Example Issue: Data Download And Description

by Dimemap Team 45 views

Hey guys! Let's dive into an example scenario where we need to figure out how to grab some data. We'll walk through the specifics, keeping it clear and concise, so anyone can follow along. This example is for the zequihg50,esgf-data category. We are dealing with an example issue, with all fields left blank. So, let's get started!

Data Purpose: Why Do We Need This Data, Anyway?

Okay, so the first thing we've got to figure out is why we're even after this data. Understanding the purpose is super important because it shapes how we get the data and what we do with it. Let's break down the key questions to consider, ensuring a clear picture of the data's role.

Who Are You?

This is all about identifying who is initiating the request. In this scenario, it's me, @zequihg50. Knowing who needs the data helps in tracking the data's use and ensuring it's properly handled and utilized. So, remember to always specify the user or the person responsible for the data download.

Who Will Download the Data?

This clarifies who will be responsible for the actual download process. It could be @zequihg50, or perhaps we're pinging someone else, like @another_user. This distinction helps in organizing the tasks and ensuring that the right person is assigned to the download and data handling duties. Ensure you specify the correct user.

Project, Paper, or Activity Context

What is the broader context surrounding the use of this data? Is it part of a project, a research paper, or maybe a specific activity? Knowing this context helps in aligning the data download with its intended purpose. It provides a clearer understanding of how the data will be applied and the goals it will support.

Detailed Project, Paper, or Activity Description

Provide a comprehensive explanation of the project, paper, or activity. This should encompass the objectives, methodologies, and potential outcomes associated with using the data. The description should allow others to grasp the significance of the data and its contribution to the overall goal. The more detail, the better!

Data Removal Timeline

Specify when the data is expected to be removed. If the data is being archived, it may remain available indefinitely, which would be specified as never. Otherwise, provide a date. If there is a data removal timeline, it helps with data management, storage planning, and archiving the datasets. This is essential for managing data lifecycle and ensuring resources are efficiently managed.

Download Method

Select a method for downloading the data. Options include using ESGF wget scripts, esgf-download, or a custom script. This choice depends on the user’s preferences and the complexity of the data requirements. Each approach offers different features and levels of customization. Choose the method that best suits your needs.

Data Download Location

Identify where the data will be downloaded. This could be /lustre/gmeteo/DATA/ESGF/REPLICA/DATA, /gpfs/..., the IFCA cloud (ping @zequihg50), or another specified location. Specifying the download location is crucial for storage management and ensuring data accessibility. Knowing the destination helps in organizing files and streamlining data retrieval.

Data Description: What Exactly Do We Want?

Now, let's get down to the nitty-gritty and describe the exact data we're after. This is where we specify the project, variables, models, and time scales we need. We'll be specific about the ESGF facets, which are essentially the filters we use to narrow down our data search. If a facet is missing, it means we're interested in all values for that facet.

CMIP6 Facets

For the CMIP6 project, we need to specify: project, activity_id, institution_id, source_id, experiment_id, member_id, table_id, variable_id, grid_label, and frequency. These facets help to filter the data precisely. Let's break down each one.

  • project: This specifies the overall project, such as CMIP6 itself.
  • activity_id: Defines the specific activity or scenario, like ScenarioMIP.
  • institution_id: Indicates the institution providing the model.
  • source_id: Specifies the climate model, e.g., ACCESS-CM2.
  • experiment_id: Determines the climate experiment, e.g., ssp370 or ssp585.
  • member_id: Identifies the ensemble member.
  • table_id: Denotes the data table, e.g., day for daily data.
  • variable_id: Specifies the climate variable, e.g., tas for surface air temperature.
  • grid_label: Describes the grid resolution, such as gr.
  • frequency: Indicates the data's temporal resolution.

CMIP5 Facets

For the CMIP5 project, specify: project, product, institute, model, experiment, time_frequency, realm, cmor_table, ensemble, and time_frequency. These facets help to filter the data precisely. Let's break down each one.

  • project: This specifies the overall project, such as CMIP5 itself.
  • product: Defines the data product.
  • institute: Indicates the institution providing the model.
  • model: Specifies the climate model.
  • experiment: Determines the climate experiment.
  • time_frequency: Indicates the data's temporal resolution.
  • realm: Specifies the data realm.
  • cmor_table: Denotes the data table.
  • ensemble: Identifies the ensemble member.
  • time_frequency: Indicates the data's temporal resolution.

CORDEX Facets

For the CORDEX project, include: project, product, domain, institute, driving_model, experiment, ensemble, rcm_name, rcm_version, time_frequency, and variable. These facets help to filter the data precisely. Let's break down each one.

  • project: This specifies the overall project, such as CORDEX itself.
  • product: Defines the data product.
  • domain: Specifies the regional domain.
  • institute: Indicates the institution providing the model.
  • driving_model: Specifies the driving model.
  • experiment: Determines the climate experiment.
  • ensemble: Identifies the ensemble member.
  • rcm_name: Denotes the regional climate model name.
  • rcm_version: Specifies the version of the regional climate model.
  • time_frequency: Indicates the data's temporal resolution.
  • variable: Specifies the climate variable.

Generic Facets

In addition to the project-specific facets, we have a generic facet: latest. This facet specifies whether to retrieve all ESGF versions of the dataset or just the most recent one (true or false). This is useful for ensuring that you are working with the most up-to-date data.

Example

Here's an example to make things super clear. Let's say we need all members for the ACCESS-CM2 model at daily resolution for two future scenarios. Our request would look like this:

project=CMIP6
activity_id=ScenarioMIP experiment_id=ssp370,ssp585
source_id=ACCESS-CM2
table_id=day
variable_id=tas
grid_label=fr
latest=true

This request specifies that we want data from the CMIP6 project, the ScenarioMIP activity with the ssp370 and ssp585 experiments. We're looking for data from the ACCESS-CM2 model, specifically daily data (table_id=day), the surface air temperature variable (variable_id=tas), a specific grid (grid_label=fr), and only the latest version of each dataset (latest=true).