James V. Geiger

Land information system: An interoperable framework for high resolution land surface modeling

Abstract Knowledge of land surface water, energy, and carbon conditions are of critical importance due to their impact on many real world applications such as agricultural production, water resource management, and flood, weather, and climate prediction. Land Information System (LIS) is a software framework that integrates the use of satellite and ground-based observational data along with advanced land surface models and computing tools to accurately characterize land surface states and fluxes. LIS employs the use of scalable, high performance computing and data management technologies to deal with the computational challenges of high resolution land surface modeling. To make the LIS products transparently available to the end users, LIS includes a number of highly interactive visualization components as well. The LIS components are designed using object-oriented principles, with flexible, adaptable interfaces and modular structures for rapid prototyping and development. In addition, the interoperable features in LIS enable the definition, intercomparison, and validation of land surface modeling standards and the reuse of a high quality land surface modeling and computing system.

Evaluating Clouds in Long-Term Cloud-Resolving Model Simulations with Observational Data

Abstract Two 20-day, continental midlatitude cases are simulated with a three-dimensional (3D) cloud-resolving model (CRM) and are compared to Atmospheric Radiation Measurement Program (ARM) data. Surface fluxes from ARM ground stations and a land data assimilation system are used to drive the CRM. This modeling evaluation shows that the model simulates precipitation well but overpredicts clouds, especially in the upper troposphere. The evaluation also shows that the ARM surface fluxes can have noticeable errors in summertime. Theoretical analysis reveals that buoyancy damping is sensitive to spatial smoothers in two-dimensional (2D) CRMs, but not in 3D ones. With this theoretical analysis and the ARM cloud observations as background, 2D and 3D simulations are compared, showing that the 2D CRM has not only rapid fluctuations in surface precipitation but also spurious dehumidification (or a decrease in cloud amount). The present study suggests that the rapid precipitation fluctuation and spurious dehumidif...

An Integrated Hydrologic Modeling and Data Assimilation Framework

The Land Information System (LIS) is a multiscale hydrologic modeling and data assimilation framework that integrates the use of satellite and ground-based observational data products with advanced land-surface modeling tools to aid several application areas, including water resources management, numerical weather prediction, agricultural management, air quality, and military mobility assessment.

High-performance land surface modeling with a Linux cluster

The Land Information System (LIS) was developed at NASA to perform global land surface simulations at a resolution of 1-km or finer in real time. Such unprecedented scales and intensity pose many computational challenges. In this article, we demonstrate some of our approaches in high-performance computing with a Linux cluster to meet these challenges and reach our performance goals. These approaches include job partition and a job management system for parallel processing on the cluster, high-performance parallel input/output based on GrADS-DODS (GDS) servers, dynamic load-balancing and distributed data storage techniques, and highly scalable data replication with peer-to-peer (P2P) technology. These techniques work coherently to provide a high-performance land surface modeling system featuring fault tolerance, optimal resource utilization, and high scalability. Examples are given with LISs high-resolution modeling of surface runoff during 2003 to illustrate LISs capability to enable new scientific explorations.

Middleware-Based Sensor Web Integration

The Earth observation sensor web enables multiple-way interaction between earth observing sensors, sensor networks, Earth science models, and decision support systems. To achieve this goal, flexible and reliable integration between these disparate components is needed. In this study, a middleware-based, message-driven integration paradigm is proposed and implemented with the Land Information Sensor Web (LISW), to link a high-performance land surface modeling system with sensor simulators and other sensor web components, under a service-oriented architecture. OGC Sensor Web Enablement standard is adopted for interoperability. The middleware played a key role in enabling an integrated real-time sensor web with demonstrated simplicity, resilience and flexibility. We recommend that middleware-based integration should be adopted as a standard model in a wide range of sensor web applications, to replace the conventional point-to-point, client-server model.

A Land Information Sensor Web (LISW) Study in Support of Land Surface Studies

Land Surface Model (LSM) predictions are regular in time and space, but these predictions are influenced by errors in model structure, input variables, parameters and inadequate treatment of sub-grid scale spatial variability. Consequently, LSM predictions are significantly improved through observation constraints made in a data assimilation framework. Several multi-sensor satellites are currently operating which provide multiple global observations of the land surface, and its related near-atmospheric properties. However, these observations are not optimal for addressing current and future land surface environmental problems. To meet future earth system science challenges, NASA will likely develop constellations of smart satellites in sensor web configurations that provide timely on-demand data and analysis to users, and that can be reconfigured based on the changing needs of science and available technology. The prototype Land Information Sensor Web (LISW) project is aimed at integrating the Land Information System (LIS) in a sensor web framework which allows for optimal 2-way information flow that enhances land surface studies using sensor web observations, and in turn allows sensor web reconfiguration to minimize overall system uncertainty. These synthetic experiments provide a controlled environment in which to examine the end-to-end performance of the prototype, the impact of various design sensor web design trade-offs and the eventual value of sensor webs for particular prediction or decision support. In this paper, the progress of the LISW study will be presented, especially in scenario experiment design, sensor web framework and uncertainties in current land surface modeling.

A prototype of land information sensor web (LISW)

To meet future earth system science challenges, NASA will develop constellations of smart satellites in sensor web configurations that provide timely on-demand data and analysis to users, and that be reconfigured based on the changing needs of science and available technology. Sensor webs can eclipse the value of disparate sensor components by reducing response time and increasing scientific value, especially when integrated with science analysis, data assimilation, prediction modeling and decision support tools. The prototype Land Information Sensor Web (LISW) is a project sponsored by NASA, trying to integrate the Land Information System (LIS) in a sensor web framework which allows for optimal 2-way information flow that enhances land surface modeling using sensor web observations, and in turn allows sensor web reconfiguration to minimize overall system uncertainty. This prototype is based on a simulated interactive sensor web, which is then used to exercise and optimize the sensor web - modeling interfaces. These synthetic experiments provide a controlled environment in which to examine the end-to-end performance of the prototype, the impact of various design sensor web design trade-offs and the eventual value of sensor webs for particular prediction or decision support. The Study of virtual Land Information Sensor Web (LISW) is expected to provide some necessary priori knowledge for designing and deploying the next generation Global Earth Observing System of systems (GEOSS). In this paper, the progress of the LISW study will be presented, especially in scenario experiment design, sensor web framework and uncertainties in current land surface modeling.

Cloud field assimilation using simulated Triana satellite data

Abstract In the next few years, NASA plans to launch satellite Triana, a deep space Earth observatory that will take a full-disk view of the sunlit side of the Earth. Triana carries two instruments, EPIC, which will deliver Science products such as total precipitable water, cloud height, aerosol index, total ozone, and a global visible cloud field image, and NISTAR, which obtains precise radiometry integrated over the entire sunlit disk. Using a contemporary atmospheric model (namely the Eta model), we have started a project whose goal is to simulate some of the Triana observations and to assess the impact of Triana data for weather and climate predictions. In this paper, we report on the results of numerical experiments assimilating temperature profiles with and without cloud liquid water for inferring tropical and extra-tropical atmospheric states. We also assess the impact of initializing cloud liquid water for short term forecasts and data assimilation cycles.

Implementation of an eta belt domain on parallel systems

We extend the Eta weather model from a regional domain into a belt domain that does not require meridional boundary conditions. We describe how the extension is achieved and the parallel implementation of the code on the Cray T3E and the SGI Origin 2000. We validate the forecast results on the two platforms and examine how the removal of the meridional boundary conditions affects these forecasts. In addition, using several domains of different sizes and resolutions, we present the scaling performance of the code on both systems.