|
|
|
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 |
|
|
|
|
|
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 |
|
|
|
|
|
|
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! |
|
|
|
|
|
|
|
Definition 1: using data to force a model
ie. precipitation and evapotranspiration to force a LSM |
|
|
|
|
|
|
|
|
Continuous (ie. variational) |
|
|
|
|
Sequential (ie. Kalman filter) |
|
|
|
|
|
|
|
|
|
|
|
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). |
|
|
|
|
|
|
|
|
|
|
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. |
|
|
|
|
|
|
|
|
|
|
|
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. |
|
|
|
|
|
|
|
|
|
|
|
|
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 ? |
|
|
|
|
|
|
|
|
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. |
|
|
|
|
|
|
|
|
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 |
|
|
|
|
|
|
|
|
|
|
|
|
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. |
|
|