# Illustration: Precipitation Forecasts in Innsbruck

The second example models 3 day-accumulated precipitation sums using a Logistic distribution censored at zero accounting for non-negative precipitation sums. The use case builds heavily on the vignette Heteroscedastic Censored and Truncated Regression with crch given in crch by Messner, Mayr, and Zeileis (2016).

## 1 Precipitation forecasts

This use-case discusses a weather forecast example application of censored regression models. The example is taken from the vignette Heteroscedastic Censored and Truncated Regression with crch provided for the package crch by Messner, Mayr, and Zeileis (2016).

Weather forecasts are usually based on numerical weather prediction (NWP) models, which take the current state of the atmosphere and calculate future weather by numerically simulating the main atmospheric processes. However, due to uncertain initial conditions and unknown or unresolved processes, these numerical predictions are always subject to errors. To estimate these errors, many weather centers produce what are called ensemble forecasts: multiple NWP runs that use different initial conditions and model formulations. Unfortunately, these ensemble forecasts cannot account for all sources of error, so they are often still biased and uncalibrated. Therefore, they are often calibrated and corrected for systematic errors through statistical post-processing.

One popular post-processing method is heteroscedastic linear regression where the ensemble mean is used as regressor for the location and the ensemble standard deviation or variance is used as regressor for the scale (Gneiting et al. 2005). The following example applies heteroscedastic censored regression with a logistic distribution assumption to precipitation data in Innsbruck (Austria).

The `RainIbk`

data set contains observed 3 day-accumulated precipitation amounts (`rain`

) and the corresponding 11 member ensemble forecasts of total accumulated precipitation amount between 5 and 8 days in advance (`rainfc.1`

, `rainfc.2`

, … `rainfc.11`

). In previous studies it has been shown that it is of advantage to model the square root of precipitation rather than precipitation itself. Thus all precipitation amounts are square rooted before ensemble mean and standard deviation are derived. Furthermore, events with no variation in the ensemble are omitted:

## 2 From linear regression to distributional regression models

For comparison we fit a homoscedastic linear regression model using the least squares approach for `rain`

with `ensmean`

as regressor. As a more appropriate model for `rain`

accounting for non-negative precipitation sums, we fit a hetereoscedastic censored model assuming an underlying logistic response distribution with `ensmean`

as regressor for the location and `log(enssd)`

as regressor for the scale:

In the scatterplot of `rain`

against `ensmean`

it can be seen, that precipitation is clearly non-negative with many zero observations. Thus the censored regression model `m_hclog`

is more suitable than the linear regression model to estimate the underlying relationship:

## 3 Model evaluation: Marginal calibration

The significant underfitting of zero observations is also very apparent in the *rootogramm* of the linear model, as it does not correctly account for the point mass at zero. Additionally, a weak wavelike pattern indicates a slight overfitting of precipitation sums between zero and 5mm and an underfitting of precipitation sums above. In contrast, the censored logistic regression `m_hclog`

provides a pretty good marginal fit, where the expected squared frequencies closely match the squared observed frequencies:

## 4 Model evaluation: Probabilistic calibration

The *PIT histogram* of the linear model `m_lm`

is skewed, indicating a misfit in terms of the probabilistic calibration, whereas the *PIT histogram* of the linear model is rather uniformly distributed.

For a better comparison of the two model fits, alternatively a line-style *PIT histogram* can also be shown:

`ggplot2::autoplot(c(p1, p2), colour = c(3, 4), style = "line", single_graph = TRUE, confint_col = 1, confint = "line", ref = FALSE)`

To further analyse the misfit of the linear model in terms of probabilistic calibration, we transform the PIT residuals to the normal scale and increase the number of breaks in the *PIT histogram*. Transforming to the normal scale spreads the values at the tails of the distribution further apart, and increasing the breaks prevents potential masking of small scale patterns.

We can see that the distribution of PIT residuals is right skewed with more values between -2 and 0, but less values between 0 and 2 compared to the standard normal distribution:

`pithist(m_lm, breaks = 50, trafo = qnorm, xlim = c(-3, 3))`

This is also reflected in the pattern of the *Q-Q plot* of the linear model: Due to the high frequency in values between -2 and 0, the observed quantile residuals increase slower in this region relative to the standard normal quantiles. However, the low frequency of quantile residuals above zero leads to an higher increase in quantile residuals compared to the standard normal quantiles.

In summary, the *Q-Q plot* of the linear model has a positive curvature, bending up at both ends of the distributional tails. The censored logistic regression `m_hclog`

follows more or less the reference line indicating normally distributed quantile residuals:

```
q1 <- qqrplot(m_lm, plot = FALSE)
q2 <- qqrplot(m_hclog, plot = FALSE)
ggplot2::autoplot(c(q1, q2), colour = c(3, 4), alpha = 0.8, single_graph = TRUE, simint = FALSE, confint ="line")
```

Consistent with the pattern of right-skewed PIT residuals corresponding to a “too left-skewed” fitted distribution, a U-shaped pattern is evident in the *worm plot* of the linear model; the censored logistic is more or less flat around zero.

## 5 References

*Monthly Weather Review*133 (5): 1098–118. https://doi.org/http://dx.doi.org/10.1175/MWR2904.1.

*The R Journal*8 (1): 173–81. https://doi.org/10.32614/RJ-2016-012.