How to Import GFS Forecast Products

The following code shows how to import GFS forecast products to a workspace.

Prerequisites

To run this code you need:

• A running Python 3.x Environment

• A valid WASDI Account

• A valid Config file

If this is not clear, you probably need to take a look to the Python Tutorial before.

Recipe

Note

Assume you have at least one workspace and you have configured it in the config.json file.

To search for GFS products, the following fields are mandatory:

• Platform (this is always going to be ‘GFS’)

• Start Date

• End Date

• Bounding Box

Four additional search parameters, specific for GFS products, are also mandatory:

• Product Type: Specifies the meteorological variable or product (e.g., temperature, wind, precipitation)

• Product Level: Defines the vertical atmospheric level (e.g., surface, 500 hPa, 2m above ground)

• Model Run: Indicates the model cycle runtime (00, 06, 12, 18 UTC)

• Forecast Time: Sets the forecast hour of product (from 000 to 384 hours)

Note

The aforementioned search filters: producttype, productlevel, modelRun, and forecastTime, should be added to a dictionary, that you can then pass to the method wasdi.searchEOImages through the optional parameter aoParams

Let’s see an example of code to search for GFS precipitation forecast products:

def getPrecipitationForecastProducts():
    wasdi.wasdiLog("** GFS Precipitation Forecast Products **")

    # Create the Bounding Box Object: usually you will read it from the parameters
    oBBox = wasdi.getParameter("BBOX", None)

    # If it is null we show here how to initialize manually
    if oBBox is None:
        oBBox = {
            "northEast": {
                "lat": 44.2879447888337,
                "lng": 9.5
            },
            "southWest": {
                "lat": 43.5,
                "lng": 8.4
            }
        }

    # Set Search Date
    sSearchDate = wasdi.getParameter("SEARCH_DATE", "2025-01-01")

    # Define parameters for the search
    aoParams = {
        "producttype": "PRATE",
        "productlevel": "surface",
        "modelRun": "06",
        "forecastTime": "f000"
    }

    # Search for products with the specified parameters
    aoProductsFoundArray = wasdi.searchEOImages(
        sPlatform="GFS",
        oBoundingBox=oBBox,
        sDateFrom=sSearchDate,
        sDateTo=sSearchDate,
        sProvider='AUTO',
        aoParams=aoParams)

    # if we found a product:
    if len(aoProductsFoundArray) > 0:
        wasdi.wasdiLog(f"Found precipitation product for date - {oSearchDate.strftime('%Y-%m-%d')}, run - 06, forecast time - 00 hr UTC.")

        # Import the images
        wasdi.importProductList(aoProductsFoundArray)

        for oProduct in aoProductsFoundArray:
            sProductName = oProduct.get("fileName", "")

            # Ensure the file name ends with .grb2
            if not sProductName.endswith(".grb2"):
                sProductName += ".grb2"

What it does:

• Initializes the input variables

• Searches for the corresponding GFS products for the given date

• Checks that at least one product is found

• Imports the product to the workspace

• Ensures that the filename of the product ends with its format “.grb2”

Note

The developer can decide what is needed in the search parameters, by specifying it in the params.json file.

Note

The Bounding Box Format used here is the one used by the User Interface when renderAsStrings is missing or false. The Bounding Box format when renderAsStrings: true is “NORTH,WEST,SOUTH,EAST”.

Note

The Date is formatted by the User Interface as “YYYY-MM-DD”. A single search date is given for both the start and end date, as we are looking for the product from this specific date.

Note

Ensure that the Product name always ends with “.grb2”, so that the product can be accessed for further processing.