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Latent negative precipitation for the delineation of a zero-precipitation area in spatial interpolations

ABSTRACT: The spatial interpolation of precipitation has been employed in a number of fields, including by spatially downscaling the Global Circulation Model (GCM) to a finer scale. Most precipitation events become more sporadic when the coverage area increases (i.e., a portion of the points experience zero precipitation). However, spatial interpolations of precipitation generally ignore these dry areas, and the interpolated grids are filled with certain precipitation amounts. Subsequently, no delineation of dry and wet regions can be made. Therefore, the current study suggested a novel approach to determine dry areas in spatial interpolations of precipitation events by assigning latent negative precipitation (LNP) to points with observed precipitation values of zero. The LNP-assigned points are then employed in a spatial interpolation. After that, the dry region can be determined using the negative region (i.e., points with zero precipitation). The magnitude of LNP can be defined by multiplying the precipitation values of neighboring stations by a tuning parameter. The LNP method and the tuning parameter are tested on weather stations covering South Korea. The results indicate that the proposed LNP method can be suitable for the spatial interpolation of precipitation events by delineating dry and wet regions. Additionally, the tuning parameter plays a special role in that it increases in value with longer precipitation durations and denser networks. A value of 0.5–1.5 can be suggested for the tuning parameter as a rule of thumb when high accuracy for final products of interpolated precipitation is not critical. For future studies, the LNP model derived herein can be tested over much larger areas, such as the United States, and the model can also be easily adopted for other variables with spatially sporadic values.

SUBMITTER: Lee T

PROVIDER: S-EPMC8516931 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThroughout their lifespans, humans continually interact with the microbial world, including those organisms which live in and on the human body. Research in this domain has revealed the extensive links between the human-associated microbiota and health. In particular, the microbiota of the human gut plays essential roles in digestion, nutrient metabolism, immune maturation and homeostasis, neurological signaling, and endocrine regulation. Microbial interaction networks are frequently estimated from data and are an indispensable tool for representing and understanding the conditional correlation between the microbes. In this high-dimensional setting, zero-inflation and unit-sum constraint for relative abundance data pose challenges to the reliable estimation of microbial interaction networks.Methods and resultsTo identify the microbial interaction network, the zero-inflated latent Ising (ZILI) model is proposed which assumes the distribution of relative abundance relies only on finite latent states and provides a novel way to solve issues induced by the unit-sum and zero-inflation constrains. A two-step algorithm is proposed for the model selection of ZILI. ZILI is evaluated through simulated data and subsequently applied to an infant gut microbiota dataset from New Hampshire Birth Cohort Study. The results are compared with results from Gaussian graphical model (GGM) and dichotomous Ising model (DIS). Providing ZILI is the true data-generating model, the simulation studies show that the two-step algorithm can identify the graphical structure effectively and is robust to a range of parameter settings. For the infant gut microbiota dataset, the final estimated networks from GGM and ZILI turn out to have significant overlap in which the ZILI tends to select the sparser network than those from GGM. From the shared subnetwork, a hub taxon Lachnospiraceae is identified whose involvement in human disease development has been discovered recently in literature.ConclusionsConstrains induced by relative abundance of microbiota such as zero inflation and unit sum render the conditional correlation analysis unreliable for conventional methods such as GGM. The proposed optimal categoricalization based ZILI model provides an alternative yet elegant way to deal with these difficulties. The results from ZILI have reasonable biological interpretation. This model can also be used to study the microbial interaction in other body parts.

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Latent negative precipitation for the delineation of a zero-precipitation area in spatial interpolations

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