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9 results for “moisture sources”
Calculated moisture sources for the Yangtse River Valley for past, present and future climate using a Lagrangian moisture source diagnostic
<p>This dataset contains calculated moisture sources for the Yangtse River Valley (110–122°E and 27–33°N, eastern China) for past, present and future climate using a Lagrangian moisture source diagnostic. The dataset comprises gridded monthly moisture source data files and monthly time series files for a Last Glacial Maximum (LGM) simulation and a Pre-Industrial reference simulation (PRE) with CAM5.1 using prescribed sea surface temperatures, and a control simulation (CTL, 2001-2010) and a climate scenario run with representative concentration pathway 6 (RCP, 2061-2070) with the coupled NorESM-1M model. Each file covers a 10-year time period, computed with the Lagrangian moisture source diagnostic WaterSip (Sodemann et al., 2008).</p>
Datasets for the article "Moisture source controls on water isotopes in Antarctic precipitation - insights from water tracers in ECHAM6-wiso"
<p>This is the dataset used for the manuscript Qinggang Gao, Louise C Sime, Alison J Mclaren, et al. Moisture source controls on water isotopes in Antarctic precipitation -insights from innovative water tracers in ECHAM6-wiso. <em>ESS Open Archive .</em> December 10, 2024. DOI: 10.22541/essoar.173386109.93218804/v1.</p> <p>The corresponding code used for the manuscript can be found at https://github.com/l975421700/a_basic_analysis.</p>
Moisture origins of the Amazon carbon source region
<p>Data belonging to the article:</p> <p>Staal, A., Koren, G., Tejada, G. & Gatti, L.V. (2023). Moisture origins of the Amazon carbon source region. Environmental Research Letters 18, 044027. DOI:10.1088/1748-9326/acc676</p> <p>The data include the moisture sources (in mm) of each of the "regions of influence" (Gatti et al. 2021, Nature) for each quarter during 2010-2018, as well as the masks of the regions of influence. Fore further details, see Staal et al. (2023) and the attributes of the netcdf files.</p>
Lagrangian moisture sources for an arid region in northeast Greenland
<p>dataset of lagrangian moisture sources (February 1979-May 2017) that was used in a research article with the title "Lagrangian detection of precipitation moisture sources for an arid region in northeast Greenland: relations to the North Atlantic Oscillation, sea ice cover and temporal trends from 1979 to 2017" published in the Weather and Climate Dynamics journal (<a href="https://wcd.copernicus.org/articles/2/1/2021/">https://wcd.copernicus.org/articles/2/1/2021/</a>, more information in readme.md)</p> <p><strong>When using this dataset, please refer to the original publication in addition to this Zenodo repository:</strong></p> <p>- Schuster, L., Maussion, F., Langhamer, L., Moseley, G.E.: Lagrangian detection of precipitation moisture sources for an arid region in northeast Greenland: relations to the North Atlantic Oscillation, sea ice cover, and temporal trends from 1979 to 2017, Weather and Climate Dynamics, 2, 1-17, https://doi.org/10.5194/wcd-2-1-2021, 2021</p>
Moisture sources data of Tianshan Mountains
<p>This version of the data has modified some of the problems of previous versions: the timestamp and data storage have been modified to make it clear to other users.</p> <p>This dataset was used in a manuscript entitled "Lagrangian analysis of moisture sources of Tianshan Mountain precipitation" that has been submitted to JGR: Earth and Space Science</p> <p>Files:<br> The dataset contains two folders: one for evaporative moisture within the Planetary Boundary Layer (PBL), and the other for evaporative moisture above the PBL. Each folder has four NetCDF files, representing each of the four divisions of the Tianshan Mountains.</p> <p>License:<br> This dataset is licensed under a Creative Commons Attribution 4.0 International License (CC-BY).</p> <p>How to cite the data?<br> When using this dataset, please refer to the original publication in addition to this Zenodo repository:</p> <p>Guan. X, Langhamer, L., Schneider, C.: "Lagrangian analysis of moisture sources of Tianshan Mountain precipitation", 2022 (submitted to JGR: Earth and Space Science)</p> <p>For questions please write to xuefengg@hu-berlin.de</p>
A 40-year moisture source data for Tibetan Plateau precipitation using a 3D Lagrangian approach
<p>This repository contains the dataset that reproduces the work by Cheng et al. (2024). The zip files contain data in netCDF format. They consist of</p> <ol> <li>Data of Figures 1-5 in the article (Cheng et al. 2024)</li> <li>Moisture sources of precipitation in the Tibetan Plateau (TP) <ul> <li><code>TP_moisture_source_1971-2010.nc</code>: Global map (lon,lat,time) of 40 years of moisture source (mm/day) contributing to the TP precipitation based on the FLEXPART-WaterSip approach</li> <li><code>SR_1971-2010_TP_grids_1x1_XXX.nc</code>: Fractional contributions of different circulation regimes to each of 302 1˚x1˚ grids on the TP</li> </ul> </li> <li>Multi-product ensemble mean precipitation and evapotranspiration</li> <li>Boundary data of TP river basins used in the study</li> </ol> <p>For any enquiries, feel free to contact Dr. Tat Fan (Franklin) Cheng at <a href="mailto:franklin.cheng@ust.hk">franklin.cheng@ust.hk</a>. Please cite our two recent articles if you found the dataset useful. Thank you!</p> <p><strong>References</strong></p> <blockquote> <p>Cheng, T. F., Chen, D., Wang, B., Ou, T. & Lu, M. (2024). Human-induced warming accelerates local evapotranspiration and precipitation recycling over the Tibetan Plateau. <em>Commun Earth Environ </em>5, 388. <a href="https://doi.org/10.1038/s43247-024-01563-9">https://doi.org/10.1038/s43247-024-01563-9</a> </p> <p>Cheng, T. F., & Lu, M. (2023). Global Lagrangian Tracking of Continental Precipitation Recycling, Footprints, and Cascades. <em>Journal of Climate</em>, 36, 1923–1941. <a href="https://doi.org/10.1175/JCLI-D-22-0185.1">https://doi.org/10.1175/JCLI-D-22-0185.1 </a></p> </blockquote>
An Integrated Approach for enhanced SMAP Soil Moisture Retrieval: Multi-Source Data Fusion and Data-Driven Machine Learning
<p><span>Accurate satellite-based soil moisture (SM) retrieval is essential for hydrometeorological and agroecological applications, yet traditional physical models for L-band SM retrieval are hindered by uncertainties stemming from inaccuracies in prior parameters. This work combines multi-source data fusion and a physically-guided machine learning framework to develop a Soil Moisture Active Passive (SMAP) SM retrieval model (Fusion-LightGBM, F-LGB) that bypasses the need for static prior parameters, resulting in a new SM product. The retrieval benchmark is a new seamless SM data constructed by combining Triple Collection correlation coefficients (TC-R) and the Maximized-R method, which demonstrates superior temporal correlation on 20 International Soil Moisture Network (ISMN) <em>in-situ</em> networks compared to existing SM data, including ECMWF Reanalysis v5-Land (ERA5-Land), SMAP Level 4 (SMAP L4), and Global Land Data Assimilation System (GLDAS) Noah. The machine learning model incorporates input variables that represent the Tau-Omega model’s radiative transfer process, including brightness temperature, vegetation optical depth, soil temperature, and an external variable for precipitation. In the 2015-2020 validation set, F-LGB demonstrated the highest correlation (mean R = 0.72, significantly surpassing the second-best SMAP-INRAE-BORDEAUX (SMAP-IB) SM and deep neural network (DNN) SM at 0.67) and the lowest ubRMSE (mean value of 0.052 m<sup>3</sup>/m<sup>3</sup>, better than 0.055 m<sup>3</sup>/m<sup>3</sup> for both DNN and SMAP-IB). F-LGB performed well across diverse land covers, vegetation densities, and climates, with SHAP analysis showing H-polarized brightness temperature as crucial, especially in areas with low to moderate vegetation. This new machine learning-based SMAP SM product may improve global satellite-based SM estimation capabilities.</span></p>
Source contributions to two super dust storms over Northern China in March 2021 and the impact of soil moisture
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Moisture-tagging simulation to investigate the link between the moisture sources and interannual oxygen isotope variability of the Indian Summer Monsoon precipitation
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