Datasets

Subject
Creator
Type
Date

subject: EAPS

Total is 64 Results
Evaluating the variability of surface soil moisture simulated within CMIP5 using SMAP data

10.4231/A0QC-7E03

Pierre Gentine , Qianlai Zhuang ORCID logo , Seungbum Kim , Xuan Xi ORCID logo

12/01/2021

This dataset includes the comparison results of 17 land surface models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) globally using the Soil Moisture Active Passive (SMAP) products and codes for analysis.

EAPS Fourier Analysis Land Surface Model Matlab SMAP Soil Moisture

Stormslam survey results

10.4231/ZVCB-PM81

Loran Carleton Parker , Robin Lynn Tanamachi ORCID logo

01/05/2022

De-identified results of pre- and post-course surveys given to students in EAPS 13800 (Spring 2021) who used the online game Stormslam for engagement.

EAPS Meteorology Teaching and learning

A model intercomparison analysis for controls on C accumulation in North American peatlands

10.4231/982H-T007

Bailu Zhao ORCID logo , Claire Treat , Qianlai Zhuang ORCID logo , Steve Frolking ORCID logo

02/23/2022

This dataset contains the Peatland Terrestrial Ecosystem Model and Holocene Peatland Model simulation results for three peatland sites, all in text files. Three sites include: Mariana fen , Bear bog and Innoko bog.

EAPS Holocene Peatland Carbon HPM P-TEM RCP scenarios

Bias in CMIP6 historical U.S. severe convective environments driven by bias in mean-state near-surface moist static energy

10.4231/42ZJ-A891

Daniel Robert Chavas ORCID logo , Funing Li ORCID logo

03/08/2022

This database provides data (in NetCDF format) for reproducing figures in the manuscript of Chavas&Li (2022).

climate modeling EAPS Earth and Atmospheric Sciences severe convective storms thunderstorm

Code file for Rmax prediction model and relevant data used in the analyses (Chavas and Knaff 2022, Weather and Forecasting)

10.4231/3FQ4-1E11

Daniel Robert Chavas ORCID logo

04/15/2022

Simple MATLAB code to predict Rmax from R34kt as explained in Chavas and Knaff (2022) Eq. 2-4 + Eq 7.

climate impacts damages EAPS Hurricanes Risk Analysis Risk assessment Weather

Water vapor stable isotope memory effects of common tubing materials

10.4231/Y13T-6775

Alexandra Meyer ORCID logo , Lisa Welp-Smith ORCID logo

06/27/2024

A laboratory experiment testing the water vapor isotopic memory of a few common air intake tubing materials (PFA, FEP, PTFE, HDPE, and copper, plus Dekabon and Bev-A-Line XX). Memory metrics for common materials are quantified at 2 different temperatures.

carryover deuterium excess EAPS isotopic Los Gatos memory effect water isotopes Water Vapor

Quantifying the impacts of wildfires on soil thermal, hydrological and carbon dynamics in northern Eurasia from 2003 to 2016

10.4231/ZJM7-A207

Qianlai Zhuang ORCID logo , Yiming Xu

05/02/2024

It contains simulation results of TEM in Eurasia during 2003-2016.

Carbon Dynamics EAPS Terrestrial Ecosystem Model (TEM) Wildfire

A Hybrid Physics-Guided Deep Learning Modeling Framework for Predicting Surface Soil Moisture

10.4231/NR0B-EJ07

Qianlai Zhuang ORCID logo , Xinyu Liu , Xuan Xi ORCID logo

05/24/2024

This dataset contains the main materials for predicting site-level surface soil moisture based on a developed hybrid physics-guided deep learning modeling framework.

EAPS Long short-term memory (LSTM) Physics-Guided Deep Learning Soil Moisture Terrestrial Ecosystem Model (TEM)

West African Heat Low Climatology and Comparison with Dry Tropical Cyclones

10.4231/GMQS-DJ89

Aaron Paul Kruskie ORCID logo , Daniel Robert Chavas ORCID logo

07/29/2024

This repository contains the data files and scripts required to recreate the figures for Kruskie et al. 2024 (in review), which examines the climatology of heat lows in West Africa and their relationship to tropical cyclones using potential...

Climatology EAPS sensible heat flux tropical cyclone West Africa

A Hybrid Physics-Guided Deep Learning Modeling Framework for Predicting Surface Soil Moisture

10.4231/SBB0-V865

Qianlai Zhuang ORCID logo , Xinyu Liu , Xuan Xi ORCID logo

08/21/2024

This dataset contains the main materials for predicting site-level surface soil moisture based on a developed hybrid physics-guided deep learning modeling framework.

EAPS Long short-term memory (LSTM) Physics-Guided Deep Learning Soil Moisture Terrestrial Ecosystem Model (TEM)

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Results 21 - 30 of 64