Synergetic use of millimeter- and centimeter-wavelength radars for retrievals of cloud and rainfall parameters

A remote sensing approach for simultaneous retrievals of cloud and rainfall parameters in the vertical column above the US Department of Energy's (DOE) Climate Research Facility at the Tropical Western Pacific (TWP) Darwin site in Australia is described. This approach uses vertically pointing measurements from a DOE <i>K</i><sub>a</sub>-band radar and scanning measurements from a nearby C-band radar pointing toward the TWP Darwin site. Rainfall retrieval constraints are provided by data from a surface impact disdrometer. The approach is applicable to stratiform precipitating cloud systems when a separation between the liquid hydrometeor layer, which contains rainfall and liquid water clouds, and the ice hydrometeor layer is provided by the radar bright band. Absolute C-band reflectivities and <i>K</i><sub>a</sub>-band vertical reflectivity gradients in the liquid layer are used for retrievals of the mean layer rain rate and cloud liquid water path (CLWP). C-band radar reflectivities are also used to estimate ice water path (IWP) in regions above the melting layer. The retrieval uncertainties of CLWP and IWP for typical stratiform precipitation systems are about 500–800 g m<sup>−2</sup> (for CLWP) and a factor of 2 (for IWP). The CLWP retrieval uncertainties increase with rain rate, so retrievals for higher rain rates may be impractical. The expected uncertainties of layer mean rain rate retrievals are around 20%, which, in part, is due to constraints available from the disdrometer data. The applicability of the suggested approach is illustrated for two characteristic events observed at the TWP Darwin site during the wet season of 2007. A future deployment of W-band radars at the DOE tropical Climate Research Facilities can improve CLWP estimation accuracies and provide retrievals for a wider range of stratiform precipitating cloud events

Remote sensing data from CLARET: A prototype CART data set

The data set containing radiation, meteorological , and cloud sensor observations is documented. It was prepared for use by the Department of Energy's Atmospheric Radiation Measurement (ARM) Program and other interested scientists. These data are a precursor of the types of data that ARM Cloud And Radiation Testbed (CART) sites will provide. The data are from the Cloud Lidar And Radar Exploratory Test (CLARET) conducted by the Wave Propagation Laboratory during autumn 1989 in the Denver-Boulder area of Colorado primarily for the purpose of developing new cloud-sensing techniques on cirrus. After becoming aware of the experiment, ARM scientists requested archival of subsets of the data to assist in the developing ARM program. Five CLARET cases were selected: two with cirrus, one with stratus, one with mixed-phase clouds, and one with clear skies. Satellite data from the stratus case and one cirrus case were analyzed for statistics on cloud cover and top height. The main body of the selected data are available on diskette from the Wave Propagation Laboratory or Los Alamos National Laboratory

Optically Thin Liquid Water Clouds: Their Importance and Our Challenge

Many of the clouds important to the Earth's energy balance, from the tropics to the Arctic, are optically thin and contain liquid water. Longwave and shortwave radiative fluxes are very sensitive to small perturbations of the cloud liquid water path (LWP) when the liquid water path is small (i.e., < g/sq m) and, thus, the radiative properties of these clouds must be well understood to capture them correctly in climate models. We review the importance of these thin clouds to the Earth's energy balance, and explain the difficulties in observing them. In particular, because these clouds are optically thin, potentially mixed-phase, and often (i.e., have large 3-D variability), it is challenging to retrieve their microphysical properties accurately. We describe a retrieval algorithm intercomparison that was conducted to evaluate the issues involved. The intercomparison included eighteen different algorithms to evaluate their retrieved LWP, optical depth, and effective radii. Surprisingly, evaluation of the simplest case, a single-layer overcast cloud, revealed that huge discrepancies exist among the various techniques, even among different algorithms that are in the same general classification. This suggests that, despite considerable advances that have occurred in the field, much more work must be done, and we discuss potential avenues for future work

Internet of Things for Environmental Sustainability and Climate Change

Our world is vulnerable to climate change risks such as glacier retreat, rising temperatures, more variable and intense weather events (e.g., floods, droughts, and frosts), deteriorating mountain ecosystems, soil degradation, and increasing water scarcity. However, there are big gaps in our understanding of changes in regional climate and how these changes will impact human and natural systems, making it difficult to anticipate, plan, and adapt to the coming changes. The IoT paradigm in this area can enhance our understanding of regional climate by using technology solutions, while providing the dynamic climate elements based on integrated environmental sensing and communications that is necessary to support climate change impacts assessments in each of the related areas (e.g., environmental quality and monitoring, sustainable energy, agricultural systems, cultural preservation, and sustainable mining). In the IoT in Environmental Sustainability and Climate Change chapter, a framework for informed creation, interpretation and use of climate change projections and for continued innovations in climate and environmental science driven by key societal and economic stakeholders is presented. In addition, the IoT cyberinfrastructure to support the development of continued innovations in climate and environmental science is discussed

Index-Based Cost-Effectiveness Analysis vs. Least-Cost River Basin Optimization Model: Comparison in the Selection of a Programme of Measures at the River Basin Scale

Increasing water scarcity challenges conventional approaches to managing water resources. More holistic tools and methods are required to support the integrated planning and management of fresh water resources at the river basin level. This paper compares an index-based cost-effectiveness analysis (IBCEA) with a least-cost river basin optimization model (LCRBOM). Both methods are applied to a real case study to design a cost-effective portfolio of water demand and supply management measures that ensures compliance with water supply and environmental targets. The IBCEA is a common approach to select programmes of measures in the implementation of the EU Water Framework Directive. We describe its limitations in finding a least-cost solution at the river basin level and highlight the benefits from implementing a LCRBOM. Both methods are compared in a real case study, the Orb river basin, in the south of France. The performances of the programmes of measures selected by the two methods are compared for the same annual equivalent cost. By ignoring the spatial and temporal variability of water availability and water demands in the river basin and the interconnection among its elements, the aggregated approach used in the standard IBCEA can miss more cost-effective solutions at the river basin scale.This paper is based on work conducted as part of several projects over more than 6 years. It benefited from the financial and technical support of the Agence de l'Eau Rhone Mediteranee et Corse; Conseil General de l'Herault; Conseil Regional du Languedoc Roussillon et ONEMA. Funding was partly provided by the IMPADAPT project /CGL2013-48424-C2-1-R) from the Spanish ministry MINECO (Ministerio de Economia y Competitividad) and European FEDER funds. Corentin Girard is supported by a grant from the University Lecturer Training Programme (FPU12/03803) of the Ministry of Education, Culture and Sports of Spain. We are very grateful to Y. Caballero (BRGM), S. Chazot (BRLi), E. Vier and F. Aigoui (GINGERGROUP) and L. Rippert and his team from the SMVOL for their help during the project and for the data provided. We thank as well the two anonymous reviewers, the Associated Editor and Editor-in-Chief of Water Resources Management, for their useful and encouraging comments during the review process.Girard-Martin, CDP.; Rinaudo, J.; Pulido-Velazquez, M. (2015). Index-Based Cost-Effectiveness Analysis vs. Least-Cost River Basin Optimization Model: Comparison in the Selection of a Programme of Measures at the River Basin Scale. 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Review on computational methods for Lyapunov functions

Lyapunov functions are an essential tool in the stability analysis of dynamical systems, both in theory and applications. They provide sufficient conditions for the stability of equilibria or more general invariant sets, as well as for their basin of attraction. The necessity, i.e. the existence of Lyapunov functions, has been studied in converse theorems, however, they do not provide a general method to compute them. Because of their importance in stability analysis, numerous computational construction methods have been developed within the Engineering, Informatics, and Mathematics community. They cover different types of systems such as ordinary differential equations, switched systems, non-smooth systems, discrete-time systems etc., and employ di_erent methods such as series expansion, linear programming, linear matrix inequalities, collocation methods, algebraic methods, set-theoretic methods, and many others. This review brings these different methods together. First, the different types of systems, where Lyapunov functions are used, are briefly discussed. In the main part, the computational methods are presented, ordered by the type of method used to construct a Lyapunov function

Bidirectional Coupling between Astrocytes and Neurons Mediates Learning and Dynamic Coordination in the Brain: A Multiple Modeling Approach

In recent years research suggests that astrocyte networks, in addition to nutrient and waste processing functions, regulate both structural and synaptic plasticity. To understand the biological mechanisms that underpin such plasticity requires the development of cell level models that capture the mutual interaction between astrocytes and neurons. This paper presents a detailed model of bidirectional signaling between astrocytes and neurons (the astrocyte-neuron model or AN model) which yields new insights into the computational role of astrocyte-neuronal coupling. From a set of modeling studies we demonstrate two significant findings. Firstly, that spatial signaling via astrocytes can relay a “learning signal” to remote synaptic sites. Results show that slow inward currents cause synchronized postsynaptic activity in remote neurons and subsequently allow Spike-Timing-Dependent Plasticity based learning to occur at the associated synapses. Secondly, that bidirectional communication between neurons and astrocytes underpins dynamic coordination between neuron clusters. Although our composite AN model is presently applied to simplified neural structures and limited to coordination between localized neurons, the principle (which embodies structural, functional and dynamic complexity), and the modeling strategy may be extended to coordination among remote neuron clusters

Astrocyte networks and intercellular calcium propagation

International audienceAstrocytes organize in complex networks through connections by gap junction channels that are regulated by extra-and intracellular signals. Calcium signals generated in individual cells, can propagate across these networks in the form of intercellular calcium waves, mediated by diffusion of second messengers molecules such as inositol 1,4,5-trisphosphate. The mechanisms underpinning the large variety of spatiotemporal patterns of propagation of astrocytic calcium waves however remain a matter of investigation. In the last decade, awareness has grown on the morphological diversity of astrocytes as well as their connections in networks, which seem dependent on the brain area, developmental stage, and the ultra-structure of the associated neuropile. It is speculated that this diversity underpins an equal functional variety but the current experimental techniques are limited in supporting this hypothesis because they do not allow to resolve the exact connectivity of astrocyte networks in the brain. With this aim we present a general framework to model intercellular calcium wave propagation in astrocyte networks and use it to specifically investigate how different network topologies could influence shape, frequency and propagation of these waves