A Transparent Truth Layer for Earth’s Climate Record

dClimate.net WTHR
dClimate is a decentralized network for climate data. The
aim is to build an ecosystem where participants provide
realized and forecasted data for various climatic variables
in exchange for monetary rewards -- or, if they do notwish to monetize, to simply make their data available via
a modern architecture that supports data immutability
and is easy to use. Our mission is to democratize the data
and models that describe Earth’s climate.
Climate data collection,interpretation, and reporting, as they currently exist, are a series ofglobal processes that are behemothic in scale, while also being
fractured and fragile in their organization. This has a dampening
effect on the many entities seeking to contribute or consume
climate data -- scientific projects, government policy making efforts,
and private sector entities being the major categories. The current
status quo also stymies innovation when it comes to new datacollection and interpretation initiatives, as the barrier to entry is highand the path to success outside of a nonprofit model is unclear.

dClimate’s mandate is to build a new home
for this critically important climate record,
and to build it in such a way thatencourages openness and innovation.

Contents
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Tokenomics

14

00

Why Climate

Data Matters

dClimate.net | WTHR

01

Why Climate Data Matters
Climate data refers to a vast corpus of records representing the entire terrestrial
biome. This includes weather, crop output, forest biomass, soil health, carbon
emissions, disease transmission -- essentially any timescaled record which describes
nature or the human species’ relationship with nature. Within these broad record
categories, datasets are expressed in terms of specific climatic variables. Some typical
weather variables are temperature, humidity, rainfall, wind speed and direction, andsoil moisture.
But there are projects compiling datasets for

For many of us in urban areas, the only reason tocountless other weather-like variables such as

look up a weather forecast is to decide when toatmospheric pressure, glacial elevation, ozone,

carry an umbrella -- but such forecasts aremethane, and atmospheric dust levels,

extremely crucial across many regions and industry

longwave and shortwave surface radiation, soil

domains around the globe. In developing countries,

and vegetation evaporation, ice temperature,

accurate forecasts of upcoming droughts orriver discharge and runoff, snow density, icemonsoon failure can help governments to prepare

sheet velocity, water quality indicators, seismic

food assistance programs in advance (Hellmuth etearthquake events, ocean color, sea level -- theal. 16-17). 

list goes on.

Hyper-local weather forecasts are becoming
essential in shipping and trucking, where

Climate change research and policy
implementations become impossible without

optimizing routes around weather is key todelivering goods in a timely manner (Walker). 

historical climate data. But that is just the

A storm can cause a cascading series of events

beginning when it comes to the public sector. 

where a container ship is unduly forced to remain

Governments also need climate data to makedecisions around water inf rastructure,
agriculture, disaster relief efforts, health policy,
macroeconomic policy, military spending,foreign policy, energy policy, highway
inf rastructure, environmental policy, scientificin port, delaying vessel berths to such a degree that
incoming vessels have no choice but to incur thelarge operational and regulatory costs associatedwith idling in a bay, undocked -- forecasts in the
shipping sector are needed to plan for this (Hellenic
Shipping News). 

funding -- at the government level, weather,On the leisure side, forecasts are important forand more broadly climate, come into theplanning outdoor events such as concerts andequation in some shape or form in essentiallysports. Construction companies need goodevery domain. 

forecasts to avoid various costly scenarios

How much pollution are we generating? Whatis the state of a given ecosystem? What willassociated with rain and wind during key building
stages (Freeman). 

the electricity needs of a given city be? What isWind farms see their output rise and fall not justthe likelihood of a crop failure? How expensivewith wind speed, but with wind direction as well

will it be to maintain a building in a hurricane(Gallego et al. 9).

zone? Where is the best place to put a nuclear
power plant or hydroelectric dam? Is there
enough wind to justify a wind farm here? Is
there enough visibility for an airport here?
Climate data matters to all of this.

For example, Xcel Energy, a Minneapolis based
utility with $11.5 billion in annual sales and a large
wind-power division has been able to save its
customers more than $60 million in fuel costs overseven years using private forecasts (Adams). 

Forecasting

All of these entities and more need good forecasts

In the past few decades, weather forecasting has

mitigation strategies to smooth out their output

come a long way. Providing forecasts is no longer
an exclusive role reserved for government weather
agencies. An influx of new, private companies andorganizations have entered the forecasting market(Freedman).

to optimize operations, and good climate risk
when plans can’t be changed.

02

Why Climate Data Matters

Parametric  
Financial Instruments

Traditional insurance covers loss, and as such, necessitates a “boots on
the ground” approach when the time comes to evaluate how muchmoney should be paid out to a policyholder. 

This can work in wealthy, dense population centers, but it is not
scalable across more geographically expansive domains (Sibiko et al.
1-2). 

Human loss adjusters introduce a large degree of uncertainty andsubjectivity to the process, bringing delays, disputes, high overhead,and occasionally fraud (Hammond).

Parametric financial instruments are a scalable alternative to
traditional insurance policies, and are a compelling solution forbusiness entities seeking to mitigate their climate risk. 

Rather than creating a situation where both parties need to agree on
whether subjective circumstances on the ground constitute a loss, aparametric instrument simply pays out based on objective data
generated by 3rd parties. The classic example of a parametriccoverage is a put or call option for a publicly traded stock that pays
out if the given stock is trading at a certain price at a specific time. 

This works because a stock’s price is public, uncontested information.
Thus, when it comes to creating parametric products for climate risk,
having uncontested, highly available climate data is foundational.

The unscalability of traditional insurance has resulted in hundreds ofbillions of dollars of uninsured losses due to a host of causes fromhurricanes to micro-droughts to wildfires. In agriculture alone, $1
trillion of crops are estimated to be uninsured each year, and thisestimate does not count the millions of agribusiness entities exposed
to weather. 

These financial losses not only hang over farmers’ heads and act as anoccasional blow to earnings, they actually prevent farmers from
specializing into certain crop areas, driving down profits and in manycases perpetuating a cycle of subsistence (G.E. Roesch-McNally etal.1-2). 

As climate change increases general weather volatility, businesses inagriculture and beyond will need parametric financial instruments tobuild resilience against climate change instead of waiting for disasteraid after the fact.

03

Why Climate Data Matters

Economic Opportunity  of Climate Data

Economic growth is increasingly intersecting withenvironmental and climatic constraints. As
climate change and other environmental
stressors build up, the economic value of
understanding our climate systems and their
historical evolution increases. This has become
increasingly clear in recent years as losses fromnatural sources have been on the rise.

According to IBM, 90 percent of crop losses are
due to weather events (Walker). Over the past few
years, extreme weather has forced the U.S.
Federal Crop Insurance Program to pay out
indemnities ranging from $3.5 billion in 2016 to
17.5 billion in 2012 (Walker).

More broadly, according to Morgan Stanley,climate disasters have cost North America $415
billion in the last three years, much of that due towildfires and hurricanes (Cho). 

Losses from climate and weather risks exist acrossthe economy with vulnerabilities showing in eventhe largest companies. 215 of the world’s 500
biggest companies could lose an estimated one
trillion dollars due to climate change, beginningwithin five years (Cho).

G500 -  Companies providing details on substantive risks

300

294

294
215

Identified being exposed
to substantial risks

Identified risk

driver type

Disclosed financial
impact drivers

Provided potential
financial impact figures(Source: Carbon Disclosure Project)

Alphabet, Google’s parent company, will likely have to deal with rising cooling costs for its data
centers (Plumer). Hitachi Ltd.’s suppliers in Southeast Asia could be disrupted by increased
rainfall and flooding (Plumer). 

Beyond pure economic costs, the human cost of shifting weather patterns has been risingwith billions of livelihoods at risk from increased droughts and floods while over a billion
people globally are estimated to be at risk from rising heat in cities and towns (Earth Institute).

04

Why Climate Data Matters

The rising costs of natural disruptions increase thevalue of weather and climate forecasting, which
dClimate is well poised to capture. The private
weather industry is currently estimated at $7 billion(National Weather Service) while the weather
forecasting systems market is estimated to reach$3.3 billion by 2025 (Markets and Markets).

This increased demand for weather forecastingcomes as companies and public sector agencies
need more real-time and localized information to
optimize supply chains, create proactive disasterplans, and manage uncertainties stemming fromadverse weather.

Hyper Local weather forecasting is a particularly
rapidly growing market with hosts of newcompanies competing for business (Adams). 

As the space spawns ever more new entities, it canbe difficult for a user to navigate complex
considerations around what forecasts would work
best for their use case. A 2013 article from theWharton School estimated that overall revenues forclimate and weather companies were about $3
billion and that, in aggregate, the industry was
worth some $6 billion (Adams). 

A 2017 report from the National Weather Service
included a prediction that the sector could
quintuple in size.

It is estimated that the cost of curtailing carbon
emissions will be in the hundreds of billions or
higher and accurate estimation of carbon
emissions, an important climatic variable, will bea key part of this process (EPA). There are many
companies involved in the estimation of carbon
footprint where the industry is expected to rise
to $12 billion by 2025, highlighting growing
demand (Markets and Research). dClimate will
be well poised to capture this growth as carbon
footprint estimation models are a key use casefor implementation.
More broadly, ESG (Environmental Social Governance) considerations are becoming important for asset
managers and financial institutions to consider for their investments. Such financial institutions will
increasingly need objective, validated data on environmental parameters related to their investments
such as emissions, pollution, and other key metrics. For example, a public pension investing in a mining
company or a power plant will need to assess the environmental impact of their investment via satellite
monitoring of mine waste or emissions. Such analysis is increasing in demand but data is often delayedor lacking and analysis is cumbersome across a vast array of satellite data sources. dClimate will enable
investment firms to have a standardized platform to access environmental impact data as well asinteract with innovative companies offering relevant analysis on such topics.

Climate Data: 

A Fragmented Space

dClimate.net | WTHR

05

Climate Data: A Fragmented Space
Raw climate data is largely produced by academic research projects andgovernment partnerships. Weather stations in airports, government
facilities, and academic institutions record observations for variables suchas rainfall, temperature, snowfall, and wind speed, and submit theseobservations to an international aggregation pipeline.
This aggregation pipeline is the manifestation ofmany nested bureaucracies submitting andforwarding their observations progressively farther
and farther up the pyramid, with the results
eventually finding their way to public release via
endpoints controlled by various globalorganizations such as NOAA, NASA, WMO, andothers (Bell-Pasht and Krechowicz).

In recent decades, satellites have come to play anincreasingly important role. Satellites take raw
observations in the UV, visible light, IR, microwave,
and radio spectrums (National Aeronautics andSpace Administration) of vast swathes of planet
earth, and flow them through a similar aggregation
process to what is in place for weather stations. 

Climate datasets based on satellite observations
are generally not as accurate as those based on
weather stations, but satellites make up incoverage what they lack in accuracy (Abraham).
Many areas have few or no major weather stations
set up -- the nation of Cambodia, for example, has
one major weather station -- so satellites are very
important for large segments of the world. 

However, the terrestrial weather station networkneeds to be expanded to get better quality data inmany important parts of the globe.

Once satellite and weather station observations
have been collected and released in the form of“primary datasets,” researchers and forecasters
ingest them to build models and create new,“secondary datasets.” The secondary datasets are inturn released at new endpoints controlled andmaintained by these groups (National Oceanic andAtmospheric Administration). 

This process can repeat several times with tertiary,
quaternary, etc, datasets being released along theway. What results is a fragmented marketplace ofdata where idiosyncratic formats abound, andwhere there is no way to measure up paid dataset
providers against one another (Schomm et al. 12).

Even with the free datasets, the situation is so
confusing for the end-user that many data
consumers end up paying for expensive private
subscription services that simply standardize andrepackage data (Ciklum). This adds another layer of
opacity and gatekeeping to the ecosystem.

Compounding the confusion is the sheer numberof entities providing climate data. Many privatecompanies focus on a particular area where climate
data is lacking or may offer enhanced resolution or
frequency of the data. In some regions of the world,
especially emerging markets, these needs are
acute as government data networks are sparse andunreliable. 

Without data on climate, crops, or other key inputs,
it is difficult to plan or prepare for natural disasters
or even deal with routine seasonal patterns. 

Even in developed regions, many rural areas wherea host of data ranging from weather to soil to plantdiseases is crucial, there are vast gaps where
private companies have stepped in to provide
better data. 

More climate data is a good thing, but thedownside of the heterogeneous nature of theproviders is that there is little standardization orcomparison between different providers and evenbasic information on which companies offer thehighest quality data for a given area. 

For example, how can a farmer in the United Statesor Brazil assess which companies provide the best
data on soil quality for their farm area? Or how cana real estate developer assess which company has
the best flood data for a prospective property
development site? dClimate’s mandate is to
standardize these varied datasets and provide easy
to understand comparisons between them. 

This would give data consumers a clear way tocompare quality, resolution, and temporalfrequency for any dataset, enabling them to quickly
choose the best dataset for their needs.

06

Climate Data: A Fragmented Space

Collection & Aggregation
Status Quo

Interpretation & Access
Status Quo

There is currently no easy way for a hobbyist or
small company to set up a weather station and feed
into a wider collection/aggregation effort. The major
pipelines only work with established entities likegovernments and airports. 

Given that 70% of companies around the world are
impacted by weather with $5.7 trillion of direct andindirect impact just in the US, (Dobbie and Evershed4) and given that the volatility of weather isincreasing (Lazo et al.), there needs to be a bettersystem for incentivizing wider terrestrial datacollection. However, there are very few suchincentive structures to encourage data contribution
and most weather station data networks areoperated by non-profits, governments, andacademic institutions with highly varying degrees ofcoverage. As expected, such coverage is extremely
poor in lower income areas or in low population
density areas, but needs are growing rapidly toenable better forecasting and adaptation to shifting
climate patterns.

Once raw data has been collected and aggregated,
it must be accessed via the government controlled
endpoints. Accessing the correct files is a difficult,largely manual process. One must navigate throughan elaborate directory structure of poorly
documented acronyms and abbreviations. Then,
once files have been found, it comes time to learn
whatever esoteric local conventions the maintainer
uses in their respective formatting and structuring.
Servers are often down and do not follow modern
security best practices -- FTP is still widely used.
Datasets tend to be indexed on time rather than on
location, so getting values for one location overtime requires downloading hundreds of gigabytes
of files and then combing through it all to extract
the few bytes that are actually needed rather thanmaking a simple query.

Data releases are often changed after the fact withno record of what the changes were as newobservations from the slower weather stations
come in on a staggered basis. One important
dataset called PRISM revises every datapoint up to
six times after their initial release, once per month,and none of these revisions are formally tracked.
This creates huge problems for any parametricagreements that might reference this data, asparametric agreements require a single,
uncontested source of truth to determine a payout.
It should be said that this is not a fault of the
projects themselves necessarily -- PRISM is a small
academic operation -- but rather the fault of an
ecosystem that places the burden of data hostingon the publisher. Dataset maintainers end up
responsible for maintaining web infrastructure forthese critical datasets, a task that is outside theirwheelhouse, especially when parametric comes
into play. They should be free to focus on science.

The reason that accessing climate datasets is sodire is twofold: a ponderous bureaucracy runs theinitial aggregation, and then multiple levels ofrecombination and rehosting occur in the form of
secondary datasets. Each publisher is trying to be“the destination” for data in general because no
generalized marketplace exists where all the
publishing entities can play nice with each other.
There is no Amazon of climate data. What results isa situation where accessing climate data -- typically
written off as a triviality in an early stage startup -becomes an overwhelmingly burdensome barrier
to entry. Whether it is a new participant who is justcurious and wants to take a look, or a startup thatwants to provide better data and forecasting for themarket, or even a well funded insurance entity thatis trying to build out a business based on weather
data, all these participants hit the brick wall sooner
or later.

70%

is impacted by weather

World’s GDP
Beyond standard weather data, carbon emission,
biomass, wind speed, cloud cover, crop yields, soil
moisture, and a host of other key variables are
currently of poor resolution in many areas of theworld. Some, such as soil moisture, are essential for
agriculture and drougt forecasting, while otherssuch as cloud cover and wind speed data are needed
to plan wind and solar energy projects. Because of
the highly centralized nature of the climate datacollection and aggregation status quo, there is little
incentive for smaller players to get involved andcontribute much needed observations. Collection
efforts are slow and bureaucratic, still mostly relying
on government space programs with high barriers toentry for private players.

07

Climate Data: A Fragmented Space

Data Analysis Status Quo
Many crucial types of analysis related to the climate ecosystem are expensive,
making them inaccessible to all but the largest firms. 

Two key areas we can point to are cleaned data and natural catastrophe
simulations. Cleaned weather station data is important for any type of climate
analysis as weather stations are prone to maintenance, breakdowns, and othersetbacks which lead to missing data. 

While airport weather stations are the ones with the best data quality, manyare still often missing large sections of their history. As we expand from thesestandard airport stations, the missing data problems rise significantly withrural areas and many urban areas having persistent problems with missing
data. To remedy this situation, there are a number of “clean and fill” algorithms
that can be run utilizing nearby stations or satellites to impute missing
observations and provide a consistent weather history for a given station. 

Cleaned data of this type from private sources can be extremely expensive
even when the algorithms are not particularly complex. dClimate will host suchalgorithms providing clean weather station data for analysis, forecasting andinsurance use cases in an accessible, transparent, and affordable manner. 

Another major use case is modeling for natural catastrophe impact. This isimportant for activities like hurricane preparation, or for pricing large scalecatastrophe insurance, or for understanding the likely impact of climatechange on vulnerable areas. 

For hurricanes, such modeling requires combining storm track data with dataon building types and home insured values. For earthquakes, it would require
earthquake simulation models with building type and insurance values. Suchdata is available across different academic and government sources but
putting it all together is generally done by private entities that charge very
high prices for their simulation results. 

This creates a situation where these products become affordable only for large
corporations such as reinsurers. The results also tend to be opaque anddifficult to compare across private providers. dClimate will incentivize
publisher nodes to run simulations in the natural catastrophe space, thereby
opening the gates to small and medium sized entities. 

By democratizing disaster analysis, dClimate aims to cultivate a range ofparticipants to prepare, analyze, and insure events of the highest natural
impact.

00

dClimate:  
An End to the Climate 
Data Status Quo

dClimate.net | WTHR

08

dClimate: An End to the Climate Data  
Status Quo
A single, decentralized ingestion and distribution
system should be implemented for all climate data.
This will enable participants to provide andconsume climate data that is clean, immutable,transparent, highly available, and monetizable.
Once ingestion has been standardized and hostinghas been decentralized, public and private sectors
can begin to shed their behemothic data
aggregation pipelines, and move toward adecentralized reporting system where allparticipants report directly to the same immutable
hosting network rather than going through a long
chain of intermediaries. Finally, through adecentralized, open marketplace, forecasts andnew climate variable reporting will be free toblossom with WTHR as the governancemechanism and stablecoin as the incentive.

dClimate Stakeholders
dClimate is a novel ecosystem for organizing theclimate data space -- a space which is composedof participating entities ranging vastly in size,
focus area, and technological comfort. This begs
the question, what are the incentives for different
entities to participate in dClimate’s reimagining of
the climate data ecosystem? Here we discuss how
the network would incentivize different
participants. 	

Data Consumers
For consumers of climate data -- companies looking
for better weather forecasting, farmers looking for
better crop yield models, or NGOs looking for carbon
sequestration data, to name a few examples -- theexisting landscape of climate data is daunting. There
are thousands of companies selling solutions fordifferent special cases sitting on top of large
government agencies offering vast amounts of raw
data in archaic form. For all but the most expert
clients, it is a difficult space to navigate. For example,
a flour mill that imports wheat from Russia mayneed to find the company which had the best
forecast record for wheat output in the last few years
to prepare for contingencies in case of crop failure.
Or, consider an event group responsible for throwing
outdoor concerts that needs to find the best
forecasting company with a focus on short termstorm forecasts. Finally, consider a parametricprovider wishing to sell an agreement to a fracking
operation for hedging against low subterranean
temperatures. There are many confusing options forthese use cases, with the result being that a
consumer is forced to rely on subjective and often
cherry picked marketing materials provided byopaque private data providers.

	

For a climate data consumer, dClimate offers aprotocol-level transparency coupled with a featurerich and intuitive UI. Through a simple and easy
rating system, data consumers will be able to quickly
judge which data publishers offer the most value fora particular need. Clients will also be able to set up
payments with ease using digital stablecoin rather
than any volatile token payment method. The
validator nodes on the network will offer quality
control that is not available when shopping for dataor forecasts in the current system. Finally, acommunity of other users will be available to share
information and lessons with.

09

dClimate: An End to the Climate Data Status Quo

Data Publishers

GovernanceFor publishers dClimate offers access to a sales
channel that is very difficult for all but the largest
firms to build. For smaller companies which may
have superior models or data, the struggle isimmense with client acquisition being haphazard
and expensive. 	

A Decentralized Autonomous Organization (DAO)
is needed to ensure that the right data collection is
incentivized and validated. For example, weather
data provided by a node has very different value if
it is just adding New York City data--already widely
available--versus in an important emerging market
with little reporting, such as Sudan. Validation ofrealized weather data for quality and tampering isanother area where specialized functions areneeded and general data checks are insufficient.

And for a large company with a dedicated sales
force and a constant stream of client business,
dClimate offers an alternative sales vector that lets
them spend less on distribution and more on data
quality. The lack of organization in the current
market leads to few gathering areas for users who
would be interested in data or forecasts, leaving
data publishers with high costs in time and dollars
to find clients. 	

Once prospective clients are found, conversion is
yet another long and painful process. This isbecause it is difficult to stand out, especially if the
publisher is not a brand name. It takes time tocheck if a particular company is providing the rightkind of data or forecasts and then checking if theseare superior to other competitors. In this case, themarket significantly penalizes smaller companies
with smaller marketing budgets even if theirproduct is superior.

dClimate puts publishers on a level playing field
and allows the companies with the best data tonaturally succeed. If a given publisher’s weather
forecast model is best-in-class for forecasting
hurricanes or their model is a major improvement
in forecasting wind speed for wind farms, dClimate
sets them up for success. 	

Client acquisition and marketing costs are
considerably lower and once set up, the network
handles the validation, metrics, and payment forservices. Given that the costs of adding a new
publisher to the ecosystem will be minimal,
dClimate makes publishing models and data aneasy, low downside exercise. We expect this to bring
forth many more players to the space, especiallythose with models that might currently be stuck at
the academic level for lack of VC funding to build amarketing engine. 

The DAO’s validation role is also important forforecasts. Evaluating forecasts is a complex
problem and nodes that provide weather forecasts
need to be rewarded appropriately. Forecasts ofweather data come in the form of probability
distributions, not point estimates (such as 60%
chance of rain). However, realizations are single
outcomes (did it rain or did it not?). To measureerrors between these disparate inputs and outputs,
there are different accuracy scores that are used.
Furthermore, some providers may excel at
forecasting 1 week out, others 2 weeks out, and yetothers a season out. For users needing weather
forecasts at different horizons and for different
climate variables, all these metrics matter.

	

A decentralized network that can present forecast
comparisons in a transparent manner and enableappropriate rewards for higher quality forecasts
while also solving the inefficiencies in datareporting and hosting will end the stagnation in
the industry and bring increased access, lower
costs, better security, better accuracy, and bothwider and deeper data coverage. This will benefit
everyone currently involved in the space, and will
also immensely grow the utility of climate data sothat it can reach its true potential with regards toreal world applications.

10

How does dClimate.net operate?

dClimate is architected with four key layers that together provide a decentralized infrastructure
for sourcing, delivering, storing, and incentivizing the use of high-quality climate data:
Governance, Oracle, Blockchain and Data Storage, and Marketplace.

Governance Layer
dClimate token holders

DAO Web Application
1 Proposes and votes on:

DAO smart contracts
2 Passes resolution andwrites results on-chain-Chainlink node selection-Node payout amounts-Core and parameters to run for nodes

Oracle Layer

3 DAO enters into service
Publisher
adapter

4 Chainlink node encrypts

Chainlink node5

weather data and sends to
IPFS through publisher adapter

Fetches and posts releasecontent IDs (hash)level arguments with
Chainlink node network

alidator
adapterV

6 Chainlink node validates andposts releases skill scores
through validator adapter

Blockchain & Data Storage
IPFS

Marketplace Layer

7

Publisher Smart Contract

8 Compares skill scores and pays

etrieves and decrypts
release content
R

Consumerin stablecoin which are thenautomatically converted in LINK
for Chainlink node compensation

11

How does dClimate.net operate?

Governance Layer

Oracle Layer

The Governance Layer is run by the dClimate DAO,

For the oracle layer, we’ve chosen to adopt thewhich will have two main responsibilities: 1) deploy

existing Chainlink Network as an already widelyand maintain validators and 2) create bounties.

used and provably secure solution. The oracle layer

These represent ways for the DAO to incentivize

is run by Chainlink nodes which perform three key

the network’s growth, and will be implemented byjobs: 1) retrieve weather data f rom publishers anda proposal/voting mechanism. Successful proposals

store it in an encrypted format on IPFS (a peer towill be implemented in the dClimate codebase via

peer data sharing network), 2) post the resulting

the DAO smart contracts. Because proposals and

IPFS hash onto the blockchain to be referenced byvoting will be highly technical and domain-specificconsumers, and 3) validate the climate data byin nature, it is important to cultivate a strong

generating Skill Scores and publishing the Skill

knowledge base of participants within the DAO. 

Scores on the blockchain in the publisher smart

Validators are special types of network participantsthat apply certain DAO-approved algorithms as ameans of evaluating the quality of Publishers’ data.
These algorithms generate Skill Scores for the
Publishers that are shared with consumers in orderto aid them in filtering between publishers. For
forecasts, these scores will be historical hit/misstables, with a bottom line accuracy score that willtake into account the time horizon to the predicted
event. For realized data, Skill Scores will containreferences to data gaps, possible errors, distancef rom the closest national weather station, signifiers

contract as a form of verifiable performance
history on specific publishers. Through the usageof Chainlink External Adapters, which are selectedby the dClimate DAO, oracle nodes perform
validation computations off-chain and only post
the final resulting Skill Score on-chain. As a result,
Chainlink oracles act as the bridge between
disparate on-chain and off-chain systems, such asdata publishers, IPFS, and the blockchain, as wellas the computation engine for generating privacy(encrypting publisher data) and runningalgorithms (validation).

of f raudulent behavior, and other metrics, with a

Publishers can run their own official Chainlink

bottom line credibility score that also takes into

node or take advantage of professionally-managed

account how widely used the data is.

Chainlink node operators that simplify nodedClimate leverages Chainlink, the market-leading
blockchain oracle network, to serve as its validator
node network. Chainlink external adapters will be
implemented to codify the validator algorithms
used off-chain. Thus, the dClimate DAO votes on
which Chainlink node operators are allowed toserve as validators, what kind of Chainlink adapters
the Validator Nodes will run for each climate
variable, what payout amounts will be to Validator
Nodes, and various other decisions like bounty
incentives for specific in-demand datasets.
Bounties help the network grow by incentivizing
deployment of new inf rastructure andinterpretation methods, as well as wider adoptionof the network by “status quo” climate data
participants. 

The dClimate DAO enforces the fulfillment ofvarious oracle jobs via Service Level Agreements(SLAs), which define the exact jobs carried out by
the Chainlink oracle validators and therewards/punishments for not meeting suchrequirements. The SLA is stored and enforced
directly on the blockchain, providing the DAO witha pluggable governance f ramework capable ofenforcing key functions on dClimate as voted on byits members. 

maintenance and ensure reliability, redundancy,
and upgradability. These nodes cryptographicallysign each piece of data they provide to dClimate
using a private key that only they possess, which
serves as proof of the data’s origination f rom a
specific publisher and allows for the publisher’sdata to be tracked historically over time.
Additionally, as both the IPFS hash identifier foreach dataset and Skill Scores for each provider are
posted on-chain, an immutable record isgenerated that data consumers can reference at
any time.

12

How does dClimate.net operate?

Blockchain and Data Storage

Marketplace

The blockchain and data storage layer is designed
to: 1) store publisher data on IPFS in a confidential
manner until it’s purchased by a consumer, 2) hosta publisher smart contract on the blockchain,
which serves as a historical and immutable
reputation system of publishers’ Skills Scores that
consumers can reference, 3) host the DAO
governance smart contract on the blockchain
responsible for certain governance procedures
stated above like permissions on validators nodes,
validator node payments, validator algorithms, etc,
and 4) host the on-chain Service Level Agreement
smart contract between the DAO and Validator
Nodes that outlines the exact jobs of the validatorsand enforces the correct rewards/punishments tovalidators based on their performance. Byleveraging the immutability of the blockchain in
combination with the decentralized data storage
capabilities of IPFS, a hybrid approach is achieved
as climate data is stored off-chain on IPFS in a
cost-efficient manner with only a small hash andSkill Scores posted on-chain to ensure
censorship-resistant discoverability. 

When a consumer wishes to access a datapoint ina monetized release, they submit an on-chain
stablecoin payment to the Publisher smart
contract and specify the IPFS hash of the data thatthey wish to obtain. The stablecoin payment is
paid to the data publisher, with the Oracle Nodebeing paid in the Chainlink Network’s native
cryptocurrency token, LINK, via a backend
conversion such as using a decentralizedexchange. This payment process operates entirely
on-chain, ensuring consumers can pay for datasetsin an entirely permissionless manner.

The process is accomplished by the Chainlink node
generating an incrementally nonced private key,which they then use to encrypt the climate datathat a publisher wishes to monetize. The
encrypted data is then posted to the IPFS networkand pinned as a new record. The key generation,
encryption, and IPFS hosting will be managed by
the Chainlink node. Once the encrypted data has
been posted on IPFS, the Chainlink node posts thedata’s IPFS hash and metadata to the Publisher
smart contract. IPFS libraries will be implemented
at the climate variable level, such as dWeather,dYield, dCarbon, dBiomass, dSoil, etc, all under theumbrella of the wider dClimate project.

The Chainlink node will monitor the smart
contract’s events to confirm that a fee has been
paid by a consumer. Upon receipt, the node will
encrypt the incrementally nonced release key thatprovides access to the Publisher’s IPFS data using
the data consumer’s public key and post it to thesmart contract on-chain. The Consumer can thenretrieve this encrypted release key, decrypt it using
their personal private key, use the resulting release
key to decrypt the desired dataset stored on IPFS,
and subsequently gain access to high quality data
validated by the dClimate ecosystem.

The consumer will also pay a stablecoin fee forusing the marketplace, specifically to the
Publisher smart contract as a means of evaluating
publishers and their data. This fee is also converted
to LINK on the backend and paid to the Chainlink
node.

13

Additional Privacy Schema

Overview of DECO and how it generates a three-party handshake between network participants(source)

To provide publishers and consumers an additional layer of privacy, the dClimate ecosystem will use
Chainlink for its DECO functionality, a privacy-preserving oracle protocol that ensures sensitive
datasets are kept private, while allowing consumers to perform computations on the data using zeroknowledge proofs. Through the creation of a three-party handshake between the publisher’s web
server, the Chainlink prover, and the consuming verifier, data can be kept private end-to-end andnever revealed in its entirety. 

14Tokenomics
Total token supply: 100,000 WTHR
30%

23%

will be sold in the WTHR offering.

20%

will be reserved for the dClimate Inc. in order toparticipate in network governance and cover costs
associated with network construction and upkeep.

of tokens will be granted to devs and researchorganizations to further the development and data
breadth on the network as determined bygovernance. Funds can also be used for customer
acquisition through contests, referral bonuses, newsign up rewards, and general marketing as part ofgreater efforts to grow the community.

17%

10%

The tokens granted to devs, community, and held
in reserves to pay staking rewards will be unable tobe used for governance until transferred.
Governance votes can also take place to nominate
and select recipients of these grants.

WTHR is a fixed supply token and so the token
inflation rate will decline to 0% as network revenues
start to ramp up. Staking rewards will not increase
the total supply of the token, but instead will be
supplied from the initial reserve. The WTHR held to
pay staking rewards will follow an emissions curve
described by:will be allocated to Arbol Inc. for funding thestart-up costs. Arbol will be an anchor client of theplatform by providing initial, cleaned data for use
and functioning as the first customer for the
network as well.

will be held in reserves, which will be used to
pay early staking rewards via an automatedsmart contract along a gradually decaying
emissions curve.

Staked WTHR tokens will allow a token holder toparticipate in governance. Initially, the WTHR
holders will stake WTHR and earn WTHR as
rewards. These rewards will be much higher atinitial stages of the network in order to incentivize
early adoption.

f(x) = 10000e-1*Emissions Factor*x

where Emissions Factor is set to a fixed 0.25.

WTHR Emission Curve
Tokens in Staking Reserves (WTHR),
thousands
10
8
6
4
2
0

1

2

3

4

5

6

7

8

9

10

11

12

Months 
Post Launch

15

Tokenomics

This emission function rewards early adopters who aim to actively participate in governance and network
growth. The yield generated for WTHR holders is described by the function

f(x) = tt+1e(-1*Emissions Factor*x) This implies that the yield in the first month is roughly 30%,
then 15% the month after, and 10% in the month after that.

WTHR Yield
Monthly Yield (%)
30
25
20
15
10
5

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Months 
Post Launch

After the first twelve months, network revenues will be distributed to token holders as a royalty payment.
The royalty payments will start in small percentages of total revenue in order to ensure that the network isable to grow infrastructure and development, and gradually rise to a Royalty Cap of 70% of total revenue
based on a logarithmic curve set using our earlier described Emissions Factor. The royalty rate as a functionof revenue is described by

There is no guarantee that WTHR Tokens will pay yield in the future. There is a significant risk that WTHR token holders will receive no remuneration related to their possession of WTHR tokens.

16

Arbol Inc: A dClimate Anchor Participant

dClimate’s key founding members are also the founders and Key Employees of Arbol
Inc. Arbol is an insurtech platform for parametric products that pay quickly and fairly
covering external risks such as unexpected weather. Arbol uses Big Data, Machine
Learning, and Smart Contracts to bring transparency and remove the delays anddisputes that plague traditional insurance policies.

dClimate’s key founding members are also the

As Arbol grows in size and scope, dClimate willfounders and Key Employees of Arbol Inc. Arbol

grow in conjunction as Arbol will be an anchor

is an insurtech platform for parametric products

user of dClimate. Arbol’s data inf rastructurethat pay quickly and fairly covering external risks

already utilizes a decentralized f ramework and thesuch as unexpected weather. Arbol uses Big

company’s core philosophy is based around theData, Machine Learning, and Smart Contracts todecentralization of parametric insurance. In thisbring transparency and remove the delays andcontext, Arbol’s use of dClimate is a natural next

disputes that plague traditional insurance

step towards building a decentralized climate data

policies. Arbol’s products serve a range of

network which creates a positive feedback loop ofindustries including agriculture, energy,

usage growth of the platform and dClimate’s

maritime, leisure, and a host of other bespoke

network. 

risks. An example product would be a contract
with a farmer that pays out if rainfall around a
farm as measured by local data sources is lower

Arbol’s platform is expected to show exponential

than average. Such a contract would offset losses

growth in the upcoming year after quickly gaining

to the farmer’s crop stemming f rom deficient

traction with institutional clients in its first eightrainfall. Beyond rainfall data, a plethora ofmonths of facilitating live transactions. Notional

contracts can be structured for different risk

risk facilitated across the platform is projected tofactors including temperature, snow, soil

rise f rom $15 million to in excess of $1 billion in justmoisture, or wind speed. 

one year. Arbol also projects to transact $65 million
in gross premium through its platform in theupcoming year, a substantial increase f rom theGiven Arbol’s focus on parametric insurance,$2.1 million in gross premium managed in the firstdata is a key pillar of Arbol’s business. As Arbol

eight months. 

grows, its needs for data underlying its contracts
will grow substantially. As discussed above,
weather and climate data are not monolithic

Arbol currently serves over 350 institutional clientsconcepts but different subsets of data that are

across several industries with a footprint of 35,000

suited for different regions and situations. The

farmers covered. Those numbers are also expected

data to be used in applications such as insurance

to rise in the upcoming year as Arbol expands its

require high levels of quality over a long history.

existing line of product offerings to include

Another layer of key importance are forecasts

programs for businesses in the maritime, energy,

and simulations that are important to calculate aand hospitality industries.

fair value premium for a range of weather andother parametric contracts. Examples of thiswould be the rainfall simulations for the contract
with the farmer above or more complex
simulations of hurricane damage incorporating
simulated paths and real estate data for anatural catastrophe insurance policy.

17dClimate DAO
The dClimate DAO is a distributed autonomous
organization to guide the dClimate network’s activities.
The DAO will vote on key issues such as bounties for the
addition of weather stations or new partnerships thatthe network may enter into.
The dClimate DAO is a distributed autonomous organization to guide thedClimate network’s activities. The DAO will vote on key issues such as
bounties for the addition of weather stations or new partnerships that the
network may enter into. 

The DAO’s mission will be to manage a thriving network with inputs fromthe community of WTHR governance token holders. dClimate Inc., a legal
entity set up in Delaware, will be a member of the DAO and also
responsible for coordinating and implementing the DAO’s decisions
subject to such decisions being feasible from a legal and operational
standpoint. dClimate Inc. will have a board of directors representing
experienced professionals spanning the weather and technology spaces.

18

Works Cited
Abraham, John.

The Guardian“New Study Questions the Accuracy of Satellite
Atmospheric Temperature Estimates”

Guardian News and Media,
7 Nov. 2014,

Bell-Pasht , Kyra, and Dana Krechowicz.

World MeteorologicalOrganization“Why Does Access to Good Climate Data Matter?”ho , Renee

C“How Climate Change Impacts the Economy?”i l mEarth Institute at Columbia
University“Pros and Cons of Big Data.

27 June 2019

Dobie, Greg, and Tim Evershed.

The Guardian“The Weather Business: How Companies Can Protect Against
Increasing Weather Volatility.”Gu“Climate Change May Soon Hit Billions of People. Many
Cities Are Already Taking Action.”ardian News and Media,
ov. 2014,

www.ciklum.com/blog/prosv-and-consof-big-data/

www.agcs.allianz.com/content/dam/one
marketing/agcs/agcs/reports/AGCS-We
ather-Risk-Report.pdf"

7 Ntate of the PlanetS

arth Institute at Columbia
niversity , 21 June 2018

E

U

PA

The Guardian“The Social Cost of Carbon.”

Guardian News and Media,
7 Nov. 2014,

Andrew Freedman

The Washington Post

“Weather Is Turning into Big Business. And That Could
Be Trouble for the Public .”9 Jan. 2017

Abraham, John.

The Guardian“New Study Questions the Accuracy of Satellite
Atmospheric Temperature Estimates”

WP Company, 5 Dec. 2019

Abraham, John.

The Guardian“New Study Questions the Accuracy of Satellite
Atmospheric Temperature Estimates”

Guardian News and Media,
7 Nov. 2014,

E

public.wmo.int/en/resources/bulletin/w
hy-does-access-good-climate-data-mat
ter.

World MeteorologicalOrganization, 6 Dec. 2019
Ciklumarth Institutepublic.wmo.int/en/resources/bulletin/w
hy-does-access-good-climate-data-matter.

6 Dec. 2019

C k u

E

www.theguardian.com/environment/cli
mate-consensus-97-per-cent/2014/nov/
07/new-study-disputes-satellite-temper
ature-estimates.

blogs.ei.columbia.edu/2018/06/20/climat
e-change-may-soon-hit-billions-of-urba
n-dwellers-many-cities-are-already-taki
ng-action/

www.theguardian.com/environment/cli
mate-consensus-97-per-cent/2014/nov/
07/new-study-disputes-satellite-temper
ature-estimates.

www.washingtonpost.com/business/201
9/11/25/weather-is-big-business-its-veeri
ng-toward-collision-with-federal-gover
nment/.

www.washingtonpost.com/business/201
9/11/25/weather-is-big-business-its-veeri
ng-toward-collision-with-federal-gover
nment/.

www.theguardian.com/environment/cli
mate-consensus-97-per-cent/2014/nov/
07/new-study-disputes-satellite-temper
ature-estimates.

19

Appendix
A. Weather Station Bounty Thought Experiment01

A DAO participant creates a proposal detailing the need for rainfall data in South Sudan. They explain that parametricweather agreements are needed for agriculture in the region and that global datasets are insufficient, and that a weather
station should be set up to collect terrestrial observations. The proposal specifies the Helium blockchain-enabled weather
station, which collects rainfall data and contains several “trustless” features -- GPS, an inaccessible execution environment,
and direct data upload to IPFS/dWeather to name a few. The proposal also contains code (see Appendix B) that can be
deployed to a Chainlink adaptor that will validate submissions as well as a small stablecoin bounty for fulfillment of thevalidator’s conditions, to be paid out directly to the publisher node. 

02

A vote is taken by the DAO, and the proposal passes. The decision is made to deploy the chainlink adaptor containing thecode in the proposal.

03

Some time goes by and nobody appears to be interested in deploying a weather station at that level of bounty. DAO
participants and potential data consumers add additional funds to the bounty.

04

A hobbyist purchases the Helium weather station and installs it at the specified location. Regular data posts meeting thevalidator’s requirements are made. The validator code posts high skill scores to the Publisher’s smart contract, and aftersome time, the Bounty smart contract, which looks at the publisher’s skill scores, pays out the full amount to the station
operator.

05

Parametric agreements using this data are implemented and consume the data. The hobbyist receives revenue fromthese payments.

06

After a few years, the hobbyist continues to enjoy a regular revenue stream from the data consumers, some of which isused to upkeep the weather station. The DAO receives a portion of this stream as well and the hobbyist publisher enjoys ahigh skill score for meeting the requirements of the validator.

07

Eventually, the station can be replaced with a unit that has better data collection capabilities. The hobbyist creates theirown proposal to the DAO asking for a bounty to deploy a new station, and the cycle repeats.

B. Example DAO Proposal Chainlink Adaptor# An example Chainlink adaptor for a weather station validator proposal.

# If the proposal associated with this adaptor is accepted by the DAO, this code will be deployed to a “validator node”(Chainlink adaptor) which will validate new climate data releases.

PUBLISHER_SMART_CONTRACT_ADDRESS = %%%%;STATION_PUBLIC_KEY = %%%%; # The station device’s public key/MAC ID

STATION_LOCATION = (6.120700, 30.984107); # South Sudan

EXPECTED_READINGS_PER_DAY = 48; # Expecting an observation every half hour to unlock bounty

EXPECTED_DOWNTIME = 1/40; # Allowing for one missed observation out of every 40 for battery changes, etc.

REFERENCE_DATASET = “chirps_05_final”; # A satellite dataset for sanity checks.

# This function is called by the Publisher’s smart contract with each new Release.

function validateRelease(releaseCID, decryptionKey)

decryptionKey = decryptionKey.decrypt(); # The decryption key comes encrypted, so the validator must decrypt first.

releaseContent = ipfsClient.get(releaseCID); # Retrieve the encrypted release content.

decryptedContent = releaseContent.decrypt(decryptionKey); # Decrypt the release content with the decryptionKey.

assert(validateSignature(releaseContent, STATION_PUBLIC_KEY)); # Post must be generated by “locked down” weather
station software and signed.

assert(validateLocation(releaseContent, STATION_LOCATION)); # Validate that the observations were taken at the
specified location, taking into account an expected amount of GPS drift/jitter.

skillScore = validateReadingsPerDay(releaseContent, EXPECTED_READINGS_PER_DAY, EXPECTED_DOWNTIME,REFERENCE_DATASET); # Generate a skill score based on the release versus what is expected.

blockchainClient.postSkillScore(skillScore, releaseCID, PUBLISHER_SMART_CONTRACT_ADDRESS); # Publish the release’s
skill score for all to see.

20

C. Forecast Bounty Thought Experiment
01

02

03

04
05

1. A DAO participant creates a proposal detailing the need for good local forecasts of Indian monsoon outcomes. They
explain that shipping, agriculture, and construction efforts are affected by this, and provide notices of intent by aparametric seller, and by several shipping and construction entities expressing their desire for these forecasts as well as
their projected market value. The proposal also contains code that can be deployed to a Chainlink adaptor that willvalidate these forecasts against satellite images and global rainfall datasets as the source of truth. The proposal specifies a45, 30, and 15 day time horizon for the forecasts, and the level of interest for each horizon. No bounty is provided.

A local meteorologist begins posting 45, 30, and 15 day forecasts to dClimate. The monsoons are a yearly event, so several
years must pass before a statistically significant skill score can be generated based on this data alone. The meteorologist
has been making local predictions about Indian monsoons for several decades, however, and wishes to be given a higher
skill score sooner. They make an appeal to the DAO in the form of a proposal asking to be given a higher skill score basedon their previous, albeit unverified forecasts. 

The DAO decides to vote no on the proposal, but a second proposal is made in its place by a different network contributor.
This proposal specifies that the meteorologist’s skill score will remain unchanged, but that the meteorologist’s profile
page will receive a new kind of “badge” that contains reference to a DAO approved summary of the meteorologist’s work
history and scientific methods, as well as references to the meteorologist’s scientific publications.

Two construction companies who are willing to pay for forecasts based on the meteorologist’s credentials alone do so. The
meteorologist receives a small income stream from this, a part of which passes to the DAO. 

Several years pass, and the meteorologist’s skill score has emerged as the best for Indian monsoons, with 92%, 94%, and
95% skill scores at the 45, 30, and 15 day horizons respectively. Many construction, shipping, and parametric entities are
subscribing to the meteorologist’s forecasts, with a portion of the revenues passing to the DAO. Secondary weather
reporting entities begin subscribing as well. The meteorologist enjoys a healthy income stream, and the DAO is rewarded
for its labor in helping to bootstrap the meteorologist’s forecasting efforts.

D. Data Interpretation Bounty Thought Experiment
01

02
03

04

A DAO participant creates a proposal expressing the need for new methods for inferring atmospheric CO2 levels globally.
The proposal explains that there are several sustainability funds and carbon swap entities wishing to purchase access tothis data, but that none is currently available, and that the instrumentation required for taking direct global observationsof CO2 would be unrealistically burdensome to deploy. Several DAO participants place large bounties on a satisfactory
method as defined by a validator node. A training set of known good CO2 leve ls is posted as part of the validator code. As
new training data comes in, the validator code is updated to include it.

One year passes. During this time, several entities have made submissions that match the training set. However, as new
training data comes out, their posts are found to have been an overfit to the training set, and the bounty is not paid out.

A chemist begins posting global CO2 releases that have been generated from satellite IR observations and global
temperature observations. He keeps his method private, but over the course of a year, it is shown that the method
matches new training data. After a year of new matches, the entire bounty is paid out to the chemist over the course of asecond year of matching observations. Concurrent with the bounty award, the publisher receives a high skill score that
continues to be validated against the new training sets.

Sustainability funds and carbon swap entities purchase the data from the publisher at a high price, and portion of theproceeds flow to the DAO.

21

Disclaimer
The distribution, reproduction, or other use of all or any portion of this presentation and other documents delivered in connection herewith isprohibited. This presentation is for informational purposes onlyand is prepared without regard to any particular user'sinvestment objectives orfinancial situation. Any business dealings arising from or relating to this proposal will be conducted in accordance with all applicable laws andregulations. The presenting company has not been registered under the US Securities Act of 1933, nor is it registered or qualified under securities
laws of any jurisdiction. This presentation does not constitute an offer to sell, or a solicitation of an offer to buy any securities. Any investment
would involve significant risk given the early stage of the company andthe blockchain industry. This document herewith should not be construed
as investment, legal, or tax advice in any way. The information contained herein is believed to be accurate but no assurances can be given that all
information contained in this presentation is in fact correct. Given the volatile nature of the region, information that was accurate may no longer
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