Improved accuracy in defining initial conditions for fully-coupled numerical weather prediction models (NWP) along
with continuous internal bias corrections for baseline data generated by uncoupled Land Surface Models (LSM), is
expected to lead to improved short-term to long-range weather forecasting capability. Because land surface parameters
are highly integrated states, errors in land surface forcing, model physics and parameterization tend to accumulate in the
land surface stores of these models, such as soil moisture and surface temperature. This has a direct effect on the model's
water and energy balance calculations, and will eventually result in inaccurate weather predictions.
Surface soil moisture and surface temperature estimates obtained with a recently improved retrieval algorithm from the
Advanced Microwave Scanner Radiometer (AMSR) aboard NASA's Earth Observing System (EOS) Aqua satellite are
evaluated against model output of the Community Noah Land Surface Model operated within the Land Information
System (LIS) forced with atmospheric data of the NCEP Global Data Assimilation System (GDAS). The surface
temperature retrievals and Noah LSM output are further evaluated against local measurements from the Mesonet
observational grid in Oklahoma.
Preliminary analysis presented here shows a potential to improve simulated surface temperature estimates of the Noah
model by assimilating satellite derived surface temperature fields. The potential for updating (top) soil moisture seems to
be more restricted, mainly as a result of the relatively thick top soil layer of the model as compared to the passive
microwave emanation depth.
ERA-40 stands for ECMWF Re Analysis and refers to the rerun of the European Centre of Medium Range Weather Forecast (ECMWF) Numerical Weather Prediction (NWP) model for the period September 1957- August 2002 utilizing all state-of-the-art information and satellite data input presently available. From this dataset the top layer volumetric soil water is extracted and evaluated against surface moisture retrievals derived from the SMMR instrument on board the Nimbus-7 satellite for the European window. The evaluation of NWP model output with observed data is relevant to the initialization of land surface conditions in these models, which is important for accurate short term to long range meteorological and hydrological prediction. Because land surface parameters are highly integrated states, errors in land surface forcing, model physics and parameterization tend to accumulate in the land surface stores, such as soil moisture, of these models This has a direct effect on the model's water and energy balance calculations, and will eventually result in inaccurate weather predictions. It is expected that improved accuracy in defining initial conditions for NWPs along with continuous internal bias corrections for baseline data generated by uncoupled Land Surface Models (LSM), will lead to highly improved shortterm to long-range weather forecasting capability. Preliminary analysis presented here reveals the off set between the two data sets, although distinct, is relatively constant, which suggests a potential for improved initialization and bias correction by an optimized accuracy and spatial representation of the soil moisture data fields.
ERA-40 stands for ECMWF Re Analysis and refers to the rerun of the European Centre of Medium Range Weather Forecast (ECMWF) Global Circulation model for the period September 1957- August 2002 utilizing all state-of-the-art information and satellite data input presently available. Here, a selection of the ERA-40 atmospheric output data at the surface is used to force the catchment hydrological model LISFLOOD to simulate historic river flows for the whole of Europe on a 5km grid resolution. Once evaluated against observed rainfall and point flow data, the output constitutes an extensive and coherent 40+ year database of pan-European calibrated river flow time series, providing a wealth of information and potential for a range of evaluation purposes. For example, statistical analyses could serve to detect flooding and/or drying frequency trends. Alternatively, the output dataset could be used to assess flood alert levels in a consistent and uniform manner at any point for any river in Europe. The set would also be extremely useful as a basis for scenario studies, investigating the impact of policy and decision making such as de/aforestation and water reservoir management on flow regimes. Further, in the context of the general scientific consensus of an expected increasing trend in extreme events, the database may serve as a resource of information for extrapolated future scenarios.
The ECMWF Re Analysis (ERA-40) refers to the rerun of the European Centre of Medium Range Weather Forecast (ECMWF) Numerical Weather Prediction (NWP) model for the period September 1957- August 2002 employing all state-of-the-art information and satellite data input presently available. A selection of the reanalysis atmospheric output data can potentially be used to run a hydrological model to simulate historic river flows for the whole of Europe. Once evaluated against observed time series of rainfall and river flow, the output would constitute an extensive and coherent 40+ year database of pan-European calibrated river flow time series, providing a wealth of information and allowing a range of evaluation possibilities.
Here, in order to separate the meteorological model performance from the hydrological model performance, the ERA-40 near-surface rainfall aggregates, which come as a by-product of the ECMWF NWP system, are evaluated against interpolated fields of observed surface precipitation. The ERA-40 rainfall fields consist of forecast data with a 36 hr lead time at midday and midnight and a 6 hr lead time at 6:00 and 18:00 UTC, allowing different combinations of lead and base times to compute daily rainfall aggregates. The evaluation of these aggregated precipitation fields against observed totals is relevant to the spin-up time of the ECMWF NWP system and the forecast reliability with increasing lead time. Interpolated fields of observed daily rainfall totals are provided by the Monitoring Agriculture with Remote Sensing (MARS) database (1990-2001) based at the European Commission Joint Research Centre (JRC).
The potential of satellite passive microwave remote sensing for the monitoring of surface moisture in semi-arid areas is generally recognized. While the largely unknown behavior of a.o. the vegetation parameters in the radiative transfer algorithm requires the validation of the satellite derived surface moisture for a restricted calibration time period, it often proves difficult due to the lack of adequate surface moisture field measurements on the regional scale and/or for the corresponding lifetime of the space platform. The present study describes the indirect validation of surface moisture derived from historical Nimbus/SMMR data (1978-1987) collected over the Upper Guadiana Catchment in central Spain using recharge estimates from historical surface flow measurements. The recharge estimates are used to calibrate a soil moisture balance model, which is applied in the absence of surface moisture measurements. If the followed approach is successful, satellite passive microwave inversion techniques may be used to assess recharge estimates. Since the recharge rate corresponds to the maximum abstraction yield without depletion of the groundwater reservoir, the approach may provide a tool for the sustainable development of areas suffering from the overexploitation of water resources.
Nimbus/SMMR data have been used together with an extensive data base of soil moisture (1984-1987) to study the potential for large scale surface moisture monitoring. In order to study the influences of vegetation on the signals gathered from space, a radiative transfer model has been used to simulate the below canopy emissivity. Vegetation characteristics were subsequently estimated from inverse modeling and related to NDVI (synergistic approach) and to H- and V-polarization signatures (dual-polarization approach). Results of both approaches will be presented and compared. In order to validate the dual-polarization concept, which was applied to account for the radiative transfer properties of the vegetation, field measurements of single scattering albedo and optical depth were carried out recently for an agricultural crop.