Estimating large scale land surface fluxes : the use of remote sensing data with SVAT and NWP models

Current soil-vegetation-atmosphere transfer (SVAT) and numerical weather prediction (NWP) models use increasingly complex descriptions of the physical mechanisms governing land surface processes. They require large numbers of soil and land surface parameters controlling the vertical fluxes. Uncertainty in land surface parameterisation and the nature of landscape controls may lead to considerable uncertainty in predicted land surface fluxes and state variables such as soil moisture. There also remains significant uncertainty in the data used to calibrate such models and constrain the model's parameter set. Determining the spatial and temporal variability in land surface processes over large areas and long time periods is a difficult task. This variability underlines the need for proper determination of the SVAT parameters used in the land surface parameterisation. Improved characterisation at larger landscape scales involves aggregation over heterogeneous surfaces. Such aggregation has frequently been approached in a lumped fashion, requiring single effective parameter values which would allow SVAT and NWP models to yield realistic regional-scale surface fluxes. Stochastic methods have been developed which successfully reduce the uncertainty of the most important surface characteristics or SVAT parameters by comparing random model output to observed time series of remotely sensed variables. This approach acknowledges and is based on the inherent uncertainty of both SVAT model parameterisations and the actual measurement of thermal signatures of the land surface. Various case studies have shown that the remote sensing in combination with SVAT modelling has significant potential in improving the estimation of land surface fluxes and derived variables. These studies focus on the use of satellite derived surface characteristics (land use) and on estimation of surface turbulent fluxes from RS data. Successful surface flux estimation has so far been limited to cases where the horizontal and spatial variability in surface temperature and reflectance is large and dominates the distribution of surface fluxes. As an example, we describe a recent algorithm for deriving surface fluxes and surface wetness from remotely sensed surface temperatures and albedo which has been used successfully for soil moisture assimilation in a NWP model case study. However, turbulent fluxes at the land surface and atmospheric boundary flow are controlled by factors which cannot be directly monitored from space platforms, in particular the aerodynamic properties of the land surface. For long term monitoring or estimation of surface fluxes, additional information on the aerodynamics is therefore crucial. For this, routine surface flux estimation is probably best achieved when combined with or embedded within a NWP analysis system. Following a review of several recent studies on the assimilation of satellite derived heating rates in regional atmospheric mesoscale models, we will discuss progress towards the use of a stochastic method to match measured and simulated surface temperature changes (heating rates) in order to obtain improved estimates of several key land surface parameters.