Remote Sensing Data Products for Land Surface Data Assimilation System Application

2013 ◽  
pp. 3-43
Author(s):  
Yunjun Yao ◽  
Shunlin Liang ◽  
Tongren Xu
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
System Application ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Surface Data ◽  
Data Products ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Impacts of High-Resolution Land Surface Initialization on Regional Sensible Weather Forecasts from the WRF Model

2008 ◽  
Vol 9 (6) ◽  
pp. 1249-1266 ◽  
Author(s):  
Jonathan L. Case ◽  
William L. Crosson ◽  
Sujay V. Kumar ◽  
William M. Lapenta ◽  
Christa D. Peters-Lidard
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Land Surface ◽  
Initial Conditions ◽  
Surface Model ◽  
Surface Data ◽  
Initial Soil ◽  
Eta Model ◽  
Data Assimilation System ◽  
Assimilation System

Abstract This manuscript presents an assessment of daily regional simulations of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model initialized with high-resolution land surface data from the NASA Land Information System (LIS) software versus a control WRF configuration that uses land surface data from the National Centers for Environmental Prediction (NCEP) Eta Model. The goal of this study is to investigate the potential benefits of using the LIS software to improve land surface initialization for regional NWP. Fifty-eight individual nested simulations were integrated for 24 h for both the control and experimental (LISWRF) configurations during May 2004 over Florida and the surrounding areas: 29 initialized at 0000 UTC and 29 initialized at 1200 UTC. The land surface initial conditions for the LISWRF runs came from an offline integration of the Noah land surface model (LSM) within LIS for two years prior to the beginning of the month-long study on an identical grid domain to the subsequent WRF simulations. Atmospheric variables used to force the offline Noah LSM integration were provided by the North American Land Data Assimilation System and Global Data Assimilation System gridded analyses. The LISWRF soil states were generally cooler and drier than the NCEP Eta Model soil states during May 2004. Comparisons between the control and LISWRF runs for one event suggested that the LIS land surface initial conditions led to an improvement in the timing and evolution of a sea-breeze circulation over portions of northwestern Florida. Surface verification statistics for the entire month indicated that the LISWRF runs produced a more enhanced and accurate diurnal range in 2-m temperatures compared to the control as a result of the overall drier initial soil states, which resulted from a reduction in the nocturnal warm bias in conjunction with a reduction in the daytime cold bias. Daytime LISWRF 2-m dewpoints were correspondingly drier than the control dewpoints, again a manifestation of the drier initial soil states in LISWRF. The positive results of the LISWRF experiments help to illustrate the importance of initializing regional NWP models with high-quality land surface data generated at the same grid resolution.

Progress in integrating remote sensing data and hydrologic modeling

2014 ◽  
Vol 38 (4) ◽  
pp. 464-498 ◽  
Author(s):  
Xiaoyong Xu ◽  
Jonathan Li ◽  
Bryan A. Tolson
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Land Surface ◽  
Hydrologic Modeling ◽  
Snow Water Equivalent ◽  
Research Effort ◽  
Remote Sensing Data ◽  
Area Index ◽  
Sensing Data ◽  
The Past

Remote sensing and hydrologic modeling are two key approaches to evaluate and predict hydrology and water resources. Remote sensing technologies, due to their ability to offer large-scale spatially distributed observations, have opened up new opportunities for the development of fully distributed hydrologic and land-surface models. In general, remote sensing data can be applied to land-surface and hydrologic modeling through three strategies: model inputs (basin information, boundary conditions, etc.), parameter estimation (model calibration), and state estimation (data assimilation). There has been an intensive global research effort to integrate remote sensing and land/hydrologic modeling over the past few decades. In particular, in recent years significant progress has been made in land/hydrologic remote sensing data assimilation. Hence there is a demand for an up-to-date review on these efforts. This paper presents an overview of research efforts to combine hydrologic/land models and remote sensing products (mainly including precipitation, surface soil moisture, snow cover, snow water equivalent, leaf area index, and evapotranspiration) over the past decade. This paper also discusses the major challenges remaining in this field, and recommends the directions for further research efforts.

Retrieving accurate soil moisture over the Tibetan Plateau using multi-source remote sensing data assimilation with simultaneous state and parameter estimations

2021 ◽  
Author(s):  
Weijing Chen ◽  
Chunlin Huang ◽  
Zong-Liang Yang ◽  
Ying Zhang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Tibetan Plateau ◽  
Data Assimilation ◽  
Brightness Temperature ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Accurate Estimation ◽  
The Tibetan Plateau ◽  
Sensing Data

AbstractData assimilation provides a practical way to improve the accuracy of soil moisture simulation by integrating a land surface model and satellite data. This study establishes a multi-source remote sensing data assimilation framework by incorporating a simultaneous state and parameter estimation method to acquire an accurate estimation of the soil moisture over the Tibetan Plateau. The brightness temperature of the Advanced Microwave Scanning Radiometer 2 (AMSR2) is directly assimilated into the coupled system of the Common Land Model (CoLM) and a microwave radiative transfer model (RTM) to improve the soil moisture simulation. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature product and the Beijing Normal University (BNU) leaf area index product are employed to not only improve the estimation of temperature and vegetation variables from the CoLM, but also provide more accurate background information for the RTM during the brightness temperature assimilation. In situ measurements from the Naqu network are used to evaluate the results. The model simulation showed an obvious underestimation of soil moisture and overestimation of soil temperature, which was alleviated by the assimilation experiments, particularly in the shallow soil layers. The estimated parameters also showed advantages in the soil moisture simulation when compared with the default parameters. The assimilation experiment presents promising results in the combination of model and multi-source remote sensing data for estimating soil moisture over the complex mountainous region in Tibet.

scholarly journals Constraining a land surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System

2016 ◽  
Author(s):  
G. J. Schürmann ◽  
T. Kaminski ◽  
C. Köstler ◽  
N. Carvalhais ◽  
M. Voßbeck ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Land Surface ◽  
Data Stream ◽  
Atmospheric Co2 ◽  
Surface Model ◽  
Terrestrial Biosphere ◽  
Multiple Observations ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We describe the Max Planck Institute Carbon Cycle Data Assimilation System (MPI-CCDAS) built around the tangent-linear version of the land surface scheme of the MPI-Earth System Model v1 (JSBACH). The simulated terrestrial biosphere processes (phenology and carbon balance) were constrained by observations of the fraction of photosynthetically active radiation (TIP-FAPAR product) and by observations of atmospheric CO2 at a global set of monitoring stations for the years 2005–2009. The system successfully, and computationally efficiently, improved average foliar area and northern extra-tropical seasonality of foliar area when constrained by TIP-FAPAR. Global net and gross carbon fluxes were improved when constrained by atmospheric CO2, although the system tended to underestimate tropical productivity. Assimilating both data streams jointly allowed the MPI-CCDAS to match both observations (TIP-FAPAR and atmospheric CO2) equally well as the single data stream assimilation cases, therefore overall increasing the appropriateness of the resultant parameter values and biosphere dynamics. Our study thus highlights the role of the TIP-FAPAR product in stabilising the underdetermined atmospheric inversion problem and demonstrates the value of multiple-data stream assimilation for the simulation of terrestrial biosphere dynamics. The constraint on regional gross and net CO2 flux patterns is limited through the parametrisation of the biosphere model. We expect improvement on that aspect through a refined initialisation strategy and inclusion of further biosphere observations as constraints.

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