Cited by 213Meteorological Phenomena and SimulationsForecasting Techniques and ApplicationsClimate variability and models

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Measures of skill and value of ensemble prediction systems, their interrelationship and the effect of ensemble size

Richardson, David S.·2001·Quarterly Journal of the Royal Meteorological Society

DOI 10.1002/qj.49712757715

Open this paper in CoChat — free account

Abstract

Ensemble forecasts provide probabilistic predictions for the future state of the atmosphere. Usually the probability of a given event E is determined from the fraction of ensemble members which predict the event. Hence there is a degree of sampling error inherent in the predictions. In this paper a theoretical study is made of the effect of ensemble size on forecast performance as measured by a reliability diagram and Brier (skill) score, and on users by using a simple cost‐loss decision model. The relationship between skill and value, and a generalized skill score, dependent on the distribution of users, are discussed. The Brier skill score is reduced from its potential level for all finite‐sized ensembles. The impact is most significant for small ensembles, especially when the variance of forecast probabilities is also small. The Brier score for a set of deterministic forecasts is a measure of potential predictability, assuming the forecasts are representative selections from a reliable ensemble prediction system (EPS). There is a consistent effect of finite ensemble size on the reliability diagram. Even if the underlying distribution is perfectly reliable, sampling this using only a small number of ensemble members introduces considerable unreliability. There is a consistent over‐forecasting which appears as a clockwise tilt of the reliability diagram. It is important to be aware of the expected effect of ensemble size to avoid misinterpreting results. An ensemble of ten o…

Integrity signals

Registry

Crossref · 10.1002/qj.49712757715

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Statistical Methods in the Atmospheric Sciences
Ramsey, P. H., & Wilks, D. S. (1996) · Technometrics · doi:10.2307/1271312 · Cited by 7,681
Skill and relative economic value of the ECMWF ensemble prediction system
Richardson, D. S. (2000) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.49712656313 · Cited by 491
Self-citation
Preface
Wilks, D. S. (1995) · International geophysics/International geophysics series · doi:10.1016/s0074-6142(06)80036-7 · Cited by 319
On the Evolution of the Southern Oscillation
Trenberth, K. E., & Shea, D. J. (1987) · Monthly Weather Review · doi:10.1175/1520-0493(1987)115 · Cited by 316
The Finley Affair: A Signal Event in the History of Forecast Verification
Murphy, A. H. (1996) · Weather and Forecasting · doi:10.1175/1520-0434(1996)011 · Cited by 246
Some Aspects of Circulation and Climate over the Eastern Equatorial Atlantic
HASTENRATH, S. L., & Lamb, P. (1977) · Monthly Weather Review · doi:10.1175/1520-0493(1977)105 · Cited by 82
On the Relationship between the Accuracy and Value of Forecasts in the Cost–Loss Ratio Situation
Murphy, A. H., & Ehrendorfer, M. (1987) · Weather and Forecasting · doi:10.1175/1520-0434(1987)002 · Cited by 69
A Note on the Utility of Probabilistic Predictions and the Probability Score in the Cost-Loss Ratio Decision Situation
Murphy, A. H. (1966) · Journal of applied meteorology · doi:10.1175/1520-0450(1966)005 · Cited by 52
Untitled work
(1995) · doi:10.1175/1520-0493(1995)123

Show all 9 references

Research works printed but not linked: 1

Talagrand O. Vautard R.andStrauss B.1997‘Evaluation of probabilistic prediction systems’. Pp.157–157in Proceedings of the ECMWF workshop on predictability 20–22 October 1997 ECMWF Shinfield Park Reading UK

Sorted by citations

Items 101–200 of 213

Using Extreme Wind-Speed Probabilistic Forecasts to Optimize Unit Scheduling Decision
Wu, Y., Wu, Y.-C., Chang, H.-L., & Hong, J. (2021) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2021.3132342 · Cited by 23
A Bayesian approach to model-based clustering for binary panel probit models
Aßmann, C., & Boysen‐Hogrefe, J. (2010) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2010.04.016 · Cited by 23
What Is the True Value of Forecasts?
Millner, A. (2009) · Weather Climate and Society · doi:10.1175/2009wcas1001.1 · Cited by 23
Lagged ensembles in sub‐seasonal predictions
Vitart, F., & Takaya, Y. (2021) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.4125 · Cited by 22
Effect of doubling the ensemble size on the performance of ensemble prediction in the warm season using MOGREPS implemented at the KMA
Kay, J. K., Kim, H. M., Park, Y.-Y., & Son, J. (2013) · Advances in Atmospheric Sciences · doi:10.1007/s00376-012-2083-y · Cited by 22
Demonstrating the value of larger ensembles in forecasting physical systems
Machete, R. L., & Smith, L. A. (2016) · Tellus A Dynamic Meteorology and Oceanography · doi:10.3402/tellusa.v68.28393 · Cited by 22
Sequential Aggregation of Probabilistic Forecasts—Application to Wind Speed Ensemble Forecasts
Zamo, M., Bel, L., & Mestre, O. (2020) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/rssc.12455 · Cited by 21
A Component-Resampling Approach for Estimating Probability Distributions from Small Forecast Ensembles
Dettinger, M. D. (2006) · Climatic Change · doi:10.1007/s10584-005-9001-6 · Cited by 21
Postprocessing of Ensemble Weather Forecasts Using a Stochastic Weather Generator
Chen, J., Brissette, F. P., & Li, Z. (2013) · Monthly Weather Review · doi:10.1175/mwr-d-13-00180.1 · Cited by 20
Estimation of Ambiguity in Ensemble Forecasts
Eckel, F. A., Allen, M. S., & Sittel, M. C. (2011) · Weather and Forecasting · doi:10.1175/waf-d-11-00015.1 · Cited by 20
Verification of Quantitative Precipitation Reforecasts over the Southeastern United States
Baxter, M. A., Lackmann, G. M., Mahoney, K. M., Workoff, T. E., & Hamill, T. M. (2014) · Weather and Forecasting · doi:10.1175/waf-d-14-00055.1 · Cited by 19
Influence of trends on subseasonal temperature prediction skill
Wulff, C. O., Vitart, F., & Domeisen, D. I. V. (2022) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.4259 · Cited by 18
Statistical postprocessing of dual‐resolution ensemble precipitation forecasts across Europe
Gascón, E., Lavers, D. A., Hamill, T. M., Richardson, D. S., Bouallègue, Z. B., Leutbecher, M., & Pappenberger, F. (2019) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.3615 · Cited by 18
Self-citation
Getting the Most out of Ensemble Forecasts: A Valuation Model Based on User–Forecast Interactions
Millner, A. (2008) · Journal of Applied Meteorology and Climatology · doi:10.1175/2008jamc1899.1 · Cited by 18
Effective mesoscale, short-range ensemble forecasting
Eckel, F. A. (2003) · Cited by 18
How much does simplification of probability forecasts reduce forecast quality?
Doblas-Reyes, F. J., Coelho, C. A. S., & Stephenson, D. B. (2008) · Meteorological Applications · doi:10.1002/met.50 · Cited by 17
Hydrometeorological Short-Range Ensemble Forecasts in Complex Terrain. Part I: Meteorological Evaluation
McCollor, D., & Stull, R. B. (2007) · Weather and Forecasting · doi:10.1175/2008waf2007063 · Cited by 17
Analysis of the Relative Operating Characteristic and Economic Value Using the LAPS Ensemble Prediction System in Taiwan
Chang, H.-L., Yang, S., Yuan, H., Lin, P., & Liou, Y. (2015) · Monthly Weather Review · doi:10.1175/mwr-d-14-00189.1 · Cited by 16
Assessment of NCMRWF Global Ensemble System with differing ensemble populations for Tropical cyclone prediction
Chakraborty, P., Sarkar, A., Kumar, S., George, J. P., Rajagopal, E. N., & Bhatla, R. (2020) · Atmospheric Research · doi:10.1016/j.atmosres.2020.105077 · Cited by 15
Reliability of Regression-Corrected Climate Forecasts
Tippett, M. K., DelSole, T., & Barnston, A. G. (2014) · Journal of Climate · doi:10.1175/jcli-d-13-00565.1 · Cited by 15
A bias-correction and downscaling technique for operational extended range forecasts based on self organizing map
Sahai, A. K., Borah, N., Chattopadhyay, R., Joseph, S., & Abhilash, S. R. (2016) · Climate Dynamics · doi:10.1007/s00382-016-3214-4 · Cited by 15
Subseasonal Forecast Skill of Evaporative Demand, Soil Moisture, and Flash Drought Onset from Two Dynamic Models over the Contiguous United States
Lesinger, K., Tian, D., & Wang, H. (2024) · Journal of Hydrometeorology · doi:10.1175/jhm-d-23-0124.1 · Cited by 14
Predicting and presenting plausible future scenarios of wind power production from numerical weather prediction systems: a qualitative ex ante evaluation for decision making
Cutler, N. J., Outhred, H. R., MacGill, I. F., & Kepert, J. D. (2011) · Wind Energy · doi:10.1002/we.485 · Cited by 14
Forecasts of “normal”
Mason, S. J., Ferro, C. A. T., & Landman, W. A. (2020) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.3968 · Cited by 13
Skillful decadal prediction of German Bight storm activity
Krieger, D., Brune, S., Pieper, P., Weiße, R., & Baehr, J. (2022) · Natural hazards and earth system sciences · doi:10.5194/nhess-22-3993-2022 · Cited by 13
Forecast Verification for S2S Timescales
Coelho, C. A. S., Brown, B. G., Wilson, L., Mittermaier, M., & Casati, B. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-811714-9.00016-4 · Cited by 13
Multi-model seasonal forecasts for the wind energy sector
Lee, D. Y., Doblas-Reyes, F. J., Torralba, V., & González-Reviriego, N. (2019) · Climate Dynamics · doi:10.1007/s00382-019-04654-y · Cited by 13
A Regional Ionospheric Storm Forecasting Method Using a Deep Learning Algorithm: LSTM
Ban, P., Guo, L., Zhao, Z., Sun, S., Xu, T., Xu, Z., & Sun, F. (2023) · Space Weather · doi:10.1029/2022sw003061 · Cited by 13
Modeling the Distribution of Precipitation Forecasts from the Canadian Ensemble Prediction System Using Kernel Density Estimation
Peel, S., & Wilson, L. J. (2008) · Weather and Forecasting · doi:10.1175/2007waf2007023.1 · Cited by 13
Application of the NCEP Ensemble Prediction System to Medium-Range Forecasting in South Africa: New Products, Benefits, and Challenges
Tennant, W. J., Tóth, Z., & Rae, K. J. (2007) · Weather and Forecasting · doi:10.1175/waf979.1 · Cited by 13
Long-term ensemble forecast of snowmelt inflow into the Cheboksary Reservoir under two different weather scenarios
Gelfan, A. N., Михайлович, М. В., Motovilov, Y. G., & SOLOMATINE, D. P. (2018) · Hydrology and earth system sciences · doi:10.5194/hess-22-2073-2018 · Cited by 13
User decisions, and how these could guide developments in probabilistic forecasting
Rodwell, M. J., Hammond, J., Thornton, S., & Richardson, D. S. (2020) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.3845 · Cited by 12
Self-citation
Decide Now or Wait for the Next Forecast? Testing a Decision Framework Using Real Forecasts and Observations
Jewson, S. P., Scher, S., & Messori, G. (2021) · Monthly Weather Review · doi:10.1175/mwr-d-20-0392.1 · Cited by 11
Relative impact of model quality and ensemble deficiencies on the performance of ensemble based probabilistic forecasts evaluated through the Brier score
Atger, F. (2004) · Nonlinear processes in geophysics · doi:10.5194/npg-11-399-2004 · Cited by 11
Convergence of forecast distributions in a 100,000‐member idealised convective‐scale ensemble
Tempest, K. I., Craig, G. C., & Brehmer, J. R. (2022) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.4410 · Cited by 13
Spatial heterogeneity in the timing of birch budburst in response to future climate warming in Ireland
Caffarra, A., Zottele, F., Gleeson, E., & Donnelly, A. (2013) · International Journal of Biometeorology · doi:10.1007/s00484-013-0720-5 · Cited by 10
Development and prospects of the regional MiKlip decadal prediction system over Europe: predictive skill, added value of regionalization, and ensemble size dependency
Reyers, M., Feldmann, H., Mieruch, S., Pinto, J. G., Uhlig, M., Ahrens, B., Früh, B., Modali, K., Laube, N., Moemken, J., et al. (2019) · Earth System Dynamics · doi:10.5194/esd-10-171-2019 · Cited by 10
Comments on “The Discrete Brier and Ranked Probability Skill Scores”
Tippett, M. K. (2008) · Monthly Weather Review · doi:10.1175/2008mwr2594.1 · Cited by 10
The Utility of Convection-Permitting Ensembles for the Prediction of Stationary Convective Bands
Barrett, A. I., Gray, S. L., Kirshbaum, D. J., Roberts, N. M., Schultz, D. M., & Fairman, J. G. (2015) · Monthly Weather Review · doi:10.1175/mwr-d-15-0148.1 · Cited by 10
Skill of the extended range prediction ( NERP ) for Indian summer monsoon rainfall with NCMRWF global coupled modelling system
Gera, A., Gupta, A., Mitra, A. K., Nagarjuna Rao, D., Momin, I. M., Rajeeavan, M. N., Milton, S. F., Martin, G., Martin, M. J., Waters, J. C., et al. (2021) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.4216 · Cited by 9
Ensemble Fog Prediction
Du, J., & Zhou, B. (2017) · Springer atmospheric sciences · doi:10.1007/978-3-319-45229-6_10 · Cited by 9
A dipole of tropical cyclone outgoing long‐wave radiation
Smith, M., & Toumi, R. (2020) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.3912 · Cited by 9
Jumpiness in Ensemble Forecasts of Atlantic Tropical Cyclone Tracks
Richardson, D. S., Cloke, H., Methven, J. A., & Pappenberger, F. (2023) · Weather and Forecasting · doi:10.1175/waf-d-23-0113.1 · Cited by 8
Self-citation
Theory and practice of phase‐aware ensemble forecasting
Schulte, J. A., & Georgas, N. (2018) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.3285 · Cited by 8
Predicting Subseasonal Tropical Cyclone Activity Using NOAA and ECMWF Reforecasts
Switanek, M. B., Hamill, T. M., Long, L. N., & Scheuerer, M. (2022) · Weather and Forecasting · doi:10.1175/waf-d-22-0124.1 · Cited by 7
Estimation of the area under ROC curve with censored data
Wang, Q., Yao, L., & Lai, P. (2008) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2008.06.006 · Cited by 7
Assessing the Skill of Yes/No Forecasts for Markov Observations
Briggs, W. M., & Ruppert, D. (2006) · Monthly Weather Review · doi:10.1175/mwr3190.1 · Cited by 7
An Extended Procedure for Implementing the Relative Operating Characteristic Graphical Method
Semazzi, F. H. M., & Mera, R. (2006) · Journal of Applied Meteorology and Climatology · doi:10.1175/jam2397.1 · Cited by 6
Improvements in precipitation simulation over South America for past and future climates via multi-model combination
Coutinho, M. D. L., Lima, K. C., & Silva, C. M. S. E. (2016) · Climate Dynamics · doi:10.1007/s00382-016-3346-6 · Cited by 6
Dependence of Probabilistic Quantitative Precipitation Forecast Performance on Typhoon Characteristics and Forecast Track Error in Taiwan
Teng, H., Done, J., Lee, C., & Kuo, Y. (2019) · Weather and Forecasting · doi:10.1175/waf-d-19-0175.1 · Cited by 6
Sub-Ensemble Coastal Flood Forecasting: A Case Study of Hurricane Sandy
Schulte, J. A. (2017) · Journal of Marine Science and Engineering · doi:10.3390/jmse5040059 · Cited by 6
Testing the reliability of forecasting systems
Bröcker, J. (2021) · Journal of Applied Statistics · doi:10.1080/02664763.2021.1981833 · Cited by 6
Evaluation, Calibration, and Application of Probabilistic 100-m Wind Speed Forecasts Produced by the WRF Ensemble Prediction System in Taiwan
Chou, S.-C., Chang, H.-L., Tóth, Z., Lin, P., Hong, J., Wu, Y., Chen, G., Lu, K.-C., & Cheng, C.-P. (2024) · Journal of Applied Meteorology and Climatology · doi:10.1175/jamc-d-23-0210.1 · Cited by 5
Probability forecasts of ice accretion on wind turbines derived from multiphysics and neighbourhood ensembles
Strauss, L., Serafin, S., & Dorninger, M. (2022) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.4311 · Cited by 5
How to assess climate change impact models: uncertainty analysis of streamflow statistics via approximate Bayesian computation (ABC)
Romero-Cuéllar, J., & Francés, F. (2023) · Hydrological Sciences Journal · doi:10.1080/02626667.2023.2231437 · Cited by 5
The Effect of Ensemble Size on the Mean Squared Error and Spread–Error Relationship
Dirkson, A., & Buehner, M. (2025) · Monthly Weather Review · doi:10.1175/mwr-d-24-0189.1 · Cited by 5
What can be expected from a semi-distributed multi-model approach for streamflow forecasting? Tailoring the structure and size of a super-ensemble on the Rhône basin
Thébault, C., Perrin, C., Legrand, S., Andréassian, V., Thirel, G., & Delaigue, O. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.133589 · Cited by 5
Ensemble mean forecast skill and applications with the T213 ensemble prediction system
Li, S., Wang, Y., Yuan, H., Song, J., & Xu, X. Y. (2016) · Advances in Atmospheric Sciences · doi:10.1007/s00376-016-6155-2 · Cited by 5
Forecast System Design, Configuration, and Complexity
Takaya, Y. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-811714-9.00012-7 · Cited by 5
The adaptability of typical precipitation ensemble prediction systems in the Huaihe River basin, China
Wang, H., Zhong, P., Zhu, F., Lü, Q., Ma, Y., & Xu, S. (2020) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-020-01923-9 · Cited by 5
Reliable Probabilistic Quantitative Precipitation Forecasts from a Short-Range Ensemble Forecasting System during the 2005/06 Cool Season
Yussouf, N., & Stensrud, D. J. (2008) · Monthly Weather Review · doi:10.1175/2007mwr2314.1 · Cited by 5
The probabilistic skill of extended-range heat wave forecasts over Europe
Korhonen, N., Hyvärinen, O., Kollanus, V., Lanki, T., Jokisalo, J., Kosonen, R., Richardson, D. S., & Jylhä, K. (2025) · Natural hazards and earth system sciences · doi:10.5194/nhess-25-1865-2025 · Cited by 4
Self-citation
Are we misdiagnosing ensemble forecast reliability? On the insufficiency of spread–error and rank‐based reliability metrics
Dirkson, A., & Buehner, M. (2026) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.70186 · Cited by 4
Ensemble Forecasting
Wilks, D. S. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815823-4.00008-0 · Cited by 4
Improved predictions of rare events using the Crooks fluctuation theorem
Gundermann, J., Siegert, S., & Kantz, H. (2014) · Physical Review E · doi:10.1103/physreve.89.032112 · Cited by 4
The Development of Seasonal and Inter-Annual Climate Forecasting
Harrison, M. (2005) · Springer eBooks · doi:10.1007/1-4020-4166-7_10 · Cited by 3
Subseasonal Prediction of Tropical Cyclone Precipitation
García‐Franco, J. L., Lee, C., Tippett, M. K., Camargo, S. J., Kim, D., Molod, A. M., & Lim, Y. (2025) · Weather and Forecasting · doi:10.1175/waf-d-24-0185.1 · Cited by 2
Calibration and validation of seasonal forecasts
Ferranti, L. (2012) · Cited by 2
Variability and combination as an ensemble of mineral dust forecasts during the 2021 CADDIWA experiment using the WRF 3.7.1 and CHIMERE v2020r3 models
Menut, L. (2023) · Geoscientific model development · doi:10.5194/gmd-16-4265-2023 · Cited by 2
Probabilistic temperature forecast by using ground station measurements and ECMWF ensemble prediction system
Boi, P. (2004) · Meteorological Applications · doi:10.1017/s1350482704001380 · Cited by 2
Evaluating ensemble forecasts by the Ignorance score -- Correcting the finite-ensemble biasPreprint
Siegert, S., Ferro, C. A. T., & Stephenson, D. B. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.8249 · Cited by 2
Improving the skill of medium range ensemble rainfall and temperature forecasts over India using MoES grand ensemble (MGE)-Part-II
Abhijith, V., Dube, A., Mamgain, A., Singh, H. N., Ashrit, R., Verma, S., George, J. P., & Prasad, V. S. (2025) · Meteorology and Atmospheric Physics · doi:10.1007/s00703-025-01107-6 · Cited by 1
Large-ensemble generation based on mesoscale ensemble prediction system using machine learning
Nakayama, S., Ohtake, H., Takamatsu, T., Oozeki, T., & Yamaguchi, K. (2026) · Energy Reports · doi:10.1016/j.egyr.2025.109002 · Cited by 1
Classification of Use Cases for Ensemble Weather Forecasts
Mylne, K., Roberts, N. M., Walters, D., Barciela, R. M., Petch, J. C., Wells, O., Willington, S., Suri, D., Steele, E. C. C., Titley, H. A., et al. (2025) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-24-0183.1 · Cited by 1
Forecast verification for subseasonal-to-seasonal timescales
Coelho, C. A. S., Brown, B. G., Wilson, L., Mittermaier, M., Casati, B., & Takaya, Y. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-31538-1.00024-5 · Cited by 1
Verification of Calibrated Multimodel Subseasonal Precipitation Predictions Cascaded From Global to Regional Scale Over Ceará State in Brazil
Coelho, C. A. S., Vasconcelos, F. C., Cardoso, D., Martins, E. S. P. R., & Guimarães, B. S. (2026) · Meteorological Applications · doi:10.1002/met.70179 · Cited by 1
Contribution of lagged members to the performance of a global ensemble prediction system
Shanker, G., Sarkar, A., Mamgain, A., Prasad, S. K., Bhatla, R., & Mitra, A. K. (2022) · Atmospheric Research · doi:10.1016/j.atmosres.2022.106451 · Cited by 1
Diagnostic Verification of Atmospheric Water Cycle Predicted by Regional Mesoscale Models and Ensemble Systems
Polade, S. D. (2012) · Cited by 1
A Score for Probability Forecasts of Binary Events Based on the User’s Cost–Loss Relations
Ivarsson, K.-I. (2019) · Weather and Forecasting · doi:10.1175/waf-d-19-0013.1 · Cited by 1
The Effect of Decision Analysis on Power System Resilience and Economic Value during a Severe Weather Event
Wu, Y., Chen, Y.-C., Chang, H.-L., & Hon, J.-S. (2021) · IEEE Industry Applications Society Annual Meeting (IAS) · doi:10.1109/ias48185.2021.9677422 · Cited by 1
Relative economic value of global ensemble prediction system of NCMRWF , India, for extreme weather events
Shanker, G., Sarkar, A., Mamgain, A., Bhatla, R., & Prasad, V. S. (2024) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.4674 · Cited by 1
On the Utility of Ensemble Rainfall Forecasts Over River Basins in India
Dube, A., & Ashrit, R. (2022) · Pure and Applied Geophysics · doi:10.1007/s00024-025-03682-6 · Cited by 1
On the provision, reliability, and use of hurricane forecasts on various timescales
Jarman, A. S. (2014) · London School of Economics and Political Science Theses Online (London School of Economics and Political Science) · Cited by 1
Sequential Aggregation of Probabilistic Forecasts - Applicaton to Wind Speed Ensemble Forecasts
Zamo, M., Bel, L., & Mestre, O. (2021) · doi:10.5194/egusphere-egu21-11193 · Cited by 1
Parviennusteen satunnaishäiriö- ja jalostushäiriömenetelmä Lorenz-96-mallissa
Siiskonen, V. (2016) · Työväentutkimus Vuosikirja
Error dynamics in ensemble Kalman-filter systems
Houtekamer, P. L. (2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198723844.003.0010
Statistical Models for Characterizing and Reducing Uncertainty in Seasonal Rainfall Pattern Forecasts to Inform Decision Making
AlMutairi, B. S. (2017) · KiltHub Repository · doi:10.1184/r1/6723173
Financial Markets and Climate Models: An Empirical Study on Corn FuturesPreprint
Miftakhova, A., & Pohl, W. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3330535
References
Jolliffe, I. T., & Stephenson, D. B. (2011) · Wiley eBooks · doi:10.1002/9781119960003.refs
References
(2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815823-4.09992-2
References
(2018) · Elsevier eBooks · doi:10.1016/b978-0-12-811714-9.09989-7
Skills of the user-relevant ocean indicators
McAdam, R. (2022) · doi:10.3289/eurosea_d7.4
Comment on gmd-2022-306
(2023) · Geoscientific model development · doi:10.5194/gmd-2022-306-rc1
Accuracy Analysis of ensemble precipitation forecast data based on TIGGE
Wang, H. E., Li, S., Jiang, W., & Diao, Y. (2022) · International Conference on Wearables, Sports and Lifestyle Management (WSLM) · doi:10.1109/wslm54683.2022.00010
Comment on gmd-2022-306
(2023) · Geoscientific model development · doi:10.5194/gmd-2022-306-cec1
Comment on gmd-2022-306
(2023) · Geoscientific model development · doi:10.5194/gmd-2022-306-rc2
Comment on gmd-2022-306
Menut, L. (2023) · Geoscientific model development · doi:10.5194/gmd-2022-306-ac1
Comment on nhess-2024-75
(2024) · Natural hazards and earth system sciences · doi:10.5194/nhess-2024-75-rc1
Review of nhess-2024-75
(2024) · Natural hazards and earth system sciences · doi:10.5194/nhess-2024-75-rc2
Reply on RC1
Korhonen, N. (2024) · Natural hazards and earth system sciences · doi:10.5194/nhess-2024-75-ac1

Authors: Richardson, D. S.
Affiliations: European Centre for Medium-Range Weather Forecasts (GB)
Corresponding author: Richardson, David S.
ORCID: Richardson, David S.
Venue: Quarterly Journal of the Royal Meteorological Society
Publisher: Wiley
Published: 2001-10
Volume: 127
Issue: 577
Pages: 2473-2489
ISSN: 0035-9009 (ISSN-L)
1477-870X
DOI: 10.1002/qj.49712757715
OpenAlex: W2089463042
Cited by: 212
References: 9
Citations by year: 2026: 8 · 2025: 9 · 2024: 10 · 2023: 8 · 2022: 11 · 2021: 7 · 2020: 6 · 2019: 13 · 2018: 14 · 2017: 6 · 2016: 8 · 2015: 8 · 2014: 12 · 2013: 7 · 2012: 6
FWCI: 3.05
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor, 1905 d)
Locations: Quarterly Journal of the Royal Meteorological Society (publishedVersion)
Topics: Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
Keywords: ensemble prediction systems, ensemble size, Brier skill score, reliability diagrams, forecast reliability, sampling error, forecast value, probabilistic forecasting, predictability, extreme events, user sensitivity

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Measures of skill and value of ensemble prediction systems, their interrelationship and the effect of ensemble size

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks