# Presentation: 188292

## Snow and Vegetation:Remote Sensing and Modeling(Activities in Land-Atmosphere Interactions at the University of Arizona, Tucson)Michael BarlageJoint Center Funded Work - PI Xubin Zeng 

## Derivation of a New Maximum Snow Albedo Dataset Using MODIS DataM.Barlage, X.Zeng, H.Wei, K.Mitchell; GRL 2005

## Motivation

- Maximum snow albedo is used as an end member of the interpolation from snow- to non-snow covered grids
- Current dataset is based on 1-year of DMSP observations from 1979
- Current resolution of 1°
- Create new dataset using 4+ years of MODIS data with much higher resolution

## Albedo and Land Cover

## NDSI and NDVI 

- NDSI and NDVI

## NDSI and Snow Albedo

## Current Logic Structure

- NDSI > 0.4

- MODIS
- QC = good

- Global Maximum Snow Albedo

- Band 2
-  > 0.11

- 0.05o MODIS
- Albedo

- LANDUSE

## Final 0.05° Maximum Snow Albedo

- Comparison with RK

- 0.05deg MODIS

- RK Figure 5

## High-resolution Improvements

## Application of MODIS Maximum Snow Albedo to NLDAS

- Over 10 W/m2 difference in southern Canada and mountain regions of United States
- *Note: 0.05° maximum albedo dataset downscaled to 0.125° to use in NLDAS

- Upward Sensible Heat Difference
- North America Land Data Assimilation System: 0.125° NOAH model forced with EDAS output
- Winter simulation: From Nov. 1997 to May 1998

## Application of MODIS Maximum Snow Albedo to WRF-NMM/NOAH

-  WRF-NMM Model: 10min(0.144°) input dataset converted from 0.05° by simple average; model run at 12km; initialized with Eta output;
-  Winter simulation: 24hr simulation beginning  12Z 31 Jan 2006

## Application of MODIS Maximum Snow Albedo to WRF-NMM/NOAH

-  Again we see up to 0.5 C decreases in 2-m temperature in regions of high snow cover and significant albedo change
-  Also see greater than 0.5 C increase in 2-m temperature in several regions

## Application and Derivation of Global Green Vegetation Fraction Using NDVIJ.Miller, M.Barlage, X.Zeng, H.Wei; GRL 2006Zeng et al. 2000; Zeng et al. 2003

## GVF calculationZeng et al. 2000

- GVF calculationZeng 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

- NLDAS GVF Data

- Noah 1/8 degree monthly

- MODIS 2km 16-day

## NLDAS GVF Data

-  More realistic annual variation in GVF for needleleaf forest land cover
-  Systematically higher in all land cover categories
-  Winter difference up to 0.6 in evergreen needleleaf regions
-  Grass/Crop increases 0.1-0.2 throughout the annual cycle
-  Some decreases in deciduous broadleaf in summer up to 0.4

- NLDAS GVF Results

-  Addition of new GVF dataset results in an increase of transpiration (~15W/m2) and canopy evaporation (~3W/m2)
-  Balanced by a decrease in ground evaporation (~10W/m2)
-  Overall increase in LHF(~8W/m2) is balanced by decreases in SHF(~6W/m2) and Lwup(~2W/m2)

- NLDAS GVF Results

- crop

- grass

-  Addition of new GVF dataset results in an increase of transpiration (up to 35W/m2) and canopy evaporation (up to 8W/m2)
-  Balanced by a decrease in ground evaporation (up to 20W/m2)
-  Overall increase in LHF(up to 20W/m2) is balanced by decreases in SHF(up to 10W/m2) and Lwup(5W/m2)

- AVRHH GVF Results

-  Initial results from analysis using Le Jiang’s 24-year NDVI climatology
-  Data resolution is 0.144o so Ncv numbers are substantially different than higher resolution data
-  Include higher resolution land data to account for sub-grid vegetation variability??

## An Empirical Formulation of Soil Ice Fraction Based on In Situ DataM. Decker and X.Zeng; GRL 2006

## Ice Fraction Observations

-  Observations from measurements in Alaska, Mongolia and Tibet
-  Large variation with saturation percentage
-  New formulation fits most data well, but does overpredict for tundra land cover

## Ice Fraction: New vs. Noah

-  Of the models investigated, Noah formulation is closest to observed character
-  Too dependent on C-H b parameter
-  Doesn’t freeze any water for high b when soil is dry
- Doesn’t freeze enough water for saturated soil
-  Net result in CLM: Reduces ground temperature by up to 3K in winter

- Noah b=4.5

- Noah b=5.5

- ECMWF

- New

## Solar zenith angle dependence of desert and vegetation albedoZ. Wang,M.Barlage,X.Zeng,R.E.Dickinson,C.Schaaf; GRL 2005Z.Wang, X.Zeng, M.Barlage; JGR 2006

## MODIS Zenith Angle Dependence

- MODIS albedo as a function of cos() at 30 desert sites globally
- Similar shape in both black-sky and white-sky dependence

## Zenith Angle Dependence Formulations

- () = (60°) * {1 + B1 * g1() + B2 * g2() }

- () = (60°) * [1 + C] / [ 1 + 2C * cos() ]

- Two parameter model

- One parameter model

- Two parameter model Bn parameters are determined for using the30 desert locations and are found to be B1 = 0.346 and B2 = 0.063
- C parameter in one parameter model is assumed to be 0.4

## Model Tests with Zenith Angle Dependence

- Sensitivity tests of the new formulation using the Noah model over HAPEX-Sahel site
- Albedo dependence on zenith angle increases absorbed solar by 20 W/m2 which is balanced by increases in sensible and ground heat flux

## Use MODIS albedo/BRDF data to identify deficiencies in the solar zenith angle dependence of land surface albedo in the CERES, ISCCP, and UMD surface solar flux datasets (e.g.,CERES dataset below, in red)

## Other Current Activities

-  Dynamical vegetation modeling: coexistence of shrubs and grassland in current land surface models; sub-grid clustering of vegetation
-  Stratus cloud parameterization, liquid water content, and marine boundary layer height using EPIC data
-  Sea-ice turbulence parameterization using SHEBA data
-  Snowpack snow grain size parameterization
-  Under-canopy and within-canopy turbulence modeling
-  Humidity inversions in polar regions from soundings, reanalysis, and modeling
-  Convection initiation and parameter space analysis
-  Surface controls of upper atmosphere temperature and radiational climate controls

## Our Research

- Look for areas of model improvement, especially those which can be explored by remotely sensed data
- Develop new datasets or formulations to solve these problems
- Test new datasets to determine improvements in either model prediction or representation
- Goal: Be a bridge between the modeling and remote sensing community

## Motivation

## MODIS products used:

-  Broadband Albedo: 0.05° CMG, all available v4; major data component
-  Land Cover: 1km global, v4; used to determine fill values outside snow area
-  Spectral Albedo/NBAR: 0.05° CMG, all available v4; used to calculate NDSI to determine snow regions, also to mask water

## Raw MODIS Albedo Data

- Tucson: little variation; no snow
- Minnesota: cropland; obvious annual cycle
- Canada: annual snow cycle; little summer variation
- Moscow: some cloud complications

## Maximum “Good” Albedo

## How can you be sure it’s snow?

- NDSI: Exploiting the differences in spectral signature between visible and NIR albedo.

## Maximum Snow Albedo

## Merging Land Use and Albedo 

- Merging Land Use and Albedo

- High spread in albedo among same land use type...
- What value to use?

## Data Flag Layer

- Decision Tree:
- Grey: “Good” snow-covered albedo
- Red: Fill with average of same land cover in 2° area surrounding
- Blue: If red filter < 100 values, fill with latitude average
- Light blue: If higher, replaced non-snow covered value
- Green: Albedo > 0.84 decreased to global ice average of 0.84

- Comparison with RK

- 0.05deg MODIS

- RK Figure 5

## Application of MODIS Maximum Snow Albedo to NCEP Land Surface Model

- Up to 0.2 difference in high/mid latitudes can greatly affect surface energy balance, snow depth, and snow melt timing
- *Note: 0.05° maximum albedo dataset downscaled to 1° to compare with NOAH data

- *

## Application of MODIS Maximum Snow Albedo to NLDAS

- Over 10 W/m2 difference in southern Canada and mountain regions of United States
- *Note: 0.05° maximum albedo dataset downscaled to 0.125° to use in NLDAS

- Upward Shortwave Difference
- North America Land Data Assimilation System: 0.125° NOAH model forced with EDAS output
- Winter simulation: From Nov. 1997 to May 1998

## Application of MODIS Maximum Snow Albedo to WRF-ARW/NOAH

- Maximum Snow Albedo and Difference
-  WRF-ARW Model: 10min(0.144°) input dataset converted from 0.05° by simple average; model run at 40km; initialized with Eta output;
-  Winter simulation: 24hr simulation beginning  00Z 10 Feb 2005
-  Significant albedo change of greater than 0.05 over most of the Western U.S.

## Application of MODIS Maximum Snow Albedo to WRF-ARW/NOAH

- Simulation Snow Depth and Difference
-  Only small differences in simulated snow depth
-  Note pattern of snow cover

## Application of MODIS Maximum Snow Albedo to WRF-ARW/NOAH

- Simulation Sensible Heat Flux and Difference
-  Up to 5 W/m2 differences in SHF
-  Mostly decreases due to lack of snow in lower Plains

## Application of MODIS Maximum Snow Albedo to WRF-ARW/NOAH

-  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

## What’s next?

- Working with Ken Mitchell’s group on validation beyond sensitivity tests in coupled systems such as WRF
- Implement into operational GFS and NAM

## Introduction

- Use satellite Normalized Difference Vegetation Index (NDVI) data to improve land surface model representation of vegetated surface
- Derive global 2km green vegetation fraction(GVF) using MODIS data
- Compare with existing Noah GVF
- Implement into NLDAS

## Tucson Landscape

## Remote Sensing Products Used

-  NDVI(MODIS/AVHRR): 1-2km global, v4, 2000-2004 available; filled product of Eric Moody
-  Land Cover(MODIS): 1 minute global, v4

## GVF calculationZeng et al. 2000

- GVF calculationZeng et al. 2000

- Use NDVI =
- where r1 and r2 are the 1km MODIS red and NIR reflectance
- For each reflectance:
- Combine equations to obtain seasonal max:

## NDSI and NDVI 

- NDSI and NDVI

## MODIS NDVI Histograms

- Global GVF Data

- NLDAS GVF Data

- GVF for the 7 most prevalent land cover types in NLDAS
-  More realistic annual variation in GVF for needleleaf forest land cover
- Systematically higher in all land cover categories

- NLDAS GVF Data

- NLDAS GVF Data

## Conclusions and Ongoing Work

- Inclusion of GVF makes a significant difference to land surface representation
- Removes annual variation in GVF for forest land cover types
- Technique can be used at any resolution
- Initial results indicate surface energy budget redistribution which could be important in future
- Use 12-year AVHRR data and 1km MODIS

## Use MODIS albedo/BRDF data to identify deficiencies in the solar zenith angle dependence of land surface albedo in the NCAR, NCEP, and NASA land models (e.g., NASA Catchment                 model below, in red)

- Use MODIS albedo/BRDF data to identify deficiencies in the solar zenith angle dependence of land surface albedo in the NCAR, NCEP, and NASA land models (e.g., NASA Catchment                 model below, in red)