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1. Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models

Author

Feldmann, Monika, Beucler, Tom, Gomez, Milton, and Martius, Olivia

Subjects
Physics - Atmospheric and Oceanic Physics, Computer Science - Machine Learning
Abstract

Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. The recently released suite of AI-based weather models produces medium-range forecasts within seconds, with a skill similar to state-of-the-art operational forecasts for variables on single levels. However, predicting severe thunderstorm environments requires accurate combinations of dynamic and thermodynamic variables and the vertical structure of the atmosphere. Advancing the assessment of AI-models towards process-based evaluations lays the foundation for hazard-driven applications. We assess the forecast skill of three top-performing AI-models for convective parameters at lead-times of up to 10 days against reanalysis and ECMWF's operational numerical weather prediction model IFS. In a case study and seasonal analyses, we see the best performance by GraphCast and Pangu-Weather: these models match or even exceed the performance of IFS for instability and shear. This opens opportunities for fast and inexpensive predictions of severe weather environments., Comment: Main body: 17 pages, 4 Figures. SI: 11 pages, 3 Figures. Under review at Geophysical Research Letters

Published
2024

2. Lessons Learned: Reproducibility, Replicability, and When to Stop

Author

Gomez, Milton S. and Beucler, Tom

Subjects
Computer Science - Machine Learning, Physics - Atmospheric and Oceanic Physics
Abstract

While extensive guidance exists for ensuring the reproducibility of one's own study, there is little discussion regarding the reproduction and replication of external studies within one's own research. To initiate this discussion, drawing lessons from our experience reproducing an operational product for predicting tropical cyclogenesis, we present a two-dimensional framework to offer guidance on reproduction and replication. Our framework, representing model fitting on one axis and its use in inference on the other, builds upon three key aspects: the dataset, the metrics, and the model itself. By assessing the trajectories of our studies on this 2D plane, we can better inform the claims made using our research. Additionally, we use this framework to contextualize the utility of benchmark datasets in the atmospheric sciences. Our two-dimensional framework provides a tool for researchers, especially early career researchers, to incorporate prior work in their own research and to inform the claims they can make in this context., Comment: Main Text: 6 pages with 2 Figures Supplementary Text: 7 Pages with 3 figures and 3 tables Submitted to AMS AIES Lessons Learned (https://journals.ametsoc.org/view/journals/aies/aies-overview.xml)

Published
2024

3. Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery

Author

S., Saranya Ganesh, Beucler, Tom, Tam, Frederick Iat-Hin, Gomez, Milton S., Runge, Jakob, and Gerhardus, Andreas

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Physics - Atmospheric and Oceanic Physics, Physics - Computational Physics
Abstract

Robust feature selection is vital for creating reliable and interpretable Machine Learning (ML) models. When designing statistical prediction models in cases where domain knowledge is limited and underlying interactions are unknown, choosing the optimal set of features is often difficult. To mitigate this issue, we introduce a Multidata (M) causal feature selection approach that simultaneously processes an ensemble of time series datasets and produces a single set of causal drivers. This approach uses the causal discovery algorithms PC1 or PCMCI that are implemented in the Tigramite Python package. These algorithms utilize conditional independence tests to infer parts of the causal graph. Our causal feature selection approach filters out causally-spurious links before passing the remaining causal features as inputs to ML models (Multiple linear regression, Random Forest) that predict the targets. We apply our framework to the statistical intensity prediction of Western Pacific Tropical Cyclones (TC), for which it is often difficult to accurately choose drivers and their dimensionality reduction (time lags, vertical levels, and area-averaging). Using more stringent significance thresholds in the conditional independence tests helps eliminate spurious causal relationships, thus helping the ML model generalize better to unseen TC cases. M-PC1 with a reduced number of features outperforms M-PCMCI, non-causal ML, and other feature selection methods (lagged correlation, random), even slightly outperforming feature selection based on eXplainable Artificial Intelligence. The optimal causal drivers obtained from our causal feature selection help improve our understanding of underlying relationships and suggest new potential drivers of TC intensification., Comment: 11 pages, 4 figures, 1 table, Supporting Information, Accepted for an oral presentation at the Climate Informatics 2023

Published
2023

4. Enhancing generalizability of data-driven urban flood models by incorporating contextual information.

Author

Cache, Tabea, Gomez, Milton Salvador, Beucler, Tom, Blagojevic, Jovan, Leitao, João Paulo, and Peleg, Nadav

Abstract

Fast urban pluvial flood models are necessary for a range of applications, such as near real-time flood nowcasting or processing large rainfall ensembles for uncertainty analysis. Data-driven models can help overcome the long computational time of traditional flood simulation models, and the state-of-the-art models have shown promising accuracy. Yet the lack of generalizability of data-driven urban pluvial flood models to both unseen rainfall and distinctively different terrain, at the fine resolution required for urban flood mapping, still limits their application. These models usually adopt a patch-based framework to overcome multiple bottlenecks, such as data availability and computational and memory constraints. However, this approach does not incorporate contextual information of the terrain surrounding the small image patch (typically 256m×256m). We propose a new deep-learning model that maintains the high-resolution information of the local patch and incorporates a larger surrounding area to increase the visual field of the model with the aim of enhancing the generalizability of data-driven urban pluvial flood models. We trained and tested the model in the city of Zurich (Switzerland), at a spatial resolution of 1 m, for 1 h rainfall events at 5 min temporal resolution. We demonstrate that our model can faithfully represent flood depths for a wide range of rainfall events, with peak rainfall intensities ranging from 42.5 to 161.4 mmh-1. Then, we assessed the model's terrain generalizability in distinct urban settings, namely, Lucerne (Switzerland) and Singapore. The model accurately identifies locations of water accumulation, which constitutes an improvement compared to other deep-learning models. Using transfer learning, the model was successfully retrained in the new cities, requiring only a single rainfall event to adapt the model to new terrains while preserving adaptability across diverse rainfall conditions. Our results indicate that by incorporating contextual terrain information into the local patches, our proposed model effectively simulates high-resolution urban pluvial flood maps, demonstrating applicability across varied terrains and rainfall events. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Ecosistemas comunicativos para enseñar y aprender con mediaciones tecnológicas

Author

Bravo Gomez, Milton Camilo and Pacheco Lora, Martha Cecilia

Subjects
Ecosistemas comunicativos, Educación, Communication, Educación mediada, Communicative ecosystems, Mediated education, Tendencias, Trends, Comunicación, Education
Abstract

Esta monografía concibe la importancia de las tendencias en ecosistemas comunicativos en educación, los cuales ayudan a determinar cuáles son los mejores mecanismos y herramientas para que la enseñanza, realizada por medio de los canales tecnológicos, sea de calidad. Debido a los nuevos acontecimientos sociales, culturales y económicos y políticos, estas ecologías comunicativas desplegaron en gran manera, en comparación a como se operaban antes. Hoy en día, estas tienden a ser muy manejadas a nivel formativo. Así, el presente estudio se llevó a cabo mediante la búsqueda y recuperación avanzada de investigaciones que, bajo una minuciosa selección de catalogación documental, conformó una muestra teórica de referencias confiables para identificar con calidad y transparencia antecedentes sobre la configuración comunicativa de la enseñanza y aprendizaje con mediaciones tecnológicas. Finalmente, se dedujeron conclusiones que determinan que la educación mediada está teniendo un gran adelanto con relación a las diferentes fuentes de comunicación que son priorizadas para usos pedagógicos, y más aún porque la implementación de nuevos recursos para la comunicación educativa obligó a los profesores y estudiantes a capacitarse para utilizar más tecnologías debido a la vivencia de una época contemporánea de transformación cognitiva - audiovisual - digital. Dedicatoria .......................................................................................................................... 2 Agradecimientos ................................................................................................................. 3 Resumen .............................................................................................................................. 7 Abstract ............................................................................................................................... 8 Introducción ........................................................................................................................ 9 1. Objetivos ................................................................................................................. 12 1.1. Objetivo general .................................................................................................. 12 1.1. Objetivos específicos........................................................................................... 12 2. Antecedentes ........................................................................................................... 13 3. Desarrollo temático ................................................................................................. 15 3.1. Marco conceptual ................................................................................................ 15 3.1.1. Ecosistemas Comunicativos en la educación mediada ................................. 15 3.1.2. Ecosistemas ................................................................................................... 16 3.1.3. Comunicación ............................................................................................... 17 3.1.4. La evolución del soporte didáctico en la interacción de la tecnología con los docentes y estudiantes dentro del ecosistema comunicativo ................................................. 18 3.1.5. El ser humano y la comunicación ................................................................. 18 3.1.6. Comunicación y educación ........................................................................... 20 3.2. Bases conceptuales y fundamentos teóricos del potencial de la educación mediante ecosistemas tecnomediados ....................................................................................... 25 3.2.1. Los ecosistemas tecnomediados .................................................................... 25 3.2.2. La página web ............................................................................................... 27 3.2.3. Aulas virtuales ............................................................................................... 29 3.2.4. Las redes sociales .......................................................................................... 29 3.2.5. Las herramientas audiovisuales..................................................................... 30 3.3. Modelo didáctico de un ecosistema .................................................................... 31 3.4. El rol del estudiante ............................................................................................. 32 3.5. El rol del docente................................................................................................. 33 3.6. La confrontación teórica acerca de la mediación tecnológica en el ecosistema educativo ........................................................................................................................... 34 3.7. Viabilidad de los ecosistemas comunicativos en la educación mediada ............. 35 4. Conclusión .............................................................................................................. 38 5. Referencias bibliográficas ....................................................................................... 42 Pregrado Licenciado(a) en Informática Monografías

Published
2023

9. Panorama de la parteras. : El caso de la partería en el Pacífico colombiano

Author

Barona, Rocío Carvajal, Gómez, Milton Cesar Gómez, Valencia, Martha Cecilia Navarro, Arévalo, María Teresa Varela, Valencia, Elena Sofía Angulo, Acuña, Nathalia Restrepo, Barona, Rocío Carvajal, Gómez, Milton Cesar Gómez, Valencia, Martha Cecilia Navarro, Arévalo, María Teresa Varela, Valencia, Elena Sofía Angulo, and Acuña, Nathalia Restrepo

Published
2019
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10. Soft Soil Remediation with Permeable Low-Density Cellular Concrete (PLDCC)

Author

Sutmoller Nico, Gomez Milton, and Kevern John

Subjects
Engineering (General). Civil engineering (General), TA1-2040
Abstract

This paper addresses the feasibility of using permeable low-density cellular concrete (PLDCC) in soft soil remediation applications. In discussing this feasibility, the paper will review and evaluate data from recent studies conducted at the University of Missouri Kansas City related to the physical properties of PLDCC, including permeability, infiltration, and void structure. This paper will cover the most typical applications for the utilization of PLDCC and take a brief look at the constructability and cost considerations. Three specific case studies are examined, including a wall abutment project in Bellingham, WA, a structural foundation project in New Orleans, LA, and a sinkhole remediation project in Chester County, PA. All three of these case studies utilized 25 to 35 pcf (pounds per cubic foot) (400 kg/m3 to 560 kg/m3) PLDCC for soft soil remediation.

Published
2019
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