| Year |
Citation |
Score |
| 2018 |
Zhang Y, Pan M, Sheffield J, Siemann AL, Fisher CK, Liang M, Beck HE, Wanders N, MacCracken RF, Houser PR, Zhou T, Lettenmaier DP, Pinker RT, Bytheway J, Kummerow CD, et al. A Climate Data Record (CDR) for the global terrestrial water budget: 1984–2010 Hydrology and Earth System Sciences. 22: 241-263. DOI: 10.5194/Hess-22-241-2018 |
0.577 |
|
| 2018 |
Siemann AL, Chaney N, Wood EF. Development and Validation of a Long-Term, Global, Terrestrial Sensible Heat Flux Dataset Journal of Climate. 31: 6073-6095. DOI: 10.1175/Jcli-D-17-0732.1 |
0.609 |
|
| 2018 |
Siemann AL, Chaney N, Wood EF. Sensitivity and Uncertainty of a Long-Term, High-Resolution, Global, Terrestrial Sensible Heat Flux Data Set Journal of Geophysical Research. 123: 4988-5000. DOI: 10.1029/2017Jd027785 |
0.57 |
|
| 2016 |
Siemann AL, Coccia G, Pan M, Wood EF. Development and Analysis of a Long-Term, Global, Terrestrial Land Surface Temperature Dataset Based on HIRS Satellite Retrievals Journal of Climate. 29: 3589-3606. DOI: 10.1175/Jcli-D-15-0378.1 |
0.638 |
|
| 2016 |
Koch J, Siemann A, Stisen S, Sheffield J. Spatial validation of large-scale land surface models against monthly land surface temperature patterns using innovative performance metrics Journal of Geophysical Research: Atmospheres. 121: 5430-5452. DOI: 10.1002/2015Jd024482 |
0.601 |
|
| 2015 |
Coccia G, Siemann AL, Pan M, Wood EF. Creating consistent datasets by combining remotely-sensed data and land surface model estimates through Bayesian uncertainty post-processing: The case of Land Surface Temperature from HIRS Remote Sensing of Environment. 170: 290-305. DOI: 10.1016/J.Rse.2015.09.010 |
0.657 |
|
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