A Land Data Assimilation System for Soil Moisture and Temperature: An Information Content Study (original) (raw)
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Journal of Geophysical Research, 2004
The multiple-agency/university North American Land Data Assimilation System (NLDAS) project is designed to provide enhanced soil and temperature initial conditions for numerical weather/climate prediction models. Currently, four land surface models (LSMs) are running in NLDAS both in retrospective mode and in real-time mode. All LSMs are driven by the same meteorologic forcing data and are initiated at the same time with the same relative soil wetness. This study intercompares these NLDAS soil moisture fields with each other and ...
Journal of Hydrometeorology, 2011
The contributions of precipitation and soil moisture observations to soil moisture skill in a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Soil moisture skill (defined as the anomaly time series correlation coefficient R) is assessed using in situ observations in the continental United States at 37 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at 4 USDA Agricultural Research Service (“CalVal”) watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land ...
Journal of Geophysical Research, 2003
1] North American Land Data Assimilation System (NLDAS) land surface models have been run for a retrospective period forced by atmospheric observations from the Eta analysis and actual precipitation and downward solar radiation to calculate land hydrology. We evaluated these simulations using in situ observations over the southern Great Plains for the periods of May-September of 1998 and 1999 by comparing the model outputs with surface latent, sensible, and ground heat fluxes at 24 Atmospheric Radiation Measurement/Cloud and Radiation Testbed stations and with soil temperature and soil moisture observations at 72 Oklahoma Mesonet stations. The standard NLDAS models do a fairly good job but with differences in the surface energy partition and in soil moisture between models and observations and among models during the summer, while they agree quite well on the soil temperature simulations. To investigate why, we performed a series of experiments accounting for differences between model-specified soil types and vegetation and those observed at the stations, and differences in model treatment of different soil types, vegetation properties, canopy resistance, soil column depth, rooting depth, root density, snow-free albedo, infiltration, aerodynamic resistance, and soil thermal diffusivity. The diagnosis and model enhancements demonstrate how the models can be improved so that they can be used in actual data assimilation mode.
Monthly Weather Review, 2017
This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humid...
Journal of Applied Meteorology, 1997
In this study, a simple method is described and tested for deriving initial soil moisture fields for numerical weather prediction purposes using satellite imagery. Recently, an algorithm was developed to determine surface evaporation maps from high-and low-resolution satellite data, which does not require information on land use and synoptic data. A correction to initial soil moisture was calculated from a comparison between the evaporation fields produced by a numerical weather prediction model and the satellite algorithm. As a case study, the method was applied to the Iberian Peninsula during a 7-day period in the summer of 1994. Two series of short-term forecasts, initialized from a similar initial soil moisture field, were run in parallel: a control run in which soil moisture evolved freely and an experimental run in which soil moisture was updated daily using the simple assimilation procedure. The simple assimilation resulted in a decrease of the bias of temperature and specific humidity at 2-m height during the daytime and a small decrease of the root-mean-square error of these quantities. The results show that the surface evaporation maps, derived from the satellite data, contain a signal that may be used to assimilate soil moisture in numerical weather prediction models.
Remote Sensing, 2020
Soil moisture is a key variable used to describe water and energy exchanges at the land surface/atmosphere interface. Therefore, there is widespread interest in the use of soil moisture retrievals from passive microwave satellites. In the assimilation of satellite soil moisture data into land surface models, two approaches are commonly used. In the first approach brightness temperature (TB) data are assimilated, while in the second approach retrieved soil moisture (SM) data from the satellite are assimilated. However, there is not a significant body of literature comparing the differences between these two approaches, and it is not known whether there is any advantage in using a particular approach over the other. In this study, TB and SM L2 retrieval products from the Soil Moisture and Ocean Salinity (SMOS) satellite are assimilated into the Canadian Land Surface Scheme (CLASS), for improved soil moisture estimation over an agricultural region in Saskatchewan. CLASS is the land sur...
Remote Sensing, 2022
The correction of Soil Moisture (SM) estimates in Land Surface Models (LSMs) is considered essential for improving the performance of numerical weather forecasting and hydrologic models used in weather and climate studies. Along with surface screen-level variables, the satellite data, including Brightness Temperature (BT) from passive microwave sensors, and retrieved SM from active, passive, or combined active–passive sensor products have been used as two critical inputs in improvements of the LSM. The present study reviewed the current status in correcting LSM SM estimates, evaluating the results with in situ measurements. Based on findings from previous studies, a detailed analysis of related issues in the assimilation of SM in LSM, including bias correction of satellite data, applied LSMs and in situ observations, input data from various satellite sensors, sources of errors, calibration (both LSM and radiative transfer model), are discussed. Moreover, assimilation approaches are ...
A simple assimilation method to ingest satellite soil moisture into a limited-area NWP model
Meteorologische Zeitschrift, 2014
Recently several studies discussed the potential and operational use of satellite soil moisture measurements in new land surface analysis feeding global Numerical Weather Prediction (NWP) models. This work seeks to establish whether a limited-area NWP model might benefit from the assimilation of remotely sensed soil moisture data. The question is important because it is well known that even small errors in the initial conditions could amplify in the future states and lead to erroneous predictions. On the other hand, remotely sensed soil moisture observations are attractive because they offer a synoptic point of view and their reliability with respect to in-situ measurements is demonstrated.
2.10 Analysis of Water Balance Simulation of Land Data Assimilation System
The multi-agency/university North American Land Data Assimilation System (N-LDAS) project is designed to provide enhanced soil and temperature initial conditions for numerical weather/climate prediction models by using real-time observed precipitation and solar insolation data. Currently four different land surface models (LSMs) are running in N-LDAS in both retrospective mode as well as in realtime. All LSMs are initiated at the same time with the same relative soil wetness. This study examines the degree of correlation between the ...