第129回計算科学コロキウムを、11月15日（金）に開催いたします。多数のご来聴をお待ちしております。

場所：筑波大学　計算科学研究センター　会議室A
日時：2019年11月15日（金）10:00-12:00

講演タイトル：
Understand and improve uncertainties in land-surface model

講演者氏名：Dr.Fei Chen  
Research Applications Laboratory (RAL)
National Center for Atmospheric Research (NCAR), Boulder, CO, USA

使用言語：英語

アブストラクト：
　Uncertainties in land-surface models (LSMs) parameterization and parameters, in atmosphere forcing conditions used to drive LSMs severely limit their applications in understanding the role of land-atmosphere interactions on weather and climate. To assess these uncertainties, we conducted physical ensemble simulations for selected observation sites. The Noah with multiparameterization (Noah-MP) community land model was used to perform 1152 physics ensemble experiments. We will present results of using two statistical methods (natural selection and Tukey’s test) to identify the range of uncertainties associated with atmospheric forcing conditions, vegetation parameter, and sub-processes, and to mitigate those uncertainties to obtain similar performance to the ensemble mean of the “best” ensemble experiment. We will also discuss impacts of these uncertainties on regional climate simulations.

　Advancing the understanding of the nexus among food, energy, and water systems has recently emerged as a new science frontier, and the research community started modeling agricultural management in earth-system models to develop an integrated modeling tool for investigating relevant land-atmosphere interactions and agriculture sustainability issues. We discuss progress in developing of agricultural management (crop-growth, irrigation, tile drainage) models in Noah-MP and WRF-Crop. Especially, we will focus on the uncertainties in transitioning these agriculture models from field scales to continental scales.

世話人　　　：日下博幸

第128回計算科学コロキウムを、11月12日（火）に開催いたします。多数のご来聴をお待ちしております。

場所：　筑波大学　計算科学研究センター　会議室A
日時：　2019年11月12日（火）10:00-12:00

講演タイトル：
Recent progress and challenge in observing and modeling effects of urbanization

講演者氏名：　Dr.Fei Chen 
Research Applications Laboratory (RAL)
National Center for Atmospheric Research (NCAR), Boulder, CO, USA

使用言語：　英語

アブストラクト：

Today’s changing climate poses two formidable challenges: 1) the projected climate change by IPCC will lead to more frequent occurrences of heat waves, severe weather, and floods, and 2) the current trend of population increase and urban expansion is expected to continue. The combined effect of global climate change and rapid urban growth, accompanied with economic and industrial development, will inevitably make people living in cities more vulnerable to a number of the urban environmental problems. It is imperative to employ integrated modeling systems to accurately represent the land-atmosphere interactions cross various temporal and spatial scales in order to assess such problems. In this paper, we will discuss community efforts in modeling urbanization effects in regional and global models (e.g., WRF and CESM), including recent enhancements to urban hydrologic models and new modeling capabilities in addressing urban heat-island mitigation strategies. This paper will also discuss recent observation projects and the observed effects of urbanization on precipitation extremes. We will conclude this paper with a short discussion on current challenges in urban modeling.

世話人　　　：日下博幸

第127回計算科学コロキウムを、8月19日（月）に開催いたします。多数のご来聴をお待ちしております。

場所　　　　：筑波大学　計算科学研究センター　ワークショップ室
日時　　　　：2019年8月19日（月）14:00 – 15:00

講演タイトル：VERSE: Versatile Graph Embeddings from Similarity Measures (WWW’18)

講演者氏名　：Panagiotis Karras 准教授（デンマーク オーフス大）   

使用言語　　：英語

Abstract:
Embedding a web-scale information network into a low-dimensional vector space facilitates tasks such as link prediction, classification, and visualization. Past research has addressed the problem of extracting such embeddings by adopting methods from words to graphs, without defining a clearly comprehensible graph-related objective. Yet, as we show, the objectives used in past works implicitly utilize similarity measures among graph nodes. In this paper, we carry the similarity orientation of previous works to its logical conclusion; we propose VERtex Similarity Embeddings (VERSE), a simple, versatile, and memory-efficient method that derives graph embeddings explicitly calibrated to preserve the distributions of a selected vertex-to-vertex similarity measure. VERSE learns such embeddings by training a single-layer neural network. While its default, scalable version does so via sampling similarity information, we also develop a variant using the full information per vertex. Our experimental study on standard benchmarks and real-world datasets demonstrates that VERSE, instantiated with diverse similarity measures, outperforms state-of-the-art methods in terms of precision and recall in major data mining tasks and supersedes them in time and space efficiency, while the scalable sampling-based variant achieves equally good result as the non-scalable  full variant.

世話人：天笠俊之