Photo by Bill Wetzel, Jr.


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Significant technological advances have occurred over the past decade in the National Weather Service Forecast Offices which have improved the quality of numerical weather prediction model forecasts and facilited the analysis of gridded model data and observations. However, developments in forecasting winter season precipitation have not paralleled these technological advances, and there remains considerable reliance on empirically-derived rules of thumb. Many of the techniques for predicting snowfall accumulations currently used at NWSFOs were developed prior to the availability of sophisticated gridded analysis programs, and generate 12- or 24-hour forecasts by simply extrapolating current observations or numerical model forecasts of synoptic variables (e.g., 200 hPa temperature, 500 hPa vorticity, or 700 hPa mixing ratio). Basing forecasts on extrapolation, never very satisfactory beyond the 3- to 6-hour range, is now unnecessary given the analysis tools currently available to forecasters.

Instead, the fundamental elements, or ingredients, involved in a winter precipitation event can be analyzed. An ingredients-based analysis allows forecasters to base their predictions more directly on the physical processes involved, thus enabling them to tailor a forecast to the unique conditions characteristic of each event. In contrast, empirically-derived rules of thum are generally based on canonical scenarios that assume set conditions, and the reliability of the techniques diminishes as conditions vary from these scenarios.

The ingredients-based forecast technique is developed here as an operational tool to help forecasters analyze and predict winter precipitation events. The ingredients-based methodology (IM) provides a systematic approach to forecasting winter weather by establishing a framework for interpreting numerical forecast model output and observations. The five key ingredients for a winter precipitation event diagnosed by the technique presented here are: forcing for ascent, moisture, instability (i.e., gravitational, inertial, or slantwise instability), precipitation efficiency (specifically cloud microphysical properties), and temperature.

This page contains a discussion of the motivation for the ingredients-based methodology (IM) and background information including:


Ingredients-based forecast methodologies have been studied for more than two decades, primarily in the context of severe weather. It was originally developed to forecast the initiation of deep moist convection (McNulty 1978, 1995; Doswell, 1987; Johns and Doswell, 1992). This methodology included three ingredients--instability, moisture, and lift--and looked for all three to be present in order for deep moist convection to occur. More recently, Doswell et al. (1996) proposed an ingredients basis for the prediction of rainfall associated with flash floods. Starting with the premise that heavy precipitation is the result of sustained high rainfall rates which are a direct consequence of the rapid ascent of moist air, the authors qualitatively predicted the instantaneous rainfall rate R by assuming that it is proportional to the vertical flux of moisture. This notion is formalized in the relationship,

R = E w q

where ascent rate w, mixing ratio q, and precipitation efficiency E constitute the three ingredients in this approach. Precipitation efficiency serves as the constant of proportionality and is defined as the ratio of the mass of water falling as precipitation to the influx of water vapor mass into the cloud. Given estimates of rainfall rate R, they predicted total precipitation (P) by considering a fourth ingredient, precipitation duration (t_d), P = R t_d.

Nietfeld and Kennedy (1998) adjusted the approach developed in Doswell et al. (1996) for consideration of forecasting snowfall amounts. They proposed air temperature, snowfall rate, and snowfall duration as the three ingredients in a snow event. The snowfall rate R ingredient is further described as R = E w q, where q includes the ambient mixing ratio and that anticipated by moisture advection. The ascent rate (w) is diagnosed by considering the synoptic and sub-synoptic scale mechanisms for lift, and the efficiency ingredient (E) includes the degree of saturation of the air mass, cloud physics pertaining to snowflake formation, and the ratio of snow to liquid water. Although they discuss the ingredients terminology in the context of winter weather, the approach of Nietfeld and Kennedy (1998) was essentially designed to serve as a conceptual model and has not been developed to have operational utility.

In a conference presentation, Janish et al. (1996) developed an ingredients-based approach to diagnosing the areal distribution of precipitation type for application in an operational environment. This approach uses winter weather composite charts to combine hand analyses of three variables (moisture, temperature profile, and vertical motion) onto a single map. Their method provides useful guidance for short-term forecasts of precipitation type. Consideration of similar maps constructed using numerical forecast output of the three central variables was suggested as a means of making longer range forecasts of precipitation type. The present study expands upon the concepts in Janish et al. (1996). The ingredients-based methodology presented here includes more of the physically relevant elements involved in a winter precipitation event (i.e., precipitation duration and intensity) and thus provides for more comprehensive forecast guidance.

Definition and Choice of Ingredients

The ingredients-based methodology presented here is based on a stricter definition of ingredient than that employed in previous studies. Here, an ingredient is defined as a fundamental physical component or process that contributes to the development of a meteorological event. This definition excludes intermediate parameters such as precipitation rate and duration which are important in the diagnosis of a precipitation event, but are dependent on the more elementary physical ingredients. Because precipitation rate and duration are derived from fundamental ingredients, they do not lend themselves for use in a physically-based forecast that can be easily tailored to event-specific conditions.

Use of duration as an ingredient in the manner of Doswell et al. (1996) and Nietfeld and Kennedy (1998) implicitly assumes that the rainfall rate will remain constant throughout the duration of the event. Instead, using the definition of an ingredient employed here, the storm duration can be assessed through an evaluation of the selected ingredients through all forecast hours of a numerical model. If the necessary ingredients are expected to be present at a given forecast hour, then forecasters can predict a high precipitation potential. If an important ingredient is not expected to be present, the precipitation potential will be low.

Central to an understanding of the IM is the clear distinction between an ingredient and a diagnostic. Ingredients represent the physical components or processes directly involved in a meteorological event, while diagnostics are the observable or computed quantities which can be used to assess the presence and strength of an ingredient. Previous work has often blurred this distinction, as illustrated by the use of the mixing ratio "ingredient" by Doswell et al. (1996) and Nietfeld and Kennedy (1998). Mixing ratio is actually only one of a number of parameters that can be used to quantify the moisture availability, and thus is more appropriately considered a diagnostic of the moisture ingredient. In the ingredients-based methodology for forecasting winter season precipitation presented here, parameters will be introduced to diagnosis each ingredient; however, the technique is not dependent on these specific diagnostics. Because the ingredients-based approach is grounded on the physical components and processes involved, it has the flexibility to incorporate new diagnostics for these ingredients to meet the specific needs, interests, and theory of the application.

The following five ingredients are employed in the ingredients-based methodology (IM) for forecasting winter season precipitation here:

The first, second, and fourth ingredients are similar to ingredients used by Nietfeld and Kennedy (1998). The fifth ingredient was implicit in their study which considered only snow events. The third ingredient, however, has not been previously considered as an ingredient for winter season precipitation. This omission has likely occurred because instability is not a necessary ingredient for precipitation, and because until recently convenient diagnostics for identifying winter season instability were not readily available nor well understood by forecasters. As previously mentioned, reduced stability is not a necessary condition for winter season precipitation, but can significantly amplify the response of an air column to forcing for vertical motion. The instability ingredient is included in the ingredients-based approach using convenient diagnostics which are readily available to operational forecasters.

In summary, the ingredients-based methodology follows directly from the following physical processes involved in a winter season precipitation event. To generate any amount and type of precipitation, some mechanism to force ascent (ingredient 1) in a region with sufficient moiture availability (ingredient 2) is required. The intensity of the ensuing precipitation can be modulated by the presence of instability (ingredient 3) and the cloud microphysical properties (ingredient 4). Finally, the precipitation type is related to the temperature profile (ingredient 5). A forecast using this ingredients-based approach involves evaluating diagnostics of each ingredient at various levels in the atmosphere for every forecast hour for which gridded data are available, to determine which ingredients are present over the forecast area.

The application of the ingredients-based approach to forecasting winter season precipitation is discussed in more depth on the Ingredients Maps Page.

Traditional Forecast Techniques from an Ingredients Perspective

The traditional techniques used for forecasting winter precipitation events are largely empirical relationships established from observations of consistent patterns in the development of weather systems. Because of the abundance of observational evidence on which these techniques are based, there is good reason to trust their prognostic accuracy in similarly configured synoptic situations. However, there are many circumstances in which these techniques have failed to provide accurate forecasts. An investigation of each technique from an ingredients perspective can assist in determining the reason for the failure and help to identify the conditions under which it should or should not be applied. For example, a technique that does not consider variations in the strength of the forcing for ascent should only be applied under conditions characterized by ``normal'' forcing. Here, normal is defined as the strength of forcing present in the cases from which the empirical technique was derived, or for those cases where the technique's reliability has been demonstrated in operational situations. This section presents a discussion of the ingredients considered by some of the popular traditional techniques in operation at NWSFOs. A discussion of these techniques can be found on the
Traditional Techniques Page.

The table below identifies the ingredients addressed in these empirical methods. Some techniques do not directly include an ingredient, but acknowledge its importance by instructing the forecaster to consider it independently (indicated in the table as "LtF", for Left to Forecaster).

Synoptic ClimatologyCookGarciaMagicLEMO
Forcing for Ascent No No LtF Yes No
Instability No No No No No
Moisture No No Yes LtF No
Temperature No Yes No Yes No
Efficiency* No No No No No

* Note: any empirical technique might include efficiency mechanisms indirectly.

As shown in the table, no technique considers more than two ingredients, and the most commonly overlooked element in forecasting winter precipitation events is the instability ingredient. Although it is not a necessary ingredient for snowfall, reduced stability can strongly influence precipitation rates.

There is some room for interpretation in identifying the ingredients used in the traditional forecast techniques presented in the table above. Because these techniques were derived empirically, certain ingredients may be included implicitly. For example, the Magic Chart, which predicts snowfall based on the NVD of air parcels reaching the 700 hPa surface, may implicitly consider stability because air parcels in an unstable environment will experience greater vertical displacement in a given time interval than those in a stable environment. Additionally, any empirical technique may include efficiency mechanisms implicitly because the synoptic patterns identified by an empirical technique may have specific characteristic microphysical properties which are not directly accounted for but nonetheless exist in a majority of similar cases.

Advantages of an Ingredients-Based Methodology

Empirical snowfall prediction techniques which base their forecasts on observations of consistent patterns can be thought of as conceptual models developed from the average behavior of many events. Because of the natural variability in the development of weather systems, there will always be synoptic or thermodynamic conditions which do not fit the empirical model and thus will not be properly forecasted. In contrast, one of the primary advantages of the ingredients-based methodology for winter season precipitation is that its validity is not restricted to specific synoptic or thermodynamic conditions. Because the ingredients approach is based on a physical understanding of the processes involved in a precipitation event instead of empirically-derived formulae, it provides forecasters with the flexibility to accommodate a variety of conditions.

Other advantages of the ingredients-based approach to forecasting winter season precipitation are associated with its interpretation of quantitative precipitation forecasts (QPF) generated by numerical models. Instead of relying on a ``black box'' utilization of QPF estimates, forecasters can use the ingredients approach to diagnose the mechanisms responsible for an event. This is especially important in situations modulated by instability. Under these conditions, if forecasters are aware of the potential for convective precipitation, higher accumulations can be anticipated and included in a forecast. Additionally, the ingredients approach provides a means of comparing the forecasts of different model runs . By identifying the roles played by each ingredient in the numerical models, one can account for differences in model-generated QPF. For example, forecasters can evaluate whether there are differences in forcing patterns or moisture, or whether any forecasts are modulated by instability. As conditions begin to evolve, the modeled ingredients can be compared with observed values of the ingredients and actual precipitation patterns to assist in deciding which model to choose.

The ingredients-based methodology can also be used to identify regional differences in precipitation. In contrast, many traditional forecast techniques focus on one station or a single cross-section and predict the snowfall for that small area; thus, they do not provide information about neighboring areas or the overall distribution of precipitation without tedious repetition of the technique. Using the ingredients-based approach, forecasters can examine ingredients on isobaric surfaces and anticipate localized regions of stronger forcing for ascent, isolated areas of instability, or boundaries of moisture and thus anticipate spatial variations in precipitation accumulation. This is particularly important when a forecast area is near the boundary of a region of high precipitation potential. In this situation, if the synoptic features evolve slightly differently from the predictions of the numerical forecast models, there may be considerably more or less snowfall over the forecast area.

While model-generated QPF only provides an estimate of time-integrated precipitation, the ingredients-based approach to forecasting winter season precipitation provides a depiction of the forecasted instantaneous precipitation intensity and distribution. This is useful in comparisons with radar observations of actual precipitation patterns for assessing how well a model is verifying. The instantaneous depiction of precipitation is also helpful in forecasting the timing of periods of increased or decreased precipitation intensity.

Finally, the ingredients-based aproach can be used to improve upon empirical techniques for estimating snowfall accumulation. Although this approach does not independently provide a quantitative estimate of snowfall accumulation, it can be coupled with an existing empirical snowfall prediction technique to provide an estimate of the accumulation. The Garcia Method (Garcia, 1994) is employed for this purpose in step 5 of the ingredients-based technique (make link). Further analysis of case studies and real-time applications may lead to the development of a quantitative relationship between the magnitudes of the ingredients and the expected snowfall totals. However, development of such a quantitative relationship may compromise the physical basis and flexibility inherent in this approach, and would be useful only when employed in conjunction with an analysis of the individual ingredients.

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Winter Forecast Ingredients Page