From 018a69854aaa534085a54bc0e885732c5af5c6a5 Mon Sep 17 00:00:00 2001 From: leavesntwigs Date: Thu, 30 Jul 2026 12:20:54 -0600 Subject: [PATCH 1/4] adding plotly to the environment for titan display --- binder/environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/binder/environment.yml b/binder/environment.yml index 490acebb6..820ff3a8d 100644 --- a/binder/environment.yml +++ b/binder/environment.yml @@ -79,4 +79,5 @@ dependencies: # needed for LROSE / pyrad - metpy - pip + - plotly From afc4789440c55014a199effe57f9f14123a0a7e4 Mon Sep 17 00:00:00 2001 From: leavesntwigs Date: Fri, 21 Aug 2026 17:33:29 +0200 Subject: [PATCH 2/4] updating schedule --- schedule.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/schedule.md b/schedule.md index b36262a11..a861c273e 100644 --- a/schedule.md +++ b/schedule.md @@ -29,6 +29,7 @@ Please make sure to be there well before 9 o'clock to find your way and have eno - Xradar (IO layer) - PyArt +- wradlib - LRose - Baltrad @@ -79,8 +80,7 @@ Show how individual packages integrate into a complete workflow. - 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 - +#### c. Reflectivity Calibration with Spaceborne Radar +- GPM-API Introduction +- Ground-Spaceborne Radar Volume Matching +- Ground Radar Reflectivity Calibration From 205fd774e97f65e7acf853d7889002d2e4fb37cf Mon Sep 17 00:00:00 2001 From: leavesntwigs <29667431+leavesntwigs@users.noreply.github.com> Date: Fri, 21 Aug 2026 18:05:07 +0200 Subject: [PATCH 3/4] Update schedule.md --- schedule.md | 41 +++++++++++------------------------------ 1 file changed, 11 insertions(+), 30 deletions(-) diff --git a/schedule.md b/schedule.md index a861c273e..a5b39481c 100644 --- a/schedule.md +++ b/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 - wradlib -- LRose -- Baltrad - -## 4. Workflow as a Notebook +- 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. Afternoon: Hands-On Tutorial -### 5.1 Project Options - -#### a. In Depth Review of 4.* Workflow -- Algorithm analysis, modification, and extension -- Incorporation of interoperable open-source tools - -#### b. TITAN (LROSE) +#### 1. TITAN (LROSE) - Storm tracking and analysis workflows - Radar-to-dynamics applications -#### c. Reflectivity Calibration with Spaceborne Radar +#### 2. Reflectivity Calibration with Spaceborne Radar - GPM-API Introduction - Ground-Spaceborne Radar Volume Matching - Ground Radar Reflectivity Calibration + +## 6. General Discussion From 073755ec422ea27ef2432f0d9cb1cd3afdfb3972 Mon Sep 17 00:00:00 2001 From: leavesntwigs <29667431+leavesntwigs@users.noreply.github.com> Date: Fri, 21 Aug 2026 18:15:23 +0200 Subject: [PATCH 4/4] Update outline.md --- outline.md | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/outline.md b/outline.md index 1c42d96b4..81e7cd02f 100644 --- a/outline.md +++ b/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