added imperial slides, features photo, and made links work on the training page

.github/workflows/publish.yaml

@@ -6,7 +6,7 @@ env:
 on:
   # Trigger the workflow every time you push to the `main` branch
   push:
-    branches: ["pdv_comet"]
+    branches: ["workshop_news","deploy"]
   # Allows you to run this workflow manually from the Actions tab on GitHub.
   workflow_dispatch:
 

config/_default/menus.yaml

@@ -37,6 +37,10 @@ main:
     url: /#tools
     weight: 30
     hasChildren: true
+  - parent: Tools
+    name: curepy
+    url: tools/curepy
+    weight: 34
   - parent: Tools
     name: comet_maths
     url: tools/comet_maths

content/_index.md

@@ -26,7 +26,7 @@ sections:
           url: "latest-news/"
     design:
       spacing:
-        padding: [300, 100, 300, 100]
+        padding: [0, 0, 0, 0]
         margin: [0, 0, 0, 0]
       # For full-screen, add `min-h-screen` below
       css_class: "dark"

content/latest-news/25-10-01-VHRODA-workshop/index.md

@@ -0,0 +1,36 @@
+---
+title: VH-RODA workshop 2025
+summary: Hands-on tutorial on the CoMet Toolkit at the ESA VH-RODA workshop 2025.
+date: 2025-10-01
+authors:
+  - admin
+tags:
+  - Poster
+  - Conference
+  - LPS
+image:
+  #caption: 'Image credit: [**Unsplash**](https://unsplash.com)'
+---
+
+We’re pleased to invite you to a hands-on tutorial session on the CoMet (Community Metrology) Toolkit, taking place during ESA’s Very High-resolution Radar & Optical Data Assessment (VH-RODA) 2025 Workshop which will be held at ESA-ESRIN, Frascati (Italy), from 17-21 November 2025. The tutorial itself will take place:
+
+🗓 Wednesday 19th November
+
+🕒 12:50 – 13:40
+
+📍 ESA-ESRIN (Frascatti, Italy), Big hall
+
+> The **CoMet Toolkit** (www.comet-toolkit.org) is an open-source suite of Python tools for handling and propagating uncertainties and error-correlation in measurement data. Originally designed for Earth Observation (EO) applications, CoMet’s flexible tools can be applied to any dataset involving uncertainties — making it highly relevant for anyone working with measurement functions and data analysis in Python.
+
+### 🗸 In this tutorial, we will:
+
+- Introduce the key concepts behind metrological uncertainty propagation 
+- Walk through the core components of the toolkit:
+  - **punpy** – for robust uncertainty propagation with support for error-correlation structures 
+  - **obsarray** – for managing and storing uncertainty metadata in a self-describing, traceable format
+- Work through a sensor calibration example using Google Colab-based Jupyter notebooks
+
+
+This session will be tailored to implement rigorous, traceable uncertainty handling in satellite Cal/Val processing chains.
+
+### 🗸 No installation is required beforehand – all examples will run in Google Colab!

content/latest-news/26-03-12-Spring-2026-workshops/index.md

@@ -19,53 +19,53 @@ For any events where public registration is available, we will post the details
 
 ---
 
-# 1. Imperial College London
+## 1. Imperial College London
 
-🗓 Thursday 19th March
+🗓 **When:** Thursday 19th March
 
-📍 Imperial College London (London, UK)
+📍 **Where:** Imperial College London (London, UK)
 
-![imperial](assets/media/imperial.jpg)
+![imperial](assets/media/imperial.png)
 
 ---
 
-# 2. Wavelength Conference 2026
+## 2. Pangeos
 
-🗓 **When:** Thursday 9th April
+🗓 **When:** Monday 23rd March
 
-📍 **Where:** National Physical Laboratory (Teddington, UK)
+📍 **Where:** Online
 
-🔗 **More information:** https://rspsoc.org.uk/
+🔗 **More information:** https://pangeos.eu/
 
-![wavelength](assets/media/wavelength.png)
+![pangeos](assets/media/pangeos.png)
 
 ---
 
-# 3. ARIA
+## 3. Wavelength Conference 2026
 
-🗓 **When:** Wednesday 15th April
+🗓 **When:** Thursday 9th April
 
 📍 **Where:** National Physical Laboratory (Teddington, UK)
 
-🔗 **More information:** https://aria.org.uk/
+🔗 **More information:** https://rspsoc.org.uk/
 
-![aria](assets/media/aria.jpeg)
+![wavelength](assets/media/wavelength.png)
 
 ---
 
-# 4. Pangeos
+## 4. ARIA
 
-🗓 **When:** Wednesday 23rd April
+🗓 **When:** Wednesday 15th April
 
-📍 **Where:** Online
+📍 **Where:** National Physical Laboratory (Teddington, UK)
 
-🔗 **More information:** https://pangeos.eu/
+🔗 **More information:** https://aria.org.uk/
 
-![pangeos](assets/media/pangeos.png)
+![aria](assets/media/aria.jpeg)
 
 ---
 
-# 5. Met4EO
+## 5. Met4EO
 
 🗓 **When:** 20th-22nd May
 

content/meteor/index.md

@@ -1,13 +1,19 @@
 ---
 title: MetEOR Coming Soon!
-date: 2025-11-13
+summary: What is the MetEOR Toolkit?
+date: 2025-11-01
 
-image: meteor_logo.png
+# Featured image
+# Place an image named `featured.jpg/png` in this page's folder and customize its options here.
+image:
 
 authors:
+  - RasmaOrmane
   - admin
 ---
 
+<!-- Welcome 👋 -->
+
 ## ❔ What is MetEOR?
 
-Met4EO is developing the **"Metrology for Earth Observation & Radiometry"** (MetEOR) toolkit, a set of Python tools to enable performing comparisons between various satellite sensors. This includes all the tools in the matchup pipeline used for the CEOS-PVP, as well some additional tools for BRDF modelling and atmospheric correction.
+As part of the [**Met4EO**](https://www.QA4EO.org/met4eo) project, NPL is developing the **"Metrology for Earth Observation & Radiometry"** (MetEOR) toolkit, a set of Python tools to enable performing comparisons between various satellite sensors. This includes all the tools in the matchup pipeline used for the [**CEOS-PVP**](https://www.ceos-pvp.org/), as well some additional tools for BRDF modelling and atmospheric correction.

content/projects/index.md

@@ -18,7 +18,7 @@ Here's the ever-growing list of the projects and organisations which have utilis
 
 📋 **Overview:** QA4EO is a Quality Assurance framework for Earth Observation (EO) data, providing guidance and best practices across the EO community. 
 
-🔗 **Links:** [**QA4EO Website**](https://www.QA4EO.org/)
+🔗 **URL:** [**QA4EO Website**](https://www.QA4EO.org/)
 
 ☄️ **Involvement:** The CoMet toolkit received funding from QA4EO for its development and provides the tools for the practical implementation of some of the QA4EO steps to an uncertainty budget.
 
@@ -28,7 +28,7 @@ Here's the ever-growing list of the projects and organisations which have utilis
 
 📋 **Overview:** HYPERNETS developed an automated network of hyperspectral radiometers for validating surface reflectance of water and land for satellite missions.
 
-🔗 **:Links:** [**HYPERNETS Website**](https://www.hypernets.eu/network/summary)
+🔗 **URL:** [**HYPERNETS Website**](https://www.hypernets.eu/network/summary)
 
 ☄️ **Involvement:** The CoMet toolkit is used within the HYPERNETS_processor to propagate uncertainties from lab calibration through each of its products. See also [this case study page](/user-guide/case-studies/hypernets).
 
@@ -38,7 +38,7 @@ Here's the ever-growing list of the projects and organisations which have utilis
 
 📋 **Overview:** The CHIME L2 project performs atmospheric correction for the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME).
 
-[//]: # (🔗 **Links:** [****]())
+[//]: # (🔗 **URL:** [****]())
 
 ☄️ **Involvement:** CoMet is used for the uncertainty propagation through the atmospheric correction algorithms.
 
@@ -49,7 +49,7 @@ Here's the ever-growing list of the projects and organisations which have utilis
 
 📋 **Overview:** FLEX validation supports the calibration and validation of ESA’s Fluorescence Explorer mission using ground-based and airborne measurements.
 
-[//]: # (🔗 **Links:** [****]())
+[//]: # (🔗 **URL:** [****]())
 
 ☄️ **Involvement:** The CoMet toolkit is used to ensure the validation results are accompanied by uncertainties, and thus can be reliably interpreted.
 
@@ -59,7 +59,7 @@ Here's the ever-growing list of the projects and organisations which have utilis
 
 📋 **Overview:** LIME is the Lunar Irradiance Model of the European Space Agency (ESA), which aims to determine an improved lunar irradiance model with sub-2% radiometric uncertainties.
 
-🔗 **Links:** [**LIME Website**](https://lime.uva.es/)
+🔗 **URL:** [**LIME Website**](https://lime.uva.es/)
 
 ☄️ **Involvement:** These uncertainties are propagated through the LIME toolbox using CoMet.
 
@@ -69,15 +69,15 @@ Here's the ever-growing list of the projects and organisations which have utilis
 
 📋 **Overview:** Met4EO aims to develop, apply and extend a metrological approach to the uncertainty analysis and comparison of satellite data products.
 
-[//]: # (🔗 **Links:** [****]())
+[//]: # (🔗 **URL:** [****]())
 
 ☄️ **Involvement:** The CoMet toolkit will be used in a range of its case studies.
 
 ## 🗸 TACOS
 
 📋 **Overview:** The TRUTHS mission Accompanying Consolidation Towards Operations Study (TACOS) performs scientific studies in preparation of the TRUTHS mission.
 
-[//]: # (🔗 **Links:** [****]())
+[//]: # (🔗 **URL:** [****]())
 
 ☄️ **Involvement:** The CoMet toolkit is used for uncertainty propagation in various parts.
 
@@ -87,7 +87,7 @@ Here's the ever-growing list of the projects and organisations which have utilis
 
 📋 **Overview:** FRM4SOC sets out to define above-water ocean colour radiometry best practice for measurement and uncertainty propagation, for two radiometers in common usage, being implementing into an open source community processor.
 
-🔗 **Links:** [**FRM4SOC Website**](https://frm4soc.org/)
+🔗 **URL:** [**FRM4SOC Website**](https://frm4soc.org/)
 
 ☄️ **Involvement:** CoMet has been used heavily to propagate uncertainties through a complicated measurement system and provide uncertainty outputs for L1 and L2 products.
 
@@ -97,7 +97,7 @@ Here's the ever-growing list of the projects and organisations which have utilis
 
 📋 **Overview:** RPV4PICS advances reflectance product validation for satellite data using improved BRDF and aerosol modelling over Pseudo Invariant Calibration Sites (PICS).
 
-[//]: # (🔗 **Links:** [****]())
+[//]: # (🔗 **URL:** [****]())
 
 ☄️ **Involvement:** Uncertainties were propagated using CoMet.
 
@@ -107,7 +107,7 @@ Here's the ever-growing list of the projects and organisations which have utilis
 
 📋 **Overview:** - The Metrological Analysis of CIMR Radiometry (MACRAD) study.
 
-[//]: # (🔗 **Links:** [****]())
+[//]: # (🔗 **URL:** [****]())
 
 ☄️ **Involvement:** Uses CoMet to propagate uncertainties for the Copernicus Imaging Microwave Radiometer (CIMR) mission.
 
@@ -118,7 +118,7 @@ Here's the ever-growing list of the projects and organisations which have utilis
 
 📋 **Overview:** - FDR4ATMOS develops methodologies for full‐disclosure radiometry in atmospheric remote sensing to support climate monitoring.
 
-[//]: # (🔗 **Links:** [****]())
+[//]: # (🔗 **URL:** [****]())
 
 [//]: # ()
 [//]: # (☄️ **Involvement:**)
@@ -129,7 +129,7 @@ Here's the ever-growing list of the projects and organisations which have utilis
 
 📋 **Overview:** The Digital Twin Earth Synthetic Scene Generator and Observation Simulator (DTE-S2GOS) project focuses on developing a new pre-operational service to be implemented as a component of the ESA DestinE Platform.
 
-🔗 **Links:** [**S2GOS Website**](https://dte-s2gos.rayference.eu/)
+🔗 **URL:** [**S2GOS Website**](https://dte-s2gos.rayference.eu/)
 
 ☄️ **Involvement:** Validation of this service needs a metrological approach, which is implemented using CoMet.
 
@@ -139,7 +139,7 @@ Here's the ever-growing list of the projects and organisations which have utilis
 
 📋 **Overview:** DEFLOX is a project supporting the development of the FloX system for Solar induced Chlorophyll Fluorescence observation. 
 
-[//]: # (🔗 **Links:** [****]() )
+[//]: # (🔗 **URL:** [****]() )
 
 ☄️ **Involvement:** The CoMet Toolkit is being used to develop uncertainty propagation through the FLOX processing chain.
 
@@ -149,7 +149,7 @@ Here's the ever-growing list of the projects and organisations which have utilis
 
 📋 **Overview:** - FRM4FLUO establishes standards for the calibration and validation of satellite-derived fluorescence measurements of vegetation.
 
-[//]: # (🔗 **Links:** [****]())
+[//]: # (🔗 **URL:** [****]())
 
 [//]: # (☄️ **Involvement:**)
 
@@ -160,8 +160,18 @@ Here's the ever-growing list of the projects and organisations which have utilis
 
 📋 **Overview:** Global Climate Observing System (GCOS) ensures access to long-term climate observations. Within GCOS, the GCOS Reference Upper-Air Network (GRUAN) plays a key role by providing reference-quality atmospheric balloon data.
 
-🔗 **Links:** [**GRUAN Website**](https://www.gruan.org/)
+🔗 **URL:** [**GRUAN Website**](https://www.gruan.org/)
 
 ☄️ **Involvement:** A case study where the CoMet Toolkit has been successfully implemented with GRUAN data to acquire covariance information of GRUAN data. Read more on [the case study page](/user-guide/case-studies/gruan).
 
-![GCOS GRUAN](gruan.jpeg)
+![GCOS GRUAN](gruan.jpeg)
+
+## 🗸 St3TART-FO
+
+📋 **Overview:** The St3TART-FO project is aimed at providing an operational framework for Fiducial Reference Measurements (FRM) in support of the validation activities of the Sentinel-3 (S3) radar altimeter over land surfaces of interest, including inland water bodies (lakes, reservoirs, rivers including estuarian areas), as well as sea ice and land ice areas (ice caps, mountain glaciers, etc.).
+
+🔗 **URL:** [**St3TART Website**](https://sentinel3-st3tart.noveltis.fr/)
+
+☄️ **Involvement:** CoMet has been used to implement uncertainty propagation within the St3TART-FO project.
+
+![St3TART-FO](St3TART-FO.png)

content/tools/curepy/index.md

@@ -0,0 +1,46 @@
+---
+# widget: blank
+# headless: true
+# ... Put Your Section Options Here (title etc.) ...
+title: curepy
+# subtitle:
+# weight: 10  # section position on page
+# design:
+#   # Choose how many columns the section has. Valid values: 1 or 2.
+#   columns: '1'
+authors:
+  - admin
+date: 2026-03-31
+
+---
+
+## ❔ What is *curepy*?
+
+
+**curepy** is a Python package designed to propagate uncertainties through inverse problems. 
+The name *curepy* stands for **“Comet Uncertainties for REtrievals in PYthon”**. 
+
+Currently, only a *beta* version is available for *curepy*. Please use with caution. 
+
+
+## 📍 Where can *curepy* be found?
+
+Curepy can be installed via pip, and is available through:
+- Github [here](https://github.com/comet-toolkit/curepy)
+- *curepy* documentation [here](https://curepy.readthedocs.io/en/latest/)
+- Example jupyter notebook in the [example section](/user-guide/examples/).
+
+## 📋 What can *curepy* be used for?
+
+**curepy** provides tools to solve inverse problems in which a state vector and its associated uncertainties is retrieved from measurements, optional prior information, and optionally taking into account any required ancillary parameters.
+
+In inverse problems, uncertainties arise not only from measurement noise but also from the ill‑posedness of the retrieval. To obtain meaningful results, these uncertainties must be rigorously characterised and propagated through the inversion process. This includes handling **error-correlations** in the measurement data, as well as uncertainties introduced by priors, forward‑model assumptions, and model sensitivities. curepy quantifies how each of these factors affects the retrieved state, providing a robust framework for assessing retrieval reliability and uncertainty.
+
+To make this process straightforward, curepy implements tools that allows users to propagate uncertainties through any measurement or forward model function written in Python, ranging from simple analytical expressions to full numerical processing chains (including external components such as radiative‑transfer models), as long as they can be wrapped in a Python function.
+
+Uncertainty propagation through the retrieval can be performed using:
+
+- Markov Chain Monte Carlo (MCMC)
+- Optimal Estimation (OE)
+
+Measurement values and uncertainties can be supplied manually or via an *obsarray* dataset. **curepy** also provides tools to analyse retrieval outputs, quantify uncertainties, compare results, and export them again as obsarray datasets.

content/user-guide/examples/index.md

@@ -89,6 +89,24 @@ This example covers the following concepts:
   - Extrapolation
   - 1D interpolation along high-resolution example
 
+## ☄️ Curepy retrieval 
+
+### 🗸 How to obtain uncertainties for inverse problems?
+
+A jupyter notebook for **retrievals** with uncertainties can be found [here](https://colab.research.google.com/github/comet-toolkit/comet_training/blob/main/curepy_example.ipynb).
+
+This example allows you to get familiar with the [**curepy**](/tools/curepy) tool, and covers the following concepts:
+
+  - Propagate uncertainties through a basic inverse problem (harmonisation of two sensors). 
+  - Pass all relevant inputs to curepy
+    1. Measurements
+    2. Measurement function
+    3. Prior
+    4. Ancillary params
+  - Run curepy retrieval to obtain state vector with uncertainties and error correlation
+    1. MCMC
+    2. Optimal Estimation
+
 ## 🗂️ Project specific examples
 
 In this section, we have compiled a list of external projects and examples that have utilised the CoMet Toolkit. 

content/user-guide/theory/intro-to-uncertainties/index.md

@@ -12,7 +12,7 @@ image:
   caption: 'Image credit: [**Unsplash**](https://unsplash.com)'
 ---
 
-Navigating the world of uncertainties can be quite tricky, as oftentimes terms such as _errors_ and _uncertainties_ are used interchangeably, even though they do not describe the same thing.
+Navigating the world of uncertainties can be quite tricky, as often terms such as _errors_ and _uncertainties_ are used interchangeably, even though they do not describe the same thing.
 
 In this brief introduction to uncertainties and their propagation, we will highlight the most important concepts and signpost you to useful resources.