Date: July 2016- Ongoing

Description: Precipitation Estimation from Remotely Sensed Information using Artifical Neural Networks (PERSIANN) is one of the popular satellite-based precipitation estimations which has three products in different spatio- temporal resolutions. In majority of cases, these data are used in policy making and therefore the users normally don’t have necessary technical capacity to work with these special file formats. Thus, a user-friendly precipitation database will be a great asset   in monitoring long-term climate patterns, particularly in studying the climate change patterns in arid and semi-arid areas of West Asia (including: Afghanistan, Pakistan, Azerbaijan, Iran, Iraq, Tajikistan and Turkmenistan) which lack this vital    dataset.

The Grid-based Precipitation Dataset for West Asia in the first step will extract and provide an online user friendly as well as visual version of the different PERSIANN products for the region of West Asia for multi-national application to enhance their effectiveness in the formulation and implementation in drought monitoring and water management strategies as well as climate model verifications which will ultimately contribute to better climate change prediction. The second step would be to collect ground observation (in progress for Iran, Iraq and Pakistan) to be merged with sattelite-based precipitation and develop the most accurate and precise grid-based precipitation datasets for the region.

Main Objectives:

  • An online and easy to use version of PERSIANN precipitation data produced for the West Asia
  • Research dataset transformed into user-friendly public knowledge
  • Verification of the grid-based precipitation data using available ground observations