Transcript Recent Results and Planned Changes for the Noah LSM at NCEP

Recent Results and Near-Future
Planned Changes for the Noah LSM
at NCEP
Ken Mitchell, Mike Ek, Helin Wei, Vince Wong, Youlong
Xia, Rongqian Yang, Jesse Meng,
Yihua Wu, Weizhong Zheng
Unified Noah LSM Development Workshop
NCAR
25-26July 2007
Strategic Issues of this
Noah LSM Workshop
(for later workshop reference: not subject of this talk)
• Migrating all Noah developers to “Unified Noah” in
public WRF Repository
• Centralized “version control” Noah software
• Migrating to ESMF and LIS
– Maintaining ESMF compatibility in WRF repository
• Coordinating Noah development paths
• Using new satellite-derived land surface fields
• “Strategic” (major) Noah physics improvements
– See next frame for examples
• “Tactical” (modest) Noah physics improvements
– This is emphasis of remainder of this talk and emphasis of
current Noah upgrades in NCEP Land Team
Strategic (major) improvements to Noah physics:
often by collaborators outside of NCEP and NCAR
(not subject of this talk)
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Groundwater state and Topmodel approach (U. Texas)
Multi-layer snowpack (U. Texas)
Multi-layer vegetation canopy (U. Texas)
Ball-Berry canopy resistance (Purdue U.)
Snow albedo treatment (U. Arizona)
Irrigation treatment (NASA HSB)
Van Genuchten soil hydraulics (NASA HSB)
Spatially varying soil layer thickness
Unified surface layer treatment (over land)
Others (TBD at this workshop)
Recent Results and Plans for Noah LSM at NCEP:
Outline of Remainder of Talk
• GLDAS-LIS/Noah: Jesse Meng
– T126 and T382
– Component in next NCEP Global Reanalysis
• Climate Forecast System (CFS): Rongqian Yang
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– Impacts of Noah and T126 GLDAS
Global Forecast System (GFS):Helin Wei, Weizhong Zheng
– Impact of T382 GLDAS
– Impact of new weekly realtime global vegetation fraction (GVF)
– Surface layer scheme: Zot changes
• N. American WRF model (NAM): Mike Ek, Vince Wong
– Unified Noah: Impacts
– Surface layer scheme: Zot changes
• NLDAS: Youlong Xia, Helin Wei
– improve Noah physics (seasonal LAI, canopy resist., snow albedo, sfc layer, infiltration)
– Add seasonal prediction capability (with U. Washington and Princeton U.)
• Hurricane WRF Model (HWRF): Yihua Wu
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– Replace surface slab model with Noah LSM
PILPS:
– San Pedro (Luis Bastidas) and LBA (U. Texas/Yang & Niu)
• Joint Center for Satellite Data Assim (JCSDA): Weizhong Zheng
– New satellite land surface fields (mostly MODIS)
– Land surface skin temp (LST) and land surface emissivity
CFS Land Experiments: 4 configurations
Land Experiments of T126 CFS with CFS/Noah and CFS/OSU
Choice of Land Model
Choice of
Land
Initial
Conditions
CFS/Noah
CFS/OSU
GR2/OSU
GR2/OSU (CONTROL)
GLDAS/Noah
GLDAS/Noah Climo
“GR2” denotes NCEP/DOE Global Reanalysis 2
CFS Summer-Season Land Experiments
Objective: Demonstrate Impact on CFS of: A) new land model (Noah LSM vs OSU LSM)
B) new land initial conditions (GLDAS vs GR2)
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25-year (1980-2004) 10-member 6-month T126 CFS runs ( GFS-OP3T3, MOM-3 )
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Four configurations of T126 CFS:
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A) CFS/OSU/GR2:
B) CFS/Noah/GR2:
C) CFS/Noah/GLDAS:
D) CFS/Noah/GLDAS-Climo:
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Initial conditions: 00Z daily from Apr 19-23 29,30, and May 1-3
- OSU LSM, initial land states from GR2 (CONTROL)
- Noah LSM, initial land states from GR2
- Noah LSM, initial land states from T126 GLDAS/Noah
- Noah LSM, initial land states from GLDAS/Noah climo
Key Theme From Results that Follow:
Configuration B is clearly the worst configuration for CONUS JJA precip
Soil Moisture Comparison:
T126 GLDAS/Noah vs. T62 Global Reanalysis-2/OSU
Monthly Time Series (1985-2004) of Area-mean
Illinois 2-meter Soil Moisture [mm]:
Observations (black), GLDAS/Noah (purple), GR2/OSU (green)
Total
Climatology
Anomaly
The climatology of GLDAS/Noah soil moisture is higher and closer to the
observed climatology than that of GR2/OSU, while the anomlies of all three show
generally better agreement with each other (though some exceptions)
GLDAS/Noah (top) versus GR2/OSU (bottom)
2-meter soil moisture (% volume)
May 1st Climatology
01 May 1999
Anomaly
GLDAS/Noah
GR2/OSU
GLDAS/Noah
GR2/OSU
Left column: GLDAS/Noah soil moisture climo is generally higher then GR2/OSU
Middle column: GLDAS/Noah soil moisture anomaly pattern agrees better
than that of GR2/OSU with observed precipitation anomaly (right column: top)
Top: observed 90-day
Precipitation Anomaly
(mm) valid 30 April 99
Bottom: Climatology
GLDAS/Noah (top row) versus GR2/OSU (bottom row)
2-meter soil moisture (% volume): GLDAS/Noah values are higher
Climatology (left column) is from 25-year period of ~1981-2005)
May 1st Climatology
GLDAS/Noah
GR2/OSU
01 May 1999 Anomaly
GLDAS/Noah
GR2/OSU
JJA Precip Correlation Skill w Different LSMs and ICs
Noah/
GLDAS
Noah/
GR2
Worst
OSU/
GR2
Noah/
GLDAS
Climo
10 Members each case (same initial dates)
JJA Precip Correlation Skills South America
Noah/
GLDAS
Noah/
GR2
Best
Noah/
GLDAS
Climo
OSU/
GR2
JJA
Mean SST Correlation Skill w Different LSM/ICs
Globally
Noah/
GLDAS
Noah/
GR2
Noah/
GLDAS
Climo
OSU/
GR2
10 Members each case (same initial dates)
Realtime T382 GLDAS/Noah
System: dew. Daily execution.
Software: LIS (Land Information System)
Land surface model: Noah (as in operational GFS/GDAS)
Forcing: GDAS hourly sflux
CMAP precip (1-7 days behind realtime)
Output: Global land states validate at 00z.
GLDAS-based gdas1.t00z.sfcanl
CONUS CPC Precip Anomaly
http://www.cpc.ncep.noaa.gov/cgi-bin/anom_realtime.sh
CONUS GDAS vs CMAP precip
Up to 50 mm differences in
30-day precip accumulation
of 200 mm.
CONUS GDAS/SMC
CONUS LIS/SMC – GDAS/SMC
The Impact of using
GLDAS/Noah initial land states
on GFS Forecasts
(T382 GFS Static Runs)
Experiments:
GFS/GLDAS: 31 static 6-day T382 forecasts with GLDAS/Noah
land states as initial conditions
Period: May 4-June 3, 2007
Hypothesis:
GDAS tends to overestimate precip over
eastern half of CONUS in warm season-->wet
soil moisture-->high evaporation-->cool
temperature
With CMAP precip in T382 GLDAS:
less precip over east half of CONUS-->lower
soil moisture-->lower evaporation-->warmer
temperature
As in previous slide, but for CONUS
Daytime (18-21h) 40-100 cm Soil Moisture averaged
from 20070511 to 20070610
tested GFS – ops GFS
Mean Daytime (18-21h) Surface Latent Heat Flux
(W/m**2)
from 20070511 to 20070610
tested GFS – ops GFS
Valid period: 20070507-20070603
Threat
Score
07 May – 03 Jun
Bias
CONUS:
Precip scores
12-36h
West
CONUS
East CONUS
New NESDIS fields of weekly realtime global green
vegetation fraction (GVF)
and their impact on
T382 GFS forecasts
GVF Data Set:
(i) Old climatology GVF monthly data (Gutman)
(ii) Multi-year mean GVF weekly data (24 years: 1982-2005)
(iii) Real-time weekly GVF data (from 1982 to present)
GFS Sensivity Case Selection:
Summer 2006: August 1, 2006
Global GVF Data: August 01, 2006
• Old climatology GVF data (Gutman);
• Mult-year mean GVF weekly data (24 years);
• Difference of two climatology datasets;
• Anomaly of real-time weekly GVF data;
Climatology: for Aug 01
Old GVF: Upper left
New GVF: Upper right
Difference (New-minus-Old):
Lower right
New GVF is generally higher
CONUS version of previous frame
Climatology: for Aug 01
Old GVF: Upper left
New GVF: Upper right
Difference (New-minus-Old):
Lower right
New GVF is generally higher
Top Panel:
Climatology of New GVF for Aug 01
Bottom Right Panel:
Realtime Anomaly for 01 August 2006:
(New Weekly Realtime GVF minus
Climatology of New Realtime GVF)
Bottom Left Panel:
CONUS version of bottom right panel
Two Sensitivity Tests with GFS: August 01, 2006
• (1) Impact of climatology difference:
– Control:
– Test:
Execute GFS with climatology of old GVF product
Execute GFS with climatology of new GVF product
• (2) Impact of weekly anomaly:
– Control:
Execute GFS using climatology of new GVF product
– Test:
Execute GFS using the realtime weekly field of new GVF product
• GFS: 7 days forecast, starts from Aug.01,2006.
• Only Experiment 1 is completed to date and its results shown in next
frame (Plots of Test-minus-Control Differences: Tsfc, Ta, Sensible heat flux and Latent heat flux at 1800
UTC for GFS 36-hour forecast valid at 1800 UTC from 0000 UTC initial conditions on 01 August 2006)
•Test-minus-Control Difference Fields: Tskin (upper left), 2-m air T (upper right),
•Sensible heat flux (lower left), Latent heat flux (lower right)
GFS 36-hour forecast valid at 1800 UTC from 0000 UTC initial time on 01 Aug 06)
Where differences
are significant,
higher GVF values
in new vs old climo
Result in
cooler Tskin
and higher surface
latent heat flux.
Mike Ek will present recent work with
Unified Noah LSM in
WRF/NMM (NAM) mesoscale model
plus:
1 - surface layer test results in NAM
2- iterative surface energy balance in Noah
NLDAS Phase 2:
1 – Add seasonal prediction component
2- Employ improved versions of Noah (and VIC, Mosaic, SAC)
3- Add CLM
Following frames focus on improvements to Noah LSM:
-- seasonal LAI
-- canopy resistance (vapor pressure deficit function, soil moisture stress)
-- infiltration parameters (Ksat, Kdt) as tested in DMIP-2
Impact in NLDAS simulations of a bundle of
following seven changes to Noah LSM
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-- replace constant LAI treatment with a seasonally and spatially varying LAI
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-- allow a seasonally varying vertical profile of root density
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-- change the function for the vapor-pressure deficit term in the canopy resistance
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-- change the upper threshold of soil moisture at which the vegetation feels a soil
moisture deficit
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-- change the minimum stomatal resistance parameter for a few vegetation classes
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-- change the treatment for the diurnal variation of surface albedo
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-- change the single parameter in the formulation for the roughness length for heat
(to increase the daytime aerodynamic conductance)
– Decrease CZIL from 0.10 to 0.05
Adding Seasonal LAI treatment to Noah:
Use current green veg fraction (GVF), scale between
annual min and max values of LAI
Seasonal LAI Approach
LAI  LAI
min
 ( LAI
max
 LAI
min
) *
where , a  ( f  f min ) /( f max  f min )
LAImin and LAImax are from Brian Cosgrove’s table.
Exp: scaling LAI (left hand Y-axis) by means of green veg fraction (right hand Y-axis)
This example for a winter-wheat location in central Oklahoma
LAI
Greenness
Changing humidity stress and soil moisture stress functions in Jarvis treatment in Noah
To reduce low LH biases during the Summer
Using a narrow range of this
tends to overestimate the
evaporation during wet
periods (spring) and
underestimate the
evaporation during dry
periods (summer).
SMHIGH and SMLOW
From Chen et al. 1996
BXEXP
Bundle of 7 changes to Noah LSM physics reduces Noah’s generally negative runoff bias
CONTROL
BUNDLE
Normalized bias of mean-annual basin average runoff from 2-year uncoupled Noah LSM
simulations (Oct 97 to Sep 99) for selected unregulated basins in the NLDAS test bed for a control
version (left panel) and test version (right panel) of the Noah LSM. The test version (labeled
“BUNDLE”) includes the seven physical changes listed in Section 5 of the progress report. The
changes in the test version reduce the negative runoff bias in the east half of the CONUS. Those
basins in central Great Plains with high positive bias (dark green) are likely basins were streamflow
is diverted for irrigation and hence should be discarded from the set of validating basins.
Normalized bias is defined as [(model-minus-observed)/observed]
Diurnal cycle (x-axis) by month of year (y-axis) of differences between
Noah simulated and observed surface latent heat flux
CONTROL
Monthly mean diurnal cycle (horizontal axis)
by month (vertical axis) of the difference
of model-minus observed surface latent heat
flux as computed from the average of such
differences at 24 ARM/CART flux stations for
a 21-month simulation in the NLDAS test bed
of three configurations of the Noah LSM
described in the text.
BUNDLE
of 7 Noah
changes
BUNDLE +
GRNDWTR
Noah simulations for DMIP-2:
improving Noah streamflow simulations
by tuning Ksat and Kdt parameters
New Ksat =
5.5 * (Old Ksat)
New Kdt =
2.59 - 0.044*(new Ksat above)
Mean annual cycle from six years (Oct 96 – Sep 02) of observed and Noah LSM simulated monthly
streamflow for the Blue River and Eldon River basins in Oklahoma. Changes to two Noah runoff
related parameters recommended by the MOPEX project reduces the low bias in the simulation.
As in previous figure, but for monthly time series spanning six years (Oct 96 – Sep 02).
PILPS San Pedro (semi-arid: S. Arizona)
Lead: Luis Bastidas
• See PILPS San Pedro arcticle in most recent
issue of GEWEX Newsletter
– Noah simulations fared reasonably well
• Runoff timing one of the best of participating models
• Runoff magnitude much too low (as in almost all models)
• Latent heat flux was low
New Land-Surface Fields
from JCSDA thrusts
To be shown on Day 2 of
workshop
New Satellite-Based Land-Surface Fields
from JCSDA thrusts
To be shown on Day 2 of workshop
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Landuse (Mark Friedl, Boston U.)
Albedo: snow-free (Mark Friedl, Boston U.)
LAI (Mark Friedl, Boston U.)
Deep snow albedo (Xubin Zeng, U. Arizona)
Greenness fraction
– Realtime weekly (NESDIS/STAR)
– Monthly climo (Xubin Zeng, U. Arizona)
• Land Surface emissivity (Ben Ruston, NRL)
NCEP recommended modest
changes to Noah
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Seasonal LAI
Modify Ksat and Kdt parameters
Change humidity stress function in Jarvis
Modify RSmin for some veg classes
Modifyy SMCREF and SMCWLT
Modify Zot for stable conditions