Soil Moisture Data Assimilation Using the 1D Kalman Filter

^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

Model Forcing and Parameters

Vegetation parameters derived from relationships with corn height.

Meteorological forcing data measured in the field with an automatic weather station.

Simulation Results

Plans ?

Modify model to have a 10cm surface soil moisture to be compatible with observations.

Calibrate model to soil moisture and runoff.

Assimilate surface (10cm) soil moisture for simulation with poor initial condition in both calibrated and uncalibrated model. Compare with no assimilation.

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Motivation

Gain experience with the 1D Kalman filter in a series of controlled experiments.

What are the soil moisture needs for NSIPP and 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

Effect of Temporal Resolution

Spatial Dissagregation

Effect of Spatial Resolution

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.

Conclusions

Daily observations achieved the best results.

The greatest impact of temporal resolution was for 1 to 5 days, with greater time between observations having a marginal degradation.

Spatial resolution less than the model resolution achieved the best results. Greater resolution produced only slightly worse results.

Observations at half the spatial resolution of the model is a good compromise.

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	^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








	Vegetation parameters derived from relationships with corn height.
	Meteorological forcing data measured in the field with an automatic weather station.


	Modify model to have a 10cm surface soil moisture to be compatible with observations.

	Calibrate model to soil moisture and runoff.

	Assimilate surface (10cm) soil moisture for simulation with poor initial condition in both calibrated and uncalibrated model. Compare with no assimilation.


	Gain experience with the 1D Kalman filter in a series of controlled experiments.
	What are the soil moisture needs for NSIPP and 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.


	Daily observations achieved the best results.
	The greatest impact of temporal resolution was for 1 to 5 days, with greater time between observations having a marginal degradation.
	Spatial resolution less than the model resolution achieved the best results. Greater resolution produced only slightly worse results.
	Observations at half the spatial resolution of the model is a good compromise.