Evaluation and inter-comparison of modern day reanalysis datasets over Africa and the Middle East

Monday, 14 December 2015
Poster Hall (Moscone South)
Kristi R Arsenault1,2, Shraddhanand Shukla3, Mike Hobbins4, Christa D Peters-Lidard1 and James P Verdin5, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)SAIC, Greenbelt, MD, United States, (3)University of California Santa Barbara, Santa Barbara, CA, United States, (4)Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, (5)USGS/EROS, Boulder, CO, United States
Reanalysis datasets are potentially very valuable for otherwise data-sparse regions such as Africa and the Middle East. They are potentially useful for long-term climate and hydrologic analyses and, given their availability in real-time, they are particularity attractive for real-time hydrologic monitoring purposes (e.g. to monitor flood and drought events). Generally in data-sparse regions, reanalysis variables such as precipitation, temperature, radiation and humidity are used in conjunction with in-situ and/or satellite-based datasets to generate long-term gridded atmospheric forcing datasets. These atmospheric forcing datasets are used to drive offline land surface models and simulate soil moisture and runoff, which are natural indicators of hydrologic conditions. Therefore, any uncertainty or bias in the reanalysis datasets contributes to uncertainties in hydrologic monitoring estimates. In this presentation, we report on a comprehensive analysis that evaluates several modern-day reanalysis products (such as NASA’s MERRA-1 and -2, ECMWF’s ERA-Interim and NCEP’s CFS Reanalysis) over Africa and the Middle East region. We compare the precipitation and temperature from the reanalysis products with other independent gridded datasets such as GPCC, CRU, and USGS/UCSB’s CHIRPS precipitation datasets, and CRU’s temperature datasets. The evaluations are conducted at a monthly time scale, since some of these independent datasets are only available at this temporal resolution. The evaluations range from the comparison of the monthly mean climatology to inter-annual variability and long-term changes. Finally, we also present the results of inter-comparisons of radiation and humidity variables from the different reanalysis datasets.