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Dataset for "On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates"
<h1>Code and Dataset for "On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates"</h1> <p>This dataset accompanies the research paper titled <strong>"On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates"</strong>, currently under review for the AGU Journal GRL. The study introduces a novel Regional Climate Model (RCM) emulator focusing on high-resolution climate downscaling for the New Zealand region. For additional insights and access to the codebase utilized in this research, please refer to our <a href="https://github.com/nram812/On-the-Extrapolation-of-Generative-Adversarial-Networks-for-downscaling-precipitation-extremes">Github Repository</a>.</p> <p>The code can also be found as a ".zip" file: *On-the-Extrapolation-of-Generative-Adversarial-Networks-for-downscaling-precipitation-extremes-main. </p> <h2>Aims</h2> <p>Our study focuses on two important gaps in the literature regarding the extrapolation of empirical downscaling algorithms. First, we examine how well relationships learned from a historical period extrapolate to future unobserved climates. We compare two widely used algorithms, a GAN and a deterministic CNN baseline, that use a similar architecture (i.e. convolutional layers) trained in a model-as-truth framework to downscale daily precipitation over New Zealand. We evaluate their accuracy in capturing climate change signals in mean and extreme precipitation. Second, we explore whether training on future vs. only historical periods combined with different-sized training datasets can improve extrapolation skill. </p> <h2>Geographic Focus</h2> <p>Our research focuses only on the New Zealand Region (165°E-184°W, 33°S-51°S).</p> <p> </p> <h2>Data Overview</h2> <h3>Training and Evaluation Data</h3> <p>The training data used in this study (for our RCM emulator) spans the historical period and future period (SSP370) of simulation. It comprises daily accumulated precipitation as the primary target variable, alongside large-scale predictor variables. </p> <ul> <li> <p><strong>Resolution:</strong> The target variable is presented at a 12km resolution, reflecting the highest resolution face of RCM for the New Zealand region. Predictor variables are coarsened to a 1.5-degree resolution from original CCAM outputs using conservative interpolation. </p> </li> <li> <p><strong>Period Coverage:</strong></p> <ul> <li>Training Data: 1960-2100 (Depending on Experiment, see Table 1 for list of experiment configurations)</li> <li>Validation Data: 1985-2014 + 2070-2099 (to compute the climate change signal)</li> </ul> </li> <li> <p><strong>Models:</strong></p> <ul> <li>Training on: ACCESS-CM2</li> <li>Validated on: EC-Earth3, NorESM2-MM, CNRM-CM6-1, AWI-MR-1 </li> </ul> </li> </ul> <h3>File Structure</h3> <ul> <li> <p><strong>Training Data:</strong></p> <ul> <li>Target/Ground Truth (Y): <code>target_ACCESS-CM2_hist_ssp370_pr.nc</code></li> <li>Predictor (X): <code>predictor_ACCESS-CM2_hist_ssp370.nc</code></li> </ul> </li> <li> <p><strong>Evaluation Data:<br></strong>All other GCMs can be accessed in one single file, predictor and target variables have the dimensions (time, lat, lon, GCM).</p> <ul> <li>Target/Ground Truth (Y): <code>Other_GCMs_hist_SSP370_target_fields_pr.nc</code></li> <li>Predictor (X): <code>Other_GCMs_hist_SSP370_predictor_fields.nc</code></li> </ul> </li> </ul> <h2>Methodological Insights</h2> <ul> <li> <p><strong>Regional Climate Model</strong>, Our Regional Climate Model training data is from the Conformal Cubic Atmospheric Model (CCAM) which is a global non-hydrostatic atmospheric model renowned for its variable-resolution cubic grid. . For more information about CCAM, please see the following <a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023JD038530">paper</a>.</p> </li> <li> <p><strong>Predictor and Target Variables:</strong> Daily-averaged large-scale prognostic variables, including zonal wind, meridional wind, temperature, and specific humidity, are employed as predictors at the 500mb and 850mb pressure levels. These are normalized (see the GitHub repository for the mean and standard deviation fields). Precipitation is taken as is from CCAM and accumulated for each given day. Static predictors are also used in our model, which is stored in a GitHub repository.</p> </li> <li> <p><strong>Training Framework:</strong> Our dataset benefits from the "perfect framework" training strategy, which uses CCAM-coarsened predictor variables. For more information about the perfect and imperfect training frameworks, see the following <a title="review" href="https://journals.ametsoc.org/view/journals/aies/3/2/AIES-D-23-0066.1.xml">review</a></p> </li> </ul> <table> <tbody> <tr> <td> <p><strong>Algorithm</strong></p> </td> <td> <p><strong>Training Data</strong></p> </td> <td> <p><strong>Period</strong></p> </td> </tr> <tr> <td> <p>Deterministic Baseline</p> </td> <td> <p>Historical</p> </td> <td> <p>1960-2014 (~21,000 days)</p> </td> </tr> <tr> <td> <p>Deterministic Baseline</p> </td> <td> <p>Future (SSP370)</p> </td> <td> <p>2044-2099 (~21,000 days)</p> </td> </tr> <tr> <td> <p>Deterministic Baseline</p> </td> <td> <p>Historical and Future (SSP370)</p> </td> <td> <p>1960-2099 (~51,000 days)</p> </td> </tr> <tr> <td> <p>Residual GAN</p> </td> <td> <p>Historical</p> </td> <td> <p>1960-2014</p> </td> </tr> <tr> <td> <p>Residual GAN</p> </td> <td> <p>Future (SSP370)</p> </td> <td> <p>2044-2099</p> </td> </tr> <tr> <td> <p>Residual GAN</p> </td> <td> <p>Historical and Future (SSP370)</p> </td> <td> <p>1960-2099</p> </td> </tr> </tbody> </table> <p><strong>Table 1:</strong> The six RCM emulator experiments performed in this study.</p>
Downscaled North American Multi-Model Ensemble Forecast for the Pacific Northwest USA
<h1>Downscaled North American Multi-Model Ensemble Forecast of Meteorological Variables for the Pacific Northwest</h1> <p>Monthly retrospective hindcasts (1982-2010) and forecasts (2011-2020) of temperature and precipitation are acquired for the Pacific Northwest region of the United States from five models (CFSv2, NASA GEOS5v2, CanCM4i, GEM-NEMO, and NCAR-CCSM) participating in the North American Multi-Model Ensemble project <a href="https://www.zotero.org/google-docs/?WlFE7n">(Kirtman et al., 2014)</a>. These models, detailed in Table 1 with more recent information available in <a href="https://www.zotero.org/google-docs/?PjZZHw">(Becker et al., 2022)</a>, are initialized monthly to provide a forecast of 0-9 months at a 1.0̊ × 1.0̊ spatial resolution. The multi-model ensemble mean (ENSMEAN) is then generated for each initialization by simply averaging all considered models and their ensemble members. Monthly ENSMEAN forecast is bias-corrected and spatially downscaled to 1/24th degree using the methodology described in <a href="https://www.zotero.org/google-docs/?jwPLzP">Wood et al. (2002)</a> and <a href="https://www.zotero.org/google-docs/?2b3lkj">Barbero et al. (2017)</a> using historical meteorological data <a href="https://www.zotero.org/google-docs/?zN8gKh">(gridMET; Abatzoglou, 2013)</a> as the baseline. Then, the downscaled ENSMEAN data are temporally disaggregated to daily timescales using an analog approach. The closest analog month for the ENSMEAN forecast is found from the gridMET dataset by minimizing the root mean square error (RMSE) of monthly gridMET and forecast precipitation (excluding gridMET data for the target month). Other daily meteorological variables (such as maximum and minimum temperature, maximum and minimum relative humidity, wind speed, and specific humidity) are extracted from the same analog month to use as input for the coupled crop-hydrology model. As a last step to the analog approach, the process corrects the bias between the forecast and analog month to ensure that monthly mean temperature and accumulated precipitation match those of the original forecast. </p> <p>Table 1. List of NMME models used to create Ensemble Mean.</p> <div> <table> <tbody> <tr> <td>Model </td> <td>Model Expansion </td> <td>Ensemble Size </td> <td>References</td> </tr> <tr> <td>NCEP- CFSv2 </td> <td>Climate Forecast System, version 2 </td> <td>24 </td> <td><a href="https://www.zotero.org/google-docs/?XTr15U">(Saha et al., 2014)</a></td> </tr> <tr> <td>NASA GEOS5v2</td> <td>Goddard Earth Observing System, version 5 </td> <td>4</td> <td><a href="https://www.zotero.org/google-docs/?fQzQZL">(Molod et al., 2020)</a></td> </tr> <tr> <td>CanCM4i </td> <td>Fourth Generation Canadian Coupled Global Climate Model </td> <td>10 </td> <td><a href="https://www.zotero.org/google-docs/?AoXCO8">(Merryfield et al., 2013)</a></td> </tr> <tr> <td>GEM - NEMO </td> <td>Global Environmental Multiscale Model – Nucleus for European Modelling of the Ocean </td> <td>10 </td> <td><a href="https://www.zotero.org/google-docs/?N4JwM9">(Lin et al., 2020)</a></td> </tr> <tr> <td>NCAR - CCSM </td> <td>Community Climate System Model </td> <td>10 </td> <td><a href="https://www.zotero.org/google-docs/?NMkImR">(Kirtman & Min, 2009)</a></td> </tr> </tbody> </table> </div> <p>The dataset has *.mat files which are MATLAB data files. </p> <h3>References</h3> <ol> <li> <p>Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121–131. <a href="https://doi.org/10.1002/joc.3413">https://doi.org/10.1002/joc.3413</a></p> </li> <li> <p>Barbero, R., Abatzoglou, J. T., & Hegewisch, K. C. (2017). Evaluation of Statistical Downscaling of North American Multimodel Ensemble Forecasts over the Western United States. Weather and Forecasting, 32(1), 327–341. https://doi.org/10.1175/WAF-D-16-0117.1</p> </li> <li> <p>Becker, E. J., Kirtman, B. P., L’Heureux, M., Muñoz, Á. G., & Pegion, K. (2022). A Decade of the North American Multimodel Ensemble (NMME): Research, Application, and Future Directions. Bulletin of the American Meteorological Society, 103(3), E973–E995. <a href="https://doi.org/10.1175/BAMS-D-20-0327.1">https://doi.org/10.1175/BAMS-D-20-0327.1</a></p> </li> <li> <p>Kirtman, B. P., & Min, D. (2009). Multimodel Ensemble ENSO Prediction with CCSM and CFS. Monthly Weather Review, 137(9), 2908–2930. https://doi.org/10.1175/2009MWR2672.1</p> </li> <li> <p>Kirtman, B. P., Min, D., Infanti, J. M., Kinter, J. L., Paolino, D. A., Zhang, Q., Dool, H. van den, Saha, S., Mendez, M. P., Becker, E., Peng, P., Tripp, P., Huang, J., DeWitt, D. G., Tippett, M. K., Barnston, A. G., Li, S., Rosati, A., Schubert, S. D., … Wood, E. F. (2014). The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction. Bulletin of the American Meteorological Society, 95(4), 585–601. <a href="https://doi.org/10.1175/BAMS-D-12-00050.1">https://doi.org/10.1175/BAMS-D-12-00050.1</a></p> </li> <li> <p>Lin, H., Merryfield, W. J., Muncaster, R., Smith, G. C., Markovic, M., Dupont, F., Roy, F., Lemieux, J.-F., Dirkson, A., Kharin, V. V., Lee, W.-S., Charron, M., & Erfani, A. (2020). The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2). Weather and Forecasting, 35(4), 1317–1343. <a href="https://doi.org/10.1175/WAF-D-19-0259.1">https://doi.org/10.1175/WAF-D-19-0259.1</a></p> </li> <li> <p>Merryfield, W. J., Lee, W.-S., Boer, G. J., Kharin, V. V., Scinocca, J. F., Flato, G. M., Ajayamohan, R. S., Fyfe, J. C., Tang, Y., & Polavarapu, S. (2013). The Canadian Seasonal to Interannual Prediction System. Part I: Models and Initialization. Monthly Weather Review, 141(8), 2910–2945. <a href="https://doi.org/10.1175/MWR-D-12-00216.1">https://doi.org/10.1175/MWR-D-12-00216.1</a></p> </li> <li> <p>Molod, A., Hackert, E., Vikhliaev, Y., Zhao, B., Barahona, D., Vernieres, G., Borovikov, A., Kovach, R. M., Marshak, J., Schubert, S., Li, Z., Lim, Y.-K., Andrews, L. C., Cullather, R., Koster, R., Achuthavarier, D., Carton, J., Coy, L., Friere, J. L. M., … Pawson, S. (2020). GEOS-S2S Version 2: The GMAO High-Resolution Coupled Model and Assimilation System for Seasonal Prediction. Journal of Geophysical Research: Atmospheres, 125(5), e2019JD031767. https://doi.org/10.1029/2019JD031767</p> </li> <li> <p>Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.-T., Chuang, H., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., Dool, H. van den, Zhang, Q., Wang, W., Chen, M., & Becker, E. (2014). The NCEP Climate Forecast System Version 2. Journal of Climate, 27(6), 2185–2208. https://doi.org/10.1175/JCLI-D-12-00823.1</p> </li> <li> <p>Wood, A. W., Maurer, E. P., Kumar, A., & Lettenmaier, D. P. (2002). Long-range experimental hydrologic forecasting for the eastern United States. Journal of Geophysical Research: Atmospheres, 107(D20), ACL 6-1-ACL 6-15. https://doi.org/10.1029/2001JD000659</p> </li> </ol>
Comparing the Influence of Global Warming and Urban Anthropogenic Heat on Extreme Precipitation in Urbanized Pearl River Delta Area Based on WRF Dynamical Downscaling
<p>The simulation outputs from the Weather Research and Forecasting (WRF) v3.8.1 coupled with single layer urban canopy model from three experiments (HIST_AH300, HIST_AH0, and RCP85_AH300).</p> <p>Variables include hourly precipitation, wind, specific humidity, relative humidity, convective available potential energy, convective inhibition, temperature, model height, land use land cover, and topography.</p> <p> </p>
Downscaling climatic data 2021-2100 in North Korea
<p>Please refer to the 'Readme' file.</p>
Earlier onset and shortened Meiyu season during the Last Interglacial based on dynamical downscaling simulations
<p>This dataset includes the model outputs that could be used to reproduce the figures in our paper. A detailed description of the dataset is given in the word file.</p>
Multivariate projected ensemble of "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain"
<p>Multivariate statistically downscaled projected ensemble for different combinations of GCM-RCMs of both stochastic and deterministic runs for eight historical runs, eight RCP85 runs and one RCP26 run. Selection of good perfoming GCM-RCM combinations in NetCDF format. Variables: precipitation, water vapour pressure, radiation, wind speed, and, maximum, mean and minimum temperature.</p>
Supporting material for PyESDv1.0.1 An open-source Python framework for empirical-statistical downscaling of climate information
<p>The nature and severity of climate change impacts varies significantly from region to region. Consequently, high-resolution climate information is needed for meaningful impact assessments and the design of mitigation strategies. This demand has led to an increase in the coupling of Empirical Statistical Downscaling (ESD) models to General Circulation Model (GCM) simulations of future climate. Here, we present a new open-source Python package (<em>pyESD; </em>github.com/Dan-Boat/PyESD) that implements several Perfect Prognosis ESD (PP-ESD) methods and the whole downscaling cycle. The latter includes routines for data preparation, predictor selection and construction, model selection and training, evaluation, utility tools for relevant statistical tests, visualization, and more. The package includes a collection of well-established Machine Learning algorithms and allows the user to choose a variety of estimators, cross-validation schemes, objective function measures, hyperparameter optimization, etc., in relatively few lines of code. The package is highly modular and flexible and allows quick and reproducible downscaling of any climate information, such as precipitation, temperature, wind speed, or even glacial retreat. The dataset presented here serves as supporting material for the package description and evaluation manuscript</p>
Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks [datasets]
<p>Outputs used in:</p> <p><em>van der Meer, M., de Roda Husman, S., Lhermitte, S.: </em>Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks</p> <ul> <li>MAR(ACCESS1-3)_monthly_SMB.nc: MAR outputs with monthly values of SMB and components over the Antarctic ice sheet (1980--2100)</li> <li>MAR(ACCESS1-3)-stereographic_monthly_GCM_like.nc: MAR outputs upscaled to GCM resolution (1980--2100)</li> <li>ACCESS1-3-stereographic_monthly_cleaned.nc: GCM monthly outputs over the Antarctic ice sheet (1980--2100)</li> </ul> <p>The up-to-date working versions of our experiments and source code can be found and are available on our GitHub: <a href="https://github.com/marvande/RCM-Emulator">https://github.com/marvande/RCM-Emulator</a> and at this link: <a href="https://doi.org/10.5281/zenodo.7875967">https://doi.org/10.5281/zenodo.7875967</a></p> <p>Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. You should also refer to and cite the following paper:</p> <p><strong>Cite as: </strong>Marijn van der Meer, Sophie de Roda Husman, S Lhermitte. Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks. <em>Authorea.</em> December 27, 2022 <br> DOI: <a href="https://doi.org/10.22541/essoar.167214210.02213149/v1">10.22541/essoar.167214210.02213149/v1</a> </p>
Experimental downscaled TROPOMI SIF dataset for continental Europe
<p>The present dataset represent the attempt done within the Sen4GPP project to produce a prototype downscaled SIF for continental Europe. The objective was to adapt an existing downscaling methodology (Duveiller et al. 2020) and apply it to selected sentinel data in order to downscale TROPOMI SIF data (Guanter et al. 2022) from a 10 km grid to a 1 km grid. The method relies on a locally calibrated model linking fine spatial resolution explanatory variables to the coarse spatial resolution target using a moving window. In this case, the explanatory variables are the Sentinel-3 OLCI green vegetation index (OGVI), and Sentinel-3 SLSTR daytime land surface temperature (LST), which are preprocessed into 8-daily composites by project partner U. of Southampton. For details on the downscaling algorithm, the reader is directed to the ATBD document of the Sen4GPP project.</p> <p>The dataset covers the TROPOMI period from 2018-05-11 until 2020-12-29 for continental Europe. The data is in provided in sinusoidal projection widely used with the MODIS land products, and it covers the area of the MODIS tiles v2 to v5 and h17 to h20. The dataset is divided in separate NetCDF files, with each file covering the entire spatial domain for a single time slice, and each slice representing a period of 8-days. The main variable of interest in each individual file is the predicted downscaled SIF at 1km (variable name: sif) that is mapped on the main dimensions (easting, northing) that cover 4800 by 4800 pixels of circa 1km in the Sinusoidal projection.</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 8
<p>Future projections of precipitation by the BM10 model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 5
<p>Future projections of precipitation by the BM1 model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 3
<p>Future projections of 2-meter minimum temperature by the CNN models (BM1, BM10 and BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 2
<p>Future projections of 2-meter maximum temperature by the CNN models (BM1, BM10 and BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 11
<p>Future projections of 2-meter maximum, mean and minimum temperatures by the BMlinear model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 1
<p>Input data (ERA5 and the seven GCMs) used to train the CNN models (BMlinear, BM1, BM10 and BMdense) used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 9
<p>Historical projections of all predictands (2-meter maximum, mean and minimum temperatures, and precipitation) by all CNN models (BMlinear, BM1, BM10, BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia". Each CNN model architecture is available in the file "model.json" and its optimized weights for each case are available in the file "model_weights.h5".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 7
<p>Future projections of precipitation by the BMdense model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
Climatology of Sundowner Winds in Coastal Santa Barbara, California, Based on 30 yr High Resolution WRF Downscaling
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The microDelta: Downscaling robot mechanisms enables ultra-fast and high precision movement
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Downscaled climate projections of future mesopelagic habitat in the California Current Ecosystem
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