Comparing the value of Southern Oscillation Index-based climate forecast methods for Canadian and US wheat producers

Hill, Harvey; Park, Jaehong; Mjelde, James W.; Rosenthal, W. D.; Love, H. Alan; Fuller, Stephen W.

doi:10.1016/s0168-1923(99)00154-9

Cited by 30 publications

(23 citation statements)

References 12 publications

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“…The skill level demonstrated for seasonal rainfall forecasting is only moderate; while clearly better than climatological and randomly guessed forecasts, it is less than that achievable for Australian seasonal (threemonth) temperature forecasting (FJB05 and F08b), but arguably of greater economic importance. (Hill et al (2000) have reported on the economic value of SOI-based seasonal forecasts to Canadian and USA wheat producers, while Chen et al (2002) have investigated the benefits which flow from use of the QG forecasting system specifically in the USA context.) We note that the QG system, with its selection of analogue years (used in the construction of the stratified climatology) is much more useful from an agricultural modelling perspective than the Bureau system.…”

Section: Discussionmentioning

confidence: 99%

A comparison of two seasonal rainfall forecasting systems for Australia

Fawcett¹,

Stone²

2010

AMOJ

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Section: Discussionmentioning

confidence: 99%

A comparison of two seasonal rainfall forecasting systems for Australia

Fawcett¹,

Stone²

2010

AMOJ

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“…The system has been used extensively for decision making in Australia (e.g., Hammer et al 2000) and elsewhere (e.g., Hill et al 2000Hill et al , 2004Selvaraju et al 2004). Years were categorized into five analog sets according to their similarity regarding oceanic and/or atmospheric conditions as measured by SOI phases just prior to the 3-month forecast period.…”

Section: Methodsmentioning

confidence: 99%

Inferential, Nonparametric Statistics to Assess the Quality of Probabilistic Forecast Systems

Maia¹,

Meinke²,

Lennox³

et al. 2007

Monthly Weather Review

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Many statistical forecast systems are available to interested users. To be useful for decision making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and its statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of "quality." However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what quality entails and how to measure it, leading to confusion and misinformation. A generic framework is presented that quantifies aspects of forecast quality using an inferential approach to calculate nominal significance levels ( p values), which can be obtained either by directly applying nonparametric statistical tests such as Kruskal-Wallis (KW) or Kolmogorov-Smirnov (KS) or by using Monte Carlo methods (in the case of forecast skill scores). Once converted to p values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. The analysis demonstrates the importance of providing p values rather than adopting some arbitrarily chosen significance levels such as 0.05 or 0.01, which is still common practice. This is illustrated by applying nonparametric tests (such as KW and KS) and skill scoring methods [linear error in the probability space (LEPS) and ranked probability skill score (RPSS)] to the five-phase Southern Oscillation index classification system using historical rainfall data from Australia, South Africa, and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. It is found that nonparametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system, or quality measure. Eventually such inferential evidence should be complemented by descriptive statistical methods in order to fully assist in operational risk management.

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“…For example, forecasts based on the state of the climate system, especially the state of ENSO and Pacific SSTs in general have the potential to provide useful information to fishermen, farmers, water resource managers, and industry (Dettinger et al 1999;Stern and Easterling 1999;Hill et al 2000;Hamlet et al 2002;Huppert et al 2002).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Pacific SSTs: Linear Inverse Model Predictions of the PDO

et al. 2008

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A linear inverse model (LIM) is used to predict Pacific (30°S-60°N) sea surface temperature anomalies (SSTAs), including the Pacific decadal oscillation (PDO). The LIM is derived from the observed simultaneous and lagged covariance statistics of 3-month running mean Pacific SSTA for the years 1951-2000. The model forecasts exhibit significant skill over much of the Pacific for two to three seasons in advance and up to a year in some locations, particulary for forecasts initialized in winter. The predicted and observed PDO are significantly correlated at leads of up to four seasons, for example, the correlation exceeds 0.6 for 12-month forecasts initialized in January-March (JFM). The LIM-based PDO forecasts are more skillful than persistence or a first-order autoregressive model, and have comparable skill to LIM forecasts of El Niño SSTAs. Filtering the data indicates that much of the PDO forecast skill is due to ENSO teleconnections and the global trend.Within LIM, SST anomalies can grow due to constructive interference of the empirically determined modes, even though the individual modes are damped over time. For the Pacific domain, the basinwide SST variance can grow for ϳ14 months, consistent with the skill of the actual predictions. The optimal structure (OS), the initial SSTA pattern that LIM indicates should increase the most rapidly with time, is clearly relevant to the predictions, as the OS develops into a mature ENSO and PDO event 6-10 months later. The OS is also consistent with the seasonal footprinting mechanism (SFM) and the meridional mode (MM); the SFM and MM involve a set of atmosphere-ocean interactions that have been hypothesized to initiate ENSO events.

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Comparing the value of Southern Oscillation Index-based climate forecast methods for Canadian and US wheat producers

Cited by 30 publications

References 12 publications

A comparison of two seasonal rainfall forecasting systems for Australia

A comparison of two seasonal rainfall forecasting systems for Australia

Inferential, Nonparametric Statistics to Assess the Quality of Probabilistic Forecast Systems

Forecasting Pacific SSTs: Linear Inverse Model Predictions of the PDO

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