Publications

Articles 

[50] Bonas, M., Datta, A., Wikle, C.W., Boone, E.L., Alamri, F.S., Hari, B.V., Kavila, I., Simmons, S.J., Jarvis, S.M., Burr, W.S., Pagendam, D., Chang, W. and Castruccio, S.  (2024+). Assessing Predictability of Environmental Time Series with Statistical and Machine Learning Models, Environmetrics, in press.

[49] Meis, M., Pirani, M., Euan, C., Castruccio, S., Simmons, S., Stroud, J., Blangiardo, M., Wikle, C.K., Wheeler, M., Naumova, E., Bravo, L., Miller, C., and Gel, Y.  (2024+). Catalyse virtual collaboration: the experience of the TIES working groups, Environmetrics, in press.

[48] Ultee, L., Robel, A.A. and Castruccio, S.  (2024). A Stochastic Parametrization of Ice Sheet Surface Mass Balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0), Geoscientific Model Development, 17, 1041-1057.

[47] Zhang, J., Crippa, P., Genton, M.G. and Castruccio, S. (2024). Sensitivity Analysis of Wind Energy Resources with Bayesian non-Gaussian and nonstationary Functional ANOVA, Annals of Applied Statistics, 18(1), 23-41.

[46] Bonas, M., Wikle, C. and Castruccio, S.  (2024). Calibrated Forecasts of Quasi-Periodic Climate Processes with Deep Echo State Networks and Penalized Quantile Regression, Environmetrics, 35(1), e2833.

[45] Zhang, J., Bonas, M., Bolster, D., Fuglstad, G.-A. and Castruccio, S. (2024). High Resolution Global Precipitation Downscaling with Latent Gaussian Models and Nonstationary SPDE Structure, Journal of the Royal Statistical Society - Series C, 73(1), 65-81.

[44] Alifa, M., Castruccio, S., Bolster, D., Bravo, M. and Crippa, P. (2023). Uncertainty reduction and environmental justice in air pollution epidemiology: the importance of minority representation,  GeoHealth, 7(10), e2023GH000854.

[43] Bonas, M., and Castruccio, S.  (2023). Calibration of Spatio-Temporal Forecasts from Citizen Science Urban Air Pollution Data with Sparse Recurrent Neural Networks, Annals of Applied Statistics, 17(3), 1820-1840.

[42] Huang, H., Castruccio, S., Baker, A. and Genton, M.G. (2023). Saving storage in climate ensembles: A model-based stochastic approach  (with discussion), Journal of Agricultural, Biological and Environmental Sciences, 28, 324-344.

[41] Wikle, C.W., Datta, A. Hari, B.V., Boone, E.L., Sahoo, I., Kevila, I., Castruccio, S., Simmons, S.J., Burr, W.S. and Chang, W. (2023). An Illustration of Model Agnostic Explainability Methods for Machine Learning Applied to Environmental Data, Environmetrics, 34( 1), e2772 

[40] Alifa, M., Castruccio, S. , Bolster, D., Bravo, M. and Crippa, P. (2022) . Information entropy tradeoffs for efficient uncertainty reduction in estimates of air pollution mortality, Environmental Research, 212, 113587. 

[39] Huang, H., Castruccio, S. and Genton, M.G. (2022) . Forecasting High-Frequency Spatio-Temporal Wind Power with Dimensionally Reduced Echo State Networks, Journal of the Royal Statistical Society - Series C, 71(2), 449-466. 

[38] Hu, W., Fulgstad, G.-A. and Castruccio, S. (2022). A Stochastic Locally Diffusive Model with Neural Network-Based Deformations for Global Sea Surface Temperature, Stat, 11(1), e431.

[37] Shen, P., Crippa, P. and Castruccio, S. (2021). Assessing Urban Mortality from Wildfires with a Citizen Science Network, Air Quality, Atmosphere & Health, 14, 2015-2027

[36 ] Zhang, J., Crippa, P., Genton, M.G. and Castruccio, S. (2021). Assessing the Reliability of Wind Power Operations under a Changing Climate with a Non-Gaussian Bias Correction, Annals of Applied Statistics, 15(4), 1831-1849 

[35] Hu, W. and Castruccio, S. (2021). Approximating the Internal Variability of Bias-Corrected Global Temperature Projections with Spatial Stochastic Generators, Journal of Climate, 34(20), 8409-8418

[34] Aquino, B., Castruccio, S., Gupta, V. and Howard, S. (2021). Spatial Modeling of MIR Spectral Data With Thermal Compensation Using INLA, Applied Optics, 60(27), 8609-8615

[33] Crippa, P., Alifa, M., Bolster, D., Genton, M.G. and Castruccio, S. (2021). A temporal model for vertical extrapolation of wind speed and wind energy assessment, Applied Energy, 301, 117378

[32 ] Lenzi, A., Castruccio, S., Rue, H. and Genton, M.G. (2021). Improving Bayesian Local Spatial Models in Large Data Sets, Journal of Computational and Graphical Statistics, 30(2), 349-359

[31 ] Chen, W.,  Castruccio, S.,  and Genton, M.G. (2021). Assessing the Risk of Disruption of Wind Turbine Operations in Saudi Arabia using Bayesian Spatial Extremes, Extremes, 24(2), 267-292 

[30 ] Sicard, P., Crippa, P., De Marco, A. , Castruccio, S., Giani, P., Cuesta, J., Paoletti, E., Feng, Z., Anav, A. (2021). High Spatial Resolution WRF-Chem Model over Asia: Physics and Chemistry Evaluation. Atmospheric Environment, 244, 118004

[29] Tagle, F., Genton, M.G., Yip, A., Mostamandi, S., Stenchikov, G. and Castruccio, S. (2020). A High-Resolution Bi-Level Skew-t Stochastic Generator for Assessing Saudi Arabia's Wind Energy Resources (with discussion), Environmetrics, 31(7), e2628

[28 ] Giani, P., Castruccio S., Anav, A., Hu, W. and Crippa, P. (2020). Short- and Long-Term Health Impacts of Air Pollution Reductions from COVID-19 Lockdowns in China and Europe, The Lancet Planetary Health, 4(10),  e474-e482 

[27 ] Edwards, M.,  Castruccio S.  and Hammerling, D. (2020). Marginally Parametrized Spatio-Temporal Models and Stepwise Maximum Likelihood Estimation, Computational Statistics and Data Analysis, 151, 107018

[26] Fuglstad, G.-A. and Castruccio, S. (2020). Compression of Climate Simulations with a Nonstationary Global Spatio-Temporal SPDE Model. Annals of Applied Statistics, 14(2),  542-559

[25] Giani, P., Tagle, F.,  Genton, M.G. Castruccio, S. and Crippa, P. (2020). Closing the Gap between Wind Energy Targets and Implementation for Emerging Countries, Applied Energy, 269, 115085

[24] Tagle, F., Castruccio, S. and Genton, M.G. (2020). A Hierarchical bi-Resolution Spatial Skew-t Model. Spatial Statistics, 35, 100398

[23] Castruccio, S., Hu, Z., Sanderson, B., Karspeck, A. and Hammerling, D. (2019). Reproducing Internal Variability with Few Ensemble Runs. Journal of Climate, 32, 8511-8522

[22] Edwards, M., Castruccio, S. and Hammerling, D. (2019). A multivariate Global Spatio-Temporal Stochastic Generator for Climate Ensembles, Journal of Agricultural, Biological and Environmental Sciences, 24(3), 464-483.

[21] Jeong, J., Yan, Y., Castruccio, S. and Genton, M.G. (2019). A Stochastic Generator of Global Monthly Wind Energy with Tukey g-and-h Autoregressive Processes, Statistica Sinica, 29, 1105-1126.

[20] Tagle, F., Castruccio, S., Crippa, P. and Genton, M.G. (2019). A Non-Gaussian Spatio-Temporal Model for Daily Wind Speeds Based on a Multivariate Skew-t Distribution, Journal of Time Series Analysis, 40, 312-326.

[19] Porcu E., Castruccio S., Alegría, A. and Crippa, P. (2019). Axially Symmetric Models for Global Data: a Journey between Geostatistics and Stochastic Generators, Environmetrics, 30(1), e2555

[18] Castruccio S., Genton, M.G. and Sun, Y. (2019). Visualising Spatio-Temporal Models with Virtual Reality: From Fully Immersive Environments to Apps in Stereoscopic View (with discussion), Journal of the Royal Statistical Society - Series A, 182(2), 379-387. (read before the Royal Statistical Society on September 5th, 2018)

[17] Chen, W., Castruccio, S., Genton, M.G. and Crippa, P. (2018). Current and Future Estimates of Wind Energy Potential in Saudi Arabia, Journal of Geophysical Research - Atmospheres, 123, 6443-6459.

[16] Castruccio, S., Ombao, H. and Genton, M.G. (2018). A Multi-Resolution Spatio-Temporal Model for Brain Activation and Connectivity in fMRI Data, Biometrics, 74, 823-833.

[15] Castruccio, S. and Genton, M.G. (2018). Principles for Inference on Big Spatio-Temporal Data from Climate Models, Statistics and Probability Letters, 136, 92-96.

[14] Mead, M.I., Castruccio S., Latif, M.T., Nadzir, M.S.M., Dominik, D., Thota, A. and Crippa, P. (2018). Impact of the 2015 Wildfires on Malaysian Air Quality and Exposure: A Comparative Study of Observed and Modeled Data, Environmental Research Letters, 13(4), 044023.

[13] Jeong J., Castruccio S., Crippa P. and Genton M.G. (2018). Reducing Storage of Global Wind Ensembles with Stochastic Generators. Annals of Applied Statistics. 12(1), 490-509.

[12] Parreno-Centeno M., van Moorsel A. and Castruccio S. (2017). Smartphone Continuous Authentication Using Deep Learning Autoencoders. 15th Annual Conference on Privacy, Security and Trust (PST), Calgary, Alberta, Canada, 2017, pp.147-1478.

[11] Crippa, P., Castruccio, S. and Pryor, S.C. (2017). Forecasting Ultrafine Particle Concentrations from Satellite and In-Situ Observations. Journal of Geophysical Research - Atmospheres. 122(3), 1828-1837.

[10] Castruccio, S. and Guinness, J. (2017). An Evolutionary Spectrum Approach to Incorporate Large-scale Geographical Descriptors on Global Processes. Journal of the Royal Statistical Society - Series C. 66(2), 329–344.

[9] Castruccio, S. (2016). Assessing the Spatio-temporal Structure of Annual and Seasonal Surface Temperature for CMIP5 and Reanalysis. Spatial Statistics. 18, 179-193.

[8] Crippa, P., Castruccio, S., Archer-Nicholls, S., Lebron, G.B., Kuwata, M., Thota, A., Sumin S., Butt, E., Wiedinmeyer, C. and Spraklen, D.V. (2016). Population Exposure to Hazardous Air Quality Due to 2015 Fires in Southeast Asia. Scientific Reports. 6, Article number: 37074.

[7] Castruccio, S., Huser, R. and Genton, M.G. (2016). High-order Composite Likelihood Inference for Max-Stable Distributions and Processes. Journal of Computational and Graphical Statistics. 25(4), 1212-1229.

[6] Castruccio, S. and Genton, M.G. (2016). Compressing an Ensemble with Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature. Technometrics. 58(3), 319-328.

[5] Genton, M.G., Castruccio, S., Crippa, P., Dutta, S., Huser, R., Sun, Y. and Vettori, S. (2015). Visuanimation in Statistics. Stat, 4, 81-96. 

[4] Castruccio, S. and Genton, M.G. (2014). Beyond Axial Symmetry: An Improved Class of Models for Global Data. Stat, 3, 48-55.

[3] Castruccio, S., McInerney, D.J., Stein, M.L., Liu, F., Jacob, R.L. and Moyer, E.J. (2014). Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs. Journal of Climate, 27(5), 1829-1844.

[2] Castruccio, S. and Stein, M.L. (2013). Global Space-time Models for Climate Ensembles. Annals of Applied Statistics, 7(3), 1593-1611.

[1] Castruccio, S., Bonaventura, L. and Sangalli, L.M. (2012). A Bayesian Approach to Geostatistical Interpolation with Flexible Variogram Models. Journal of Agricultural, Biological, and Environmental Statistics, 17(2), 209-227.

Discussions and Editorials

[3] Abdulah, S., Castruccio, S., Genton M.G. and Sun, Y. (2022), Editorial: Large-Scale Spatial Data Science, Journal of Data Science. 20(4), 437-438 

[2] Castruccio S. (2018). Discussion of 'Computationally Efficient Multivariate Spatio-Temporal Models for High-Dimensional Count-Valued Data'. Bayesian Analysis, 13(1), 282-283

[1] Castruccio S. and Genton M.G. (2015). Discussion of 'Comparing and Selecting Spatial Predictors using Local Criteria'. TEST, 24, 31-34.

Preprints (arxiv only)

[1] Bonas, M. , Richter, D.H. and  Castruccio S. (2023). A Physics-Informed, Deep Double Reservoir Network for Forecasting Boundary Layer Velocity.  (arXiv)

[2] Kim, M., Genton, M.G., Huser, R. and Castruccio, S. (2023). A Neural Network-Based Approach to Normality Testing for Dependent Data. (arXiv)

[3] Menicali, L. , Richter, D.H. and  Castruccio S. (2023). Physics-Informed Priors with Application to Boundary Layer Velocity.  (arXiv)