an operational ingredients-based methodology for forecasting midlatitude winter season precipitation...

An Operational Ingredients-Based Methodology for Forecasting Midlatitude Winter Season Precipitation

http://speedy.meteor.wisc.edu/~swetzel/winterreference: Wetzel and Martin, 2001. Weather and Forecasting,16 (1), 156-167.

Suzanne Wetzel SeemannJonathan E. MartinScott Bachmeier

October 4, 2001 5th 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


Ingredients-Based Forecast Methodology


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



A diagnostic is the observable or derived quantity that can be used to assess the presence and strength of an ingredient.




A diagnostic is the observable or derived quantity that can be used to assess the presence and strength of an ingredient.



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



2. Atmospheric Stability: Will there be an enhanced response to the forcing?


4. Precipitation Efficiency: How will cloud microphysical characteristics affect the precipitation rate?




2. Atmospheric Stability: Will there be an enhanced response to the forcing?


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?



Ingredient 1: Forcing for Ascent


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)


Top right white contours:QG forcing diagnostic

(for an 80km grid)



Diagnostic of Non-QG Forcing: Full Wind Frontogenesis

• Includes ageostrophic “thermally direct/indirect” circulations.



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.



Bottom left white contours:Non-QG forcing diagnostic

Full-wind frontogenesis

Ingredient 2: Atmospheric Stability


Instability Diagnostic

Conditional instability (CI or CSI) is diagnosed where PVes is negative

Saturated equivalent potential vorticity

PVes “combines vertical [CI] and slantwise [CSI] instabilities and so becomes an all-purpose convection potential tool.” (McCann, 1995)

Ingredient 2: Atmospheric Stability (cont’d)


Colored contours:Instability diagnostic

Ingredients 1 & 2 Combined: PVQ


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)


white contours

colored contours

green contours

Ingredient 3: Moisture


Moisture Diagnostics1) Absolute Moisture: Mixing Ratio2) Degree of Saturation: Relative Humidity

red contours: mixing ratio (g/kg)filled contours: relative humidity (%)

Ingredient 4: Precipitation Efficiency


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: http://www.crh.noaa.gov/arx/micrope.html

Ingredient 4: Precipitation Efficiency (cont’d)


Ice crystal growth after initiationDepositional Growth, maximized around -15 oCGrowth by Aggregation, maximized around 0 oC

How do clouds initiate ice from supercooled liquid droplets? Without ice nuclei, T < - 40 oC With ice nuclei present, T < -10 to -20 oC

Baumgardt: -12 oC to -14 oC recommended range for a high likelihood of ice -10 oC operational cutoff point for no ice in a cloud

Ingredient 5: Temperature


1. Wet-bulb temperature < 0 at all levels above the surface: Snow likely

Ingredient 5: Temperature


1. Wet-bulb temperature < 0 at all levels above the surface: Snow likely

colored contours

850 hPa Temperature (oC), shaded where negative

2. Wet-bulb temperature > 0 at some level above the surface, decreasing monotonically: 850 hPa 0 to -4 oC 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)


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)


* 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 oC) & No ice introduced from above

Elevated Warm Layer Cooler Layer Beneath

Possible ice nucleation(T < - 10 oC)

Rain or Freezing Rain

Snow or Ice Pellets

Application: Ingredients Maps


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


600:650 mb



700:750 mb



800:850 mb

00 UTC January 27, 1996

Cross-Section Ingredients Maps


• Assist in determining precipitation type and efficiency

• Identify layers of instability at levels not captured by the isobaric ingredients maps (800-850, 700-750, 600-650 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)


6-hour ETA model forecast valid at 06Z March 13, 1997600:650 hPa 700:750 hPa

No negative PVes in WI


6-hour ETA model forecast valid at 06Z March 13, 1997 Cross Section Ingredients Map

Mg: red

colored:

white dashed


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


• Precipitation onset and duration: If an area of forcing coincides with relative humidity > 80%, some precipitation is likely.



• 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 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 850 0 to -4 oC transition region Inspection of forecast and observed soundings is essential

Steps In Preparing an Ingredients-Based Forecast


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


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 hPaOnset of Precipitation: 12 UTC 26 January 1996

Cross-sectionOnset of Precipitation: 12 UTC 26 January 1996

Onset of Precipitation: 12 UTC 26 January 1996

800:850 hPa

600:650 hPa700:750 hPa

Period of Peak Intensity00 UTC 27 January 1996

Peak Intensity: 00 UTC 27 January 1996 Cross-section

Near Ending 12 UTC 27 January 1996 800:850 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


Limitations


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


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


“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)



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).




Current (0Z and 12Z ETA) Ingredients Maps, scripts to generate the ingredients maps, links to AWIPS ingredients maps, and other information is available at http://speedy.meteor.wisc.edu/~swetzel/winter

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).


THIS IS THE END OF THE SLIDES I USED IN NORTH PLATTE

There are some additional slides after this point that were not included



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.

Near Ending: 12 UTC 27 January Cross-section

Summary of Some Useful Diagnostics


Many other diagnostics can be incorporated into the IM to meet the specific needs of a forecast area or to include additional theory.

Checklist?


…or Reality?

an operational ingredients-based methodology for forecasting midlatitude winter season precipitation...

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