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:

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 ???HybridTraditional Grid 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  T d  T sounding 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  T d 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 “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 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 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 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)