A hierarchical stochastic model of large‐scale atmospheric circulation patterns and multiple station daily precipitation

Wilson, Larry L.; Lettenmaier, Dennis P.; Skyllingstad, Eric D.

doi:10.1029/91jd02155

Cited by 93 publications

(45 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The approach of Wilson et al (1992) is hierarchical and becomes difficult to handle for a medium to large number of stations. The method of Bardossy and Plate (1991) uses a censored power Normal distribution and the procedure needs to resolve the problem of correlation based on rainfall occurrences and intensity.…”

Section: Discussionmentioning

confidence: 99%

“…Several series of circulation patterns and corresponding rainfall occurrences were simulated and the statistics of the simulated and the observed data were similar. Wilson et al (1992) developed a stochastic model of weather states and daily rainfall at multiple rainfall sites. Four classification techniques were investigated to obtain a single index of the regional weather state for each day of the study period.…”

Section: Conditional Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Stochastic generation of annual, monthly and daily climate data: A review

Srikanthan

McMahon²

2001

Hydrol. Earth Syst. Sci.

293

175

View full text Add to dashboard Cite

The generation of rainfall and other climate data needs a range of models depending on the time and spatial scales involved. Most of the models used previously do not take into account year to year variations in the model parameters. Long periods of wet and dry years were observed in the past but were not taken into account. Recently, Thyer and Kuczera (1999) developed a hidden state Markov model to account for the wet and dry spells explicitly in annual rainfall. This review looks firstly at traditional time series models and then at the more complex models which take account of the pseudo-cycles in the data. Monthly rainfall data have been generated successfully by using the method of fragments. The main criticism of this approach is the repetitions of the same yearly pattern when only a limited number of years of historical data are available. This deficiency has been overcome by using synthetic fragments but this brings an additional problem of generating the right number of months with zero rainfall. Disaggregation schemes are effective in obtaining monthly data but the main problem is the large number of parameters to be estimated when dealing with many sites. Several simplifications have been proposed to overcome this problem. Models for generating daily rainfall are well developed. The transition probability matrix method preserves most of the characteristics of daily, monthly and annual characteristics and is shown to be the best performing model. The two-part model has been shown by many researchers to perform well across a range of climates at the daily level but has not been tested adequately at monthly or annual levels. A shortcoming of the existing models is the consistent underestimation of the variances of the simulated monthly and annual totals. As an alternative, conditioning model parameters on monthly amounts or perturbing the model parameters with the Southern Oscillation Index (SOI) result in better agreement between the variance of the simulated and observed annual rainfall and these approaches should be investigated further. As climate data are less variable than rainfall, but are correlated among themselves and with rainfall, multisite-type models have been used successfully to generate annual data. The monthly climate data can be obtained by disaggregating these annual data. On a daily time step at a site, climate data have been generated using a multisite type model conditional on the state of the present and previous days. The generation of daily climate data at a number of sites remains a challenging problem. If daily rainfall can be modelled successfully by a censored power of normal distribution then the model can be extended easily to generate daily climate data at several sites simultaneously. Most of the early work on the impacts of climate change used historical data adjusted for the climate change. In recent studies, stochastic daily weather generation models are used to compute climate data by adjusting the parameters appropriately for the future climates assumed.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Conditional Modelsmentioning

confidence: 99%

Stochastic generation of annual, monthly and daily climate data: A review

Srikanthan

McMahon²

2001

Hydrol. Earth Syst. Sci.

293

175

View full text Add to dashboard Cite

show abstract

“…A few attempts were made to compare the performance of all or some of these methods (e.g. Overland and Hiester, 1980;Key and Crane, 1986;ElKadi and Smithson, 1992;Wilson et al, 1992;Huth, 1996a). A main consensus from such studies is that none of the four methods is superior to the others in all aspects -each method has its advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

On two different objective procedures for classifying synoptic weather types over east Australia

Jiang

Cheung

Luo

et al. 2011

Intl Journal of Climatology

View full text Add to dashboard Cite

Synoptic weather typing depends on the method and its implementation. This paper demonstrates the utility of a 2-phase batch self-organising map procedure (CP2) in comparison with a procedure (CP1) consisting of T-mode principal component analysis followed by convergent K-means clustering, for classifying the synoptic weather types over east Australia. Four classifications were obtained for the 52-year NCEP/NCAR dataset, one from CP1 and three from CP2. These classifications were examined in terms of grouping quality, mean type maps, type frequencies, lifetime and transitions, and in relation to the Southern Oscillation Index (SOI).The results showed that the classifications derived from different procedures are inter-confirmative and capture a similar set of major synoptic situations influencing east Australia, each having counterparts in previous studies and conforming well to local synoptic experience. In particular, this study demonstrates that CP2 is a promising tool for the purpose of weather typing with a two-fold utility: CP2 can perform data projection (neighbourhood width > = 1) and provide a flexible means for visualising the broad distribution of the daily weather patterns in the dataset; and the procedure can also conduct cluster analysis (neighbourhood width = 0) and produce results equivalent to those from CP1. It was found that performing data clustering or data projection may affect the inference associated with type frequencies. While focusing on methodology, the analysis has also shown that the frequency of synoptic weather types fluctuates on the seasonal, interannual, and decadal scales. From the 1970s to 1995 there was a decline in the occurrence of a few weather types associated with an east-coast trough extending over New South Wales from the north, and a slight increase in the activity of some anticyclonic types. Significant correlations were also identified between SOI and frequencies of synoptic weather types on the annual and seasonal timescales.

show abstract

“…resampling of observed data conditioned by large-scale climate variables [106,109,118,[128][129][130][131][132]; weather generator (with the option of conditioning parameters upon large-scale climate variables) [126,[133][134][135][136][137][138][139][140][141][142][143][144]; and, regression-based methods [144][145][146][147][148][149][150][151][152][153]. The specific advantages and disadvantages of these three methods are summarised in Table 9.…”

Section: Statistical Downscalingmentioning

confidence: 99%

Representing Climate and Extreme Weather Events in Integrated Assessment Models: A Review of Existing Methods and Options for Development

Goodess

Hanson

Hulme

et al. 2003

Integrated Assessment

View full text Add to dashboard Cite

The lack of information about future changes in extreme weather is a major constraint of Integrated Assessment Models (IAMs) of climate change. The generation of descriptions of future climate in current IAMs is assessed. We also review recent work on scenario development methods for weather extremes, focusing on those issues which are most relevant to the needs of IAMs. Finally, some options for implementing scenarios of weather extremes in IAMs are considered.Keywords: Integrated Assessment Models, climate change, scenarios, extreme weather events, general circulation models, regional climate models. INTEGRATED ASSESSMENT AND CLIMATE CHANGEOne of the essential characteristics of integrated assessment is the simultaneous consideration of the multiple dimensions of environmental problems such as climate change. A number of formal integrated assessment models (IAMs) for climate change have been developed over the last decade, starting with the models IMAGE 1.0 and ESCAPE in the early 1990s [1,2]. These models were constructed using modules that are reduced-form versions of more complex models, for example of the climate system, the economy and ecosystems. Most climate modules in IAMs generate zero (i.e., globally-averaged; e.g., PAGE) or one (i.e., zonallyaveraged; e.g., IMAGE) dimensional descriptions of future climate, usually at a mean (e.g., 30-year average) seasonal or annual resolution [3][4][5]. Some IAMs (e.g., IMAGE 2.2 and AIM) then generate spatially explicit, i.e., two-dimensional, descriptions of future climate, usually by accessing stored patterns of climate change derived from more complex General Circulation Model (GCM) experiments [6][7][8].These approaches to generating future climate descriptions in IAMs, which are reviewed in Section 2, are computationally efficient and allow multiple experiments to be easily conducted in an integrated framework. The climate output is then input into an ecosystem, agriculture or health impacts module (e.g., AIM), or used directly to estimate the economic cost of climate damage from a lookup climate damage function (e.g., DICE). In either case, the current lack of any information about changes in daily or extreme weather (the focus of this review) is a major weakness. Agriculture, for example, is likely to be as, or more, sensitive to changes in daily weather sequences and the occurrence of extreme weather events than to changes in mean monthly or seasonal climate [9][10][11]. Potential changes in extremes and the ensuing changes in risk are also important for sectors such as water resources and insurance [12,13]. Climate damage functions that express the economic impact of climate change as a function of global-(or regional-) mean climate alone are likely to underestimate the economic damage associated with extreme events such as flooding and storms (e.g., in the UK, the October 1987 windstorm event is estimated to have cost insurers 3.1 billion US dollars, with further economic losses of 2.7 billion US dollars [14]). The lack of information about chan...

show abstract

A hierarchical stochastic model of large‐scale atmospheric circulation patterns and multiple station daily precipitation

Cited by 93 publications

References 53 publications

Stochastic generation of annual, monthly and daily climate data: A review

Stochastic generation of annual, monthly and daily climate data: A review

On two different objective procedures for classifying synoptic weather types over east Australia

Representing Climate and Extreme Weather Events in Integrated Assessment Models: A Review of Existing Methods and Options for Development

Contact Info

Product

Resources

About