References and further literature

An ECMWF Newsletter is published quarterly. It covers topics in meteorology and the operational activities at the Centre, including short descriptions of operational changes to the analysis and forecasting system. The Newsletter is widely distributed and is available at the ECMWF web site http://www.ecmwf.int/publications/newsletters/

Proceedings from the Centre’s annual seminar and workshops are distributed widely to national weather services and scientific institutions of the meteorological community.

The ECMWF web site www.ecmwf.int is the main repository for its documentation. Comprehensive information on the analysis and forecasting system, the archive and dissemination can be found there. Proceedings from the Centre’s annual seminar and workshops are published there, as is ECMWF’s series of Technical Memoranda, describing scientific and technical aspects of the Centre’s work. Please refer to:

 www.ecmwf.int/publications

Useful references mentioned in the User Guide:

www.ecmwf.int/products/forecasts/seasonal/documentation/system4/index.html (Seasonal forecast documentation and User Guide)

Andersson, E. J-N Thépaut, 2008: ECMWF’s 4D-Var data assimilation system –the genesis and ten years in operations, ECMWF Newsletter 115, 8-12

Balsamo, G., S. Boussetta, E. Dutra, A. Beljaars, P. Viterbo, B. Van den Hurk, 2011: Evolution of land surface processes in the IFS, ECMWF Newsletter, 127, 17-22.

Bechtold, P., M. Köhler, T. Jung, F. Doblas-Reyes, M. Leutbecher, M. J. Rodwell , F. Vitart, G. Balsamo, 2008: Advances in simulating atmospheric variability with the ECMWF model: from synoptic to decadal time-scales. Q.J.R.Meterol. Soc., 134, 1337-1351, also available as ECMWF Tech. Memo. 556

Bright, D. and P.A.Nutter, 2004: On the challenges of identifying the ''best'' ensemble member in operational forecasting, 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction, J11.3

Buizza, R., M.Leutbecher, L.Isaksen, J.Haseler, 2010: Combined use of EDA- and SV-based perturbations in the EPS, ECMWF Newsletter 123, 22-28.

Doswell, C.A. III, 2004: Weather forecasting by humans - Heuristics and decision making. Wea. Forecasting, 19, 1115-1126.

Ferranti, L. and S. Corti 2011: New clustering products, ECMWF Newsletter 127, 6-12, Spring 2011

Göber, M., E. Zsótér, D. Richardson, 2008: Could a perfect model ever satisfy a naïve forecaster? On grid box mean versus point verification, Meteorological Applications 15:3, 359–365,

Hamill, T.M., 2003: Evaluating Forecasters' Rules of Thumb: A Study of d(prog)/dt, Weather and Forecasting, vol. 18:5, 933-37

Hersbach, H. P. Janssen, 2007: Operational assimilation of surface wind data from the MetOp ASCAT scatterometer at ECMWF, ECMWF Newsletter, 113, 6-8.

Hewson, T.D., 2009: Tracking fronts and extra-tropical cyclones. ECMWF Newsletter. 121, 9-19.

Hewson, T.D. & H.A. Titley, 2010: Objective identification, typing and tracking of the complete life-cycles of cyclonic features at high spatial resolution. Meteorol. Appl., 17, 355-381.

Isaksen, L., J.Haseler, R. Buizza, M. Leutbecher, 2010: The new Ensemble of Data Assimilations, ECMWF Newsletter 123, 15-21

Jolliffe, I.T. and D.B. Stephenson, D.B. Eds, 2003: Forecast Verification: A Practitioner's Guide in Atmospheric Science, Wiley and Sons, Chichester

Jung, T., G. Balsamo, P. Bechtold, A. Beljaars, M. Köhler, M. Miller, J.-J. Morcrette, A. Orr, M. Rodwell, A. Tompkins, 2010 : The ECMWF model climate: Recent progress through improved physical parametrizations. Quart. J. Roy. Meteor. Soc., 136(650), 1145-1160, doi: 10.1002/qj.63 also available as ECMWF Tech. memo. 623.

Lalaurette F. 2003: Early detection of abnormal weather conditions using a probabilistic extreme forecast index. Quarterly Journal of the Royal Meteorological Society, Volume 129, Issue 594, 3037–3057, Part A

Lewis, J., 1994: Cal Tech’s program in meteorology: 1933 – 1948. Bull. Amer. Meteor. Soc., 75, 69 -81, in particular 73-74

Lorenz, E.N., 1970: Forecast for another century of weather progress.  A Century of Weather Progress. Amer. Meteor. Soc., 18-24.

Murphy, A.H. 1973: A new vector partition of the probability score. Journal of Applied Meteorology 12 (4): 595–600. http://ams.allenpress.com/archive/1520-0450/12/4/pdf/i1520-0450-12-4-595.pdf.

Murphy, A. H. 1986: A New Decomposition of the Brier Score: Formulation and Interpretation, Monthly Weather Review, 114:12, 2671-2673

Miller M., R. Buizza, J. Haseler, M. Hortal, P. Janessen and A. Untch, 2010: Increased resolution in the ECMWF deterministic and ensemble prediction systems, ECMWF Newsletter No. 124 pp.10-16,

Nurmi, P., 2003: Recommendations on the verification of local weather forecasts. ECMWF Tech. Mem. 430.

Palmer, T., R. Buizza, R. Hagedorn, A. Lawrence, M. Leutbecher, and L. Smith, 2006: Ensemble prediction: A pedagogical perspective. ECMWF Newsletter, 106, 10–17.

Persson, A., 1991: Kalman filtering - a new approach to adaptive statistical interpretation of numerical forecasts. Lectures and papers presented at the WMO training workshop on the interpretation of NWP products in terms of local weather phenomena and their verification, WMO, Wageningen, the Netherlands, XX-27-XX-32.

Persson, A and B. Strauss, 1995: On the skill and consistency in medium-range weather forecasts, ECMWF Newsletter 70, 12-15.

Zsoter, E., R. Buizza  & R. Richardson, 2009: 'Jumpiness' of the ECMWF and UK Met Office EPS control and ensemble-mean forecasts. Mon. Wea. Rev., 137, 3823-3836.

Wilks, D., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Elsevier, 627 pp.

Zsótér, E. 2006: Recent developments in extreme weather forecasting, ECMWF Newsletter 107, 8-1

[1] The  “regression to the mean” effect was first discussed by Francis Galton (1822-1911) who found that tall (short) fathers tended to have tall (short) sons, but on average slightly shorter (taller) than themselves.

^[2]Note that the terminology here may be different from that used in other books. We refer to the definitions given by Nurmi (2003) and the recommendations from the WWRP/WGNE working group on verification.

[3] The FR should not be confused with the false alarm ratio FAR=F/(H+F), i.e. the proportion of false alarms, given the event was forecast, which is one of the main parameters, together with the HR, in ROC diagrams.

[4] A “do not know” forecast does not necessarily mean “50-50”. It could mean the climatological probability. In fact, unless the climatological rain frequency is 50% a “50-50” statement actually provides the non-trivial information that the risk is higher or lower than normal.

[5] As discussed in Section 4.3.4 a good NWP model should not over- or under-forecast at any forecast range. This is yet another example of how computer -based forecasts differ from customer -orientated forecasts.