OSIRISSYNTHESISREPORTNOVEMBER 2023

Table of Contents
1.Executive Summary
2. Project Timeline
3. Outcome 1: Tools to interpret
breeding outcomes at sites as afunction of spatially and seasonallyvarying agro-climatic weather drivers
1.1a: Needs Assessment 1:
Weather and Climate Tools in
Support of Data-Driven Seed Trial
Analysis1.1b & 1.2: Osiris Reanalysis
Veriﬁcation Report
1.3b: OSIRIS Reanalysis
Veriﬁcation Report
1.4a: Data Access Frameworks for
Next-Gen Weather Technologies
1.4b: CGIAR Technical Report
Enhanced Crop Variety Evaluation(Use Case 1)
1.5a&c: Satellite Constellation for
Rainfall Monitoring in East Africa
1.5b: Deployment of ﬁve laser
disdrometers to enhance evaluationof Pathﬁnder satellite in E Africa

Personas Involved and their Needs
for Weather Intelligence in Supportof Improved Breeding Outcomes
3.1b: Gender Roles in Seed Trial
Programs and the Impact of Weather
Intelligence
3.2b: Capacity building workshop forusing decision support tool
6. Outcome 4
4.3: External Rainfall Data Analysis
4.4: Data Science - Assessment of
the IRI Climate Data Tool’s (CDT)
functionality to enhance TAHMO
station observations as well as the
CBAM gridded reanalysis
temperature data
7. Workshops and Stakeholder Events
during P1
1.

Kenya Trip 1

2. Needs Assessment Workshop
with Seed Breeders in East Africa
3.

AGRF Rwanda 2022

4.

GSMA MWC Africa Event 2022

1.6b: Global Access Strategy

5. Mercy Corps AgriFin 6th Annual
Learning Event 2022

4. Outcome 2: Tools to apply insightsfrom trial sites to farm ﬁelds

6. Kenya & Rwanda Trial
Management and Gender Case
Studies

2.1: Needs Assessment 2:
Importance of Weather and Climate
Tools in Supporting Crop Modelingfor Improved Breeding Outcomes
2.2a: Use Case 2 - Analysis of
APSIM crop simulation outcomesusing CBAM vs NASA to assess
ROI in Rwanda

5. Outcome 3: Tools for managers infarm ﬁelds to use weather data
3.1a: Needs Assessment 3: Assessing
the Trial Management Ecosystem, the

7: Co-design Workshop for a
Next-Generation Weather
Intelligence Tool for Trial Managers
8: Partnership Launch with Kenya
Space Agency for Data
Dissemination
9: Exhibition at the Inaugural Africa
Climate Summit & TomorrowNow
Side Event About Transformative
Weather Intelligence
10: Niche User Innovation, Design &Feedback Workshop Convened byRegrow Ag in Kigali and Nairobi

2

Executive Summary

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The OSIRIS project was an ambitious 18-monthundertaking to demonstrate the power of next
generation weather data, tools and analytics torevolutionize small-scale agriculture on a data-sparse
African continent. By enabling close partnerships withkey weather intelligence, seed breeding, and cropmodeling ecosystem stakeholders, including CGIAR,
Regrow, and Tomorrow.io, the project assessed thetransformative potential of weather and climate
technologies for enhanced seed breeding outcomes.
Concentrating on closing the weather data gap andtransforming forecasts into practical insights, theproject has signiﬁcantly advanced understanding of therequirements of trial managers and data scientists. This
progress has led to the development of a new highresolution historical reanalysis data set for East Africa
that has been made available through multiple open
platforms. These efforts are crucial in facilitating
informed decision-making, optimizing crop yields, andstrengthening food security.
OSIRIS was oriented around the hypothesis that
innovative weather data and tools can improvesmallholder economics and food security in ourchanging climate.
The hypothesis was tested through the focused
application of breeding trials to develop advanced
crop varieties in East Africa. Data/tools weredeveloped to enable and assess the usefulness ofadvanced weather technologies through the lens ofspeciﬁc users at different value chain points, including
the crop modeling community and on-site farmmanagers, especially women.
In conclusion, the OSIRIS project's results offer
encouraging evidence supporting the hypothesis that
innovative weather data and tools can signiﬁcantly
enhance the smallholder ecosystem, and bolster foodsecurity in the face of climate change. Validation
studies conducted with key partners, including CGIAR
and Regrow, have afﬁrmed the substantial beneﬁts andimpact of utilizing higher resolution historical reanalysis
data on breeding outcomes, underscoring the vital roleof data accessibility in these efforts.

3

Executive Summary
The key learnings from the project:
1. Six key factors currently inﬂuence the adoption of
weather data & tools across the seed breeding ecosystem:
●
●
●
●
●
●

Data accessibility
Data quality
Data relevance
Timely delivery of data
Data presentation
Affordability

2. Data accessibility remains the single biggest factor
inﬂuencing the uptake of weather data & tools in East
Africa.
3. Validation is critical for de-risking anditerating/improving weather technologies for local
adoption/uptake. Validations require infrastructure andexpertise external to the companies engaged for foodsystem-speciﬁc applications.
4. Next-generation weather innovations have the potential
to provide critical value to food systems severely impacted
by limitations in the quality and accessibility of weather &climate data. The potential of the data being generated
goes well beyond the three use cases tested in Osiris phaseone. Several different models could enable maximum reach,
adoption, impact and sustainability of data.
5. New commercial weather assets such as Tomorrow.io’s
CBAM can dramatically improve the usability andusefulness of data for R&D and small-scale producer (SSP)
applications. There is also a massive opportunity for future
space-radar capabilities identiﬁed within the seed breeding
ecosystem and broader SSP applications. Efﬁcient andeffective access to data is as important to breedingscientists as the quality and accuracy of weather andclimate data.
6. The role of trial managers differs greatly between trial
management programs across East Africa with different
personas, each with speciﬁc weather intelligence needs.

S
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M
M
A
R
Y

7. The temperature measurements derived from the CBAM
reanalysis outperformed all existing climate data sets andenhanced crop variety modeling outcomes by more than40%.
8. The rainfall and solar radiation measurements derived
from CBAM requires signiﬁcant bias adjustments to ensure
future adoption.

4

Project Kick-Off
April 2022
Needs Assessment I
30 July 2022
Osiris Reanalysis
Validation Report
30 July 2022
Evaluation framework
Report30 July 2022
Needs Assessment II
30 Aug 2022
CBAM data accessible
15 Dec 2022
Next generation data
access protocols andframework documented
31 Jan 2023

Needs Assessment III
31 Jan 2023
Co - design workshop
report28 Feb 2023
Gender Focused report
01 Mar 2023
Farmer facing tool
design document
30 April 2023
CBAM Validation
Report31 May 2023
CGIAR Analysis
30 June 2023
Radar Simulation
Modelling
30 Aug 2023
Client - side code
library
31 Aug 2023
Global Access Strategy
Document
31 Aug 2023
Signed LOI’s & MOU,s ofcollaborators & Stakeholders
Initial Baseline GAS

30 Sep 2023

Deployment of laser
disdrometers OSSE
Experiments
31 Aug 2023

OSIRIS I Final Report
30 Nov 2023

Figure 1: OSIRIS Project Timeline5Project Progress
Reporting per
Outcome
Outcome 1: Tools to interpret
breeding outcomes at sites as afunction of spatially and seasonallyvarying agro-climatic weather drivers

Outcome 1 encompassed the dynamic
downscaling and reanalysis of weather datafor crucial regions in Sub-Saharan Africa,
utilizing Tomorrow.io’s proprietary
cloud-native CBAM. This process included acomparative analysis of this product withexisting benchmarks such as CHIRPS, NASA
POWER, and ERA5, speciﬁcally for
precipitation and surface meteorology.
Furthermore, the outcome involved a thoroughassessment of the enhancements in thesedata resulting from the integration of datafrom precipitation radar satellites, which arescheduled for launch by Tomorrow.io.
Outcome 1 deliverables included:●
The undertaking of a needs assessment
study to identify needs andopportunities for improved weather
intelligence in support of seed trials●
The development of an evaluation andvalidation framework for Tomorrow.io’s
CBAM Reanalysis over East Africa●
The development of a data store withground-based data for assimilation
and/or performance benchmarking●
A summary of the results ofTomorrow.io’s Comprehensive Bespoke
Atmospheric Model (CBAM) reanalysis●
The development of a data portal andcustom API

Recommendation:
To address the challenge ofcoarse, inaccurate, oragronomically irrelevant data, andthe limitations in current tools for
retrieving and summarizing timeseries data for trial outcomes, theproject proposes the creation ofan advanced benchmark dataset
for East Africa.
This dataset will becomplemented by thedevelopment of an online platform
and tools designed for ease ofaccess and utilization by breeders,
including those at CGIAR. Thisinitiative will be aimed atenhancing the quality andrelevance of agricultural data,
thereby improving trial outcomes
and overall agricultural practices
in the region
In the following, we willsummarize each activity andsynthesize the key outcomes,
learnings, and recommendations.

6Figure 2: Crop Modeling Value Chain

1.1a - Needs Assessment 1:
Weather and Climate Tools in
Support of Data-Driven Seed Trial
Analysis
Hypothesis
Next-generation weather technologies
have the potential to improve breeding
trial outcomes by ensuring better quality
input data of higher spatial and temporal
resolution in support of enhanced trial
analysis.
The goal of this needs assessment was
to identify the needs, limitations, andopportunities for improved weather
innovations that have the potential totransform agriculture, help overcome
climate challenges, and initiate the next
plant breeding revolution.

According to CGIAR research, efﬁcient
access via API’s to quality climate data
was identiﬁed to be the single mostimportant environmental factor in cropmodels. This ﬁnding suggested the need forscientists or seed breeders to accesshigh-quality climate data to develop newcrop varieties.
The study recommends the provision ofhigh-resolution temperature, rainfall, andsolar radiation data through efﬁcient APIs.
This data could include providing regional
climate reanalysis to support trial analysis
on independent local observations,improving precipitation measurements, andincreasing spatial and temporal resolution.

7

Figure 3: Location of 5 rainfall stations used to evaluating CBAM’smodel performance on precipitation

1.1b & 1.2: Osiris Reanalysis
Framework Report
This report summarizes the proposed evaluation and validation framework of Tomorrow.io’s
CBAM Reanalysis over East Africa. It provides an evaluation focused on known and valuable
quantities in the agricultural sector (i.e., precipitation and surface temperatures) while using
familiar and relatable metrics in the sciences, particularly in atmospheric and climate
science. This evaluation was critical in understanding breeding outcomes in assimilating
geospatial inputs.
The key insights of this report can besummarized as follows:
1. The evaluation focused on precipitationand surface temperatures, essential
factors in the agricultural sector.
2. The evaluation framework uses familiar
and relatable metrics in atmospheric andclimate science.
3. Independent, non-assimilated
observations representative of thebioclimatic diversity and geographic
uniqueness are used for validation.
4. Two observation networks, TAHMO and
NCAR GLOBE, are identiﬁed for use in thereanalysis.

5. Data quality and completeness of theobservation networks are assessed,
including a comparison with existing meters
for data quality.
6. The document discusses data quality
assessment, including using medianabsolute deviation (MAD) as a summary
statistic.
7. The report also included theestablishment of deﬁnite thresholds fordiscrete meteorological criteria, such as
precipitation occurrence and accumulation.
Overall, the report aimed to evaluate theperformance of CBAM Reanalysis interms of precipitation and surface
temperatures using familiar metrics inatmospheric and climate science.

8

1.3a: Cloud-accessible Climate
Reanalysis data set for East Africa
Tomorrow.io’s CBAM is a customized
version of the Weather Research andForecasting (WRF) model conﬁgured
with two-nested domains: a 12-km
domain covering much of northeastern
Africa and a 4-km domain to cover theregion of interest which includes Kenya,
Tanzania, Uganda, Burundi, andRwanda (Figure 1). The CBAM
reanalysis utilizes the European
Center for Medium-Range Weather
Forecasting Fifth Generation
Atmospheric Reanalysis (ERA5) as
the initial and time-dependent
boundary conditions. All publicly
available METAR and radiosondedata are assimilated into the reanalysis
using four-dimensional data
assimilation.
The CBAM reanalysis has been
completed from January 1, 2012,
through December 31, 2021,
providing hourly analysis. Output ﬁelds
include 2-m temperature, 2-m
dewpoint, 2-m relative humidity, 10-m
wind speed and direction, 10-m wind
gust, surface pressure, mean sea levelpressure, 1-hour accumulated rainfall,and global horizontal irradiance (GHI).

Figure 4. Left: CBAM nested domain conﬁguration.
Right: CBAM domain 2 reanalysis area with terrain(m; shading).

Image 1: TomorrowNow & Kenya Space Agency
teams at the ofﬁcial partnership launch

Through partnership with Kenya Space Agency, TomorrowNow is hosting the CBAM reanalysis
dataset on a data sharing platform by Source Cooperative, a neutral, non-proﬁt data-sharing
utility that allows trusted organizations to share data without purchasing a data portal SaaS
subscription or managing infrastructure.
CBAM Global Access Repository: https://beta.source.coop/repositories/ksa/1000/description

9

1.3b: OSIRIS Reanalysis
Veriﬁcation Report
This report provides the results of the Comprehensive Bespoke Atmospheric Model(CBAM) reanalysis against available observations and independent reanalysis products,
i.e., CHIRPS, AgERA5 NASA Powers, with attention to enhanced spatial and temporalvariance for key agrometeorological variables.
The main ﬁndings and conclusions of theveriﬁcation report on the Tomorrow.io
Osiris Reanalysis and its comparison toother datasets are as follows:
1. Tomorrow.io’s CBAM daily total
precipitation skill surpassed AgERA5 andNASA Power datasets while exceedingthat of the CHIRPS dataset for small
precipitation thresholds but signiﬁcantly
overpredicted large rainfall events.
2. Accuracy of daily temperature indicators(mean, minimum, and maximum) from
CBAM exhibited improvement relative toNASA POWER and similar performance to
AgERA5.
3. The mean absolute error and bias indaily total solar radiation from CBAM wereslightly higher compared to AgERA5,
closely aligning with NASA POWER. A
comprehensive evaluation was limited due
to insufﬁcient ground validation sensors,necessitating further investigation prior toextended use. CBAM data had higher
spatial and temporal resolution than
gridded data sets, enabling agronomic
analysis and applications at ﬁner scales.

4. Further improvements in CBAM
reanalysis are being investigated toreduce bias and errors and bring thereanalysis closer to observed values.
In summary, the report presents
CBAM's performance against
observations and reanalysis products,
i.e., CHIRPS, AgERA5 NASA Powers,
with attention to enhanced spatial andtemporal variance for key
agrometeorological variables.
This analysis paid special attention toenhanced spatial and temporalvariance for key agro-met variables as
an activity under Outcome 1. The
veriﬁcation report highlighted
Tomorrow.io's CBAM reanalysis
performance as superior in
precipitation skill and comparable intemperature indicators. It emphasized
CBAM data's higher spatial andtemporal resolution, which is suitablefor ﬁne-scale agronomic analysis.
However, ongoing efforts aim to
improve accuracy and reduce bias inrainfall and solar radiation
measurements.

10

1.4a: Data Access Frameworks for
Next-Gen Weather Technologies
The report assesses data access protocols
and recommends a framework relevant toweather intelligence platforms that will allow
actors from the seed breeding ecosystem toanalyze data and enable analysis-ready data
(ARD) discovery, access, and distribution. The
aim was to optimize data access to weather
intelligence platforms cost-effectively andefﬁciently for seed breeders and provide
recommendations for current and future data
access protocols.
The report includes data storage, discovery,
access, and distribution recommendations.
1. Storagea. Data such as netCDF ﬁles andcloud-optimized geoTIFFs (CoGs) can be
stored in an object store like AWS S3.
b. Structured or relational data, like weather
station or laser disdrometer data, is better
stored in a relational database like
PostgreSQL with the PostGIS spatial
extension.
2. Discovery
a. Data intended for widespread use should bewell-indexed and searchable via general web
search tools and search tools on specialist
platforms.
b. Comprehensive metadata is crucial for
successful search, citation, and trust.

3. Access and Distribution
a. Weather intelligence companies areencouraged to open access to their data
for scientiﬁc users, who often have low or
no budget and require fast access to large
datasets and computing resources.
b. Scientiﬁc users need access to raw data,
ideally in an "analysis ready" format, alongwith sufﬁcient metadata and the ability toﬁlter or select subsets based on dimensions
or metadata ﬁelds.
c. Data should be available in open,
widely-used formats and protocols to servea wider community.

Additionally, the report also gives
recommendations to weather intelligencecompanies. Some of theserecommendations include:
1. Open access to data that meets
scientiﬁc computing requirements,
including raw and "analysis-ready" data.
2. Provide sufﬁcient metadata to describe
and ﬁlter/select subsets of data based on
dimensions or metadata ﬁelds.
3. Consider providing resources forscientiﬁc users to perform their
computations.

c. The STAC (Spatio-Temporal Asset Catalog)
speciﬁcation is recommended for describing
geospatial information and enabling easierindexing and discovery.

11

1.4b: CGIAR Technical Report
Enhanced Crop Variety Evaluation(Use Case 1)
This report analyzes the impact and value of the CBAM temperature, rainfall, and solar
radiation data in supporting crop variety analytics through the Plackett-Luce modelcompared to existing models such as NASA Power and ERA5.
The ﬁndings of this study are summarized asfollows:
1. CBAM temperature measurements indicate
an enhanced ability to delineate
agroecological zones in Rwanda. Precipitation
estimates from CBAM, however, degraded themodel results compared to the other models.
Conversely, both NASA POWER and AgERA4
demonstrate improved performance ingroundnut production in Tanzania.
2. CBAM shows enhanced model performancefor various recommendations in complex
terrains, like the case of Rwanda highlands.
Conversely, NASA POWER and AgERA5
performed well in Tanzania, where data
samples are limited and the landscape ishomogenous.
3. CBAM is a reliable data source for ranking
Rwandan potato varieties. Decision makers in
Rwanda can utilize CBAM to track
agroclimatic variability and recommend types,
considering performance and risk mitigation.
In Tanzania, CBAM is less strong than theother models. All three models identiﬁed
ICGV-SM 16593 as the top groundnut variety
regarding winning probability and worst
regret.

A comparison of the three climate models
indicated variations in accuracy andreliability among the models in predicting
crop yields and climate conditions.
Regarding accuracy, the CBAM model
enhanced performance in delineating
agroecological zones in Rwanda based on
temperature measurements. However, theprecipitation estimates from CBAM
degraded the model results compared tothe other models. On the other hand, bothNASA POWER and AgERA5 demonstrated
improved performance in groundnut
production in Tanzania.
Regarding reliability, CBAM demonstrated
enhanced model performance for various
recommendations in complex terrains likethe Rwanda highlands. This suggests that
CBAM is better suited for areas withdiverse topography. In contrast, NASA
POWER and AgERA5 performed well in
Tanzania, where data samples are limited,
and the terrain is more homogenous.
The benchmarking results highlighted theimportance of selecting the appropriate
climate model based on the target region's
speciﬁc agro-ecological conditions andterrain characteristics. The accuracy andreliability of each model can varydepending on the location and the speciﬁc
crop being evaluated.

12

1.5a&c: Satellite Constellation for
Rainfall Monitoring in East Africa
The report covers the development of radar
simulation and rain retrieval methods byTomorrow.io for the East Africa region, using
training data from NASA/JAXA's GPM mission
observations. These algorithms areundergoing validation with data fromTomorrow.io's Pathﬁnder R1 and R2 satellites.
The launch of the full TRAKR constellation,
including the Rain-mapping, Adaptive
Ka-band Radar (TRAKR) satellites, isanticipated by late 2025. Furthermore,
Tomorrow.io intends to deploy a series of
microwave sounders capable of estimating
precipitation proﬁles. While these instruments
offer more frequent revisits, they have a lower
resolution than the TRAKR satellites.

The key insights and takeaways from thereport include:
1. Tomorrow.io is implementing a novel
two-step retrieval algorithm for accurate
precipitation rates using Ka-band radarmeasurements. The algorithm uses adata-driven neural network technique thatprovides statistics of the probability
distribution of precipitation proﬁles. The
training data for the algorithm is sourced
from the dual-frequency retrievals from theNASA/JAXA GPM mission.
2. The Tomorrow.io Radar Constellation is
expected to improve spatiotemporal
coverage compared to ground-based
measurements. The sounder constellation
will provide more frequent observations
and critical inputs for weather forecasts.
3. The TRAKR and TMS constellations
could improve nowcast lead time by nearly
an hour. The typical lead times forwarnings driven by real-time precipitation
data and nowcasts are 15-30 minutes.

Figure 5: First science data collected by the
Tomorrow-R1 Pathﬁnder on June 24, 2023
Left: Satellite ground track overlaidon GOES-18
true-color composite satellite image. Right:
Proﬁles of measured reﬂectivity, retrieved
precipitation particle size, water content, andprecipitation rate

4. The NWS Warn-on-Forecast project
aims to improve warning lead times by 10
to 20 minutes. The accuracy of the
nowcast and precipitation retrievals varies
over time, with radar observations losing
value relative to geostationary
observations after 1-2 hours.

This report successfully demonstrated the new rainfall retrieval algorithm based onsatellite inputs and the performance of simulated radar data. The outcomes provide
insight into understanding precipitation as an agro-climatic weather driver.

13

1.5b: Deployment of ﬁve laser
disdrometers to enhance evaluationof Pathﬁnder satellite in East Africa
The project initiated the deployment of a set of state-of-the-art laser disdrometers at ﬁve
locations across Kenya and Rwanda in convectively active areas of the continent. Thesesensors will accurately measure the true surface rainfall rate and provide detailed informationon the size distribution of individual precipitation particles, a technical detail crucial fortransforming radar observations to rain rate.
Successful deployment of two
disdrometers has already been achieved.
The inaugural sensor found its home at
Maseno University in Kisumu, Kenya,
while the second was set up in Kayonza,
Rwanda, through a collaboration with
One Acre Fund. Plans are in place toinstall the remaining three sensors by
February 2024. These will be under the
management of the Kenya Meteorological
Department, with locations set for
Nairobi, Mombasa, and Moi University

Image 2: A laser disdrometer deployed in
Maseno University (Kisumu, Kenya)

Locations

Reliable Partner

Nairobi, Kenya

KMD

Eldoret, Kenya

Moi Univ

Kisumu, Kenya

Maseno Univ

Mombassa, Kenya

KMD

Kayonza, Rwanda

OAF

Figure 6: Location of sites for 5 deployed laser
disdrometers in East Africa

141.6b: Global Access Strategy
The document describes
TomorrowNow.org and Tomorrow.io's
commitment to ensuring global
accessibility of developments from the
OSIRIS Project. Their mission is to makenew technologies and ideas available,
accessible, and affordable for users in
Africa, particularly in East Africa.
The document emphasizes thedissemination of knowledge and theavailability of products and services
resulting from the project at a reasonable
cost to those in need. It also highlights theimportance of managing intellectual
property assets for global access.
A detailed technical document is being
developed to provide more information on
data sets, background technology, IP
rights, platforms for dissemination, and thedevelopment and post-project plan for
OSIRIS.

The key insights and takeaways from thestrategy are as follows:
1. Contracts have been signed with subawardees and relevant partners such as
Tomorrow.io and TAHMO
2. Contracts have been drawn up with
relevant parties such as OAF, Kenya
Space Agency and KMD
3. TAHMO network has deployed three
disdrometers and with remaining two to beinstalled in January 2024. All data isavailable via API
4. Open access to OSIRIS CBAM reanalysis
data for East Africa is provided throughplatforms such as Kenya Space Agency
and Radient Earth Source Cooperative
global platforms
https://beta.source.coop/repositories/ksa/1000

15Project Progress
Reporting per
Outcome
Outcome 2: Tools toapply insights from trialsites to farm fields

Validating tools to extend knowledge from trial farm sites to user farmsites.
Outcome 2 extended crop simulation of the 1000Farms and One Acre
Fund Fund trial data across Kenya and Rwanda based on the APSIM
model with inputs from CBAM and other climate models. The project aimed
to demonstrate the impact and value of better quality climate data on
APSIM model outputs for speciﬁc crops such as beans and maize.
Main deliverables included:●

The undertaking of a needs assessment with Regrow (Niche project)
to better understand the needs within Crop Simulation modeling●Preparation of a validation report for CBAM data through APSIM
crop simulation●

Development and customization of the Bestiapop Python package
to automatically generate and visualize CBAM gridded climate datafor crop model applications within the APSIM software

Recommendation:
To overcome data quality and access limitations associated with gridded
data in existing models and the lack of tools for applying these models tolarge, gridded datasets, the project proposes the development ofuser-friendly tools that simplify access to validated and localized data for
multi-site trial locations.
Additionally, we propose the development of design methods for
effectively applying predictive models to these gridded datasets, thereby
enhancing the utility and accuracy of model outputs for practical
agricultural applications.

16

2.1: Needs Assessment 2:
Importance of Weather andClimate Tools in Supporting Crop
Modeling for Improved Breeding
Outcomes.
Hypothesis
Innovative new weather technologies have thepotential to improve crop modeling efforts
currently limited by the quality of available
gridded climate data.
The goal of this needs assessment was tounderstand further the role of improved
weather innovations that can transform
agriculture, speciﬁcally focusing on the needs,
limitations, and opportunities for improved
weather innovations within crop simulation
modeling and its potential to enhance seed
breeding outcomes.
High-quality weather data was essential for
accurate yield situation modeling, whichhelps understand crop responses to climate
variability and change. Weather data also
supports risk analysis and helps improve trial
data quality. Furthermore, it supports theclassiﬁcation of cultivars based on
environmental factors.

Developing countries in Africa facechallenges in obtaining quality weather
data due to the scarcity and poor quality ofobservation data in many agriculturalareas. A need was identiﬁed for weather
data with higher spatial resolution (<5km)
to capture the variability in climaticconditions across different regions. This will
enable more accurate and localized cropmodeling and seed breeding.
As a recommendation, efforts should
encourage enhanced collaboration
between stakeholders, weather data
providers, and breeding programs toensure better data collection and sharing.
Data standardization is also necessary to
make datasets efﬁciently usable. Finally,
there is a need to enhance the quality ofclimate models and data outputs. This
includes producing model performance
results, clearly deﬁning model uncertainties
and limitations, and incorporating
innovative weather technologies toimprove the accuracy of climate data.

17

2.2a: Use Case 2 - Analysis of
APSIM crop simulation outcomesusing CBAM vs NASA to assess
ROI in Rwanda
This report assesses the accuracy of threeglobal climate datasets for maize yield
simulations using Rwanda's APSIM crop
production model. The objectives were:●

Both NP and CBAM raw weather data
displayed unbiased precipitation, but
CBAM radiation produced
questionable results, negatively
impacting model outcomes. The
higher radiation elevates soil
evaporation, which can contribute towater stress in crops, resulting inlower simulated yield duringmodeling. Thus, CBAM weather data
tends to limit simulated crop yield due
to signiﬁcantly higher radiation values
than weather stations and NASA
POWER data.

●

There was an acknowledged need for
gap-ﬁlling to ﬂesh out the full set of
required inputs for APSIM simulations;as such, it was impossible to compare
either NASA POWER or CBAM
against the weather station data
across all variables for all crop trials.
The variable distances between theweather station and the crop trial
locations and elevation differenceswere also expected to impact the yield
estimations and bias comparisons.
Finally, for trials in which the exact
trial location was not known, we
estimated the location based on thenearest distribution center, which cancreate challenges in directlycomparing data sources.

1. Assess similarities/differences between
weather station data (WS), NASA POWER(NP), and Custom Bespoke Atmospheric Model(CBAM) datasets. The variables under scrutiny
encompass minimum and maximum
temperature, rain, and radiation.
2. Assess the accuracy of simulated maize
yield using weather stations, NASA POWER,
and CBAM weather data relative to observed
maize yields from maize trials in Rwanda.
CBAM temperature data has shown evidenceof improving crop simulation model outcomes
and improving yield estimates at trial sites.
However, CBAM rainfall and solar radiation
measurements negatively impacted model
trial outcomes and required further
investigation. It is recommended that future
research delve into the sensitivity analysis ofthese variables to enhance the understandingof their impact further.
The main ﬁndings and conclusions can besummarized as follows:●

The gridded data from CBAM for
maximum temperature showed a stronger
overall agreement and better predictive
performance than NP. In contrast, theminimum temperature data from CBAM
and NP showed similar predictive
accuracy.

18

Project Progress
Reporting per
Outcome
Outcome 3: Tools for
managers in farm fields
to use weather data

Outcome 3 involved the engagement of trial managers tobetter understand the value of farmer/user-facing tools in thecontext of weather intelligence and seed trials. A tightfeedback loop between the technical teams and users will
ensure that the voice of users, especially women, arerepresented in the tools' design and outputs from the outset.
Studies have shown that while women only own 20% of theland, they account for roughly half the smallholder farmers
and, in Africa, are responsible for producing 70% of the food.
Outcome 3 deliverables included:●

The undertaking of a needs assessment with trial
management organizations●

A gender-focused report to assess the role of womenin two trial programs●

Capacity building workshop for using decision support
tool

Recommendation:
To bridge the gap between research and operational aspectsof trial sites, address the limitations of agroclimatic weather
forecasts, and enhance the usability of weather intelligencefor operational staff, we recommend developing decision
support tools tailored to the speciﬁc needs and information
access patterns of 1000Farms sites, considering
gender-speciﬁc roles and responsibilities.

19

3.1a: Needs Assessment 3:
Assessing the Trial Management
Ecosystem, the Personas Involved
and their Needs for Weather
Intelligence in Support of Improved
Breeding Outcomes
Hypothesis
Improved Weather Intelligence supported bywell-designed decision support tools has thepotential to improve trial management
outcomes through focused interventionsaligned with the different personas androles within trial management.
The goal of this needs assessment was tounderstand how the seed breeding
ecosystem interacts with each other and thevalue generated from this interaction.
The Seed Breeding Ecosystem comprises
numerous role players historically centeredaround governments and the private sector.
sector. The potential value and importanceof community participatory breeding
programs - such as the CGIAR 1000Farms
program - is currently being demonstrated
across East Africa with the participation of
thousands of small-scale farmers.
They play an important role in meeting foodsystems' multiple challenges by supportingfood security and nutrition, livelihoods,
sustainable resource use, and climatechange mitigation. This can lead to
improved planning and management of
trials, ultimately enhancing the success andproductivity of breeding outcomes.

Figure 7: Weather intelligence and the seed
breeding ecosystem in East Africa
This assessment recommended that byaddressing the challenges of accessing
quality weather information and decision
support tools, these advancements cancontribute to more efﬁcient and effective
trial management practices.
Additionally, customized weather andclimate data products and tools can caterto the speciﬁc needs of different personas
involved in trial management, such as
analysts and crop advisors.

20

3.1b: Gender Roles in Seed Trial
Programs and the Impact ofWeather Intelligence
The goal of this study was to highlight theneed to deliver gender-based insights toenhance the usability and operationalizationof weather data through developing
weather intelligence-enabled decision
support tools that take into account gender
perspectives in the context of seed trial
programs.
The study outcomes indicate that womentend to adopt weather information fasterthan men. However, the uptake of thisinformation is subject to many factors, suchas cultural, economic, and educational.
Female farmers are also keen on getting
short-term forecasts compared to their malecounterparts. This was attributed to theirfarming practices. Male farmers were more
likely to be cash-crop farmers, while female
farmers were more likely to need weather
information for activities like harvesting.

Hypothesis
Improved Weather Intelligence supported
by well-designed decision-support tools
has the potential to improve trial
management outcomes through focusedinterventions aligned with the different
personas, especially women and roles
within trial management.

21

Image 3: USSD Platform visualization

3.2b: Capacity building workshop
for using decision support tool
The co-creation workshop brought together
key stakeholders from the weather
forecasting and trial management
ecosystem to co-create and design support
tools to improve weather intelligence
adoption by trial managers.
The following are key takeaways from theworkshop:Participants acknowledged that weather
information is a vital resource that helps
individuals and organizations makeinformed decisions about safety,
transportation, agriculture, and more.
However, using and sharing weather
information has challenges, including
technology and language barriers, lack ofactionable information, insufﬁcient or
inaccurate data, lack of trust, andinsufﬁcient partnerships. The workshop
emphasized the crucial role of weather
information in decision-making acrossvarious sectors. The consensus was that
more efforts are needed to ensurecommunities and stakeholders utilize
weather information effectively.

Key recommendations included providing
weather forecasts packaged and presented
to intended users with a consideration of thefollowing factors:
1. Access to accurate weather information
and actionable insights
2. Appropriate communication of weather
information
3. Timeliness4. Integration of weather information into
existing platforms such as SMS, WhatsApp,
mobile and web apps, and radio or TV
broadcasts
5. Weather visualization tools andinformation should be designed to consider
the differentiated gender roles at thehousehold level.

Image 4: Short-term forecasts
visualization

22

Project Progress
Reporting per
Outcome
Outcome 4External weather and climate data and tool evaluation andanalysis by industry partners.
Outcome 4 was included in the project to extend weather
and climate data and tool evaluations and to develop
protocols to better support the seed breeding ecosystem. Thisoutcome included project activities such as:●

External rainfall data analysis●

Assessment of the IRI Climate Data Tool’s (CDT)
functionality to enhance TAHMO station observations
as well as the CBAM gridded reanalysis temperature
data

23

Figure 8: Monthly Climatology of CHIRPS2, CBAM, and MERRA2 and TAHMO in-situ
observations at TA00067 (top) and TA000182 (bottom). All the products have clearly
captured the bi-modal nature of rainfall (MAM and OND).

4.3: Kenya Rainfall Data
AnalysisAn external validation study performed by the Jomo Kenyatta University of Agriculture andTechnology rigorously examined CBAM's accuracy in predicting precipitation by comparing
its output with calibrated rain gauges in and around the Machakos region in Kenya.

The ﬁndings indicated a consistentpositive bias in CBAM's daily, monthly,
and annual rainfall estimations,signifying an overestimation.
Notably, CBAM demonstrated
proﬁciency in capturing short-duration
rainfall, particularly during the short
rainfall season (OND), while MERRA2
excelled in longer durations, speciﬁcallyduring the long rainfall season (MAM).

Despite high Probability of Detection(POD) scores for both CBAM and
MERRA2, the study raised concerns
about the high False Alarm Rate (FAR),
suggesting the models' tendency to
predict rainfall even without actual
precipitation.
Furthermore, MERRA2 exhibited ahigher correlation than CBAM whenassessing the Machakos region on
monthly and annual scales.

244.4: Data Science - Assessmentof the IRI Climate Data Tool’s(CDT) functionality to enhance
TAHMO station observations aswell as the CBAM gridded
reanalysis temperature data
This report underscored the pivotal role of meticulous data curation, bias correction
methodologies, and innovative tools like the CDT in advancing the accuracy, quality, andutility of temperature reanalysis data for Kenya.
The following are key insights from the report:

Assessment of the IRI Climate Data Tool
(CDT) and its Potential Impact on
Temperature Data Enhancement:●●●

A quality-controlled observation
dataset, sourced from TAHMO andUCAR data, improved the accuracyof temperature patterns in Kenya.

Development of Quality-Controlled,
Gap-Filled Observation andBias-Corrected CBAM Reanalysis
Datasets for Kenya:●

The Climate Data Tool (CDT)
demonstrated user-friendly andeffective handling of temperature
data, contributing to enhanced
reanalysis outcomes.

Additional bias correction
techniques were identiﬁed ashaving signiﬁcant potential toenhance Kenya's CBAM
temperature reanalysis further.

●

Bias correction techniques applied
to the CBAM temperature
reanalysis dataset resulted innotable accuracy improvements forboth maximum and minimum
temperature variables.

The Climate Data Toolkit (CDT)
proved valuable in generating
quality-controlled observation
datasets, highlighting its role in
improving data accuracy.

●

The CDT emerged as an effective
tool for augmenting CBAM
temperature reanalysis data
through efﬁcient bias correction
methods.

25

Workshops and Stakeholder
Events during P1
Kenya Trip 1
The 18th April 2022 - 29th April 2022 trip to Kenya aimed to
kick-start the OSIRIS Project in East Africa. This involved
engaging stakeholders and partners, holding farmer workshops,
participating in a conference, and engaging in team-building
activities. Some key trip highlights included farm visits in Kiambu
County, a Greenshade Self Help Group workshop, and meetings
with stakeholders in Kenya's agriculture and weather/climate
ecosystem.

The trip identiﬁed Kenya as a suitable launch region foradvanced weather services, with a favorable ecosystem andstrong local support. Overall, this paved the way forcollaboration, innovation, and sustainable solutions to address
climate risks and support the resilience of the agricultural sector
in Kenya.

26

Workshops and Stakeholder
Events during P1
Needs Assessment Workshop withSeed Breeders in East Africa
TomorrowNow initiated the Osiris Needs Assessment 3 with aworkshop held from 5th to 9th September 2022, coinciding witha CGIAR ClimMob training course at CIMMYT. This workshop
brought together trial managers from organizations including
One Acre Fund, KALRO, Bayer, CGIAR, and CIMMYT. It focused onunderstanding different personas in trial management and theirrequirements for weather information, as well as enhancing
stakeholder engagement and coordinating the OSIRIS project.
Participants, comprising scientists and trial ofﬁcers, engaged in
mapping the trial management ecosystem and explored how toeffectively utilize the CGIAR CIAT ClimMob tool for managing
seed breeding sites.

27

Workshops and Stakeholder
Events during P1
AGRF Rwanda 2022
The Africa Green Revolution Forum (AGRF) convened in Kigali,
Rwanda, on 7th-10th September 2022, focused on empowering
youth in agriculture and advancing resilient food systems.
Ronald Diang’a represented TomorrowNow as a panelist at theYouth Town Hall session, where he shared TomorrowNow’s
mission, milestones through various projects, and its vision for
Africa.
Key takeaways from the forum centered around two crucial
themes. Firstly, there was a strong emphasis on the necessity ofa multi-stakeholder approach to better support and engage theyouth in agriculture. The panel highlighted the critical role ofcollaboration in transforming food systems. Secondly, the event
underscored the transformative potential of technology,
recognizing it as a powerful tool to support and amplify youth
participation in agriculture sustainably.

28

Workshops and Stakeholder
Events during P1
GSMA MWC Africa Event 2022
The GSMA MWC Africa Event in October 2022 brought together key players intelco, digital inclusion, and mobile technology to address climate resilience inAfrica. TomorrowNow, represented by Kenneth Chepkwony, contributed to thepanel discussion on building climate resilience through digital technologies. The
panel discussions delved into TomorrowNow's projects, emphasizing the role ofwarning systems in enhancing community resilience.
Insights revealed the importance of partnerships with input providers,
government extension services, and community leaders to build trust with
farmers. Additionally, the event shed light on the current priority for donors andorganizations- shifting mindsets from mitigation to adaptation. These key
learnings underscored the potential of digital technologies to address climate
challenges while emphasizing the collaborative efforts needed to bridge gaps in
information access and community engagement.

29

Workshops and Stakeholder
Events during P1
Mercy Corps AgriFin 6th Annual
Learning Event 2022
The Mercy Corps AgriFin 6th Annual Learning Event on 22nd November, 2022
provided a forum where participants and organizers shared experiences oftested digital technologies and practices that work to enable smallholder farmer
resilience.
TomorrowNow had a booth and presented the OSIRIS project to farmer-facingorganizations at the conference. The discussions underscored the importance of
accurate weather data for effective decision-making in the agriculture sector.
However, a prevailing challenge identiﬁed was the generalized nature ofweather-linked extension services, causing information overload and "farmer
fatigue." The event revealed a willingness among organizations to invest in data
access, signaling a recognition of its potential to make a signiﬁcant difference inquality compared to existing solutions.

30

Workshops and Stakeholder
Events during P1
Kenya and Rwanda Trial Management
and Gender Case Studies
In Kenya, KALRO Katumani's gender-focused exploration of seed trial
operations on 29th - 30th November, 2022 revealed a comprehensive approachto address the intersection of gender dynamics and weather needs.
The team at TomorrowNow also conducted a workshop with farmers registeredunder the Greenshade Self Help Group (90% women) to understand theirperspectives on the importance of weather information in addition to the genderissues that deﬁne the efﬁcacy of their seed trial activities.
In Rwanda, TomorrowNow visited One Acre Fund’s Karongi headquarters andexplored their gender dynamics and weather information within seed trial
management. Through Key Informant Interviews (KIIs) on 3rd-7th December
2022, it was identiﬁed that weather information is not perceived as critical bymost farmers, prompting recommendations for targeted solutions.
In Rwanda and Kenya, the exploration of seed trial operations reveals a sharedcommitment to understanding the intersection of gender dynamics and weather
needs in agriculture. One Acre Fund in Rwanda emphasized the signiﬁcance of
weather information in seed trial management, proposing targeted solutions
such as incentives and training to enhance adoption. KALRO Katumani in Kenya,
through collaborative efforts, underscored the crucial role of accurate, localized
weather information for farmers and trial ofﬁcers. Both organizations
recognized the impact of women in their agricultural ecosystems, with genderconsiderations extending from seed preferences to community participation. The
collective takeaway emphasized the need for tailored solutions, recognizing
each context's unique challenges and opportunities.

31

Workshops and Stakeholder
Events during P1
Co-design Workshop - Next-Generation
Weather Intelligence Tools for Trial Managers
TomorrowNow.org organized a participatory co-design workshop on 28th
February 2023 in Nairobi, Kenya to generate ideas for a decision support tool fortrial managers supported by the latest weather technologies with a focus on
relevance, presentation and visualization.
Participants at the co-design workshop included seed trial managers and key
climate adaptation stakeholders representing Kenya Meteorological
Department, Biovision Africa Trust, One Acre Fund, ICPAC, Tomorrow.io, ESRI,International Maize and Wheat Improvement Center (CIMMYT), Kenya
Agricultural & Livestock Research Organization (KALRO), Kenya Space Agency,
and Mercy Corps AgriFin Sprout Open Content.
A key outcome of the co-design workshop was a set of actionable
recommendations and a comprehensive framework showing possible design
features for a farmer-facing user interface & seed trial managers' tool for
improved weather information.
In summary, it was recommended that short-term and seasonal forecasts need
to be packaged and presented to intended users with a consideration of thefollowing key factors:●●●

Access to accurate weather information and actionable insights
Appropriate communication of weather information
Timeliness

Integration of weather information into existing platforms such as SMS,
WhatsApp, mobile and web apps, and radio or TV broadcasts

32

Workshops and Stakeholder
Events during P1
Partnership Launch with Kenya Space
Agency for Data Dissemination
TomorrowNow and the Kenya Space Agency launched a new partnershipon 9th July, 2023 to jointly leverage the power of space data to support the
Kenyan government, key stakeholders, and farmers for food security andclimate adaptation. As part of the OSIRIS Global Access Strategy all
historical reanalysis data produced in the project will be made freely
available through the Kenya data portal.
Our partnership with the Kenya Space Agency will jointly provide
space-derived data decision support to stakeholders in agriculture toenhance food security and build climate resilience to ensure the efﬁcientutilization of space technology through local capacity building andpartnerships for increased investment in the sector.

33

Workshops and Stakeholder
Events during P1
Exhibition at the Inaugural Africa Climate
Summit & TomorrowNow Side Eventfocused on Transformative Weather
Intelligence
TomorrowNow shared its vision and progress at the Africa Climate
Summit #ACS23 (4th -8th September 2023) as an exhibitor alongside techpartner Tomorrow.io, and hosted a side event in partnership withTomorrow.io, Regen Organics & the Kenya Agricultural & LivestockResearch Organization (KALRO).
Themed “Transforming Africa's Food System through Weather Intelligenceand Partnership”, TomorrowNow’s side event crowded in the perspectiveof public, private and NGO leaders to highlight cutting-edge space andgenerative AI technology and transformative ﬁnancing being embraced in
Kenya to transform weather and climate information services.

34

Workshops and Stakeholder
Events during P1
Niche User Innovation, Design & Feedback
Workshop Convened by Regrow Ag in
Kigali and Nairobi
TomorrowNow.org joined Regrow & partners including University of
Nebraska-Lincoln, NASA Harvest & One Acre Fund in Kigali and Nairobi
from 23rd - 27th October 2023 for the Niche User Innovation Design &Feedback Workshop deep dive sessions into the Niche Project led byRegrow & funded by the Bill & Melinda Gates Foundation.
We used the opportunity to highlight the progress that TomorrowNow.org
has made through Project Osiris to understand the weather & climate dataneeds of seed breeding programs in East Africa, conduct data validation
studies, and develop key weather/climate technologies as key inputs withinseed breeding services & agro-climate modeling applications.
We learned from the workshop conveners and participants thathigh-quality weather data can make a huge difference to the outcomes ofcrop variety placement and ultimately contribute to inclusive local climate
adaptation.

35

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