EMS Annual Meeting Abstracts
Vol. 18, EMS2021-204, 2021
EMS Annual Meeting 2021
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

CSTools: the MEDSCOPE Toolbox for Climate Forecasts postprocessing

Núria Pérez-Zanón1, Louis-Philippe Caron2, Silvia Terzago3, Bert Van Schaeybroeck4, Lauriane Batté5, M. Carmen Alvarez-Castro6, Susana Corti3, Marta Dominguez7, Federico Fabiano3, Silvio Gualdi6, Jost von Hardenberg3,8, Llorenç Lledó1, Nicolau Manubens1, Paola Marson5, Stefano Materia6, Eroteida Sánchez7, Verónica Torralba6, Deborah Verfaillie1, and Danila Volpi3
Núria Pérez-Zanón et al.
  • 1Barcelona Supercomputing Center (BSC) (nuria.perez@bsc.es)
  • 2Ouranos, Montreal, Quebec
  • 3National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Italy
  • 4Royal Meteorological Institute of Belgium
  • 5MeteoFrance/DCSC/AVH, France
  • 6Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Italy
  • 7Delegación territorial (DT) Cantabria, Agencia Estatal de Meteorología (AEMET)
  • 8Dept. of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Italy

Climate forecasts need to be postprocessed to obtain user-relevant climate information, to develop and implement strategies of adaptation to climate variability and to trigger decisions. Several postprocessing methods are gathered into CSTools (short for Climate Service Tools) for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products. 

Besides an overview of the methods and documentation available in CSTools, a practical example is demonstrated. The objective of this practical example is to postprocess a seasonal forecast with a set of CSTools functions in order to obtain the required data to produce forecasts of mountain snow resources. Quantile mapping bias-correction and RainFARM stochastic downscaling methods are applied to raw seasonal forecast daily precipitation data to derive 1 km resolution fields. Bias-adjusted and downscaled precipitation data are then employed to drive a snow model, SNOWPACK, and generate snow depth seasonal forecasts at selected high-elevation sites in North-Western Italian Alps. 

The computational resources required by CSTools to process the forecasts will be discussed. This assessment is relevant given the memory requirements for the use case: while seasonal forecast data occupies ~10MB (8 x 8 grid cells, 215 forecast time steps for 30 different initializations with 25 members each), the data post-processed reaches ~1TB (the RainFARM downscaling requires a refinement factor 100 for the SNOWPACK model increasing the spatial resolution to 800 x 800 grid cells and creating 10 stochastic realizations for each ensemble member). In addition to one strategy using conventional loops, startR is introduced as an efficient alternative. startR is an R package that allows implementing the MapReduce paradigm, i.e. chunking the data and processing them either locally or remotely on high-performance computing systems, leveraging multi-node and multi-core parallelism where possible.

How to cite: Pérez-Zanón, N., Caron, L.-P., Terzago, S., Van Schaeybroeck, B., Batté, L., Alvarez-Castro, M. C., Corti, S., Dominguez, M., Fabiano, F., Gualdi, S., von Hardenberg, J., Lledó, L., Manubens, N., Marson, P., Materia, S., Sánchez, E., Torralba, V., Verfaillie, D., and Volpi, D.: CSTools: the MEDSCOPE Toolbox for Climate Forecasts postprocessing, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-204, https://doi.org/10.5194/ems2021-204, 2021.

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