Futoshi Yamauchi, Yanyan Liu, James Warner and Noam David The 8th Tokyo International Conference on African Development (TICAD8) Side Event: How Japan’s know-how can help address Africa’s food and nutrition challenges: Interventions and impacts SEP 28, 2022 - 6:00 TO 7:30PM JST

1. Using Commercial Microwave Links to Improve
Weather Forecasting and Emergency Response to
Rainstorms
Futoshi Yamauchi, Yanyan Liu, James Warner (IFPRI)
Noam David (AtmosCell/Tel Aviv)
September 28, 2022

2. Overview of Presentation
1. Introduction
2. Current progress
• Collaboration established
• Development of 2D CML rainfall
maps
• Short term Rain forecasting
• SMS interventions followed by
phone surveys
3. Next steps
• Within the project: data analysis
understand feedback and impacts of
SMS interventions
• Beyond the project: improve and
expand the alert system to all Ethiopia
4. Possible extensions of our project

3. 1.1 Introduction: Floods are a perennial problem in Ethiopia
• Large scale river floods occur most
commonly in the lowland areas, with
flash floods occurring in the highlands
• 250,000 people and 200 education and
healthcare facilities nationally are
affected by river flooding per year.

4. Specialized rainfall monitoring techniques
o Rain gauge: Low spatial coverage and sites
o Satellites: Lack of accuracy near ground level (cloud cover)
o Rainfall radars: Very limited use (Bole Airport), costly for implementation
Rain gauge Satellite Radars
1.2 Introduction: Current tools for rain monitoring

5. 1.3 Introduction: The CML-based rainfall algorithm
Why CML?
✓ Near real-time - every 15 minutes
✓ Near the ground surface
✓ High spatial-temporal resolution
✓ Low cost - already available
✓ No privacy issues – no phone records
needed

6. 2.1 Current progress - Collaboration established to obtain real-time CML data
• Collaboration with EIAR and Ethio
Telecom and AtmosCell established
• Infrastructure in place to
automatically retrieve the needed
data from the cellular provider’s
system
• Near real-time data of 15-minute
frequencies, from July 2021 onward
• Data covering Addis Ababa, Amhara,
Oromia, and SNNPR
Such large-scale real-time CMLs
data access is unprecedented in
any developing countries!

7. 2.2 Compare performance of CML rain with existing satellite products
• Climate Hazards Group InfraRed Precipitation (CHIRP)
• Based on infrared Cold Cloud Duration (CCD) rain estimates using high resolution satellite
images
• Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)
• Products yield from incorporating land station data from five public data streams and several
private archives to perform inverse distance weighting on CHIRP
• European Centre for Medium-Range Weather Forecasts - Reanalysis v5 (ERA5)
• Based on its physical and dynamic atmosphere model
Spatial Resolution Temporal Resolution Delay
CML 0.05 X 0.05 degree 15-minute 15 minutes
CHIRP 0.05 X 0.05 degree Daily 5 days
CHIRPS 0.05 X 0.05 degree Daily 3 weeks
ERA5 0.25 X 0.25 degree Hourly 5 days

8. Compare CML with satellite products – daily accumulative
Pearson Correlation* RMSD
RG vs CML 0.7040 (0.0000) 9.3847
RG vs CHIRP 0.1313 (0.3738) 12.0297
RG vs CHIRPS 0.2826 (0.0516) 13.8115
RG vs ERA5 0.4243 (0.0274) 10.5552
*: p-value of the Pearson correlation coefficient in parenthesis.
RG=Rain gauge; RMSD=root-mean-square deviation
• CML is more accurate than CHIRP, CHIRPS and ERA5.

9. 2.3 Develop algorithm of CML rain forecasting
• Layer I of the algorithm:
Generating a 2D map of the
accumulated rainfall amounts
• Layer II of the algorithm: Short
term Forecasting ('Nowcasting’)
based on 2D maps and some
additional features
• The algorithm issues an alert
once it identifies dynamics that
indicate the onset of a significant
rainfall event
Example: 2D rain intensity mapping and forecasting in
Addis Ababa on July 31, 2021

10. 2.4 Current progress – SMS rainstorm alert operations (1)
• Operation 1 – small-scale pilot informant survey
• 1,005 informants in 50 towns in 4 zones in the Amhara Region
• In-person survey followed by phone surveys
• Objective:
• Communication—Can the data be received, processed and SMS
sent to correct location in real time?
• Comprehension—Is the SMS prediction accurate and
understood by recipient?
• Response—In the event of rainstorms did respondent take
appropriate, timely action?

11. 2.4 Current progress – SMS rainstorm alert operations (2)
• Operation 2: larger-scale operation
- Recipients of SMS messages: 12,000 randomly
selected cellphone users from 1740 kebeles
- Interviewees: 1080 interviewees (540 from 180
control kebeles and 540 from 180 treatment
kebeles)
- Objective:
- Test the SMS alert system at larger scale
- Evaluate the impacts of SMS alert system on
behavioral responses

12. 3. Next steps
• Within the project:
• Data analysis to understand
• feedback of SMS interventions
• impacts of SMS interventions on behavioral responses including evacuations and
preventive actions
• Final project report
• Beyond the project
• Improve the alert system including forecasting algorithm and SMS operation
• Expand the alert system to all Ethiopia

13. 4. Other possible extensions of our project
• Incorporate hydrological models
and machine learning methods with
rain forecasts for flooding
predictions and alert systems
• Optimal placement of weather
radars and stations
• Crop yield monitoring
• Poverty and malnutrition mapping
• Rainfall-based index insurance
• Real-time malaria risk maps
Entrepreneurs, researchers
End users: donors (famine early warning
etc.), farmers, microinsurance providers,
meteorology departments, etc.
• Promote a sustainable business model
CML data
CML rainfall
products
Inform
demand