Publications
Peer-Reviewed Manuscripts
2024
- Hammerling, D.M., Eyring, V., Collins, W.D., Gentine, P., Barnes, E.A., Barreiro, M., Beucler, T., Bocquet, M., Bretherton, C.S., Christensen, H.M., Dagon, K. and Gagne, D.J., 2024. Pushing the frontiers in climate modelling and analysis with machine learning. Nature Climate Change, pp.1-13.
- Daniels, W.S., Jia, M. and Hammerling, D.M., 2024. Detection, localization, and quantification of single-source methane emissions on oil and gas production sites using point-in-space continuous monitoring systems. Elementa: Science of the Anthropocene, 12(1).
- Jia, M., Daniels, W. and Hammerling, D., 2024. Comparison of the Gaussian plume and puff atmospheric dispersion models for methane modeling on oil and gas sites, in revision, preprint: ChemRxiv
- Daniels, W., Jia, M. and Hammerling, D., 2024. Estimating methane emission durations using continuous monitoring systems, in revision, preprint: ChemRxiv
- Jia, M., Sorensen, T. and Hammerling, D., 2024. Optimizing continuous monitoring sensor placement on oil and gas sites, in review, preprint: ChemRxiv
- Khaliukova, O., Zhu, Y., Daniels, W., Ravikumar, A., Ross, G., Roman-White, S., George, F. and Hammerling, D., 2024. Investigating aerial data pre-analysis schemes and site-level methane emission aggregation methods at LNG facilities, in review, preprint: ChemRxiv
2023
- Krock, M.L., Kleiber, W., Hammerling, D. and Becker, S., 2023. Modeling Massive Highly Multivariate Nonstationary Spatial Data with the Basis Graphical Lasso. Journal of Computational and Graphical Statistics, 32(4), pp.1472-1487.
- Daniels, W.S., Wang, J.L., Ravikumar, A.P., Harrison, M., Roman-White, S.A., George, F.C. and Hammerling, D.M., 2023. Toward multiscale measurement-informed methane inventories: reconciling bottom-up site-level inventories with top-down measurements using continuous monitoring systems. Environmental Science & Technology, 57(32), pp.11823-11833.
- Baker, A.H., Pinard, A. and Hammerling, D.M., 2023. On a structural similarity index approach for floating-point data. IEEE Transactions on Visualization and Computer Graphics.
2022
- Blake, L.R., Porcu, E. and Hammerling, D.M., 2022. Parametric nonstationary covariance functions on spheres. Stat, 11(1), p.e468.
- Sather, H., Pinard, A., Baker, A.H. and Hammerling, D.M., 2022, November. What can real information content tell us about compressing climate model data?. In 2022 IEEE/ACM 8th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD) (pp. 29-36). IEEE.
- Elvidge, C.D., Zhizhin, M., Keith, D., Miller, S.D., Hsu, F.C., Ghosh, T., Anderson, S.J., Monrad, C.K., Bazilian, M., Taneja, J., Sutton, P.C., Hammerling, D., 2022. The VIIRS Day/Night Band: a flicker meter in space?. Remote Sensing, 14(6), p.1316.
- Daniels, W.S., D.M. Hammerling, R.R. Buchholz, H.M. Worden, and F. Ahamad, 2022. Predicting Fire Season Intensity in Maritime Southeast Asia with Interpretable Models. Journal of Geophysical Research: Atmospheres, Vol. 127, No. 17.
- Wang, J.L., Daniels, W.S., Hammerling, D.M., Harrison, M., Burmaster, K., George, F.C. and Ravikumar, A.P., 2022. Multiscale methane measurements at oil and gas facilities reveal necessary frameworks for improved emissions accounting. Environmental science & technology, 56(20), pp.14743-14752.
2021
- Hammerling, D.M. and Baker, A.H., 2021. Advancing data compression via noise detection. Nature Computational Science, 1(11), pp.711-712.
- Ahn, D.H., Baker, A.H., Bentley, M., Briggs, I., Gopalakrishnan, G., Hammerling, D.M., Laguna, I., Lee, G.L., Milroy, D.J. and Vertenstein, M., 2021. Keeping science on keel when software moves. Communications of the ACM, 64(2), pp.66-74.
- Blake, L.R., Khaliukova, O., Pinard, A., Nychka, D., Hammerling, D. and Bandyopadhyay, S., 2021. Discussion on Competition for Spatial Statistics for Large Datasets. Journal of Agricultural, Biological and Environmental Statistics, 26(4), pp.596-598.
- Huang, H., Blake, L.R., Katzfuss, M. and Hammerling, D.M., 2021. Nonstationary spatial modeling of massive global satellite data. arXiv preprint arXiv:2111.13428.
2020
- McNeely, T., Lee, A.B., Wood, K.M. and Hammerling, D., 2020. Unlocking GOES: A statistical framework for quantifying the evolution of convective structure in tropical cyclones. Journal of Applied Meteorology and Climatology, 59(10), pp.1671-1689.
- Edwards, M., Castruccio, S. and Hammerling, D., 2020. Marginally parameterized spatio-temporal models and stepwise maximum likelihood estimation. Computational Statistics & Data Analysis, 151, p.107018.
- Dalmasso, N., Vincent, G., Hammerling, D. and Lee, A.B., 2020. HECT: High-Dimensional Ensemble Consistency Testing for Climate Models. arXiv preprint arXiv:2010.04051.
- Poppick, A., Nardi, J., Feldman, N., Baker, A.H., Pinard, A. and Hammerling, D.M., 2020. A statistical analysis of lossily compressed climate model data. Computers & Geosciences, 145, p.104599.
- Pinard, A., Hammerling, D.M. and Baker, A.H., 2020, December. Assessing differences in large spatio-temporal climate datasets with a new python package. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 2699-2707). IEEE.
2019
- Edwards, M., Castruccio, S. and Hammerling, D., 2019. A multivariate global spatiotemporal stochastic generator for climate ensembles. Journal of Agricultural, Biological and Environmental Statistics, 24, pp.464-483.
- Heaton, M.J., Datta, A., Finley, A.O., Furrer, R., Guinness, J., Guhaniyogi, R., Gerber, F., Gramacy, R.B., Hammerling, D., Katzfuss, M. and Lindgren, F., 2019. A case study competition among methods for analyzing large spatial data. Journal of Agricultural, Biological and Environmental Statistics, 24, pp.398-425.
- Abramowitz, G., Herger, N., Gutmann, E., Hammerling, D., Knutti, R., Leduc, M., Lorenz, R., Pincus, R. and Schmidt, G.A., 2019. ESD reviews: Model dependence in multi-model climate ensembles: Weighting, sub-selection and out-of-sample testing. Earth System Dynamics, 10(1), pp.91-105.
- Castruccio, S., Hu, Z., Sanderson, B., Karspeck, A. and Hammerling, D., 2019. Reproducing internal variability with few ensemble runs. Journal of Climate, 32(24), pp.8511-8522.
- Milroy, D.J., Baker, A.H., Hammerling, D.M., Kim, Y., Jessup, E.R. and Hauser, T., 2019, June. Making root cause analysis feasible for large code bases: a solution approach for a climate model. In Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing (pp. 73-84).
2018
- Guinness, J. and Hammerling, D., 2018. Compression and conditional emulation of climate model output. Journal of the American Statistical Association, 113(521), pp.56-67.
- Milroy, D.J., Baker, A.H., Hammerling, D.M. and Jessup, E.R., 2018. Nine time steps: ultra-fast statistical consistency testing of the Community Earth System Model (pyCECT v3. 0). Geoscientific Model Development, 11(2), pp.697-711.
- Crowell, S.M., Randolph Kawa, S., Browell, E.V., Hammerling, D.M., Moore, B., Schaefer, K. and Doney, S.C., 2018. On the ability of space‐based passive and active remote sensing observations of CO2 to detect flux perturbations to the carbon cycle. Journal of Geophysical Research: Atmospheres, 123(2), pp.1460-1477.
- Hammerling, D., Katzfuss, M. and Smith, R., 2019. Climate change detection and attribution. In Handbook of Environmental and Ecological Statistics (pp. 789-817). Chapman and Hall/CRC.
- Nychka, D., Hammerling, D., Krock, M. and Wiens, A., 2018. Modeling and emulation of nonstationary Gaussian fields. Spatial statistics, 28, pp.21-38.
- Buchholz, R.R., Hammerling, D., Worden, H.M., Deeter, M.N., Emmons, L.K., Edwards, D.P. and Monks, S.A., 2018. Links between carbon monoxide and climate indices for the southern hemisphere and tropical fire regions. Journal of Geophysical Research: Atmospheres, 123(17), pp.9786-9800.
2017
- Katzfuss, M., Hammerling, D. and Smith, R.L., 2017. A Bayesian hierarchical model for climate change detection and attribution. Geophysical Research Letters, 44(11), pp.5720-5728.
- Baker, A.H., Xu, H., Hammerling, D.M., Li, S. and Clyne, J.P., 2017. Toward a multi-method approach: Lossy data compression for climate simulation data. In High Performance Computing: ISC High Performance 2017 International Workshops, DRBSD, ExaComm, HCPM, HPC-IODC, IWOPH, IXPUG, P^ 3MA, VHPC, Visualization at Scale, WOPSSS, Frankfurt, Germany, June 18-22, 2017, Revised Selected Papers 32 (pp. 30-42). Springer International Publishing.
- Katzfuss, M. and Hammerling, D., 2017. Parallel inference for massive distributed spatial data using low-rank models. Statistics and Computing, 27, pp.363-375.
- Baker, A.H., Milroy, D.J., Hammerling, D.M. and Xu, H., 2017, November. Quality assurance and error identification for the Community Earth System Model. In Proceedings of the First International Workshop on Software Correctness for HPC Applications (pp. 8-13).
- Hammerling, D., 2017. Climate Change Detection and Attribution: Letting Go of the Null?. CHANCE, 30(4), pp.26-29.
2016
- Milroy, D.J., Baker, A.H., Hammerling, D.M., Dennis, J.M., Mickelson, S.A. and Jessup, E.R., 2016. Towards characterizing the variability of statistically consistent Community Earth System Model simulations. Procedia computer science, 80, pp.1589-1600.
- Baker, A.H., Hu, Y., Hammerling, D.M., Tseng, Y.H., Xu, H., Huang, X., Bryan, F.O. and Yang, G., 2016. Evaluating statistical consistency in the ocean model component of the Community Earth System Model (pyCECT v2. 0). Geoscientific Model Development, 9(7), pp.2391-2406.
- Baker, A.H., Hammerling, D.M., Mickelson, S.A., Xu, H., Stolpe, M.B., Naveau, P., Sanderson, B., Ebert-Uphoff, I., Samarasinghe, S., De Simone, F. and Carbone, F., 2016. Evaluating lossy data compression on climate simulation data within a large ensemble. Geoscientific Model Development, 9(12), pp.4381-4403.
- Salazar, E., Hammerling, D., Wang, X., Sansó, B., Finley, A.O. and Mearns, L.O., 2016. Observation-based blended projections from ensembles of regional climate models. Climatic Change, 138, pp.55-69.
2015
- Nychka, D., Bandyopadhyay, S., Hammerling, D., Lindgren, F. and Sain, S., 2015. A multiresolution Gaussian process model for the analysis of large spatial datasets. Journal of Computational and Graphical Statistics, 24(2), pp.579-599.
- Baker, A.H., Hammerling, D.M., Levy, M.N., Xu, H., Dennis, J.M., Eaton, B.E., Edwards, J., Hannay, C., Mickelson, S.A., Neale, R.B. and Nychka, D., 2015. A new ensemble-based consistency test for the Community Earth System Model (pyCECT v1. 0). Geoscientific Model Development, 8(9), pp.2829-2840.
- Bukovsky, M.S., Carrillo, C.M., Gochis, D.J., Hammerling, D.M., McCrary, R.R. and Mearns, L.O., 2015. Toward assessing NARCCAP regional climate model credibility for the North American monsoon: future climate simulations. Journal of Climate, 28(17), pp.6707-6728.
- Hammerling, D.M., Kawa, S.R., Schaefer, K., Doney, S. and Michalak, A.M., 2015. Detectability of CO2 flux signals by a space‐based lidar mission. Journal of Geophysical Research: Atmospheres, 120(5), pp.1794-1807.
2014
- Hammerling, D., Cefalu, M., Cisewski, J., Dominici, F., Parmigiani, G., Paulson, C., & Smith, R. L. (2014). Completing the results of the 2013 Boston marathon. PLoS One, 9(4), e93800.
2013
- Wang, J., Brown, D. G., & Hammerling, D. (2013). Geostatistical inverse modeling for super-resolution mapping of continuous spatial processes. Remote sensing of environment, 139, 205-215.
2012
- Hammerling, D. M., Michalak, A. M., & Kawa, S. R. (2012). Mapping of CO2 at high spatiotemporal resolution using satellite observations: Global distributions from OCO‐2. Journal of Geophysical Research: Atmospheres, 117(D6).
- Hammerling, D. M., Michalak, A. M., O’Dell, C., & Kawa, S. R. (2012). Global CO2 distributions over land from the Greenhouse Gases Observing Satellite (GOSAT). Geophysical Research Letters, 39(8).
2011
- Salazar, E., Sansó, B., Finley, A. O., Hammerling, D., Steinsland, I., Wang, X., & Delamater, P. (2011). Comparing and blending regional climate model predictions for the American Southwest. Journal of agricultural, biological, and environmental statistics, 16, 586-605.
Technical Reports
2024
- Ward, K., Daniels, W.S., and D.M. Hammerling (2024): Comparison of Co-located Laser and Metal Oxide Continuous Monitoring Systems. Payne Institute Commentary Series: Commentary.
- Daniels, W.S., D.M. Hammerling, and Bazilian, M. (2024): New Method for Tracking Down Methane Emissions on Oil and Gas Sites. Payne Institute Commentary Series: Commentary.
2021
- Pinard, A., A. Baker, and D.M. Hammerling (2021): Examining Variations in the Optimal Compression Level of Spatiotemporal Datasets Determined Using the Data Structural Similarity Index Measure (DSSIM), NCAR Technical Note NCAR/TN-570+STR, doi:10.5065/4q49-t141
- Wang, Z., Peterson, C., Zhou, Q., Subramanian, R., Kunke, J., Baker, A., Moyer, E., and Hammerling, D. (2021): Characterizing Internal Variability and Detecting Changes in Model and Computational Parameters in a Century-Long CESM Ensemble NCAR Technical Note NCAR /TN-569+STR. doi:10.5065/m291-1410
- Pinard, A., A. Baker, and D.M. Hammerling (2021): A Statistical Approach to Obtaining a Data Structural Similarity Index Cutoff Threshold, NCAR Technical Note NCAR/TN-568+STR, 22 pp, doi:10.5065/jc2r-5289
- Duggal, M., W. Daniels, D.M. Hammerling and R. Buchholz, (2021): Optimizing Genetic Algorithm Parameters for Atmospheric Carbon Monoxide Modeling, NCAR Technical Note NCAR/TN-566+STR, 31pp, doi:10.5065/h45f-c987
- Blake, L.R., H. Huang, B. Vanderwende,and D.M. Hammerling (2021): The Deep-Tree Approach: An Improved Parallel Matlab Implementation of the Multi-resolution Approximation for Massive Spatial Data on High-Performance Computing Systems, NCAR Technical Note NCAR/TN-565+STR, 15 pp, doi:10.5065/pzzt-wj18
2020
- W. Daniels, D.M. Hammerling, and R. Buchholz, (2020): regClimateChem: An R Package for Data Driven Variable Selection Applied to Atmospheric Carbon Monoxide, NCAR Technical Note NCAR/TN-562+STR, 34pp, doi:10.5065/e8xj-3k89
2019
- Blake, L.R., H. Huang, B. Vanderwende,and D.M. Hammerling (2019): A Shallow-Tree Multi-resolution Approximation for Distributed and High-Performance Computing Systems, NCAR Technical Note NCAR/TN-559+STR, 32 pp, doi:10.5065/hvvq-j471
- McNeely, T., A.B. Lee, D.M. Hammerling, and K. Wood, (2019): Quantifying the Spatial Structure of Tropical Cyclone Imagery, NCAR Technical Note NCAR/TN-557+STR, 18pp, doi:10.5065/5frb-ws04
- Huang, H., L. R. Blake, and D. M. Hammerling, (2019): Pushing the Limit: A Hybrid Parallel Implementation of the Multi-resolution Approximation for Massive Data, NCAR Technical Note NCAR/TN-558+STR, 32 pp, doi:10.5065/nnt6-q689
2018
- Jurek, M., and D. M. Hammerling, 2018: Parallel Implementation of the Multi-resolution Approximation for Large-scale Spatial Gaussian Models in Python. NCAR Technical Note NCAR/TN-553+STR, 35 pp, doi:10.26024/c04h-fd33
- Molinari, S. J., D. J. Milroy, and D. M. Hammerling, 2018: A Statistical Investigation of the CESM Ensemble Consistency Testing Framework. NCAR Technical Note NCAR/TN-554+STR, 142 pp, doi:10.26024/bfdr-nz31
- Lenssen, N. J. L., A. Hannart, and D.M. Hammerling, 2018: Simulation Testbed for Trend Detection and Attribution Methods. NCAR Technical Note NCAR/TN-555+STR, 26 pp, doi:10.26024/xfmm-hj36
- Blake, L. R., P. Simonson, and D.M. Hammerling, 2018: Parallel implementation and computational analysis of the multi-resolution approximation. NCAR Technical Note NCAR/TN-551+STR, 45 pp, doi:10.5065/D6XW4HNH
- Nardi, J., N. Feldman, A. Poppick, A. Baker, and D. M. Hammerling, 2018: Statistical Analysis of Compressed Climate Data. NCAR Technical Note NCAR/TN-547+STR, 60 pp, doi:10.5065/D6HQ3XQJ
- Simonson, P., and D.M. Hammerling, 2018: Refactoring Data-Driven Model Selection Code for Improvements in Interpretability, Generality, and Computational Expense. NCAR Technical Note NCAR/TN-546+STR, 25 pp, doi:10.5065/D6ZW1JRF
2017
- Rodríguez-Jeangros, and D.M. Hammerling (2017), Informing the Prediction of Compression Method and Level for Climate Model Data Using Variable Features. NCAR Technical Note NCAR/TN-539+STR, 32 pp, doi:10.5065/D69C6W4B
- Milroy, S. Chen, B. Vanderwende, and D. M. Hammerling (2017), Accelerating CMIP data analysis with parallel computing in R. NCAR Technical Note NCAR/TN-534+STR, 37 pp, doi:10.5065/D61V5CP8
- Chen, T. Geltser, K. Hawley, D. M. Hammerling, and D. Lombardozzi (2017), Ozone Concentration and Foliar Injury Analysis at Purchase Knob Garden. NCAR Technical Note NCAR/TN-538+STR, 20 pp, doi:10.5065/D6F18XGS
2016
- W. Kaufman, P. Ma, D. M. Hammerling, and D. Lombardozzi (2016), Ozone and Foliar Damage Analysis: NCAR and St. Louis. NCAR Technical Note NCAR/TN-530+STR, 31 pp, doi:10.5065/D6WH2NCQ
- N. Lenssen, D. Nychka, D. M. Hammerling, and S. A. McGinnis (2016), A Tutorial for Using ‘Rmpi’ on the NCAR/Wyoming Supercomputer. NCAR Technical Note NCAR/TN-524+EDD, doi:10.5065/D6X63K5S
2015
- V. B. Ramakrishnaiah, R. R. P. Kumar, J. Paige, and D. M. Hammerling (2015), Accelerating ‘fields’ by revamping the Cholesky Decomposition. NCAR Technical Note NCAR/TN-518+STR, 25 pp, doi:10.5065/D6QF8QXR
- J. Paige, I. Lyngaas, V. Ramakrishnaiah, D. M. Hammerling, R. Kumar, and D. Nychka (2015), Incorporating MAGMA into the ‘fields’ spatial statistics package. NCAR Technical Note NCAR/TN-519+STR, 29 pp, doi:10.5065/D6KP8078
- J. Paige, D. Nychka, and D. M. Hammerling (2015), ‘fieldsMAGMA’: A MAGMA-accelerated extension to the ‘fields’ spatial statistics R package. NCAR Technical Note NCAR/TN-520+STR, 42 pp, doi:10.5065/D6FX77HJ
- K. Jucks, S Neeck, J Abshire, D Baker, E Browell, A Chatterjee, D Crisp, S Crowell, S Denning, D. M. Hammerling, F Harrison, J Hyon, S Kawa, B Lin, B Meadows, R Menzies, A Michalak, B Moore, K Murray, L Ott, P Rayner, O Rodriguez, A Schuh, Y Shiga, G Spiers, J Wang, and T Zaccheo (2015), Active sensing of CO2 emissions over nights, days, and seasons (ASCENDS) mission, NASA Science Mission Definition Study
2013
- L. Xie, D. M. Hammerling, B. Liu, M. Fuentes (2013), 2013 Atlantic Tropical Cyclone Outlook, Technical Report