ESSD benchmark output data

<p>This dataset contains the ESSD benchmark output data. Visit <a href="https://github.com/EUPP-benchmark/ESSD-benchmark">https://github.com/EUPP-benchmark/ESSD-benchmark</a> for more information.</p> <p>This dataset is provided as supplementary material with:</p> <ul> <li>Demaeyer, J., Bhend, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A., Ben Bouall&egrave;gue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., Jobst, D., Mer&scaron;e, J., Mlakar, P., M&ouml;ller, A., Mestre, O., Taillardat, M., and Vannitsem, S.: The EUPPBench postprocessing benchmark dataset v1.0, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2022-465">https://doi.org/10.5194/essd-2022-465</a>, in review, 2023.</li> </ul> <p>Please cite this article if you use (a part of) this code for a publication.</p> <p><br> Description of the methods used to get the ESSD benchmark output data<br> -------------------------------------------------------------------------------------------------------</p> <p>&nbsp;- ANET: NN post processing method using ensemble member encoders and dynamic attention<br> &nbsp;&nbsp;&nbsp; &nbsp;Repository: <a href="https://github.com/EUPP-benchmark/ESSD-ANET">https://github.com/EUPP-benchmark/ESSD-ANET</a><br> &nbsp;&nbsp;&nbsp; &nbsp;<br> &nbsp;- AR-EMOS: EMOS with heteroscedastic autoregressive error adjustments<br> &nbsp;&nbsp;&nbsp; &nbsp;Repository: <a href="https://github.com/EUPP-benchmark/ESSD-AR-EMOS">https://github.com/EUPP-benchmark/ESSD-AR-EMOS</a><br> &nbsp;&nbsp;&nbsp; &nbsp;<br> &nbsp;- ASRE: Accounting for systematic and representativeness errors<br> &nbsp;&nbsp;&nbsp; &nbsp;Repository: <a href="https://github.com/EUPP-benchmark/ESSD-ASRE">https://github.com/EUPP-benchmark/ESSD-ASRE</a><br> &nbsp;&nbsp;&nbsp; &nbsp;<br> &nbsp;- DRN: Distributional regression network<br> &nbsp;&nbsp;&nbsp; &nbsp;Repository: <a href="https://github.com/EUPP-benchmark/ESSD-DRN">https://github.com/EUPP-benchmark/ESSD-DRN</a><br> &nbsp;&nbsp;&nbsp; &nbsp;<br> &nbsp;- DVQR: D-vine copula based postprocessing<br> &nbsp;&nbsp;&nbsp; &nbsp;Repository: <a href="https://github.com/EUPP-benchmark/ESSD-D-Vine-Copula">https://github.com/EUPP-benchmark/ESSD-D-Vine-Copula</a><br> &nbsp;&nbsp;&nbsp; &nbsp;<br> &nbsp;- EMOS: Ensemble model output statistics<br> &nbsp;&nbsp;&nbsp; &nbsp;Repository: <a href="https://github.com/EUPP-benchmark/ESSD-EMOS">https://github.com/EUPP-benchmark/ESSD-EMOS</a><br> &nbsp;&nbsp;&nbsp; &nbsp;<br> &nbsp;- RC: Reliability Calibration (IMPROVER)<br> &nbsp;&nbsp;&nbsp; &nbsp;Repository: <a href="https://github.com/EUPP-benchmark/ESSD-reliability-calibration">https://github.com/EUPP-benchmark/ESSD-reliability-calibration</a><br> &nbsp;&nbsp;&nbsp; &nbsp;<br> &nbsp;- MBM: Member-By-Member postprocessing<br> &nbsp;&nbsp;&nbsp; &nbsp;Repository: <a href="https://github.com/EUPP-benchmark/ESSD-mbm">https://github.com/EUPP-benchmark/ESSD-mbm</a></p>