in Intergovernmental Panel on Climate Change (IPCC), 4th Assessment Report, Working Group 1: The Physical Basis of Climate Change, (2007) 1
Quarterly Journal of the Royal Meteorological Society 133 (2007) 1309-1325
Ensemble prediction systems aim to account for uncertainties of initial conditions and model error. Ensemble forecasting is sometimes viewed as a method of obtaining (objective) probabilistic forecasts. How is one to judge the quality of an ensemble at forecasting a system? The probability that the bounding box of an ensemble captures some target (such as 'truth' in a perfect model scenario) provides new statistics for quantifying the quality of an ensemble prediction system: information that can provide insight all the way from ensemble system design to user decision support. These simple measures clarify basic questions, such as the minimum size of an ensemble. To illustrate their utility, bounding boxes are used in the imperfect model context to quantify the differences between ensemble forecasting with a stochastic model ensemble prediction system and a deterministic model prediction system. Examining forecasts via their bounding box statistics provides an illustration of how adding stochastic terms to an imperfect model may improve forecasts even when the underlying system is deterministic. Copyright © 2007 Royal Meteorological Society.
NATURE 439 (2006) 576-579
SOLA Meteorological Society of Japan 2 (2006) 33-36
In this study, we investigate the impact of Multi-Center Grand Ensemble (MCGE) forecasts, consisting of three operational ensemble forecasts by the Japan Meteorological Agency (JMA), the National Centers for Environmental Prediction, and the Canadian Meteorological Center. We verified the skill of MCGE forecasts in comparison with that of JMA ensemble forecast using root mean square error, anomaly correlation, and Brier skill score for 500 hPa geopotential height and 850 hPa temperature in the Northern Hemisphere (20°N-90°N) in September 2005.Our results show that MCGE forecasts are more skillful than single-center ensemble forecast without considering weight among ensemble members and bias corrections. This implies that considering weight or bias corrections may result in further improvement of MCGE forecasts, specifically in probabilistic forecasts.
Erratum: "Changing frequency of occurrence of extreme seasonal temperatures under global warming" (Geophysical Research Letters (2005) vol. 32 10.1029/2005GL023365)
Geophysical Research Letters 33 (2006)
CLIMATE RESEARCH 33 (2006) 19-26
Changing frequency of occurrence of extreme seasonal temperatures under global warming (vol 32, art no L20721, 2005)
GEOPHYSICAL RESEARCH LETTERS 33 (2006) ARTN L07712
GEOPHYSICAL RESEARCH LETTERS 33 (2006) ARTN L07708
Tellus, Series A: Dynamic Meteorology and Oceanography 57 (2005) 265-279
Insight into the likely weather several months in advance would be of great economic and societal value. The DEMETER project has made coordinated multi-model, multi-initial-condition simulations of the global weather as observed over the last 40 years; transforming these model simulations into forecasts is non-trivial. One approach is to extract merely a single forecast (e.g. best-first-guess) designed to minimize some measure of forecast error. A second approach would be to construct a full probability forecast. This paper explores a third option, namely to see how often this collection of simulations can be said to capture the target value, in the sense that the target lies within the bounding box of the forecasts. The DEMETER forecast system is shown to often capture the 2-m temperature target in this sense over continental areas at lead times up to six months. The target is captured over 95% of the time at over a third of the grid points and maintains a bounding box range less than that of the local climatology. Such information is of immediate value from a user's perspective. Implications for the minimum ensemble size as well as open foundational issues in translating a set of multi-model multi-initial-condition simulations into a forecast are discussed; in particular, those involving 'bias correction' are consider. Copyright © Blackwell Munksgaard, 2005.
Influence of a stochastic parameterization on the frequency of occurrence of North Pacific weather regimes in the ECMWF model
GEOPHYSICAL RESEARCH LETTERS 32 (2005) ARTN L23811
Recurrent climate winter regimes in reconstructed and modelled 500 hPa geopotential height fields over the North Atlantic/European sector 1659-1990
CLIMATE DYNAMICS 24 (2005) 809-822
ANNUAL REVIEW OF EARTH AND PLANETARY SCIENCES 33 (2005) 163-193
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 86 (2005) 519-+
The rationale behind the success of multi-model ensembles in seasonal forecasting - II. Calibration and combination
TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY 57 (2005) 234-252
The rationale behind the success of multi-model ensembles in seasonal forecasting - I. Basic concept
TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY 57 (2005) 219-233
Philos Trans R Soc Lond B Biol Sci 360 (2005) 1991-1998
The development of multi-model ensembles for reliable predictions of inter-annual climate fluctuations and climate change, and their application to health, agronomy and water management, are discussed.
A forecast quality assessment of an end-to-end probabilistic multi-model seasonal forecast system using a malaria model
TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY 57 (2005) 464-475