46 PROGNOS: A Meteorological Service of Canada (MSC) Initiative …
295
the diagnostic and visualization features for enhanced monitoring and consultation
of experimental forecasts.
An iterative development approach is adopted to expand the PROGNOS system
in order to better serve research and development projects. The next major milestone
consists of adapting the data ingestion and pre-processing routines in PROGNOS to
use historical records from the PROGNOS database. From there, PROGNOS will
extend its modeling to consider additional predictands and NWP sources as well as
season and model transition schemes. Over the medium to long term, PROGNOS
will be integrating gridded post-processing methods to meet the evolving air quality
and meteorological forecast program requirements.
References
1. L. Breiman, Random forests. Mach. Learn. 45, 5–32 (2001)
2. M. Cassotti, F. Grisoni, R. Todeschini, Reshaped sequential replacement algorithm: an efficient
approach to variable selection. Chemometr. Intell. Lab. Syst. 133, 136–148 (2014)
3. R.E. Kalman, A new approach to linear filtering and prediction problems. Trans. ASME, Ser.
D, J. Basic Eng. 82(1), 35–45 (1960)
4. J. Marshall, SSM v7.8 documentation (2006), https://goo.gl/xyi4ER
5. D. Racette, ECCC Maestro sequencer code on Github (2017), https://github.com/racetted/
maestro
6. G. Schwarz, Estimating the dimension of a model. Ann. Stat. 6, 461–464 (1978)
7. R. Tibshirani, Regression shrinkage and selection via the lasso. J. Royal Stat. Soc. Ser. B, 58(1),
267–288 (1996)
8. K. Ushey, J. McPherson, J. Cheng, A. Atkins, J.J. Allaire, Packrat: a dependency management
system for projects and their r package dependencies. R package version 0.4.8-1 (2016), https://
CRAN.R-project.org/package=packrat
9. R. Verret, D. Vigneux, J. Marcoux, F. Petrucci, C. Landry, L. Pelletier, G. Hardy, Scribe 3.0
a product generator, in Preprints 13th International Conference on Interactive Information
and Processing Systems for Meteorology, Oceanography and Hydrology, 2–7 February 1997
(AMS, Long Beach, California, 1997), pp. 392–395
10. L.J. Wilson, M. Vallée, The Canadian updateable model output statistics (UMOS) system:
Design and development tests. Weather Forecast. 17, 206–222 (2002)
295
the diagnostic and visualization features for enhanced monitoring and consultation
of experimental forecasts.
An iterative development approach is adopted to expand the PROGNOS system
in order to better serve research and development projects. The next major milestone
consists of adapting the data ingestion and pre-processing routines in PROGNOS to
use historical records from the PROGNOS database. From there, PROGNOS will
extend its modeling to consider additional predictands and NWP sources as well as
season and model transition schemes. Over the medium to long term, PROGNOS
will be integrating gridded post-processing methods to meet the evolving air quality
and meteorological forecast program requirements.
References
1. L. Breiman, Random forests. Mach. Learn. 45, 5–32 (2001)
2. M. Cassotti, F. Grisoni, R. Todeschini, Reshaped sequential replacement algorithm: an efficient
approach to variable selection. Chemometr. Intell. Lab. Syst. 133, 136–148 (2014)
3. R.E. Kalman, A new approach to linear filtering and prediction problems. Trans. ASME, Ser.
D, J. Basic Eng. 82(1), 35–45 (1960)
4. J. Marshall, SSM v7.8 documentation (2006), https://goo.gl/xyi4ER
5. D. Racette, ECCC Maestro sequencer code on Github (2017), https://github.com/racetted/
maestro
6. G. Schwarz, Estimating the dimension of a model. Ann. Stat. 6, 461–464 (1978)
7. R. Tibshirani, Regression shrinkage and selection via the lasso. J. Royal Stat. Soc. Ser. B, 58(1),
267–288 (1996)
8. K. Ushey, J. McPherson, J. Cheng, A. Atkins, J.J. Allaire, Packrat: a dependency management
system for projects and their r package dependencies. R package version 0.4.8-1 (2016), https://
CRAN.R-project.org/package=packrat
9. R. Verret, D. Vigneux, J. Marcoux, F. Petrucci, C. Landry, L. Pelletier, G. Hardy, Scribe 3.0
a product generator, in Preprints 13th International Conference on Interactive Information
and Processing Systems for Meteorology, Oceanography and Hydrology, 2–7 February 1997
(AMS, Long Beach, California, 1997), pp. 392–395
10. L.J. Wilson, M. Vallée, The Canadian updateable model output statistics (UMOS) system:
Design and development tests. Weather Forecast. 17, 206–222 (2002)