Remote Sensing,
Land Surface Modelling and Data Assimilation
Jeffrey Walker | |
The University of Melbourne | |
Garry Willgoose and Jetse Kalma | |
The University of Newcastle | |
Paul Houser, Rolf Reichle and Randal Koster | |
NASA Goddard Space Flight Center | |
http://www.civag.unimelb.edu.au/~jwalker |
Importance of Land
Surface States
(soil moisture, soil temperature, snow)
Early warning systems | ||
Flood prediction infiltration, snow melt | ||
Socio-economic activities | ||
Agriculture yield forecasting, management (pesticides etc), sediment transport | ||
Water management irrigation | ||
Policy planning | ||
Drought relief | ||
Global change | ||
Weather and climate | ||
Evapotranspiration latent and sensible heat | ||
Albedo | ||
Soil Moisture vs Sea Surface Temp
Knowledge of soil moisture has a greater impact on the predictability of summertime precipitation over land at mid-latitudes than Sea Surface Temperature (SST). | |
Same forcing and initial conditions but different predictions of soil moisture! |
Soil Moisture Coverage: Veg (Mean PR)
Soil Moisture Coverage: RFI (SD PR)
Definition 1: using data to force a
model ie. precipitation and evapotranspiration to force a LSM |
|
Continuous (ie. variational) |
Sequential (ie. Kalman filter) |
Case 1: 1D Synthetic
Study
(Walker et al., AWR 2001)
Kalman Filter Update Every Hour
Kalman Filter update Every 5 Days
Demonstrated the usefulness of near-surface soil moisture (and temperature) measurements. | |
Require a statistical assimilation
scheme (ie. a scheme which can potentially alter the entire profile). |
|
Require as linear forecasting model as possible to ensure stable updating (ie. q-based model rather than a y-based model). | |
Case 2: 1D Field
Study
(Walker et al., JHM 2001)
Kalman Filter Update Every 5 Days
Updating of the model is only as good as the models representation of the soil physics (ie. need an unbiased model). | |
Porosity and residual soil moisture are the most important parameters as they set the bounds better to use soil wetness? | |
Assimilation is most useful during dry-down as the model will reset itself at the extremes. | |
Case 3: 3D 6ha Field
Study
(Walker et al., WRR In Review)
Modified Kalman Filter Application
The soil moisture profile cannot be retrieved when the deep soil layer becomes decoupled from the near-surface layer (ie. during extended drying periods). | |
Simulation results may be degraded slightly if simulation and observation values are already close. | |
The updating interval is relatively unimportant when using a calibrated model with accurate forcing. |
Case 4: 1.5D Continental
Synthetic Study
(Part 1: Demonstration)
(Walker
and Houser, JGR 2001)
Errors in Assimilated Moisture: 1
Errors in Assimilated Moisture: 2
Demonstrated a methodology for generating soil moisture initialisation states. | |
Soil moisture assimilation performs best for regions with shallower soils; particularly depths less than 3m. | |
Important that all of the dominant physical processes in the LSM are identified and included in the covariance forecasting. | |
Structure of the model effects the retrieval time (ie. layered vs catchment deficit). | |
What are the defensible requirements of a remote sensing mission for measurement of surface soil moisture? | ||
Polarisation horizontal | ||
Wavelength L-band | ||
Look Angle < 50° | ||
Observation Accuracy ? | ||
Temporal Resolution ? | ||
Spatial Resolution ? | ||
Spatial Distribution of MC Error
Typically, observations of surface soil
moisture must have an accuracy of better than 5% v/v. |
|
The greatest impact of temporal resolution was for 1 to 5 days, with greater time between observations having a marginal degradation. | |
Observations at one-quarter to one-half the spatial resolution of the model/application is a good compromise. | |
To achieve the best results, it is important that the model forcing and observations be unbiased, or the bias modelled as part of the assimilation framework. | |
EKF and EnKF both provided satisfactory estimates of soil moisture. | |
EKF cheaper (for 1.5D; equivilant to 4
ensembles), but EnKF more accurate for 6 (or more) ensemble members. |
|
EnKF has the potential to include
higher order moments than mean and std dev (ie. skewness) |
|
EnKF more promising for including horizontal correlations. | |
Case 5: 1.5D Continental
Field Study
(Work in Progress)
Land Surface Initialisation for Seasonal-to-Interannual Prediction
Goal: Provide global land surface initialisation states for the retrospective period of 1979 to present. | ||
A two-tiered approach. | ||
Tier 1: Spin-up the land surface model off-line from the GCM using the best atmospheric forcing data available. Minimises forcing bias! | ||
Tier 2:Assimilate passive microwave remote sensing observations of near-surface soil moisture from SMMR and SSM/I. Minimises model and forcing bias! | ||
Observational Data Sets | |||
NCAR Northern Hemisphere Sea Level Pressure | |||
01/1899 present; 5 x 5 degrees | |||
Climate Research Unit (University of East Anglia) Temperature and Precipitation | |||
01/01 - 12/98; 0.5 x 0.5 degrees | |||
Center for Climatic Research (University of Delaware) Terrestrial Temperature and Precipitation | |||
01/50 - 12/96 ; 0.5 x 0.5 degree | |||
Global Precipitation Climatology Project (GPCP) | |||
01/86 - 03/95; 2.5 x 2.5 degree | |||
Langley Eight Year Shortwave and Longwave Surface Radiation Budget | |||
07/83 - 06/91 - in process of being extended; 2.5 x 2.5 degree | |||
Re-Analysis Atmospheric Forcing Data Sets | |||
ECMWF Re-analysis Advanced Global Data | |||
4x/day, 01/79 - 12/93 | |||
1.125 degrees (Gaussian) | |||
NCEP/NCAR Re-analysis | |||
4x/day, 01/48 12/99 | |||
2.5 x 2.5 degrees | |||
Bias Correct Using Monthly Mean Observational Data Sets | |||
SMMR Soil Moisture Observations
Soil Moisture Time Series: Illinois
Soil Moisture: Lat 50, Lon -100
How can we evaluate the soil moisture assimilation? | ||
Soil moisture ideal but limited data available. United states has 19 stations in Illinois, 6 stations in Iowa and transect of 89 points in New Mexico for SMMR period. | ||
Analysis increments only provides a check for systematic biases. | ||
Runoff - data available but assumes that soil moisture is the only reason for poor estimates. | ||
Evapotranspiration data not available and assumes that soil moisture is the only reason for poor estimates. | ||
Precipitation forecasts assumes soil moisture is the only reason for poor forecasts. | ||
Other ? | ||
Other assimilation work | ||
Complete the global SMMR assimilation Ni et al. | ||
SMMR/AMSR assimilation Australia Walker et al. | ||
Continental snow assimilation Sun et al. | ||
TRMM assimilation Entin et al. | ||
G-LDAS Rodell et al. | ||
Runoff assimilation Walker et al. |