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”.

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.


	^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


	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 – ?


	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”.


	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.


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


	Time Series Histogram

	Precipitation (mm/day) Profile Soil Moisture (%v/v)


	Evapotranspiration RMS Error (mm/day)






	Evapotranspiration Bias (mm/day)


	Profile Soil Moisture RMS Error (v/v)






	Profile Soil Moisture Bias (v/v)





	Precip Bias (mm/day) Soil Depth (mm) Avg Pro Soil Moist (v/v)


	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.