Initialization of the Land Surface in Seasonal-to-Interannual Predictions

Jeffrey P. Walker

Universities Space Research Association

NASA/Goddard Space Flight Center

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

Overview

Development of an optimal methodology for initializing the land surface in NASA’s seasonal-to-interannual prediction project (NSIPP)

The importance of the soil moisture land surface states in seasonal-to-interannual predictions

The initialization approach

A demonstration of the approach using a synthetic study

Future directions

Soil Moisture v Sea Surface Temp

Knowledge of soil moisture has a greater impact on the predictability of summertime precipitation over land at mid-latitudes than Sea Surface Temperature (SST).

Importance of Soil Moisture

Initialization Approach

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, 1948-1999

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

The Catchment Discretization

Hydrologic unit is the catchment

Level 5 Pfafstetter

» 4500 km²

Re-analysis: Grid to Catchment

Observations: Grid to Catchment

Bias Correction: Air Temperature

ISLSCP v ECMWF

Tier 2: Assimilation

SMMR Polarization Ratio (Mean)

SMMR Polarization Ratio (Std Dev)

Why Assimilate Soil Moisture?

Accurate soil moisture forecasts are necessary for accurate predictions of precipitation.

LSM spin-up does not guarantee correct soil moisture initialization.

Errors in soil moisture forecasts result from:

The memory of errors in LSM soil moisture initialization.

Errors in atmospheric forcing data.

Errors in LSM physics.

Errors in soil and topographic data.

What is Data Assimilation ?

The process of finding the model representation which is most consistent with the observations.

Catchment Hydrological Regimes

The Catchment-Based LSM

srfexc = srfexc – es – srflow + i + a

rzexc = rzexc – ev + srflow – rzflow + b

catdef = catdef + et – rzflow + baseflow + c

where a, b and c are correction terms to ensure mass balance

Using a first-order Taylor series expansion:

Numerical Experiments

“Truth” Data Set

Spin-up catchment-based LSM for 1987 using ISLSCP forcing data.

Run the simulation for 1987 using ISLSCP forcing data and output surface moisture data as “observations”.

Open Loop Data Set

Degrade the soil moisture prognostic variable spin-up states to make the catchments artificially wet.

Run the simulation for 1987 using ISLSCP forcing data.

Assimilated Data Set

Degrade the soil moisture prognostic variable spin-up states to make the catchments artificially wet (as for the open loop).

Run the simulation for 1987 using ISLSCP forcing data.

Assimilate the surface “observations” once every 3 days using the Kalman-filter.

Soil Moisture: January 30

Soil Moisture: July 31

Soil Moisture: December 29

“Errors” in Assimilated Moisture: 1

“Errors” in Assimilated Moisture: 2

Soil Depth

Monthly Evapotranspiration

Monthly Runoff

Summary

A surface measurement of soil moisture may be used to correct the entire soil moisture profile.

Assimilation of soil moisture has a positive impact on the water and energy budgets.

Soil moisture assimilation performs best for regions with shallower soils; particularly depths less than 3m.

Near-Term Goals

Identify the maximum level of error which surface observations can have before the assimilation is no longer useful.

Identify the minimum frequency of surface observations before there is a significant degradation of the assimilation results.

Assimilate Dr. Owe’s surface soil moisture data from SMMR over North America and evaluate.

Move towards a global implementation of the assimilation.

Importance of Snow

In the northern hemisphere the snow cover ranges from 7% to 40% during the annual cycle.

The high albedo, low thermal conductivity and large spatial/temporal variability impact both the energy and water budgets.

Snow adjacent to bare soil causes mesoscale wind circulations.

Direct replacement with observations does not account for model bias.

Snow Assimilation: NCEP ETA

Snow Assimilation: NSIPP

Development of a Kalman filtering based snow assimilation strategy which overcomes the current limitations with assimilation of snow water equivalent, snow depth, and snow cover.

Investigate the utility of novel snow observation products in such an assimilation strategy. Such observations include snow melt signature and fractional snow cover.

Provide a basis for global implementation of an assimilation scheme for snow observation products, both for near-real-time forecasting and for the accurate initialization of seasonal-to-interannual predictions in the NSIPP fully-coupled GCM.


	Jeffrey P. Walker

	Universities Space Research Association
	NASA/Goddard Space Flight Center

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


	Development of an optimal methodology for initializing the land surface in NASA’s seasonal-to-interannual prediction project (NSIPP)
		The importance of the soil moisture land surface states in seasonal-to-interannual predictions
		The initialization approach
		A demonstration of the approach using a synthetic study
		Future directions



	Knowledge of soil moisture has a greater impact on the predictability of summertime precipitation over land at mid-latitudes than Sea Surface Temperature (SST).


	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, 1948-1999
		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







	Hydrologic unit is the catchment
		Level 5 Pfafstetter
		» 4500 km²


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


	The process of finding the model representation which is most consistent with the observations.



	srfexc = srfexc – es – srflow + i + a
	rzexc = rzexc – ev + srflow – rzflow + b
	catdef = catdef + et – rzflow + baseflow + c

	where a, b and c are correction terms to ensure mass balance


	Using a first-order Taylor series expansion:


	“Truth” Data Set
		Spin-up catchment-based LSM for 1987 using ISLSCP forcing data.
		Run the simulation for 1987 using ISLSCP forcing data and output surface moisture data as “observations”.
	Open Loop Data Set
		Degrade the soil moisture prognostic variable spin-up states to make the catchments artificially wet.
		Run the simulation for 1987 using ISLSCP forcing data.
	Assimilated Data Set
		Degrade the soil moisture prognostic variable spin-up states to make the catchments artificially wet (as for the open loop).
		Run the simulation for 1987 using ISLSCP forcing data.
		Assimilate the surface “observations” once every 3 days using the Kalman-filter.


	A surface measurement of soil moisture may be used to correct the entire soil moisture profile.
	Assimilation of soil moisture has a positive impact on the water and energy budgets.
	Soil moisture assimilation performs best for regions with shallower soils; particularly depths less than 3m.


	Identify the maximum level of error which surface observations can have before the assimilation is no longer useful.
	Identify the minimum frequency of surface observations before there is a significant degradation of the assimilation results.
	Assimilate Dr. Owe’s surface soil moisture data from SMMR over North America and evaluate.
	Move towards a global implementation of the assimilation.


	In the northern hemisphere the snow cover ranges from 7% to 40% during the annual cycle.
	The high albedo, low thermal conductivity and large spatial/temporal variability impact both the energy and water budgets.
	Snow adjacent to bare soil causes mesoscale wind circulations.
	Direct replacement with observations does not account for model bias.


	Development of a Kalman filtering based snow assimilation strategy which overcomes the current limitations with assimilation of snow water equivalent, snow depth, and snow cover.
	Investigate the utility of novel snow observation products in such an assimilation strategy. Such observations include snow melt signature and fractional snow cover.
	Provide a basis for global implementation of an assimilation scheme for snow observation products, both for near-real-time forecasting and for the accurate initialization of seasonal-to-interannual predictions in the NSIPP fully-coupled GCM.