Can We Estimate the Uncertainty Level of Satellite Long-Term Precipitation Records?
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

For very narrow results

When looking for a specific result

Best used for discovery & interchangable words

Recommended to be used in conjunction with other fields



Document Data
Clear All
Clear All

For additional assistance using the Custom Query please check out our Help Page


Can We Estimate the Uncertainty Level of Satellite Long-Term Precipitation Records?

Filetype[PDF-4.01 MB]


  • Journal Title:
    Journal of Applied Meteorology and Climatology
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    Several decades of continuous improvements in satellite precipitation algorithms have resulted in fairly accurate level-2 precipitation products for local-scale applications. Numerous studies have been carried out to quantify random and systematic errors at individual validation sites and regional networks. Understanding uncertainties at larger scales, however, has remained a challenge. Temporal changes in precipitation regional biases, regime morphology, sampling, and observation-vector information content, all play important roles in defining the accuracy of satellite rainfall retrievals. This study considers these contributors to offer a quantitative estimate of uncertainty in recently-produced global precipitation climate data records. Generated from inter-calibrated observations collected by a constellation of Passive Microwave (PMW) radiometers over the course of 30 years, this data record relies on Global Precipitation Measurement (GPM) mission enterprise PMW precipitation retrieval to offer a long-term global monthly precipitation estimates with corresponding uncertainty at 5° scales. To address changes in the information content across different constellation members the study develops synthetic datasets from GPM Microwave Imager sensor, while sampling- and morphology-related uncertainties are quantified using GPM’s Dual-frequency Precipitation Radar (DPR). Special attention is given to separating precipitation into self-similar states that appear to be consistent across environmental conditions. Results show that the variability of bias patterns can be explained by the relative occurrence of different precipitation states across the regions and used to calculate product’s uncertainty. It is found that at 5° spatial scale monthly mean precipitation uncertainties in Tropics can exceed 10%.
  • Keywords:
  • Source:
    Journal of Applied Meteorology and Climatology (2023)
  • DOI:
  • ISSN:
  • Format:
  • Publisher:
  • Document Type:
  • Rights Information:
  • Compliance:
  • Main Document Checksum:
  • Download URL:
  • File Type:

Supporting Files

  • No Additional Files
More +

You May Also Like

Checkout today's featured content at

Version 3.26.1