What Do Rain Gauges Tell Us about the Limits of Precipitation Predictability?*

Gianotti, Daniel J. Short; Anderson, Bruce T.; Salvucci, Guido D.

doi:10.1175/jcli-d-12-00718.1

Cited by 11 publications

(12 citation statements)

References 32 publications

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“…Studies of the United States have found higher PP in the U.S. West (Yilmaz and DelSole 2010;Feng et al 2011), with possible feedbacks in the central Great Plains (Koster et al 2004) using gridded precipitation data. Using station data, Gianotti et al (2013) found the seasonal PP for rain gauge precipitation occurrence to be somewhat higher than other studies found for total precipitation, 30% on average across the United States, with some locations higher than 70% in certain seasons.…”

Section: Introductioncontrasting

confidence: 66%

“…Our occurrence models are variable-order Markov chains with daily varying transition probabilities and chain orders (see Gianotti et al 2013). At each station, a data-pooling window is selected using the AIC c to improve transition probability estimates.…”

Section: A Stochastic Modelingmentioning

confidence: 99%

“…At our common measurement time scales (e.g., daily), spatially based physical process models dominate our precipitation forecasting methodologies (Lynch 2008), whereas the chaotic behavior of the atmosphere beyond a few weeks renders these methods nearly useless for forecasts at seasonal scales or longer (Lorenz 1965). In time series analyses of precipitation point data, the perceived deterministic time scale switches; there is significant persistence in aggregated seasonal and annual totals (particularly when tied to slowly changing atmospheric indices; Katz and Parlange 1998), but daily data are generally considered random variables because of low daily persistence (Katz and Parlange 1998) as well as the complex spatial behavior of precipitation (Olsson and Niemczynowicz 1996;Gong et al 2003).…”

Section: Introductionmentioning

confidence: 99%

“…Given its importance, significant research has addressed the issue of isolating predictable characteristics of climatescale precipitation variance, using both process models (Koster et al 2004;Moron et al 2006;Jia and DelSole 2011) and observational approaches (Madden et al 1999;Anderson et al 2009;Robertson et al 2009;Gianotti et al 2013). PP is generally established from signal-tonoise ratios (Ferguson et al 2011), variance partitioning (Koster et al 2004;Jia and DelSole 2011;Boer 2009), or through the divergence of states from initial model conditions (Collins 2002).…”

Section: Introductionmentioning

confidence: 99%

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The Potential Predictability of Precipitation Occurrence, Intensity, and Seasonal Totals over the Continental United States*

2014

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Using weather station data, the parameters of a stationary stochastic weather model (SSWM) for daily precipitation over the contiguous United States are estimated. By construct, the model exactly captures the variance component of seasonal precipitation characteristics (intensity, occurrence, and total amount) arising from high-frequency variance. By comparing the variance of the lower-frequency accumulations (on the order of months) between the SSWM and the original measurements, potential predictability (PP) is estimated. Decomposing the variability into contributions from occurrence and intensity allows one to establish two contributing sources of total PP. Aggregated occurrence is found to have higher PP than either intensity or the seasonal total precipitation, and occurrence and intensity are found to interfere destructively when convolved into seasonal totals. It is recommended that efforts aimed at forecasting seasonal precipitation or attributing climate variability to particular processes should analyze occurrence and intensity separately to maximize signal-to-noise ratios. Significant geographical and seasonal variations exist in all PP components.

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Section: Introductioncontrasting

confidence: 66%

Section: A Stochastic Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Potential Predictability of Precipitation Occurrence, Intensity, and Seasonal Totals over the Continental United States*

2014

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“…The use of seasonal forecasts in such a system is mainly dependent on the actual predictability of drought conditions, which are in turn dependent on the predictability of precipitation (Gianotti et al, 2013). Dynamical seasonal forecasting has evolved significantly in the last 20 years, from early studies using simplified models (e.g.…”

Section: Introductionmentioning

confidence: 99%

Global meteorological drought – Part 2: Seasonal forecasts

Dutra

Pozzi

Wetterhall

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. Global seasonal forecasts of meteorological drought using the standardized precipitation index (SPI) are produced using two data sets as initial conditions: the Global Precipitation Climatology Centre (GPCC) and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis (ERAI); and two seasonal forecasts of precipitation, the most recent ECMWF seasonal forecast system and climatologically based ensemble forecasts. The forecast evaluation focuses on the periods where precipitation deficits are likely to have higher drought impacts, and the results were summarized over different regions in the world. The verification of the forecasts with lead time indicated that generally for all regions the least reduction on skill was found for (i) long lead times using ERAI or GPCC for monitoring and (ii) short lead times using ECMWF or climatological seasonal forecasts. The memory effect of initial conditions was found to be 1 month of lead time for the SPI-3, 4 months for the SPI-6 and 6 (or more) months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value with skills at least equal to and often above that of climatological forecasts. Furthermore, it is very difficult to improve on the use of climatological forecasts for long lead times. Our results also support recent questions of whether seasonal forecasting of global drought onset was essentially a stochastic forecasting problem. Results are presented regionally and globally, and our results point to several regions in the world where drought onset forecasting is feasible and skilful.

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Detectability of historical trends in station‐based precipitation characteristics over the continental United States

2015

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The goal of this paper is to detect secular trends in observed, station-based precipitation variations and extreme event occurrences over the United States. By definition, detectable trends are those that are unlikely to have arisen from internal variability alone. To represent this internal variability, we use station-specific, seasonally varying, daily time scale stationary stochastic weather models-models in which the simulated interannual-to-multidecadal precipitation variance is purely the result of the random evolution of daily precipitation events within a given time period-to first estimate the trends in various means and extremes that can occur even with fixed, climatological daily precipitation characteristics. Detection of secular trends in the observed climate-whether naturally or anthropogenically induced-can then be defined relative to this stochastic variability, i.e., as trends in the means and/or extremes that are likely to have occurred only through a change in the underlying precipitation characteristics. The derived results have two important ramifications. First, they identify "hot spot" regions in which trends in precipitation characteristics are already emerging from within the envelope of stochastic variability, including (but not limited to) positive trends in annual occurrence across most of the U.S. and positive trends in annual intensity and heavy-event accumulations across the Interior Plains and around the Great Lakes. Further, they identify "sentinel" metrics which show the greatest detectability-e.g., annual precipitation occurrence and intensity-as well as those which show the least detectability and hence are unlikely to produce any statistically meaningful signals-e.g., seasonal total precipitation and extremes.

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What Do Rain Gauges Tell Us about the Limits of Precipitation Predictability?*

Cited by 11 publications

References 32 publications

The Potential Predictability of Precipitation Occurrence, Intensity, and Seasonal Totals over the Continental United States*

The Potential Predictability of Precipitation Occurrence, Intensity, and Seasonal Totals over the Continental United States*

Global meteorological drought – Part 2: Seasonal forecasts

Detectability of historical trends in station‐based precipitation characteristics over the continental United States

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