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Barbara G. Brown
,
Richard W. Katz
, and
Allan H. Murphy

The so-called fallowing/planting problem is an example of a decision-making situation that is potentially sensitive to meteorological information. In this problem, wheat farmers in the drier, western portions of the northern Great Plains must decide each spring whether to plant a crop or to let their land lie fallow. Information that could be used to make this decision includes the soil moisture at planting time and a forecast of growing-season precipitation. A dynamic decision-making model is employed to investigate the economic value of such forecasts in the fallowing/planting situation.

Current seasonal-precipitation forecasts issued by the National Weather Service are found to have minimal economic value in this decision-making problem. However, relatively modest improvements in the quality of the forecasts would lead to quite large increases in value, and perfect information would possess considerable value. In addition, forecast value is found to be sensitive to changes in crop price and precipitation climatology. In particular, the shape of the curve relating forecast value to forecast quality is quite dependent on the amount of growing-season precipitation.

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Cameron R. Peterson
,
Kurt J. Snapper
, and
Allan H. Murphy

An experiment was conducted in which forecasters expressed temperature forecasts in terms of intervals of variable width and fixed probability. The use of such intervals, called credible intervals, permits forecasters to describe the uncertainty inherent in their temperature forecasts in a meaningful, quantitative way. The results of the experiment indicate that forecasters can use credible intervals to quantify this information, information which may be important to potential users of these forecasts. Several recommendations are made regarding credible interval temperature forecasting on an operational basis.

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Karl-Ivar Ivarsson
,
Rune Joelsson
,
Erik Liljas
, and
Allan H. Murphy

Abstract

This paper describes new operational and experimental forecasting programs at the Swedish Meteorological and Hydrological Institute (SMHI) designed to provide users with more detailed and more useful weather forecasts. User groups currently served by these programs include construction contractors, farmers, electric power companies, street and highway departments, and ski resorts. The programs represent a major component of a SMHI-wide effort to develop products to meet the needs of the public and private sectors in Sweden for meteorological and hydrological information.

An important feature of these programs is that many of the forecasts are expressed in probabilistic terms, and some results of the probability forecasting components of four programs are presented here. These subjective forecasts specify the likelihood of occurrence of various precipitation, wind speed, temperature, and cloud amount events, and they generally involve relatively short lead times and/or valid periods. The probabilistic forecasts of measurable precipitation are found to be reasonably reliable and definitely skillful. Some forecasts of larger precipitation amounts and the wind speed forecasts for shorter lead times also demonstrate positive skill, and the probabilistic temperature forecasts appear to be quite reliable. On the other hand, most of the experimental and operational probability forecasts reveal some degree of overforecasting, which tends to increase as lead time increases and as the climatological probability of the event decreases. As a result, the wind speed forecasts for longer lead times, some forecasts of precipitation amount, and the cloud amount forecasts exhibit negative skill.

Some factors that may have contributed to the deficiencies in the forecasters' performance are identified. The need to refine various components of the forecasting system is emphasized, and current efforts to implement such refinements at SMHI are outlined.

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Allan H. Murphy
,
Wu-ron Hsu
,
Robert L. Winkler
, and
Daniel S. Wilks

Abstract

This paper summarizes the results of an experiment in which National Weather Service forecasters formulated probabilistic quantitative precipitation forecasts (QPFs) during a 17-month period in 1981–82. These forecasts expressed the likelihood that certain threshold amounts of precipitation would be equaled or exceeded in 12-hour periods at four locations in Texas. The forecasters had no previous experience in quantifying the uncertainty in such forecasts, but they did receive feedback regarding their collective performance at the end of the first year of the experiment. In the evaluation of the experimental results, particular attention is focused on three issues: 1) the reliability and skill of the subjective QPFs; 2) the effects of feedback and experience on the quality of these forecasts; and 3) the relative performance of the subjective probabilistic QPFs and objective probabilistic QPFs produced by the model output statistics system.

The subjective probabilistic QPFs possess positive skill, although they exhibit considerable overforecasting for larger precipitation amounts. Moreover, the feedback provided to the forecasters evidently contributed to modest increases in the reliability and skill of their forecasts. In this regard, the quality of the subjective and objective QPFs is generally comparable in the first year of the experiment. However, after the receipt of the feedback, the skill of the subjective forecasts exceeded the skill of the objective forecasts. These results are considered to be encouraging regarding the ability of forecasters to formulate reliable and skillful probabilistic QPFS, but more extensive experiments should be undertaken to investigate this and related issues in greater detail.

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Allan H. Murphy
,
Richard W. Katz
,
Robert L. Winkler
, and
Wu-Ron Hsu

Abstract

The purposes of this paper are to describe a dynamic model for repetitive decision‐making in the cost–loss ratio situation and to present some theoretical and numerical results related to the optimal use and economic value of weather forecasts within the framework of the model. This model involves the same actions and events as the standard (i.e., static) cost–loss ratio situation, but the former (unlike the latter) is dynamic in the sense that it possesses characteristics (e.g., decisions, events) that are related over time. We assume that the decision maker wants to choose the sequence of actions over an n‐occasion time period that minimizes the total expected expense. A computational technique known as stochastic dynamic programming is employed to determine this optimal policy and the total expected expense.

Three types of weather information are considered in studying the value of forecasts in this context: 1) climatological information; 2) perfect information; and 3) imperfect forecasts. Climatological and perfect information represent lower and upper bounds, respectively, on the quality of all imperfect forecasts, with the latter considered here to be categorical forecasts properly calibrated according to their past performance. Theoretical results are presented regarding the form of the optimal policy and the relationship among the total expected expenses for these three types of information. In addition, quality/value relationships for imperfect forecasts are described.

Numerical results are derived from the dynamic model for specific values of the model parameters. These results include the optimal policy and the economic value of perfect and imperfect forecasts for various time horizons, climatological probabilities, and values of the cost–loss ratio. The relationship between the accuracy and value of imperfect forecasts also is examined.

Several possible extensions of this dynamic model are briefly discussed, including decision‐making problems involving more actions and/or events, more complex structures of the costs and losses, and more general forms of imperfect forecasts (e.g., probability forecasts).

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Allan H. Murphy
,
Sarah Lichtenstein
,
Baruch Fischhoff
, and
Robert L. Winkler

Previous studies have suggested that the general public misinterprets probability of precipitation (PoP) forecasts, leading some meteorologists to argue that probabilities should not be included in public weather forecasts. Upon closer examination, however, these studies prove to be ambiguous with regard to the nature of the misunderstanding. Is the public confused about the meaning of the probabilities or about the definition of the event to which the probabilities refer? If event misinterpretation is the source of the confusion, then elimination of the probabilities would not reduce the level of misunderstanding.

The present paper summarizes a study of 79 residents of Eugene, Oreg., who completed a questionnaire designed to investigate their understanding of and attitude toward precipitation probability forecasts. Results indicate that the event in question frequently is misunderstood, with both traditional precipitation forecasts and PoP forecasts producing similar levels of event misinterpretation. On the other hand, the probabilities themselves are well understood. Moreover, most respondents revealed a preference for the use of probabilities to express the uncertainty inherent in precipitation forecasts. Although the sample size was limited, the results of this study strongly support the inclusion of probabilities in public forecasts of precipitation occurrence. The paper concludes with a brief discussion of some implications of these results for operational weather forecasting.

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P. W. Thorne
,
R. J. Allan
,
L. Ashcroft
,
P. Brohan
,
R. J. H Dunn
,
M. J. Menne
,
P. R. Pearce
,
J. Picas
,
K. M. Willett
,
M. Benoy
,
S. Bronnimann
,
P. O. Canziani
,
J. Coll
,
R. Crouthamel
,
G. P. Compo
,
D. Cuppett
,
M. Curley
,
C. Duffy
,
I. Gillespie
,
J. Guijarro
,
S. Jourdain
,
E. C. Kent
,
H. Kubota
,
T. P. Legg
,
Q. Li
,
J. Matsumoto
,
C. Murphy
,
N. A. Rayner
,
J. J. Rennie
,
E. Rustemeier
,
L. C. Slivinski
,
V. Slonosky
,
A. Squintu
,
B. Tinz
,
M. A. Valente
,
S. Walsh
,
X. L. Wang
,
N. Westcott
,
K. Wood
,
S. D. Woodruff
, and
S. J. Worley

Abstract

Observations are the foundation for understanding the climate system. Yet, currently available land meteorological data are highly fractured into various global, regional, and national holdings for different variables and time scales, from a variety of sources, and in a mixture of formats. Added to this, many data are still inaccessible for analysis and usage. To meet modern scientific and societal demands as well as emerging needs such as the provision of climate services, it is essential that we improve the management and curation of available land-based meteorological holdings. We need a comprehensive global set of data holdings, of known provenance, that is truly integrated both across essential climate variables (ECVs) and across time scales to meet the broad range of stakeholder needs. These holdings must be easily discoverable, made available in accessible formats, and backed up by multitiered user support. The present paper provides a high-level overview, based upon broad community input, of the steps that are required to bring about this integration. The significant challenge is to find a sustained means to realize this vision. This requires a long-term international program. The database that results will transform our collective ability to provide societally relevant research, analysis, and predictions in many weather- and climate-related application areas across much of the globe.

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