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Tuesday, January 28, 2020. 12:00PM. Webinar: A Practical Stochastic Weather Generator for Exploring Variability in Projected Precipitation Time Series. Mark Maimone, CDM Smith. Sponsored by Consortium for Climate Risk in the Urban Northeast. More information here. 

In addition to addressing the need for realistic projected future precipitation time series, another important aspect of using these projections is to recognize the potential range of natural variability that can be expected associated with these projections. This problem was addressed by developing an innovative and practical approach to creating a stochastic weather generator that utilizes the adjusted future time series to explore potential variability in projected precipitation patterns. The Weather Generator is based on the use of the projected future precipitation time series as a probability set, and represents a simple to implement approach for generating multiple future time series that can both explore the range of future projections, as well as provide rough guidance on the relative probabilities of extreme future time series.

Using the Stochastic Weather Generator to calculate the potential natural variability in precipitation, it can then be used to develop the minimum and maximum IDF curves likely to occur for present and future rainfall patterns, compared to the actual IDF curves produced from local rain gage data. The importance of the innovative approach presented in this study is that it is both easy to implement, addresses a key challenge with GCM output, and transferable to many areas of the US, addressing the need for actionable climate change information in the field of urban stormwater management.