Soil‐moisture nudging experiments with a single‐column version of the ECMWF model

Abstract

The soil-moisture nudging technique suggests using model forecast errors in near-surface air temperature and relative humidity to re-initialize (update) soil moisture in atmospheric models. This study investigates the application of soil-moisture nudging using a single-column version of the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The model was applied at 16 sites selected to sample a range of climates and land covers across the globe, with atmospheric forcing taken from the ECMWF operational analysis for 15 June 1994 and 15 December 1994. When observation errors are set to zero, the Optimal Interpolation technique for deriving estimates of nudging coefficients shows computational instability because strong correlation between near-surface temperature and near-surface relative humidity forecast errors makes the coefficient matrix of the linear equations ill-posed. Therefore, Principal Component Analysis (PCA) is used as a pre-processor to identify the independent and dependent components of temperature and relative humidity errors. With PCA, the soilmoisture nudging coefficients appropriate to the dominant principal component become stable and effective for correcting soil moisture within days. Such coefficients, when derived for the northern hemisphere summer over the 16 sites, are sufficiently consistent to propose using their all-site, daily-average values in a globally applicable soil-moisture analysis scheme. Tests of this method at the First ISLSCP (International Satellite Land Surface Climatology Program) Field Experiment (FIFE) site confirm that soil-moisture nudging can provide good estimates of near-surface weather variables and surface fluxes in a numerical weather prediction (NWP) model which gives poor simulation of precipitation (thereby poor soil moisture). However, the PCA method is unable to give an accurate determination of the location of the soil moisture within soil layers that are accessible to the atmosphere. Further, these tests show that, if the NWP model has good simulation of precipitation but poor simulation of surface radiation, soil-moisture nudging could wrongly vary the soil moisture so as to provide good estimates of the surface sensible-heat flux, but poor estimates of surface latent-heat flux. An approach to distinguish error sources from surface radiation and soil moisture is necessary for further improvement.

Land, Water and Energy Data Assimilation

“…There are several soil moisture estimation data assimilation techniques that use a one-dimensional optimal estimation approach, including studies by Milly (1986), Katul et al (1993), Parlange et al, (1993), Entekhabi et al (1994), Galantowicz et al (1999), Calvet et al (1998) and Castelli et al (1999). Several additional studies have used low-level atmospheric observations to infer soil moisture using one-dimensional optimal variation assimilation approaches (Mahfouf, 1991;Bouttier et al, 1993;Hu et al, 1999;Callies et al, 1998;Rhodin, et al, 1999). In these approaches the calculation of soil moisture is a 'parametric approach' and not physically based.…”

Perspectives in using a remotely sensed dryness index in distributed hydrological models at the river‐basin scale

“…Improving the predictive capability of these models requires consideration of this variability (Gao et al, 1996) and much effort has been dedicated to this in recent years (e.g. Houser et al, 1998;Hu et al, 1999). A large part of the progress in recent years has been driven by the environmental concern about the future climate.…”

“…This has initiated research mainly in improving meteorological and climate forecasting (e.g. Hu et al, 1999;GEWEX, 2001).…”

Improving NWP through radar rainfall-driven land surface parameters: A case study on convective precipitation forecasting

“…[27,24,14]), routine observations of these quantities suitable for data assimilation are currently unavailable over large areas of the world. Further, the existing observations of soil state conditions face substantial technical issues that severely limit their potential of use [19]. Thereby, land surface models (LSMs) are considered as viable tools for the definition of the land surface conditions that are required in the atmospheric NWP models.…”