The following HTML text is provided to enhance online
readability. Many aspects of typography translate only awkwardly to HTML.
Please use the page image
as the authoritative form to ensure accuracy.
Completing the Forecast: Characterizing and Communicating Uncertainty for Better Decisions Using Weather and Climate Forecasts
intention that the recommendation also applies to any relevant group or activity within NOAA, such as the Office of Oceanic and Atmospheric Research (OAR). Recognizing the breadth and depth of the task presented to the committee, NWS urged the committee to think in terms of “teaching us how to fish as opposed to giving us a fish.” The committee approached the task accordingly, and explains its approach to each task at the beginning of each chapter. The remainder of this first chapter summarizes the issues that are central to the committee’s charge, most of which are examined in more detail in subsequent chapters.
1.1 THE UNCERTAIN ATMOSPHEREAND HYDROSPHERE
Uncertainty in hydrometeorological predictions, often described in terms of probability (Box 1.1), varies by weather/climate situation, location, and length of forecast. As demonstrated in the seminal work of Lorenz (1963, 1965, 1968, 1969), the origins of this uncertainty include (1) lack of an accurate, complete description of the initial three-dimensional state of the atmosphere; (2) incomplete and inaccurate descriptions of physical processes and other inadequacies in the modeling systems; and (3) the chaotic character of the atmosphere, in which small uncertainties in the starting point of a forecast or in the forecasting system can result in large differences as the prediction unfolds. In addition to the inherent sources of uncertainty in the atmosphere, there are also uncertainties in Earth surface characteristics and fluxes that contribute to the overall uncertainty in hydrometeorological prediction (e.g., Beven, 1989).
Numerical prediction is the basis for most hydrometeorological forecasts beyond several hours. All numerical weather forecasts begin with a three-dimensional description of the atmosphere, known as an initialization. Such initializations have improved during the past several decades as more observing systems have become available and as data assimilation systems, which combine observations with previous forecasts to provide a coherent, three-dimensional description of the atmosphere, have improved. Nevertheless, all observations have errors, data assimilation systems have inadequacies, and even the improved observational networks have substantial gaps on all scales. The result is that the three-dimensional descriptions of the atmosphere, even from large centers such as NWS’s National Centers for Environmental Prediction (NCEP), are inevitably imperfect. Such imperfect initializations lead to errors and therefore uncertainty in the forecasts that increases with forecast projection forward in time.
Computer forecast models also possess other sources of error that result in degraded prediction. Some errors result from inadequacies in the model descriptions of the physics of the atmosphere, such as radiation, cloud physics, and surface drag. Other errors result from lack of sufficient horizontal and vertical resolution, the scales at which the numerical simulation can accurately forecast the evolving atmosphere and hydrosphere. Because computer resources are finite, there are always small scales that are not properly described. Additional errors accrue due to approximations in numerics, how the forecast models are integrated into the future. Still other errors arise because of fundamental uncertainties in boundary fluxes, especially surface fluxes such as latent and sensible heat fluxes, and surface properties. The results of model inadequacies are often apparent early in forecasts and generally increase in time, resulting in increasing uncertainty as the forecasts progress.
In summary, the chaotic character of the atmosphere, coupled with inevitable inadequacies in observation quality and data assimilation, model physics and numerics, boundary conditions, and model resolution, result in forecasts that always contain uncertainties that generally increase with forecast lead time and vary by the type of weather situation and location. Uncertainty is thus a fundamental characteristic of hydrometeorological prediction, and no forecast iscomplete without a description of its uncertainty.
1.2 THE EVOLUTION OF HYDROMETEOROLOGICALUNCERTAINTY PREDICTION
Early forecasters, faced with large gaps in their nascent science, understood the uncertain nature of the hydrometeorological prediction process and were comfortable with expressing uncertainties in their forecasts. Cleveland Abbe (Figure 1.2), who organized the first American forecast group as part of the U.S. Army Signal Corps, did not use the term “forecast” for his first prediction in 1871, but rather employed the term “probabilities,” resulting in him being known as “Old Probabilities” or “Old Probs” (Box 1.2). A few years later, the term “indications” was substituted for probabilities and by 1889 the term “forecasts” received official sanction (Murphy, 1977).
As meteorology evolved during the late 19th and early 20th centuries into a more exact science based on explicit physical laws, the weather forecasting community increasingly presented deterministic5 predictions, with the uncertainties eventually succumbing to improving knowledge, technology, and observations (Murphy, 1977). The advent of numerical weather prediction around 1950 and its early successes strengthened this deterministic viewpoint. Forecast skill6 rapidly improved in the 1950s and 1960s as faster computers allowed higher spatial resolution and increasing sophistication in the numerical prediction models. But also during this period, the research of Lorenz (1963, 1965, 1968) and others demonstrated that forecast skill was inherently limited in a chaotic atmosphere in which small initialization
A deterministic forecast provides only one prediction of the future state of a system, with no information regarding forecast uncertainty.
Forecast skill is a statistical measure of the relative accuracy of forecasts compared to an alternative forecast such as climatology or persistence.