Record Search Query: [ISO_Topic_Category='BIOTA']
North American Land Data Assimilation System (NLDAS)
Entry ID: NOAA_NLDAS
Abstract: North American (NLDAS) is being developed that will lead to more accurate reanalysis and forecast simulations by numerical weather prediction (NWP) models. Specifically, this system will reduce the errors in the stores of soil moisture and energy which are often present in NWP models and which degrade the accuracy of forecasts. NLDAS is currently running retrospectively and in near real-time on a ... 1/8th-degree grid resolution. The system is currently forced by terrestrial (NLDAS) precipitation data, space-based radiation data and numerical model output. In order to create an optimal scheme, the projects involve several LSMs, many sources of data, and several institutions. Data from the project can be accessed on the NLDAS forcing pages, the NLDAS model output pages, as well as on the NLDAS Realtime Image Generator page.
The land surface component of the hydrological cycle is fundamental to the overall functioning of the atmospheric and climate processes. Spatially and temporally variable rainfall and available energy, combined with land surface heterogeneity cause complex variations in all processes related to surface hydrology. The characterization of the spatial and temporal variability of water and energy cycles are critical to improve our understanding of land surface-atmosphere interaction and the impact of land surface processes on climate extremes. Because the accurate knowledge of these processes and their variability is important for climate predictions, most Numerical Weather Prediction (NWP) centers have incorporated land surface schemes in their models. However, errors in the NWP forcing accumulate in the surface and energy stores, leading to incorrect surface water and energy partitioning and related processes. This has motivated the NWP community to impose ad hoc corrections to the land surface states to prevent this drift. A methodology under development here is to implement a Land Data Assimilation System (LDAS), which consists of uncoupled models forced with observations, and is therefore not affected by NWP forcing biases. This research is being implemented in near real time using existing Surface Vegetation Atmosphere Transfer Schemes (SVATS) by NCEP, NASA, Princeton University, and the University of Washington at 1/8 degree (about 14 kilometer) resolution across North America and at 1/4 degree resolution globally to evaluate these critical science questions. The LDAS is forced with real time output from numerical prediction models, satellite data, and radar precipitation measurements. Model parameters will be derived from the existing high-resolution vegetation and soil coverages. The model results will be aggregated to various scales to assess water and energy balances and these will be validated with various in-situ observations. Ultimately, observations of LDAS storages (soil moisture, temperature, snow) and fluxes (evaporation, sensible heat flux, runoff) will be used to further validate and constrain the LDAS predictions using data assimilation techniques.
[Summary provided by NOAA.]
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