Abstract
Abstract Three methods for estimating potential seasonal predictability of precipitation from a single realization of daily data are assessed. The estimation methods include a first-order Markov chain model proposed by Katz (KZ), and an analysis of covariance (ANOCOVA) method and a bootstrap method proposed by the authors. The assessment is based on Monte Carlo experiments, ensemble atmospheric general circulation model (AGCM) simulations, and observation-based data. For AGCM time series, ANOCOVA produces the most accurate estimates of weather noise variance, despite the fact that it makes the most unrealistic assumptions about precipitation (in particular, it assumes precipitation is generated by a Gaussian autoregressive model). The KZ method significantly underestimates noise variance unless the autocorrelation of precipitation amounts on consecutive wet days is taken into account. Both AGCM and observation-based data reveal that the fraction of potentially predictable variance is greatest in the tropics, smallest in the extratropics, and undergoes a strong seasonal variation. The three methods give consistent estimates of potential predictability for 67% of the globe.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
