A Range-Based Approach for Long-Term Forecast of Weather Using Probabilistic Markov Model

Weather forecasts serve to incline individual behaviors and interactions, commercial intentions and organizational efforts. A normal user is usually indifferent to weather statistics and corresponding value predictions but obtains an approximate idea from the average weather conditions. Forecasts justifying overall conditions for a duration which is usually rely on previous observations. Correspondingly, they extend the probability of inducing incorrect predictions as relatively insignificant variations consequently compound to substantial errors. As such, long term predictions are usually limited and unreliable. This paper aims to bridge this gap, by adopting a range specific approach to a probabilistic markov model (PMM). To develop a certainty in availability, we employ a cloud server to house for the analytics. We have achieved a considerable rise in accuracy in the results, along with a simplistic convenience for the user as compared to other available state-of- the-art methods.