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).

Precipitation Predictability Index

Importance of Soil Moisture

Initialization Approach
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!

SMMR Polarization Ratio (Mean)

SMMR Polarization Ratio (Std Dev)

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

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

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.