# Updating meteorological forecasts, part 1

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As Mark Twain said “the
art of prophecy is very difficult, especially about the future” (well, actually I am not sure Mark Twain was the first one to say so,
but if you’re interested by that sentence, you can look here). I have been rather surprised to see how Canadians can
be interested in weather, and weather forecasts (see e.g. here for a
first study). For instance, on that website (here),
we can have forecasts of the temperature (but also rain and wind, which
is interesting when you ride a bike) over the next week, almost on an
hourly basis (I show here only GFS forecasts (*Global Forecast System*,here), which is quite standard, see here or there, but other meteorological models are considered on the website)

So, to look at this problem, Vincent (alias @Vicnent) helped me (again, since he helped me already to get backups of betting websites during the soccer world cup, in June) to make backups of those tables…

- A descriptive study

6 hours latter (21st of October (06:00)), we have the following

On the other hand, consider forecasts made for the 25th of October (11:00) we have

while for 3 hours later (25th of October (14:00)) we have

and again for 3 hours later (25th of October (17:00)) we have So obviously, 5 days earlier, there has been a bump of optimism, or perhaps simply a typing mistake since we jump from 4 before and after to 14 during 6 hours

^{*}. If we can expect to have different predictions for different days (even within the day since it is usually cooler during the night), we can try to see if bumps, or forecast errors can be understood.

Here, it is difficult to observe the scatterplot of temperature,

That graph was based on the following points, with here the scatterplot of prediction made on the 21st of October (00:00)

with a smoothed version below (using standard splines),

or, if we consider forecasts made for the 25th of October (11:00) we have

with again, a smoothed version,

The code is the following,

donnees=read.table("http://perso.univ-rennes1.fr/ arthur.charpentier/pred-meteo2.txt") x=donnees$DATE1+donnees$HEURE1/24 x[x<15]=x[x<15]+31 y=donnees$DATE2+donnees$HEURE2/24 y[y<15]=y[y<15]+31 z=y-x h=donnees$TEMPERATURE D=data.frame(x,y,z,h) X0=sort(unique(x)) Y0=sort(unique(y)) Z0=sort(unique(z)) matY.X=matrix(NA,length(X0),length(Y0)) library(splines) for(i in 1:length(X0)){ dd=D[D$x==X0[i],] ax=dd$y ay=dd$h a=data.frame(ax,ay) ra=lm(ay~bs(ax,df=5),data=ba) X=Y0[(Y0>min(ax))&(Y0<max(ax))] iX=which((Y0>min(ax))&(Y0<max(ax))) Y=predict(ra,newdata=data.frame(ax=X)) matY.X[i,iX]=Y }So, let us look at levels of iso-temperature, as a function of the date the prediction is made (the

*x*-axis) and the horizon (the

*y*-axis). First, we we fix the date of the prediction, we get (from the code above)

and then, when we fix the horizon of the prediction

It looks like the small

*heat wave*we experienced at the end of October has been perfectly predicted... And here, it looks like the only pattern we can observe is the

*standard*evolution of temperature: there are no bumps due to forecasting problems..

- Modeling predicted temperatures

where denotes the date the prediction was made and the date the horizon, or

This was easy with the gam framework... For instance, consider the following code (some regressions mentioned below will be used later on)

library(mgcv) library(splines) REG1=gam(h~s(x,y),data=D,family=gaussian) REG2=lm(h~bs(x)+bs(y),data=D) REG3=gam(h~s(x)+s(y),data=D,family=gaussian) REG4=gam(h~s(x)+s(y),data=D,family=gaussian(link="log")) REG5=gam(h~s(y),data=D,family=gaussian) D$e=residuals(REG5) REG6=gam(e~s(x,y),data=D,family=gaussian)Here is the prediction we had with the additive model

and for the multiplicative model

(colors are different because of the log scaling but the shape is similar).

For the component on (in blue), we have, with the additive modeland for the multiplicative model

On the other hand, for the component on (in red) we have, with the additive model

and for the multiplicative model

Note that we observe something which looks like the

*true*temperature (we actually had)

So finally, on average, those predictions are consistent with the true temperature we experienced. But so far, we cannot say anything about prediction errors. If we consider a bivariate spline regression, i.e.

for some smoothed function, we have, again, the same kind of predictions,

It looks like what we've seen before. And because we have those horizontal lines, only the horizon should be considered.... So let us study the following model

where we obtain the following prediction

(which is similar with previous graph) but if we fit a spline on

*residuals*obtained from that regression, i.e.

where

we have the following distribution for This is where we can see prediction errors. We can observe that meteorologists have been a bit surprised by the heat-wave we observed just after the 25th. But let us wait a few more days to get more data, and perhaps we'll have a look at something that

*I*do care about: prediction of rainfall....

^{*}perhaps I should mention that we do not have backups of tables, but backups of html pages... then I use simple linguistic tools available on R to extract the information where it should be....

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