We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Quantifying the predictability of a predictand: Demonstrating the diverse roles of serial dependence in the estimation of forecast skill.
- Authors
Jarman, Alexander S.; Smith, Leonard A.
- Abstract
Predictability varies. In geophysical systems, and related mathematical dynamical systems, variations are often expressed as serial dependence in the skill with which the system is, or can be, predicted. It is well known, of course, that estimation is more complicated in cases where the time series sample in hand does not reflect an independent from the target population; failure to account for this results in erroneous estimates both of the skill of the forecast system and of the statistical uncertainty in the estimated skill. This effect need not be indicated in the time series of the predictand; specifically, it is proven by example that linear correlation in the predictand is neither necessary nor sufficient to identify misestimation. It has been shown that temporal correlations in forecast skill give rise to biased estimates of skill of a forecast system, and progress has been made on accounting for this effect in probability‐of‐precipitation forecasts. Related effects are explored in probability density forecasts of a continuous target in three different dynamical systems (demonstrating that linear correlation in the predictand is neither necessary nor sufficient), and a simple procedure is presented as a straightforward, good practice test for the effect when estimating the skill of a forecast system. Serial dependence in forecast skill results in misleading estimates of the quality of a forecast system when evaluated in a time‐series fashion. A wide variety of impacts are possible in addition to systematic biases established elsewhere which demonstrate that sample size corrections are required to correctly interpret the statistical significance of the estimated skill. Extensions to forecast systems which display (a) no linear correlation in the predictand yet serial dependence in the prediction skill, (b) linear correlation in the predictand yet no serial dependence in skill, and (c) linear correlation in both the predictand and skill are discussed; empirical approaches to estimated sample size corrections (when analytic results are not available) are introduced.
- Subjects
WEATHER forecasting; GEOPHYSICAL instruments; DYNAMICAL systems; TIME series analysis; STATISTICAL correlation
- Publication
Quarterly Journal of the Royal Meteorological Society, 2019, Vol 145, Issue 718, p40
- ISSN
0035-9009
- Publication type
Article
- DOI
10.1002/qj.3384