Apipattanavis S., 2008: Stochastic nonparametric methods for multi-site weather generation and flood frequency estimation: applications to construction delay,hydrology and agricultural modeling. PhD dissertation, University of Colorado,199 pages.
Apipattanavis S., G. P. Podest谩, B. Rajagopalan, and R. W. Katz, 2007: A semiparametric multivariate and multisite weather generator. Water Resour. Res., 43,W11401, doi: 10.1029/ 2006WR005 714.10.1029/2006WR005714b759c5039097ef55476c11b32c619242http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2006WR005714%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2006WR005714/full[1] We propose a semiparametric multivariate weather generator with greater ability to reproduce the historical statistics, especially the wet and dry spells. The proposed approach has two steps: (1) a Markov Chain for generating the precipitation state (i.e., no rain, rain, or heavy rain), and (2) a k -nearest neighbor ( k -NN) bootstrap resampler for generating the multivariate weather variables. The Markov Chain captures the spell statistics while the k -NN bootstrap captures the distributional and lag-dependence statistics of the weather variables. Traditional k -NN generators tend to under-simulate the wet and dry spells that are keys to watershed and agricultural modeling for water planning and management; hence the motivation for this research. We demonstrate the utility of the proposed approach and its improvement over the traditional k -NN approach through an application to daily weather data from Pergamino in the Pampas region of Argentina. We show the applicability of the proposed framework in simulating weather scenarios conditional on the seasonal climate forecast and also at multiple sites in the Pampas region.
Apipattanavis S., F. Bert, G. P. Podest谩, and B. Rajagopalan, 2010a: Linking weather generators and crop models for assessment of climate forecast outcomes. Agriculture and Forest Meteorology, 150, 166- 174.10.1016/j.agrformet.2009.09.0122313aad8-3abd-4fac-96a3-b073a7fd4d7cf8d3cc50142c093a44f6093a61f58e21http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0168192309002287refpaperuri:(67dfeb34cd99b40a9720e4bbde318492)http://www.sciencedirect.com/science/article/pii/S0168192309002287Agricultural production responses to climate variability require salient information to support decisions. We coupled a new hybrid stochastic weather generator (combining parametric and nonparametric components) with a crop simulation model to assess yields and economic returns relevant to maize production in two contrasting regions (Pergamino and Pilar) of the Pampas of Argentina. The linked models were used to assess likely outcomes and production risks for seasonal forecasts of dry and wet climate. Forecasts involving even relatively small deviations from climatological probabilities of precipitation may have large impacts on agricultural outcomes. Furthermore, yield changes under alternative scenarios have a disproportionate effect on economic risks. Additionally, we show that regions receiving the same seasonal forecast may experience fairly different outcomes: a forecast of dry conditions did not change appreciably the expected distribution of economic margins in Pergamino (a climatically optimal location) but modified considerably economic expectations (and thus production risk) in Pilar (a more marginal location).
Apipattanavis S., K. Sabol, K. Molenaar, B. Rajagopalan, Y. Xi, B. Blackard, and S. Patil, 2010b: Integrated framework for quantifying and predicting weather-related highway construction delays. Journal of Construction Engineering and Management, 136, 1160- 1168.
Barnston A. G., S. H. Li, S. J. Mason, D. G. DeWitt, L. Goddard, and X. F. Gong, 2010: Verification of the first 11 years of IRI's seasonal climate forecasts. Journal of Applied Meteorology and Climatology, 49, 493- 520.10.1175/2009JAMC2325.1b5be6b0351c18c2641f3369338aa3e4bhttp%3A%2F%2Fwww.researchgate.net%2Fpublication%2F249603814_Verification_of_the_First_11_Years_of_IRI%27s_Seasonal_Climate_Forecastshttp://www.researchgate.net/publication/249603814_Verification_of_the_First_11_Years_of_IRI's_Seasonal_Climate_ForecastsAbstract This paper examines the quality of seasonal probabilistic forecasts of near-global temperature and precipitation issued by the International Research Institute for Climate and Society (IRI) from late 1997 through 2008, using mainly a two-tiered multimodel dynamical prediction system. Skill levels, while modest when globally averaged, depend markedly on season and location and average higher in the tropics than extratropics. To first order, seasons and regions of useful skill correspond to known direct effects as well as remote teleconnections from anomalies of tropical sea surface temperature in the Pacific Ocean (e.g., ENSO related) and in other tropical basins. This result is consistent with previous skill assessments by IRI and others and suggests skill levels beneficial to informed clients making climate risk management decisions for specific applications. Skill levels for temperature are generally higher, and less seasonally and regionally dependent, than those for precipitation, partly because of correct forecasts of enhanced probabilities for above-normal temperatures associated with warming trends. However, underforecasting of above-normal temperatures suggests that the dynamical forecast system could be improved through inclusion of time-varying greenhouse gas concentrations. Skills of the objective multimodel probability forecasts, used as the primary basis for the final forecaster-modified issued forecasts, are comparable to those of the final forecasts, but their probabilistic reliability is somewhat weaker. Automated recalibration of the multimodel output should permit improvements to their reliability, allowing them to be issued as is. IRI is currently developing single-tier prediction components.
Beersma J. J., T. Adri Buishand, 2003: Multi-site simulation of daily precipitation and temperature conditional on the atmospheric circulation. Climate Research, 25, 121- 134.10.3354/cr0251215b9ed9a3b8ffefa8eef3b5d7b329ef31http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F250220884_Multi-site_simulation_of_daily_precipitation_and_temperature_conditional_on_the_atmospheric_circulationhttp://www.researchgate.net/publication/250220884_Multi-site_simulation_of_daily_precipitation_and_temperature_conditional_on_the_atmospheric_circulationABSTRACT Nearest-neighbour resampling was used to generate multi-site sequences of daily precipitation and temperature in the Rhine basin. The simulation is conditional on the values of 3 continuous indices of the atmospheric circulation, An advantage of nearest-neighbour resampling is that the spatial correlations of the daily precipitation and temperature data are automatically preserved in the simulated data. Comparison of different resampling models showed that the simulation of the precipitation and temperature for a new day should not only be conditioned on the circulation characteristics of that day but also on the simulated precipitation and temperature for the previous day, in order to achieve the appropriate level of persistence and variability in the generated data. With a hydrological application in mind, 980 yr multi-site simulations of daily precipitation and temperature were performed conditional on a simulated time series of circulation indices that was obtained with a second resampling model. The distribution of the extreme 10 d area-average precipitation amounts in these long-duration simulations was compared with the distribution of the historical 10 d area averages. Again, the models in which the precipitation and temperature of the previously simulated day were taken into account performed best, but even these models somewhat underestimate the quantiles of the distribution of the 10 d area-average precipitation. The long-duration simulations demonstrate that nearest-neighbour resampling is capable of producing much larger 10 d area-average precipitation amounts than the historical maximum.
Benestad R. E., I. Hanssen-Bauer, and D. L. Chen, 2008: Empirical Statistical Downscaling. World Scientific,228 pps.10.1029/2004EO420002ccfe1c8aab43c45e14e70569577e439fhttp%3A%2F%2Fwww.worldscientific.com%2Fdoi%2Fpdf%2F10.1142%2F9789812819147_bmatterhttp://www.worldscientific.com/doi/pdf/10.1142/9789812819147_bmatterABSTRACT Publisher’s description: Empirical-statistical downscaling (ESD) is a method for estimating how local climatic variables are affected by large-scale climatic conditions. ESD has been applied to local climate/weather studies for years, but there are few – if any – textbooks on the subject. It is also anticipated that ESD will become more important and commonplace in the future, as anthropogenic global warming proceeds. Thus, a textbook on ESD will be important for next-generation climate scientists. Contents: 61 Downscaling strategies 61 Predictors and preprocessing 61 Linear techniques 61 Nonlinear techniques 61 Predictions and diagnostics 61 Shortcomings and limitations 61 Reducing uncertainties 61 Downscaling extremes and PDFs 61 Weather generator 61 Implementing ESD
Briggs W. M., D. S. Wilks, 1996: Extension of the Climate Prediction Center long-lead temperature and precipitation outlooks to general weather statistics. J.Climate, 9, 3496- 3504.10.1175/1520-0442(1996)0092.0.CO;20b1efd88a77cb9bd7dc3592aeb3e636dhttp%3A%2F%2Fwww.researchgate.net%2Fpublication%2F253795108_Extension_of_the_Climate_Prediction_Center_Long-Lead_Temperature_and_Precipitation_Outlooks_to_General_Weather_Statisticshttp://www.researchgate.net/publication/253795108_Extension_of_the_Climate_Prediction_Center_Long-Lead_Temperature_and_Precipitation_Outlooks_to_General_Weather_StatisticsAbstract The long-lead monthly and seasonal forecasts issued by the Climate Prediction Center literally pertain only to average temperature and total precipitation outcomes, but implicitly contain information regarding other quantities that are correlated with these two variables. This paper presents a method for estimating the conditional probability distribution for any such quantity that is a computable statistic of available daily climatological data, through weighted bootstrap resampling conditional on particular joint (temperature and precipitation) forecast probabilities. Examples illustrating implementation and particular results are provided.
Buishand, T. A., 1978: Some remarks on the use of daily rainfall models. J. Hydrol., 36, 295- 308.10.1016/0022-1694(78)90150-62c0b613fc49037e400411d299f756900http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2F0022169478901506http://www.sciencedirect.com/science/article/pii/0022169478901506Features of daily rainfall processes are described using data from different sites of the world. The process of rainfall occurrence is modelled by an alternating renewal process or by a Markov chain. It is shown that rainfall amounts within a wet spell are often neither independent nor identically distributed.There are, however, features of the rainfall process which are hardly sensitive to the choice of the process for the occurrence of wet days or to some assumptions about the behaviour of the rainfall amounts, for example the distribution of 30-day totals and the distribution of monthly extremes.
Caldwell J., B. Rajagopalan, and E. Danner, 2014: Statistical modeling of daily water temperature attributes on the Sacramento River. Journal of Hydrologic Engineering, 20,04014065, doi: 10.1061/(ASCE)HE.1943-5584.0001023.\clearpage10.1061/,DanaInfo=dx.doi.org+(ASCE)HE.1943-5584.0001023995630cd9d106eda249b974015daca02http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F266373690_Statistical_Modeling_of_Daily_Water_Temperature_Attributes_on_the_Sacramento_Riverhttp://www.researchgate.net/publication/266373690_Statistical_Modeling_of_Daily_Water_Temperature_Attributes_on_the_Sacramento_RiverABSTRACT Abstract The Sacramento River is the largest river in California, and an important source of water for agricultural, municipal, and industrial users. Input to the Sacramento River comes from Shasta Lake and is controlled by operators of Shasta Dam, who are challenged with ...
Cleveland, W. S., 1979: Robust locally-weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74, 829- 836.10.2307/2286407ddae677d-3812-49c5-884b-9ab2e4e6dfcce1bd4dfc9921c84aac321c7dbbafd513http%3A%2F%2Fwww.tandfonline.com%2Fdoi%2Fabs%2F10.1080%2F01621459.1979.10481038refpaperuri:(32841f08be6d1618288965dd41b045c0)http://www.tandfonline.com/doi/abs/10.1080/01621459.1979.10481038and small if it is not. A robust fitting procedure is used that guards against deviant points distorting the smoothed points. Visual, computational, and statistical issues of robust locally weighted regression are discussed. Several examples, including data on lead intoxication, are used to illustrate the methodology.
Furrer E. M., R. W. Katz, 2007: Generalized linear modeling approach to stochastic weather generators. Climate Research, 34, 129- 144.10.3354/cr0341293e467321dbfb248a564851bf1c9e5cd5http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F250221852_Generalized_linear_modeling_approach_to_stochastic_weather_generatorshttp://www.researchgate.net/publication/250221852_Generalized_linear_modeling_approach_to_stochastic_weather_generatorsStochastic weather generators are a popular method for producing synthetic sequences of daily weather. We demonstrate that generalized linear models (GLMs) can-provide a general modeling framework, allowing the straightforward incorporation of annual cycles and other covariates (e.g. an index of the El Nino-Southern Oscillation, ENSO) into stochastic weather generators. We apply the GLM technique to daily time series of weather variables (i.e. precipitation and minimum and maximum temperature) from Pergamino, Argentina. Besides annual cycles, the fit is significantly improved by permitting both the transition probabilities of the first-order Markov chain for daily precipitation occurrence, as well as the means of both daily minimum and maximum temperature, to depend on the ENSO state. Although it is more parsimonious than typical weather generators, the GLM-based weather generator performs comparably, particularly in terms of extremes and overdispersion.
Giorgi F., L. O. Mearns, 1991: Approaches to the simulation of regional climate change: A review. Rev. Geophys., 29, 191- 216.10.1029/90RG02636a144800f03cdaf6c8275ba6a055b19a2http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F90RG02636%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/90RG02636/fullThe increasing demand by the scientific community, policy makers, and the public for realistic projections of possible regional impacts of future climate changes has rendered the issue of regional climate simulation critically important. The problem of projecting regional climate changes can be identified as that of representing effects of atmospheric forcings on two different spatial scales: large-scale forcings, i.e., forcings which modify the general circulation and determine the sequence of weather events which characterize the climate regime of a given region (for example, greenhouse gas abundance), and mesoscale forcings, i.e., forcings which modify the local circulations, thereby regulating the regional distribution of climatic variables (for example, complex mountainous systems). General circulation models (GCMs) are the main tools available today for climate simulation. However, they are run and will likely be run for the next several years at resolutions which are too coarse to adequately describe mesoscale forcings and yield accurate regional climate detail. This paper presents a review of these approaches. They can be divided in three broad categories: (1) Purely empirical approaches, in which the forcings are not explicitly accounted for, but regional climate scenarios are constructed by using instrumental data records or paleoclimatic analogues; (2) semiempirical approaches, in which GCMs are used to describe the atmospheric response to large-scale forcings of relevance to climate changes, and empirical techniques account for the effect of mesoscale forcings; and (3) modeling approaches, in which mesoscale forcings are described by increasing the model resolution only over areas of interest. Since they are computationally inexpensive, empirical and semiempirical techniques have been so far more widely used. Their application to regional climate change projection is, however, limited by their own empiricism and by the availability of data sets of adequate quality. More recently, a nested GCM-limited area model methodology for regional climate simulation has been developed, with encouraging preliminary results. As it is physically, rather than empirically, based, the nested modeling framework has a wide range of applications.
Hansen J. W., T. Mavromatis, 2001: Correcting low-frequency variability bias in stochastic weather generators. Agricultural and Forest Meteorology, 109, 297- 310.10.1016/S0168-1923(01)00271-4f1d604a527f3b7582a512982088a30a1http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0168192301002714http://www.sciencedirect.com/science/article/pii/S0168192301002714ABSTRACT Stochastic weather generators used with agricultural simulation models tend to under predict interannual variability of generated climate, often resulting in distortion of simulated agricultural or hydrological variables. This study presents a stochastic weather generator that attempts to improve interannual variability characteristics by perturbing monthly parameters using a low-frequency stochastic model, and evaluates the effectiveness of the low-frequency component on interannual variability of generated monthly climate and simulated crop variables. Effectiveness of the low-frequency correction was tested by comparing results based on observed weather sequences to those generated from the same underlying stochastic model without and with the low-frequency component. For monthly precipitation and maximum and minimum temperatures at 25 locations in the continental USA, the low-frequency correction reduced total error and eliminated negative bias of interannual variability, and reduced the number of station-months with significant differences between observed and generated interannual variability, but over-represented variability of precipitation frequency. For 11 crop scenarios, the low-frequency correction reduced the number of instances in which mean simulated yields and development times differed for observed and generated weather, and improved all measures of interannual variability of simulated yields and development times. We conclude that the approach presented here to disaggregate and separately model the high- and low-frequency components of weather variability can effectively address the negative bias of interannual variability of monthly climatic means found in some stochastic weather generators, and improve crop simulation applications of stochastically-generated weather. Further refinement is needed to better represent interannual variability of both precipitation occurrence and intensity processes, and to rectify over-correction of interannual temperature variability.
Hastie T. J., R. J. Tibshirani, 1990: Generalized Additive Models. Chapman andHall.10.1002/0471667196.ess0297.pub28ad3ce4302b0a35837caba9fc0e0f294http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2F0470011815.b2a09018%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1002/0470011815.b2a09018/pdfLikelihood based regression models, such as the normal linear regression model and the linear logistic model, assume a linear (or some other parametric) form for the covariate effects. We introduce the Local Scotinq procedure which replaces the liner form C Xjpj by a sum of smooth functions C Sj(Xj)a The Sj(.) ‘s are unspecified functions that are estimated using scatterplot smoothers. The technique is applicable to any likelihood-based regression model: the class of Generalized Linear Models contains many of these. In this class, the Locul Scoring procedure replaces the linear predictor VI = C Xj@j by the additive predictor C ai ( hence, the name Generalized Additive Modeb. Local Scoring can also be applied to non-standard models like Cox’s proportional hazards model for survival data. In a number of real data examples, the Local Scoring procedure proves to be useful in uncovering non-linear covariate effects. It has the advantage of being completely automatic, i.e. no “detective work ” is needed on the part of the statistician. In a further generalization, the technique is modified to estimate the form of the link function for generalized linear models. The Local Scoring procedure is shown to be asymptotically equivalent to Local Likelihood estimation, another technique for estimating smooth covariate functions. They are seen to produce very similar results with real data, with Local Scoring being considerably faster. As a theoretical underpinning, we view Local Scoring and Local Likelihood as empirical maximizers of the ezpected log-likelihood, and this makes clear their connection to standard maximum likelihood estimation. A method for estimating the “degrees of freedom” of the procedures is also given.
Hostetler S. W., J. R. Alder, and A. M. Allan, 2011: Dynamically downscaled climate simulations over North America: Methods, evaluation, and supporting documentation for users. U.S. Geological Survey Open-File Report 2011-1238, 64 pp.
Katz R. W., M. B. Parlange, 1998: Overdispersion phenomenon in stochastic modeling of precipitation. J.Climate, 11, 591- 601.10.1175/1520-0442(1998)011<0591:OPISMO>2.0.CO;27622c4937ae65878b21cf9acb18cc1a7http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F37421916_Overdispersion_phenomenon_in_stochastic_modeling_of_precipitationhttp://www.researchgate.net/publication/37421916_Overdispersion_phenomenon_in_stochastic_modeling_of_precipitationSimple stochastic models fit to time series of daily precipitation amount have a marked tendency to under-estimate the observed (or interannual) variance of monthly (or seasonal) total precipitation. By considering extensions of one particular class of stochastic model known as a chain-dependent process, the extent to which this "overdispersion" phenomenon is attributable to an inadequate model for high-frequency variation of pre-cipitation is examined. For daily precipitation amount in January at Chico, California, fitting more complex stochastic models greatly reduces the underestimation of the variance of monthly total precipitation. One source of overdispersion, the number of wet days, can be completely eliminated through the use of a higher-order Markov chain for daily precipitation occurrence. Nevertheless, some of the observed variance remains unexplained and could possibly be attributed to low-frequency variation (sometimes termed "potential predictability"). Of special interest is the fact that these more complex stochastic models still underestimate the monthly variance, more so than does an alternative approach, in which the simplest form of chain-dependent process is conditioned on an index of large-scale atmospheric circulation.
Kim Y., R. W. Katz, B. Rajagopalanc, G. P. Podest谩, and E. M. Furrer, 2012: Reduced overdispersion in stochastic weather generators using a generalized linear modeling approach. Climate Research, 53, 13- 24.d241e216-31ae-48ad-a0aa-f96fe1b30b25676fcbbc67305b658d169196c976e5cchttp%3A%2F%2Fams.confex.com%2Fams%2F87ANNUAL%2Fwebprogram%2FPaper119147.htmlrefpaperuri:(b0cfcaaabe1c0d265537f979b7be18fc)http://ams.confex.com/ams/87ANNUAL/webprogram/Paper119147.htmlWe demonstrate how an approach based on generalized linear models (GLMs) can provide a general modeling framework for incorporating climate states into parametric stochastic weather generators. One advantage of the GLM approach is that software is readily available for fitting such models (e.g., the function glm in the open source statistical programming software R, available at www.r-project.org). Long ago, GLMs were advocated by Stern and Coe (1984) for the stochastic modeling of daily precipitation, and more recently by Chandler (2005) for stochastic modeling of individual daily weather variables more generally. This past work has demonstrated how temporal dependence and annual cycles, as well as climate states, can be incorporated into a stochastic model for a single weather variable. Here we extend this approach to treat several daily weather variables simultaneously, as required to construct a stochastic weather generator.
MacDonald I. L., W. Zucchini, 1997: Hidden Markov and Other Models for Discrete-Valued Time Series. Chapman and Hall.10.2307/1271194e30fdb0bc283e723bc256d2ead48f1b2http%3A%2F%2Fwww.ams.org%2Fmathscinet-getitem%3Fmr%3D1692202http://www.ams.org/mathscinet-getitem?mr=1692202Publication &raquo; Hidden Markov and other Models for Discrete-Valued Time Series.
Mannig B., Coauthors, 2013: Dynamical downscaling of climate change in Central Asia. Global and Planetary Change, 110, 26- 39.10.1016/j.gloplacha.2013.05.008625532a67fe498ac72b2d1c88951445dhttp%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0921818113001331http://www.sciencedirect.com/science/article/pii/S0921818113001331The high-resolution regional climate model (RCM) REMO has been implemented over the region of Central Asia, including western China. A model run forced by reanalysis data (1/2° resolution), and two runs forced by a GCM (one run with 1/2° and one run with 1/6° resolution) have been realized. The model has been evaluated regarding its ability to simulate the mean climate of the period 1971–2000. It has been found that the spatial pattern of mean temperature and precipitation is simulated well by REMO. The REMO simulations are often closer to observational data than reanalysis data are, and show considerably higher spatial detail. The GCM-forced simulations extend to the year 2100 under the A1B scenario. The climate change signal of temperature is largest in winter in the northern part of the study area and over mountainous terrain. A warming up to 702°C is projected until the end of the 21st century. In summer, warming is strongest over the southern part of Central Asia. Changes in precipitation are spatially more heterogeneous.
McCullagh P., J. A. Nelder, 1989: Generalized Linear Models.2nd ed. Chapman and Hall, 206 pages.10.2307/2347392608f301c1d030aeb394f2b139e9f869chttp%3A%2F%2Fwww.researchgate.net%2Fpublication%2F236853442_Generalized_Linear_Models_2nd_Edhttp://www.researchgate.net/publication/236853442_Generalized_Linear_Models_2nd_EdAddresses a class of statistical models that generalizes classical linear models-extending them to include many other models useful in statistical analysis. Incorporates numerous exercises, both theoretical and data-analytic Discusses quasi-likelihood functions and estimating equations, models for dispersion effect, components of dispersion, and conditional likelihoods Holds particular interest for statisticians in medicine, biology, agriculture, social science, and engineering
Rajagopalan B., V. Lall, 1999: A k-nearest neighbor simulator for daily precipitation and other weather variables. Water Resour. Res., 35, 3089- 3101.10.1029/1999WR900028443ab1cc-80c6-46f3-a9e2-34634c00986fcc47fad308d972116f82d1e9455a18f2http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F1999WR900028%2Ffullrefpaperuri:(ae3e2e60cf2f8d3d7d05e98da212826b)http://onlinelibrary.wiley.com/doi/10.1029/1999WR900028/fullAbstract. A multivariate, nonparametric time series simulation method is provided to generate random sequences of daily weather variables that “honor ” the statistical properties of the historical data of the same weather variables at the site. A vector of weather variables (solar radiation, maximum temperature, minimum temperature, average dew point temperature, average wind speed, and precipitation) on a day of interest is resampled from the historical data by conditioning on the vector of the same variables (feature vector) on the preceding day. The resampling is done from the k nearest neighbors in state space of the feature vector using a weight function. This approach is equivalent to a nonparametric approximation of a multivariate, lag 1 Markov process. It does not require prior assumptions as to the form of the joint probability density function of the variables. An application of the resampling scheme with 30 years of daily weather data at Salt Lake City, Utah, is provided. Results are compared with those from the application of a multivariate autoregressive model similar to that of Richardson [1981].
Richardson C. W., 1981: Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour. Res., 17, 182- 190.10.1029/WR017i001p00182da8b7f70cab36aaea83f8cb062afebd5http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2FWR017i001p00182%2Fabstracthttp://onlinelibrary.wiley.com/doi/10.1029/WR017i001p00182/abstractLong samples of weather data are frequently needed to evaluate the long-term effects of proposed hydrologic changes. The evaluations are often undertaken using deterministic mathematical models that require daily weather data as input. Stochastic generation of the required weather data offers an attractive alternative to the use of observed weather records. This paper presents an approach that may be used to generate long samples of daily precipitation, maximum temperature, minimum temperature, and solar radiation. Precipitation is generated independently of the other variables by using a Markov chain-exponential model. The other three variables are generated by using a multivariate model with the means and standard deviations of the variables conditioned on the wet or dry status of the day as determined by the precipitation model. Daily weather samples that are generated with this approach preserve the seasonal and statistical characteristics of each variable and the interrelations among the four variables that exist in the observed data.
Stern R. D., R. Coe, 1984: A model fitting analysis of daily rainfall data. Journal of the Royal Statistical Society: Series A, 147, 1- 34.10.2307/2981736a04558af2f593575ae4195d7178cce71http%3A%2F%2Fwww.jstor.org%2Fstable%2F2981736http://www.jstor.org/stable/2981736We have recently shown that bimorph piezoelectric PVDF films induce formation of periosteal bone in vivo and attributed this phenomenon to a piezoelectric effect. In the present study films were implanted in rabbits to encircle the femoral diaphysis. Specimens obtained after 6 and 12 days were subjected to routine processing for electron microscopy as well as fixation using the Ka-pyroantimonate technique. The electron micrographs revealed that initial osteoblastic differentiation and formation of collagenous matrix were followed by Ca accumulation in mitochondria. Calcification of the matrix progressed with deposition of mineralizing nodules and their fusion to form larger calcified masses. This was associated with disappearance of the pyroantimonate positive material from mitochondria. These ultrastructural observations confirm that bimorph films induce bone formation and disclose some features of the calcification process of the osseous callus.
Verdin A., B. Rajagopalan, W. Kleiber, and R. W. Katz, 2015: Coupled stochastic weather generation using spatial and generalized linear models. Stochastic Environmental Research and Risk Assessment, 29, 347- 356.10.1007/s00477-014-0911-634ca5b591ace44557a7838b2bc54d4a7http%3A%2F%2Flink.springer.com%2F10.1007%2Fs00477-014-0911-6http://link.springer.com/10.1007/s00477-014-0911-6We introduce a stochastic weather generator for the variables of minimum temperature, maximum temperature and precipitation occurrence. Temperature variables are modeled in vector autoregressive framework, conditional on precipitation occurrence. Precipitation occurrence arises via a probit model, and both temperature and occurrence are spatially correlated using spatial Gaussian processes. Additionally, local climate is included by spatially varying model coefficients, allowing spatially evolving relationships between variables. The method is illustrated on a network of stations in the Pampas region of Argentina where nonstationary relationships and historical spatial correlation challenge existing approaches.
Wilby R. L., T. M. L. Wigley, 1997: Downscaling general circulation model output: A review of methods and limitations. Progress in Physical Geography, 21, 530- 548.10.1177/03091333970210040377fd0191d053b25f7f12e08c08179b7bhttp%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F30030794057http://ci.nii.ac.jp/naid/30030794057General circulation models (GCMs) suggest that rising concentrations of greenhouse gases may have significant consequences for the global climate. What is less clear is the extent to which local (subgrid) scale meteorological processes will be affected. So-called 'downscaling' techniques have subsequently emerged as a means of bridging the gap between what climate modellers are currently able to provide and what impact assessors require. This article reviews the present generation of downscaling tools under four main headings: regression methods; weather pattern (circulation)-based approaches; stochastic weather generators; and limited-area climate models. The penultimate section summarizes the results of an international experiment to intercompare several precipitation models used for downscaling. It shows that circulation-based downscaling methods perform well in simulating present observed and model-generated daily precipitation characteristics, but are able to capture only part of the daily precipitation variability changes associated with model-derived changes in climate. The final section examines a number of ongoing challenges to the future development of climate downscaling.
Wilby R. L., T. M. L. Wigley, D. Conway, P. D. Jones, B. C. Hewitson, J. Main, and D. S. Wilks, 1998: Statistical downscaling of general circulation model output: A comparison of methods. Water Resour. Res., 34, 2995- 3008.10.1029/98WR025778759003a8d770613118171c428931d59http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F98WR02577%2Fabstracthttp://onlinelibrary.wiley.com/doi/10.1029/98WR02577/abstractA range of different statistical downscaling models was calibrated using both observed and general circulation model (GCM) generated daily precipitation time series and intercompared. The GCM used was the U.K. Meteorological Office, Hadley Centre's coupled ocean/atmosphere model (HadCM2) forced by combined CO 2 and sulfate aerosol changes. Climate model results for 1980&ndash;1999 (present) and 2080&ndash;2099 (future) were used, for six regions across the United States. The downscaling methods compared were different weather generator techniques (the standard &ldquo;WGEN&rdquo; method, and a method based on spell-length durations), two different methods using grid point vorticity data as an atmospheric predictor variable (B-Circ and C-Circ), and two variations of an artificial neural network (ANN) transfer function technique using circulation data and circulation plus temperature data as predictor variables. Comparisons of results were facilitated by using standard sets of observed and GCM-derived predictor variables and by using a standard suite of diagnostic statistics. Significant differences in the level of skill were found among the downscaling methods. The weather generation techniques, which are able to fit a number of daily precipitation statistics exactly, yielded the smallest differences between observed and simulated daily precipitation. The ANN methods performed poorly because of a failure to simulate wet-day occurrence statistics adequately. Changes in precipitation between the present and future scenarios produced by the statistical downscaling methods were generally smaller than those produced directly by the GCM. Changes in daily precipitation produced by the GCM between 1980&ndash;1999 and 2080&ndash;2099 were therefore judged not to be due primarily to changes in atmospheric circulation. In the light of these results and detailed model comparisons, suggestions for future research and model refinements are presented.
Wilby R. L., S. P. Charles, E. Zorita, B. Timbal, P. Whetton, and L. O. Mearns, 2004: Guidelines for use of climate scenarios developed from statistical downscaling methods. Supporting material for Data Distribution Centre of Intergovernmental Panel on Climate Change. [Available online at http://www.ipcc-data.org/guidelines/dgmno2400v1092004.pdf].33efd45a38959695ba9e90a151e8a0fahttp%3A%2F%2Fwww.citeulike.org%2Fgroup%2F14742%2Farticle%2F8861447http://www.citeulike.org/group/14742/article/8861447Search all the public and authenticated articles in CiteULike. Include unauthenticated resultstoo (may include "spam") Enter a search phrase. You can also specify a CiteULike article id(123456),. a DOI (doi:10.1234/12345678). or a PubMed ID (pmid:12345678).
Wilks D. S., 1989: Conditioning stochastic daily precipitation models on total monthly precipitation. Water Resour. Res., 25, 1429- 1439.10.1029/WR025i006p014295d5ddc77cb0bdd13d27e475bcbad7f27http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2FWR025i006p01429%2Fabstracthttp://onlinelibrary.wiley.com/doi/10.1029/WR025i006p01429/abstractABSTRACT Chain-dependent stochastic daily precipitation models are fit to dry, near-normal, and wet subsets of monthly total precipitation data, using category definitions consistent with the 30-day forecasts issued by the Climate Analysis Center of the National Oceanic and Atmospheric Administration. The resulting models are compared to those derived unconditionally from entire data records. It is found that for the 10 selected North American stations investigated, the unconditional models produce distributions of total monthly precipitation having too few dry and wet months as compared to the observations, while appropriate probability mixtures of the three conditional models can accurately reproduce the climatological distributions of total monthly precipitation. Application of the conditional precipitation models to generation of daily data consistent with certain longer-term aspects of the observations is also illustrated.
Wilks D. S., R. L. Wilby, 1999: The weather generator game: A review of stochastic weather models. Progress in Physical Geography, 23, 329- 357.
Xu Z. F., Z. L. Yang, 2012: An improved dynamical downscaling method with GCM bias corrections and its validation with 30 years of climate simulations. J.Climate, 25, 6271- 6286.10.1175/JCLI-D-12-00005.165e9224f-1c85-4251-9b9e-a6cda57917a3f252401a541820250a58a59c2e801f56http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F233409970_An_Improved_Dynamical_Downscaling_Method_with_GCM_Bias_Corrections_and_Its_Validation_with_30_Years_of_Climate_Simulationsrefpaperuri:(bca895dcf28a91598081e2808aa73821)http://www.researchgate.net/publication/233409970_An_Improved_Dynamical_Downscaling_Method_with_GCM_Bias_Corrections_and_Its_Validation_with_30_Years_of_Climate_SimulationsAbstract An improved dynamical downscaling method (IDD) with general circulation model (GCM) bias corrections is developed and assessed over North America. A set of regional climate simulations is performed with the Weather Research and Forecasting Model (WRF) version 3.3 embedded in the National Center for Atmospheric Research's (NCAR's) Community Atmosphere Model (CAM). The GCM climatological means and the amplitudes of interannual variations are adjusted based on the National Centers for Environmental Prediction (NCEP)鈥揘CAR global reanalysis products (NNRP) before using them to drive WRF. In this study, the WRF downscaling experiments are identical except the initial and lateral boundary conditions derived from the NNRP, original GCM output, and bias-corrected GCM output, respectively. The analysis finds that the IDD greatly improves the downscaled climate in both climatological means and extreme events relative to the traditional dynamical downscaling approach (TDD). The errors of downscaled climatological mean air temperature, geopotential height, wind vector, moisture, and precipitation are greatly reduced when the GCM bias corrections are applied. In the meantime, IDD also improves the downscaled extreme events characterized by the reduced errors in 2-yr return levels of surface air temperature and precipitation. In comparison with TDD, IDD is also able to produce a more realistic probability distribution in summer daily maximum temperature over the central U.S.鈥揅anada region as well as in summer and winter daily precipitation over the middle and eastern United States.
Yates D., S. Gangopadhyay, B. Rajagopalan, and K. Strzepek, 2003: A technique for generating regional climate scenarios using a nearest neighbor algorithm. Water Resour. Res., 39,1199, doi: 10.1029/2002WR001769.
Yoon J. H., L. Y. R. Leung, and J. Correia Jr., 2012: Comparison of dynamically and statistically downscaled seasonal climate forecasts for the cold season over the United States. J. Geophys. Res., 117,D21109, doi: 10.1029/2012JD017650.10.1029/2012JD017650f3bf845c94d1eced785842297b527453http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2012JD017650%2Fcitedbyhttp://onlinelibrary.wiley.com/doi/10.1029/2012JD017650/citedby[1] This study compares two approaches, dynamical and statistical downscaling, for their potential to improve regional seasonal forecasts for the United States (U.S.) during the cold season. In the MultiRCM Ensemble Downscaling (MRED) project, seven regional climate models (RCMs) are used to dynamically downscale the Climate Forecast System (CFS) seasonal prediction over the conterminous U.S. out to 5 months for the period of 1982鈥2003. The simulations cover December to April of next year with 10 ensemble members from each RCM with different initial and boundary conditions from the corresponding ensemble members. These dynamically downscaled forecasts are compared with statistically downscaled forecasts produced by two bias correction methods applied to both the CFS and RCM forecasts. Results of the comparison suggest that the RCMs add value in seasonal prediction application, but the improvements largely depend on location, forecast lead time, variables, and skill metrics used for evaluation. Generally, more improvements are found over the Northwest and North Central U.S. for the shorter lead times. The comparison results also suggest a hybrid forecast system that combines both dynamical and statistical downscaling methods have the potential to maximize prediction skill.