3d-rainfall-generator

In this project we investigate if it is possible to combine the [mulGETS][doi:10.1016/j.jhydrol.2007.06.035] rainfall generator, [Stochastic Storm Transposition][http://dx.doi.org/10.1016/j.jhydrol.2013.03.003] and general data science to create long term continuous rainfall time series at multiple site locations.

Things to do:

• Implement the mulGETS model in python.
• Compare correlation of mulGETS and observation data.
• Compare annual statistics of mulGETS model to observation data.
  • Also compare low frequency variability of the model output ([This paper][https://doi.org/10.1016/j.jhydrol.2019.05.047] suggest that this are modelled poorly by this model).
  • If this is the case, investigate if a time varying Markov chain can fix this problem.
• Autoencode data from a [RainyDay][https://github.com/danielbwright/RainyDay2] output with Principal Component Analysis (PCA).
  • The autoencoding is used to determine if certain days from the RainyDay can be substituted into the mulGETS model output.
• Autoencode data from RainyDay using:
  • Densely connected neural network.
  • Deeply connected neural network.
• Develop a "plugin" to RainyDay to allow long term simulation of rainfall in time (subdaily) and space (data dependent resolution)

The mulGETS model

The general procedure and methodology is presented in [this][doi:10.1016/j.jhydrol.2007.06.035] paper, but the general outline is:

• Fit a Markov chain to every rainfall station of interest. An example of 23 years of daily rainfall is available in this github.
• Determine the correlation matrix needed, to simulate the observed correlation of either rainfall occurrence and precipitation amount.
• Establish link between occurrence index (number of "wet" station at once, relative to the interstation correlation) and average seasonal precipitation. Use this information to construct a multi-exponential distribution for each station.

Results

In this section we will run through the output of the mulGETS model.

Spatial coherence

Figure 1 displays the observed and simulated correlation of both daily rainfall occurrences and daily rainfall amounts.

A	B
Figure 1: (A) Interstation correlation. (B) Precipitation amount correlation.

The mulGETS model is quite succesfull at simulating these two parameters. For some reason there is a small error on the right-hand figure (the precipitation amounts). Our initial suspension is that it could be caused by either: the fact that the occurence correlation matrix converges on a somewhat high error (0.446) where the mulGETS paper reports on an error in the 1e-3. The paper also mentions making adjustment to the multi-exponential distribution, which we have not done.

Overall the results presented on figure shows that the mulGETS model retains the spatial coherence of the rainfall field.

Annual and seasonal precipitation

Figure 2 displays the observed mean annual and seasonal precipitation, compared with the simulated ones from the mulGETS model.

A	B
C	D