The tenth IMSC, Beijing, China, 2007 - International Meetings on ...

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chosen since it completely defines the variability of cases and hence the verification score. <strong>The</strong> trouble is that weather is highly correlated in time and space thus having much less variability then the independent normative model indicates. <strong>The</strong> effect of correlation can be incorporated by using randomly SIMULATED VERIFICATION: generated forecasts and observation with known correlation. SYNTHETIC VERIFICATION is the closed form result which would occur from a series of simulated verifications and thus an explicit form shows the effects of skill, event climate probability and other factors on a verification score and one easily programmed simulation of correlated forecasts and verifying observation is the TRANSNORMAL model which generates multivariate normal deviates which are then transformed into weather variables via their cumulative distribution. <strong>The</strong> transormal model can relate with closed form expressions quantitative, category and probability forecasts CLIMATE PREDICTIONS differ from weather forecasts in several ways instead of being for a single location at one time, they are often valid for a large region and an extended valid time thus they are often expressed as distributions. Plus generally because of longer lead times, they generally have lower skill and accuracy. Gringorten (1952) proposed an S score to verify probability density forecasts. Plus synthetic verification indicates it is quite sensitive to low skill forecasts.<strong>The</strong> S score was able to show that one year ahead seasonal temperature analog forecasts forecasts produced by ETAC had significant improvement over chance. CFARC MODEL a global extension of the transnormal model generates weather forecasts and observation on a global scale thus providing a normative model for judging significance of such things as widely separated telaconnections and global climate change. CLIMATE CHANGE VERIFICATION: By using observations from one period as a prediction of a later period, climate change can be verified. We note from experience that the climate is always changing thus the question: has the climate changed is trivial since the answer is always Yes! <strong>The</strong> real question is has the climate changed in an important manner? A Glossary of over 28 verification terms is included. For example: skill: takes into account the difficulty of the forecast in accessing the ability of a forecaster or forecast method. Skill attempts to compare verification of easy forecasts with hard forecasts. Generally, accuracy will be lower when: 1. the forecast event is rare, 2. data is sparse or missing, or 3. the weather situation is complex; it does not fall into an easily recognized pattern. See Skill Score. 2 ROC Verification methodology using Relative Operating Characteristics (ROC), is derived from signal detection theory. This methodology is intended to provide information on the characteristics of systems upon which management decisions can be taken.(WMO2000) Skill Score, SS: Is a generalized form to convert any score into a fractional improvement over some standard. Thus: SS = [S(f) - S(s)] / [S(p) - S(s)] where S(f) is a any score based on forecasts, S(s) is the score based on a standard such a persistence or climatology or chance , and S(p) is the perfect score. For forecasts better then the standard, SS has the range [0, 1] with 0 when forecasts have the same accuracy as the standard and 1 when when forecasts are∞the forecasts are perfect. SS is negative with range to - less than standard. When the perfect score is zero as in the Brier score or MSE, it simplifies to: SS= 1 - S(f)/S(s). WARNING: When the denominator can become small, skill scores are subject to considerable variability. 73
See Heidke Skill Score. Ref. Heidke (1926) and Murphy (1976). Bibliography Murphy, A., 1993: What is a good forecast? An essay on the nature of goodness in weather forecasting, Weather and Forecasting, 8, 281-293. Tufte, Edward R., 1983: <strong>The</strong> Visual Display of Quantitative Information, Graphics Press, Cheshire, CT, pp 197. WMO (200) Standardised Verification System (SVS) for Long-Range Forecasts Integrated seasonal climate forecasts for South America Speaker: Caio Coelho Caio Coelho Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), Instituto Nacional de Pesquisas Espaciais (INPE), Brazil caio@cptec.inpe.br Seasonal climate forecasts are forecasts of the expected climatic conditions in the forthcoming 3-6 months. <strong>The</strong>se forecasts are currently produced using empirical (statistical) and dynamical (physical) models. Given the availability of these two modeling approaches one might question the feasibility of producing a single and well calibrated integrated forecast that gather all available information at the time the forecast is issued. This talk will illustrate how empirical and dynamical coupled model rainfall seasonal forecasts for South America are currently being integrated (i.e. combined and calibrated) using a Bayesian approach know as forecast assimilation. Empirical forecasts are produced using a simple multivariate regression model that uses the previous month Pacific and Atlantic sea surface temperature as predictors for South American rainfall of the following three month season. Dynamical forecasts are from the European Seasonal to Inter-annual Prediction (EURO-SIP) coupled multi-model system composed by ECMWF, UK Met Office and Météo-France. Such an empirical-dynamical multi-model integrated system is part of EUROBRISA (A EURO-BRazilian Initiative for improving South American seasonal forecasts). Preliminary results of rainfall seasonal forecasts for South America produced by each model and by the integrated system together with skill assessment measures, which indicate regions where forecasts have best quality, will be presented. 74
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1Oth International
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Preface It is truly a great pleasur
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Speaker: Dan Cooley................
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Speaker: Qian Li ..................
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Speaker: Ediang Okuku Archibong....
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Speaker: Yi Wang...................
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Climatological Dataset.............
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Bridging the gap between simulated
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Invited Session: Spatial patterns o
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Joint Program on the Science and Po
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e to adopt a different calibrated l
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those retained, and [iii] performan
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A range of existing statistical app
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palaeo-environmental data from sout
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Whilst it has been noted that such
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State Key Laboratory of Numerical M
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address new policies for changing c
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and show promise for analyzing tren
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1901-2005 with complete data of all
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Page 42 and 43: as Homogen Data, Gap, Index, Statis
Page 44 and 45: Penalized maximal t-test for detect
Page 46 and 47: Iran,Tehran, Azad university, Resea
Page 48 and 49: We present applications of several
Page 50 and 51: A 50-member ensemble is produced wi
Page 52 and 53: ichard@stats.ucl.ac.uk Nadja Leith
Page 54 and 55: Answering questions about empirical
Page 56 and 57: In a series of climate change impac
Page 58 and 59: climatological annual frequency of
Page 60 and 61: Detection of urban warming in recen
Page 62 and 63: Detection of Human Influence on pan
Page 64 and 65: Extending attribution studies: post
Page 66 and 67: Francis W. Zwiers, Gabriele C. Hege
Page 68 and 69: 1, In the middle-pattern of El Niñ
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Page 74 and 75: A multiple surrogate data study of
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Page 80 and 81: Parallel Sessions:Regional Studies
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Page 86 and 87: Climate Risk Management in North-Ea
Page 88 and 89: Analyzing of synoptic intence showe
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Page 96 and 97: Empirical probabilistic seasonal fo
Page 98 and 99: Rajesh J. Department of Atmospheric
Page 100 and 101: Andrew Moore Uni. of California at
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Page 104 and 105: data can ever be generated. In fact
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Page 110 and 111: Key words:- principal component reg
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Page 122 and 123: Verification of Climate Forecasts (
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D. Semeradova Institute of Landscap
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Kulkarni Ashwini, Sabade S S and Kr
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The magnitude of t
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Richard E. Chandler University Coll
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convective cloud clusters when they
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1999; Smith et al, 2001; Gibbons, 2
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dmanatsa@buse.ac.zw Wisemen Chingom
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The Relation Betwe
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conditioned on some indices of the
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ari@ludens.elte.hu J. Bartholy Dept
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ainfall as predictand. Additionally
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Impact of ENSO PHASES on Amazonia S
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time lag. Both perturbed analysis a
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Relationship Between Ensemble Mean
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ways were considered. Four zones, f
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- Monthly ratios of synthetic and o
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Total ozone trends are typically st
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Impact of Urban Expansion on Region
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Calibrating and evaluating reanalys
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To explore an optimal climate decis
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