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GAPP. N. American Regional Reanalysis Land Surface : Configuration, Physics, Results, Cautions. Ken Mitchell Mike Ek, Ying Lin, Eric Rogers, P. Shafran, D. Jovic, R. Yang W. Higgins, W. Shi, Y. Fan, J. Janowiak, E. Yarosh. NCEP/ EMC, NCEP/CPC.

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slide1

GAPP

N. American Regional Reanalysis

Land Surface:

Configuration, Physics, Results, Cautions

Ken Mitchell

Mike Ek, Ying Lin, Eric Rogers, P. Shafran, D. Jovic, R. Yang

W. Higgins, W. Shi, Y. Fan, J. Janowiak, E. Yarosh

NCEP/ EMC, NCEP/CPC

Regional Reanalysis Workshop, AMS Meetings, 11 Jan 05

NCEP: Where America's Climate and Weather Services Begin

land modeling support at ncep
Land Modeling Support at NCEP
  • NOAA Office of Global Programs (OGP)
    • GAPP: GEWEX America Prediction Project
    • GCIP: GEWEX Continental International Project
  • These OGP programs are major sponsors of advancements in land surface modeling and land data assimilation in NCEP’s Environmental Modeling Center (EMC)
slide3
The N. American Regional Reanalysis:A Coupled Atmosphere-Land 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 model’s temperature, moisture, cloud and rain field to be more consistent with observed precipitation.

See NARR Article in May 2004 issue of GEWEX/NEWS

slide4

Noah Land Surface Model in NARR

  • 4 soil layers: 10, 30, 60, 100 cm
  • -- total soil moisture
  • -- liquid soil moisture
  • -- soil temperature
  • cold season processes(Koren et al 1999)
  • -- snowpack --- snow water equivalent (SWE) --- snow depth (i.e. snow density)
  • -- frozen soil freeze/thaw physics
  • - surface evaporation components
  • -- Et: transpiration through plant canopy
  • --- Jarvis treatment of canopy resistance
  • -- Ed: direct soil evaporation
  • -- Ec: canopy evaporation
  • --- dew/frost, intercepted precipitation
  • -- Es: snow sublimation
  • Vegetation phenology from NESDIS
  • -- based on AVHRR NDVI
  • -- fraction of green vegetation cover
  • -- monthly global 0.144-deg 5-year climatology
  • -- derived by Gutmann and Ignatov 1998)
slide5

GREEN VEGETATION FRACTION PHENOLOGY:

via NESDIS/ORA based on NDVI from AVHRR

From global, monthly 0.144 degree green vegetation fraction database (Gutman and Ignatov, 1998).

Monthly values are interpolated to daily values in the NARR.

slide6

Example NARR Cold Season States: 01 Jan 92, 21Z

Snow depth (cm)

Shown later: Daily update increment of snowpack state from

U.S. Air Force analysis must be included in land surface water budget.

key references for noah lsm physics performance
Chen et al. (1996, JGR, GCIP Special Issue 1)

Canopy resistance and soil hydraulics/thermodynamics

Koren et al. (1999, JGR, GCIP Special Issue 2)

Snowpack and frozen soil physics

Ek et al. (2003, JGR, GCIP Special Issue 3)

Improvements to subsurface heat fluxes under snow and vegetation

Berbery et al. (2003, JGR, GCIP Special Issue 3)

Ops Eta model Mississippi land surface water and energy budget

Mitchell et al. (2004, JGR)

Evaluation in uncoupled N. American LDAS (NLDAS)

Also see various PILPS and GSWP papers

Uncoupled Noah LSM performance

Key references for Noah LSM physics & performance
land surface subset of narr output
Land Surface Subset of NARR output
  • NARR Land-surface subset: – 720 Gb / 25-year
    • 75 Mb/day tar file of Land A, B, C files
      • versus 450 Mb/day for primary output
    • via NCEP NOMADS server (free anonymous access)
    • Land A files: surface forcing (155 Gb for 25-years) -- can drive offline LSMs
    • Land B files: includes prognostic land states (soil moisture/temp., snow)
    • Land C files: includes surface water and energy fluxes
      • from which to calculate land surface water and energy budgets
      • 3-hour accumulated water fluxes (mm)
      • 3-hour averaged energy fluxes (watts/m**2)
  • Also need Merged B file to:
    • calculate daily snow update increment in surface water budget
    • obtain instantaneous surface energy fluxes (vs. 3-hr mean in Land C file)
  • Fixed field file (18 fields, veg and soil type, land mask, etc)
    • Soil and veg parameter fields (soil porosity, wilting point, min stomatal resistance)
18 narr fixed fields
18 NARR Fixed Fields
  • Examples:
    • Terrain height
    • Land mask
    • Latitude
    • Longitude
    • Soil type (Zobler classes: 7 classes)
    • Vegetation type (SiB classes: 12)
    • Number of soil layers in root zone
    • Maximum albedo over deep snow
    • Wilting point
    • Soil porosity
    • Minimal stomatal resistance
slide10

Example Fixed Fields

Maximum albedo over deep snow

Vegetation Class: 12 Sib classes

hourly output at over 1300 sites especially convenient in 72 hour free forecasts
Hourly output at over 1300 sites:(especially convenient in 72-hour free forecasts)

Examples shown here:

  • Atmospheric sounding
  • Near surface forcing
  • Land surface fluxes
  • -- latent & sensible heat flux
slide12

NARR Land Surface Examples:

Drought of 1988 vs. Flood of 1993

slide13

Difference of 1993 minus 1988

June + July Mean Precipitation

slide14

NARR:

Soil moisture availability (percent of saturation)

Top 1-meter of soil column

1993

1988

Average during 16-31 July(at 21 GMT)

slide15

NARR:

Atmospheric Boundary Layer Depth (m)

1993

1988

Average during 16-31 July(at 21 GMT)

slide17

Ops 12-km EDAS

2004 mean July 18Z-21Z average latent heat flux(sign convention: negative values are upward flux from surface, i.e. surface energy sink)

32-k Regional Reanalysis

Global Reanalysis 1

NLDAS

slide18

Observed Latent Heat Flux (LH): 3 stations( Tilden Meyers: NOAA/ARL, eddy correlation stations )(Brotzge and Crawford, JHM, 2003: such stations give 20% low bias summer LH)

narr summer latent heat flux in eastern half of conus appears to have modest high bias
NARR Summer Latent Heat Flux in eastern half of CONUS appears to have modest high bias
  • Hypothesis as to why: Ops EDAS legacy in NARR
    • Ops EDAS has low precipitation forcing bias
      • Ops EDAS assimilates radar dominated precip estimates
      • Radar-dominated precip estimates have low bias over summer CONUS
    • Above ops EDAS precip forcing bias tends to yield dry soil moisture bias in Ops EDAS
    • Ops Eta model canopy resistance was tuned to lower values to maintain reasonable evaporation rates given low soil moisture bias
    • Ops Eta model canopy resistance was not re-tuned to higher values for Regional Reanalysis, which has less low bias in its precipitation forcing, which is anchored to a daily gauge analysis
slide20
Example of low precipitation bias in ops EDAS:Observed (Higgins/Shi CPC) minus ops EDASprecipitation total (mm) for August 2003
high bias in narr eastern conus warm season evaporation overly depletes soil moisture over illinois
High bias in NARR eastern-CONUS warm season evaporation overly depletes soil moisture over Illinois

Obs:

solid black

NLDAS:

dashed green

NARR:

dashed black

Courtesy:

Y. Fan &

H. Van den Dool

slide22
NARR Monthly Water Budget: 1998-2002 Mississippi Basin(compare with ops EDAS by Berbery et al. (2003, JGR)

(ops Eta min)

slide23
NARR Monthly Water Budget: 1998-2002 Mississippi Basin(compare with ops EDAS by Berbery et al. (2003, JGR)

Black: no snow update

Green: with snow update

slide24

Must include daily NARR snowpack update in the NARR land surface water budgetAt 00Z each day, NARR snowpack depth is updated from external daily Air Force snowdepth analysis

00Z

NARR

Anal

SWE (mm)

21Z

NARR

Anal –Ges

SWE (mm)

is zero

00Z

NARR

Anal –Ges

SWE (mm)

is nonzero

03Z

NARR

Anal –Ges

SWE (mm)

is zero

snowpack physics in noah lsm version 2 6 in narr has systematic tendency to melt snowpack too early
Snowpack physics in Noah LSM Version 2.6 in NARR has systematic tendency to melt snowpack too early
  • This systematic behavior was solved in Noah Versions 2.7 and higher, but not in time to make it into the production NARR (despite concerted attention to this problem)
  • Hence daily snowpack increment will tend to be positive (increase snowpack SWE)
    • Acts as a source of water mass to land water budget
  • Above two factors will likely yield a high bias in season-total snowmelt
narr verification near surface meteorology
NARR Verification: Near Surface Meteorology
  • Compared to Global Reanalysis:
    • NARR has smaller 2-m temperature bias and rms error in both winter and summer
    • NARR has less diurnal cycle error
slide27

January 1998

2-m Temperature 1st Guess

July 1998

RMS

RMS

Bias

Bias