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83 results for “typhoons”
Considerations for high-resolution regional meteorological wind modelling over complex terrain: a typhoon case study for assessing forestry damage (data)
<p>This is the experiment data.</p> <p>The Weather Research and Forecasting (WRF) model is a popular and easily used as a numerical weather prediction (NWP) model, but configuring WRF to produce accurate results can be time-consuming. This is especially so when simulating extreme events, over complex terrain, or at high resolutions. In this study, a strong wind event from Tropical Cyclone (TC) Thad in year 1981 was simulated at 200 m resolution over an experiment forest in a mountainous region of Hokkaido island, Japan. The simulation configuration is challenging, in order to cover a larger area to produce a TC with appropriate track and intensity, and at the same time to resolve the smallest domain of sub-km grid spacing with computational stability. A mixed nesting method was applied with two-way nesting up for the first three domains, followed a separate simulation over the smallest domain. The mixed method could produce 10 min wind speed distributions similar to that of the full simulation with two-way nesting of all four domains, if a 30-minute boundary update interval was used for the separate simulation. Mixed nesting improves the efficiency of the simulation process, since the larger phenomenon scale and smaller human impact scale can be tuned separately. </p>
The Maintenance of Long-Lived Concentric Eyewall in Simulated Typhoon Lekima (2019)
<p>The model-simulated data used in this study are uploaded here. Due to the large number, the original simulation data are available on request (qnn_nancy@yahoo.com).</p>
Bottom layer temperature and sea level elevation during the Typhoon Lekima
<p>This data is the direct observations of bottom layer temperature and sea level elevations during typhoon Lekima in August 2019.</p>
radar observations and WRF simulations of Typhoon Yagi (2024)
<p>This data set contains radar observations and WRF modal simulations (with Thompson and WDM6 microphysics schemes) of super Typhoon Yagi (2024).</p>
Data for Origins of UTLS Turbulence: Insights from the RRJ-ClimCORE Mesoscale Reanalysis - The ACCLIP Flight Over the Super Typhoon Hinnamnor (2022)
<p>Data used in our paper entitled "Origins of UTLS Turbulence: Insights from the RRJ-ClimCORE Mesoscale Reanalysis - The ACCLIP Flight Over the Super Typhoon Hinnamnor (2022)".</p> <p>The user may use these data only for the purpose of scientific evaluation of this paper and secondary distribution is prohibited. See README file for more details.</p>
Air quality data from the article "Typhoon-associated air quality over the Guangdong–Hong Kong–Macao Greater Bay Area, China: machine-learning-based prediction and assessment"
<p>This dataset consists of 26 files. The descriptions of the files are as follows:</p> <ul> <li>aqi_TY.csv, pm25_TY.csv, pm10_TY.csv, so2_TY.csv, no2_TY.csv and o3_TY.csv are the observed values of AQI and concentrations of PM<sub>2.5</sub>, PM<sub>10</sub>, SO<sub>2</sub>, NO<sub>2</sub> and O<sub>3</sub> of 36 monitoring stations used in model establish stage on TY days. The time range is June 2014 to December 2020.</li> <li>aqi_NTY.csv, pm25_NTY.csv, pm10_NTY.csv, so2_NTY.csv, no2_NTY.csv and o3_NTY.csv are the observed values of AQI and concentrations of PM<sub>2.5</sub>, PM<sub>10</sub>, SO<sub>2</sub>, NO<sub>2</sub> and O<sub>3</sub> of 36 monitoring stations used in model establish stage on NTY days. The time range is June 2014 to December 2020.</li> <li>station_info.csv is the detailed information of the 36 monitoring stations used in model establish stage, including station number, city, longitude and latitude.</li> <li>aqi_TY_testing.csv, pm25_TY_testing.csv, pm10_TY_testing.csv, so2_TY_testing.csv, no2_TY_testing.csv and o3_TY_testing.csv are the observed values of AQI and concentrations of PM<sub>2.5</sub>, PM<sub>10</sub>, SO<sub>2</sub>, NO<sub>2</sub> and O<sub>3</sub> of 3 monitoring stations used for testing the model on TY days. The time range is June 2014 to December 2020.</li> <li>aqi_NTY_testing.csv, pm25_NTY_testing.csv, pm10_NTY_testing.csv, so2_NTY_testing.csv, no2_NTY_testing.csv and o3_NTY_testing.csv are the observed values of AQI and concentrations of PM<sub>2.5</sub>, PM<sub>10</sub>, SO<sub>2</sub>, NO<sub>2</sub> and O<sub>3</sub> of 3 monitoring stations used for testing the model on NTY days. The time range is June 2014 to December 2020.</li> <li>sta_testing.csv is the detailed information of the 3 monitoring stations used for testing the model, including station number, city, longitude and latitude.</li> </ul>
Increasing Typhoon Threats to the Expanding Maize Fields in Northeast China
<ul> <li>The excel data were used in the random forest model.</li> <li>The random forest regression model based on python can calculate the relative importance of each independent variable as well as the partial dependency plot.</li> <li>The text file is the Java script used to calculate the relative change value of the Vegetation Index.</li> </ul>
Data for A Physical Model for the Observed Inverse Energy Cascade in Typhoon Boundary Layers
<p>This repository contains dataset for the paper entitled "A Physical Model for the Observed Inverse Energy Cascade in Typhoon Boundary Layers". The magnitude of inverse energy cascade flux is revised in version 2.0 according to Xia et al. (2009) (https://doi.org/10.1063/1.3275861). </p>
Data from: Soil fauna trophic multifunctionality mediates the release of elements from decomposing typhoon generated leaf litter
Open the record for dataset details and reuse information.
Tree dynamic response and survival in a category-5 tropical cyclone: The case of super typhoon Trami
Open the record for dataset details and reuse information.
Data from: Multiple canopy opening effects on recruited saplings in a typhoon-disturbed tropical rainforest, Taiwan
Open the record for dataset details and reuse information.
Data of tangential wind speed of Typhoon Trami (2018) derived by Tsujino et al. (2021)
<p>Time series data of tangential wind speed in the eye and eyewall of Typhoon Trami (2018) from 0000 UTC 25 to 0600 UTC 27 September 2018, derived by Tsujino et al. (2021) using the data observed by Himawari-8 satellite. More information is also available at <a href="http://wwwoa.ees.hokudai.ac.jp/people/horinouchi-lab/TC/data_en.html">http://wwwoa.ees.hokudai.ac.jp/people/horinouchi-lab/TC/data_en.html</a>.</p>
Four-Dimensional wind fields retrieved from FY4B GIIRS observations during Typhoon Khanun (2023)
<p>This is the output of four-dimensional wind fields retrieved from FY4B intensified observations during Typhoon Khanun in 2023. Apart from regular quality flag indicating cloud contamination over each target FOV, the number of total cloud contaminated neighboring FOVs included in the calculation for each target is also given as a reference of potential bias of retrievals.</p>
Typhoon track tracking and forecasting algorithm based on multi-source information
<p>Typhoon track tracking and forecasting algorithm based on multi-source information</p>
Dataset in the article "Sedimentary record off the Yangtze River estuary and its response to typhoons and human activities over the past 70 years"
<p>The original data presented here is related to the article “Sedimentary record off the Yangtze River estuary and its response to typhoons and human activities over the past 70 years”</p>
Formation and transport of fluid mud triggered by typhoon events in front of the subaqueous Changjiang Delta
<p>1_SSC_calibration.xlsx is used for producing figure 2.</p> <p>2_Wind_Wave.xlsx is used for producing figure 3.</p> <p>3_SSC_contour_and_conducitivity.xlsx is used for producing figure 4.</p> <p>4_SSC_Current_Profile.xlsx is used for producing figure 5.</p> <p>5_Grain_Size.xlsx is used for producing figure 6.</p> <p>6_Current_And_Fluid_Mud_Dynamics.xlsx is used for producing figures 1, 4 and 7 and tables 1 and 2.</p> <p>7_Sediment_Flux.xlsx is used for producing figure 8.</p>
Typhoon effect on pelagic ecosystem
<p><span>This dataset contains data collected onboard the R/V Ocean Researcher I during the pre-typhoon (July 6–8, 2018) and post-typhoon (July 13–16, 2018) periods across transects T1 (i.e., Sts. 5, 35, and 34) and T2 (i.e., Sts. 4, 3, 2, 1, and 1A) in the southern East China Sea described in the paper: "</span><span>C.-C. Chen, C.-h. Hsieh, Y.-H. Cheng, W.-J. Huang, W.-C. Chou, F.-K. Shiah, G.-C. Gong (2023). </span><span>Effect of a tropical cyclone on the pelagic ecosystem of a continental shelf</span><span>, Limnology and Oceanography (submitted) ". Main results of this study included fifteen figures as described in the paper, and please refer it for further details.</span></p>
The Rainfall Responses of Typhoon Soudelor (2015) to Radiative Processes of Cloud Species
<p>The data provides the scripts and data need to produce Figures and Tables in the manuscript "The Rainfall Responses of Typhoon Soudelor (2015) to Radiative Processes of Cloud Species" submitted to Journal of Geophysical research-Atmosphere.</p>
Net Modulation of Upper Ocean Thermal Structure by Typhoon Kalmaegi (2014)
<p>Field data and model output associated with the manuscript entitled "Net Modulation of Upper Ocean Thermal Structure by Typhoon Kalmaegi (2014)"</p>
Raw in situ observational data sets for manuscript entitled "Effect of Typhoon Kalmaegi (2014) in northern South China Sea explored using Muti-platform satellite and Buoy observations data"
<p> Raw in situ observational data sets for manuscript entitled "Effect of Typhoon Kalmaegi (2014) in northern South China Sea explored using Muti-platform satellite and Buoy observations data" , which has been submitted to Progress in Oceanography for reviewing. Only data sets collected by field observations were uploaded. Other data sets can be download from the relevant open access websites (see the descriptions in the manuscript).</p>
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.