Stochastic daily precipitation model with a heavy-tailed component

Neykov, N. M.; Neytchev, P. N.; Zucchini, Walter

doi:10.5194/nhess-14-2321-2014

Cited by 16 publications

(15 citation statements)

References 33 publications

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“…GWGEN diverges from the original WGEN by using a hybrid-order Markov chain to simulate precipitation occurrence (Wilks, 1999a) and a hybrid gamma-GP distribution (Furrer and Katz, 2008;Neykov et al, 2014) to estimate precipitation amount. Temperature, cloud cover, and wind speed are calculated following Richardson (1981), using cross correlation and depending on the wet/dry state of the day.…”

Section: Model Developmentmentioning

confidence: 99%

“…The gamma distribution, however, shows poor performance in simulating highprecipitation events consistent with observations. Furrer and Katz (2008) and Neykov et al (2014) suggest that a hybrid probability density function, based on both gamma and the GP distribution, has superior accuracy in simulating extreme precipitation events when compared to gamma alone. Because of its superior accuracy and ease of implementation, we therefore adopt the hybrid gamma-GP distribution for simulating precipitation amount in GWGEN.…”

Section: Precipitation Amountmentioning

confidence: 99%

“…The GP scale parameter σ on the other hand is calculated during the simulation following Neykov et al (2014) via…”

Section: Precipitation Amountmentioning

confidence: 99%

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A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0

Sommer

Kaplan

2017

Geosci. Model Dev.

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Abstract. While a wide range of Earth system processes occur at daily and even subdaily timescales, many global vegetation and other terrestrial dynamics models historically used monthly meteorological forcing both to reduce computational demand and because global datasets were lacking. Recently, dynamic land surface modeling has moved towards resolving daily and subdaily processes, and global datasets containing daily and subdaily meteorology have become available. These meteorological datasets, however, cover only the instrumental era of the last approximately 120 years at best, are subject to considerable uncertainty, and represent extremely large data files with associated computational costs of data input/output and file transfer. For periods before the recent past or in the future, global meteorological forcing can be provided by climate model output, but the quality of these data at high temporal resolution is low, particularly for daily precipitation frequency and amount. Here, we present GWGEN, a globally applicable statistical weather generator for the temporal downscaling of monthly climatology to daily meteorology. Our weather generator is parameterized using a global meteorological database and simulates daily values of five common variables: minimum and maximum temperature, precipitation, cloud cover, and wind speed. GWGEN is lightweight, modular, and requires a minimal set of monthly mean variables as input. The weather generator may be used in a range of applications, for example, in global vegetation, crop, soil erosion, or hydrological models. While GWGEN does not currently perform spatially autocorrelated multi-point downscaling of daily weather, this additional functionality could be implemented in future versions.

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Section: Model Developmentmentioning

confidence: 99%

Section: Precipitation Amountmentioning

confidence: 99%

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A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0

Sommer

Kaplan

2017

Geosci. Model Dev.

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“…GWGEN is based on the WGEN weather generator (Richardson, 1981), using the method of defining the model parameters based on monthly summaries described by Geng et al (1986) and Geng and Auburn (1987). GWGEN diverges from the original 25 WGEN by using a second-order Markov chain to simulate precipitation occurrence (Wilks, 1999a), and a hybrid Gamma-GP distribution (Furrer and Katz, 2008;Neykov et al, 2014) to estimate precipitation amount. Temperature, cloud cover, and wind speed are calculated following (Richardson, 1981), using cross correlation and depending on the wet/dry-state of the day.…”

Section: Model Developmentmentioning

confidence: 99%

“…The gamma distribution, however, shows poor performance in simulating high-precipitation events consistent with observations. Furrer and Katz (2008) and Neykov et al (2014) suggest that a hybrid probability density function, based on both gamma and the generalized pareto where α > 0 is the shape, and θ > 0 the scale parameter. The generalized pareto (GP) distribution is defined via…”

Section: Precipitation Amountmentioning

confidence: 99%

A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0

Sommer¹,

Kaplan²

2017

Preprint

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While a wide range of earth system processes occur at daily and even sub-daily timescales, many global vegetation and other terrestrial dynamics models historically used monthly meteorological forcing, both to reduce computational demand and because global datasets were lacking. Recently, dynamic land surface modeling has moved towards resolving daily and subdaily processes, and global datasets containg daily or sub-daily meteorology have become available. These meteorological datasets, however, cover only the instrumental era of the last ca. 120 years at best, are subject to considerable uncertainty, 5 and represent extremely large data files with associated computational costs of data input/output and file transfer. For periods before the recent past or into the future, global meteorological forcing can be provided by climate model output, but the quality of these data at high temporal resolution is low, particularly for daily precipitation frequency and amount. Here we present GWGEN, a globally applicable statistical weather generator for the temporal downscaling of monthly climatology to daily meteorology. Our weather generator is parameterized using a global meteorological database and simulates daily values of five 10 common variables: minimum and maximum temperature, precipitation, cloud cover, and windspeed. GWGEN is lightweight, modular, and requires a minimal set of monthly mean variables as input. The weather generator may be used in a range of applications, for example, in global vegetation, crop, soil erosion, or hydrological models. While GWGEN does not perform spatially autocorrelated multi-point downscaling of daily weather, this additional functionality could be easily implemented in future versions. 15Geosci. Model Dev. Discuss., also for a wide range of studies on biodiversity and species distribution (e.g., Elith et al., 2006), vegetation trace gas emissions (e.g., Guenther et al., 1995), 5 and even the geographic distribution of human diseases (e.g., Bhatt et al., 2013) Over subsequent years, the global gridded monthly climate datasets were improved (New et al., 1999(New et al., , 2002, developed with very high spatial resolution (Hijmans et al., 2005), and expanded beyond climatological mean climate to cover continuous timeseries over decades (Harris et al., 2014;Mitchell and Jones, 2005;New et al., 2000). The latter was an essential requirement for forcing dynamic global vegetation models (DGVMs) (e.g., Sitch et al., 2003). However despite increasing quality, spatial 10 resolution, and temporal extent in these datasets, the basic time step remained monthly, partly for legacy reasons -models had been developed in an earlier era subject to computational limitations and therefore used a monthly timestep for efficiency even if this was no longer strictly a constraint -and partly because of the challenge in developing a global, high-resolution climate dataset with a daily or shorter timestep still presented a major data management challenge.On the other hand, there was increasing awareness that ...

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On modeling positive continuous data with spatiotemporal dependence

2020

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In this article, we concentrate on an alternative modeling strategy for positive data that exhibit spatial or spatiotemporal dependence. Specifically, we propose to consider stochastic processes obtained through a monotone transformation of scaled version of 2 random processes. The latter is well known in the specialized literature and originates by summing independent copies of a squared Gaussian process. However, their use as stochastic models and related inference has not been much considered. Motivated by a spatiotemporal analysis of wind speed data from a network of meteorological stations in the Netherlands, we exemplify our modeling strategy by means of a nonstationary process with Weibull marginal distributions. For the proposed Weibull process we study the second-order and geometrical properties and we provide analytic expressions for the bivariate distribution. Since the likelihood is intractable, even for a relatively small data set, we suggest adopting the pairwise likelihood as a tool for inference. Moreover, we tackle the prediction problem and we propose to use a linear prediction. The effectiveness of our modeling strategy is illustrated by analyzing the aforementioned Netherland wind speed data that we integrate with a simulation study. The proposed method is implemented in the R package GeoModels.

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Stochastic daily precipitation model with a heavy-tailed component

Cited by 16 publications

References 33 publications

A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0

A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0

A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0

On modeling positive continuous data with spatiotemporal dependence

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