Eksen Project Noah S Arch Hotel Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.GLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive.This data product, reprocessed in January 2020, is for GLDAS-2.1 Noah 3-hourly 0.25 degree data from the main production stream and it is a replacement to its previous version.The 3-hourly data product was simulated with the Noah Model 3.6 in Land Information System (LIS) Version 7. The data product contains 36 land surface fields from January 2000 to present. The GLDAS-2.1 data are archived and distributed in NetCDF format. The GLDAS-2.1 products supersede their corresponding GLDAS-1 products.The GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.In October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.If you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.

Context

Compiled dataset of flood reports that include specific coordinates, average precipitation, land elevation and reported flood height in Metro Manila, Philippines. The following data was acquired by applying spatial kriging (using ARCGIS, and some python scripts) on flood reports, elevation rasters and average precipitation all around Metro Manila based on data acquired from public government data. The initial intention for this dataset was to create a heat map to find a correlation between all the parameters in this dataset.

Content

Latitude - lat Longitude - lon Flood Height - flood_height

0 - No flood 1 - Ankle High 2 - Knee High 3 - Waist High 4 - Neck High 5 - Top of Head High 6 - 1-storey High 7 - 1.5-storey High 8 - 2-storeys or Higher

Elevation - elevation (meters) Precipitation - precipitat (millimetres/hour)

Acknowledgements

Credits to these websites which have made it possible to derive these datasets: Project NOAH - http://noah.up.edu.ph/ NAMRIA - http://www.namria.gov.ph/

Inspiration

Metro Manila is always flooded due to frequent tropical storms in the country. I wanted to identify which areas are quite prone to flooding, and which ones are potential evacuation centers.

P.S This was a hobby project of mine when I was in studying in the university. I have been looking at your comments/feedback and I can no longer find my raw dataset which included the dates of the flood reports, and other relevant data for predictive analysis. Fortunately, the websites still have the raw data you might be looking for. I'll try to update this dataset if I find time.

This U.S Geological Survey data release contains soil moisture, actual evapotranspiration, precipitation, and potential evapotranspiration by watershed for the United States from 1980 to 2020. Hourly values from NASA's North American Land Data Assimilation System Phase 2 (NLDAS-2) Noah model were aggregated to daily values and extracted by Geospatial Attributes of Gages for Evaluating Streamflow, version II (GAGES-II) watershed. The daily aggregated values include average soil moisture at four depths, 0 to 10 centimeters (SoilM_0_10cm), 10 to 40 centimeters (SoilM_10_40cm), 40 to 100 centimeters (SoilM_40_100cm), and 100 to 200 centimeters (SoilM_100_200cm); and total actual evapotranspiration (Evap), precipitation (Rainf), and potential evapotranspiration (PotEvap). Each daily value is presented in a table with a row from January 1, 1980 to December 31, 2020. Each column represents a GAGES-II watershed. References: Falcone, J.A., 2011, GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow: U.S. Geological Survey dataset, https://doi.org/10.3133/70046617. Xia, Y.L., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L.F., Alonge, C., Wei, H.L., Meng, J., Livneh, B., Lettenmaier, D., Koren, V., Duan, Q.Y., Mo, K., Fan, Y., and Mocko, D., 2012, Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products: Journal Of Geophysical Research-Atmospheres, v. 117, D03109, https://doi.org/10.1029/2011jd016048. Xia, Y.L., Mitchell, K., Ek, M., Cosgrove, B., Sheffield, J., Luo, L.F., Alonge, C., Wei, H.L., Meng, J., Livneh, B., Duan, Q.Y., and Lohmann, D., 2012, Continental-scale water and energy flux analysis and validation for North American Land Data Assimilation System project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow: Journal Of Geophysical Research-Atmospheres, v. 117, D03110, https://doi.org/10.1029/2011jd016051.

The total land water storage anomalies are aggregated from the Global Land Data Assimilation System (GLDAS) NOAH model. GLDAS outputs land water content by using numerous land surface models and data assimilation. For more information on the GLDAS project and model outputs please visit https://ldas.gsfc.nasa.gov/gldas. The aggregated land water anomalies (sum of soil moisture, snow, canopy water) provided here can be used for comparison against and evaluations of the observations of Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO over land. The monthly anomalies are computed over the same days during each month as GRACE and GRACE-FO data, and are provided on monthly 1 degree lat/lon grids in NetCDF format. Currently, the days included in these monthly anomaly computation are same as GRACE-FO monthly Level-2 RL06.3 JPL solutions.

The license for this dataset was provided to me by my school on behalf of divvy bikes. For my project I was provided zip files I could download that would show me raw data on customers experience and any related data about the trip. No personal information was shared or provided. The license to this data can be found here https://ride.divvybikes.com/data-license-agreement

Protein-Protein, Genetic, and Chemical Interactions for NOAH-2 (Caenorhabditis elegans) curated by BioGRID (https://thebiogrid.org); DEFINITION: noah-2 encodes a PAN and ZP domain-containing protein that is related to the Drosophila extracellular matrix component NompA (no-mechanoreceptor-potential A); loss of noah-2 function via RNAi indicates that NOAH-2 activity is essential for molting; in addition, NOAH-2 appears to be required for embryonic and larval development, reproduction, coordinated locomotion, and the overall health of the animal.

The goal of NLDAS is to construct quality-controlled, and spatially and temporally consistent, land-surface model (LSM) datasets from the best available observations and model output to support modeling activities. Specifically, this system is intended to reduce the errors in the stores of soil moisture and energy which are often present in numerical weather prediction models, and which degrade the accuracy of forecasts. NLDAS is currently running in near real-time on a 1/8th-degree grid over central North America; retrospective NLDAS datasets and simulations also extend back to January 1979. NLDAS constructs a forcing dataset from gauge-based observed precipitation data (temporally disaggregated using Stage II radar data), bias-correcting shortwave radiation, and surface meteorology reanalyses to drive several different LSMs to produce model outputs of surface fluxes, soil moisture, and snow cover. NLDAS is a collaboration project among several groups: NOAA/NCEP's Environmental Modeling Center (EMC), NASA's Goddard Space Flight Center (GSFC), Princeton University, the University of Washington, the NOAA/NWS Office of Hydrological Development (OHD), and the NOAA/NCEP Climate Prediction Center (CPC). NLDAS is a core project with support from NOAA's Climate Prediction Program for the Americas (CPPA). Data from the project can be accessed from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) as well as from the NCEP/EMC NLDAS website. This service provides access to NASA's North American Land Data Assimilation System (NLDAS) hourly Mosaic land surface model data.