Downloads · February initialization · FMAM and its months

Files and code

• Screening: all scores, per-year values, diagnostics and screening statistics (JSON)7.3 MB · SHA-256 a687a8b437bd…
• Per-cell scores and distribution ratios (NetCDF)4.0 MB · SHA-256 b43b2b59cadc…
• Confirmation: full-pipeline scores, selections and tests (JSON)3.5 MB · SHA-256 f7428bf3d807…
• Corrected forecast members of every method (NetCDF)18.7 MB · SHA-256 7f53db565787…
• Output manifest: SHA-256 of every screening output file (JSON)3 kB
• MVS ratio-limit study: screening (JSON)2.0 MB · SHA-256 4c91f25448b3…
• MVS ratio-limit study: confirmation (JSON)1.2 MB · SHA-256 2d3ffb5084da…
• Independent validation record: every method scored on the seasons after the benchmark (JSON)0.1 MB · SHA-256 7e8d1be1e676…
• Shadow forecast Feb 2026 (NetCDF; not published)42 kB
• Full write-up (docs/47) at commit 9f39792Definitions, design, all result tables and the references, as of the code that produced these results
• Code at commit 9f39792scripts/bias_methods.py (methods), scripts/bias_benchmark.py (screening), scripts/bias_confirmation.py (confirmation), config/bias_correction/feb_fmam.json (settings)

Run provenance

References

1. Boé, J., Terray, L., Habets, F., & Martin, E. (2007). Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies. International Journal of Climatology, 27, 1643–1655. doi:10.1002/joc.1602
2. Cannon, A. J., Sobie, S. R., & Murdock, T. Q. (2015). Bias correction of GCM precipitation by quantile mapping: how well do methods preserve changes in quantiles and extremes? Journal of Climate, 28, 6938–6959. doi:10.1175/JCLI-D-14-00754.1
3. Delle Monache, L., Hacker, J. P., Zhou, Y., Deng, X., & Stull, R. B. (2006). Probabilistic aspects of meteorological and ozone regional ensemble forecasts. Journal of Geophysical Research: Atmospheres, 111, D24307. doi:10.1029/2005JD006917
4. Ferro, C. A. T. (2014). Fair scores for ensemble forecasts. Quarterly Journal of the Royal Meteorological Society, 140, 1917–1923. doi:10.1002/qj.2270
5. Gudmundsson, L., Bremnes, J. B., Haugen, J. E., & Engen-Skaugen, T. (2012). Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations — a comparison of methods. Hydrology and Earth System Sciences, 16, 3383–3390. doi:10.5194/hess-16-3383-2012
6. Hay, L. E., Wilby, R. L., & Leavesley, G. H. (2000). A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States. JAWRA Journal of the American Water Resources Association, 36, 387–397. doi:10.1111/j.1752-1688.2000.tb04276.x
7. Johnson, S. J., Stockdale, T. N., Ferranti, L., et al. (2019). SEAS5: the new ECMWF seasonal forecast system. Geoscientific Model Development, 12, 1087–1117. doi:10.5194/gmd-12-1087-2019
8. Leander, R., & Buishand, T. A. (2007). Resampling of regional climate model output for the simulation of extreme river flows. Journal of Hydrology, 332, 487–496. doi:10.1016/j.jhydrol.2006.08.006
9. Lenderink, G., Buishand, A., & van Deursen, W. (2007). Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach. Hydrology and Earth System Sciences, 11, 1145–1159. doi:10.5194/hess-11-1145-2007
10. Manzanas, R., Gutiérrez, J. M., Bhend, J., et al. (2019). Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset. Climate Dynamics, 53, 1287–1305. doi:10.1007/s00382-019-04640-4
11. Michelangeli, P.-A., Vrac, M., & Loukos, H. (2009). Probabilistic downscaling approaches: application to wind cumulative distribution functions. Geophysical Research Letters, 36, L11708. doi:10.1029/2009GL038401
12. Piani, C., Weedon, G. P., Best, M., et al. (2010). Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. Journal of Hydrology, 395, 199–215. doi:10.1016/j.jhydrol.2010.10.024
13. Schmidli, J., Frei, C., & Vidale, P. L. (2006). Downscaling from GCM precipitation: a benchmark for dynamical and statistical downscaling methods. International Journal of Climatology, 26, 679–689. doi:10.1002/joc.1287
14. Switanek, M. B., Troch, P. A., Castro, C. L., et al. (2017). Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes. Hydrology and Earth System Sciences, 21, 2649–2666. doi:10.5194/hess-21-2649-2017
15. Teutschbein, C., & Seibert, J. (2012). Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. Journal of Hydrology, 456–457, 12–29. doi:10.1016/j.jhydrol.2012.05.052
16. Themeßl, M. J., Gobiet, A., & Leuprecht, A. (2011). Empirical-statistical downscaling and error correction of daily precipitation from regional climate models. International Journal of Climatology, 31, 1530–1544. doi:10.1002/joc.2168
17. Torralba, V., Doblas-Reyes, F. J., MacLeod, D., Christel, I., & Davis, M. (2017). Seasonal climate prediction: a new source of information for the management of wind energy resources. Journal of Applied Meteorology and Climatology, 56, 1231–1247. doi:10.1175/JAMC-D-16-0204.1
18. Vrac, M., Drobinski, P., Merlo, A., et al. (2012). Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment. Natural Hazards and Earth System Sciences, 12, 2769–2784. doi:10.5194/nhess-12-2769-2012