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Intensive Testbed Weekly Summary: January 27th–February 3rd, 2025

Category
WISER-EWSA Testbed
Date
One of the Forecasting teams at testbed 2

The T2-Z Intensive Testbed launched on January 30th, and will run until February 7th, 2025. Prior to the start, a three-day training programme (January 27–29) was led by Cynthia Nemutudi from South African Weather Service (SAWS), where forecasters from ZMD and INAM received training on nowcasting products, Impact Based forecasting, Common Alert Protocol (CAP) messages and the standard operations procedures (SOPs) for nowcasting and synoptic forecasting in preparation for T2-Z.

 

For T2-Z, forecasters from ZMD, INAM, and SAWS were assigned to teams focusing on synoptic forecasting, nowcasting, evaluation, and community engagement In Kanyama. The ZMD nowcasting team operates 24 hours, with three shifts:

  • 08:00–17:00 CAT (main shift)
  • 16:00–01:00 CAT
  • 00:00–09:00 CAT

 

During T2-Z, ZMD will be issuing nowcasts for Kanyama every 2 hours, and community observers (SMDCs) will provide structured feedback via Google Forms, WhatsApp messages, photos, and videos three times a day.

INAM will be issuing daily weather updates to Boane’s community observers and will provide nowcasts when an Intensive Nowcasting Event (INE) occurs in Mozambique. When necessary, these nowcasts will be distributed nationally. Additionally, INAM will be sending daily rain gauge data to facilitate the evaluation and verification of nowcasts.

 

During the first week of T2-Z, SAWS forecasters were unable to join due to a network disruption at their offices. However, starting from today, Monday, February 3rd, forecasters from SAWS joined online and resume issuing nowcasts for Katlehong.

 

Kick-off Meeting Highlights

 

The Testbed was officially launched with a speech by Mr Edson Nkonde, PR Zambia Meteorological Department, followed by opening remarks from:

  • Doug Parker (WISER-EWSA PI, University of Leeds)
  • Hellen Msemo (WMO representative)
  • Nico Kroese (SAWS, WISER-EWSA representative)

 

During the kick-off meeting, it was emphasized that international collaboration is a key driver in advancing scientific research and forecasting applications. Additionally, the adoption of new technologies is essential for improving forecasts and warnings in Southern Africa. Finally, community engagement through co-production plays a crucial role in enhancing adaptation to extreme weather events, ensuring that weather information, including nowcasts, is valuable and actionable for end-users.

 

Key Achievements – Week 1 of the Intensive Testbed

  • ZMD introduced voice notes in English, Nyanja, and other local languages, to disseminate nowcasts, improving the accessibility of weather information for a broader audience.
  • Nowcasting teams are utilizing a diverse range of forecasting products, including CRR, RDT, RoA, and Lightning data, to enhance the accuracy of nowcasts.
  • Forecasters from ZMD and INAM are actively engaged in the evaluation of nowcasts, contributing to improving forecast accuracy.
  • The evaluation team is conducting:
    • Cross-validation of retrieval products against rain gauge data to assess accuracy.
    • Assessment of forward prediction models (CRR, RoA) to understand their performance and limitations in capturing nowcasted events.
    • Analysis of AI-driven nowcasting models, including Jawairaa’s CNN-based nowcast of convective cores, to evaluate their effectiveness in predicting convective rainfall development.
  • Evaluation of the King-size Testbed (from October 1st, 2024 to January 27th,2025) is underway, to assess the effectiveness of nowcasting operations during this period.

 

Looking Ahead

  • Continued daily nowcasting operations and evaluation of forecast accuracy.
  • Assessing the accessibility of nowcasts – ensuring that maps, warnings, and advisories are clear and understandable to all users.
  • Refining the nowcasting template based on feedback from community observers in response to nowcasts issued by SAWS, INAM, and ZMD.
  • Addressing time constraints – the current nowcast template is time-consuming, leading to lost opportunities for monitoring real-time weather developments.
    • Possible improvements include simplifying the process, incorporating colour-coded rainfall intensity, and ensuring long-term sustainability of nowcasting operations.
  • Expanding capacity-building for SDMCs to support the dissemination of nowcasting information more effectively.
  • Integrating AI prediction models for improved nowcasting through intercomparison of new products developed by the team’s scientists.
  • Enhancing training opportunities for forecasters on new nowcasting models:
    • Facilitating collaboration between model developers and forecasters.
    • Sharing instructional materials through different platforms.
    • Inviting PhD students to provide training sessions.
    • Incorporating training materials into platforms like Moodle for long-term learning.