The TempRisk Apollo mission leverages vast amounts of weather data and “machine learning” in order to better predict uncertainty in a highly nonlinear dynamic system governed by chaos. TempRisk Apollo adds value to traditional methods of long-range weather forecasting by quantifying the inherent uncertainty in long-range temperature predictions in several ways.
Category: TempRisk Updates
We’re excited to announce the next generation of TempRisk! TempRisk Apollo provides the market’s most sophisticated temperature guidance in a one-stop shop for all your forecast needs. It optimally blends TempRisk’s unique, objective temperature output with numerical guidance for the full spectrum of temperature probabilities.
This newest tool from EarthRisk Technologies allows easy comparison of TempRisk’s output to numerical guidance. ForecastRisk captures the inherent uncertainty associated with weather forecasts by showing the spread in numerical ensemble members that make up a numerical mean in comparison with TempRisk’s statistical output.
A Look at What’s New in TempRisk 5.1: Announcing Path Analysis and the Ensemble Matrix We are excited to release TempRisk 5.1! This upgrade introduces Path Analysis and the TempRisk Ensemble Matrix driven by ensemble data. Other features include overlaying the TempRisk Index (veriﬁcation) on the Ensemble Scorecard, dynamic updates on Scorecard pages based on input changes,…
TempRisk v5.0: Improved Model = Stronger Performance TempRisk 5.0, our Summer 2013 package, is a lean and ﬁnely tuned model that utilizes multiple levels of pattern ﬁltering to identify the highest quality patterns. The Naive Bayes model remains in play, but now pattern signiﬁcance is determined by multi-day occurrence, correlations, sample size, qualitative ﬁltering and yearly diversity….