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Concerns about the potential effects of anthropogenic climate change have led to a closer examination of how climate varies in the long run, and how such variations may impact rainfall variations at daily to seasonal time scales. For South Florida in particular, the influences of the low-frequency climate phenomena, such as the El Nino Southern Oscillation (ENSO) and the Atlantic Multi-decadal Oscillation (AMO), have been identified with aggregate annual or seasonal rainfall variations. Since the combined effect of these variations is manifest as persistent multi-year variations in rainfall, the question of modeling these variations at the time and space scales relevant for use with the daily time step-driven hydrologic models in use by the South Florida Water Management District (SFWMD) has arisen. To address this problem, a general methodology for the hierarchical modeling of low- and high-frequency phenomenon at multiple rain gauge locations is developed and illustrated. The essential strategy is to use long-term proxies for regional climate to first develop stochastic scenarios for regional climate that include the low-frequency variations driving the regional rainfall process, and then to use these indicators to condition the concurrent simulation of daily rainfall at all rain gauges under consideration. A newly developed methodology, called Wavelet Autoregressive Modeling (WARM), is used in the first step after suitable climate proxies for regional rainfall are identified. These proxies typically have data available for a century to four centuries so that long-term quasi-periodic climate modes of interest can be identified more reliably. Correlation analyses with seasonal rainfall in the region are used to identify the specific proxies considered as candidates for subsequent conditioning of daily rainfall attributes using a Non-homogeneous hidden Markov model (NHMM). The combined strategy is illustrated for the May–June–July (MJJ) season. The details of the modeling methods and results for the MJJ season are presented in this study. [ABSTRACT FROM AUTHOR]

Weather-state models have been shown to be effective in downscaling the synoptic atmospheric information to local daily precipitation patterns. We explore the ability of non-homogeneous hidden Markov models (NHMM) to downscale regional seasonal climate data to daily rainfall at a collection of gauging sites. The predictors used are: ensemble means of seasonal rainfall as forecast by the DEMETER and ECHAM models, and the preceding seasonal outgoing long-wave radiation (OLR). As the downscaling of seasonal GCM-based predictions lacks the ability to capture the intra-seasonal variability, we augment the seasonal GCM-driven inputs with statistically-driven predictions of the monthly rainfall amounts. The pooling effect of combining seasonal and monthly estimates of the regional rainfall enhances the capacity of the NHMM to simulate the stochastic characteristics of rainfall fields. The monthly rainfall prediction is derived from a wide range of climate precursors such as the El Nino-Southern Oscillation, local sea-level pressure, and sea-surface temperature. Application of the methodology to data from the Everglades National Park region in South Florida, USA is presented for the seasons May-July and August-September using a 22-year sequence of seasonal data from eight rainfall stations. The model skill in capturing the seasonal and intra-seasonal rainfall attributes at each station is demonstrated graphically and using simple statistical measures of efficiency. The hidden states derived from NHMM are qualitatively analysed and shown to correspond to the dominant synoptic-scale features of rainfall generating mechanisms, which reinforces the argument that physical processes are appropriately captured. Citation Khalil, A. F., Kwon, H.-H., Lall, U. & Kaheil, Y. H. (2010) Predictive downscaling based on non-homogeneous hidden Markov models. Hydrol. Sci. J. 55(3), 333-350. [ABSTRACT FROM AUTHOR]