# Presentation: 071927

## Testing of new satellite-derived land surface products in NCEP’s Global and Regional Data Assimilation Systems

- _Ken Mitchell_               and             _Dan Tarpley_
- NCEP/EMC (NOAA)                     NESDIS/ORA (NOAA)
*JCSDA Science Workshop *
*May 31 – June 1, 2006*

## Acknowledgements(Coordination via bi-monthly telecons hosted by NCEP/EMC)

- Joint Center for Satellite Data Assimilation
- _External_: JCSDA-funded PIs
- U.Arizona: _X. Zeng_, Boston.U: _M. Friedl_, Princeton.U: _E. Wood_, GMU: _P. Houser_
- _Internal_:
-  NCEP/EMC: Land Team
- K. Mitchell, G. Gayno, V. Wong, C. Marshall
- NESDIS/ORA: Land Team
- D. Tarpley, L. Jiang, F. Kogan, I. Laszlo. P Romanov
- NASA GSFC/HSB:
- C. Peters-Lidard, M. Rodell, B. Cosgrove, LIS Team
- NASA GSFC/GMAO:
- R. Koster, R. Reichle
- Air Force Weather Agency (AFWA)
- J. Eylander

## Goals of Land-arena in JCSDA:Improved Weather and Climate Forecast Skill Through Use and Assimilation of Satellite Land Data

**New and improved satellite products for prescribed land surface characteristics **_**(focus of this presentation)**_
- Boston U. (M. Friedl), U.Arizona (X. Zeng), NESDIS/ORA (D. Tarpley)
**Improved land surface forward radiation models**
- Princeton U. (E. Wood), NESDIS/ORA (F. Weng)
**Improved Noah land surface model physics as required to use satellite data **(e.g. add groundwater component for GRACE 4dda)
- U.Arizona (X. Zeng), NCEP/EMC (K.Mitchell), NASA/HSB (C. Peters-Lidard)
**Model sensitivity studies on newly available satellite products**
- U.Arizona (X. Zeng), NCEP/EMC, NASA/HSB, NASA/GMAO (R. Koster)
**Land data assimilation methodologies for improved initial conditions of land surface prognostic states **(via Kalman filters or Adjoint models)
- George Mason U. (P. Houser), NASA/HSB, NASA/GMAO
**Transition to operations**
- NCEP/EMC, NASA/HSB, NASA/GMAO, NESDIS/ORA

## Outline

- Vegetation type (aka: landuse class)
- Vegetation phenology (annual cycle)
- Surface albedo
- Land surface temperature (LST)
- Snow cover and snowpack
- Soil moisture
- Surface emissivity
- Research to Operations transition strategy

## Noah Land Surface Model:The land component in NCEP global and regionalmodels and their data assimilation systems(Noah LSM implemented in NCEP ops GFS/GDAS in May 2005)

## Uses of land-surface satellite products in NCEP modeling initiatives (operations or test beds)

**Define land surface characteristics**
_**Vegetation phenology**_**: annual cycle of green vegetation fraction (GVF)**
- Operations: monthly 0.14-deg monthly global climatology of GVF from AVHRR
- Test Bed:
- External/U.Arizona:  MODIS-based global 2-km bi-monthly climatology of GVF
- Internal/NESDIS: AVHRR-based realtime weekly global 0.144-deg (16-km) GVF
_**Vegetation type**_** (land use class):**
- Operations: AVHRR-based 12-class 1-deg (global model) or 24-class 1-km (regional model)
- Test Bed:
- External/Boston.U: MODIS-based 15-class 1-km global
_**Surface albedo**_**: snow-free and maximum for deep snow**
- Operations: based on Briegleb (1992, 1986) and Matthews (1983,1984) seasonal, 1-deg global
- Test Bed:
- External/Boston.U: MODIS-based monthly 5-km global (snow-free)
- :External/U.Arizona: MODIS-based global 0.05-deg maximum albedo over deep snow
**Determine initial values of land prognostic states via data assimilation**
_**Snow cover and snowpack**_**:**
- Operations: Daily multi-sensor 4-km global (Geo, AVHRR, DMSP, AMSU, MODIS)
- Test Bed:
- External/Princeton.U: 4DDA of microwave SWE estimates via forward modeling
- Internal/HSB: MODIS snow cover assimilation
- Internal/NESDIS: AMSU and GEO SWE estimates
_**Soil moisture**_  -- Test Bed only (External: GMU, Internal: HSB, GMAO)
_**Land surface temperature**_ – Test Bed only (External: GMU, Internal: HSB, GMAO)
**Verification of land surface fields** (operations and test beds)
_**Land surface temperature**_ (Internal:EMC)
_**Snow cover**_** **(External: Princeton.U, Internal: EMC and HSB)

## Global Vegetation Type Datasets(aka Landuse Datasets)

- _NCEP Operations_: AVHRR-based
- Global Model (1-deg global, SiB 12-classes, NASA/ISLSCP)
- Regional Model (1-km global, USGS 24-classes, EDC)
- CRTM (its own landuse classification, different from above two)
- _GOAL_:  unify landuse map in above 3 suites
- _NCEP Testing_:    MODIS-based
**Global 1-km**
**U.MD 15 classes, same as IGBP less 2 sparse classes)**
- M. Friedl et al. (Boston U., JCSDA)
- Fewer counts of mixed vegetation class
- Less mixed forest,
- less mixed cropland/grassland
- Less tropical rain forest, more savanna

## 0 WATER BODIES 1 EVERGREEN NEEDLELEAF FOREST 2 EVERGREEN BROADLEAF FOREST 3 DECIDUOUS NEEDLELEAF FOREST 4 DECIDUOUS BROADLEAF FOREST 5 MIXED FORESTS 6 CLOSED SHRUBLANDS 7 OPEN SHRUBLANDS 8 WOODY SAVANNAS 9 SAVANNAS 10 GRASSLANDS 12 CROPLANDS 13 URBAN AND BUILT-UP 15 SNOW AND ICE 16 BARREN OR SPARSELY VEGETATED

## With tundra class added by NCEP/EMC:

**With tundra class added by NCEP/EMC:**
**Tundra class believed to be important to CRTM surface emissivity modeling.**
**(closed and open shrubland classes divided into high latitude and lower latitude regimes)**

## MODIS-based (Red/Top) versus AVHRR-based (Blue/Bottom)

**MODIS-based (Red/Top) versus AVHRR-based (Blue/Bottom)**
**Evergreen needleleaf forest class (left) and mixed forest class (right):**
**Example of fewer mixed-vegetation pixels in MODIS**

##         Annual Cycle of Vegetation Phenology        (Green Vegetation Fraction: GVF)   Original (NESDIS):      AVHRR-based, 16-km, Gutman & Ignatov (1998)    New      (U.Arizona):   MODIS-based,  2-km, Zeng et al. (2000)

- To find NDVIveg and NDVIsoil, we introduce 2km IGBP land type classifications

|  | IGBP land | Pixel% | NDVIveg | GVF |
| --- | --- | --- | --- | --- |
| 1 | Evergreen needleleaf forest | 5.03 | 0.63 | 0.90 |
| 2 | Evergreen broadleaf forest | 9.39 | 0.69 | 0.93 |
| 3 | Deciduous needleleaf forest | 1.52 | 0.63 | 0.92 |
| 4 | Deciduous broadleaf forest | 2.50 | 0.70 | 0.90 |
| 5 | Mixed forest | 4.86 | 0.68 | 0.88 |
| 6 | Closed shrubland | 2.01 | 0.60 | 0.72 |
| 7 | Open shrubland | 13.96 | 0.60 | 0.39 |
| 8 | Woody savanna | 7.87 | 0.62 | 0.86 |
| 9 | Savanna | 7.21 | 0.58 | 0.81 |
| 10 | Grassland | 8.53 | 0.49 | 0.71 |
| 11 | Permanent wetland | 1.02 | 0.56 | 0.85 |
| 12 | Cropland | 10.89 | 0.61 | 0.86 |
| 14 | Natural vegetation | 10.80 | 0.65 | 0.85 |
| 16 | Barren | 14.22 | 0.60 | 0.11 |

- Histogram of evergreen broadleaf

**GVF annual cycle:**Original (Gutman/AVHRR) versus New (Zeng et al/MODIS)

- New GVF for the 7 most prevalent land cover types in CONUS.
-  More realistic annual cycle in new GVF for evergreen needleleaf forest (Original GVF appears to get too low in winter.)
- New GVF is systematically higher in all land cover categories (possibly too high for deciduous broadleaf forest and cropland/grassland).

## Global Albedo Datasets

**Snow-free albedo**
- _NCEP Operations_: Pre-MODIS (1-deg global, quarterly)
- Briegleb et al. (1992,1986), E. Matthews (1984),
- _NCEP Testing_:    Post-MODIS (.05-deg global, monthly)
- M. Friedl et al. (Boston U., JCSDA)
**Maximum albedo for deep snow**
- Serves as upper bound on surface albedo for deep snow in NCEP Noah LSM
- _NCEP Operations_: Pre-MODIS (1-deg)
- Robinson and Kukla (1985, JAM, DMSP-based)
- _NCEP Testing_: Post-MODIS (0.05-deg)
- Barlage and Zeng (2005, GRL, MODIS-based, JCSDA)

## Global VIS Albedo difference: example for 25 May 06 Boston U. MODIS-based minus that in ops GFS(rendered to ops GFS T382 computational grid for 60-deg zenith angle)

-  -- MODIS-based albedo is generally less than (darker) than ops GFS albedo, with notable exceptions such as semi-arid regions.
- -- Tests of the MODIS-based albedo are now underway in the NCEP global model.

## NCEP Operational 1-deg Max Snow Albedo:

**NCEP Operational 1-deg **_**Max Snow Albedo**_**:**
**from Robinson and Kukla (1985, JAM)**
**DMSP visibile imagery based**

**NCEP Test Bed 0.05-deg **_**Max Snow Albedo**_**:**
**from Barlage and Zeng (2005, GRL)**
**MODID-based**

## Application of MODIS Maximum Snow Albedo:Testing in WRF-NoahLSM coupled mesoscale model(Barlage and Zeng, U. Arizona and JCSDA)

-  Up to 0.5 C decreases in 2-m temperature in regions of high snow cover and significant albedo change
-  Greater than 0.1 C increase in 2-m temperature even when snow depth is less than 1cm

## LST: Land Surface Temperature Assimilation

- _External initiatives_
- GMU / Paul Houser: separate talk this session
- _Internal initiatives_
- GMAO / Rolf Reichle: separate talk this session
- EMC / Ken Mitchell:
- LST assimilation via Noah LSM adjoint/tangent-linear model (presented at last year’s workshop)
- LST for verifying land surface simulations – next slides

## LST: Land Surface Skin Temperature

**Satellite sources of LST**
- Geostationary (split window, sounder)
- AVHRR
- MODIS
- SSMI, AMSU, other microwave platforms
**NCEP to date has utilized mostly GOES-based LST**
- Operational in NESDIS: hourly, 1/2-deg resolution
- Test Bed at U.Md (R. Pinker): hourly, 1/8th-deg resolution
- Split window technique through GOES (I-M)
- Sounder technique most recently
- Good definition of diurnal cycle

##                                           Model vs ARM              Model vs GOES             

**        **                                  **Model vs ARM              Model vs GOES**

**July 1999**

-  (18 UTC valid time)

- Validation of LST from
- three land surface models
- (one model per row)
- over the Oklahoma region
- using LST observations from
- ARM flux-stations (left
- column) and from GOES
- (right column).  Model
- validation via GOES LST
- yields similar picture of
- model performance and bias
- as obtained from ARM LST.

## LST Difference: Noah LSM simulated minus GOES-derived (18 UTC, 18 July 1999)

**(GOES LST is obtained only where GOES retrieval infers cloud-free conditions)**

**For **_**Control Run**_** of Noah LSM**
**Noah LSM LST has notable warm bias**

**For **_**Test Run**_** of improved Noah LSM**
**(e.g. revised humidity stress function**
**In canopy resistance formulation)**
**Noah LSM warm LST bias is**
**substantially reduced.**

## Snow Cover and Snowpack Assimilation

- _External initiatives_
- U.Arizona / Mark Barlage: separate talk this session
- Princeton.U / Raf Wojcik: separate talk this session
- _Internal initiatives_
- HSB: MODIS snow cover assimilation
- NESDIS/ORA and NCEP/EMC: next slides

## NESDIS Northern Hemisphere IMS:Interactive Multi-sensor Snow-cover Analysis

**NESDIS has produced satellite-derive N.H. snow cover analyses since 1965 (nearly 40 years)**
- 1965-1996: weekly, 190-km resolution
- Jan 1997- Jan 2004: daily, 24-km resolution
**Feb 2004 – present: daily, new 4-km resolution**
**IMS production is based on mix of:**
- A) automated satellite-based snow cover retrieval
- B) human analysis of satellite imagery via interactive workstation
- time loops of geostationary and polar visible imagery
**IMS sources of automated satellite retrievals of snow:**
- GOES and Meteosat visible, SSM/I, MODIS

- Satellite-derived snow cover was the FIRST satellite product of any kind ingested into
- NCEP operational NWP models

## Example: NESDIS IMS Snow Cover Analysis of 26 Feb 2004 (4-km)

- (includes sea-ice cover)

## IMS Snow Cover Example:Pacific Northwest U.S. – 15 May 2006

- Former 24 km resolution

- New 4 km resolution

## Soil moisture assimilation

- _External initiative_
- GMU / Paul Houser: separate presentation this session
- _Internal initiative_
- GMAO / Rolf Reichle: separate presentation this session

## Land Surface Emissivity

- _External initiative_ (not funded by JCSDA)
- Tom Schmugge: U. New Mexico, MODIS-based
- _Internal initiative_
- John LeMarshall: AIRS based (hyper-spectral)
- Fuzhong Weng: IR sfc emission derived from MW

## Research to Operations Strategyfor Land Data Assimilation: Key thrusts for coming year

**Couple NASA/HSB Land Information System (LIS) to the NCEP GFS/GDAS via the Earth System Modeling Framework (ESMF)**
- NASA funded
**Substantially expand CRTM testing and focus in LIS**
- NCEP/EMC funded
- MODIS, AIRS and MW-based surface emissivity
**Substantially expand Ensemble Kalman Filter component and testing in LIS**
- Joint NASA and NCEP and USAF
**Continue the expanding collaboration between NCEP/EMC, NASA/HSB, and NASA/GMAO in land data assimilation algorithms, methods, approaches**