How do meteorologists grade their past forecasts?
Disclaimer: I am not a meteorologist.

To answer this question, you need to understand how a forecast is obtained. Basically, meteorologists run computer simulations that predict how weather systems might evolve from current measurements. In fact, data assimilation techniques (http://en.wikipedia.org/wiki/Data_assimilation) are commonly used, so that the predictions given by the model depend not only on current observations, but also upon the history of the weather system. As new data comes in, the evolving model is adjusted towards those values - but not forced to match exactly, as that would destroy the historical information.

Any one model is therefore not expected to be entirely accurate. However, if we run many models simultaneously, and include random variations to reflect the chaotic nature of the weather system (and measurement errors), we obtain something called an "ensemble forecast". This is where the 30% chance of rain comes from: 30% of the simulations predict rain in your area, while the others think it will remain dry. Average/minimum/maximum temperatures etc can also easily be extracted from the ensemble.

So, the accuracy of a forecast can be considered in different ways. It is certainly possible to compare the predictions of one ensemble member to observations: this is an integral part of the data assimilation process. However, it is not necessarily a problem if a member performs poorly on one particular occasion. Different types of measurements may be weighted differently during the comparison (so matching rainfall might be deemed 'more important' than matching temperature; this may vary depending on the type of forecast/its intended use). More generally, you have to look at the long-term statistics of the errors in the forecast. So, if you look at several months of data, did it rain on around a third of the days when you predicted a 30% chance of rain? On average, are your temperature predictions reasonably accurate? Making this assessment on a day-by-day basis is unlikely to be in keeping with the statistical basis of modern weather forecasting.