Implementation of a Rainfall Normalization Module for GSMaP Microwave Imagers and Sounders
This paper introduces the Method of Microwave Rainfall Normalization (MMN) for the Global Satellite Mapping of Precipitation (GSMaP) algorithm in its latest version (V05, algorithm version 8), released in December 2021. The method aims to mitigate the discrepancy of GSMaP rainfall estimates among passive microwave (PMW) imagers/sounders (MWIs/MWSs) due
Munehisa K. Yamamoto, Takuji Kubota
openaire +2 more sources
A novel machine learning (ML) approach combining extreme gradient boosting with quantile regression is used to create Vietnam Precipitation with Uncertainty (VNpu). Our VNpu dataset outperforms individual input products, benchmark interpolation methods and an existing gauge‐based product, particularly for heavy and extreme rainfall events. ABSTRACT The
Vinh Ngoc Tran +15 more
wiley +1 more source
Genomic introgression mapping of field-derived multiple-anthelmintic resistance in Teladorsagia circumcincta [PDF]
Preventive chemotherapy has long been practiced against nematode parasites of livestock, leading to widespread drug resistance, and is increasingly being adopted for eradication of human parasitic nematodes even though it is similarly likely to lead to ...
Bisset, Stewart A +6 more
core +4 more sources
Abstract Over tropical oceans, cumulus convection is triggered by updrafts from the atmospheric boundary layer (BL). Given mutual interactions between cumulus convective ensembles and synoptic‐ to global‐scale atmospheric phenomena, it seems important to quantitatively evaluate the updraft on spatiotemporal scales relevant to the interactions and ...
S. Yokoi +3 more
wiley +1 more source
Validation of Hourly GSMAP and Ground Base Estimate of Precipitation for Flood Monitoring in Kumamoto, Japan [PDF]
GSMaP (Global Satellite Mapping Precipitation) satellite rainfall estimates are evaluated at the hourly time scale and spatial resolution 0.1 degree latitude x 0.1 degree longitude.
Aryastana, Putu +2 more
core
Rain Rate Retrieval Algorithm for Conical-Scanning Microwave Imagers Aided by Random Forest, RReliefF, and Multivariate Adaptive Regression Splines (RAMARS) [PDF]
This paper proposes a rain rate retrieval algorithm for conical-scanning microwave imagers (RAMARS), as an alternative to the NASA Goddard profiling (GPROF) algorithm, that does not rely on any a priori information.
Dai, Qiang +4 more
core +2 more sources
GENERACIÓN DE UN PRODUCTO DE PRECIPITACIÓN DIARIA PARA LA CUENCA CHOQUEYAPU EN LA CIUDAD DE LA PAZ
En este estudio se ha generado un producto precipitación diaria para el periodo 2021 a 2023 como resultado de la combinación de estaciones locales e información de sensores remotos.
Jhonatan E. Ureña +2 more
doaj +1 more source
Abstract Peat decomposition is progressing in Southeast Asia due to lowered groundwater levels (GWL) caused by drainage. Additionally, droughts during El Niño events significantly lower the GWL, the main environmental factor that controls greenhouse gas (GHG; carbon dioxide (CO2) and methane) emissions in peatlands.
Takashi Hirano +19 more
wiley +1 more source
Multi-Sensor Imaging and Space-Ground Cross-Validation for 2010 Flood along Indus River, Pakistan [PDF]
Flood monitoring was conducted using multi-sensor data from space-borne optical, and microwave sensors; with cross-validation by ground-based rain gauges and streamflow stations along the Indus River; Pakistan.
DE GROEVE Tom +4 more
core +1 more source
Applications of Attention‐Enhanced CNN Models to Regional Precipitation Downscaling
Abstract Precipitation downscaling is essential for generating high‐resolution data from coarse‐resolution global climate models and assessing the environmental impacts of climate change at regional and local scales. Convolutional Neural Networks (CNNs) are an emerging critical deep‐learning technique that promises significant improvements over other ...
Lei Fan +6 more
wiley +1 more source

