In an effort to provide optimal inputs to downstream modeling systems (e.g., a hydrodynamics model that simulates the water circulation of a lake), we hereby strive to enhance resolution of precipitation fields from a weather model by up to 9x. We test two super-resolution models: the enhanced super-resolution generative adversarial networks (ESRGAN) proposed in 2017, and the content adaptive resampler (CAR) proposed in 2020. Both models outperform simple bicubic interpolation, with the ESRGAN exceeding expectations for accuracy. We make several proposals for extending the work to ensure it can be a useful tool for quantifying the impact of climate change on local ecosystems while removing reliance on energy-intensive, high-resolution weather model simulations.
High-resolution climatic data are essential to many applications in environmental research. Here we develop a new semi-mechanistic downscaling approach for daily precipitation that incorporates high resolution (30 arc sec) satellite-derived cloud frequency. The downscaling algorithm incorporates orographic predictors such as wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. We apply the method to the ERA5 precipitation archive and MODIS monthly cloud cover frequency to develop a daily gridded precipitation time series in 1km resolution for the years …
Indian Institute of Tropical Meteorology（印度热带气象研究所）；Indian Institute of Science Education and Research（印度科学教育与研究学院）；Indian Meteorological Department（印度气象局）；CIRES（环境科学合作研究所）；University of Colorado Boulder（科罗拉多大学博尔德分校）；NOAA/Physical Sciences Laboratory（美国国家海洋和大气管理局物理科学实验室）；Center for Atmospheric and Ocean Sciences, Indian Institute of Science（印度科学学院大气与海洋科学中心）
Deep Learning (DL) based downscaling has become a popular tool in earth sciences recently. Increasingly, different DL approaches are being adopted to downscale coarser precipitation data and generate more accurate and reliable estimates at local (~few km or even smaller) scales. Despite several studies adopting dynamical or statistical downscaling of precipitation, the accuracy is limited by the availability of ground truth. A key challenge to gauge the accuracy of such methods is to compare the downscaled data to point-scale observations which are often unavailable at such small scales. In th…
Kumamoto University（熊本大学）；University of California, Davis（加州大学戴维斯分校）；Sun Yat-Sen University（中山大学）
Deep learning has been utilized for the statistical downscaling of climate data. Specifically, a two-dimensional (2D) convolutional neural network (CNN) has been successfully applied to precipitation estimation. This study implements a three-dimensional (3D) CNN to estimate watershed-scale daily precipitation from 3D atmospheric data and compares the results with those for a 2D CNN. The 2D CNN is extended along the time direction (3D-CNN-Time) and the vertical direction (3D-CNN-Vert). The precipitation estimates of these extended CNNs are compared with those of the 2D CNN in terms of the root-…
Predicting future climatic conditions at high spatial resolution is essential for many applications and impact studies in science. Here, we present monthly time series data on precipitation, minimum- and maximum temperature for four downscaled global circulation models. We used model output statistics in combination with mechanistic downscaling (the CHELSa algorithm) to calculate mean monthly maximum and minimum temperatures, as well as monthly precipitation at 5 km spatial resolution globally for the years 2006-2100. We validated the performance of the downscaling algorithm by comparing model…
An intercomparison of generative machine learning methods for downscaling precipitation at fine spatial scales
Earth Sciences New Zealand（新西兰地球科学）；School of Computer Science, University of Auckland（奥克兰大学计算机科学学院）；NVIDIA AI Technology Centre, NVIDIA Corporation（英伟达人工智能技术中心，英伟达公司）；ARC Centre of Excellence for Weather of the 21st Century & Climate Change Research Centre, University of New South Wales（新南威尔士大学21世纪天气卓越研究中心及气候变化研究中心）；Commonwealth Scientific and Industrial Research Organisation (CSIRO)（联邦科学与工业研究组织（CSIRO））；Laboratory for Turbulence Research in Aerospace and Combustion, Department of Mechanical and Aerospace Engineering, Monash University（莫纳什大学机械与航空航天工程系航空与燃烧湍流研究实验室）
University of Cambridge（剑桥大学）；British Antarctic Survey（英国南极调查局）；The Alan Turing Institute（艾伦·图灵研究所）
A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep learning techniques to be applied to off-the-grid spatio-temporal data. This model has a substantial advantage over existing downscaling methods in that the trained model can be used to generate multisite predictions at an arbitrary set of locations, regardless of the availability of training data. The convCNP model is shown to outperform an ensemble of existing downscaling …
Evaluating Cross-region Generalization for Wavelet-Diffusion Precipitation Downscaling
University of Florida（佛罗里达大学）；Stony Brook University（石溪大学）；University of Maryland（马里兰大学）；University of Colorado Boulder（科罗拉多大学博尔德分校）；Pacific Northwest National Laboratory（太平洋西北国家实验室）
Jackson School of Geosciences（杰克逊地球科学学院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；CIRES, University of Colorado Boulder（科罗拉多大学博尔德分校环境科学研究合作研究所）；NOAA/Physical Sciences Laboratory（美国国家海洋和大气管理局物理科学实验室）；Purdue University（普渡大学）；Cockrell School of Engineering（科克雷尔工程学院）
Urban downscaling is a link to transfer the knowledge from coarser climate information to city scale assessments. These high-resolution assessments need multiyear climatology of past data and future projections, which are complex and computationally expensive to generate using traditional numerical weather prediction models. The city of Austin, Texas, USA has seen tremendous growth in the past decade. Systematic planning for the future requires the availability of fine resolution city-scale datasets. In this study, we demonstrate a novel approach generating a general purpose operator using dee…
Statistical Downscaling of Daily Precipitation from ERA5 Atmospheric Fields to IMERG over West Africa
Laboratoire d’Océanographie et des Sciences de l’Environnement et du Climat (LOSEC)Université Assane Seck de Ziguinchor（西古尔省阿萨内·塞克大学海洋与环境气候学实验室）；Institut Pierre-Simon Laplace (IPSL), Sorbonne Université, IRD（皮埃尔-西蒙·拉普拉斯研究所，索邦大学，法国国家科学研究中心）
RainScaleGAN: a Conditional Generative Adversarial Network for Rainfall Downscaling
Institute for Applied Mathematics and Information Technologies, CNR（意大利国家研究委员会应用数学与信息技术研究所）；Institute of Atmospheric Sciences and Climate, CNR（意大利国家研究委员会大气科学与气候研究所）；Politecnico di Torino（都灵理工大学）
Global spatio-temporal downscaling of ERA5 precipitation through generative AI
Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU)（气象与气候研究所——大气环境研究（IMK-IFU））；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Institute of Meteorology and Climate Research - Atmospheric Trace Gases and Remote Sensing (IMK-ASF)（气象与气候研究所——大气痕量气体与遥感（IMK-ASF））；University of Augsburg（奥格斯堡大学）
Generating High-Resolution Regional Precipitation Using Conditional Diffusion Model
School of Computing, KAIST（韩国科学技术院计算机学院）；Data Science Group, IBS（基础科学研究院数据科学组）；Department of Climate System, PNU（釜山大学气候系统系）；Center for Climate Physics, IBS（基础科学研究院气候物理中心）
Conditional diffusion models for downscaling and bias correction of Earth system model precipitation
Technical University of Munich（慕尼黑技术大学）；Munich Climate Center（慕尼黑气候中心）；TUM School of Engineering and Design（慕尼黑技术大学工程与设计学院）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Institute of Atmospheric Physics（大气物理研究所）；University of Exeter（埃克塞特大学）
University of Oxford（牛津大学）；European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）
Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is that several key processes affecting precipitation distribution and intensity occur below the resolved scale of global weather models. Generative adversarial networks (GANs) have been demonstrated by the computer vision community to be successful at super-resolution problems, i.e., learning to add fine-scale structure to coarse images. Leinonen et al. (2020) previously applied a GAN to produce ensembles of reconstruct…
PrecipCorrDiff: A Deep Learning Framework for Generating 0.0625$^\circ$ High-Resolution IMD Gridded Precipitation Data
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Government of India（印度热带气象研究所）；Vishwakarma Institute of Technology（维斯瓦卡尔马理工学院）；India Meteorological Department, Ministry of Earth Sciences, Government of India（印度气象局）；NVIDIA Graphics India Pvt. Ltd.（英伟达图形印度私人有限公司）
Downscaling with AI reveals the large role of internal variability in fine-scale projections of climate extremes
National Institute of Water and Atmospheric Research (NIWA)（国家水气和大气研究所）；ARC Centre of Excellence for Weather of the 21st Century（21世纪天气卓越研究中心）；University of New South Wales（新南威尔士大学）；Climate Change Research Centre, University of New South Wales（新南威尔士大学气候变化研究中心）；Te Aka Mātuatua School of Science, University of Waikato（怀卡托大学理学院）
Investigating the Robustness of Extreme Precipitation Super-Resolution Across Climates
Expertise Center for Climate Extremes, University of Lausanne（极端气候专家中心，洛桑大学）；Faculty of Geosciences and Environment, University of Lausanne（地球科学与环境学院，洛桑大学）；Faculty of Business and Economics (HEC), University of Lausanne（商业经济学院（HEC），洛桑大学）
Projection of changes in extreme indices of climate variables such as temperature and precipitation are critical to assess the potential impacts of climate change on human-made and natural systems, including critical infrastructures and ecosystems. While impact assessment and adaptation planning rely on high-resolution projections (typically in the order of a few kilometers), state-of-the-art Earth System Models (ESMs) are available at spatial resolutions of few hundreds of kilometers. Current solutions to obtain high-resolution projections of ESMs include downscaling approaches that consider …
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences（印度热带气象研究所，地球科学部）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Ramrao Adik Institute of Technology（拉姆劳·阿迪克技术学院）；Defence Institute of Advanced Technology（国防高级技术研究所）
Downscaling is necessary to generate high-resolution observation data to validate the climate model forecast or monitor rainfall at the micro-regional level operationally. Dynamical and statistical downscaling models are often used to get information at high-resolution gridded data over larger domains. As rainfall variability is dependent on the complex Spatio-temporal process leading to non-linear or chaotic Spatio-temporal variations, no single downscaling method can be considered efficient enough. In data with complex topographies, quasi-periodicities, and non-linearities, deep Learning (DL…
École polytechnique fédérale de Lausanne（洛桑联邦理工学院）；Federal Office of Meteorology and Climatology MeteoSwiss（联邦气象和气候学办公室 MeteoSwiss）
Generative adversarial networks (GANs) have been recently adopted for super-resolution, an application closely related to what is referred to as "downscaling" in the atmospheric sciences: improving the spatial resolution of low-resolution images. The ability of conditional GANs to generate an ensemble of solutions for a given input lends itself naturally to stochastic downscaling, but the stochastic nature of GANs is not usually considered in super-resolution applications. Here, we introduce a recurrent, stochastic super-resolution GAN that can generate ensembles of time-evolving high-resoluti…
High resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA Climatologies at high resolution for the earths land surface areas data of downscaled model output temperature and precipitation estimates of the ERA Interim climatic reanalysis to a high resolution of 30 arc seconds. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height with a…
Super-Resolution of Radar/Raingauge-Analyzed Precipitation Using Gaussian Process Regression with a Steering Kernel
Graduate School of Life and Environmental Sciences, University of Tsukuba（筑波大学生命环境科学研究科）；Meteorological Research Institute, Japan Meteorological Agency（日本气象厅气象研究所）；Faculty of Life and Environmental Sciences, University of Tsukuba（筑波大学生命环境科学 faculties）
Monthly precipitation climatologies at 1 km resolution have been produced over the Norwegian mainland for 1981-2010. The observed station normals are interpolated over a regular grid by applying a multi-linear local regression kriging (MLRK). The statistical method aims at modeling the influence of the main geographical features, such as: latitude, longitude, elevation and sea nearness on the precipitation field at a local scale. The MLRK is composed of two steps, (i) a background precipitation field is computed through a multi-linear local regression based on the geographical information, the…
PCSDiff: Diffusion-Based Bias Correction and Super Resolution Toward Practical Operational Medium-Term Precipitation Forecast
Tsinghua University（清华大学）；Huawei Technologies Co., Ltd（华为技术有限公司）；ETH Zürich（苏黎世联邦理工学院）；Beijing Forestry University（北京林业大学）；National Climate Centre, China Meteorological Administration（中国气象局国家气候中心）；National Institute of Natural Hazards, Ministry of Emergency Management of China（中国应急管理部国家自然灾害防治研究院）
