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HOME > Monitoring & Data > U.S. Climate Data & Maps > Soil Moisture Monitoring > Soil Moisture Outlooks > CAS > Description
 
 
H. M. van den Dool

1. Introduction

In order to understand how continental soil moisture is used in a constructed analogue one needs to know first how soil moisture data are obtained.

Soil moisture is not measured at enough places for a long enough time to base a monitoring system for the United States on true observations. Rather, soil moisture data are generated by models in which observations play a role, but much else is generated by smart physics. Symbolically a soil moisture model can be represented by dw/dt = P - E - R + other, where w is soil moisture, P is precipitation, E is evaporation, and R is surface runoff. The w data set we will use here has been described in Huang et al(1996), where a simple hydrological model was presented. About 5 parameters are fitted to reproduce observed runoff in Oklahoma, given observed P. The E (based on Thornwhaite's expression for potential evaporation, requiring T as input) and w are important 'data' coming out of this procedure, and will be treated as proxy-observations below. Using the Oklahoma fitted parameters elsewhere, a data set for the period 1932-present has been generated at 344 Climate Divisions in the US. {As an aside: Global gridded data for a coarse resolution 1979-1998 is available also}.

One particularly important consideration is that the interannual variability in P is 2 to 3 times larger than that in E. This places an enormous burden on having accurate P - in fact everything else is secondary. This is precisely the reason why we cannot (as yet) use soil moisture produced by such comprehensive approaches as CDAS/Reanalysis (Kalnay et al 1996), because even though NCEP's Medium Range Forecast (MRF) has a better land surface model than was used in Huang et al (1996), the MRF does not assimilate observed P, but rather generates its own precipitation in the 6 hours leading up to making the next guess field - these P estimates are unsatisfactory. We thus resort to a stand alone off line model integration of a soil model requiring only P and T as input. The GCIP Land Data Assimilation Project (LDAS) is based on much the same considerations, using however the ETA land surface model. {We do consider to use the state of the art ETA land surface model (replacing Huang et al 1996) at a future time.}

2. Constructed Analogue

We construct here an analogue in terms of just the soil moisture over the contiguous United States. Suppose wbase(x,y) is a (any) soil moisture anomaly field to which we desire an analogue. I.e. we want to minimize

Q = { wbase(x,y) - wcon(x,y)}2 where

wcon(x,y) = sum ( a j wj(x,y)) (1)

where, wj is the soil moisture anomaly observed in year j (j=1 to 66, corresponding to 1932 to 1997), x and y are spatial coordinates, and j are the weights assigned to each historical year, such that the l.h.s. matches the field we seek to reproduce. The method of finding aj is given in Van den Dool (1994). In order to reproduce the soil moisture at the end of March 1998, for example, the following list of weights was obtained:

Table 1: Weights assigned to each year, such that a linear combination as in (1), would reproduce the soil moisture conditions observed over the United States as per March 31, 1998.

Year aj Year aj Year aj Year aj Year aj Year aj Year aj
1932 -0.05 1942 -0.07 1952 0.03 1962 -0.01 1972 -0.02 1982 -0.01 1992 0.01
1933 -0.03 1943 -0.10 1953 0.01 1963 -0.03 1973 0.15 1983 0.16 1993 0.20
1934 0.01 1944 -0.07 1954 0.07 1964 0.02 1974 0.01 1984 0.05 1994 0.09
1935 -0.02 1945 -0.13 1955 -0.03 1965 -0.16 1975 -0.04 1985 -0.01 1995 0.03
1936 0.08 1946 -0.01 1956 0.03 1966 -0.13 1976 -0.04 1986 0.00 1996 0.02
1937 0.06 1947 -0.01 1957 0.01 1967 -0.05 1977 -0.13 1987 0.10 1997 0.00
1938 0.01 1948 -0.07 1958 0.10 1968 -0.02 1978 0.06 1988 -0.01
1939 -0.06 1949 -0.04 1959 -0.10 1969 0.06 1979 -0.01 1989 -0.06
1940 -0.08 1950 -0.04 1960 0.11 1970 0.06 1980 0.09 1990 0.05
1941 0.07 1951 0.02 1961 0.04 1971 -0.07 1981 -0.07 1991 0.08

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