Methods of Objective Weather Forecasting

Gringorten, Irving I.

doi:10.1016/s0065-2687(08)60311-8

Cited by 4 publications

(1 citation statement)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although analog techniques remained known to weather forecasters (e.g., Gringorten, ), they lost favor as numerical forecasts became increasingly prevalent with the advent of the digital computer. Yet, Lorenz () considered analogs in evaluating short‐term atmospheric predictability, linking the methods to the notions of chaos theory that he discovered (Lorenz, ).…”

Section: Introductionmentioning

confidence: 99%

A model‐based approach for analog spatio‐temporal dynamic forecasting

McDermott

Wikle

2015

Environmetrics

View full text Add to dashboard Cite

Analog forecasting has been applied in a variety of fields for predicting future states of complex nonlinear systems that require flexible forecasting methods. Past analog methods have almost exclusively been used in an empirical framework without the structure of a model-based approach. We propose a Bayesian model framework for analog forecasting, building upon previous analog methods but accounting for parameter uncertainty. Thus, unlike traditional analog forecasting methods, the use of Bayesian modeling allows one to rigorously quantify uncertainty to obtain realistic posterior predictive distributions. The model is applied to the long-lead time forecasting of mid-May averaged soil moisture anomalies in Iowa over a high-resolution grid of spatial locations. Sea surface temperature is used to find past time periods with similar trajectories to the current pre-forecast period. The analog model is developed on projection coefficients from a basis expansion of the soil moisture and sea surface temperature fields. Separate models are constructed for locations falling in each Iowa Crop Reporting District, and the forecasting ability of the proposed model is compared against a variety of alternative methods and metrics.

show abstract

Section: Introductionmentioning

confidence: 99%

A model‐based approach for analog spatio‐temporal dynamic forecasting

McDermott

Wikle

2015

Environmetrics

View full text Add to dashboard Cite

show abstract

The Use of Cost/benefit Studies in the Interpretation of Probability Forecasts for Agriculture and Industry: an Operational Example

Stringer¹

1970

Weather Economics

View full text Add to dashboard Cite

A hierarchical stochastic model of large‐scale atmospheric circulation patterns and multiple station daily precipitation

Wilson¹,

Lettenmaier²,

Skyllingstad³

1992

J. Geophys. Res.

View full text Add to dashboard Cite

A stochastic model of weather states and concurrent daily precipitation at multiple precipitation stations is described. Four algorithms are investigated for classification of daily weather states: k‐means clustering, fuzzy clustering, principal components, and principal components coupled with k‐means clustering. A semi‐Markov model with a geometric distribution for within‐class lengths of stay is used to describe the evolution of weather classes. A hierarchical modified Pólya urn model is used to simulate precipitation conditioned on the regional weather type. An information measure that considers both the probability of weather class occurrence and conditional precipitation probabilities is developed to quantify the extent to which each of the weather classification schemes discriminates the precipitation states (rain‐no rain) at the precipitation stations. Evaluation of the four algorithms using the information measure shows that all methods performed equally well. The principal components method is chosen due to its ability to incorporate information from larger spatial fields. Precipitation amount distributions are assumed to be drawn from spatially correlated mixed exponential distributions, whose parameters varied by season and weather class. The model is implemented using National Meteorological Center historical atmospheric observations for the period 1964–1988 mapped to 5° × 5° grid cells over the eastern North Pacific, and three precipitation stations west of the Cascade mountain range in the state of Washington. Comparison of simulated weather class‐station precipitation time series with observational data shows that the model preserved weather class statistics and mean daily precipitation quite well, especially for stations highest in the hierarchy. Precipitation amounts for the lowest precipitation station in the hierarchy, and for precipitation extremes, are not as well preserved.

show abstract

Methods of Objective Weather Forecasting

Cited by 4 publications

References 62 publications

A model‐based approach for analog spatio‐temporal dynamic forecasting

A model‐based approach for analog spatio‐temporal dynamic forecasting

The Use of Cost/benefit Studies in the Interpretation of Probability Forecasts for Agriculture and Industry: an Operational Example

A hierarchical stochastic model of large‐scale atmospheric circulation patterns and multiple station daily precipitation

Contact Info

Product

Resources

About