A conditional stochastic weather generator for seasonal to multi-decadal simulations

Verdin, A.; Rajagopalan, Balaji; Kleiber, William; Podestá, Guillermo; Bert, Federico

doi:10.1016/j.jhydrol.2015.12.036

Cited by 46 publications

(45 citation statements)

References 70 publications

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“…By contrast, stochastic weather generators are commonly used to alter daily weather characteristics, including the likelihood and persistence of wet and dry days, the intensity and seasonality of precipitation, its interannual persistence, and the magnitude and range of minimum and maximum temperature (Guo et al, ; Steinschneider & Brown, ). These approaches must maintain realistic persistence and covariance structures across space and time and potentially between multiple variables (e.g., precipitation, temperature, and wind speed) (Allard & Bourotte, ; Kwon et al, ; Steinschneider & Brown, ; Verdin et al, ), which can be supported by GCM output.…”

Section: Perspectives: Research Gaps and Opportunitiesmentioning

confidence: 99%

“…Water Resources Research . These approaches must maintain realistic persistence and covariance structures across space and time and potentially between multiple variables (e.g., precipitation, temperature, and wind speed) (Allard & Bourotte, 2015;Kwon et al, 2009;Steinschneider & Brown, 2013;Verdin et al, 2018), which can be supported by GCM output.…”

Section: 1029/2019wr025502mentioning

confidence: 99%

See 1 more Smart Citation

Climate Adaptation as a Control Problem: Review and Perspectives on Dynamic Water Resources Planning Under Uncertainty

Herman

Quinn

Steinschneider

et al. 2020

Water Resources Research

142

111

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Climate change introduces substantial uncertainty to water resources planning and raises the key question: when, or under what conditions, should adaptation occur? A number of recent studies aim to identify policies mapping future observations to actions—in other words, framing climate adaptation as an optimal control problem. This paper uses the control paradigm to review and classify recent dynamic planning studies according to their approaches to uncertainty characterization, policy structure, and solution methods. We propose a set of research gaps and opportunities in this area centered on the challenge of characterizing uncertainty, which prevents the unambiguous application of control methods to this problem. These include exogenous uncertainty in forcing, model structure, and parameters propagated through a chain of climate and hydrologic models; endogenous uncertainty in human‐environmental system dynamics across multiple scales; and sampling uncertainty due to the finite length of historical observations and future projections. Recognizing these challenges, several opportunities exist to improve the use of control methods for climate adaptation, namely, how problem context and understanding of climate processes might assist with uncertainty quantification and experimental design, out‐of‐sample validation and robustness of optimized adaptation policies, and monitoring and data assimilation, including trend detection, Bayesian inference, and indicator variable selection. We conclude with a summary of recommendations for dynamic water resources planning under climate change through the lens of optimal control.

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Section: Perspectives: Research Gaps and Opportunitiesmentioning

confidence: 99%

Section: 1029/2019wr025502mentioning

confidence: 99%

Climate Adaptation as a Control Problem: Review and Perspectives on Dynamic Water Resources Planning Under Uncertainty

Herman

Quinn

Steinschneider

et al. 2020

Water Resources Research

142

111

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…GLMs can incorporate any number of covariates to facilitate the simulation of weather sequences under various conditions, such as wet or dry years. Such covariates have included large-scale climate drivers (i.e., El Niño Southern Oscillation and North Atlantic Oscillation) and total seasonal precipitation and mean temperature trajectories, to name a few (Asong et al, 2016;Furrer & Katz, 2008;Hauser & Demirov, 2013;Verdin et al, 2018). GLM-based space-time stochastic weather generators have become more popular recently due to their ability to generate weather sequences at any desired location (e.g., grids with different spatial resolutions) and conditioned on large-scale climate drivers.…”

Section: Introductionmentioning

confidence: 99%

BayGEN: A Bayesian Space‐Time Stochastic Weather Generator

Verdin

Rajagopalan

Kleiber

et al. 2019

Water Resources Research

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We present a Bayesian hierarchical space‐time stochastic weather generator (BayGEN) to generate daily precipitation and minimum and maximum temperatures. BayGEN employs a hierarchical framework with data, process, and parameter layers. In the data layer, precipitation occurrence at each site is modeled using probit regression using a spatially distributed latent Gaussian process; precipitation amounts are modeled as gamma random variables; and minimum and maximum temperatures are modeled as realizations from Gaussian processes. The latent Gaussian process that drives the precipitation occurrence process is modeled in the process layer. In the parameter layer, the model parameters of the data and process layers are modeled as spatially distributed Gaussian processes, consequently enabling the simulation of daily weather at arbitrary (unobserved) locations or on a regular grid. All model parameters are endowed with weakly informative prior distributions. The No‐U Turn sampler, an adaptive form of Hamiltonian Monte Carlo, is used to maximize the model likelihood function and obtain posterior samples of each parameter. Posterior samples of the model parameters propagate uncertainty to the weather simulations, an important feature that makes BayGEN unique compared to traditional weather generators. We demonstrate the utility of BayGEN with application to daily weather generation in a basin of the Argentine Pampas. Furthermore, we evaluate the implications of crop yield by driving a crop simulation model with weather simulations from BayGEN and an equivalent non‐Bayesian weather generator.

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“…While the method could derive multi-site sequences simultaneously for either rainfall or temperature, each variable was still generated independently. More recently, Verdin et al (2018) developed a parametric stochastic weather generator to output downscaled daily weather sequences based on tercile seasonal climate forecasts from the International Research Institute for Climate and Society (IRI). The approach is flexible enough to incorporate any predictors and, therefore, could theoretically be applied to downscale GCM forecasts.…”

Section: Introductionmentioning

confidence: 99%

Coupling forecast calibration and data‐driven downscaling for generating reliable, high‐resolution, multivariate seasonal climate forecast ensembles at multiple sites

Schepen

Everingham

Wang

2019

Intl Journal of Climatology

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Calibration and downscaling of ensemble GCM forecasts is becoming increasingly important for hydrological and agricultural modelling in support of the management and protection of valuable natural resources. Moreover, skilful and reliable daily forecast sequences are required to drive decision support models that operate on a daily time step. While downscaling of daily GCM outputs has been developed extensively in climate impacts studies, much less attention has been paid to the downscaling of ensemble GCM forecasts, which has the confronting aspect of low and diminishing skill with increasing lead time. Evidence is building that simple bias‐correction methods that do not model correlation between forecasts and observations, nor attempt to correct spatial, temporal and inter‐variable correlations, produce downscaled forecasts that perform poorly in applications models. Thus downscaling GCM forecasts requires inclusion of a full calibration component to reduce bias, improve reliability, capture skill where it is available and remove negative skill. In this study, we propose a new methodology for coupling a full GCM forecast calibration with empirical methods to (a) instil correct temporal, spatial and inter‐variable correlations in ensembles and (b) perform a multivariate downscaling of ensembles to daily sequences at multiple sites. Through a case study application, the proposed methodology is shown to produce skilful monthly–seasonal forecasts of rainfall, temperature and solar radiation at regional spatial scales. It also produces realistic and coherent multivariate daily sequences at multiple sub‐grid locations. The new methodology can be applied to more effectively integrate climate forecasts into hydrological and crop models and to support proactive decision‐making in agriculture and natural resources management.

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A conditional stochastic weather generator for seasonal to multi-decadal simulations

Cited by 46 publications

References 70 publications

Climate Adaptation as a Control Problem: Review and Perspectives on Dynamic Water Resources Planning Under Uncertainty

Climate Adaptation as a Control Problem: Review and Perspectives on Dynamic Water Resources Planning Under Uncertainty

BayGEN: A Bayesian Space‐Time Stochastic Weather Generator

Coupling forecast calibration and data‐driven downscaling for generating reliable, high‐resolution, multivariate seasonal climate forecast ensembles at multiple sites

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