Integration of Satellite Observations with the NOAH Land Model for Snow Data

Assimilation

Xubin Zeng, Mike Barlage

Mike Brunke, Jesse Miller

University of Arizona, Tucson

Objectives

To provide better land data (particularly the snow albedo data)

To improve the relevant parameterizations in the Noah land model

To improve the weather forecasting

KEY: Data development has to be integrated with model improvement

Progress I: Snow data delivery

(a) Data generation: Delivered

(b) Utility code to read data and map data into different resolutions: Delivered

(c) Data documentation: GRL Manuscript Submitted

(d) Impact study: Preliminary Work Done

MODIS Albedo data

(a) 1 km data in 10 deg tiles; global 0.05 deg (vs. 1 deg in RK)(b) seven narrow bands, VIS (0.4-0.7 microns), NIR (0.7-5 microns), SW (0.4-5 microns) (vs. SW from 0.4-1.1 microns in RK)(c) Day 49 of 2000 - Day 177 of 2004 (vs. 75 images in 1979 and 5 images in 1978)(d) Quality flags(e) MODIS data from both Terra and Aqua(f) Both albedo and BRDF

Use of Quality Control Flags

Red: NN filledBlue: LAT filledGreen: > 0.84

Existing NOAH vs New MODIS

NDSI and Albedo

Comparison with RK0.05deg MODIS RK Figure 5

0.05° Dataset Inclusion in NLDAS

Progress II: Over Sea Ice

(a) Intercomparison of bulk algorithms for the

computation of sea ice surface turbulence

fluxes, as used in NCEP, ECMWF, NCAR,

and ARCSyM

(b) Interact with Hua-Lu Pan and Sarah Lu

at NCEP

(c) Manuscript submitted to JGR

Algorithm Intercomparison

Roughness Length Formulation

zom={7e-4-6.5e-4exp[-(u*-0.05)]}if u* ≥ 0.05 m/s,

zom=5e-5if u* < 0.05 m/s

=3Ts+10if -2°<Ts≤0°C,=4 if Ts≤-2°C

=1.4+0.2Ts

if -2°<Ts≤0°C,=1 if Ts≤-2°C

Aerodynamic winter(mid-May to mid-Sept.)

Progress III: Winter FVC/LAI data

(a) In the Noah land model Leaf-area index (LAI): a global constant Fractional vegetation cover (FVC): location and month

(b) Data deficiency: AVHRR FVC data: zero in winter even for evergreen trees MODIS LAI data: zero in winter even for evergreen trees

(c) The Noah model has overall performed well in most tests

(d) Question: How to provide variable FVC and LAI to Noah to further improve its performance (at least without degrading its performance)

NLDAS Greenness Fraction

SNCOVR Sensitivity Tests

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0.000 0.020 0.040 0.060 0.080 0.100

SNEQV(m)

SN

CO

VR

(fra

ctio

n 0

-1)

CNTL FAST SLOW

Progress IV: The Noah Testbed

(a) The Noah model testbed, set up by Ken Mitchell’s team, is an important component of the JCSDA land program

(b) First outside user(c) Mike Barlage visited Ken’s group in summer 2004(d)Mike Barlage and Jesse Miller from UA have interacted with Ken’s group members on the improvement of all aspects of the testbed

Plan for the next 6-12 months

(a) Assist Ken Mitchell’s group to further evaluate the detailed impact of the new snow albedo data on the Noah land modeling and weather forecasting

(b) Continue to improve the fractional vegetation cover (FVC) and leaf-area index (LAI) data for winter months and their interaction with snow in the Noah model

(c) Continue to improve the snow submodel in the Noah model for the better assimilation of snow data

Title: Integration of Satellite Observations with the Noah Land Model for the Snow Data AssimilationPI: Xubin Zeng, University of ArizonaObjectives: Snow-related data development and Noah model improvementProgress so far: Generate and deliver snow albedo data; deliver the utility code; and finish the preliminary impact study; Develop formulations for snow/ice roughness lengths; Evaluate wintertime vegetation-snow interaction; and Help improve the Noah model testbedFuture plan: Additional impact study of snow albedo data; Further study of wintertime vegetation data; and Improve snow processes in the Noah land model