ERA5-Land monthly averaged data January 2019

Dataset has been retrieved on the Copernicus Climate data Store (https://cds.climate.copernicus.eu/#!/home) and is meant to be used for teaching purposes only. This dataset is used in the Galaxy training on "Visualize Climate data with Panoply in Galaxy".

See https://training.galaxyproject.org/ (topic: climate) for more information.

Product type: Monthly averaged reanalysis

Variable:

10m u-component of wind, 10m v-component of wind, 2m temperature, Leaf area index, high vegetation, Leaf area index, low vegetation, Snow cover, Snow depth

Year:

2019

Month:

January

Time:

00:00

Format:

NetCDF (experimental)

An Open Context "types" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This record is part of the "Rough Cilicia" data publication.

Dataset has been retrieved on the Copernicus Climate data Store (https://cds.climate.copernicus.eu/#!/home) and is meant to be used for teaching purposes only. Data retrieved were split per year and concatenated to create two separate files. Then these two files were converted from GRIB format to netcdf using xarray (http://xarray.pydata.org/en/stable/). This dataset is used in the Galaxy training on "Visualize Climate data with Panoply in Galaxy".

See https://training.galaxyproject.org/ (topic: climate) for more information.

The python code below show how it has been retrieved on CDS:

import cdsapi

c = cdsapi.Client()

c.retrieve(
'ecv-for-climate-change',
{
'variable': [
'precipitation', 'sea_ice_cover', 'surface_air_temperature',
],
'product_type': 'monthly_mean',
'time_aggregation': '1_month',
'year': [
'1979', '2018',
],
'month': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
],
'origin': 'era5',
'format': 'zip',
},
'download.zip')

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Explore the historical Whois records related to panoply.mobi (Domain). Get insights into ownership history and changes over time.

It has never been easier to solve any database related problem using any sequel language and the following gives an opportunity for you guys to understand how I was able to figure out some of the interline relationships between databases using Panoply.io tool.

I was able to insert coronavirus dataset and create a submittable, reusable result. I hope it helps you work in Data Warehouse environment.

The following is list of SQL commands performed on dataset attached below with the final output as stored in Exports Folder QUERY 1 SELECT "Province/State" As "Region", Deaths, Recovered, Confirmed FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Deaths>0 Description: How will we estimate where Coronavirus has infiltrated, but there is effective recovery amongst patients? We can view those places by having Recovery twice more than the Death Toll.

Query 2 SELECT country, sum(confirmed) as "Confirmed Count", sum(Recovered) as "Recovered Count", sum(Deaths) as "Death Toll" FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Confirmed>0 GROUP BY country

Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries

Query 3 SELECT country as "Countries where Coronavirus has reached" FROM "public"."coronavirus_updated" WHERE confirmed>0 GROUP BY country Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries.

Query 4 SELECT country, sum(suspected) as "Suspected Cases under potential CoronaVirus outbreak" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 AND confirmed=0 GROUP BY country ORDER BY sum(suspected) DESC

Description: Coronavirus is spreading at alarming rate. In order to know which countries are newly getting the virus is important because in these countries if timely measures are taken, it could prevent any causalities. Here is a list of suspected cases with no virus resulted deaths.

Query 5 SELECT country, sum(suspected) as "Coronavirus uncontrolled spread count and human life loss", 100*sum(suspected)/(SELECT sum((suspected)) FROM "public"."coronavirus_updated") as "Global suspected Exposure of Coronavirus in percentage" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 GROUP BY country ORDER BY sum(suspected) DESC Description: Coronavirus is getting stronger in particular countries, but how will we measure that? We can measure it by knowing the percentage of suspected patients amongst countries which still doesn’t have any Coronavirus related deaths. The following is a list.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Explore the historical Whois records related to panoply-tech.com (Domain). Get insights into ownership history and changes over time.

Credit report of Panoply Wood Products Inc contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

We highly recommend to contact the GLORIA team at KIT or Jülich before using the data for scientific studies.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The data are described in the paper by M. Höpfner et al.: 'Ammonium nitrate particles formed in upper troposphere from ground ammonia sources during Asian monsoons', Nature Geoscience, 2019, https://doi.org/10.1038/s41561-019-0385-8.' The datasets are provided in netCDF format and can e.g. be visualized with the software Panoply which is available at 'https://www.giss.nasa.gov/tools/panoply/'.

Abstract: We present high resolution measurements of trace species (e.g.: O3, H2O, HNO3, PAN, C2H6, HCOOH, NH3, solid ammonium nitrate) in the Upper Troposphere and Lowermost Stratosphere (UTLS) from the Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) during the StratoClim campaign with basis in Kathmandu, Nepal, on board the high altitude research aircraft Geophysica, 2017. TechnicalRemarks: netCDF data can be opened with a variety of software tools, including Matlab, Origin, or Python. For a simple GUI solution, Panoply is recommended: https://www.giss.nasa.gov/tools/panoply/download/ Other: We highly recommend to contact the GLORIA team at KIT or Jülich before using the data for scientific studies.

The Datasets are the result of computations from a source gridded data with a resolution of 0.25° x 0.25° (Ampofo et al., 2023; Domínguez-Castro et al., 2020). The datasets are the results of the analysis of the gridded netCDF precipitation dataset using Climate Data Tools (CDT) ® version 5, a component of the statistical package R 3.5.1 and developed at the International Research Institute for Climate and Society of the Columbia University. It has a Graphical User Interface (GUI) mode and has utility functions which were used for quality control, homogenization and annual computations for the above stated indices over the 56-year study period (Ampofo et al., 2023b). Other tools used were; Panoply® (https://www.giss.nasa.gov/tools/panoply/) for making customized plots from the output netCDF datasets

This dataset contains alternative products to the official Level 3 (L3) product from Measurements of Pollution in the Troposphere (MOPITT) joint thermal infrared (TIR) – near infrared (NIR) Version 8 Carbon Monoxide (CO) retrievals (available here: https://doi.org/10.5067/TERRA/MOPITT/MOP03J_L3.008). The products are described and analysed in a paper in the journal Atmospheric Measurement Techniques by Ian Ashpole and Aldona Wiacek (2022, https://doi.org/10.5194/amt-2022-90). In short, whereas the official MOPITT L3 product is based on retrievals performed over both land AND water surface types, the products here are created separately from retrievals performed ONLY over land (“L3L”) OR water (“L3W”). The code for creating L3L and L3W is available here: https://github.com/ianashpole/MOPITT_L3L_L3W The version naming is consistent with the official MOPITT product version, although note that version 8 is the first version that these alternatives are produced for (i.e. although MOPITT product versions 1-7 exist, L3L and L3W do not). However, it is intended that L3L and L3W are created for MOPITT product versions after version 8. The dataset stored here consists of two main .zip archives: “MOPITT_v8.L3L.20010901_20190228.zip” “MOPITT_v8.L3W.20010901_20190228.zip” When unzipped, each archive contains 6057 individual NetCDF (".nc") files that correspond to the daily L3L and L3W data products for the period 2001-09-01 to 2019-02-28, inclusive. Daily files represent the satellite instrument measurements for a single day. Users are referred to the "README.txt" file for a full description of the individual file contents. Note that when unzipped, the products require ~22.5 GB of data storage each (45 GB total for both L3L and L3W). Because of this, a single file from each product has been uploaded separately (file date = “20020801”; see below for naming convention) to facilitate user experimentation before unpacking the full L3L/L3W products. Individual L3L/L3W NetCDF files are ~3.4 MB in size. The individual NetCDF files are named as follows: MOPITT_v8.L3L.from_MOPO2J.selected_variables.YYYYMMDD.nc (replace “L3L” with “L3W” in the filename for the corresponding L3W product.) The date corresponds to the YYYYMMDD that the retrievals were made. E.g. the file “MOPITT_v8.L3L.from_MOPO2J.selected_variables.20020801.nc” corresponds to the L3L product for MOPITT retrievals made on August 1st 2002. Variables contained within the file are described in detail in the "README.txt" file. NetCDF is a common format for gridded geoscientific data, easily readable by all widely used scientific programming languages (e.g. Python, R, Matlab, IDL…), as well as dedicated command line tools (e.g. cdo, gdal). Panoply (https://www.giss.nasa.gov/tools/panoply/) is an alternative application for quickly plotting these data without the requirement of coding experience. Most GIS packages can also handle NetCDF data. An example python code for reading and plotting data from a single L3L file is available here: https://github.com/ianashpole/MOPITT_L3L_L3W/blob/main/example_read_and_plot_MOPITT_L3L.ipynb

This Envisat SCIAMACHY Ozone stratospheric profiles dataset has been extracted from the previous baseline (v6.01) of the SCIAMACHY Level 2 data. The dataset is generated in the framework of the full mission reprocessing campaign completed in 2023 under the _\(ESA FDR4ATMOS project\) https://atmos.eoc.dlr.de/FDR4ATMOS/ . For optimal results, users are strongly encouraged to make use of these specific ozone limb profiles rather than the ones contained in the _\(SCIAMACHY Level 2 dataset version 7.1\) https://earth.esa.int/eogateway/catalog/envisat-sciamachy-total-column-densities-and-stratospheric-profiles-sci_ol_2p- .

The new products are conveniently formatted in NetCDF. Free standard tools, such as _\(Panoply\) https://www.giss.nasa.gov/tools/panoply/ , can be used to read NetCDF data. Panoply is sourced and updated by external entities. For further details, please consult our _\(Terms and Conditions page\) https://earth.esa.int/eogateway/terms-and-conditions .

Please refer to the _\(README\) https://earth.esa.int/eogateway/documents/20142/37627/ENVI-GSOP-EOGD-QD-16-0132.pdf file (L2 v6.01) for essential guidance before using the data.

description: NSF's Discoveries RSS feed provides information on the results of NSF's public investment in science, engineering, education and technology. Read here about the Internet, microbursts, Web browsers, extrasolar planets, and more... a panoply of discoveries and innovations that began with NSF support.; abstract: NSF's Discoveries RSS feed provides information on the results of NSF's public investment in science, engineering, education and technology. Read here about the Internet, microbursts, Web browsers, extrasolar planets, and more... a panoply of discoveries and innovations that began with NSF support.

This Envisat SCIAMACHY Level 1b Geo-located atmospheric spectra V.10 dataset is generated from the full mission reprocessing campaign completed in 2023 under the _\(ESA FDR4ATMOS project\) https://atmos.eoc.dlr.de/FDR4ATMOS/ . This data product contains SCIAMACHY geo-located (ir)radiance spectra for Nadir, Limb, and Occultation measurements (Level 1), accompanied by supplementary monitoring and calibration measurements, along with instrumental parameters detailing the operational status and configuration throughout the Envisat satellite lifetime (2002-2012).

Additionally, calibrated lunar measurements, including individual readings and averaged disk measurements, have been integrated into the Level 1b product. The Level 1b product represents the lowest level of SCIAMACHY data made available to the users. The measurements undergo correction for instrument degradation applying a scan mirror model and m-factors. However, spectra are partially calibrated and require a further step to apply specific calibrations with the SCIAMACHY Calibration and Extraction Tool [_\(SciaL1c\) https://earth.esa.int/eogateway/tools/scial1c-command-line-tool ]. For many aspects, the SCIAMACHY Level 1b version 10 product marks a significant improvement with respect to previous mission datasets, supplanting the Level 1b dataset version 8.0X with product type SCI_NL_1P. Users are strongly encouraged to make use of the new datasets for optimal results.

The new products are conveniently formatted in NetCDF. Free standard tools, such as _\(Panoply\) https://www.giss.nasa.gov/tools/panoply/ , can be used to read NetCDF data. Panoply is sourced and updated by external entities. For further details, please consult our _\(Terms and Conditions page\) https://earth.esa.int/eogateway/terms-and-conditions .

Please refer to the _\(README\) https://earth.esa.int/documents/d/earth-online/rmf_0013_sci_1p_l1v10 file for essential guidance before using the data.