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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #371599

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Enhanced large-scale validation of satellite-based land rainfall products

item CHEN, F. - Science Systems And Applications, Inc
item Crow, Wade
item CIABATTA, L. - University Of Perugia
item FLIPPUCCI, P. - University Of Perugia
item PAGEGROSSI, G. - University Of Rome
item MARRA, A.C - University Of Rome
item PUCA, S. - University Of Rome
item MASSARI, C. - University Of Perugia

Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/15/2020
Publication Date: 1/15/2021
Citation: Chen, F., Crow, W.T., Ciabatta, L., Flippucci, P., Pagegrossi, G., Marra, A., Puca, S., Massari, C. 2021. Enhanced large-scale validation of satellite-based land rainfall products. Journal of Hydrometeorology. 22:245-247.

Interpretive Summary: Agricultural drought represents the single most important environmental impact on agricultural productivity. Timely estimates of daily rainfall accumulation over land form the backbone of many agricultural drought monitoring systems. However, these estimates must be validated before they can be used with confidence. Such validation is often impossible due to spatial gaps in the availability of high-quality, rain gauge observations (to serve as an objective source of ground truth). This paper describes a new approach for validating rainfall accumulation estimates without reliance on ground-based observations. The key to this approach is the generation of a new, wholly independent, daily rainfall accumulation product derived from the inversion of satellite-based surface soil moisture retrievals. The availability of this new product allows us to apply a novel statistical approach that objectively evaluates the quality of other land rainfall products for any point on the globe. This approach will eventually be used to improve our ability to globally monitor (and therefore mitigate) the impact of drought on agriculture.

Technical Abstract: Satellite-based precipitation estimates (SPE) are generally validated using ground-based rain gauge or radar observations. However, in sparsely instrumented regions, uncertainty in these references can lead to biased assessments of SPE accuracy. As a result, at regional or continental scales, an objective basis to evaluate SPEs is currently lacking. Here, we seek to develop an improved framework for the large-scale, spatially continuous evaluation of SPEs over land via the application of collocation-based techniques (i.e., triple collocation (TC) and quadruple collocation (QC) analyses). Our collocation approach leverages the newly developed soil moisture-to-rain (SM2R) product derived from the time series analysis of satellite-based soil moisture retrievals in combination with independent rainfall datasets derived from ground observations (E-OBS) and climate reanalysis (ERA5). The proposed collocation procedure is then applied to validate four years of EUMETSAT H-SAF’s H23 daily rainfall product. Large-scale maps of the H23 correlation metric are generated using both TC and QC. In order to evaluate H23 via a more complete suite of metrics and seasonal error behavior, a high-quality benchmark rainfall product is also generated by TC-based optimal merging of SM2R, E-OBS and ERA5. Results demonstrate that the SM2R product is a useful, independent dataset to apply collocation analyses to the evaluation of rainfall products, given that most other large-scale rainfall products are based on overlapping data sources and algorithms. In particular, the availability of SM2R facilitates the large-scale evaluation SPE products such as H23 – even in areas lacking adequate ground-based observations for traditional validation.