Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2017WR022473 |
BayGEN: A Bayesian Space-Time Stochastic Weather Generator | |
Verdin, Andrew1; Rajagopalan, Balaji1,2; Kleiber, William3; Podesta, Guillermo4; Bert, Federico5 | |
2019-04-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:4页码:2900-2915 |
文章类型 | Article |
语种 | 英语 |
国家 | USA; Argentina |
英文摘要 | 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. |
领域 | 资源环境 |
收录类别 | SCI-E ; SSCI |
WOS记录号 | WOS:000468597900019 |
WOS关键词 | DAILY PRECIPITATION ; MULTISITE SIMULATION ; CLIMATE VARIABILITY ; MODEL ; TEMPERATURE ; UNCERTAINTY ; INFORMATION ; VALIDATION ; FORECAST ; MINIMUM |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/182224 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Minnesota, Minnesota Populat Ctr, Minneapolis, MN 55455 USA; 2.Univ Colorado, Cooperat Inst Res Environm Sci, Boulder, CO USA; 3.Univ Colorado, Dept Appl Math, Boulder, CO 80309 USA; 4.Univ Miami, Rosenstiel Sch Marine & Atmospher Sci, 4600 Rickenbacker Causeway, Miami, FL 33149 USA; 5.Asociac Argentina Consorcios Reg Expt Agr, Buenos Aires, Argentina |
推荐引用方式 GB/T 7714 | Verdin, Andrew,Rajagopalan, Balaji,Kleiber, William,et al. BayGEN: A Bayesian Space-Time Stochastic Weather Generator[J]. WATER RESOURCES RESEARCH,2019,55(4):2900-2915. |
APA | Verdin, Andrew,Rajagopalan, Balaji,Kleiber, William,Podesta, Guillermo,&Bert, Federico.(2019).BayGEN: A Bayesian Space-Time Stochastic Weather Generator.WATER RESOURCES RESEARCH,55(4),2900-2915. |
MLA | Verdin, Andrew,et al."BayGEN: A Bayesian Space-Time Stochastic Weather Generator".WATER RESOURCES RESEARCH 55.4(2019):2900-2915. |
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