Notes

Outline

A Synthetic Study on the Influence of Error in Surface Soil Moisture Observations on Assimilation 1,2Jeffrey P. Walker and 2Paul R. Houser 1Goddard Earth Sciences and Technology Center 2NASA/Goddard Space Flight Center http:land.gsfc.nasa.gov/~cejpw

Motivation What are the defensible requirements of a remote sensing mission for measurement of surface soil moisture? Polarization – horizontal Wavelength – L-band Look Angle – < 50° Observation Accuracy – ? Temporal Resolution – ? Spatial Resolution – ?

Methodology Overview Use a LSM to generate a “truth” data set that provides both surface soil moisture “observations” and evaluation data. Degrade the land surface forcing data and initial conditions to simulate uncertainties in this data (assume a perfect model). Run the LSM with degraded data. Run the LSM with degraded data and assimilate the “observations” with various levels of error imposed using the extended Kalman filter. Compare with the “truth”.

The Catchment-Based LSM

Data Model Input Data Atmospheric forcing data and soil and vegetation properties were taken from ISLSCP-1. Initial Conditions Spin-up catchment-based LSM for 1987 using ISLSCP forcing data. Surface Soil Moisture Observations Surface (2cm) soil moisture data output every 3 days from the “truth” run. Evaluation Data Surface, root zone and total profile soil moisture data output each day plus average evapotranspiration data output each  10 days.

Degraded Data Zero mean normally distributed perturbations with standard deviations given below added to the initial conditions, forcing and obs data.

Effect of Observation Error: 1                                      Time Series Histogram Precipitation (mm/day)                       Profile Soil Moisture (%v/v)

Effect of Observation Error: 2

Effect of Observation Error: 3 Evapotranspiration RMS Error (mm/day) Evapotranspiration Bias (mm/day)

Effect of Observation Error: 4 Profile Soil Moisture RMS Error (v/v) Profile Soil Moisture Bias (v/v)

Effect of Observation Error: 5    Precip Bias (mm/day)       Soil Depth (mm) Avg Pro Soil Moist (v/v)

Effect of Forcing Bias: 1                                       Time Series Histogram      Precipitation (mm/day)                      Profile Soil Moisture (%v/v)

Effect of Observation Error: 1                                       Time Series Histogram      Precipitation (mm/day)                      Profile Soil Moisture (%v/v)

Effect of Forcing Bias: 2                                       Time Series Histogram      Precipitation (mm/day)                      Profile Soil Moisture (%v/v)

Effect of Forcing Bias: 3

Conclusions Typically observations of surface soil moisture must have an accuracy of better than 5% v/v. If model covariance forecasts are imperfect and observation errors are large there may be a slight degradation of the model forecast. To achieve the best results, it is important that the model, model forcing and observations be unbiased, or the bias modeled as part of the assimilation framework.