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

More documents

Recommendations

Info

data can ever be generated. In fact, conventional resampling algorithms, such as the standard form of bootstrap, fail for the maximum value of a sample or when the underlying distribution is heavy-tailed. Such limitations of the resampling approach are demonstrated in this talk by example. <strong>The</strong> alternative approach to stochastic weather generation is parametric. This approach involves a formal stochastic model for time series of daily weather variables (e.g., a Markov chain model for precipitation occurrence). Because the parametric distributions (e.g., gamma) typically used cannot produce a heavy tail, precipitation extremes can be underestimated by a substantial amount. Furrer and Katz (<strong>2007</strong>) proposed a generalized linear modeling (GLM) technique for parametric stochastic weather generation. <strong>The</strong> GLM technique and its extensions are used in this talk to explore how the simulation of high precipitation amounts can be improved. In particular, the substitution of a stretched exponential (or Weibull) distribution, instead of the gamma, for the upper tail of daily precipitation amounts can be readily incorporated into the GLM approach. Extreme value theory has established that such a distribution can produce an apparent heavy tail. Requiring an extension of the GLM framework is the technique of fitting a mixture of two Weibull distributions to daily precipitation amount. <strong>The</strong> results suggest that the simulation of extreme precipitation events can be substantially improved, with a stochastic model that remains fairly parsimonious. Reference Furrer, E.M., and R.W. Katz, <strong>2007</strong>: Generalized linear modeling approach to stochastic weather generators. Climate Research (in press). Atmospheric dust dispersion from disking operation: near-field simulation Speaker: Junming wang Junming wang New Mexico State University, Plant & Environmental Sciences, USA jwang@nmsu.edu April L. Hiscox University of Connecticut, USA David R. Miller University of Connecticut, USA Ted Sammis New Mexico State University, Plant & Environmental Sciences, USA Wenli Yang, Britt A. Holmén University of California at Davis, USA This paper presents a dynamic random-walk model which simulates the field-scale PM10 (particle m) dust dispersion from an agriculture operationμmatter of diameter ≤10 87
(disking). <strong>The</strong> major inputs are the mean wind speed (wind friction velocity, u*) and direction in the simulation period, atmospheric stability, and source strength (g/s, or particle numbers/s). In each time step (usually dt
Page 1 and 2:
1Oth International
Page 3:
Preface It is truly a great pleasur
Page 6 and 7:
Speaker: Dan Cooley................
Page 8 and 9:
Speaker: Qian Li ..................
Page 10 and 11:
Speaker: Ediang Okuku Archibong....
Page 12 and 13:
Speaker: Yi Wang...................
Page 14 and 15:
Climatological Dataset.............
Page 16 and 17:
Bridging the gap between simulated
Page 18 and 19:
Invited Session: Spatial patterns o
Page 20 and 21:
Joint Program on the Science and Po
Page 22 and 23:
e to adopt a different calibrated l
Page 24 and 25:
those retained, and [iii] performan
Page 26 and 27:
A range of existing statistical app
Page 28 and 29:
palaeo-environmental data from sout
Page 30 and 31:
Whilst it has been noted that such
Page 32 and 33:
State Key Laboratory of Numerical M
Page 34 and 35:
address new policies for changing c
Page 36 and 37:
and show promise for analyzing tren
Page 38 and 39:
1901-2005 with complete data of all
Page 40 and 41:
Homogenization of daily meteorologi
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
Page 70 and 71: convergence zone (ITCZ). Th
Page 72 and 73: stationary waves occur in mid-latit
Page 74 and 75: A multiple surrogate data study of
Page 76 and 77: Linear and non-linear reconstructio
Page 78 and 79: Pseudo-proxy tests of the regulariz
Page 80 and 81: Parallel Sessions:Regional Studies
Page 82 and 83: Iterative Interpolation of Daily Pr
Page 84 and 85: disturbance in the natural ecosyste
Page 86 and 87: Climate Risk Management in North-Ea
Page 88 and 89: Analyzing of synoptic intence showe
Page 90 and 91: chosen since it completely defines
Page 92 and 93: The Roles of Ensem
Page 94 and 95: The diagram shows
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
Page 102 and 103: Parallel Sessions:Simulation tools
Page 106 and 107: egion can be explained by the rando
Page 108 and 109: on instantaneous time scales, respe
Page 110 and 111: Key words:- principal component reg
Page 112 and 113: een analysed with geostatistical to
Page 114 and 115: Iberian Peninsula and with the corr
Page 116 and 117: 89% during the 10 days of mid-June,
Page 118 and 119: The paper examines
Page 120 and 121: Special AMS Session:Verification of
Page 122 and 123: Verification of Climate Forecasts (
Page 124 and 125: the regional climate change is repr
Page 126 and 127: model has been applied to the serie
Page 128 and 129: are analyzed. It is shown that thes
Page 130 and 131: stations in Iran. The</stro
Page 132 and 133: The study of the U
Page 134 and 135: Development and analysis of a daily
Page 136 and 137: Fractal widely exists in nature, an
Page 138 and 139: Rescue of historical climate data:
Page 140 and 141: D. Semeradova Institute of Landscap
Page 142 and 143: Kulkarni Ashwini, Sabade S S and Kr
Page 144 and 145: The magnitude of t
Page 146 and 147: Richard E. Chandler University Coll
Page 148 and 149: convective cloud clusters when they
Page 150 and 151: 1999; Smith et al, 2001; Gibbons, 2
Page 152 and 153: dmanatsa@buse.ac.zw Wisemen Chingom
Page 154 and 155:
The Relation Betwe
Page 156 and 157:
conditioned on some indices of the
Page 158 and 159:
ari@ludens.elte.hu J. Bartholy Dept
Page 160 and 161:
ainfall as predictand. Additionally
Page 162 and 163:
Impact of ENSO PHASES on Amazonia S
Page 164 and 165:
time lag. Both perturbed analysis a
Page 166 and 167:
Relationship Between Ensemble Mean
Page 168 and 169:
ways were considered. Four zones, f
Page 170 and 171:
- Monthly ratios of synthetic and o
Page 172 and 173:
Total ozone trends are typically st
Page 174 and 175:
Impact of Urban Expansion on Region
Page 176 and 177:
Calibrating and evaluating reanalys
Page 178:
To explore an optimal climate decis
show all

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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?