A daily weather generator for use in climate change studies

Kilsby, C. G.; Jones, Phil; Burton, A.; Ford, Alistair; Fowler, Hayley J.; Harpham, Colin; James, Philip; Smith, Ann C.; Wilby, Robert L.

doi:10.1016/j.envsoft.2007.02.005

Cited by 414 publications

(357 citation statements)

References 21 publications

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“…Although Jones et al (2009) asserted that this model has been shown realistically to reproduce extreme values for impact studies, based on Cowpertwait et al (2002) and Kilsby et al (2007), the model performance for those extremes related to low rainfall, i.e. drought, have not been widely examined.…”

Section: Ukcp09 Weather Generatorsmentioning

confidence: 99%

Comparison of drought projections using two UK weather generators

Chun

Wheater

Onof

2013

Hydrological Sciences Journal

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Section: Ukcp09 Weather Generatorsmentioning

confidence: 99%

Comparison of drought projections using two UK weather generators

Chun

Wheater

Onof

2013

Hydrological Sciences Journal

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“…In the semi-empirical generator, a few distributions can be defined, for instance for wet and dry spell lengths and precipitation amount. In the NSRP weather generator, Kilsby et al (2007) proposed four different steps. A storm origin is described by the Poisson process.…”

Section: Weather Generatorsmentioning

confidence: 99%

Projected Change—Models and Methodology

Wibig

Maraun

Benestad

et al. 2015

Regional Climate Studies

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General (global) circulation models (GCMs) are a useful tool for studying how climate may change in the future. Although GCMs have high temporal resolution, their spatial resolution is low. To simulate the future climate of the Baltic Sea region, it is necessary to downscale GCM data. This chapter describes the two conceptually different ways of downscaling: regional climate models (RCMs) nested in GCMs and using empirical and/or statistical relations between large-scale variables from GCMs and small-scale variables. There are many uncertainties in climate models, including uncertainty related to future land use and atmospheric greenhouse gas concentrations, limits on the amount of input data and their accuracy, and the chaotic nature of weather. The skill of methods for describing regional climate futures is also limited by natural climate variability. For the Baltic Sea area, the lack of an oceanic component in RCMs and poor representation of forcing by aerosols and changes in land use are major limitations. IntroductionThe development of general circulation models (GCMs) has created a useful tool for projecting how climate may change in the future. Such models describe the climate at a set of grid points, regularly distributed in space and time and with the same density over land and ocean. Their temporal resolution is relatively high, but their spatial resolution is limited by computing power. Many important processes, such as cloud formation, convection, and precipitation, occur at spatial scales much smaller than the distance between grid points. This means that these so-called sub-grid processes are not explicitly simulated by the models, but must be approximated with simplifying algorithms referred to as parameterisations. The low spatial resolution also means that the topography, coastline, and processes at the land-air, ocean-air, and land-ocean boundaries are coarsely represented in GCMs.

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“…Synthetic weather data were created using the EARWIG weather generator (Kilsby et al, 2007), which is based on the Neyman-Scott rectangular pulses rainfall model. More specifically, we generated 3000 samples of precipitation data, each representing one year of daily rainfall records (precipitation ≥1 mm) in a 10 × 10 km grid around Heathrow Airport.…”

Section: Precision and Accuracy Of Ordinary And Gpd-based Percentilesmentioning

confidence: 99%

A test for network‐wide trends in rainfall extremes

Burauskaite-Harju

Grimvall

Brömssen

2010

Intl Journal of Climatology

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Temporal trends in meteorological extremes are often examined by first reducing daily data to annual index values, such as the 95th or 99th percentiles. Here, we report how this idea can be elaborated to provide an efficient test for trends at a network of stations. The initial step is to make separate estimates of tail probabilities of precipitation amounts for each combination of station and year by fitting a generalised Pareto distribution (GPD) to data above a user-defined threshold. The resulting time series of annual percentile estimates are subsequently fed into a multivariate Mann-Kendall (MK) test for monotonic trends. We performed extensive simulations using artificially generated precipitation data and noted that the power of tests for temporal trends was substantially enhanced when ordinary percentiles were substituted for GPD percentiles. Furthermore, we found that the trend detection was robust to misspecification of the extreme value distribution. An advantage of the MK test is that it can accommodate non-linear trends, and it can also take into account the dependencies between stations in a network. To illustrate our approach, we used long time series of precipitation data from a network of stations in The Netherlands.

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A daily weather generator for use in climate change studies

Cited by 414 publications

References 21 publications

Comparison of drought projections using two UK weather generators

Comparison of drought projections using two UK weather generators

Projected Change—Models and Methodology

A test for network‐wide trends in rainfall extremes

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