Remote Sensing,
Land Surface Modelling and Data Assimilation
Importance of Land
Surface States
(soil moisture, soil temperature, snow)
Soil Moisture vs Sea
Surface Temp
The Situation
The Problem With LSMs
Importance of Soil
Moisture
Soil Moisture Coverage:
Veg (Mean PR)
Soil Moisture Coverage:
RFI (SD PR)
Data Assimilation Defined
Continuous or Sequential
DA?
Continuous or Sequential
DA?
Extended or Ensemble KF?
DA as a Spatial
Interpolator
Case 1: 1D Synthetic
Study
(Walker et al., AWR 2001)
Synthetic Data
Direct Insertion Every
Hour
Kalman Filter Update
Every Hour
Kalman Filter update
Every 5 Days
Case 1: Lessons Learned
Case 2: 1D Field
Study
(Walker et al., JHM 2001)
Field Instrumentation
Model
Calibration/Evaluation
Kalman Filter Update
Every 5 Days
Case 2: Lessons Learned
Case 3: 3D 6ha Field
Study
(Walker et al., WRR In Review)
Modified Kalman Filter
Application
Catchment Instrumentation
3D Model Calibration
3D Profile Retrieval
Case 3: Lessons Learned
Case 4: 1.5D Continental
Synthetic Study
(Part 1: Demonstration)
(Walker
and Houser, JGR 2001)
Catchment-based LSM
Catchment Discretisation
Synthetic Demonstration
“Errors” in Assimilated
Moisture: 1
“Errors” in Assimilated
Moisture: 2
Monthly
Evapotranspiration
Monthly Runoff
Case 4.1: Lessons Learned
Case 4: 1.5D Continental
Synthetic Study
(Part 2:
Mission Requirements)
(Walker and Houser, WRR In Prep)
Motivation
Mission Requirements
Spatial Distribution of
MC Error
Case 4.2: Lessons Learned
Case 4: 1.5D Continental
Synthetic Study
(Part 3: Extended v Ensemble KF)
(Reichle,
Walker, Koster and Houser,
JHM Submitted)
Filter Calibration
Extended vs Ensemble KF
Case 4.3: Lessons Learned
Case 5: 1.5D Continental
“Field” Study
(Work in Progress)
Land Surface
Initialisation for Seasonal-to-Interannual Prediction
Tier 1: Better Forcing
Tier 2: Assimilation
SMMR Soil Moisture
Observations
Soil Moisture Time
Series: Illinois
Animation
Soil Moisture: Lat 50,
Lon -100
Evaluation of
Assimilation
Final Words
Thankyou!