CHRS - The Center for Hydrometeorology and Remote Sensing at UCI

NASA Research Projects


Integration of PERSIANN-CCS in the Next-Generation GPM Multi-Sensor Precipitation Retrieve Algorithm


Maximizing the utility of satellite precipitation observations in operational hydro-meteorological applications requires precipitation measurements in near-real time and at fine spatial and temporal resolutions. With NASA's Global Precipitation Measurement (GPM) Multi-Satellite Working Group, this project has a specific goal to develop a next-generation GPM multi-sensor precipitation retrieval algorithm, "IMERG" (Integrated Multi-satellitE Retrievals for GPM). IMERG is designed to develop a merged precipitation algorithm for use in GPM. It combines the precipitation estimates from various satellite passive microwave (PMW) sensors into half-hourly fields and provides them to both the CPC Morphing-Kalman Filter (CMORPH-KF, Joyce and Xie 2011) and the PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System) re-calibration scheme. The proposed activities will support developing, testing, and implementing the re-calibration of PERSIANN-CCS algorithm in GPM IMERG. GPM ground validation (GV) data and the national radar network will be used to validate the estimation.

The objectives of this project are to:

  1. Develop the IMERG multi-sensor precipitation retrieval algorithm with the overarching goal to minimize lead time while maintaining high-quality precipitation estimates at high resolution.
  2. Continue the development and quality improvement of PERSIANN-CCS including the integration of passive microwave rainfall information to adjust PERSIANN-CCS rainfall estimates.
  3. Perform product validation using National Weather Service Stage IV radar estimates, National Severe Storm Laboratory Q2 radar data and the GPM ground validation testbed and field campaign measurements.


Next-Generation Global Precipitation Climatology Project (GPCP) Data Products


This project will generate PERSIANN precipitation estimation supporting the next generation (Version 3) of GPCP merged precipitation products, which will involve a shift to new data streams, advanced merger techniques, and finer time and space resolutions. CHRS investigators will develop the techniques to generate precipitation analysis at 0.5-degree daily scale and provide quality control and validation of the products. The 0.5-degree daily produce will cover the period 1982-present for daily product.

The objectives of this project are to:

  1. Finalize PERSIANN working with GridSat-B1 with and without routine PMW calibration.
  2. Revise observational dataset schemes where the test year reveals issues of procedure or quality and begin routine updates.
  3. Test PERSIANN data set during the pre-DMSP era and evaluate workability of approaches and quality of resulting data sets.
  4. Ensure that the PERSIANN data set is as current (but complete and stable) as possible.