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Open AccessArticle

Precipitation Regime Classification Based on Cloud-Top Temperature Time Series for Spatially-Varied Parameterization of Precipitation Models

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Department of Water Management, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands
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Delft Institute of Applied Mathematics, Delft University of Technology, Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands
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Environmental Hydrodynamics, Deltares, 2600 MH Delft, The Netherlands
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 289; https://doi.org/10.3390/rs12020289
Received: 14 December 2019 / Revised: 10 January 2020 / Accepted: 12 January 2020 / Published: 16 January 2020
Satellite and reanalysis precipitation products perform poorly over regions with low-density ground observation networks. In order to improve space-dependent parameterization of precipitation estimation models in data-scarce environments, the delineation boundaries of precipitation regimes should be accurately identified. Existing approaches to characterize precipitation regimes by seasonal or other climatological properties do not account for small scale spatial-temporal variability. Precipitation time series can be used to account for this small-scale variability in regime classification. Unfortunately, precipitation products with global coverage perform poorly at small time scales over data scarce regions. A methodology of using satellite-based cloud-top temperature (CTT) time series as a proxy of precipitation time series for precipitation regime classification was developed, and its potential and uncertainty were analyzed. A precipitation regime in this study was defined on the basis of characteristic small-scale temporal distribution and variability of precipitation at a given place. Dynamic time warping was used to calculate the distance between two time series. Criteria to select the optimal temporal scale of time series for clustering and the number of clusters were also developed. The method was validated over Germany and applied to Tanzania, characterized by complex climatology and low density ground observations. This approach was evaluated against precipitation regime classification based on a satellite precipitation product. Results show that CTT outcompetes satellite-based precipitation for classification of precipitation regime classification. The CTT-based classification can be used as precursor to spatially adapted precipitation estimation algorithms where parameters are calibrated by gauge data or other ground-based precipitation observations, and parameterization can be used for satellite-precipitation estimates, precipitation forecasts in numerical or stochastic weather models, etc. View Full-Text
Keywords: cloud-top temperature; precipitation regime classification; time series classification; dynamic time warping cloud-top temperature; precipitation regime classification; time series classification; dynamic time warping
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MDPI and ACS Style

Lu, S.; ten Veldhuis, M.-C.; van de Giesen, N.; Heemink, A.; Verlaan, M. Precipitation Regime Classification Based on Cloud-Top Temperature Time Series for Spatially-Varied Parameterization of Precipitation Models. Remote Sens. 2020, 12, 289.

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