Transcript Document 7319967

Some possible future global
modeling developments at
NCEP
Where the Nation’s climate and weather services begin
Upgrades and/or options available in GFS
• A restructured GFS is under test
• A two time-level semi-Lagrangian semi-implicit dynamics
following ECMWF’s approach (adapted by Late Dr. Sela) is an
option
• This SLSI option is being tested at T1148 with linear grid and takes
less resource compared to the operational T574 Eulerian model.
• Generalized hybrid coordinate option which can be used with or
without enthalpy as a prognostic variable.
• NDSL semi-Lagrangian option by Dr. Henry Juang
• Near surface sea-temperature model (NST) option by Dr. Xu Li
Upgrades and/or options available in GFS
• Option of using Tiedtke’s shallow convection with modification to get
good marine stratus
• Ferrier microphysics (used in NAM)
• Relaxed Arakawa-Schubert cumulus parameterization
• Near sea surface temperature model (NST) is an option
With these available options, multi-model ensemble with a
single executable is possible.
Comparing grid-scale
microphysics schemes
FEATURE
Zhao & Carr (1997)
[Modified version in GFS]
Ferrier et al. (2002)
[In Eta, WRF option]
Prognostic
variables
Water vapor, cloud
condensate (water or ice)
Water vapor, total condensate
(cloud water, rain, cloud ice,
snow/graupel/sleet)
Condensation
algorithm
Sundqvist et al. (1989)
Asai (1965)
[used in high res models]
Precip fluxes
and storage
Top-down integration of
precip, no storage, &
instantaneous fallout.
Precip partitioned between
storage in grid box & fall out
through bottom of box
Precip type
Rain, freezing rain, snow
Rain, freezing rain,
snow/graupel/sleet (variable
rime density for precip ice)
Mixed-phase
conditions
No coexistence of
supercooled cloud water &
ice, simple melting eqn.
Mixed-phase at >-10C,
includes riming, more
sophisticated melting/freezing
Flowcharts of “Sundqvist-based”
Sundqvist et al. (1989)schemes Zhao & Carr (1997)
New process
T < 0oC
T > 0oC
ICND
REVP
Water
Vapor
IEVP
CND
Cloud
Water
RAUT RACW
DEP
IACW
IACWR
IACR
Rain
Sfc Rain
T>0, T<0oC
IMLT
Cloud Ice
Precip
Ice
(Snow/
Graupel/
Sleet)
Sfc Snow/Graupel/Sleet
GROUND
Differences in Condensation/Deposition
Zhao & Carr (1997)
RH = b*RHs + (1-b)*RHe (1)
RH grid-averaged relative humidity
RHs  in-cloud, saturated RH (1.0)
RHe  relative humidity in cloud-free
environment
b  cloud fraction (0 to 1)
Assume RHe = RH0 + b*(RHs-RH0) (2)
Combining (1) & (2) 


1  RH ) 1 / 2
b  1
1  RH o
In free atmosphere,
(RH0)atm=0.75 (land), 0.80 (sea)
In lowest 10 model levels,
RH0 increased from 0.95 at sfc to
(RHo)atm 10th level above sfc
“Partition moisture convergence”
between increasing RHe , b, & qcw
Asai (1965)
• Originally from Asai (1965)
• Adjust to target RH condensation
DQ = q -qs
q=water vapor mixing ratio
qs=saturation mixing ratio
DQ=DQ1+DQ2 (1)
DT = (L/Cp)DQ1 (2)
DQ2 = LqsDT/(RvT2) L (3)
q s  DQ1
DQ 2 
C p  Rv  T 2
Putting (2) into (3):
(4)
DQ 
1
Putting (2) & (4) into (1):
q  qs
L  qs
C p  Rv  T 2
Rapidly converges
needing at most 3
iterations for accuracy <0.1%
Precipitation Sedimentation
K, 
(a)
(b)
Height
N+1
P K-1
(D + r
N+1
P K-1
N
D
N
K
K
N*
K
= D 
N
q
K
is a function of q
(D + r V  Dt)  q
K
N+1
qK
VK-1  Dt)  q
(Note PROD
N+1
P K-1
N*
qK
k-1
(c)
N+1
K
+P
N*
K
= (D  r  V
K
qK
N+1
PK
N+1
K-1
)
 Dt)  q
K-1
N
K
+ D  PROD
N
K
Deriving hydrometeor species from total condensate
• Water vapor (qv), total condensate (qt) advected in model
• Cloud water (qw), rain (qr), cloud ice (qi), precip ice (“snow”, qs)
calculated in microphysics
• Local, saved arrays store fraction of condensate in form of ice (Fi),
fraction of liquid in form of rain (Fr). Assumed fixed with time in
column between microphysics calls. Note that 0  Fi , Fr  1 .
qt = qw + qr + qi + qs , qice = qi + qs
Fi = qice/qt , Fr = qr/(qw + qr)
Internal to microphysics
Rest of model
qi + q s
qt , Fi=1, Fr=0
qw + q i + q s
qw + q r + q s
qw + q r
-10C
0C
qt , 0<Fi <1, Fr=0
qt , 0<Fi <1,
0<F 0
qt , Fi=0, r<
0<Fr<1
1st-guess size of precipitation ice (“snow”) as a function of temperature
[D] (mm)
0.01
From Ryan (1996)
Observed size distributions of ice as
functions of temperature, fit to (M-P)
exponential spectra as
N(D)=Noexp(-lD),
0.1
1.0
10.0
No is the intercept, l is the slope, and
[D] = l-1 is the mean diameter
 HHHP (Washington state)
SMPC (California)
GM (California)
PLATT (multiple locations)
AWSE (Australia)
YL (China)
B, M (Europe)
Adjust [D] so that 0.1L-1  Ns  20L-1
Global Ice Spectra (Ryan, BAMS,
1996)
Global Ice Properties (Ryan, BAMS,
1996)
UKMO GCM
Maritime Clds
Continental Clds
Other features of NGCP01 scheme
• Algorithm discriminates between cloud ice and “snow” (precip ice)
• No cloud ice if subsaturated (prevents too much sublimation)
or if T>0C (melting)  only “snow” (precip ice) is present
• Ns = 0.2·Ni if at or above ice saturation T < -8C, -3C < T < 0C
(Ni is number of cloud ice crystals, Ns is number of “snow”)
• Ns = 0.1 ·Ni if at or above ice saturation and -8C < T < -3C
• Variable rime density  assumes accreted liquid water fills holes of
ice w/o changing volume  (Total growth)/(Depositional growth)
• Efficient lookup tables store solutions for various moments
(ventilation, accretion, mass, precipitation)
• Composite of multiple velocity-diameter relationships
• Increase in fall speed of rimed ice (Böhm, 1989)
What Happens in Areas of Strong
Ascent?
 Sequence of more
heavily rimed precip ice
1.0 (unrimed snow)  RF
 ~46.4 (sleet at 0°C)
 When Ns=(Ns)max &
[D]=[D]max, then increase
RF to accommodate
large ice mixing ratios
 (Ns)max=20 L-1,
[D]max=1 mm at 0°C
 (rqs)=1.2 g m-3 is max
for unrimed snow
Cool Images of Rimed Snow &
Graupel
(Electron Microscopy Unit at the Beltsville
Agricultural Center)
Plate
Column
Needles
Graupel
Impacts of Riming Assumption
• Electron microscope images indicate rime can build up
along outside of ice lattice, and not necessarily filter into
the air holes.
• The “sponge” model assumed for riming will lead to a
high estimation of ice-particle density (high “rime factor”,
RF) when compared to real rimed ice particles.
• But my strong suspicion is that the ice-particle densities
in this scheme produce much less graupel than those
produced from Rutledge-Hobbs (1983) and Lin et al.
(1983) 3-class ice schemes that predict cloud ice, snow,
& graupel (RH) or hail (Lin)
“THE PHYSICS WHEEL OF
PAIN”
(Modified from
Jiayu Zhou,
NOAA/OST)
1. Hydrometeor phase, cloud
optical properties, cloud
fractions, & cloud overlap
Radiation
2. Precipitation (incl. phase)
Cu
Scheme
Sfc & PBL
3. Subgrid transports,
stabilization, detrainment
4. Sfc energy fluxes, LSM
Grid Scale
Microphysics
5. Convection, PBL evolution,
precipitation
NSST & NCEP GFS
• Analysis
– Introduction of NSST (T-Profile)
• Mixed Layer T  T(z): Well-defined SST
• Obs. Treated as z-dependent as there originally are
– Observation depths for In Situ and satellite data
– Use of more observations
• Available in GSI already
• New data sets
– Assimilation
• 3DVAR in GSI  Direct assimilation of radiances
• Forecasting
– NSSTM coupled to GFS Atmospheric Forecasting
Model (AFM) in forecasting mode
NSST T (z ) and NWP: Interaction
FCST
Atmospheric
Forecasting Model
(AFM)
NSST Model
(NSSTM)
ANAL
IC X an , T an ( z )
BG X
bg
bg
, T ( z)
Radiative
Transfer Model
(CRTM)
C
Atmospheric
Analysis
(GSI)
NSST Analysis
(NSSTAN)
Rch  C[ X , T ( z )]
Rch / X , Rch / Tz
: Observation operator (relate T-Profile to the radiance)
Rch / Tz : Jacobi (the sensitivity of the radiance to T-Profile)
What is NSST?
NSST is a T-Profile just below the sea
surface.
Here, only the vertical thermal structure
due to diurnal thermocline layer warming and
thermal skin layer cooling is resolved
Tf
T ( z) Warming
 T  T ( z)  T Profile
( z)
Diurnal
'
w
f
'
c
Tw' ( z , t )  [1  z / z w (t )]Tw' (0, t )
Tw' (0, t )  T (0, t )  T ( z w , t )  0
T
Tw'
Mixed Layer
T (z )
zw
T ( z, t )  T f (t0 )  T'w' ( z, t )  Tc' ( z, t )
Tw ( z )
z
Thermocline
Deeper
Ocean
z
T (0)  TProfile
( 0)  T ( z )
Skin Layer Cooling
'
w
Tc' ( z , t )  [1  z /  c (t )]Tc' (0, t )
T ( z, t )  T f ( zw , t )  Tw' ( z, t )  Tc' ( z, t )
z  [0, z w ]
z w ~ O(5m),  c ~ O(1mm)
Assuming the linear profiles, then,
4 parameters are enough
to represent NSST: Tw' (0), z w , Tc' (0),  c
w
'
TcT
(0' (,0t ))  TT((c ,)t )TT(0(0) ,t )0 0
c
c
z
c
'
c
T ( z)
Tc'
5
NSST Analysis variable
Analysis variable Tr : a defined reference temperature, currently the
foundation temperature. Therefore, Tr  T f .
Observation operators and their Jacobi: Relate the depth
ob
dependent data, Satellite: Rch , ch (channel) dependent with skin
depth of 0.1T~ 1.0 mm in sub-layer. In Situ: T ob (z) with depth of 0.2 ~
r
15.0+ m, to
with CRTM and NSSTM for direct assimilation.
Analysis increments:
Tr :
SST:
DTran,k  Tran,k  Trbg,k  Tran,k  Tran,k 1
k: time index
an
bg
an
'
'
DTsan

T

T

D
T

D
T

D
T
,k
s ,k
s ,k
r ,k
w, k
c ,k
Products:
The boundary condition for GFS_AM :
The boundary condition for CRTM:
Tw' ( z ) (0  z  z w )
Ts  Tr  Tw' (0)  Tc' (0)
Tcrtm ( z )  Tr  Tw' ( z )  Tc' ( z ) (0  z   c )
Will be used to combine NSSTM and OGCM
The NOAA Environmental
Modeling System at NCEP
NEMS
23
What is NEMS?
• NEMS stands for
NOAA Environmental Modeling System
• A shared, portable, high performance software
superstructure and infrastructure
• For use in operational prediction models at
National Centers for Environmental Prediction
(NCEP)
• National Unified Operational Prediction
Capability (NUOPC) with Navy and Air Force
• Eventual support to community through
Developmental Test Center (DTC)
• http://www.emc.ncep.noaa.gov/NEMS/
24
NEMS motivation
– Develop a common superstructure for all NCEP
models.
– Modularize large pieces of the models with ESMF
components and interfaces.
– Divide atmospheric models down into Dynamics and
Physics components but no further.
– Take history file I/O outside the science parts and into
a common Write component.
– Keep science code and parallelization code in the
respective models the same as before.
25
NEMS core developers
Ed Colon
makefiles, scripts, regression
Nicole McKee
documentation, web, testing
Ratko Vasic
upgrades, regression, atmos coupling
Jun Wang
IO, post, configuration
Weiyu Yang
ensemble, earth coupling, ESMF
26
NEMS project developers
Tom Black
Dusan Jovic
Jim Abeles
S Moorthi
Henry Juang
NAM
Jesse Meng
Jim Geiger
Sarah Lu
Arlindo da Silva
Tom Henderson
Jim Rosinski
Land
Eugene Mirvis
DTC
GFS
GOCART
FIM
27
NEMS Component Structure
MAIN
NEMS
All boxes represent
ESMF components.
Ensemble
Coupler
EARTH(1:NM)
Atm
Ocean
Ice
GFS
NMM
NEMS
LAYER
FIM
Domains(1:ND)
Dyn
Phy
Wrt
Dyn
Phy
Chem
Wrt
Dyn
Phy
Wrt
28
Below the dashed line the source codes are organized by the model developers.
2
NEMS implementation plans
• 2011 implementation
– NMMB with nests
• 2012 implementation
– NEMS GFS Aerosol Component (NGAC)
29

12 km NAM will still
run to 84 hr, with
current output
 Fixed domain nests
run to 60 hr
– 4 km CONUS
– 6 km Alaska
– 3 km HI & PR
• Single locatable 1.33
km (CONUS) or 1.5 km
(Alaska) nest to 36hr
• Nests
• Static, 1-way
• Boundaries from
parent every
timestep
• Nest is “gridassociated” with
parent (same
orientation w.r.t.
earth)
• Moving nests and 2way interaction
under development
NMMB with nests
30
Atmosphere
NEMS GFS Aerosol
Component (NGAC)
Color Key
Generic Component
unified atmosphere
Including digital filter
Generic Coupler
Completed Instance
Dynamics
•
•
•
•
Dyn-Phy
Coupler
Physics
NMM-B
NAM Phy
Spectral
GFS Phy
Phy-Chem
Coupler
GOCART
Dynamics, physics and chemistry run on the same grid in the same decomposition
GOCART does not own aerosol tracers (i.e, do not allocate aerosol tracer fields)
PHY2CHEM coupler component transfers/converts data from physics export
state to GOCART import state
– Convert units (e.g., precip rate, surface roughness)
– Calculations (e.g., soil wetness, tropopause pressure, relative humidity, air
density, geopotential height)
– Flip the vertical index for 3D fields from bottom-up to top-down
CHEM2PHY coupler component transfers data from GOCART export state to
physics export state
– Flip vertical index back to bottom-up
31
– Update 2d aerosol diagnostic fields
NEMS delivery plans
• 2011 deliveries
–
–
–
–
–
–
GFS
GEFS
Postprocessor
FIM
Multimodel ensemble
GRIB2 output
• 2012+ deliveries
–
–
–
–
–
–
NMM nested in GFS
Moving nests
Coupled ocean atmosphere
Tiled land model
netCDF output
ARW
32
NEMS GFS Aerosol Component
Status Update by Sarah Lu
Team efforts toward building global
aerosol forecast capability at NCEP
Mark Iredell (NEMS framework)
Shrinivas Moorthi (physics)
Yu-Tai Hou (radiation-aerosol)
Henry Juang (dynamics)
Jun Wang (I/O)
Hui-Ya Chuang (post)
Weiyu Yang (replay capability)
Ho-Chun Huang (GSI, verification)
GSFC collaborators (Arlindo da Silva and Mian Chin)
Downstream application (Xu Li, Jeff McQueen, Youhua Tang)
Implementation of prognostic aerosols in the NEMS GFS
Physics with prognostic aerosols
Physics
Radiation
Radiation
Land surface processes
Direct effect
Land surface processes
Vertical diffusion
Vertical diffusion
Gravity wave drag
Gravity wave drag
Convective transport
Tracer scavenging
Convection
Convection
Large-scale condensation
Large-scale condensation
Cloud scheme
Cloud scheme
Aerosol chemistry
Color Key
Dry deposition
GOCART grid component
New routine
Modified routine
Unchanged routine
Aerosol sources
Sedimentation
Coupler: Transfers/converts data
between physics and GOCART:
convert units (e.g., precip rate),
calculations (e.g., soil wetness, relative
humidity); flip the vertical index
Wet deposition
Aerosol diagnostics
Off-line
Δt = 1 hour
In-line
Chemistry
Chemistry
Emission
Emission
Vertical
Diffusion
Vertical
Diffusion
Settling
Δt = DTF
Settling
Dry Deposition
Dry Deposition
Wet Deposition
Wet Deposition
Cloud
Convection
Cloud
Convection
Transport
Transport
This flowchart is taken from Ho-Chun Huang’s ppt
Resources
Off-line System
In-line System
Configuration
60-hr once per day (00Z),
hourly output.
96-hr once per day (00Z);
output every 6 hr;
The re-play mode
Resolution
1°X1°, L 64
T126 L64
Resource (memory)
1.24GB (max)
649 Mb
Resource (wall-time)
12531 sec (03:28:51) with
single processor
786 sec (~ 13 min), using 60
tasks
Input files
15.4GB (raw GFS output), 3.4
GB (input ready meteorology,
HPSS archive)
~ 77 Mb; siganl* (63.8 Mb) and
sfcanl (13.7 Mb)
Output files
Raw output 18.7GB (peak
21.8GB), HPSS archived
output 3.25GB
~ 1.48 GB = 17*87 Mb: sig +
sfc + flx (8.8 Mb) + aer (3.6
Mb)
The re-play mode: meteorological fields are taken from analysis (oper GDAS) and
aerosol fields are from previous day NEMS forecasts. The siganl is blended from
operational siganl and NEMS sigf24
The outcomes of GOCART aerosol fields
Off-line System
In-line System
dust/smoke
Provide dynamic dust/smoke
LBCs for regional AQ
forecasts
YES
YES
volcanic ash
Provide global volcanic
particulates transport tracking
capability and regional LBCs
YES
YES
Radiation feedback in GFS
NO
YES
Atmospheric correction in SST
retrievals
NO
YES
Include aerosol effects in
GSI/CRTM
NO
YES
Aerosol data assimilation
NO
YES
Aerosol-cloud interaction in
GFS/CFS
NO
YES
Full package
Global annual total aerosol emission, annual averaged aerosol burden, and
lifetime for dust species.
Emissions (Tg/yr)
Burden (Tg)
Lifetime (days)
GFS
641.97
32.5
18.18
GEOS4
1970
31.6
5.85
offline GOCART
3242
38.4
4.33
AeroCom
1789
[541-4036]
19.2
[1.4-33.9]
4.22
[0.92-18.4]
Note: The first column is the result of NEMS/GFS-GOCART simulations, the second column
is the result of GEOS4-GOCART on-line simulations [Colarco et al., 2010], the third column is
the result of the offline GOCART model [Chin et al., 2009], and the final column is the
average/range of the AeroCom models.
On-going and planned activities:
• Refine and optimize the system: preliminary results show weak
emissions and removals in NEMS GFS
• Aerosol verification system (AERONET, MODIS, CALIPSO)*
• Real-time system* (proposed configuration: T126 L64, 4-day forecast
from 00Z GDAS (for met fields) and NEMS (for aerosol fields)
*: in coordination with NRL, ECMWF, GSFC, UKMO, JMA
From my last presentation
Global annual total aerosol emission, annual averaged aerosol burden, and
lifetime for dust species.
Emissions (Tg/yr)
Burden (Tg)
Lifetime (days)
GFS
641.97
32.5
18.18
HYB-10
688.75
29.26
15.5
GEN-10
629.11
20.21
11.7
HYB-07
690.68
33.01
17.5
GEN-07
617.51
20.36
12.1
GOES5
1970
31.6
5.85
Note: The first column is the result of NEMS/GFS-GOCART simulations, the second column
is the result of GEOS4-GOCART on-line simulations [Colarco et al., 2010], the third column is
the result of the offline GOCART model [Chin et al., 2009], and the final column is the
average/range of the AeroCom models.
•
•
•
•
GFS physics in NEMS has been updated to be consistent with operational GFS
(R11579, committed on 23 Dec 2010)
Four 13-month NEMS experiments are conducted (sigma-P and sigma-theta-P;
2007 and 2010; RAS scheme with tracer scavenging / convective transport)
NEMS vs GEOS5:
– Emissions in NEMS are 1/3 of emissions in GEOS-5
– Lifetime in NEMS is 2-3 times longer than GEOS-5
Need to adjust tunable parameters in GOCART as host AGCM is changed from
GEOS-5 to GFS
Dust Source Function
Function of surface topographic depression, surface wetness, and
surface wind speed (Ginoux et al. 2001)
2
S s p u10

u10  ut  u10  ut
Source Flux p  
otherwise
0
S : Source function
u10: wind speed at 10 m
sp: fraction of clay and silt size
ut: threshold wind velocity

r p  ra
A
g p 1.2  0.2 log 10 wt  if wt  0.5
ut  
'
ra

otherwise

A : constant=6.5
wt: surface wetness
p : particle diameter ρp, ρa : particle and air density
This slide is taken from Ho-Chun Huang’s ppt
Global annual total aerosol emission, annual averaged aerosol burden, and
lifetime for dust species.
Emissions (Tg/yr)
Burden (Tg)
Lifetime (days)
GFS
641.97
32.5
18.18
GEN
629
20.2
11.7
HYB
689
29.3
15.5
HYBx
1288
57.9
16.5
GOES5
1970
31.6
5.85
Note: The first column is the result of NEMS/GFS-GOCART simulations, the second column
is the result of GEOS4-GOCART on-line simulations [Colarco et al., 2010], the third column is
the result of the offline GOCART model [Chin et al., 2009], and the final column is the
average/range of the AeroCom models.
•
•
HYBx: increase source function (from 0.175e-9 to 1.8*0.175e-9) and wet
scavenging (from 0.2 to 0.8)
Next steps:
– Enhance removal efficiency in NEMS/GFS-GOCART
– Evaluate the model by comparing with observations (AERONET, MODIS,
CALIOP, MISR) and modeling results (off-line GFS-GOCART, GEOS5),
– Determine model configuration and set up a NRT system
International Cooperative for Aerosol Prediction
(ICAP): Multi-model ensemble dust forecasts
Total AOD at 550 nm(march 07)
NAPPS (NRL)
NGAC (NCEP) and other centers
GEOS-5 (GSFC)
MACC (ECMWF)
Dakar, Senegal
Kuwait
Cape Verde