Transcript Varying Model Parameterizations

Varying Model Parameters And
Their Relation To Ensembles
Nick Bassill
AOS 900
Wednesday, November 4th
A Look At The Future …
• Ideally, real-time modeling systems will
eventually contain these two things:
- An ensemble with hundreds (thousands? more?)
of members
- A perfect statistical framework for accurately
interpreting this vast amount of data
• We are arguably closer to realizing the first of
these goals
• Currently, modeling systems have on the order of
101 of ensemble members
What Is An “Ensemble”?
• An ensemble is a collection of different model
simulations (“members”) performed for the same
area (for example, the Atlantic Ocean)
• However, each simulation will have different
characteristics, such as:
–
–
–
–
Different initial conditions
Different dynamical cores
Different grid spacings
Different physics options (i.e. variable
parameterizations)
An Ensemble Example – Initial Time
From: http://www.nco.ncep.noaa.gov/pmb/nwprod/analysis/
An Ensemble Example – 120 Hours
From: http://www.nco.ncep.noaa.gov/pmb/nwprod/analysis/
Or, Ensembles Can Represent Specific
Storm Tracks …
Now, Starting From The Beginning …
• The complexity of numerical models is strongly tied
to available computer power
• Therefore, models did not really exist before the
1960s
• Early models were largely idealized models not
designed for day-to-day forecasting, or even for
simulating past events
• These models often did not incorporate complex, yet
important processes such as latent heat release,
radiational effects, or boundary layer processes
The Beginning … Continued
• These models also used a very coarse
resolution (i.e. large grid spacing with few
vertical levels)
• All of these factors made ensemble creation
essentially impossible until much later
• However, once sufficient computer power
became available
Computational Advances
http://en.wikipedia.org/wiki/File:Transistor_Count_and_Moore%27s_Law_-_2008.svg
Data Storage Advances
http://en.wikipedia.org/wiki/File:Hard_drive_capacity_over_time.svg
Parameterizations
• The AMS Glossary defines a parameterization as
“the representation, in a dynamic model, of physical
effects in terms of admittedly oversimplified
parameters, rather than realistically requiring such
effects to be consequences of the dynamics of the
system”
• Therefore, in many ways parameterizations are
inherently unrealistic
• However, they are preferable to the alternative of not
including important processes
*http://amsglossary.allenpress.com/glossary/search?id=parameterization1
Parameterizations Continued
• Some common parameterizations are boundary layer
parameterizations, cumulus parameterizations,
microphysics parameterizations, and
shortwave/longwave radiation parameterizations,
among many others
• For example, a computer could never simulate the
motion of every droplet in a cloud, or the latent heat
released or absorbed by the formation or dissipation of
every cloud droplet
• However, given some understanding of the atmosphere,
a parameterization can make reasonable guesses for
things like droplet fall speed, latent heat release, phase
conversions, etc.
Cumulus Parameterizations
• Cumulus clouds generally have a scale of roughly
1 km
• However, even current real-time high-resolution
models only have a grid spacing of about 4 km
• This necessitates the need for cumulus
parameterizations (CPs) for most models
• CPs are one of the more well-known
parameterizations, as well as one of the first to be
used in sensitivity experiments
From: http://www.mmm.ucar.edu/mm5/documents/MM5_tut_Web_notes/MM5/mm5.htm
Rosenthal (1979)
• Rosenthal was the first to vary any sort of
model parameter
• However, given limited computer power,
his model was an idealized hydrostatic,
axisymmetric model of a tropical cyclone
(TC) with a 20 km horizontal grid spacing
• Rosenthal studied the effect of varying his
choice of CP on the TC in his simulation
Rosenthal Continued
• Rosenthal demonstrated that changing the CP in
his model could significantly alter the evolution
(size, intensity) of the tropical cyclone he was
simulating
• Studies like this led to other similar research, with
ever-increasing model complexity (and therefore
realism)
• As time continued, operational models were
developed that were non-hydrostatic, and which
included much more detailed and improved
parameterizations
Baik et al. (1991)
• Baik et al. performed a very similar study to that
of Rosenthal, in that he varied CPs while using
almost the identical model
• In this study, Baik et al. also varied individual
parameters within each CP, such as evaporation
and condensation
• They demonstrated that using a CP at this gridspacing produced a more realistic result than not
using one
• Similar to Rosenthal, they also found large
differences between different CPs
Maximum Low-level Tangential Wind (m/s)
Baik et al. Continued
• Changing the CP used,
as well as changing the
characteristics of latent
heat release significantly
impact the profile of
heating
• Heating maxima in the
lower troposphere are
more conducive to rapid
TC strengthening
Puri and Miller (1990)
• Puri and Miller used the operational
ECMWF model to study the sensitivity of
several Australian TCs to choice of CP
• They determined that different CPs led to
different track and intensity forecasts
• Additionally, they found that different CPs
created different storm structures, with
different heating rates
Some Differences
Lord et al. (1984)
• Lord et al. used an axisymmetric, nonhydrostatic
model with a 2 km horizontal grid spacing in order
to study the effects of varying microphysics
parameterizations (MPs) on simulations of
hurricanes without the use of a CP
• They compared a simulation with only liquid
hydrometeors to one where ice was included
• Although they both reached similar final
intensities, their evolution was quite different
From: http://www.mmm.ucar.edu/mm5/documents/MM5_tut_Web_notes/MM5/mm5-16.gif
Lord et al. Continued
• Ice processes produced downdrafts, due to
localized cooling directly below the melting layer
• These are important for features such as
concentric eyewall development
McCumber et al. (1991)
• McCumber et al. studied the sensitivity of tropical
convection to varying the MP by comparing
simulations to radar data
• They determined that more complex MPs
produced more accurate simulations (i.e. MPs
incorporating ice processes did better than those
without ice processes)
• Furthermore, different MPs produced different
heating rates, even ones which included the same
numbers of ice processes
• This was due primarily to the different fall speeds
of the different particles
From
McCumber et al.
McCumber et al.
• Microphysics are
complicated!
• Microphysics are highly
variable, depending on
how many different water
types are included, and
how they interact
Parameterization Interactions In Numerical
Weather Prediction Models
From: http://www.mmm.ucar.edu/mm5/documents/MM5_tut_Web_notes/MM5/mm5.htm
Wang and Seaman (1997)
• Wang and Seaman compared the effectiveness of
different parameterizations and grid spacings for a
variety of events
• They examined a number of variables such as light
and heavy precipitation (their focus), temperature,
sea-level pressure, and winds
• One of the key features of their study was the use
of sophisticated statistical tools (such as threat
scores and bias scores), which is the first such
analysis to be performed in a study which varies
parameterizations or grid spacings
Wang and Seaman Conclusions
• They concluded that different grid spacings and CPs
responded differently to different situations
• For example, grid spacing did not matter for forecasting
light precipitation, but smaller grid spacings improved
forecasting of heavy precipitation
• Also, some CPs performed best for warm season
precipitation, and some performed best for cold season
precipitation
• “… and the predictive skill of each CPS has a fairly large
case-to-case variation in the warm-season events. None of
the schemes consistently out performs the others by a
wide margin or in all measures of skill.”
Zhu and Zhang (2006)
• Zhu and Zhang simulate the evolution of
Hurricane Bonnie (1998), while varying the MP
variables
• They found that while the forecast tracks were
similar, the intensities varied considerably
• While the simulation without ice processes was
the weakest, they discovered that it could
artificially be made to be like the others if the
latent heat of freezing/melting were artificially
added (even though the model didn’t actually
include frozen particles)
Zhu and Zhang
Continued
• The importance of latent
heating is made very evident in
their experiments
Fovell and Corbosiero (2009)
• Fovell and Corbosiero used idealized simulations to determine the effect
varying MP parameters have on tropical cyclone motion
• They found that the MP without ice processes produced a much more
northward track, due to enhanced Beta drift caused by the storm’s large size
Fovell and Corbosiero Continued
• The absence of ice allows for a much larger anvil, and therefore
a larger storm
• However, by manipulating the fall speed of hydrometeors, any
MP simulation can effectively by made to reproduce another
Stensrud et al. (2000)
• Stensrud et al. compare two different ensembles
for short-term MCS forecasting:
- one ensemble used the same physics packages, but
different initial conditions
- one ensemble used the same initial conditions, but
different physic packages
• Like Wang and Seaman (1997), they use a number
of statistical techniques to determine which is
“best”
• Also like Wang and Seaman (1997), they
determined that different situations result in better
performances for one ensemble versus another
Stensruds et al. Conclusions
• They concluded the physics ensemble is more
successful when the large-scale forcing is weak
• The initial condition ensemble is more successful
for strong large-scale forcing
• However, the variance in ensemble members
increases 2-6 times faster for the physics ensemble
• This makes the physics ensemble more useful
from a forecasting perspective, because it will
include more potential solutions
Stensrud et al. final
suggestion:
Combine these two types of
ensemble concepts into one
to produce a superior result!
Here’s a conceptual model:
General Conclusions
• Clearly, research using numerical weather
prediction models had to wait until computer
technology advanced sufficiently
• Additionally, the concept of ensembles didn’t
really develop until an adequate amount of
research had been accomplished such that people
realized that changing initial conditions,
parameterizations, dynamical cores, etc., produced
different forecasts
• As shown in these studies (and many others),
choice of parameterization can have a profound
effect on forecasts
Current/Future Research
• Studies such as these reinforce the notion that no one
parameterization is “best” for all situations
• However, it seems possible to predict in advance
which parameterizations will be biased (and in which
way) for a given event
• This implies that a collection (or ensemble) of
parameterizations might be the best approach to
forecasting an event or series of events
• The current endeavor is to compare the effectiveness
of an ensemble comprised of different
parameterizations to an ensemble comprised of
different initial and boundary conditions
References
Baik, J.J., M. DeMaria, and S. Raman, 1991: Tropical Cyclone Simulations with the Betts
Convective Adjustment Scheme. Part III: Comparisons with the Kuo Convective
Parameterization. Mon. Wea. Rev., 119, 2889–2899.
Fovell, R.G., K.L. Corbosiero, and H.C. Kuo, 2009: Cloud Microphysics Impact on
Hurricane Track as Revealed in Idealized Experiments. J. Atmos. Sci., 66, 1764–1778.
Lord, S.J., H.E. Willoughby, and J.M. Piotrowicz, 1984: Role of a Parameterized Ice-Phase
Microphysics in an Axisymmetric, Nonhydrostatic Tropical Cyclone Model. J. Atmos.
Sci., 41, 2836–2848.
McCumber, M., W.K. Tao, J. Simpson, R. Penc, and S.T. Soong, 1991: Comparison of Ice
Phase Microphysical Parameterization Schemes Using Numerical Simulations of
Tropical Convection. J. Appl. Meteor., 30, 985–1004.
Puri, K., and M. Miller, 1990: Sensitivity of ECMWF Analyses-Forecasts of Tropical
Cyclones to Cumulus Parameterization. Mon. Wea. Rev., 118, 1709–1742.
Stensrud, D., J. Bao, and T. Wagner, 2000: Using Initial Condition and Model Physics
Perturbations in Short-Range Ensemble Simulations of Mesoscale Convective Systems.
Mon. Wea. Rev., 128, 2077–2107.
Rosenthal, S.L., 1979: The Sensitivity of Simulated Hurricane Development to Cumulus
Parameterization Details. Mon. Wea. Rev., 107, 193–197.
Wang, W., and N.L. Seaman, 1997: A Comparison Study of Convective Parameterization
Schemes in a Mesoscale Model. Mon. Wea. Rev., 125, 252–278.
Zhu, T., and D.L. Zhang, 2006: Numerical Simulation of Hurricane Bonnie (1998). Part II:
Sensitivity to Varying Cloud Microphysical Processes. J. Atmos. Sci., 63, 109–126.