In recent years , weather derivatives have become a common tool in risk management for many sectors. This has its roots in that there is no unique way to determine de value and price solutions that would be generally approved by market-participants, like in the case of the Black-Scholes formula for options on non-dividend-paying stocks is the source for a constant debate between academics and practitioners. One look for fair and truly correct prices, while the others search every-day applicable solutions. To be honest… this is somehow like alchemy. This paper has as purpose the examination of statistical characteristics of weather data, data clearing and filling techniques. The study will be referring to temperatures because that is the best analyzed phenomenon, being the most common. This was also heavily influenced by energy companies and energetic interests, because the degree days were of interest ever before weather derivatives were put for sale. Main ideas are explaining what ways of pricing and valuation are, put into perspective for this financial instrument, taking into consideration that the Black-Scholes Model is not suitable. Also here, we will present the pros and cons that we found for each method. The methods are: the Burn analysis, the index value simulation method (IVSM), the daily simulation method (DSM).On the hole, this paper wants to shed light the weather derivatives pricing methods a mix of insurance pricing and standard financial models. At the end we will prospect the discounting problem, by means of the Consumption based Capital Asset Pricing Model (CCAPM).