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Author : Amsalework Ayele Ejigu
Publisher :
Page : 0 pages
File Size : 32,46 MB
Release : 2020
Category :
ISBN :
Author : Shunlin Liang
Publisher : World Scientific
Page : 491 pages
File Size : 49,28 MB
Release : 2013-09-23
Category : Science
ISBN : 981447262X
This book is unique in its ambitious and comprehensive coverage of earth system land surface characterization, from observation and modeling to data assimilation, including recent developments in theory and techniques, and novel application cases. The contributing authors are active research scientists, and many of them are internationally known leading experts in their areas, ensuring that the text is authoritative.This book comprises four parts that are logically connected from data, modeling, data assimilation integrating data and models to applications. Land data assimilation is the key focus of the book, which encompasses both theoretical and applied aspects with various novel methodologies and applications to the water cycle, carbon cycle, crop monitoring, and yield estimation.Readers can benefit from a state-of-the-art presentation of the latest tools and their usage for understanding earth system processes. Discussions in the book present and stimulate new challenges and questions facing today's earth science and modeling communities.
Author : Shunlin Liang
Publisher : World Scientific Publishing Company Incorporated
Page : 466 pages
File Size : 33,31 MB
Release : 2013
Category : Science
ISBN : 9789814472609
Pt. 1. Observation. ch. 1. Remote sensing data products for land surface data assimilation system application / Yunjun Yao, Shunlin Liang and Tongren Xu -- ch. 2. Second-generation polar-orbiting meteorological satellites of China: the Fengyun 3 series and its applications in global monitoring / Peng Zhang -- ch. 3. NASA satellite and model land data services: data access tutorial / Suhung Shen, Gregory Leptoukh and Hongliang Fang -- pt. 2. Modeling. ch. 4. Land surface process study and modeling in drylands and high-elevation regions / Yingying Chen and Kun Yang -- ch. 5. Review of parameterization and parameter estimation for hydrologic models / Soroosh Sorooshian and Wei Chu -- pt. 3. Data assimilation. ch. 6. Assimilating remote sensing data into land surface models: theory and methods / Xin Li and Yulong Bai -- ch. 7. Estimating model and observation error covariance information for land data assimilation systems / Wade T. Crow -- ch. 8. Inflation adjustment on error covariance matrices for ensemble Kalman filter assimilation / Xiaogu Zheng ... [et al.[ -- ch. 9. A review of error estimation in land data assimilation systems / Yulong Bai, Xin Li and Qianlong Chai -- ch. 10. An introduction to multi-scale Kalman smoother-based framework and its application to data assimilation / Daniel E. Salas and Xu Liang -- pt. 4. Application. ch. 11. Overview of the North American Land data assimilation system (NLDAS) / Youlong Xia ... [et al.] -- ch. 12. Soil moisture data assimilation for state initialization of seasonal climate prediction / Wenge Ni-Meister -- ch. 13. Assimilation of remote sensing data and crop simulation models for agricultural study: recent advances and future directions / Hongliang Fang, Shunlin Liang and Gerrit Hoogenboom -- ch. 14. Simultaneous state-parameter estimation for hydrologic modeling using ensemble Kalman filter / Xianhong Xie
Author : Raj Shekhar Singh
Publisher :
Page : 119 pages
File Size : 23,30 MB
Release : 2014
Category :
ISBN :
Modeling groundwater is challenging: it is not readily visible and is difficult to measure, with limited sets of observations available. Even though groundwater models can reproduce water table and head variations, considerable drift in modeled land surface states can nonetheless result from partially known geologic structure, errors in the input forcing fields, and imperfect Land Surface Model (LSM) parameterizations. These models frequently have biased results that are very different from observations. While many hydrologic groups are grappling with developing better models to resolve these issues, it is also possible to make models more robust through data assimilation of observation groundwater data. The goal of this project is to develop a methodology for high-resolution land surface model runs over large spatial region and improve hydrologic modeling through observation data assimilation, and then to apply this methodology to improve groundwater monitoring and banking. The high-resolution LSM modeling in this dissertation shows that model physics performs well at these resolutions and actually leads to better modeling of water/energy budget terms. The overarching goal of assimilation methodology is to resolve the critical issue of how to improve groundwater modeling in LSMs that lack sub-surface parameterizations and also run them on global scales. To achieve this, the research in this dissertation has been divided into three parts. The first goal was to run a commonly used land surface model at hyper resolution (1 km or finer) and show that this improves the modeling results without breaking the model. The second goal was to develop an observation data assimilation methodology to improve the high-resolution model. The third was to show real-world applications of this methodology. The need for improved accuracy is currently driving the development of hyper-resolution land surface models that can be implemented at a continental scale with resolutions of 1 km or finer. In Chapter 2, I describe our research incorporating fine-scale grid resolutions and surface data into the National Center for Atmospheric Research (NCAR) Community Land Model (CLM v4.0) for simulations at 1 km, 25 km, and 100 km resolution using 1 km soil and topographic information. Multi-year model runs were performed over the southwestern United States, including the entire state of California and the Colorado River basin. Results show changes in the total amount of CLM-modeled water storage and in the spatial and temporal distributions of water in snow and soil reservoirs, as well as in surface fluxes and energy balance. We also demonstrate the critical scales at which important hydrological processes--such as snow water equivalent, soil moisture content, and runoff--begin to more accurately capture the magnitude of the land water balance for the entire domain. This proves that grid resolution itself is also a critical component of accurate model simulations, and of hydrologic budget closure. To inform future model progress, we compare simulation outputs to station and gridded observations of model fields. Although the higher grid resolution model is not driven by high-resolution forcing, grid resolution changes alone yield significant reductions in the Root Mean Square Error (RMSE) between model outputs and actual observations: the RMSE decreases by more than 35% for soil moisture, 36% for terrestrial water storage anomaly, 34% for sensible heat, and 12% for snow water equivalent. The results of a 100 m resolution simulation over a spatial sub-domain indicate that parameters such as drainage, runoff, and infiltration are significantly impacted when hillslope scales of ~100 meters or finer are considered. We further show how limitations in the current model physics, including no lateral flow between grid cells, can affect model simulation accuracy. The results presented in Chapter 2 are encouraging, but also highlight the limitations in improving an LSM by only increasing spatial resolution of the model and the surface datasets. As was shown with the water table depth analysis, increasing model resolution cannot compensate for parameterization errors and lack of sub-surface information in CLM. However, this problem can be solved by providing additional information to the model in the form of water table depth via data assimilation. In Chapter 3, I discuss the development and verification of a methodology for assimilating observed groundwater depth measurements from multiple wells into the high spatial resolution LSM. A kriging-based interpolation technique is employed to overcome the problem of spatially and temporally sparse observations, and the interpolated data is assimilated into the CLM4.0 at 1 km resolution in a test region in northern California. Direct insertion and Ensemble Adjusted Kalman Filter (EAKF) based techniques are used for assimilation with direct insertion, producing better results and demonstrating major improvement in the simulation of water table depth. The Linear Pearson correlation coefficient between the observed well data and the assimilated model is 0.810, as opposed to only 0.107 for the non-assimilated model. This improvement is most significant where the water table depth is greater than 5 m. Assimilation also improves soil moisture content, especially in the dry season when the water table is at its lowest. Other variables, including sensible heat flux, ground evaporation, infiltration, and runoff are also analyzed in order to quantify the effect of this assimilation methodology. Though the changes in these variables seem small, they can be very important in coupled models, and the improved simulation of groundwater in the assimilated model can explain the changes in these results. This assimilation technique has been designed for use in regions with sparse and varied observation data, and it can be easily adapted to work in LSMs with a functional groundwater component. This gives us the capability to better model groundwater for the recent past and present, and also the potential to apply climate projections to probabilistically predict groundwater for future climate-change scenarios. We have collaborated with Wellintel Inc. to implement our methodology on the ground using their observation data. We are in the process of setting up our model over a large region along the central California coast, where for the past few months Wellintel has implemented a pilot with measurements based on its water table depth measuring devices. The aim of this collaboration is to provide users with actionable water table depth data in and around their properties for the past, present, and near future. We are combining this methodology with Wellintel data to create a groundwater-management and groundwater-banking monitoring tool. This is the first time that groundwater assimilation has been simulated in a high-resolution LSM, and as such this project provides proof-of-concept and application of a unique methodology that can be run at hyper resolution with data assimilation. The assimilation method is a very powerful tool that researchers can now apply to model land surface parameters much better than previously.
Author : Shunlin Liang
Publisher : World Scientific
Page : 491 pages
File Size : 40,96 MB
Release : 2013
Category : Science
ISBN : 9814472611
This book is unique in its ambitious and comprehensive coverage of earth system land surface characterization, from observation and modeling to data assimilation, including recent developments in theory and techniques, and novel application cases. The contributing authors are active research scientists, and many of them are internationally known leading experts in their areas, ensuring that the text is authoritative.This book comprises four parts that are logically connected from data, modeling, data assimilation integrating data and models to applications. Land data assimilation is the key focus of the book, which encompasses both theoretical and applied aspects with various novel methodologies and applications to the water cycle, carbon cycle, crop monitoring, and yield estimation.Readers can benefit from a state-of-the-art presentation of the latest tools and their usage for understanding earth system processes. Discussions in the book present and stimulate new challenges and questions facing today''s earth science and modeling communities.
Author : Richard Swinbank
Publisher : Springer Science & Business Media
Page : 377 pages
File Size : 20,40 MB
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 9401000298
Data assimilation is the combination of information from observations and models of a particular physical system in order to get the best possible estimate of the state of that system. The technique has wide applications across a range of earth sciences, a major application being the production of operational weather forecasts. Others include oceanography, atmospheric chemistry, climate studies, and hydrology. Data Assimilation for the Earth System is a comprehensive survey of both the theory of data assimilation and its application in a range of earth system sciences. Data assimilation is a key technique in the analysis of remote sensing observations and is thus particularly useful for those analysing the wealth of measurements from recent research satellites. This book is suitable for postgraduate students and those working on the application of data assimilation in meteorology, oceanography and other earth sciences.
Author : William Lahoz
Publisher : Springer Science & Business Media
Page : 710 pages
File Size : 31,50 MB
Release : 2010-07-23
Category : Science
ISBN : 3540747036
Data assimilation methods were largely developed for operational weather forecasting, but in recent years have been applied to an increasing range of earth science disciplines. This book will set out the theoretical basis of data assimilation with contributions by top international experts in the field. Various aspects of data assimilation are discussed including: theory; observations; models; numerical weather prediction; evaluation of observations and models; assessment of future satellite missions; application to components of the Earth System. References are made to recent developments in data assimilation theory (e.g. Ensemble Kalman filter), and to novel applications of the data assimilation method (e.g. ionosphere, Mars data assimilation).
Author : Alexander Baklanov
Publisher : Springer Science & Business Media
Page : 239 pages
File Size : 17,62 MB
Release : 2007-10-30
Category : Science
ISBN : 0387743219
This volume presents peer-reviewed papers from the NATO Advanced Research Workshop on Atmospheric Boundary Layers held in April 2006. The papers are divided into thematic sessions: nature and theory of turbulent boundary layers; boundary-layer flows: modeling and applications to environmental security; nature, theory and modeling of boundary-layer flows; air flows within and above urban and other complex canopies: air-sea-ice interaction.
Author : National Academies of Sciences, Engineering, and Medicine
Publisher : National Academies Press
Page : 351 pages
File Size : 24,43 MB
Release : 2016-08-22
Category : Science
ISBN : 0309388805
As the nation's economic activities, security concerns, and stewardship of natural resources become increasingly complex and globally interrelated, they become ever more sensitive to adverse impacts from weather, climate, and other natural phenomena. For several decades, forecasts with lead times of a few days for weather and other environmental phenomena have yielded valuable information to improve decision-making across all sectors of society. Developing the capability to forecast environmental conditions and disruptive events several weeks and months in advance could dramatically increase the value and benefit of environmental predictions, saving lives, protecting property, increasing economic vitality, protecting the environment, and informing policy choices. Over the past decade, the ability to forecast weather and climate conditions on subseasonal to seasonal (S2S) timescales, i.e., two to fifty-two weeks in advance, has improved substantially. Although significant progress has been made, much work remains to make S2S predictions skillful enough, as well as optimally tailored and communicated, to enable widespread use. Next Generation Earth System Predictions presents a ten-year U.S. research agenda that increases the nation's S2S research and modeling capability, advances S2S forecasting, and aids in decision making at medium and extended lead times.
Author : National Research Council
Publisher : National Academies Press
Page : 192 pages
File Size : 43,6 MB
Release : 2010-10-08
Category : Science
ISBN : 030915183X
More accurate forecasts of climate conditions over time periods of weeks to a few years could help people plan agricultural activities, mitigate drought, and manage energy resources, amongst other activities; however, current forecast systems have limited ability on these time- scales. Models for such climate forecasts must take into account complex interactions among the ocean, atmosphere, and land surface. Such processes can be difficult to represent realistically. To improve the quality of forecasts, this book makes recommendations about the development of the tools used in forecasting and about specific research goals for improving understanding of sources of predictability. To improve the accessibility of these forecasts to decision-makers and researchers, this book also suggests best practices to improve how forecasts are made and disseminated.