Machine Learning Framework for High-Resolution Air Temperature Downscaling Using LiDAR-Derived Urban Morphological Features

• URL: http://arxiv.org/abs/2409.02120v1
• Date: Sat, 31 Aug 2024 12:59:21 GMT
• Title: Machine Learning Framework for High-Resolution Air Temperature Downscaling Using LiDAR-Derived Urban Morphological Features
• Authors: Fatemeh Chajaei, Hossein Bagheri,
• Abstract summary: This study presents a data-driven framework for downscaling air temperature using datasets generated by UrbClim. To extract urban morphological features, first a three-dimensional building model was created using LiDAR data and deep learning models. The framework successfully estimated air temperatures at higher resolutions, enabling the identification of local air temperature patterns at street level.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Climate models lack the necessary resolution for urban climate studies, requiring computationally intensive processes to estimate high resolution air temperatures. In contrast, Data-driven approaches offer faster and more accurate air temperature downscaling. This study presents a data-driven framework for downscaling air temperature using publicly available outputs from urban climate models, specifically datasets generated by UrbClim. The proposed framework utilized morphological features extracted from LiDAR data. To extract urban morphological features, first a three-dimensional building model was created using LiDAR data and deep learning models. Then, these features were integrated with meteorological parameters such as wind, humidity, etc., to downscale air temperature using machine learning algorithms. The results demonstrated that the developed framework effectively extracted urban morphological features from LiDAR data. Deep learning algorithms played a crucial role in generating three-dimensional models for extracting the aforementioned features. Also, the evaluation of air temperature downscaling results using various machine learning models indicated that the LightGBM model had the best performance with an RMSE of 0.352{\deg}K and MAE of 0.215{\deg}K. Furthermore, the examination of final air temperature maps derived from downscaling showed that the developed framework successfully estimated air temperatures at higher resolutions, enabling the identification of local air temperature patterns at street level. The corresponding source codes are available on GitHub: https://github.com/FatemehCh97/Air-Temperature-Downscaling.

Related papers

• Enhancing Weather Predictions: Super-Resolution via Deep Diffusion Models [0.0]
  This study investigates the application of deep-learning diffusion models for the super-resolution of weather data. We present a methodology for transforming low-resolution weather data into high-resolution outputs.
  arXiv  Detail & Related papers  (2024-06-06T14:15:12Z)
• Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method [66.80344502790231]
  We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Our proposed method outperforms other specially designed baseline models on multiple surface variables.
  arXiv  Detail & Related papers  (2024-01-22T14:02:56Z)
• Generating High-Resolution Regional Precipitation Using Conditional Diffusion Model [7.784934642915291]
  This paper presents a deep generative model for downscaling climate data, specifically precipitation on a regional scale. We employ a denoising diffusion probabilistic model conditioned on multiple LR climate variables. Our results demonstrate significant improvements over existing baselines, underscoring the effectiveness of the conditional diffusion model in downscaling climate data.
  arXiv  Detail & Related papers  (2023-12-12T09:39:52Z)
• Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation [48.26243807950606]
  Two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared. generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets.
  arXiv  Detail & Related papers  (2023-07-10T08:20:45Z)
• An evaluation of deep learning models for predicting water depth evolution in urban floods [59.31940764426359]
  We compare different deep learning models for prediction of water depth at high spatial resolution. Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model. Our results show that the deep learning models present in general lower errors compared to the other methods.
  arXiv  Detail & Related papers  (2023-02-20T16:08:54Z)
• Climate Intervention Analysis using AI Model Guided by Statistical Physics Principles [6.824166358727082]
  We propose a novel solution by utilizing a principle from statistical physics known as the Fluctuation-Dissipation Theorem (FDT) By leveraging, we are able to extract information encoded in a large dataset produced by Earth System Models. Our model, AiBEDO, is capable of capturing the complex, multi-timescale effects of radiation perturbations on global and regional surface climate.
  arXiv  Detail & Related papers  (2023-02-07T05:09:10Z)
• ClimaX: A foundation model for weather and climate [51.208269971019504]
  ClimaX is a deep learning model for weather and climate science. It can be pre-trained with a self-supervised learning objective on climate datasets. It can be fine-tuned to address a breadth of climate and weather tasks.
  arXiv  Detail & Related papers  (2023-01-24T23:19:01Z)
• Learning to Simulate Realistic LiDARs [66.7519667383175]
  We introduce a pipeline for data-driven simulation of a realistic LiDAR sensor. We show that our model can learn to encode realistic effects such as dropped points on transparent surfaces. We use our technique to learn models of two distinct LiDAR sensors and use them to improve simulated LiDAR data accordingly.
  arXiv  Detail & Related papers  (2022-09-22T13:12:54Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• Comparison between ARIMA and Deep Learning Models for Temperature Forecasting [0.0]
  This paper compares ARIMA (Auto Regressive Integrated Moving Average) model and deep learning models to forecast temperature. According to the experimental results deep learning model was able to perform better than the traditional ARIMA methodology.
  arXiv  Detail & Related papers  (2020-11-09T14:21:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.