XinZhou-1

XinZhou-1's Stars

• xiangsx/gpt4free-ts

  Providing a free OpenAI GPT-4 API ! This is a replication project for the typescript version of xtekky/gpt4free

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• hugo-toha/toha

  A Hugo theme for personal portfolio

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• jbusecke/xMIP

  Analysis ready CMIP6 data in python the easy way with pangeo tools.

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• nco/pynco

  Python bindings for NCO

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• openclimatedata/global-carbon-budget

  Global Carbon Budget Data Package

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• ai2es/WAF_ML_Tutorial_Part1

  Python code to assist in familiarizing meteorologists with machine learning

  Language:Jupyter Notebook564218

• wekeo/eo-data-visualisation

  Repository containing presentations made during the Copernicus Earth Observation Data Visualisation workshop series (16 May to 20 June 2023), including links to the resulting best practice guide.

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• eabarnes1010/target_temp_detection

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• NCAR/CESM-Lab-Tutorial

  Tutorial Jupyter Notebooks for the 'CESM-Lab' environment

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• ECMWFCode4Earth/adc-toolbox

  ESoWC 2021: Comparing Atmospheric Composition Datasets

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• swartn/cmipdata

  Processing and visualization of climate model ouput.

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• s-ragen/cmip6hack-so-project

  This is the project repository for our CMIP6 hackathon project on Southern Ocean deep convection, bottom water formation, and sea ice extent.

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• pinkychow1010/Downloading-CMIP-Data-using-Python

  This repo illustrates how public users can use cdsapi Python API to download climate model data and do some first analysis

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• matthew2e/easy-volcanic-aerosol

  An idealized forcing generator for climate simulations

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• NOC-MSM/FORTE2.0

  FORTE2.0 code prepared for GMD submission

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• pinkychow1010/VIIRS_Fire_ML_Clustering

  Detecting active fire hotspots and unsupervised clustering hotspots using VIIRS thermal remote sensing data.

  Language:Jupyter Notebook5101

• dgilford/gilford20_ligais

  Code supporting "Could the Last Interglacial Constrain Projections of Future Antarctic Ice Mass Loss and Sea-level Rise?" by Gilford et al. (2020, JGR-Earth Surface)

  Language:Jupyter Notebook4202

• pmip4/pmip_p2fvar_analyzer

  Experiment outputs are now available from the Coupled Model Intercomparison Project's 6th phase (CMIP6) and the past climate experiments defined in the Model Intercomparison Project's 4th phase (PMIP4). All of this output is freely available from the Earth System Grid Federation (ESGF). Yet there are overheads in analysing this resource that may prove complicated or prohibitive. Here we document the steps taken by ourselves to produce ensemble analyses covering past and future simulations. We outline the strategy used to curate, adjust the monthly calendar aggregation and process the information downloaded from the ESGF. The results of these steps were used to perform analysis for several of the initial publications arising from PMIP4. We provide post-processed fields for each simulation, such as climatologies and common measures of variability. Example scripts used to visualise and analyse these fields is provided for several important case studies.

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• simonrp84/Tonga_Volcano_Code

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• criess374/download_tropomi_data

  Shared code regarding S5P/TROPOMI data

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• durack1/cmip5

  Code associated with cmip analysis

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• Pederzh/Anomaly-detection-of-pollution-emissions-using-TROPOMI-satellite-data

  Monitoring established in-land human activities using pollution satellite data. Copernicus Sentinel-5P (TROPOMI) is the satellite data imagery source for remote pollution sensing on which the entire project is based. The image processing phase has taken a significant amount of effort since it was crucial to extract useful and correct information for pollution source identification and time-series analysis. Starting from the assumption that we do not know where human activities are in advance, we have developed a method for top-down detection of pollution sources in areas of interest. During our work, we have developed a Gaussian reconstruction of the emissions (GROTE) method to estimate the emissions by analyzing pollution. Once the data has been processed, we use the processed data to train a time-series machine learning method and generate data on expected pollution emissions for each identified location. Finally, our service can be integrated into the ARCOS project and raise an alert if the difference between the forecast value and the actual value exceeds the reference baseline for determining whether the pollution emissions value falls into the category of "usual" or "anomalous" behavior.

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• screbec/Siberia-fires

  Analysis of drivers of Siberian fire extremes

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• Sheridan-Tech/Update_VScode

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• ZiskinZiv/Stratospheric_water_vapor_ML

  Stratospheric water vapor and ozone analysis using Machine Learning models and Explainable AI

  Language:Python2102

• bernard-legras/STC-Australia

  Codes for processing IFS data and CALIOP data to track smoke-charged vortices in the stratosphere

  Language:Python1200

• edenau/water-vapour-unit-conversion

  💧 Converts units of a vertical profile of water vapour in Python

  Language:Jupyter Notebook1200

• JoseAgustin/sat_convert

  Conversion of ascii files ESRI or TOMS format into GrADs binary including descriptor file ctl

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• martin-king/outtenetal2022eurasiacoolingtrends

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• SNAPSI-S2S/snapsi_conda_env

  Conda environment for SNAPSI analysis

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