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The Emergence of LandSurface Modeling

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NCEP: Where America's Weather and Climate Services Begin ... Becoming a Complete Earth System Endeavor. 1 - ATMOSPHERE: troposphere, stratosphere ... – PowerPoint PPT presentation

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Title: The Emergence of LandSurface Modeling


1
The Emergence of Land-Surface Modeling in
Modern-Era NWP The NCEP Experience and
Collaborations

NWP 50-Year Anniversary Symposium 15-17 June 2004
NCEP Where America's Weather and Climate
Services Begin
2
Improving Weather and Climate Prediction Becoming
a Complete Earth System Endeavor
1 - ATMOSPHERE troposphere,
stratosphere (GARP) - initial
conditions require atmosphere data
assimilation 2 - OCEAN deep ocean, seas,
coastal ocean, sea ice (TOGA/CLIVAR)
- initial conditions require ocean
data assimilation 3 - LAND soil moisture,
snowpack, vegetation, runoff
(GEWEX/GAPP) - initial conditions require
land data assimilation
3
Historical Timeline of NCEP LSMsWith respect to
NCEP atmospheric models
  • 1955-1965 Barotropic Model
  • no land surface, no radiation, no diurnal cycle
  • 1965-1985 Multi-layer PE and LFM models
  • simple surface friction effect on wind velocity
  • surface sensible/latent heat fluxes over ocean
    only
  • assume zero sensible/latent heat flux over land
  • no diurnal cycle, no radiation
  • 1986-1995 global MRF, regional NGM Eta models
  • first viable land surface models included
  • bucket model hydrology and slab model
    thermodynamics
  • first diurnal cycle of land surface energy
    balance radiative forcing

4
Historical Timeline of NCEP LSMsWith respect to
NCEP atmospheric models
  • 1995-2004 global GFS, regional Eta WRF models
  • The Oregon State University (OSU) LSM
  • The NCEP Noah LSM descendant of the OSU LSM
  • Four soil layers
  • Includes liquid and frozen soil moisture (OHD)
  • Vertical profiles of soil moisture and soil
    temperature
  • Explicit vegetation canopy with root zone
  • Satellite NDVI-based seasonal cycle of green
    vegetation fraction (NESDIS)
  • Snowpack physics, including water content and
    density
  • Daily snow cover and snowpack analyses from
    NESDIS and AFWA
  • Dynamic snowmelt and snow sublimation
  • Stream network and streamflow simulation

5
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6
Multi-institution Land-Surface Partners1990 -
present
  • Air Force (AFWA and AFRL)
  • NESDIS Land Team (ORA)
  • NWS Office of Hydrological Development (OHD)
  • NOAA Office of Global Programs (OGP)
  • GEWEX Programs GAPP, GCIP, PILPS, ISLSCP
  • NLDAS N.American Land Data Assimilation System
  • Six university partners plus above partners
  • Many are GAPP/OGP sponsored
  • NASA Hydrological Sciences Branch and GMAO
  • NCAR WRF Land Surface Working Group
  • USWRP sponsored

7
NESDIS Interactive Multi-sensor Snow (IMS)
Product Daily 4-km Snow/Ice Analysis Used along
with AFWA Snowdepth Analysis for the
daily Initialization of snowpack in NCEP global
and regional models
28 Feb 2004
13 May 2004
8
Partitioning of Incoming Solar Radiation
34 reflected to space -- 25 reflected by
clouds -- 7 back scatter by air -- 2 reflected
by earth sfc 19 absorbed by atmos -- 17
absorbed by air -- 2 absorbed by clouds 47
absorbed by earth sfc
9
  • Land Surface Energy Balance
  • (Exp Monthly mean, mid-day summer, central U.S.)

Sd - aSd Ld - Lu H LE
G 800 - 150 400 - 550 125
300 75 Complexity of LSM driven by
representation of LE and G Sd Downward
solar insolation 800 W/m2 -aSd Reflected
solar insolation -150 Ld Downward
longwave radiation 400 -Lu Upward
longwave radiation -550 (based on land skin
temp) G Ground heat flux 75 H
Sensible heat flux 125 LE LE Latent
heat flux (evaporation) 300
10
Land Surface Water Balance (Exp monthly, summer,
central U.S.)
dS P R E dS change in soil moisture
content - 75 mm P precipitation
75 R runoff 25 E
evaporation 125 (P-R) infiltration Evapora
tion is a function of soil moisture and
vegetation type, rooting depth/density,
fractional cover, greenness. All terms in units
of mm.
11
Simple one-layer slab LSMs of 1985-1995 era at
NCEP
  • Bucket Model for hydrology
  • Surface Evaporation LE B EP
  • B Surface Wetness coefficient (fraction)
  • EP potential evaporation
  • function of atmospheric conditions
  • (humidity, wind speed, temperature)
  • Slab Model (force-restore) for ground heat flux

12
The Surface Wetness Field in the NGM
Model(Range 0.04 0.20 )
(values plotted are actual 100)
13
Land Surface Evaporation Treatmentin modern-era
land models
WHEREIN E total evapotranspiration from
combined soil/vegetation Edir direct
evaporation from top soil layer Ec evaporation
from canopy-intercepted precipitation or dew Et
transpiration through plant canopy via root
uptake, and constrained by the canopy
resistance to evaporation
14
Noah Land Model Prognostic Equations
  • Soil Moisture
  • Richards Equation for soil water movement
  • D, K functions (soil texture)
  • F? represents sources (infiltration) and sinks
    (evaporation)
  • Soil Temperature
  • C, Kt functions (soil texture, soil moisture)
  • Soil temperature information used to compute
    ground heat flux

15
Vegetation Greenness April Climatology
Developed and provided by NESDIS/ORA -- New
NESDIS realtime weekly update now being
tested by NCEP
Vegetation Greenness July Climatology
16
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17
Ground Heat Flux Evaluation in Eta Model using
FIFE Field Exp Slab/Bucket LSM versus Noah
LSM (Betts et al., 1997, MWR)
18
Validation of surface fluxes of four LSMs vs 15
ARM flux stations. Monthly mean Rnet, LE, H and G
for Jan 98 to Sep 99
19
Improving the Mesoscale NWP Forecastsvia
Land-Surface Influences
  • NWP prediction improvement goals
  • - 2 meter air temperature and humidity
  • - 10 meter wind vector
  • - PBL T and Td profiles
  • - convective stability indices
  • - integrated moisture flux convergence
  • - precipitation and cloud cover

20
July 2003 Monthly Mean Diurnal Cycle of 2-m Air
Temperature Obs vs NCEP Models (3) for Midwest
U.S. Eta, GFS/AVN, NGM
NGM
ETA
Obs
AVN
21
NCEP Eta model forecast during July
1998 Texas/Oklahoma drought, 24-hour forecast
valid 00Z 27 July 1998
In late July 1998, after nearly two months of
self-cycling the land states in the EDAS, the Eta
model successfully captured the extremely dry
soil moisture (upper left) and warm soil
temps (upper right) over the Texas/Oklahoma region
, yielding forecasts of high 2-m air temps
(lower left) and deep, dry, hot boundary
layers that verified well against raobs
(e.g., at Norman, OK lower right).
soil moisture availability (1-m)
soil temperature (5-cm)
air temperature (2-meter)
Norman, OK sonde (obssolid, modeldashed)
22
The Eta Data Assimilation System EDASA Coupled
Land Data Assimilation System with hourly
assimilation of observed precipitation
Pre-forecast data assimilation period
Free forecast period
In the forecast period between the analysis steps
of the 12h pre-forecast data assimilation period,
at each time step and at each point where
observed precipitation is available, we compare
Pmod to Pobs, then modify the models
temperature, moisture, cloud and rain field to be
more consistent with observed precipitation.
23
IMPACT OF HOURLY PRECIPITATION ASSIMILATION IN
ETA MODEL
Figure 8. (a) 1-15 July 1998 gage-observed total
precip (mm), (b) 'snapshot' of hourly Stage IV
radar/ Gage precip (06Z, 15 July 1998) EDAS
total precip of 1-15 July 1998 for (c) control
run without precip assim, and (d) test run with
hourly Stage IV precip assim EDAS soil moisture
availability ( saturation) of top 1-m soil
column valid at 12Z 15 July 1998 (e) without
precip assim, and (f) with precip assim.
24
25-Year EDAS-based Regional Reanalysis Example
of July 1988 vs. 1993
25
SAMPLE LAND-SURFACE OUTPUT FROM RR
soil moisture (percent of saturation) in top
1-meter
DROUGHT 1988 July 15 July, 21Z
FLOOD 1993 July 15 July, 21Z
26
SAMPLE LAND-SURFACE OUTPUT FROM RR
Boundary layer depth m
DROUGHT YEAR (1988) 15 July, 21Z
FLOOD YEAR (1993) 15 July, 21Z
27
Conclusions
  • Land surface modeling has advanced intensely at
    NCEP from mid 1980s to present
  • Above advancements have benefited greatly from
    multi-institution and multi-disciplinary
    partnerships
  • These land surface advancements have improved the
    skill/accuracy of NWP predictions
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