TH3.TO4.3.ppt

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IMPLEMENTING HEMISPHERICAL SNOW WATER EQUIVALENT PRODUCT ASSIMILATING WEATHER STATION OBSERVATIONS AND SPACEBORNE MICROWAVE DATA M. Takala, K. Luojus, J. Pulliainen, C. Derksen, J. Lemmetyinen, J-P. Kärnä, J. Koskinen, B. Bojkov [email_address]

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Principle of SWE algorithm III ,[object Object],[object Object],[object Object]

SWE algorithm assesment I ,[object Object],[object Object]

SWE algorithm assesment II ,[object Object],[object Object]

SWE sensitivity I ,[object Object],[object Object]

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IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology

presentation 2Daniela Mullerova

Datta-Barua, URSI AT-RASC, 2015, Canary Islands, Ionospheric-Thermospheric St...Daniel Miladinovich

Program on Mathematical and Statistical Methods for Climate and the Earth Sys...

The Statistical and Applied Mathematical Sciences Institute

Physical processes in the earth system are modeled with mathematical representations called parameterizations. This talk will describe some of the conceptual approaches and mathematics used do describe physical parameterizations focusing on cloud parameterizations. This includes tracing physical laws to discrete representations in coarse scale models. Clouds illustrate several of the complexities and techniques common to many physical parameterizations. This includes the problem of different scales, sub-grid scale variability. Discussions of mathematical methods for dealing with the sub-grid scale will be discussed. In-exactness or indeterminate problems for both weather and climate will be discussed, including the problems of indeterminate parameterizations, and inexact initial conditions. Different mathematical methods, including the use of stochastic methods, will be described and discussed, with examples from contemporary earth system models.

Short-Term Forecasting of Surface Solar Irradiance Based on Meteosat-SEVIRI D...

Sandia National Laboratories: Energy & Climate: Renewables

In the first part of the talk, we will present a sensitivity analysis of a novel sea ice model. neXtSIM is a continuous Lagrangian numerical model that uses an elastobrittle rheology to simulate the ice response to external forces. The response of the model is evaluated in terms of simulated ice drift distances from its initial position and from the mean position of the ensemble. The simulated ice drift is decomposed into advective and diffusive parts that are characterized separately both spatially and temporally and compared to what is obtained with a free-drift model, i.e. when the ice rheology does not play any role. Overall the large-scale response of neXtSIM is correlated to the ice thickness and the wind velocity fields while the free-drift model response is mostly correlated to the wind velocity pattern only. The seasonal variability of the model sensitivity shows the role of the ice compactness and rheology at both local and Arctic scales. Indeed, the ice drift simulated by neXtSIM in summer is close to the free-drift model, while the more compact and solid ice pack is showing a significantly different mechanical and drift behavior in winter. In contrast of the free-drift model, neXtSIM reproduces the sea ice Lagrangian diffusion regimes as found from observed trajectories. The forecast capability of neXtSIM is also evaluated using a large set of real buoy’s trajectories. We found that neXtSIM performs better in simulating sea ice drift, both in terms of forecast error and as a tool to assist search-and-rescue operations. Adaptive meshes, as the one used in neXtSIM, are used to model a wide variety of physical phenomena. Some of these models, in particular those of sea ice movement, use a remeshing process to remove and insert mesh points at various points in their evolution. This represents a challenge in developing compatible data assimilation schemes, as the dimension of the state space we wish to estimate can change over time when these remeshings occur. In the second part of the talk, we highlight the challenges that such a modeling framework represents for data assimilation setup. We then describe a remeshing scheme for an adaptive mesh in one dimension. The development of advanced data assimilation methods that are appropriate for such a moving and remeshed grid is presented. Finally we discuss the extension of these techniques to two-dimensional models, like neXtSIM.

Globsnow (remote sensing swe product)Evan Konarek

Program on Mathematical and Statistical Methods for Climate and the Earth Sys...

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This problem represents an interesting opportunity for scientists and statisticians to collaborate since the problem is too big for either community. The science is not well established, although fairly sophisticated ice flow models exist. They are even becoming relevant to explain some of the complexity seen in observational data. At the same time, the complex phenomena we see in observations may not be particularly relevant to assessing the risks of significant increases in sea level rise over the near future. The talk will review what we have learned about this problem through the PISCEES SciDAC project. This problem is rich with challenges and opportunities, particularly for realigning how our two communities engage each other. The talk will review the computational, scientific, and mathematical "reality checks" that might stop any reasonable person from considering this topic further. I then will point out how each of these challenges could be mitigated if these different perspectives were better integrated.

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The melting of the West Antarctic ice sheet (WAIS) is likely to cause a significant rise in sea levels. Studying the present state of WAIS and predicting its future behavior involves the use of computer models of ice sheet dynamics as well as observational data. I will outline general statistical challenges posed by these scientific questions and data sets. This discussion is based on joint work with Yawen Guan (Penn State/SAMSI), Won Chang (U. of Cincinnati), Patrick Applegate, David Pollard (Penn State)

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Program on Mathematical and Statistical Methods for Climate and the Earth Sys...

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Extreme weather events pose great potential risk on ecosystem, infrastructure and human health. Analyzing extreme weather in the observed record (satellite, reanalysis products) and characterizing changes in extremes in simulations of future climate regimes is an important task. Thus far, extreme weather events have been typically specified by the community through hand-coded, multi-variate threshold conditions. Such criteria are usually subjective, and often there is little agreement in the community on the specific algorithm that should be used. We propose the use of a different approach: machine learning (and in particular deep learning) for solving this important problem. If human experts can provide spatio-temporal patches of a climate dataset, and associated labels, we can turn to a machine learning system to learn the underlying feature representation. The trained Machine Learning (ML) system can then be applied to novel datasets, thereby automating the pattern detection step. Summary statistics, such as location, intensity and frequency of such events can be easily computed as a post-process. We will report compelling results from our investigations of Deep Learning for the tasks of classifying tropical cyclones, atmospheric rivers and weather front events. For all of these events, we observe 90-99% classification accuracy. We will also report on progress in localizing such events: namely drawing a bounding box (of the correct size and scale) around the weather pattern of interest. Both tasks currently utilize multi-layer convolutional networks in conjunction with hyper-parameter optimization. We utilize HPC systems at NERSC to perform the optimization across multiple nodes, and utilize highly-tuned libraries to utilize multiple cores on a single node. We will conclude with thoughts on the frontier of Deep Learning and the role of humans (vis-a-vis AI) in the scientific discovery process.

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Statistical estimation and inference for large data sets require computationally efficient optimization methods. Remote sensing retrievals are, in fact, estimates of the underlying true state, and their optimization routines must necessarily make compromises in order to keep up with large data volumes. A sub-group of the Remote Sensing Working Group of the SAMSI Program on Mathematical and Statistical Methods for Climate and the Earth System is investigating how optimization in Bayesian-inspired retrievals and o_-line statistical methods could be made more computationally efficient. We will report on discussions held to-date and describe how progress in the theory of data systems research can positively impact optimization methodologies.

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We present a survey of computational and applied mathematical techniques that have the potential to contribute to the next generation of high-fidelity, multi-scale climate simulations. Examples of the climate science problems that can be investigated with more depth with these computational improvements include the capture of remote forcings of localized hydrological extreme events, an accurate representation of cloud features over a range of spatial and temporal scales, and parallel, large ensembles of simulations to more effectively explore model sensitivities and uncertainties. Numerical techniques, such as adaptive mesh refinement, implicit time integration, and separate treatment of fast physical time scales are enabling improved accuracy and fidelity in simulation of dynamics and allowing more complete representations of climate features at the global scale. At the same time, partnerships with computer science teams have focused on taking advantage of evolving computer architectures such as many-core processors and GPUs. As a result, approaches which were previously considered prohibitively costly have become both more efficient and scalable. In combination, progress in these three critical areas is poised to transform climate modeling in the coming decades.

Change detection

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Program on Mathematical and Statistical Methods for Climate and the Earth Sys...

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Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Me...grssieee

bakerca2Thesis2sChristian Baker

CLIM Program: Remote Sensing Workshop, Optimization Methods in Remote Sensing...

The Statistical and Applied Mathematical Sciences Institute

Fire PosterAnders Kipp

Program on Mathematical and Statistical Methods for Climate and the Earth Sys...

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manoj1412001

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Program on Mathematical and Statistical Methods for Climate and the Earth Sys...

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New features presentation: meteodyn WT 4.8 software - Wind Energy

Development of Physics - Based Emission Prediction Model

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20 bethke hammer_timeseries_of_spectrally_resolved_solar_irradiance_data_from...

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CLIM Program: Remote Sensing Workshop, Optimization Methods in Remote Sensing...

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TH3.TO4.3.ppt

1. IMPLEMENTING HEMISPHERICAL SNOW WATER EQUIVALENT PRODUCT ASSIMILATING WEATHER STATION OBSERVATIONS AND SPACEBORNE MICROWAVE DATA M. Takala, K. Luojus, J. Pulliainen, C. Derksen, J. Lemmetyinen, J-P. Kärnä, J. Koskinen, B. Bojkov [email_address]

6. Example of SWE product

10. SWE sensitivity II

11. SWE sensitivity III

12. SWE Animation

13.