Update schedule and outline

outline.md

@@ -52,16 +52,13 @@ Participants will identify and mitigate non-meteorological signals, including gr
 
 After a lunch break, afternoon sessions offer individual projects:
 * In depth exploration of selected steps from the morning session, to provide insight into the underlying algorithms and enable participants to modify or extend them as needed.
-* A dedicated session focuses on the integration of established radar processing frameworks into modern workflows. Systems such as BALTRAD and LROSE (TITAN)  are introduced, demonstrating how their capabilities can complement Python-based tools and be incorporated into xarray-based pipelines. This session bridges research-oriented workflows with operational processing environments, providing participants with a broader perspective on the radar ecosystem.
-* A special session on Echo Top Height Analysis to identify storm structure, vertical extents, and derived products.
-
-Exercises emphasize transparency and reproducibility, enabling participants to not only generate analysis-ready datasets but also to understand and adapt the processing workflows. The course concludes with a discussion of how these outputs can be used in precipitation nowcasting, providing a direct link to the second day of the training sequence.
-
-By the end of the day, participants will have completed a full radar processing workflow, gained hands-on experience with interoperable open-source tools, and acquired the skills needed to design, modify, and apply radar processing pipelines for both research and operational purposes.
+* Hands-on Tutorials will focus on the integration of established radar processing frameworks into modern workflows, demonstrating how their capabilities can complement Python-based tools and be incorporated into xarray-based pipelines. This session bridges research-oriented workflows with operational processing environments, providing participants with a broader perspective on the radar ecosystem.
+* Exercises emphasize transparency and reproducibility, enabling participants to not only generate analysis-ready datasets but also to understand and adapt the processing workflows. The course concludes with a discussion of how these outputs can be used in precipitation nowcasting, providing a direct link to the second day of the training sequence.
+* By the end of the day, participants will have completed a full radar processing workflow, gained hands-on experience with interoperable open-source tools, and acquired the skills needed to design, modify, and apply radar processing pipelines for both research and operational purposes.
 
 ## Software and Tools
 
-All exercises will be based on open-source software within the Python ecosystem, in particular xradar, Py-ART, wradlib, xarray, and dask. The course will explicitly demonstrate how these tools can be combined into coherent and reproducible workflows, while also interfacing with external systems such as BALTRAD and LROSE.
+All exercises will be based on open-source software within the Python ecosystem. The course will explicitly demonstrate how these tools can be combined into coherent and reproducible workflows, while also interfacing with external systems such as BALTRAD and LROSE.
 
 ## Data and Case Studies
 

schedule.md

@@ -23,64 +23,45 @@ Please make sure to be there well before 9 o'clock to find your way and have eno
 
 ## 3. Introduction to Radar Packages
 
-> Each package will be presented in ~2 slides:
-> - Slide 1: Overview
-> - Slide 2: Functional focus (inputs → workflow → outputs)
-
 - Xradar (IO layer)
 - PyArt
-- LRose
-- Baltrad
-
-## 4. Workflow as a Notebook
+- wradlib
+- LROSE
+- BALTRAD
 
-Show how individual packages integrate into a complete workflow.
+## 4. Radar Processing Workflow
 
-> Output of this workflow will be used in the Sunday PySteps workshop.
+Show how individual packages integrate into a complete workflow. Output of this workflow will be used in the Sunday [PySteps workshop](https://github.com/pySTEPS/ERAD-nowcasting-course-2026).
 
-### 4.1 Workflow Overview
+### 4.1 Overview
 
 ### 4.2 Quality Control (QA / QC)
 - Noise filtering
 - Artifact removal
 - Consistency checks across scans
-- tbc
 
-### 4.3 Corrections
+### 4.3 Radar Corrections
 - Clutter correction
 - Calibration adjustments
 - Attenuation correction
-- tbc
 
 ### 4.4 QPE retrievals 
 - Precipitation estimation methods
-- Integration with local rain gauge data
-- Bias correction strategies 
-- tbc
 
 ### 4.5 Gridding
 - Conversion to Cartesian grids
 - Interpolation methods
 - Output formats for analysis and visualization
 
-## 5. Afternoon: Individual Projects
-
-### Structure Options
-- Parallel breakout groups **or**
-- Sequential guided work session
-
-### 5.1 Project Options
-
-#### a. In Depth Review of 4.* Workflow
-- Algorithm analysis, modification, and extension
-- Incorporation of interoperable open-source tools
+## 5. Afternoon: Hands-On Tutorial
 
-#### b. TITAN (LROSE)
+#### 1. TITAN (LROSE)
 - Storm tracking and analysis workflows
 - Radar-to-dynamics applications
 
-#### c. Echo Top Height Analysis (Bobby?)
-- Identification of storm structure
-- Vertical extent diagnostics
-- Derived product generation
+#### 2. Reflectivity Calibration with Spaceborne Radar
+- GPM-API Introduction
+- Ground-Spaceborne Radar Volume Matching
+- Ground Radar Reflectivity Calibration
 
+## 6. General Discussion