021) to sample MPTRAC data directly on TROPOspheric Monitoring Instrument (TROPOMI) satellite observations.
Station data
Finally, the station output is collecting the data of air parcels that are located within a search radius around a given location (latitude, longitude). The vertical position is not considered here; i.e., the information of all air parcels within the vertical column over the station is collected. In order to avoid double counting of air parcels over multiple time steps, the quantity STAT has been introduced that keeps track on whether an air parcel has already been accounted for in the station output or not. We used this type of output in studies estimating volcanic emissions from satellite observations using the backward-trajectory method (Hoffmann et al., 2016; Wu et al., 2017, 2018).