Abstract
We compare ground rainfall with purely deterministic Regional Climate Model (RCM) simulations within a Bayesian framework. A truncated normal model is fitted to the observed ground data to represent spatial variability. The predictive posterior distribution of the spatially aggregated rainfall is obtained by using a Markov chain Monte Carlo method and compared to the RCM simulations. Also, the predictive posterior distribution of the RCM output is downscaled using the truncated normal model and obtaining pointwise rainfall estimates from aerial observations which are compared to the ground observations. These two procedures allow us to determine if the differences between the two sources of information are compatible with the variability predicted by the spatial model. Also, point rainfall estimates at locations without rainfall measurements conditioned on RCM observations can be obtained. We considered a set of data from an area in Nebraska for which time is considered fixed and rainfall is accumulated monthly.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 597-612 |
| Number of pages | 16 |
| Journal | Environmetrics |
| Volume | 15 |
| Issue number | 6 |
| DOIs | |
| State | Published - Sep 2004 |
| Externally published | Yes |
Keywords
- Bayesian methods for climatology
- Change of support models
- Climate model variation
ASJC Scopus subject areas
- Statistics and Probability
- Ecological Modeling