An Operational Ingredients-Based Methodology for Forecasting Midlatitude Winter Season Precipitation reference: Wetzel and Martin, Weather and Forecasting,16 (1), Suzanne Wetzel Seemann Jonathan E. Martin Scott Bachmeier October 4, th Annual High Plains Conference North Platte, NE

Introduction to the Ingredients-Based Methodology Choice of Ingredients and Selected Diagnostics Application of the Methodology and Ingredients Maps Advantages and Limitations Outline October 4, th Annual High Plains Conference

Ingredients-Based Forecast Methodology October 4, th Annual High Plains Conference The Ingredients-Based Forecast Methodology (IM) provides a framework for a systematic assessment of the fundamental physical ingredients that influence the duration, intensity, and type of winter precipitation. Based on physical principles Flexibility to accommodate a variety of synoptic and thermodynamic conditions.

An ingredient is a fundamental physical element or process that directly contributes to the development and intensity of a precipitation event. Ingredient vs. Diagnostic October 4, th Annual High Plains Conference

An ingredient is a fundamental physical element or process that directly contributes to the development and intensity of a precipitation event. A diagnostic is the observable or derived quantity that can be used to assess the presence and strength of an ingredient. Ingredient vs. Diagnostic October 4, th Annual High Plains Conference

An ingredient is a fundamental physical element or process that directly contributes to the development and intensity of a precipitation event. A diagnostic is the observable or derived quantity that can be used to assess the presence and strength of an ingredient. Ingredient vs. Diagnostic October 4, th Annual High Plains Conference Parameters will be introduced to diagnose each ingredient; however, the IM is not dependent on these specific diagnostics.

1. Forcing for ascent : Where and how strong is the forcing? 3. Moisture : Where and how much moisture is available? Choice of Ingredients October 4, th Annual High Plains Conference

1. Forcing for ascent : Where and how strong is the forcing? 2. Atmospheric Stability : Will there be an enhanced response to the forcing? 3. Moisture : Where and how much moisture is available? 4. Precipitation Efficiency : How will cloud microphysical characteristics affect the precipitation rate? Choice of Ingredients October 4, th Annual High Plains Conference

1. Forcing for ascent : Where and how strong is the forcing? 2. Atmospheric Stability : Will there be an enhanced response to the forcing? 3. Moisture : Where and how much moisture is available? 4. Precipitation Efficiency : How will cloud microphysical characteristics affect the precipitation rate? 5. Temperature : What form will the precipitation take, and what snow-to-water ratio is expected? Choice of Ingredients October 4, th Annual High Plains Conference

Ingredient 1: Forcing for Ascent October 4, th Annual High Plains Conference Quasi-Geostrophic (QG) Forcing Diagnostic Use of the Q-vector as the sole means of diagnosing vertical motion forcing is limiting Q-Vector convergence  Forcing for upward vertical motion

Ingredient 1: Forcing for Ascent (cont’d) October 4, th Annual High Plains Conference Top right white contours: QG forcing diagnostic (for an 80km grid)

Ingredient 1: Forcing for Ascent (cont’d) October 4, th Annual High Plains Conference Diagnostic of Non-QG Forcing: Full Wind Frontogenesis Includes ageostrophic “thermally direct/indirect” circulations.

Ingredient 1: Forcing for Ascent (cont’d) October 4, th Annual High Plains Conference Diagnostic of Non-QG Forcing: Full Wind Frontogenesis Examples of other forcing mechanisms: Orographic forcing Thermodynamic forcing (diabatic & lake-effect) Includes ageostrophic “thermally direct/indirect” circulations.

Ingredient 1: Forcing for Ascent (cont’d) October 4, th Annual High Plains Conference Bottom left white contours: Non-QG forcing diagnostic Full-wind frontogenesis

Ingredient 2: Atmospheric Stability October 4, th Annual High Plains Conference Instability Diagnostic Conditional instability (CI or CSI) is diagnosed where PV es is negative Saturated equivalent potential vorticity PV es “combines vertical [CI] and slantwise [CSI] instabilities and so becomes an all-purpose convection potential tool.” (McCann, 1995)

Ingredient 2: Atmospheric Stability (cont’d) October 4, th Annual High Plains Conference Colored contours: Instability diagnostic

Ingredients 1 & 2 Combined: PVQ October 4, th Annual High Plains Conference for negative and negative for positive or positive PVQ is not intended as a numerical quantity, but as a graphical aid to identify where instability and forcing are co-located

Ingredients 1 & 2 Combined: PVQ (cont’d) October 4, th Annual High Plains Conference white contours colored contours green contours

Ingredient 3: Moisture October 4, th Annual High Plains Conference Moisture Diagnostics 1)Absolute Moisture: Mixing Ratio 2)Degree of Saturation: Relative Humidity red contours: mixing ratio (g/kg) filled contours: relative humidity (%)

Ingredient 4: Precipitation Efficiency October 4, th Annual High Plains Conference 1. Ice Nucleation (Initiation): Is ice present in the cloud? 2. Ice Crystal Growth: After ice has been initiated, how do the crystals grow to larger snowflakes? Under what conditions do maximum growth rates occur? D.A. Baumgardt, SOO NWS LaCrosse, WI:

Ingredient 4: Precipitation Efficiency (cont’d) October 4, th Annual High Plains Conference Ice crystal growth after initiation Depositional Growth, maximized around -15 o C Growth by Aggregation, maximized around 0 o C How do clouds initiate ice from supercooled liquid droplets? Without ice nuclei, T < - 40 o C With ice nuclei present, T < -10 to -20 o C Baumgardt: -12 o C to -14 o C recommended range for a high likelihood of ice -10 o C operational cutoff point for no ice in a cloud

Ingredient 5: Temperature October 4, th Annual High Plains Conference 1. Wet-bulb temperature < 0 at all levels above the surface: Snow likely

Ingredient 5: Temperature October 4, th Annual High Plains Conference 1. Wet-bulb temperature < 0 at all levels above the surface: Snow likely colored contours 850 hPa Temperature ( o C), shaded where negative 2. Wet-bulb temperature > 0 at some level above the surface, decreasing monotonically: 850 hPa 0 to -4 o C T roughly identifies the region of precipitation type transition (“rain edge” of the rain-snow boundary), always apply with caution

3. Elevated Warm Layer Precipitation type depends on whether the ice melts completely to a liquid while falling through the warm layer Ingredient 5: Temperature (cont’d) October 4, th Annual High Plains Conference

3. Elevated Warm Layer Precipitation type depends on whether the ice melts completely to a liquid while falling through the warm layer Ingredient 5: Temperature (cont’d) October 4, th Annual High Plains Conference * Degree of melting determined by relationships based on the warm layer temperature and the depth of the warm layer (Czys et al. 1996, Stewart and King 1988). Complete Melting* Partial Melting* No ice nucleation (T > - 10 o C) & No ice introduced from above Elevated Warm Layer Cooler Layer Beneath Possible ice nucleation (T < - 10 o C) Rain or Freezing Rain Snow or Ice Pellets

Application: Ingredients Maps October 4, th Annual High Plains Conference Ingredients maps facilitate the use of the IM by displaying all diagnostics together in a convenient manner Non-QG Forcing, Temperature & Efficiency Moisture & PVQ QG Forcing & Instability

Midwestern Winter Storm: January 26-27, 1996 October 4, th Annual High Plains Conference 600:650 mb

Midwestern Winter Storm: January 26-27, 1996 October 4, th Annual High Plains Conference 700:750 mb

Midwestern Winter Storm: January 26-27, 1996 October 4, th Annual High Plains Conference 800:850 mb

00 UTC January 27, 1996

Cross-Section Ingredients Maps October 4, th Annual High Plains Conference Assist in determining precipitation type and efficiency Identify layers of instability at levels not captured by the isobaric ingredients maps ( , , hPa) Assess the depth of forecasted dry or moist layers Distinguish between CI and CSI (provided the flow is 2D and the cross-section is oriented perpendicular to the shear of the geostrophic wind)

October 4, th Annual High Plains Conference 6-hour ETA model forecast valid at 06Z March 13, :650 hPa700:750 hPa No negative PVes in WI

October 4, th Annual High Plains Conference 6-hour ETA model forecast valid at 06Z March 13, 1997 Cross Section Ingredients Map Mg: red colored: white dashed

October 4, th Annual High Plains Conference 6-hour ETA model forecast valid at 06Z March 13, 1997: non-standard pressure layer 550:600 hPa Negative PVes in WI

Application of the Ingredients-Based Methodology October 4, th Annual High Plains Conference Precipitation onset and duration: If an area of forcing coincides with relative humidity > 80%, some precipitation is likely.

Application of the Ingredients-Based Methodology October 4, th Annual High Plains Conference Precipitation onset and duration: If an area of precipitation coincides with relative humidity > 80%, some precipitation is likely. Intensity of precipitation: - Related to the strength of forcing - May be limited by moisture availability and depth of moist layer - Enhanced response if forcing coincides with instability - May be modulated by efficiency mechanisms

Application of the Ingredients-Based Methodology October 4, th Annual High Plains Conference Precipitation onset and duration: If an area of precipitation coincides with relative humidity > 80%, some precipitation is likely. Intensity of precipitation: - Related to the strength of forcing - May be limited by moisture availability and depth of moist layer - Enhanced response if forcing coincides with instability - May be modulated by efficiency mechanisms Precipitation type: Rough characterization based on to -4 o C transition region Inspection of forecast and observed soundings is essential

Steps In Preparing an Ingredients-Based Forecast October 4, th Annual High Plains Conference 1. Choose a forecast area and evaluate all ingredient parameters at the 850mb, 700mb, and 600mb levels for each forecast hour. 2. Inspect cross-sections and forecast soundings. 3. Compile information into a time series of forecasted storm intensity and precipitation type. 4. Re-evaluate ingredient diagnostics with new model data. 5. Monitor conditions as the storm develops to decide how well the model-predicted ingredient diagnostics are verifying.

Case Example: January 26-27, 1996 October 4, th Annual High Plains Conference NWS Storm Report: Major snowstorm across most of Wisconsin. Total snowfall 8-18" except NW and SE corners where only a few inches fell. Maximum snow amounts were just east of LaCrosse (SW Wisconsin). At the height of the storm, thunder and lightning were observed with blizzard conditions.

800:850 hPa Onset of Precipitation: 12 UTC 26 January 1996

700:750 hPa Onset of Precipitation: 12 UTC 26 January 1996

600:650 hPa Onset of Precipitation: 12 UTC 26 January 1996

Cross-section Onset of Precipitation: 12 UTC 26 January 1996

800:850 hPa 600:650 hPa700:750 hPa Period of Peak Intensity 00 UTC 27 January 1996

Peak Intensity: 00 UTC 27 January 1996 Cross-section

Near Ending 12 UTC 27 January :850 hPa

Near Ending 12 UTC 27 January :750 hPa

Near Ending 12 UTC 27 January :650 hPa

Near Ending 12 UTC 27 January 1996

Systematic approach, provides focus and organization Flexible, not restricted to synoptic or thermodynamic conditions, provided the diagnostics are chosen carefully Aids in the interpretation of QPF: diagnoses mechanisms responsible for the event instead of ‘black box’ interpretation Helps to identify the source of differences between model scenarios Depicts forecasted instantaneous precipitation and intensity distribution. Identifies boundaries of moisture, localized regions of stronger or weaker forcing. Advantages October 4, th Annual High Plains Conference

Limitations October 4, th Annual High Plains Conference Ingredients maps rely on the accuracy of a numerical forecast model. The IM does not independently provide a quantitative precipitation forecast. Choice of diagnostics can limit the analysis.

Some Future Work October 4, th Annual High Plains Conference Assess QPV “false alarm” frequency Incorporate more diagnostics for temperature and efficiency Include an equivalent of QPV using frontogenesis instead of QG forcing More case studies and operational testing

Ingredients-Based Forecast Methodology: Final Comments October 4, th Annual High Plains Conference “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.” (Wetzel & Martin, 2002)

Ingredients-Based Forecast Methodology: Final Comments October 4, th Annual High Plains Conference Copies are available of our reply to Schultz et al.’s “Comments on an operational ingredients-based methodology for forecasting midlatitude winter season precipitation” (submitted to Weather and Forecasting, 2001). “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.” (Wetzel & Martin, 2002)

Ingredients-Based Forecast Methodology: Final Comments October 4, th Annual High Plains Conference Current (0Z and 12Z ETA) Ingredients Maps, scripts to generate the ingredients maps, links to AWIPS ingredients maps, and other information is available at Copies are available of our reply to Schultz et al.’s “Comments on an operational ingredients-based methodology for forecasting midlatitude winter season precipitation” (submitted to Weather and Forecasting, 2001). “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.” (Wetzel & Martin, 2002)

THIS IS THE END OF THE SLIDES I USED IN NORTH PLATTE There are some additional slides after this point that were not included

Application of the Ingredients-Based Methodology October 4, th Annual High Plains Conference Although analysis of the ingredient maps requires considerable subjective judgement, certain guidelines have been found to apply in most situations: With sufficient moisture and no instability, weak, moderate, and strong forcing for ascent will generally correspond to light, moderate, and heavy precipitation. The intensity of precipitation will be greater in the presence of instability and weaker when small amounts of moisture are available. Instability at any level with ample moisture and at least weak forcing can result in heavy precipitation, possibly accompanied by thunder and lightning. The depth of the moist layer may have a significant impact on the intensity of precipitation.

22 UTC January 26, 1996

Near Ending: 12 UTC 27 January Cross-section

Summary of Some Useful Diagnostics October 4, th Annual High Plains Conference Many other diagnostics can be incorporated into the IM to meet the specific needs of a forecast area or to include additional theory.

Checklist? October 4, th Annual High Plains Conference …or Reality?

01 UTC January 27, 1996