Soil Moisture Data Assimilation
Using the 1D EKF

^1,2Jeffrey Walker and ²Paul Houser

¹Goddard Earth Sciences and Technology Center

²NASA/Goddard Space Flight Center

http://land.gsfc.nasa.gov/~cejpw

Land Surface Initialization for Seasonal-to-Interannual Prediction

Goal: Provide global land surface initialization 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. Minimizes forcing bias!

Tier 2:Assimilate passive microwave remote sensing observations of near-surface soil moisture from SMMR and SSM/I. Minimizes model and forcing bias!

Tier 1: Better Forcing

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

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

Why Assimilate?

Accurate initial conditions for the land surface are necessary for accurate predictions of precipitation.

LSM spin-up does not guarantee correct land surface initialization.

Errors in land surface forecasts result from:

The memory of errors in LSM initialization.

Errors in atmospheric forcing data.

Errors in LSM physics.

Errors in soil and topographic data.

Importance of Soil Moisture

SMMR Polarization Ratio (Mean)

SMMR Polarization Ratio (Std Dev)

Synthetic Demonstration

Mission Requirements

Moisture Retrieval Algorithm

Global Day-Time Maps

Soil Moisture Time Series: Illinois

Slide 14

Soil Moisture: Lat 50, Lon -100

Evaluation of Assimilation

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 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 ?

Outlook

Move towards a global implementation of the assimilation.

Develop a set of global soil moisture initialization states for the start of each month from 1979 to 1987 (term of SMMR data).


	^1,2Jeffrey Walker and ²Paul Houser
	¹Goddard Earth Sciences and Technology Center
	²NASA/Goddard Space Flight Center

	http://land.gsfc.nasa.gov/~cejpw


	Goal: Provide global land surface initialization 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. Minimizes forcing bias!
		Tier 2:Assimilate passive microwave remote sensing observations of near-surface soil moisture from SMMR and SSM/I. Minimizes model and forcing bias!


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

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


	Accurate initial conditions for the land surface are necessary for accurate predictions of precipitation.
	LSM spin-up does not guarantee correct land surface initialization.
	Errors in land surface forecasts result from:
		The memory of errors in LSM initialization.
		Errors in atmospheric forcing data.
		Errors in LSM physics.
		Errors in soil and topographic data.


	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 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 ?


	Move towards a global implementation of the assimilation.
	Develop a set of global soil moisture initialization states for the start of each month from 1979 to 1987 (term of SMMR data).