model parameterizations: issues important for heavy rainfall forecasting mike baldwin nssl spc cimms

Model Parameterizations: Issues important for heavy rainfall forecasting

Mike Baldwin

NSSL SPC CIMMS

Skill of current operational models

• To put it kindly…

• Current NCEP models do a poor job forecasting heavy rainfall

• AVN (red), Eta (green), NGM (blue) for Jan-Sept 2000

Ingredients needed by a model in order to predict some phenomena of interest

• Adequate grid spacing– To be able to resolve the feature

• Physical processes– All those important in the development,

maintenance, and decay of the feature

• Dynamics– Accuracy, hydrostatic/non-hydrostatic

• Adequate initial/boundary conditions– To be able to capture important forcing

Ask yourself:• “Does the model I’m using have the necessary

ingredients to predict the feature(s) that I’m considering or expecting?”

YES: model guidance taken literally can be useful

NO: model by itself is of little value (but not worthless)

Either way: knowledge of model characteristics will increase the value of NWP guidance

What are Model Parameterizations?

• Techniques used in NWP to predict the collective effects of physical processes which cannot be explicitly resolved

• Sub-grid scale or perhaps near-grid scale processes:

• For example; cloud physics, convection, turbulent mixing, radiation, surface exchanges

Interaction between different processes is critical

• Especially for mesoscale models• Not only important to do a good job with

a specific physical process• All pieces have to work well together in

order for model to perform well• Several studies have shown great

forecast sensitivity to subtle changes to a parameterization

Outline

• Quick overview of convective parameterizations currently available in NWP models (both operational and research)

• Look at some research/case studies of NWP performance in heavy rain events

• Talk about future

Current EMC models use different approaches

• RUC II: Grell scheme

• Eta: Betts-Miller-Janjic (Kain-Fritsch used experimentally at NSSL & SPC)

• MRF/AVN: Grell-Pan scheme– Grell, Grell-Pan, and Kain-Fritsch schemes are Mass-Flux schemes, meaning they use simple cloud models to simulate rearrangements of mass in a vertical column

– Betts-Miller-Janjic adjusts to “mean post-convective profiles” based on observational studies

“Mass-flux” parameterization

MM5 Model Physics Options• Precipitation physics

– Cumulus parameterization schemes: • Anthes-Kuo • Grell • Kain-Fritsch • Fritsch-Chappell• Betts-Miller • Arakawa-Schubert

– Resolvable-scale microphysics schemes:• Removal of supersaturation• Hsie's warm rain scheme• Dudhia's simple ice scheme• Reisner's mixed-phase scheme • Reisner's mixed-phase scheme with graupel• NASA/Goddard microphysics with hail/graupel • Schultz mixed-phase scheme with graupel

MM5 Model Physics Options• Planetary boundary layer parameterization

• Bulk formula • Blackadar scheme • Burk-Thompson (Mellor-Yamada 1.5-order/level-2.5 scheme) • Eta scheme (Janjic, 1990, 1994) • MRF scheme (Hong and Pan 1996)• Gayno-Seaman scheme (Gayno 1994)

• Surface layer process parameterization• fluxes of momentum, sensible and latent heat• ground temperature prediction using energy balance equation • variable land use catagories (defaults are 13, 16 and 24)• 5-layer soil model• OSU land-surface model (V3 only)

MM5 Model Physics Options

• Atmospheric radiation schemes:• Simple cooling • Dudhia's long- and short-wave radiation scheme • NCAR/CCM2 radiation scheme • RRTM long-wave radiation scheme (Mlawer et al., 1997)

(copied from MM5 web page: www.mmm.ucar.edu/mm5/)

What do all convective parameterizations do?• Predict convective precipitation• Feedback onto larger scales the effects of

transports, mixing, circulations, etc. found within convective elements

• Change vertical stability• Redistribute and generate heat• Redistribute and remove moisture• Make clouds that affect surface heating and

atmospheric radiation

How do convective schemes accomplish these tasks?

• convective triggering (yes/no)

• convective intensity (how much rain?)

• vertical distribution of heating

• vertical distribution of drying

Triggering/activating

• CAPE (ALL)

• mass or moisture convergence exceeding a certain threshold (Kuo)

• positive destabilization rate (Grell)

• perturbed parcels can reach their level of free convection (KF)

• sufficient cloud layer moisture (BMJ)

Convective intensity

• proportional to mass or moisture convergence (Kuo)

• sufficient to offset large-scale destabilization rate (Grell)

• sufficient to eliminate CAPE, constrained by available moisture (KF)

• proportional to cloud layer moisture (BMJ)

Vertical distribution of heating and drying

• determined by adjusting to empirical reference profiles (BMJ, Kuo)

• estimated using a simple 1-D cloud model to satisfy the constraints on intensity (Grell, KF)

Different ways to classify convective schemes

• Molinari and Dudek (1992)• Traditional

• clear separation between convective and stratiform or grid-scale precipitation

• Hybrid• direct interaction between convective and grid-scale physics

• Fully Explicit• grid-scale cloud and precipitation physics ONLY

Fully explicit

??? Hybrid Traditional

0.1 6050403020101Grid Spacing (km)

Example: Betts-Miller-Janjic (BMJ) scheme – Eta Model

• BMJ scheme requires some CAPE• “Equilibrium-type” scheme• Deep and shallow components

– Deep = precipitating– Shallow = non-precipitating

• Critical factor in determining yes/no/amount of precipitation is the cloud layer moisture

A few quick points…

• Deep convection is given first priority

• Deep convection will fail if – cloud layer is too dry – cloud is too shallow

• Scheme defers to shallow convection if deep convection fails

• No feedback of cloud water/ice

Deep convection example

– KSBN 18h forecast from 00Z 31 May 2000 Eta run

Cloud depth

Feedback from BMJ scheme

Td

Tsounding 3h earlier

What does BMJ deep convection do?

• Stabilize the cloud layer

• Typically heats mid/upper cloud

• Dries lower part of cloud

• Does not modify the sub-cloud layer

• Feedback reduces CAPE and precipitable water

Shallow convection example

– KOKC 4h forecast from 12Z 1 Jun 2000 Eta run

Cloud depth

Feedback from BMJ scheme

T Td

sounding 4h earlier

What does BMJ shallow convection do?

• Mixes moisture up from cloud base to cloud top

• Mixes heat down from cloud top to cloud base (destabilizes the cloud layer)

• Location of cloud base & top critical for determining impact on forecast fields

• Could affect lapse rates, cap strength• Does not affect precipitable water

How to recognize BMJ shallow convection

• No convective precipitation• Forecast sounding has a smoothly

varying moisture profile up to ~200mb deep (usually concave shape)

• “Straight-line” temperature profile over the same layer, just above LCL

• Base of unusual mid-tropospheric inversion indicates cloud top

Gallus (1999) Weather and Forecasting p. 405-426

• “Eta simulations of three extreme precipitation events: Sensitivity to resolution and convective parameterization”

• Ran Eta Model at four different horizontal resolutions (78, 39, 22, and 12km) and with two convective schemes (BMJ vs. KF)

• Variations in precipitation forecasts were found to be highly case dependent

Gallus (1999) Figure 1a

• 16-17 Jun 1996• MCS formed over

central IA in warm sector ahead of sfc low

• Heavy rains also occurred north of warm front in WI

Gallus (1999) Figure 3

Gallus (1999) Figure 10

• BMJ runs

• 78, 39, 22, and 12km res

• Contours at 5mm, then every 25mm

Gallus (1999) Figure 13

• KF runs

• 78, 39, 22, and 12 km res

• Contours at 5mm, then every 25mm

Items to note for this case

• BMJ runs DO NOT produce higher precip amounts as resolution increases

• KF runs DO produce higher precip amounts as resolution increases

• BMJ produced a broad area of precipitation that covered observed region for many hours

• Peak amounts in high-res KF runs produced mainly by grid-scale precipitation scheme

Gallus (1999) Figure 1b

• 16-17 Jul 1996

• MCS/MCC developed north of warm front

• Training cells found in region of peak rain

Gallus (1999) Figure 6

Gallus (1999) Figure 7

• Omaha sounding 00 UTC 17 Jul 96

• 2000 J/kg CAPE, less than 25 J/kg CIN (above inversion)

• Example BMJ reference profiles (dashed)

Gallus (1999) Figure 15

• BMJ runs• Peak ppt

decreases as resolution increases

• Areal coverage increases with resolution

Gallus (1999) Figure 16

• KF runs

• Location errors are large, too far to the north

• Peak amounts are more reasonable and increase with resolution

Gallus (1999) Figure 1c

• 27 May 1997• Jarrell TX tornado

outbreak• Supercell-type

heavy rain event• Boundary

interaction important

Gallus (1999) Figure 8

Gallus (1999) Figure 9

• Estimated sounding near Jarrell at 18 UTC 27 May 97

• CAPE near 5000 J/kg, no CIN

Gallus (1999) Figure 19

• BMJ runs• Peak ppt

increases with resolution

• Heaviest ppt produced mainly by grid-scale scheme in high-res runs

Gallus (1999) Figure 21

• KF runs

• Peak ppt increases slightly with resolution

Gallus (1999) Figure 20

• BMJ moisture divergence at 15 UTC

• Outflow-type circulation initiated in NW Texas

• Moved to the southwest

Lessons learned…

• This study shows great sensitivity of the QPF to different convective schemes and horizontal resolutions

• No consistent behavior by either scheme from case to case

• Should expect great difficulty in developing a model to predict heavy rainfall with accuracy and consistency

Sensitivity, continued…

• Not only are models sensitive to different parameterizations

• Changes to a single parameterization can also produce significant differences in model QPF

• Spencer and Stensrud (1998) MWR for example

Future: Eta Model

• 10km Eta is coming in 2001

• should include precipitation data in data assimilation (initial conditions)– helps mainly during early part of forecast

• no major changes to model parameterizations are expected for 10km Eta implementation

Future: short-range ensemble forecasting

• varying both initial conditions and model configuration

• goal: to predict the range of possible scenarios and forecast uncertainty

• problems: – low correlation between spread and errors– difficult to produce much spread among

ensemble members

Stensrud et al (2000) MWR

• Found that varying initial conditions produced the “best” ensemble when the large-scale forcing was strong

• When the large-scale forcing was weak, varying model physics produced the “best” ensemble

MM5 ensemble (NSSL) for 3 May 1999 case (24h fcst)

• 3 different convective schemes (KF, Grell, BMJ)

• 2 different PBL schemes (BL, BT)

MM5 ensemble for 3 May 1999 case

MM5 ensemble (NSSL) for 3 May 1999 case (30h fcst)

Future: local modeling

– workstation Eta (Bob Rozumalski)• easy to set up and run• can run at high resolution in near real-time on a

cheap Linux box

– WRF (Weather Research and Forecasting) model

• next-generation NWP modeling system for research AND operations

• large development effort• test versions currently available

Future: neural networks

• what is a neural network?

• model that “learns” the relationship between input (observations, NWP output) and output (QPF)

• Hall et al (1999) for example show incredible results for local QPF (DFW)