An intraseasonal moisture nudging experiment in a tropical channel version of the WRF model: The...
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![Page 1: An intraseasonal moisture nudging experiment in a tropical channel version of the WRF model: The model biases and the moisture nudging scale dependencies.](https://reader035.fdocuments.net/reader035/viewer/2022062421/56649cba5503460f94982d74/html5/thumbnails/1.jpg)
An intraseasonal moisture nudging experiment in a tropical channel version
of the WRF model: The model biases and the moisture nudging scale
dependencies
Marcela Ulate
University of Miami
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The MJO Case of Study
LON
TIM
E
mm
/day
TRMM
m/s
U850 Anom. ERA-Interim
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TRMM Precipitation NOAA OLR
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TRMM Daily Precipitation Anomalies (Filled Contours) and NOAA Daily Outgoing Longwave Radiation Anomalies (Line Contours: Blue negative anomalies, :Red positive anomalies).
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Model Description
• WRF v3.2• 1º x 1º horizontal resolution.• 28 vertical levels.• Tropical channel domain:
Periodic Boundary conditions in the east-west direction.
• Boundary Conditions form ERA-Interim data
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Model Physics:Microphysics: WSM3-class simple ice schemeLongwave Radiation: rrtm schemeShortwave Radiation: Dudhia schemeLand Surface Model: Noah Land Surface ModelBoundary Layer: Mellor-Yamada-Janjic scheme
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The Dry Bias in WRF
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The dry bias
WRFRH – ERAIRH
P (h
Pa)
Days
P (h
Pa)
Lat Lon
P (h
Pa)
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Approach to the problem: Humidity Nudging
• Four-Dimensional Data Assimilation (FDDA) or nudging is the process where the model is set to converge at a desired rate to the analysis or observations.
• The process adds an extra tendency term to the model equations proportional to the difference between the model simulation and the analysis value at every grid point, forcing the simulation closer to the analysis value.
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Humidity Nudging
€
∂α∂t
= F(α ) + GαWα ( ˆ α 0 −α )
From Skamarock et al. 2008.
Model forcing terms Nudging Tendency term
€
Gα : nudging factor ,
€
Wα: four dimensional weight function,
€
ˆ α 0:analysis field value
€
Gα = 3.0x10−6 s
€
TG =1
Gα
= 3.85days
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WRFRH – ERAIRH
P (h
Pa)
Days
P (h
Pa)
Lat Lon
P (h
Pa)
Reduction of the dry bias
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Grid Nudging
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Variations of Nudging Vertical Weight Function
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Z P
Above PBL Vertical FDDA Weight Function (Default)
Weight Function Weight Function
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Z P
Above PBL Vertical FDDA Weight Function
Weight Function Weight Function
High
Mid
Low
Low
Mid
High
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Z P
Fixed Vertical FDDA Weight Function
Weight Function Weight Function
High
Mid
LowLow
Mid
High
€
k pblt ,IO ≈ 7 =>~ 900m
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Grid Nudging: Vertical Weight Function
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Spectral Nudging of Humidity
Spatially filter the data (minimum x,y wavelength)
Analysis data (ERA-Interim)
Nudging Tendency
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Spectral Nudging: Remove long wavelengths (small wave numbers)
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Spectral Nudging: Remove short wavelengths (high wave numbers)
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Spectral Nudging: Remove specific wavelength (specific wave numbers)
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• Removing the short wavelengths (high wave numbers) improves the control simulations.
• Mean and Long wavelengths are important in order to improve the MJO simulation.
• The model “needs” to resolve the moisture large scales-structures well enough in order to obtain a MJO-like event.
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TIM
E
LON LON
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“MJO LINE”
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Humidity Tendency due to nudging
Humidity Tendency due to cumulus scheme MJO-like precipitation simulation
Humidity Tendency due to cumulus scheme - CONTROLP
(hPa
)P
(hPa
)g/Kg day-1
g/Kg day-1
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Heating Tendency - Control
Heating Tendency – MJO-like precipitation simulation
P (h
Pa)
P (h
Pa)
K/day
K/day
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How much nudging is too much nudging?What if Ga=1 ?
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6 year WRF Simulation
(Same Configuration)
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a) b)
c) d)
e) f)
MAYOCT NOVAPRObservations
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a) b)
c) d)
e) f)
MAYOCT NOVAPRWRF
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a) b)
c) d)
ERAI, TRMM WRF
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ConclusionsSpectral and grid Nudging of water vapor mixing ratio reduces the
model dry-bias and allows the model to produce an improved MJO-like precipitation pattern and wind signal.
The moisture at mid levels of the troposphere is crucial in order to reproduce the convective signal associated with the MJO.
Without nudging, the cumulus schemes remain relative inactive i.e. lack of precipitation during the MJO event. This translates to a weak heating profile. When the MJO precipitation pattern improves, the heating profile resembles the results of other studies more closely.
The prediction of the first MJO event improves when nudging is preformed, while the initiation of the second event is not for some cases. This suggests that improving the humidity field is one component of the problem, and we need to investigate further on this matter.