auto.arima does not recognize seasonal pattern I have a daily weather data set, which has, unsurprisingly, very strong seasonal effect.

I adapted an ARIMA model to this data set using the function auto.arima from forecast package.
To my surprise the function does not apply any seasonal operations- seasonal differencing, seasonal ar or ma components. Here is the model it estimated:
library(forecast)
data<-ts(data,frequency=365)
auto.arima(Berlin)

Series: data
ARIMA(3,0,1) with non-zero mean 

Coefficients:
         ar1      ar2     ar3      ma1  intercept
      1.7722  -0.9166  0.1412  -0.8487   283.0378
s.e.  0.0260   0.0326  0.0177   0.0214     1.7990

sigma^2 estimated as 5.56:  log likelihood=-8313.74
AIC=16639.49   AICc=16639.51   BIC=16676.7

And also the forecasts using this model are not really satisfying. Here is the plot of the forecast:

Can anyone give me a hint what is wrong here?
 A: The solution to your problem is as Rob points out is to combine deterministic effects (week of the year) and stochastic effects (ARIMA structure) while isolating unusual days and detecting the possible presence of one or more level shifts and/or one or more local time trends. AUTOBOX , the software used for the analysis was in part developed by me to automatically provide robust modeling for data sets like this.
I have placed your data at http://www.autobox.com/weather/weather.txt.
The acf of the original data is  which lead to an automatic model selection of the form    . The model statistics are  with a residual plot of  The plot of the forecasts for the next 60 days is presented here 
THe Actual/Fit/Forecast graph is shown here .
It might be interesting for others to follow Prof. Hyndaman's advice and to report their final model with disgnostic checks regarding residual diagnostics and parameter tests of significance.
I am personally uncomfortable with the suggestion about first performing a fourier analysis (possibly/probably impacted by anomalies) and then doing ARIMA on the residuals is unacceptable as it is not a simultaneous solution leading to 1 equation but rather a presumptive sequence. My equation use week-of-the-month and also included an AR(1) and remedies for the unusual data points.
All software has limitations and it is good to know them. Again I reiterate why doesn't somebody try to implement Rob's suggestions and show the complete results.
A: R will not fit an ARIMA model with seasonality greater than 350. See http://robjhyndman.com/hyndsight/longseasonality/ for a discussion of this issue. The solution is to use Fourier terms for the seasonality, and ARMA errors for the short-term dynamics.
