Extreme Precipitation Analysis and Prediction for a Changing Climate

Abstract

Extreme precipitation is one of the most important climate hazards that pose a significant threat to human property and life. Understanding extreme precipitation events helps to manage their risk to society and hence reduce potential losses. This paper provides two new stochastic methods to analyze and predict various extreme precipitation events based on nonstationary models with or without the consideration of serial dependency associated with different days. These methods, together with Monte Carlo simulation and dynamic optimization, bridge nonextreme precipitation and extreme precipitation so that abundant nonextreme precipitation data can be used for extreme precipitation analysis. On an annual basis, the analysis produces distributions for the maximum daily precipitation, number of days with heavy rainfall, and maximum number of consecutive days with heavy rainfall. The accuracy of the new methods is examined, using 1 decades of empirical data in the Washington, DC metropolitan area. Based on the new methods, predictions of various extreme events are provided under different assumptions. Finally, the impact of serial dependency on results is also discussed. The result shows that for the area studied, serial dependency can further improve the analysis result.