CONVECTIVE PARAMETERIZATION

For the Lesson:

Precipitation Processes

December 1998

What is Convective Parameterization?

Cumulus or convective parameterization schemes are procedures that attempt to account for the collective influence of small-scale convective processes on large-scale model variables

All NWP models with grid spacing larger than that of individual thunderstorms or storm clusters need to parameterize the effect that convection has on larger-scale model variables in each grid box

Why is ConvectiveParameterization Important?

• Convective storms can significantly influence vertical stability and large-scale flow patterns by– Redistributing heat, moisture, and

momentum – Producing cloud cover that affects surface

temperatures

• Are there other reasons?

Discussion Questions

• What are some real-life weather scenarios that would be seriously impacted if no attempt were made to account for convective processes within NWP models?

• How might the model fields differ if they weren’t accounted for?

Formulation of Convective Parameterizations

No matter how they are formulated, all convective parameterization schemes must answer these key questions1 How does the large-scale weather pattern control

the initiation, location, and intensity of convection?

2 How does convection modify the environment?

3 What are the properties of parameterized clouds?

Convection Initiationand Intensity

Schemes can initiate convection by considering the Presence of some convective instability at a

grid point (perturbed parcels may reach LFC) Existence of low-level and/or vertically-

integrated mass/moisture convergence that exceeds some threshold at a grid point

Rate of destabilization by the environment at a grid point

Convection Initiationand Intensity Continued

Schemes can make the intensity of the convection

Proportional to the moisture or mass convergence or flux

Sufficient to offset the large-scale destabilization rate

Sufficient to eliminate the CAPE (this is constrained by the available moisture)

ConvectiveFeedback

• In the real atmosphere, convection modifies the large-scale thermodynamics via– Detrainment (creates large-scale

evaporative cooling and moistening)– Subsidence in the ambient environment

(creates large-scale warming and drying)

ConvectiveFeedback Continued

• When a model changes the vertical temperature and moisture profiles as a result of convective processes, it is referred to as convective feedback

• The issue for convective parameterization schemes used in any given model is how they determine the new vertical distribution of heating, cooling, moistening and/or drying caused once convection is triggered

Two Approaches to Convective Feedback

• Adjustment Schemes– Either nudge the vertical profile toward an

empirical reference profile – Make the profile a function of the difference

between the moist adiabat inside the cloud and the moist adiabat representative of the ambient environment

• Mass Flux Schemes – DO attempt to explicitly model convective

feedback processes at each grid point

Properties ofParameterized Clouds

• If the model includes clouds, it determines their properties by using– The moist adiabat from cloud base (older

approach) OR– A one-dimensional cloud model (of varying

complexity in different models)

How Does This HelpUse NWP?

• Knowing which approach to the questions an NWP model has taken as a result of its convective parameterization scheme helps you to

– Understand some of the inherent strengths and weaknesses of the resulting convective precipitation forecasts

– Realize that the same scheme used in two different models will likely produce different results due to the way the scheme interacts with the other components of each individual model

Comparing Schemes

NWP Models and Schemes

Discussion Questions

Combining information from the two tables, which of these operational models includes convective downdraft processes?

When running or accessing a local mesoscale model, does it account for convective downdrafts?

What implication does no knowledge of outflow boundaries have on NWP convective initiation forecasts?

Even if the model produces outflow boundaries, why might they have little impact on subsequent convection initiation in the model?

The Aviation Medium-Range Forecast Model (AVN/MRF)

• AVN/MRF uses the Grell-Pan (GP) convective parameterization scheme

• Convection initiation within a column considers– Time rate of change in stability as primary

convective trigger– Presence of positive buoyancy (must have

some)– Cap strength

The AVN/MRF Model Continued

Properties of the scheme Modifies column buoyancy toward equilibrium (done as

function of vertical motion at cloud base) Evaporation efficiency a function of wind shear strength

(over ocean only) No direct mixing between cloudy air and environmental

air All cloud water converted to rain and leaves the cloud Shallow clouds (< 250 hPa) have no downdrafts and

detrain moisture more easily than deeper clouds

The Eta Model (32-km)

Uses the Betts-Miller-Janjic (BMJ) convective parameterization scheme

BMJ scheme simultaneously nudges temperature and moisture profiles at a grid point toward a reference profile (acts to adjust model atmosphere to a post-convective environment)

Post-convective profile adjustment happens ONLY if precipitation occurs (otherwise BMJ scheme does nothing or simulates limited vertical mixing)

BMJ scheme won’t trigger convection if cloud layer too dry (regardless of amount of CAPE)

The Shallow Convection BMJ Scheme in the Eta

• Shallow portion of BMJ scheme triggers if– “Cloud” depth (resulting from lifting the most

unstable parcel)–> 10 hPa deep –< 200 hPa deep–Covers at least two model layers

The Shallow Convection BMJ Scheme in Eta Cont.

• Shallow scheme’s role - to prepare pre-convective environment via vertical mixing by transporting moisture upward

• Mimics process of condensation near cloud base (warming and drying) and evaporation near cloud top (cooling and moistening) so net change in sounding from shallow convection results in no precipitation

The Deep Convection BMJ Scheme in the Eta Model

BMJ deep convective parameterization scheme identifies the most unstable parcel in the lowest 130 hPa at each grid point

The Deep Convection BMJ Scheme in the Eta Model Cont.

Then calculates cloud depth. If > 200 hPa deep, scheme modifies– Temperature profile to be 90% of slope of moist

adiabat through cloud base– Moisture profile using a procedure that considers

the distance a parcel needs to be lifted to reach saturation and the "cloud efficiency" (CE) factor (a measure of the convective column’s ability to transport enthalpy upward, while at the same time producing as little precipitation as possible)

The Deep Convection BMJ Scheme in the Eta Model Cont.

BMJ scheme ensures if rain is produced, net latent heat release is in balance with the net moisture change due to condensation

Intensity of convection produced by BMJ scheme very moisture dependent

–More moist the column, more intense the convection

An Example of the BMJ Scheme in the Eta

It is often easy to recognize where the BMJ deep conv. param. scheme has been active by the well-defined reference profile. Example shows Eta model soundings before and after scheme has been active.

Discussion Question

What impact will the recent change in the shallow cloud depth threshold (from 290 hPa to 200 hPa) that is responsible for triggering the deep convection scheme have on Eta model precipitation forecasts?

Eta Model with Kain-Fritsch (KF) Parameterization

• An experimental Eta model output using KF parameterization is available on the Web

The Eta Model with KF Parameterization Continued

• KF scheme is a mass flux scheme similar to Grell and GP schemes running in the RUC-2 and AVN/MRF; notable differences– CIN evaluated by amount of negative area, not just

pressure depth of the cap– Large-scale destabilization not required to trigger

convection, only +CAPE– Updraft and downdraft formulations more sophisticated– Intensity of convection based on instantaneous CAPE,

rather than time rate of change of CAPE

The Nested Grid Model (NGM)

Uses a modified Kuo scheme Convection triggered at a grid point when

– Moisture convergence in the lowest six layers reaches a certain threshold

– A parcel in the lowest four layers can achieve buoyancy if lifted

– Total moisture convergence in the column below cloud base is positive

The NGMContinued

Modifies by adjustment process, including converting 80% of moisture in the column to precipitation (with a corresponding latent heat release)

Precipitation allowed to fall and evaporate, but lower layers only need to reach 48% RH before precipitation falls to next layer

The Navy Operational Global Atmospheric Prediction System (NOGAPS 4)

Uses Relaxed Arakawa-Schubert (RAS) convective parameterization scheme

This version of AS scheme “relaxes” state toward equilibrium each time invoked instead of requiring end state to be balanced

Other noteworthy difference in RAS from AS relates to handling of detrainment

Precipitation assumed to fall to ground without re-evaporation into lower layers

European Centre for Medium-Range Weather Forecasts (ECMWF) Global Model

Uses mass flux convective parameterization as part of prognostic cloud scheme initially developed by Tiedtke

Tiedtke approach uses one-dimensional model to predict population of cloud types allowing for– Shallow convection – Deep convection (including anvil cirrus) – Elevated convection

The ECMWF Global Model Continued

Cumulus scale downdrafts included in Tiedke scheme

One strength of ECMWF global model - parameterization of precipitation processes handled same way for convective clouds as for all other clouds, including those resulting from large-scale ascent

The Rapid Update Cycle RUC-2

Uses a version of Grell convective parameterization scheme

Scheme was updated from RUC-1 scheme to fix– Downdraft detrainment– Calculation of cloud top– Minimum cloud depth– Capping criteria

The Rapid Update Cycle RUC-2 Continued

Produces somewhat larger amounts of precipitation and more coherent rainfall patterns in convective areas than RUC-1

Because Grell scheme includes downdrafts, RUC-2’s convective precipitation patterns may appear more detailed than those in the Eta model (which uses BMJ, no downdrafts)

MesoscaleModels

So it is important to know which scheme choice is operational in any mesoscale model accessed (as well as other physical parameterizations, PBL, etc.)– Does it modify by adjustment or mass flux? – How well do the various schemes interact?

MesoscaleModels Continued

Mesoscale models (including 32-km Eta) can predict precipitation resulting from storms associated with some topographically induced boundaries, slantwise convection, etc.

In mesoscale models, distinction between "grid-scale precipitation" and "convective precipitation" begins to disappear

In storm-scale models (< 2 km), all precipitation can be calculated "explicitly”; no convective parameterization is necessary (although microphysical processes are still parameterized)

MM5 Performance with Various Schemes

• 1997 study compared Anthes-Kuo (AK), Grell, BM, and KF schemes in 6 heavy precipitation events (both warm & cold season); some key results regarding precipitation forecast skill1 Skill higher for cold season events than for warm

season 2 Skill better for rainfall volume than areal coverage

or peak amount3 12-km grid superior to 36-km (especially for heavy

precipitation amounts)

MM5 Performance with Various Schemes Cont.

4 KF and Grell predicted total precipitation volume and storm life-cycles well, but over-predicted light precipitation

5 BM did good job of predicting areal extent of light precipitation and maximum rain rates, but tended to over predict areas of moderate to heavy rainfall in warm season

6 AK had the most difficulty predicting warm season events

MM5 Performance with Various Schemes Cont.

7 All 4 schemes had difficulty predicting high based convection

8 Overall, KF consistently performed best of those evaluated

9 Partition of rainfall into subgrid scale (that precipitation produced by the convective parameterization [CP] scheme) and grid-scale precipitation was more sensitive to the particular CP scheme chosen than to model grid size or convective environment

Discussion Questions

Why might the forecast skill of MM5 precipitation be better in the cold season than warm?

What is probably the main reason that KF and Grell were better at predicting storm life-cycles?

What might contribute to all of the schemes having difficulty with high-based convection?

The Future

• In the next 1-2 years, the operational NCEP suite will begin to assimilate precipitation data into the model’s initial conditions. They will use radar, rain gauge, and satellite data to initialize model clouds and precipitation during the data assimilation stage.

• This is expected to improve the initial specifications of humidity, vertical motion, and instability in the models and should lead to better numerical forecasts of convection and its associated precipitation