The PERSIANN family of global satellite precipitation data: a review and evaluation of products

Abstract

Abstract. Over the past 2 decades, a wide range of studies have incorporated Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products. Currently, PERSIANN offers several precipitation products based on different algorithms available at various spatial and temporal scales, namely PERSIANN, PERSIANN-CCS, and PERSIANN-CDR. The goal of this article is to first provide an overview of the available PERSIANN precipitation retrieval algorithms and their differences. Secondly, we offer an evaluation of the available operational products over the contiguous US (CONUS) at different spatial and temporal scales using Climate Prediction Center (CPC) unified gauge-based analysis as a benchmark. Due to limitations of the baseline dataset (CPC), daily scale is the finest temporal scale used for the evaluation over CONUS. Additionally, we provide a comparison of the available products at a quasi-global scale. Finally, we highlight the strengths and limitations of the PERSIANN products and briefly discuss expected future developments.

Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements

“…Compared to previous ML studies [4,9,10], we have introduced a multiscale, multimodal and multi-task DL model for precipitation area detection and instantaneous rain rate estimation from geostationary satellite imagery and rain gauges.…”

“…The performance of our model is not only coming from its multimodality, but is also due to our careful choice of DL architecture. Indeed, where other studies used a shallow fully connected NN [4,10], we used a deep multiscale convolutional NN able to learn spatial dependence in its input at different scales.…”

“…Operational satellite precipitation remote sensing has been mostly relying on a combination of microwave instruments on Low Earth Orbit (LEO) satellites and geostationary window infrared images at wavelengths around 11 micron [3][4][5].…”

“…Instead, a direct relation between the satellite data and observed precipitations can be made using Machine Learning (ML) techniques, reducing the need for any physical assumption. The fully connected Neural Network (NN) developed in [4] is a pioneer in the ML approach, but relies on microwave measurements made from LEO satellites suffering from low temporal resolution. Thanks to the near-infrared measurements from MSG, more recent studies have been developed to use exclusively this GEO satellite and benefit from his high temporal resolution (15 min for the complete disk scan and 5 min for the rapid scan of Europe) and spatial resolution (3 km at nadir).…”

Using CHIRPS Dataset to Assess Wet and Dry Conditions along the Semiarid Central-Western Argentina

“…A wide range of satellite-derived precipitation products have emerged in the last decades, providing a spatial coverage that is superior to gauge products, considering that rain gauges had the obvious queries such as the density of site networks, the continuous time series, and the financial limitation [1]. Some of these products are the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) [2], the Climate Prediction Center Morphing (CMORPH) technique [3], the Global Satellite Mapping of Precipitation (GSMaP) [4], the TRMM Multisatellite Precipitation Analysis (TMPA) 3B42RT [5], and the Multisource Weighted-Ensemble Precipitation (MSWEP) [6]. A comprehensive overview of these products can be found in Beck et al's studies [7].…”