???? SST????

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• ????? SST???
• ?????????????

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Comparison of Terrain height in Taiwan
6km????
18km????
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54km MM5
OBS
Comparison of observed and simulation
precipitation
6km MM5
18km MM5
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• ???? (Wallace 1983)
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• ???? (Mesinger 1988)
• ????????????

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Radiation
Atmospheric temperature tendency
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Radiation

• ????????????????????????????????????????? (Kiehl
  1992)?

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WRF???????(ra_lw_physics)

• RRTM scheme Rapid Radiative Transfer Model. 16
  bands, CO2 , O3 , trace gases ,clouds (1).
• GFDL scheme 14 bands, CO2 , O3 , clouds (2).
• CAM scheme 2 bands Allows for aerosols and trace
  gases (3).

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WRF???????(ra_sw_physics)

• Dudhia scheme 1 band, with clouds and clear-sky
  absorption and scattering (1).
• Goddard shortwave 11 bands, with ozone profile
  and cloud effects (2).
• GFDL shortwave 12 bands, with ozone/ CO2 profile
  and cloud effects (99).
• CAM scheme 19 bands, with ozone/ CO2 profile and
  cloud effects. Allows for aerosols and trace
  gases (3).

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Microphysics

• Atmospheric heat and moisture tendencies
• Microphysics rates
• Surface rainfall

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Microphysics

• Water species
• water vapor, cloud water, rain, cloud ice,
  snow, and graupel (Mixing ratio of water species)
• Cloud processes
• - condensation, evaporation
• - sublimation
• - collision and coalescence
• - Bergeron process

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Grid-scale Precipitation parameterization (GP)

• Remove excess atmospheric moisture directly
  resulting from the dynamically driven forecast
  wind, temperature, and moisture fields.
• Diagnoses precipitation based on relative
  humidity (RH) in order to remove grid-scale
  supersaturation.

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Why is GP?
1. clouds and precipitation forced by grid-scale
motions cannot be predicted in complete detail
and must include at least some parameterization
2. changes the wind, temperature, and moisture
fields
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Types of Schemes

• Traditionally, GP schemes have been based solely
  on the prediction of relative humidity (RH).
• More realistic GP schemes range from accounting
  for cloud water to many types of hydrometeors and
  internal cloud processes

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Inferred cloud(1)
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• ?????????????????,????????

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Inferred cloud(2)

• Strengths
• - Run fast, at low computational cost
• - Conceptually simple and easily understood
• - Driven by the relatively well-forecast
  wind, temperature, and moisture fields - Perform
  acceptably in low-resolution models

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Inferred cloud(3)

• Limitations
• - Not physically realistic
• Precipitation is a direct result of RH
• No microphysics
• error in amount and location of
  precipitation
• independent of radiation

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Inferred cloud (3)

• Limitations
• - Data assimilation cannot incorporate cloud
  data
• less accurate initial moisture
• not always match satellite
• - All precipitation fall through in 1?t
  within 1 ?x.
• ignore suspended hydrometers and advection
• too quick onset of precipitation
• error in advected precip.
• - The precipitation rate is an average for 1
  ?x
• over or under forecasts

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Predicted Cloud schemes

• Follow a physically based sequence of forming
  clouds prior to precipitation.
• simple clouds diagnose precipitation from
  cloud water (or ice) only.
• complex clouds modeling of internal cloud
  processes, including multiple cloud and
  precipitation hydrometeor types.

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Simple cloud

• Strengths
• - improved precipitation amount and location
• - Allow direct comparisons of model cloud
  fields with satellite imagery
• - Allow assimilation of cloud data
• - better suited for higher-resolution models

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Simple cloud

• Limitations
• - More computationally expensive
• - Improvements in precipitation forecast are
  not complete
• - The precipitation rate is an average for a
  grid box
• - Microphysics are too simple to predict
  convective processes

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Complex cloud

• Strengths
• - directly predict precipitation type
• - directly predict cooling from evaporating
  and/or melting precipitation
• - Can depict convective system anvil extent
  and stratiform rain region
• - Assimilation of remote sensing data,
  environmental RH, and radiation scheme is
  further improved

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Complex cloud

• Limitations
• - expensive
• - Require sufficient model resolution to
  resolve small-scale variability affecting
  microphysical processes
• - Can be difficult to determine dominating
  hydrometeors
• - underprediction of clouds and precipitation
  early in the forecast since no hydrometeor types
  to assimilate

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WRF microphysics (mp_physics)

• Kessler scheme A warm-rain (i.e. no ice) scheme
  includes water vapor, cloud water, and rain. (1).
• Lin et al. scheme A sophisticated scheme that
  has ice, snow and graupel processes, suitable for
  real-data high-resolution simulations (2).
• WRF Single-Moment 3-class scheme A simple
  efficient scheme with vapor, cloud water/ice, and
  rain/snow, (simple-ice scheme).(3).
• WRF Single-Moment 5-class scheme A slightly more
  sophisticated version of WSM3 that allows for
  mixed-phase processes and super-cooled water.
  with vapor, cloud water,ice, and rain,snow (4).

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WRF microphysics (mp_physics)

• Eta microphysics A simple efficient scheme with
  diagnostic mixed-phase processes (5).
• WRF Single-Moment 6-class scheme A scheme with
  vapor, cloud water, rain, ice, snow and graupel
  processes suitable for high-resolution
  simulations (6).
• Thompson et al. scheme A new scheme with vapor,
  cloud water, rain, ice, snow and graupel
  processes suitable for high-resolution
  simulations (8)
• NCEP 3-class An older version of WSM3 (98).
• NCEP 5-class An older version of WSM5 (99).

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Recommendations about choice

• Probably not necessary to use a graupel scheme
  for dx gt 10 km
• - Updrafts producing graupel not resolved
• - Cheaper scheme may give similar results
• When resolving individual updrafts, graupel
  scheme should be used.
• All domains use same option

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6 hr accumulated Precipitation
2 km
1 km
WSM 6
WSM 4
WSM 3
Kessler
PLIN
0.5 km
Kim and Hong (2005)
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A real case comaprison between WSM6 and PLin12hr
Accumulated rain for Do1(27km)
WSM6
PLIN
OBS
WSM6-PLIN
Kim and Hong (2005)
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Cumulus parameterization (CP)

• Formulating the statistical effects of moist
  convection to obtain a closed system for
  predicting weather and climate
• Atmospheric heat and moisture/cloud tendencies,
  Surface rainfall

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Formulation of CP schemes

• All CP schemes must answer these key questions
• How does the large-scale weather pattern control
  the initiation, location, and intensity of
  convection?
• How does convection modify the environment?
• What are the properties of parameterized clouds?

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Formulation of CP schemes
A CP scheme need to define

• Activiation Trigger function
• Intensity Closure assumption
• Vertical distribution Cloud model or specified
  profile

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Convection Initiation

• Schemes can initiate convection by considering
  the
• Presence of some convective instability at a grid
  point
• Existence of low-level and/or vertically-integrate
  d mass/moisture convergence that exceeds some
  threshold at a grid point
• Rate of destabilization by the environment at a
  grid point

Adapted from Cliff (1998)
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Convection Intensity

• 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

Adapted from Cliff (1998)
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Convective feedback

• 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)

Adapted from Cliff (1998)
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Two Approaches to Convective Feedback

• Adjustment Schemes
• 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

Adapted from Cliff (1998)
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How Does This Help Use NWP?

• Knowing which convective parameterization scheme
  the model uses 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

Adapted from Cliff (1998)
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Kuo scheme

• Definition It adjusts the temperature and
  moisture profiles toward moist adiabatic
• Trigger CAPE and column-integrated moisture
  convergence exceeding a threshold value.
• Closure convection consumes moisture at the
  rate supplied by the large-scale wind and
  moisture fields.

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Kuo scheme

• Some of the moisture moistens the sounding while
  some falls instantly as rain.

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Kuo scheme
is column-integrated moisture convergence
is mean relative humidity of the column
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Kuo scheme

• - Strengths
• Easy to understand.
• Runs quickly
• - Limitations
• Simplistic scheme
• Does not account for the strength of cap.
• Positive feedback(latent heating induce low
  pressure deepening, which will cause further
  moisture convergence and trigger the scheme
  again)
• Many variations exist

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Betts-Miller scheme

• Definition It adjusts the sounding toward a
  pre-determined, post-convective reference profile
  derived from climatology.
• - No explicit updraft or downdraft
• - No cloud detrainment
• Trigger
• At least some CAPE
• Convective cloud depth exceeding a threshold
  value
• Moist soundings to activate
• Closure relieves instability everywhere it is
  present, given sufficient moisture.

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Betts-Miller scheme

• Shift the reference sounding to reach

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Betts-Miller scheme
Precipitation is defined as
Specific humidity
Cloud top pressure
Adjustment time
Cloud bottom pressure
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BM scheme

• - Strengths
• good in moist environments with little cap .
• Treats elevated convection better
• Runs quickly
• - Limitations
• The fixed reference profile may eliminate
  important vertical structure
• Is only triggered for soundings with deep
  moisture.
• the scheme often rains out too much water
• Does not account for any changes below cloud base

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Arakawa-Shubert scheme

• Definition
• account for the effect of cloud ensembles in a
  grid box
• Trigger
• - some boundary-layer CAPE
• - the presence of large-scale atmospheric
  destabilization with time.

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Arakawa-Shubert scheme

• Closure assumption
• Convection approximately compensates for
  changes in CAPE
• Cloud model
• Cloud detrainment at their tops, cloud
  entrainment at different height, environmental
  subsidence

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Arakawa-Shubert scheme

• Strengths
• - Accounts for the influences of entrainment,
  detrainment, cap,and compensating subsidence
• Limitations
• - May not sufficiently stabilize the model
  air
• - Is not designed for elevated convection
• - Assumes entrainment through the sides
• - Assumes that convection exists over only a
  very small fraction of the grid column
• - takes longer to run

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Grell scheme

• A modification of AS scheme
• only one cloud type instead of clouds of
  different depths
• accounts for using more CAPE rather than just
  compensating for the CAPE tendency
• With parameterized downdrafts
• Subgrid precipitation is calculated as

I1 integrated condensate in the updraft mb
cloud-base mass flux of the updraft 1-?
precipitaion efficiency
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Kain_Fritsch scheme

• Definition to rearrange mass in a column so that
  CAPE is consumed
• Trigger
• - The sounding has CAPE for source parcels
  from a low-level layer 50 to 100 hPa thick- The
  cap is small enough for a parcel to penetrate
  given a boost of a few m/s
• - The convective cloud depth exceeds a
  threshold

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Kain_Fritsch scheme

• Closure assumption the convection is determined
  by CAPE at a grid point.
• Cloud model mass-conservative, allows
  cloud-environment interaction, entrainment and
  detrainment at cloud edge, environmental
  subsidence, and evaporatively driven downdrafts.
• Convective precipitation

E precipitation efficiency S sum of vertical
fluxes of vapor and liquid at about 150hPa above
LCL.
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Kain_Fritsch scheme

• Strengths
• - Suitable for mesoscale models
• - Physically realistic in many ways
• Limitations
• - Tends to leave unrealistically deep
  saturated layers in post-convective soundings
• - Takes longer to run than simpler schemes

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Recommendations about use

• For dx 10 km probably need cumulus scheme
• For dx 3 km probably do not need scheme
• - earlier triggering of convection by cumulus
  schemes may help
• For dx3-10 km, scale separation is a question
• - No schemes are specifically designed with
  this range of scales in mind
• Issues with 2-way nesting
• - best to use same physics in both domains or
  1-way nesting

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Compared Anthes-Kuo (AK), Grell, BM, and KF
schemes in 6 heavy precipitation events (both
warm cold season) in MM5 regarding
precipitation forecast skill
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• Skill higher for cold season events than for warm
  season
• None of the schemes performs consistently better
  for warm season
• Skill better for rainfall volume than areal
  coverage or peak amount
• 12-km grid superior to 36-km (especially for
  heavy precipitation amounts)

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• KF and Grell predicted total precipitation volume
  and storm life-cycles well, but over-predicted
  light precipitation
• 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
• Anthes-Kuo had the most difficulty predicting
  warm season events

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- All 4 schemes had difficulty predicting high
based convection - Overall, KF consistently
performed best of those evaluated - Partition of
rainfall into subgrid scale and grid-scale
precipitation was more sensitive to the
particular CP scheme chosen than to model grid
size or convective environment
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????(Matsa)60h???????CP????
???,???,???(2009)
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Cumulus Parameterization (cu_physics)

• Kain-Fritsch scheme Deep and shallow convection
  sub-grid scheme using a mass flux approach with
  downdrafts and CAPE removal time scale (1).
• Betts-Miller-Janjic scheme. Operational Eta
  scheme. Column moist adjustment scheme relaxing
  towards a well-mixed profile (2).
• Grell-Devenyi ensemble scheme Multi-closure,
  multi-parameter, ensemble method with typically
  144 sub-grid members (3).
• Old Kain-Fritsch scheme Deep convection scheme
  using a mass flux approach with downdrafts and
  CAPE removal time scale (99).