Evaluation of the Analysis of Record for Calibration (AORC) Rainfall across Louisiana

Hanbeen Kim, Gabriele Villarini

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


The use of a long-term and high-quality precipitation dataset is crucial for hydrologic modeling and flood risk management. This study evaluates the Analysis of Period of Record for Calibration (AORC) dataset, a newly released product with high temporal and spatial resolutions. Our study region is centered on Louisiana because of the major flooding it has been experiencing. We compare the AORC hourly precipitation to other widely used gridded rainfall products and rain-gauge observations. To evaluate the performance of rainfall products according to different weather conditions causing severe flooding, we stratify the analyses depending on whether precipitation is associated with a tropical cyclone (TC) or not. Compared to observations, our results show that the AORC has the highest correlation coefficients (i.e., values above 0.75) with respect to observations among all rainfall products for both TC and non-TC periods. When the skill metric is decomposed into the potential skill and biases, the AORC clearly shows the highest potential skill with relatively small biases for the whole period. In addition, the AORC performs better for the TC period compared to the non-TC ones. Our results suggest that AORC precipitation shows good potential to be viable for hydrologic modeling and simulations of TC and non-TC events.

Original languageEnglish (US)
Article number3284
JournalRemote Sensing
Issue number14
StatePublished - Jul 1 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Earth and Planetary Sciences


  • AORC
  • Louisiana
  • gridded rainfall products
  • skill score
  • tropical cyclones


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