Comparison of Surface Turbulent Flux Estimation from Radiometric Surface Temperature Observations Using Retrieval- and Data Assimilation-Based Approaches
Abstract
Surface moisture and energy fluxes are extremely important in land-atmosphere interaction. Despite the importance of these fluxes, in-situ measurements are generally limited to sparse ground-based monitoring sites that are often associated with short-term field experiments or to extremely localized regions. To create operational frameworks for estimating these fluxes over longer periods and larger spatial extents will require the use of remote sensing products. While there are many potentially useful remote sensing observations that can be used to estimate land surface fluxes, radiometric surface temperature observations are often used because i) it is a variable that is implicit in the surface energy balance and ii) is measured operationally from several orbiting platforms that provide excellent spatial and temporal resolution and coverage. Techniques used to estimate surface fluxes from radiometric surface temperature generally fall into two categories: retrieval-based and data assimilation approaches. Retrieval methods provide an instantaneous estimate of the variable of interest (e.g. surface fluxes) from the observed quantity (surface temperature) based on the inversion of a physical or empirical model. Data assimilation approaches differ from retrieval methods in that they combine a physically-based model with a sequence of remote sensing observations and attempts to merge the two to produce an optimal estimate based on the relative uncertainty of all inputs. Up to this point, there has been little, if any direct comparison between retrieval- and assimilation-based techniques to assess the side-by-side performance of these two different approaches. In this study we compare the popular triangle retrieval method to a variational data assimilation approach for estimating surface turbulent fluxes from radiometric surface temperature using data from a field site in Kansas. The two methods were applied in parallel for two full summer seasons. Synthetic tests were first performed to assess the ability of the methods to cope with known input errors. Given minimal input errors, the two methods performed comparably, with the data assimilation method appearing more robust as input errors were increased. Additionally, comparison of computational burden showed significant savings by the assimilation approach. The two methods were then applied using real radiometric surface temperature inputs and estimates were compared to ground-based surface flux observations. Results show that both methods are capable of providing reasonable estimates of latent and sensible heat fluxes, with better results from the variational assimilation approach. Additionally, based on the necessary model inputs and assumptions and computational burden, it would appear that the assimilation-based approach has advantages for operational application.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2004
- Bibcode:
- 2004AGUFM.H13C0431K
- Keywords:
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- 3360 Remote sensing;
- 1818 Evapotranspiration;
- 1878 Water/energy interactions