NDRank: optimised parallel search for weather analogues

References

• Anaconda Inc. (2015). Dask | scale the python tools you love. Retrieved January 23, 2023, from https://www.dask.org/
   Google Scholar
• The Apache Software Foundation. (2006). Apache hadoop. Retrieved January 23, 2023, from https://hadoop.apache.org/
   Google Scholar
• Apache Software Foundation. (2022). Apache kafka. Retrieved January 23, 2023, from https://kafka.apache.org/
   Google Scholar
• Baumann, P., Dehmel, A., Furtado, P., Ritsch, R., & Widmann, N. (1998). The multidimensional database system rasdaman. In Proceedings of the1998 ACM SIGMOD International Conference on Management of Data (pp. 575–577). https://doi.org/10.1145/276304.276386
   Google Scholar
• Bergen, R. E., & Harnack, R. P. (1982). Long-range temperature prediction using a simple analog approach. Monthly Weather Review, 110(8), 1083–1099. doi:10.1175/1520-0493(1982)110<1083:LRTPUA>2.0.CO;2
   Web of Science ®Google Scholar
• Dagum, L., & Menon, R. (1998). OpenMP: An industry standard api for shared-memory programming. IEEE Computational Science and Engineering, 5(1), 46–55. https://doi.org/10.1109/99.660313
   Web of Science ®Google Scholar
• Delle Monache, L., Eckel, F. A., Rife, D. L., Nagarajan, B., & Searight, K. (2013). Probabilistic weather prediction with an analog ensemble. Monthly Weather Review, 141(10), 3498–3516. https://doi.org/10.1175/MWR-D-12-00281.1
   Web of Science ®Google Scholar
• Dey, C., Sanders, C., Clochard, J., & Hennessy, J. (2007). Guide to the wmo table driven code form used for the representation and exchange of regularly spaced data in binary form: Fm 92 grib (Tech. Rep. No. 98). World Meterological Organization. Retrieved from https://old.wmo.int/extranet/pages/prog/www/WMOCodes/Guides/GRIB/GRIB2_062006.pdf
   Google Scholar
• European Centre for Medium-Range Weather Forecasts. (2022). ERA5: What is the spatial reference. Retrieved January 23, 2023, from https://confluence.ecmwf.int/display/CKB/ERA5/
   Google Scholar
• Fanfarillo, A., Roozitalab, B., Hu, W., & Cervone, G. (2021). Probabilistic forecasting using deep generative models. GeoInformatica, 25(1), 127–147. https://doi.org/10.1007/s10707-020-00425-8
   Web of Science ®Google Scholar
• Google. (2008). Cloud computing services | google cloud. Retrieved January 23, 2023, from https://cloud.google.com/
   Google Scholar
• Google. (2022a). Compute-optimized machine family | compute engine documentation | google cloud. Retrieved January 23, 2023, from https://cloud.google.com/compute/docs/compute-optimized-machines
   Google Scholar
• Google. (2022b). General-purpose machine family | compute engine documentation | google cloud. Retrieved January 23, 2023, from https://cloud.google.com/compute/docs/general-purpose-machines
   Google Scholar
• Google. (2022c). Vm instance pricing | compute engine: Virtual machines | google cloud. Retrieved January 23, 2023, from https://cloud.google.com/compute/vm-instance-pricing
   Google Scholar
• Gropp, W., Lusk, E., Doss, N., & Skjellum, A. (1996). A high-performance, portable implementation of the mpi message passing interface standard. Parallel Computing, 22(6), 789–828. https://doi.org/10.1016/0167-8191(96)00024-5
   Web of Science ®Google Scholar
• gRPC Authors. (2013). gRPC a high performance, open source universal rpc framework. Retrieved January 23, 2023, from https://grpc.io/
   Google Scholar
• HashiCorp. (2013). Packer Retrieved January 23, 2023, from https://www.packer.io/ Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., … others ( 2017). Complete ERA5 from 1979: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus climate change service (C3S) data store (CDS), ECMWF [data set]. Retrieved from https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5
   Google Scholar
• Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., & O. (2017). Complete ERA5 from 1979: Fifth gen-eration of ECMWF atmospheric reanalyses of the global climate. copernicus climate change service (C3S) data store (CDS), ECMWF [dataset]. Retrieved from. https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5
   Google Scholar
• Hu, W., Cervone, G., Clemente-Harding, L., & Calovi, M. (2021). Parallel analog ensemble–the power of weather analogs. NCAR Technical Notes NCAR/TN-564+ PROC1.
   Google Scholar
• Iseh, A. J., & Woma, T. Y. 2013, December. Weather forecasting models, methods and applications. International Journal of Engineering Research and Technology, ₂https://doi.org/10.17577/IJERTV2IS120198
   Google Scholar
• Mo, R., Ye, C., & Whitfield, P. H. (2014). Application potential of four nontraditional similarity metrics in hydrometeorology. Journal of Hydrometeorology, 15(5), 1862–1880. https://doi.org/10.1175/JHM-D-13-0140.1
   Web of Science ®Google Scholar
• Raoult, B., DiFatta, G., Pappenberger, F., & Lawrence, B. (2018). Fast retrieval of weather analogues in a multi-petabytes archive using wavelet-based fingerprints. In International Conference on Computational Science – ICCS 2018 (pp. 697–710). https://doi.org/10.1007/978-3-319-93701-4_55
   Google Scholar
• Rew, R., & Davis, G. (1990). NetCDF: An interface for scientific data access. IEEE Computer Graphics and Applications, 10(4), 76–82. https://doi.org/10.1109/38.56302
   Web of Science ®Google Scholar
• Stonebraker, M., Brown, P., Zhang, D., & Becla, J. (2013). Scidb: A database management system for applications with complex analytics. Computing in Science & Engineering, 15(3), 54–62. https://doi.org/10.1109/MCSE.2013.19
   Web of Science ®Google Scholar
• Stonebraker, M., Duggan, J., Battle, L., & Papaemmanouil, O. (2013). SciDB DBMS research at MIT. IEEE Data Engineering Bulletin, 36(4), 21–30.
   Google Scholar
• Toth, Z. (1989). Long-range weather forecasting using an analog approach. Journal of Climate, 2(6), 594–607. https://doi.org/10.1175/1520-0442(1989)002<0594:LRWFUA>2.0.CO;2
   Web of Science ®Google Scholar
• Xarray core developers. (2014). N-D labeled arrays and datasets in python. Retrieved January 23, 2023, from https://xarray.pydata.org/en/stable/
   Google Scholar
• Yang, D., & Alessandrini, S. (2019). An ultra-fast way of searching weather analogs for renewable energy forecasting. Solar Energy, 185, 255–261. https://doi.org/10.1016/j.solener.2019.03.068
   Web of Science ®Google Scholar
• Zhang, Y., Kersten, M., Ivanova, M., Nes, N. (2011). SciQL: Bridging the gap between science and relational dbms. Proceedings of the 15th symposium on international database engineering & applications (pp. 124–133). https://doi.org/10.1145/2076623.2076639
   Google Scholar
• Zhang, Y., Scheers, B., Kersten, M., Ivanova, M., & Nes, N. (2012). Astronomical data processing using sciql, an sql based query language for array data. Proceedings of Astronomical Data Analysis Software and Systems 2011 (ADASS XXI). Paris, France.
   Google Scholar