Application of the Ingredients Method:
Ingredient Maps


The ingredients maps display diagnostics for the ingredients on the 850mb, 700mb, and 600mb isobaric surfaces and on a cross-section placed by the user (or pre-specified). This tool helps a forecaster to identify the role each ingredient plays in the model-generated precipitation forecast and can aid in analyzing how well a model is verifying. Additionally, this helps the forecaster to identify areas of potentially convective snow.

A full description is presented below, including:


Other related pages on this website include:




Introduction to the Ingredient Maps


The five ingredients for winter season precipitation included in the ingredients-based approach (forcing for ascent, moisture, instability, efficiency, and temperature) can help forecasters predict the precipitation potential, intensity, duration, and type. The diagnostics included on the ingredients maps are discussed on the
Forecast Ingredients Page. The quantities required to compute these diagnostics are readily available to the operational community.

The ingredients maps present one set of diagnostics of the five ingredients for precipitation in a 4-panel plot. The IM presented here does not require employment of these diagnostics as the only means of assessing the ingredients. In fact, at the heart of the IM is its flexibility to easily incorporate new means of diagnosing the ingredients. Many different sets of diagnostics can be used, without compromising the utility of the IM, as long as those diagnostics serve to assess the presence and strength of the ingredients. We invite extensions and improvements to the diagnostics employed for each ingredient, recognizing that any choice comes with limitations and that any one set of diagnostics will not be suitable for all forecasters in all regions. To date, several NWS WFOs have taken our ingredients scripts and tailored them to their own needs. We encourage other users to do the same.

In addition to the diagnostics for each individual ingredient, we also use a quantity to help identify regions where enhanced vertical motions can be expected due to the collocation of forcing and instability. It is well known that enhanced vertical motions might be expected where forcing for ascent and instability are collocated, and the diagnostic parameter PVQ can assist in identifying such collocations:

A new diagnostic parameter, PVQ, is introduced to capture this effect. PVQ is defined as ,



and computed as



Where this positive product is large there is forcing for ascent in the presence of instability, and strong upward vertical motion should be anticipated. In addition to analyses of each ingredient separately, PVQ is included in the ingredients maps.

Because the two quantities PVes and Q-vector divergence span a different range of values, the absolute magnitude of the quantity PVQ may be more sensitive to one than the other. Thus, PVQ is employed as an indicator of the potential for convective precipitation without regard to its absolute magnitude.

It is important to remember that PVes need not be negative for precipitation to fall. In fact, heavy snow often results from strong forcing alone. Therefore, contours of PVQ should only be used to identify areas of potentially convective snowfall. Many winter season precipitation events with sufficient forcing and moisture will be associated with postive PVes and, thus, zero PVQ.

In summary, PVQ is meant only to be a graphical aid that alerts a forecaster to the fact that two important ingredients (which can, synergistically, produce enhanced vertical motions) are coincident in time and space. When combined with an analysis of the five fundamental ingredients, PVQ can be a convenient, practical tool to help highlight where precipitation rates may be enhanced.


Through an analysis of the ingredients and PVQ on pressure surfaces and for the necessary cross-sections, the ingredients-based approach enables forecasters to address the following questions:

In order to take advantage of the flexibility provided by the ingredients-based approach, it is important that forecasters approach this technique from a physical perspective by developing an understanding of the processes involved in every forecast.

In addition, because all ingredient diagnostics rely on the accuracy of the model forecast, one must continually compare the model-generated ingredient parameters with observations.

Since the precipitation generation ingredients are so closely tied to the synoptic scale, quantities such as sea level pressure, thickness, and temperature should also be monitored to assess model verification. Variations in these quantities from the model-predicted values could lead to changes in the strength, timing, or location of the precipitation patterns.




Ingredient Maps


Use of the ingredients based approach to forecasting winter season precipitation can be facilitated by the construction of ingredients maps that display all ingredient diagnostics together in a convenient manner. This section introduces the ingredients maps with an example from a convective snow event that occurred in southeastern Wisconsin on January 26-27, 1996. Experience has shown that the application of the IM for three thin layers in the lower- to mid-troposphere (800-850 hPa, 700-750 hPa, and 600-650 hPa) best captures the distribution of the ingredient parameters. For storms with a deep sea level pressure minimum or intense upper level dynamics, the 500-550 hPa analysis has proven informative. However, there may be features in between these levels that are not captured by such an analysis. These features can often be identified by evaluation of ingredient cross-sections using the ingredients cross-sectional maps presented here.

The images below show the ingredients maps for the 24-hour NCEP-ETA model forecast valid at 0Z on January 27, 1996, for 800-850 hPa, 700-750 hPa, and 600-650 hPa. A key to the plots is also shown.




Ingredient Maps -- valid 0Z 27 January 1996 (click to enlarge):

850mb

700mb

600mb


Key to the Plots:

Top Left Panel:

6-hour Model-Predicted QPF (shaded),
Mean Sea Level Pressure (mb, white),
and 500-1000mb Thickness (brown)
Top Right Panel:

Saturated Equivalent Potential Vorticity (shaded)
and Q-vector Divergence (white)
Bottom Left Panel:

Temperature (shaded 0C to -20C, yellow elsewhere)
and Full-Wind Frontogenesis (white)
Bottom Right Panel:

Relative Humidity (shaded > 70%)
mixing ratio (red) and PVQ (green)


Surface Observations (click to enlarge):
22z 26 Jan
23z 26 Jan
00z 27 Jan
01z 27 Jan


Discussion of 0Z January 27 Ingredient Maps:

The 24-hour forecast of mean sea level pressure valid at 0Z on January 27, 1996 (shown on
ingredients maps above) contained a well-developed mid-latitude cyclone centered just south of the Wisconsin-Illinois border. This forecast will be used in the remainder of this section to introduce the use of ingredients maps in the ingredients-based methodology for winter season precipitation.

1. Precipitation Onset and Duration

If an area of vertical motion forcing coincides with relative humidity values of 80% or greater, some precipitation is likely. The ingredients maps valid at 0Z on January 27 show that QG forcing overlapped the contours of relative humidity greater than 80% over all of Wisconsin except the northwestern corner. For this storm, this agreement was observed throughout the 850-600 hPa atmospheric layer. In some other cases, significant variation in the vertical distribution of moisture requires additional consideration. The surface observations for Wisconsin show that at 0Z and 1Z on January 27, 1996, precipitation was indeed reported throughout most of Wisconsin with the exception of the far northwestern portion of the state.

2. Precipitation Intensity

The intensity of precipitation is related to the strength of the forcing and may be limited by the availability of moisture. Additionally, if the forcing coincides with an area of weak stability, an enhanced response to the forcing with higher precipitation rates can be expected. An area of instability at 600-650 hPa in southeastern Wisconsin coincided with moderate to strong forcing at 0Z on January 27 and can be seen as a PVQ maximum (see the ingredients maps).

Where nonzero PVQ overlaps sufficient moisture, heavy precipitation and possibly thunderstorms can occur. Although the region of positive PVQ in southeastern Wisconsin was close to the boundary of sufficient moisture, with only 70-80% relative humidity predicted for Milwaukee, moisture was abundant at lower levels and the strong vertical motions at 850 hPa and 700 hPa would have supplied the 600 hPa layer with ample moisture.

Thus, based on these ingredients maps, heavy precipitation and possible convection would be expected in southeast Wisconsin. Observations from this time indicate that thundersnow and heavy snow were indeed reported in the Milwaukee area at 0Z and 1Z (see surface observations).

Precipitation intensity can also be modulated by the efficiency ingredient. The 600 hPa and 700 hPa temperature in the ingredients maps can be used to assess the microphysical characteristics. If a region with sufficient moisture and upward vertical motion coincides with the temperature of maximum depositional ice crystal growth (-15 C) enhanced precipitation rates may result. In this case, the 600-650 hPa layer average air temperature in the vicinity of the PVQ maximum in southeast Wisconsin was -15C to -16C, providing additional evidence of the potential for high precipitation intensity.

3. Precipitation Type

The temperature profile will influence the type of precipitation, however a rough characterization of the precipitation type can be inferred from the 850 hPa 0C isotherm. A more rigorous approach to determining precipitation type would involve an analysis of forecast and observed soundings. The location of the 850 hPa 0C rain-snow line at 0Z on January 27, 1999, predicted that the precipitation at this time should be snow throughout Wisconsin. Observations show that the precipitation was indeed snow throughout the state. However, the surface temperature at 1Z was +1C at a few locations in southeastern Wisconsin (see surface observations), and Milwaukee experienced a brief changeover to light rain from 2Z to 3Z with a temperature of +3C at 2Z. This occurrence highlights the importance of evaluating soundings and other upper air observations throughout the forecast period to anticipate changes in precipitation type.




Steps Involved in Preparing an Ingredients-Based Forecast for Winter Season Precipitation


Following is a list of the steps involved in preparing a forecast for winter precipitation according to the ingredients method.
  1. Decide on a forecast area.
  2. Evaluate all ingredient parameters at the 850mb, 700mb, and 600mb levels for each forecast hour.
  3. Inspect cross-sections and forecast soundings.
  4. Compile information into a time series of storm intensity and precipitation type.
  5. Estimate the snowfall accumulation.
  6. Re-evaluate ingredient parameters as new model data becomes available and note changes.
  7. Monitor conditions as the storm develops to decide how well the ingredients are verifying.
Jump to steps:
1 2 3 4 5 6 7

Step 1: Decide on a Forecast Area

Although the ingredients approach provides a forecaster with a good overview of the distribution of precipitation across a large area, he or she can focus on one region at a time to facilitate the analysis. This forecast area is chosen by criteria similar to those for the `area of concern' defined in the Garcia method, however the ingredients method does not require any prior knowledge of the precipitation potential in the area.

Step 2: Evaluate all Ingredient Parameters

By inspecting ingredients maps frequently at a range of atmospheric levels, forecasters can use these diagnostics to help anticipate the intensity, onset time, and end time of a winter precipitation event. Because of the amount of information contained in the ingredients maps, it may be helpful to use a table to organize the ingredient parameters and to insure a systematic evaluation of each. Table 2 shows an example of such a chart which could be used for this purpose. Such a table is also useful as a means of documenting the storm for archival investigations of the ingredients-based approach. Toward this aim, it would also be useful to document the observed precipitation (type and intensity) corresponding to each forecast hour in the comment column or margins.

Table 2: Example of a table for organizing the values of some winter precipitation ingredient parameters for the 0- to 48-hour forecasts of a numerical model (adapted from Wetzel, 2000)

Step 3: Inspect cross-sections and forecast soundings.

In addition to using the ingredients approach on pressure surfaces, it is important to consider the vertical distribution of ingredients because a layer of instability or forcing could exist at a level that is not captured by any of the standard ingredients maps generated regularly (600-650 hPa, 700-750 hPa, 800-850 hPa). Diagnoses of two such a situations are presented here, followed by an example employing a cross-sectional analysis to determine whether to expect a conditional gravitational (CI) or conditional symmetric (CSI) response in a region of instability.
Top Left:
negative PVesg shaded, negative Q-vector divergence (white)
Top Right:
Mg (red), theta-es (white), negative d(theta-es)/dz shaded
Bot Left:
Relative humidity shaded > 70%, mixing ratio (red)
Bot Right:
Temperature shaded between -12C and 0C
  1. Ingredients Cross-Section for a Low-Level Instability
  2. , February 12, 1999.
  3. Ingredients Cross-Section for a mid-level Instability
  4. , March 13, 1997.
  5. Ingredients Cross-Section for a CSI Case
  6. , January 23, 1996.


Step 4: Compile information into a time series of storm intensity and precipitation type.


Once ingredients have been analyzed at all levels and for the necessary cross-sections, this information can be combined to determine if precipitation will be produced, at what intensity, for how long, and of what type. The intensity is based on the strength of forcing and possible modulations by instability, provided ample moisture is present.

The analysis of the ingredients maps requires considerable subjective judgment, however, certain guidelines have been found to apply in most situations.

Based on the information filled into the ingredients table, the ingredients approach enables the forecaster to answer the following questions:

Step 5: Estimate the snowfall accumulation.

In an operational environment, forecasts for winter season precipitation require that the snowfall accumulation be quantified. The ingredients-based methodology for forecasting winter season precipitation does not currently provide a prediction of snowfall accumulation. In future extensions of the IM, the physical basis of the ingredient information may be combined with the quantitative abilities of a traditional technique. The Garcia Method (Garcia, 1994; hereafter GM) is the most likely candidate, because of its comprehensive treatment of the moisture ingredient and because it has been widely accepted as a reliable technique for heavy snow prediction (e.g., Nietfeld and Kennedy, 1998; Gordon, 1998; Gerard et al., 1998; Cobb and Albright, 1996; Cope, 1996).

The GM was designed to answer the question of "how much," but not the question of "where" (Garcia, 1994). It predicts the maximum accumulation for a region that has been pre-defined as an "area of concern" for snow. From an ingredients perspective, this area of concern constitutes the area where forcing for ascent is expected. By leaving the diagnosis of this element to forecasters, GM lends itself nicely to an incorporation with the ingredients approach. In fact, Garcia (1994) states that "the isentropic forecast procedure outlined in this paper is not a stand-alone technique but should be part of a comprehensive approach." Here, the physical basis and flexibility of the ingredients approach is incorporated with the quantitative prediction ability of GM to obtain reasonable estimates of snowfall over a broad region.

A discussion of the procedure for integrating GM with the ingredients based approach is presented on pp. 106-114 of Wetzel (2000), available on the download page or here as pdf (1 Mb) or postscript (6.6 Mb).

Step 6: Re-evaluate ingredient parameters as new model data becomes available and note changes.

It is recommended that a forecaster perform all steps once initially and then modify the results by indicating changes and trends as needed when new model data becomes available, as opposed to repeating the whole process for each new version of data. This aids in a comparison between the new and the old model scenarios and decreases the amount of work required after the first forecast is prepared.

Step 7: Monitor conditions as the storm develops to decide how well the ingredients are verifying.

As the storm begins, verification and modification become important. Each forecasted ingredient should be compared to observable quantities to identify problems in model verification or behavior that is not QG. Since the smaller-scale precipitation generation mechanisms are so closely tied to the synoptic scale, quantities such as sea level pressure, thickness, and temperature should also be monitored. Variations in these quantities from the model prediction could lead to changes in the strength, timing, or location of the precipitation patterns. Additionally, as the precipitation develops outside of your forecast area, evaluate if this is occurring as you'd expect based on the forecasted ingredients in those areas. Are the signs of instability (convection, soundings) consistent with regions of negative PVes?