David Farnham
Affiliations: | Earth & Environmental Engineering | Columbia University, New York, NY |
Website:
https://www.davidjfarnham.com/Google:
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Publications
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| Amonkar Y, Farnham DJ, Lall U. (2022) A k-nearest neighbor space-time simulator with applications to large-scale wind and solar power modeling. Patterns (New York, N.Y.). 3: 100454 |
| Antonini EGA, Ruggles TH, Farnham DJ, et al. (2022) The quantity-quality transition in the value of expanding wind and solar power generation. Iscience. 25: 104140 |
| Tong D, Farnham DJ, Duan L, et al. (2021) Geophysical constraints on the reliability of solar and wind power worldwide. Nature Communications. 12: 6146 |
| Zeng P, Sun X, Farnham DJ. (2020) Skillful statistical models to predict seasonal wind speed and solar radiation in a Yangtze River estuary case study. Scientific Reports. 10: 8597 |
| Doss-Gollin J, Farnham DJ, Ho M, et al. (2020) Adaptation over Fatalism: Leveraging High-Impact Climate Disasters to Boost Societal Resilience Journal of Water Resources Planning and Management. 146: 1820001 |
| Doss‐Gollin J, Farnham DJ, Steinschneider S, et al. (2019) Robust Adaptation to Multiscale Climate Variability Earth’S Future. 7: 734-747 |
| Farnham DJ, Doss-Gollin J, Lall U. (2018) Regional Extreme Precipitation Events: Robust Inference From Credibly Simulated GCM Variables Water Resources Research. 54: 3809-3824 |
| Farnham DJ, Steinschneider S, Lall U. (2017) Zonal Wind Indices to Reconstruct CONUS Winter Precipitation Geophysical Research Letters. 44: 12,236-12,243 |
| Farnham DJ, Lall U. (2015) Predictive statistical models linking antecedent meteorological conditions and waterway bacterial contamination in urban waterways. Water Research. 76: 143-59 |