A Feature Model of Coupling Technologies for Earth System Models

Couplers that link together two or more numerical simulations are well-known abstractions in the Earth System Modeling (ESM) community. In the past decade, reusable software assets have emerged to facilitate scientists in implementing couplers. While there is a large amount of overlap in the features supported by software coupling technologies, their implementations differ significantly in terms of both functional and non-functional properties. Using a domain analysis method called feature analysis, we explore the spectrum of features supported by coupling technologies used to build today's production ESMs. The results of the feature analysis will enable automatic code generation of ESM couplers

Data Assimilation Enhancements to Air Force Weathers Land Information System

The United States Air Force (USAF) has a proud and storied tradition of enabling significant advancements in the area of characterizing and modeling land state information. 557th Weather Wing (557 WW; DoDs Executive Agent for Land Information) provides routine geospatial intelligence information to warfighters, planners, and decision makers at all echelons and services of the U.S. military, government and intelligence community. 557 WW and its predecessors have been home to the DoDs only operational regional and global land data analysis systems since January 1958. As a trusted partner since 2005, Air Force Weather (AFW) has relied on the Hydrological Sciences Laboratory at NASA/GSFC to lead the interagency scientific collaboration known as the Land Information System (LIS). LIS is an advanced software framework for high performance land surface modeling and data assimilation of geospatial intelligence (GEOINT) information

Automatic Configuration of CESM/CCSM4 on Amazon EC2 Cloud

The goal is to develop a prototype configurator service that automatically creates Amazon EC2 machine instances running on the EC2 Cloud containing ready-to-run configurations of the Community Earth System Model (CESM). The CESM Climate Configurator (C3) service takes in high-level experimental configuration tasks as an XML specification. In the future this will be encapsulated by a web-based graphical user interface. From the high-level configuration input C3 determines the closest match from a list of preconfigured Amazon Machine Images (AMI), and starts an instance (a running AMI on the EC2 cloud). C3 then creates and sends a shell script which finishes the configuration of the instance to the requested experiment, and returns a link to the running instance with the specified simulation ready to run

Performance of Full Text Search in Structured and Unstructured Peer-to-Peer Systems

Abstract — While structured P2P systems (such as DHTs) are often regarded as an improvement over unstructured P2P systems (such as super-peer networks) in terms of routing efficiency, it is not clear which architecture is better for full text search. This paper provides a quantitative comparison of full text keyword search in structured and unstructured P2P systems. We examine three techniques (and optimizations to those techniques) proposed in the literature: using a DHT along with inverted lists and Bloom filters; using a super-peer network; and using a random walk over an unstructured network. We use real Web documents and user queries to measure the cost for both document publishing and query processing, in terms of bandwidth and response time. Our results show that all three techniques use roughly the same bandwidth to process queries (with the super-peer technique having a slight edge). The structured network provides the best response time (30 percent better than a super-peer network), but has a high cost of document publishing, using six times as much bandwidth as the super-peer system. The random walk technique requires no publishing, but has a very long response time unless multiple random walks operate in parallel. I

Hepatic coenzyme Q redox balance of fishes as a potential bioindicator of environmental contamination by polycyclic aromatic hydrocarbons

In this communication, we introduce a novel biomarker of aquatic contamination based on the xenobiotic-induced response of the hepatic coenzyme Q (CoQ) redox balance of fishes to polycyclic aromatic hydrocarbons (PAHs). The method is demonstrated by comparing changes in the liver CoQ redox balance with that measured using the CYP1A-based, 7-ethoxyresofurin-O-deethylase activity assay, on administration of benzo[a]pyrene (BaP) and β-naphthoflavone (BNF) to Barramundi (Lates calcarifer). Both assays showed comparable dose-dependent effects in fish treated with BaP or BNF. Perturbation in the constitutive hepatic CoQ redox balance of fishes may thus provide a simple biomarker of aquatic PAH contamination

Ockham's razor gone blunt: coenzyme Q adaptation and redox balance in tropical reef fishes

The ubiquitous coenzyme Q (CoQ) is a powerful antioxidant defence against cellular oxidative damage. In fishes, differences in the isoprenoid length of CoQ and its associated antioxidant efficacy have been proposed as an adaptation to different thermal environments. Here, we examine this broad contention by a comparison of the CoQ composition and its redox status in a range of coral reef fishes. Contrary to expectations, most species possessed CoQ8 and their hepatic redox balance was mostly found in the reduced form. These elevated concentrations of the ubiquinol antioxidant are indicative of a high level of protection required against oxidative stress. We propose that, in contrast to the current paradigm, CoQ variation in coral reef fishes is not a generalized adaptation to thermal conditions, but reflects species-specific ecological habits and physiological constraints associated with oxygen demand

Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF-Hydro System

The NASA LIS/WRF-Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi-scale surface hydrological modeling capabilities of the WRF-Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub-surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF-Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open-loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF-Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications.https://doi.org/10.1029/2021WR02986

Supporting the climate community by providing common metadata for climate modelling digital repositories: The metafor project

A poster to highlight common metadata for climate modelling repositories to support the climate community. There is more interest than ever in the results of climate models; users are no longer limited to the scientific and academic communities, and can now be found in as diverse areas as local government, policy and the general public. Climate modeling is a complex process, which requires accurate and complete metadata (data describing data) in order to identify, assess and use the climate data stored in digital repositories and made available to these users