Journal
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
Volume 144, Issue -, Pages 191-205Publisher
WILEY
DOI: 10.1002/qj.3243
Keywords
conditional bias; ensembles; hydrometeorology; local scale; precipitation extremes; satellite infrared brightness temperature; satellite quantitative precipitation estimation; uncertainty
Categories
Funding
- National Aeronautics and Space Administration [NNX16AE39G, NNX16AL23G]
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The uncertainty structure of satellite-based passive infrared quantitative precipitation estimation (QPE) is largely unknown at fine spatio-temporal scales, and requires more than just one deterministic best estimate to adequately cope with the intermittent, highly skewed distribution that characterizes precipitation. An investigation of this subject has been carried out within the framework of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). A new method, PIRSO (Probabilistic QPE using InfraRed Satellite Observations), is proposed to advance the use of uncertainty as an integral part of QPE. Probability distributions of precipitation rates are computed instead of deterministic values using a model quantifying the relation between satellite infrared brightness temperatures and the corresponding true precipitation rate. Ensembles of brightness temperatures-to-precipitation rate relationships are derived at a 30 min/0.04 degrees scale. This approach conditions probabilistic quantitative precipitation estimates (PQPE) on the precipitation rate and typology. PIRSO's components were estimated based on a data sample covering two warm seasons over the conterminous USA. Precipitation probability maps outperform the deterministic PERSIANN-CCS QPE. PIRSO is shown to mitigate systematic biases from deterministic retrievals, quantify uncertainty, and advance the monitoring of precipitation extremes. It also provides the basis for precipitation probability maps and satellite precipitation ensembles needed for satellite multi-sensor merging of precipitation, early warning and mitigation of hydrometeorological hazards, and hydrological modelling.
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