modelling the high resolution structure of frontal rainbands

1 12/09/2002 © Crown copyright

Modelling the high resolution structure of frontal rainbands

Talk Outline Resolution dependence of extra-tropical cyclone

modelling The impact of ice evaporation on frontal dynamics

Peter Clark, Richard Forbes, Humphrey Lean

Met Office (JCMM)

Research at the Joint Centre for Mesoscale Meteorology “Stormscale” NWP: the Unified Model at ~1km resolution Convection / Microphysics / Validation using radar / Data assimilation


FASTEX IOP 16 Cyclone12Z 17/02/1997

Cloud Head

Convection


IOP 16 Radar and Dropsonde dataC130 Run 5C130 Run 1

Figures show C130 w, IWC, windP3 Radar reflectivity

Roberts and Forbes (2002, Atmos. Sci. Lett.)


FASTEX IOP 16 Cyclone Emerging multiple cloud heads

06Z 09Z09Z 12Z


Non-Hydrostatic Simulations

60km global

Horizontal Resolutions:

24km, 12km, 4km, 2km LAMs, ~300x250 grid

points

Vertical Resolutions:

2km with 45, 90 and 135 levels

i.e. 400m, 200m and 130m mid-trop. layer

spacing approx.

12km ‘AC’ scheme analysis valid at 0Z, 9 hour forecast


FASTEX IOP 16 Cyclone Simulation

12 km

4 km

2 km


800 hPa Vertical Velocity as a function of resolution ; FASTEX IOP

16 Cyclone60 km

24 km12 km

4 km 2 km

-0.15 -0.75m/s


Power Spectrum of vertical velocity

Possible aliasing from fronts(not present in 2 km run)

Same Region at 60, 24, 12, 4, 2 km grid

Excluding frontal region


Frontal Collapse to gridscale

12 km

4 km

2 km


Ratio of Vertical Velocity Power Spectrum to Spectrum at 2 km

Normalized By Grid Scale

dx/2dx/5



16 Cyclone60 km

24 km12 km

4 km 2 km

-0.15 -0.75m/s

Cross Section 1

09Z (T+9) 12/02/1997


Cross Frontal Velocity as a function of resolution ; FASTEX IOP 16

Cyclone

60 km

24 km

12 km

4 km

2 km

-15 0.0m/s 5-10

450 km

1000 hPa

0 hPa


Slantwise instability of front: 2km Simulations

400 hPa

45 Levels~ 400 m

90 Levels~ 200 m

135 Levels ~ 130 m

200 km

~75 hPa

Slope: 1/20-1/30

950 hPa


Slantwise instability of front: 2km 90 level simulations

No sublimation cooling

Reference



16 Cyclone60 km

24 km12 km

4 km 2 km

-0.1 0.3m/s

Averaged to 60 km grid


Vertical Velocity at 800 hPa24 km L45 12 km L45 4 km L45

2 km L45 2 km L90 2 km L135


Vertical Velocity at 800 hPaaveraged to 60 km

24 km L45 12 km L45 4 km L45

2 km L45 2 km L90 2 km L135


Vertical VelocityCross section

24 km L45 12 km L45 4 km L45

2 km L45 2 km L90 2 km L135


Vertical Velocity Averaged to 60 km

24 km L45 12 km L45 4 km L45

2 km L45 2 km L90 2 km L135


Dry PV at 800 hPa24 km L45 12 km L45 4 km L45

2 km L45 2 km L90 2 km L135


PV Cross Section

3x10-6-3x10-6

24 km L45 12 km L45 4 km L45

2 km L45 2 km L90 2 km L135


PV Cross Section (120 km blowup)

24 km L45 12 km L45 4 km L45

2 km L45 2 km L90 2 km L135


PV Cross SectionAveraged to 60 km

3x10-60

24 km L45 12 km L45 4 km L45

2 km L45 2 km L90 2 km L135


PV Generation

Ice Fallout

Saturated Ascent

Saturated Descent

-

+

-• On the larger scale, symmetric dipole/tripole anomalies cancel each other out.

•Monopole dominates far-field response.

• Dipole/tripole component of a PV anomaly has only a weak influence on the far field.


Conclusions Model properly represents scales greater than about 5 gridlengths

Probably some frontal aliasing at resolutions of 4km+

Over a short forecast, high resolution simulation has only a small impact on larger scales - greater near surface fronts than in slantwise ascent

High resolution has little net impact on overall diabatic heating and on PV generation, i.e. slantwise overturning is not a strong net generator (saturated descent)

This may be why climate models work (at all!)


Impact of evaporative cooling on frontal dynamics

Cooling due to rain/snow evaporation significantly enhances frontal downdraughts– A number of papers have shown the importance of downdraughts in convective systems

– Observations of strong narrow frontal downdraughts: Thorpe and Clough (1991), Browning et al. (1997)

– 2D semi-geostrophic models of frontogenesis: Huang and Emanuel (1991) , Parker and Thorpe (1995)

– 3D idealised front in the Met Office Unified Model (UM)

– FASTEX frontal cyclone case studies using the UM: Forbes and Clark (2003, submitted to QJRMS)

For high resolution forecasting, NWP models require the correct distribution of diabatic heating and evaporative cooling.

Main surface “weather” impacts: Distribution of precipitation, wind gusts


Hypotheses Ice evaporation is rapid in the subsaturated zone beneath a frontal surface

leading to intense cooling in a shallow layer with a significant local dynamical impact.

The Unified Model does not represent the profile of evaporative cooling accurately, resulting in an incorrect dynamical response.

The UM can be improved by varying microphysical parameters within their bounds of uncertainty.

Method Evaluate the model against observations. Understand the uncertainty in the formulation of microphysics

parametrizations. Determine the sensitivity of the moisture/dynamical fields to these

uncertainties. Predict parameter changes to give a closer fit to the observations.


Idealised Front

Refe

rence

fore

cast

Downdraught is significantly weaker when there is no ice evaporative cooling

Diabatic Heating Rate (K/hr)

Cloud Ice (g/kg) Vertical Wind (m/s)


FASTEX IOP16:Dynamical impact of evaporative cooling

Plan view of vertical velocity at 800hPa with and without ice

evaporative cooling

Evaporative cooling leads to enhanced descent beneath the frontal cloud band and enhanced ascent in the frontal updraught


Does the model have the correct characteristics in the evaporation cooling zone beneath frontal updraughts ?

Few observations of downdraughts, so use proxy data. The important parameters are the vertical ice flux and the depth scale of the cooling. Can derive ice water content and evaporation depths from radar observations.

Perform statistical comparison between model and observations (1 year of data).– Timeseries from operational Unified Model forecasts Includes mixed-

phase microphysics parametrization (Wilson and Ballard, 1999)

– Timeseries of radar reflectivity data from the vertically pointing 94GHz cloud radar based at Chilbolton. Convert to ice water content and average to model resolution.

Algorithm extracts vertical profiles which contain ice evaporation beneath stratiform cloud.


Observations of Evaporation

02 Apr 2000 11 Dec 1999

94GHz Radar Derived Ice Water Content (below 0degC)

Radar data provided by Robin Hogan (Reading Univ.) and RCRU (RAL)


Model/Obs Comparison

Operational model 12km resolution

Radar averaged to

12km

Evaporation depth scale cumulative probability from the operational Unified Model and the radar data averaged to 12km

Probability of ice evaporative depth scale from 1 year of data



Average ice evaporative depth scale from the Chilbolton 94 GHz cloud radar and the operational UM for 20 separate days in Oct, Nov, Dec 1999.



Probability of ice water content for the model and radar


What are the reasons for the difference between the model and the observed depth scales ?

Hypotheses:

– Inadequate vertical resolution (model layers are 500-750m)

– Too much ice

– Relative humidity forcing in the model subsaturated zone is too moist

– Parametrized ice particle evaporation rate is too low

– Parametrized ice particle terminal fall speed is too high

Reasons for Model/Obs Differences


Microphysics Parametrization

Uncertainty in microphysical parameters– Various sources of uncertainty in microphysics parametrization schemes due to:

» insufficient knowledge of the real world

» simplification of the processes in the parametrization.

– The ice evaporation rate and ice terminal fall speed parametrizations in the model could have a bias of a factor of 2.

Sensitivity of forecasts to parameter uncertainty – Determined the sensitivity of the model to ice evaporation rate and fall speed variations within the bounds of uncertainty (analytical, 1D evaporation model, 3D frontal cyclone

forecasts)

– Increasing ice evaporation rate -> stronger narrower downdraughts

– Decreasing ice fall speed -> stronger narrower downdraughts, higher ice content


FASTEX IOP 16: Sensitivity

Increasing the ice evaporation rate increases frontal development Increasing ice terminal fall speed decreases frontal development

Sensitivity of the model to changes that affect ice sublimation


IOP 16 Sensitivity Forecasts:Surface precipitation

Increasing ice fall speed

Increasing dep/sublim

rate


Summary of the Model Evaluation

Model Errors

– Ice evaporation depths are too deep (by a factor of 2-3)

– Ice water contents are too low (by up to a factor of 2) (?)

Suggested changes:

– decrease ice fall speed (consistent with observed ice fallspeeds)

– increase ice evaporation rate (consistent with ventilation assumption)

– increase vertical resolution to at least 250m in mid-troposphere

Predicted Impacts

– Higher ice water content and shallower, stronger frontal downdraughts.


FASTEX IOP 16: Validation

Comparison of the model ice evaporative depth scales with 94GHz radar observation

statistics

Average depth scale

Reference:1260 m

Modified: 780 m

Obs: 640 m (160m)


Conclusions Ice evaporation is rapid in the subsaturated zone beneath a frontal surface leading

to intense cooling in a shallow layer. – Evaporation depth scale < 1km beneath stratiform frontal cloud

– Average cooling rate of 1 K/hr.

The Unified Model does not represent the profile of evaporative cooling accurately, resulting in an incorrect dynamical response.

– UM underestimates the amount of ice and overestimates the evaporative depth scales

– This leads to a significant underestimate in the cooling and strength of frontal circulations.

– Due to poor vertical resolution, weak ice evaporation rate, high fall speed and moist bias.

The UM can be improved by varying microphysical parameters within their bounds of uncertainty.

– Many uncertainties in the microphysics of complex irregular ice particles and in microphysics parametrization formulation.

– Estimate an uncertainty of up to a factor of two in the fall speed and evaporation rate.

– Modifiying these two parameters improves the model fit with observations.

modelling the high resolution structure of frontal rainbands

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