an overview of the ncep eta model
DESCRIPTION
An Overview of the NCEP Eta Model. COMET/UCAR SOO Symposium on NWP. 28 March 2000. Presented by Thomas Black. EMC/Mesoscale Modeling Branch. EDAS slides by Eric Rogers. Outline. Brief model description Eta Data Assimilation System Physics Examples of products/statistics Future. Domain. - PowerPoint PPT PresentationTRANSCRIPT
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An Overview of the NCEP Eta Model
28 March 2000
COMET/UCAR SOO Symposium on NWP
EDAS slides by Eric Rogers
Presented by Thomas Black
EMC/Mesoscale Modeling Branch
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Outline
• Brief model description
• Eta Data Assimilation System
• Physics
• Examples of products/statistics
• Future
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Domain
• Semi-staggered Arakawa E grid
• 32km horizontal resolution
• 45 vertical eta layers
• Silhouette step topography
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Eta Domains Past & Present
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32km DomainTopography w/ Water Points
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Topography w/ Water Points32km CONUS
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Sigma and Eta Coordinates
MSL
zgpPGF
sfcp
p
P
ground 1
1
At point P:p
0 z
P
1
1
0ref
sfcref
p
zp
At point P:0 p
zis small
is small
ground
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Eta Coordinate
Mean Sea LevelP=PMSL
Reference heights and temperatures taken from the standard atmosphere
Z= ZREF
LM=1
LM-1
LM-2
at P = LM-2 PSMSL
Z=0
LM-3
P= LM-2 PMSL
P= LM-3 PMSL
P= LM-1 PMSL
at P = LM-1 PSMSL
Z= ZREF
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Eta Model 45-Layer Distribution
1000 hPa
850 hPa
700 hPa
500 hPa
250 hPa
25 hPa27 hPa29
29
2726262423242630
31
32
32
32
33
33
33
33
33
32
31
29
2723211918181716141313
13131212121211 86
5 2
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The Semi-Staggered E Grid
H
V
H
V
H
V
H
V
H
V
H
V
H
V
H
V
H
V
H
V
H
V
H
V
H
d
H mass point
V velocity point
d
constanttransformed
latitude
constanttransformed
longitude
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GOAL : Produce best possible initial conditions for the Eta Model forecast*
• KEY COMPONENTS
- State of the art analysis (variational)
- Consistency between assimilating and forecast model (resolution, physics, dynamics)
- Intelligent selection and use of observations
* NOT necessarily the same as fitting all the observations exactly
Eta Data Assimilation System
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What is 3D-VAR?
• An analysis technique that attempts to minimize analysis error
• Takes background (forecast) and observation error into account
• Variational method allows use of “non-traditional” data sources, such as GOES precipitable water
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ETA 3DVAR ANALYSIS
• Loosely patterned after NCEP global SSI analysis
• Analysis variables:
- Stream function- Potential function
- Temperature- Specific humidity
- Surface pressure - Geopotential height
• More adaptable than OI for using new data types (e.g., NEXRAD radial velocities used in Eta-10 runs during 1996 Olympics)
(Parrish et al. 1996 NWP Preprint Volume)
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3D-VAR vs. OI
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EDAS Original Configuration Eta-48 fcst 00Z/12Z Eta-29 fcst 03Z/15Z
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WHY DO CYCLING?
• Initial conditions more consistent with
• Less spinup of divergence, cloud,
• More accurate representation of soil
forecast model
precipitation, and TKE
moisture
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Observations Used By ETA 3DVAR • Upper air data- Rawinsonde height/temperature/wind/moisture- Dropwindsondes- Wind Profilers- NESDIS thickness retrievals from polar orbiting satellites (oceans only)- VAD winds from NEXRAD- Aircraft (conventional and ACARS) winds/temps- Satellite cloud drift winds- SSM/I and GOES precipitable water retrievals- Synthetic tropical cyclone data
• Surface data- Surface land wind/temperature/moisture- Ships and buoys- SSM/I oceanic surface winds
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DATA QUALITY CONTROL• CQC: Complex QC of raob height/temps (baseline, hydrostatic, lapse rate, radiation correction, etc.)• ACQC : Quality control of conventional aircraft data (remove duplicates, track checks, create “superobs”)
• 3DVAR : Analysis performs gross check vs. first guess:
- Temperature : +/- 15oC- Wind :+/- 25 ms-1
- RH : +/- 90%- Precipitable water : +/- 12 g/kg- Height : +/- 100 m
• SDMEDIT : NCEP Senior Duty Meteorologist can flag all or parts of suspect raobs
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Use of Surface Data: Eta OI vs. Eta 3DVAR
Eta OI Analysis
Eta 3DVAR Analysis
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• New 3DVAR tested in July 1998 and showed improved fit to surface and raobs (especially moisture)
• Re-tuned 3DVAR implemented on 3 November 1998
• We thought everything was OK…..
BUT……..BUT……..
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Solid = Eta Short Dash = NGM Long Dash = AVN/MRF
24-H ACCUMULATED PRECIPITATION EQUITABLE THREAT SCORES: ALL FCSTS
12/1/97 - 2/28/98 12/1/98 - 2/28/99
10-15% drop in Eta skill between 1997-98 and 1998-99
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Persistent synoptic error in Eta-32 during winter of 98-99: weaker and faster Eastern Pacific troughs/cyclones than observed
Example : 48-h Forecasts valid 1200 UTC 17 March 1999
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PROBLEM 1: November 98 change degraded mass/wind balance in 3DVAR
• If mass / wind balance well-behaved, positive height correction is coincident with center of anticyclonic wind correction 850 mb ANL-GUESS height/wind
80KM EDAS valid 00Z 3/15/99• Note 10 degree longitude displacement between centers of wind and height correction
• Problem is most severe in regions and at analysis times without widespread raob data but with large amounts of wind or mass only data (e.g., satellite winds)
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SOLUTION : Improve geostrophic coupling of mass/wind analysis corrections in 3DVAR (5/99)
Original 3DVAR analysis Improved 3DVAR analysis
Note: Improved height/wind coupling near Aleutians
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PROBLEM 2: Horizontal/vertical correlations too narrow : observation had VERY limited impact on analysis away from its level
• One observation test : Insert one height observation 10 m greater than first guess at 200, 500, 900 mb and measure impact in horizontal/vertical
SOLUTION: Expand the influence of the observations (5/99)
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Original 32-km 3DVAR Improved 32-km 3DVAR
200 mb
900mb
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Performance of new 3DVAR : 3 December 1998 to 16 January 1999 test at 80 km resolution
24-h accumulated precipitation threat scores: All forecasts Dashed = Modified 3DVAR Solid = Operational 3DVAR
Equ
itabl
e T
hrea
t Sco
re
Threshold (in)
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Split Explicit Integration: Dynamics
• Fundamental prognostic variables– T, u, v, q, Psfc, TKE, cloud water/ice
• Inertial gravity wave adjustment– forward-backward scheme (t=90s)
• Vertical advection– Euler-backward scheme– centered in space– piecewise linear for q
The Forecast Model
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Split Explicit Integration: Dynamics
• Horizontal advection– modified Euler-backward scheme– Janjic advection in space– conservative, (nearly) shape-preserving
scheme for H20
– upstream advection near boundaries
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Split Explicit Integration: Physics
• Betts-Miller-Janjic convection
• Mellor-Yamada level 2.5 turbulent exchange
• GFDL radiation
• explicit cloud water/ice prediction
• 4-layer NOAH land surface package
• 2 horizontal diffusion
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One-way Boundary Conditions
• 3-hour tendencies
• 6 hour old AVN forecast used
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Runstream Schematic of Eta Model Integration
gridscale cloudgridscale precipconvectionturbulence
horizontaladvection
verticaladvection
inertialgrv wave
adjustment
13 14 1511 1210987654321 16
timestep
t = 90 s
Radiative temperature tendency updates
Shortwave: 40 timesteps (1 hour)Longwave: 80 timesteps (2 hours)
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The Betts-Miller-Janji Convection Schemein the Eta Model
References: Betts, 1986 (QJRMS) Betts and Miller, 1986 (QJRMS) Janji, 1994 (MWR)
Deep (precipitating) convection
Temperature reference profile
Moisture reference profile
Convective adjustment
Modification for “precipitation efficiency”
Shallow (non-precipitating) convectionTemperature reference profile
Moisture reference profile
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Find the Deep Convective Clouds
1. For all ‘parcels’ within 0.2xPsfc mb of the ground find Psat andES
2. At each point, select parcel with the maximum ES
3. Given Psat, choose cloud base as the model level just below it
4. Adjust cloud base if needed:(a) at least 25 mb above middle of lowest layer(b) at least one model layer above lowest layer
5. Compute Tmad above cloud base using ES and P in lookup tables
6. Set cloud top at highest level where Tmad<T-T (currently T=0)
7. Gather all clouds at least 0.2xPsfc mb deep
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Betts-Miller Reference Temperature Profile
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Construction of 1st Guess Humidity Reference Profile
2. Linearly interpolate DSP’s for values between these 3 levels
3. Define the reference humidity profile as qsat in each layer
For Deep Convection
1. Define ‘deficit from saturation pressure’ (DSP) for cloud bottom,freezing level, and cloud top (larger DSPdrier state)
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The Enthalpy Correction
Modify the profiles to ensure enthalpy in the cloud columnis conserved during adjustment
0 dpHH
top
bot
p
pmodref
)2(
)1(2(
)2(
)
ref
refref
Tq
vLpcHTT
from but before as computed ref
Tsatq
Corrections:
Initially:
Currently the above procedure is repeated two times
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Final Deep Convective Adjustment
2. Convective rainfall amount is
3. At any point, deep adjustment is ignored and
1. Relax model profiles of T and q toward the reference profileswhere relaxation time equals 2400 s
oldrefcnv
oldnew TTTTt
oldrefcnv
oldnew qqt
ltop
lbotloldlref
cnv pqqt
gwP
,,
a “swap” to shallow convection occurs if:
(a) S < 0(b) precipitation is negative
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Deep Convective Adjustment of Temperature
cloud top
REFERENCE TEMPERATURE
Upward Transport of HeatUpward Transport of Heat
cloud base
AMBIENT TEMPERATURE
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Modification for ‘Precipitation Efficiency’
So define precipitation efficiencyprecipitation efficiency as the ratio:
Numerator: Q arising from entropy change
Examination of Eta integrations shows:
As convective precipitation increases, entropy changes decrease.
pTc
STC
p
E 1
Denominator: Q arising from precipitation (H=0)
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USE E TO MODERATE HEAVY RAIN
larger E less mature systemThus,
(A) Modify the humidity reference profile
smaller E more mature system
IN LONG-LIVED MATURE SYSTEMS
(B) Modify the relaxation time
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Humidity Reference Profile Limits
cloud top
HUMIDITY HUMIDITY REFERENCE REFERENCE
PROFILEPROFILE
DSP’s vary between DRY (fast) and MOIST (slow) limits
cloud base
DRY PROFILE
LIMIT
MOIST PROFILE
LIMIT
E=0.2E=1
q
p
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Values of DSP Limits
Dry limits
Top -1875 Pa
Freezing -5875 Pa
Bottom -3875 Pa
MOIST DSP limits equal 0.85 times DRY limits.
At t=0, DSP’s are set to the DRY limits.
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Modification of Relaxation Time
OR
For simplicity assume F is linear. Then empirically:
Multiply the standard change due to adjustment by some quantity Fwhich is a function of the precipitation efficiency
oldrefcnv
oldnew TTTTt
'
)(EFqqt
qq oldrefcnv
oldnew
0.7 < F < 1.0 for 0.2 < E < 1.0
)(EFt
oldrefcnv
oldnew TTTT
oldrefcnv
oldnew qqqq t
'
where)(
'EF
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Find the Shallow Convective Clouds
2. Gather all clouds that are:
(a) greater than 10 mb deep
(b) less than 0.2xPsfc mb deep
1. Find the tops of the “swapped” clouds
(a) set a preliminary top at pbot - 0.2xPsfc mb (pbot > 450 mb)
(b) reset top to level where maximum (RH)/p occurs
(c) at least two model layers deep
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Construction of Temperature Reference ProfileFor Shallow Convection
cloud top
REFERENCE TEMPERATURE
cloud base
Mixing Line
bottop
bottop
pp
Correct Tref assuming (cpT p) = 0
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Shallow Convection
• Moisture profile calculation
• Forces a net positive entropy change
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Turbulent Exchange
References: Mellor and Yamada, 1974 (J. Atmos. Sci.) Mellor and Yamada, 1982 (Rev. Geo. Space Sci.)
Janji, 1994 (Mon. Wea. Rev.)
Vertical advection occurs through transport
Turbulent vertical diffusion of variable A is given by:
Fundamental task is to determine exchange coefficient KFundamental task is to determine exchange coefficient KAA
by resolvable vertical motion
Turbulent diffusionTurbulent diffusion occurs through transport by subgrid scale turbulent eddies
z
AK
zt
AA
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Modes of Turbulent Exchange
SURFACE
free atmosphere free atmosphere
surface layersurface layer
ALM -1
ALM
Az0
LM-1
LM
z0
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Exchange in the Free Atmosphere
Use second order closure scheme of Mellor-Yamada Level 2.5
Exchange coefficients for heat and momentum given by:
HH SQlK MM SQlK
l is the mixing length
Q2/2 is the turbulent kinetic energy (TKE)
SH , SM are quantities determined from MY level 2.5
TKETKE is a fully prognostic variable needed to compute exchange coefficients in the free atmosphere
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The predictive relationship for TKE is:
A variety of approaches have been used to solve the production/dissipation tendency, generally with imposed limits on Q
22
22 Q
zSQl
z
Q
dt
dq Ps + Pb -
prod/dissp
Eta Model technique: Cast in terms of (l / Q)
124
24
1
1B
Q
l
Q
l
Q
l
Q
l
Q
l
t
Write in finite difference form and solve for (l / Q)
Place physical constraints on l and not on Q
Use new Q to compute new KH and KM
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Exchange in the Surface Layer
Use similarity theory between the surface and the middle of the lowest layer
Vertical change of variable A is described by:
)(*
Fzk
S
z
A
S* = F/u* where F is flux, u* is friction velocity
F is a prescribed function (empirical)
= z/L where L is Monin-Obukov length
For neutral static stability or small z, 1 integration of A / z yields log profile
k is the von Karman constant (~ 0.4)
Note: L is a function of heat and momentum fluxes and thus of the exchange coefficients
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Take u* and L from the previous timestep then iterate:
Replace F with the standard relationship:
12
12
zz
AAKF A
Integrate A / z for the general case between two levels:
Fku
FAA
* 12
where F is an integral function of F
Solve for KA:
L
zkuzz
K
F
A
*12
KA L KA L KA new surface exchange coefficients
Considerable application of theory and computation is needed to determine values at the lower boundary
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Cloud Top Pressure
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Cloud Top Temperature
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Cloud Base Height
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850mb Cloud Water (kg/kg)
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850mb Cloud Ice (kg/kg)
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Winter Precipitation Type
Area Tw > -4 C< 3000 deg m?
Coldest T in a saturated layer < 269K?
Area sfc basedTw < 0 C
< -3000 deg m?OR
Net area with respectto 0 C < -3000 deg m
and sfc based Tw > 0 C< 50 deg m?
Lowest levelT > 0 C ?
snow
freezing rain
ice pellets
rain
Y
N
Y
N
Y
N
N
Y
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Example of precip type
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Eta/AVN/NGM Equitable Threat Scores1 Jan - 31 Oct 1999
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Impact of Fall 1999 Eta Degradation
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Impact of Fall 1999 Eta Degradation
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Impact of Fall 1999 Eta Degradation
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Output Grid Resolution
• Be aware of the resolution of the grid being viewed
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AVN Vorticity Output
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24-h Eta-32 Vorticity Output : Coarse
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24-h Eta-32 Vorticity Output: Fine
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00-h Eta-32 Vorticity Output: Fine
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Subsets (“tiles”) of 32km grid
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Future Directions of Eta Effort with the Class VIII Computer
• 72-84 hours for on-time runs
• 10km/60lyr resolution
• Expanded domain
• Cloud and precipitation assimilation
• Microphysics
• 4D-VAR
• Short range ensembles
• Nonhydrostatic model
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Rainfall Data Assimilation
• During the 12h pre-forecast assimilation period at each timestep compare the model predicted rainfall to observed
• Adjust the model’s latent heating profile accordingly (Carr and Baldwin, 1991)
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Cloud Data Assimilation(Zhao et al, 1998, 12th NWP, Phoenix, AZ)
• Data sources– real-time Neph Analyses (USAFGWC)– hourly radar/gauge observations
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Cloud Data Assimilation
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• c
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22km Domain / Topography
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22km CONUSTopography w/ Water Points
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30-h Eta-10 Rainfall Forecast
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Eta Workstation Version
• Pontiac, MI running 10km– including lake temps from GLERL– other changes to the model
• NSSL/SPC running Kain-Fritsch version• Code available from NCEP: contact Matt
Pyle ([email protected])• Code available from COMET: contact
Bob Rozumalski ([email protected])