Assessment of SCaMPR and NEXRAD Q2 precipitation estimates using Oklahoma Mesonet observations

Ronald Stenz, Xiquan Dong, Baike Xi, Robert J. Kuligowski

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Although satellite precipitation estimates provide valuable information for weather and flood forecasts, infrared (IR) brightness temperature (BT)-based algorithms often produce large errors for precipitation detection and estimation during deep convective systems (DCSs).AsDCSs produce greatly varying precipitation rates below similar IR BT retrievals, using IR BTs alone to estimate precipitation in DCSs is problematic. Classifying a DCS into convective-core (CC), stratiform (SR), and anvil cloud (AC) regions allows an evaluation of estimated precipitation distributions among DCS components to supplement typical quantitative precipitation estimate (QPE) evaluations and to diagnose these IR-based algorithmbiases. This paper assesses the performance of the National Mosaic and Multi-Sensor Next Generation Quantitative Precipitation Estimation System (NMQ Q2), and a simplified version of the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm, over the state of Oklahoma using Oklahoma Mesonet observations. While average annual Q2 precipitation estimates were about 35% higher than Mesonet observations, strong correlations exist between these two datasets for multiple temporal and spatial scales. Additionally, the Q2-estimated precipitation distribution among DCS components strongly resembled the Mesonet-observed distribution, indicatingQ2 can accurately capture the precipitation characteristics of DCSs despite its wet bias. SCaMPR retrievals were typically 3-4 times higher than Mesonet observations, with relatively weak correlations during 2012. Overestimates from SCaMPR retrievals were primarily caused by precipitation retrievals from the anvil regions of DCSs when collocated Mesonet stations recorded no precipitation. A modified SCaMPR retrieval algorithm, employing both cloud optical depth and IR temperature, has the potential to make significant improvements to reduce the wet bias of SCaMPR retrievals over anvil regions of a DCS.

Original languageEnglish (US)
Pages (from-to)2484-2500
Number of pages17
JournalJournal of Hydrometeorology
Volume15
Issue number6
DOIs
StatePublished - 2014
Externally publishedYes

Keywords

  • Algorithms
  • Microwave observations
  • Radars/Radar observations
  • Remote sensing
  • Satellite observations
  • Surface observations

ASJC Scopus subject areas

  • Atmospheric Science

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