Improving Precipitation Estimation Using Convolutional Neural Network

Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. We introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation-related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation-related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation-related parameterization schemes using a data-driven approach

Information-Driven Housing

This paper suggests a new information-driven framework is needed to help consumers evaluate the sustainability of their housing options. The paper provides an outline of this new framework and how it would work

High-Throughput System for the Early Quantification of Major Architectural Traits in Olive Breeding Trials Using UAV Images and OBIA Techniques

The need for the olive farm modernization have encouraged the research of more efficient crop management strategies through cross-breeding programs to release new olive cultivars more suitable for mechanization and use in intensive orchards, with high quality production and resistance to biotic and abiotic stresses. The advancement of breeding programs are hampered by the lack of efficient phenotyping methods to quickly and accurately acquire crop traits such as morphological attributes (tree vigor and vegetative growth habits), which are key to identify desirable genotypes as early as possible. In this context, an UAV-based high-throughput system for olive breeding program applications was developed to extract tree traits in large-scale phenotyping studies under field conditions. The system consisted of UAV-flight configurations, in terms of flight altitude and image overlaps, and a novel, automatic, and accurate object-based image analysis (OBIA) algorithm based on point clouds, which was evaluated in two experimental trials in the framework of a table olive breeding program, with the aim to determine the earliest date for suitable quantifying of tree architectural traits. Two training systems (intensive and hedgerow) were evaluated at two very early stages of tree growth: 15 and 27 months after planting. Digital Terrain Models (DTMs) were automatically and accurately generated by the algorithm as well as every olive tree identified, independently of the training system and tree age. The architectural traits, specially tree height and crown area, were estimated with high accuracy in the second flight campaign, i.e. 27 months after planting. Differences in the quality of 3D crown reconstruction were found for the growth patterns derived from each training system. These key phenotyping traits could be used in several olive breeding programs, as well as to address some agronomical goals. In addition, this system is cost and time optimized, so that requested architectural traits could be provided in the same day as UAV flights. This high-throughput system may solve the actual bottleneck of plant phenotyping of "linking genotype and phenotype," considered a major challenge for crop research in the 21st century, and bring forward the crucial time of decision making for breeders

Are Delayed Issues Harder to Resolve? Revisiting Cost-to-Fix of Defects throughout the Lifecycle

Many practitioners and academics believe in a delayed issue effect (DIE); i.e. the longer an issue lingers in the system, the more effort it requires to resolve. This belief is often used to justify major investments in new development processes that promise to retire more issues sooner. This paper tests for the delayed issue effect in 171 software projects conducted around the world in the period from 2006--2014. To the best of our knowledge, this is the largest study yet published on this effect. We found no evidence for the delayed issue effect; i.e. the effort to resolve issues in a later phase was not consistently or substantially greater than when issues were resolved soon after their introduction. This paper documents the above study and explores reasons for this mismatch between this common rule of thumb and empirical data. In summary, DIE is not some constant across all projects. Rather, DIE might be an historical relic that occurs intermittently only in certain kinds of projects. This is a significant result since it predicts that new development processes that promise to faster retire more issues will not have a guaranteed return on investment (depending on the context where applied), and that a long-held truth in software engineering should not be considered a global truism.Comment: 31 pages. Accepted with minor revisions to Journal of Empirical Software Engineering. Keywords: software economics, phase delay, cost to fi

Spitzer Science Center within an Enterprise Architecture

The Spitzer Science Center’s (SSC) evolutionary development approach, coupled with a flexible, scaleable hardware and software architecture has been key in Spitzer’s ability to handle an explosion of data products, evolving data definitions, and changing data quality requirements. Spitzer is generating (depending on the campaign and instrument) about 10 TB of pre-archive data every 14 to 20 days. This generally reduces to between 3 TB and 6 TB of standard products, again depending on the campaign and instrument. This paper will discuss (1) the Spitzer Science Center’s responses to evolving data, quality, and processing requirements and (2) how robust or not was the original architecture to allow Spitzer to accommodate on-going change

Purposive Teaching Styles for Transdisciplinary AEC Education: A Diagnostic Learning Styles Questionnaire

With the progressive globalisation trend within the Architecture, Engineering, and Construction (AEC) industry, transdisciplinary education and training is widely acknowledged as being one of the key factors for leveraging AEC organisational success. Conventional education and training delivery approaches within AEC therefore need a paradigm shift in order to be able to address the emerging challenges of global practices. This study focuses on the use of Personalised Learning Environments (PLEs) to specifically address learners’ needs and preferences (learning styles) within managed Virtual Learning Environments (VLEs). This research posits that learners can learn better (and be more readily engaged in managed learning environments) with a bespoke PLE, in which the deployment of teaching and learning material is augmented towards their individual needs. In this respect, there is an exigent need for the Higher Educational Institutions (HEIs) to envelop these new approaches into their organisational learning strategy. However, part of this process requires decision-makers to fully understand the core nuances and interdependencies of functions and processes within the organisation, along with Critical Success Factors (CSFs) and barriers. This paper presents findings from the development of a holistic conceptual Diagnostic Learning Styles Questionnaire (DLSQ) Framework, comprised of six interrelated dependencies (i.e. Business Strategy, Pedagogy, Process, Resources, Systems Development, and Evaluation). These dependencies influence pedagogical effectiveness. These finding contribute additional understanding to the intrinsic nature of pedagogy in leveraging transdisciplinary AEC training within organisations (to improve learner effectiveness). This framework can help organisations augment and align their strategic priorities to learner-specific traits