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Improvement of Land Surface Parameters and States: Diagnosing Forecast and Model Deficiencies. Michael Barlage (NCAR) Xubin Zeng (UA), Patrick Broxton ( UA) , Fei Chen (NCAR). 12 th JCSDA Science Meeting – 22 May 2014. Introduction.

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Improvement of land surface parameters and states diagnosing forecast and model deficiencies

Improvement of Land Surface Parameters and States: Diagnosing Forecast and Model Deficiencies

Michael Barlage (NCAR)

XubinZeng (UA), Patrick Broxton (UA), Fei Chen (NCAR)

12th JCSDA Science Meeting – 22 May 2014


Temperature biases in the Noah model can reduce the number of satellite observations that are assimilated

In addition, snow melts too quickly and to correct for this, water is added during data assimilation, which results in too much melt

This presentation documents deficiencies in the current forecast system and attributes part of them to model structural deficiencies

Advances in model structure aim to improve surface temperature and snow simulation, increase atmospheric assimilation, and increase land surface assimilation

Noah lsm deficiencies
Noah LSM Deficiencies

Challenges with Noah LSM Structure

Flagstaff WRF/Noah v3.2 T2m simulation (green) compared to METAR observations(black)

Forecast Hour (Initialized at 12Z daily)

  • Cold bias during the day results from capped surface temperature at freezing

  • Bias recovers during the night

  • When snow is gone, bias is low


Days in Feb

KFLG Forecast Bias (ºC)

Challenges with noah lsm structure
Challenges with Noah LSM Structure

  • May 2007 temperature time series for a single location in Arctic System Reanalysis (3D-Var, land assimilation of vegetation, snow and albedo)

  • Observations in blue, analysis in red and model forecast in green

  • Pre-snowmelt period cold bias exists, assimilation helps

  • Significant cold bias exists during melt period (up to 15°C)

  • Post-melt period performance is quite good

Challenges with noah lsm structure1
Challenges with Noah LSM Structure

  • Simultaneously with low temperature biases, snow continuously gets assimilated during spring

  • Due to model structure, this snow melts during the 24 hours until the next assimilation cycle

  • This reinforces the cold bias and inserts more water into the system, potentially causing adverse effects to hydrology prediction

NCEP operational NAM model 24-hour forecasted snow minus analysis snow shows excessive melting during the entire month of March

Snow water equivalent in gfs
Snow Water Equivalent in GFS

  • SWE (kg/m^2) (Forecast – Analysis)

*White areas: SWE <10 kg/m^2 in forecast and analysis

For GFS, compare the forecast with a lead time of 4 days with the coincident analysis for each day in 2013








Challenges with Noah LSM Structure

Noah LSM in NCEP Eta, MM5 and WRF Models(Pan and Mahrt 1987, Chen et al. 1996, Chen and Dudhia 2001,

Ek et al., 2003)

Noah-MP LSM in WRF and NCEP CFS (Yang et al., 2011; Niu et al., 2011)




Multiple surface temperatures and distinct canopy


Single surface temperature








Snow (x,y)




Noah and Noah-MP LSM Structure Comparison

  • Six-month simulations using coupled atmosphere-land model from March – June 2010

  • Compare only grids with 100% snow cover and evergreen needleleaf trees

  • When temperatures are below freezing, Noah-MP is warmer but consistent with Noah

  • When temperature approaches freezing, Noah temperature cannot get much above freezing

Noah and Noah-MP LSM Structure Comparison

  • Compare to daily MODIS/Aqua land surface temperature at 13:30 overpass

  • Noah peaks near-freezing

  • Noah-MP is warmer than MODIS by 2-4K but distribution is much better than Noah

Snow water equivalent simulated by six lsms

Noah and Noah-MP LSM Structure Comparison

Snow Water Equivalent simulated by six LSMs

Noah and Noah-MP can produce similar snow through a modified snow albedo formulation

Chen, et al. 2014

D iurnal cycle of surface albedo niwot ridge jan mar and jul 2007

Noah and Noah-MP LSM Structure Comparison

Diurnal cycle of surface albedo: Niwot Ridge Jan, Mar, and Jul 2007

  • Large variation among modeled winter albedo

  • Noah: larger seasonal variations

  • Noah-MP: drop during March spring melt

AmeriFluxObs, MODIS, Noah, VIC, SAST, LEAF, CLM, Noah-MP

Chen, et al. 2014

Albedo: 0.66 (Cline, 1997) over snow, 0.34 (MODIS, Jin et al., 2002),

Monthly daytime min max and mean absorbed sw and sensible heat w m 2 for jan mar may and jul

Noah and Noah-MP LSM Structure Comparison

Monthly daytime min, max and mean absorbed SW and sensible heat (W/m2) for Jan, Mar, May and Jul


  • Comparison of observed (O), Noah (N), and Noah-MP (M).

  • Noah has less absorbed solar radiation resulting in colder surface and lower (or negative) sensible heat flux











Jan 2007

Mar 2007












Chen, et al. 2014

Advantages with Noah-MP LSM Structure


Noah-MP uses a two-stream radiative transfer treatment through the canopy based on Dickinson (1983) and Sellers (1985)

  • Canopy parameters:

    • Canopy top and bottom

    • Crown radius, vertical and horizontal

    • Vegetation element density, i.e., trees/grass leaves per unit area

    • Leaf and stem area per unit area

    • Leaf orientation

    • Leaf reflectance and transmittance for direct/diffuse and visible/NIR radiation

  • Multiple options for spatial distribution

    • Full grid coverage

    • Vegetation cover equals prescribed fractional vegetation

    • Random distribution with slant shading

shaded fraction

Advantages with Noah-MP LSM Structure

  • Over a Noah-MP grid, individual tree elements can be randomly distributed and have overlapping shadows

  • Noah-MP albedo is calculated based on canopy parameters

  • Noah prescribes snow-free and snow-covered albedo from satellite climatology

SE Minnesota in Google Maps

Advantages with Noah-MP LSM Structure

  • Using prescribed vegetation fraction varying from 5% to 100% as radiation fraction

  • Increasing vegetation fraction increases snow, decreases albedo

Advantages with Noah-MP LSM Structure

  • Using randomly distributed shadows as radiation-active fraction through use of sun angle and canopy morphology

  • Complex interaction between vegetated and shadowed fraction and canopy/snow radiation absorption

Continue to Develop Satellite Land Datasets

MODIS 500m land cover climatology delivered to WRF v3.6 (Broxton et al., 2014)

Deciduous Needleleaf

Evergreen Needleleaf

Deciduous Broadleaf

Mixed Forest

Closed Shrubland

Open Shrubland

Woody Savanna



Permanent Wetland






Not Land

Wooded Tundra

Mixed Tundra

Bare Ground Tundra

Deciduous Broadleaf

Continue to Develop Satellite Land Datasets

Original Pixel Data

Final Smooth Climatology

Remove suspect data

Fill missing data



-Individual value of year

2001 2002 2003 2004 2005 2006 2007 2008


-black: median

-yellow: tile climo (Savanna)

Continue to Develop Satellite Land Datasets

  • WRF satellite-derived input datasets tend to produce too little vegetation outside of the tropics

  • Using fraction of photosynthetic absorbed radiation as a vegetation proxy


Can’t always blame the LSM

  • Monthly mean WRF radiation June 2010 (Dudhia scheme)

  • ERA-Interim monthly mean radiation June 2010

  • Up to 100 W/m2 difference

  • 20 – 40% too high

Relationship to Land Data Assimilation

  • Noah model land data assimilation

    • Favorable to directly assimilate (use) “bulk” land surface properties

      • Albedo

      • Green vegetation fraction (via NDVI or EVI)

      • Leaf Area Index (LAI)

    • Bulk surface treatment causes problems when heterogeneity is necessary (e.g., snow and vegetation)

  • Noah-MP model land data assimilation

    • Increased prognostic states for assimilation

      • LAI through dynamic vegetation model

      • Albedo needs to be treated differently (parameter estimation)

      • Vegetation fraction: what does it mean in the model?

    • More available states that can inform surface emissivity models

      • Prognostic LAI, partition of canopy water into ice/liquid

  • Both models use similar soil moisture treatment for soil moisture assimilation


Temperatures: significant biases can occur in the current Noah model, many of these are due to structural limitations when heterogeneities exist at the surface.

Snow: In Noah, generally there is too little snow during the spring. Assimilation replaces this snow, it immediately melts and gets added to the soil or surface runoff.

Are we reaching a structural limit in the Noah model? Do we need to move toward a more process-based model to capture important states that can informed satellite assimilation?

Before the eventual (hopefully) update of the operational LSM, can we exploit the benefits of a more complex model, e.g., in a system such as LIS?