The Adoptium provides high-performance, cross-platform, open-source prebuilt Java runtime Temurin JDK. All Temurin JDK binaries and scripts are open source licensed and available for free. The version of the Temurin JDK distributed with the IDV is jdk-21.0.6+7. This is a significant step forward in modernizing our software ecosystem, as Java 21 offers many advantages in terms of performance, security, and long-term support. See the Temurin JDK Library for more details information.

Updated the following Java 3D libraries to the latest versions: j3dcore, j3dutils, vecmath, and jogamp. Replaced the lagacy Java Extension mechanism with the explicit classpath-based linking to ensure better modularity, compatibility with modern Java versions, and ease of deployment across platforms.

Renewed code signing certificates for both Window and macOS platforms in compliance with Microsoft's tightened security requirement. The Windows installer is now signed using the updated certificate to ensure compatibility with Windows security policies. The macOS installer has been signed and notarized by Apply, allowing the IDV to run on macOS without requiring users to bypass Gatekeeper warnings.

The version of the netCDF-Java library currently distributed with the IDV is 5.9.0. See the netCDF-Java Library for more details information.

The IDV provides a suite of grid data transformation tools for processing raw datasets, making them suitable for applications like machine learning:

• Scalers - Standard, Robust, and Min-Max
• Transformer - Quantile, Power, Normalizer, Uniform Distribution and Normal Distribution
• Classifier
• Median Filter
• 2D Grid functions - Grid Min, Grid Max, Average, and Percentile

These functions integrate IDV’s diverse data access capabilities into workflows for machine learning, scientific modeling, and more, giving users the flexibility to efficiently prepare data for a wide range of analytical needs.

• Added an online document with Jython functions for ML pre/post-processing.
• Designed a new workflow including:
  • Time aggregation of dataset from TDS server
  • Spatial subsetting (via shapefiles or polygon XGRF files)
  • Jython formula pre processing datasets
• Enabled export of results to CSV or netCDF, making it easy to integrate IDV-processed data into workflows for machine learning, scientific modeling, and other applications.

This flexibility allows users to efficiently prepare data for diverse analytical needs.

Updated Jython from version to 2.7.4.

We fixed a bug associated with the excessive data loading in this release. Another bug fixed is to handle the redirect of the http urls.

For a list of outstanding-known problems, see the Known Problems page.