Description

A focused course, tailored for you

The Climate Risk Engineer's Asset-Level Hazard Model Build

Stand up a defensible physical and transition risk pipeline that survives client questions on data lineage, scenario choice, and downscaling assumptions.

The score is easy. The methodology question behind the score is what eats the week. Which scenario, which downscaling layer, which hazard dataset, which return period, which asset-to-issuer rollup, which imputation rule for the assets that did not geocode. A climate risk engineer ships a number; an analyst on the buy side asks the chain of how it was built, and that chain has to be written down before the next client call.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

An associate climate risk engineer at an index and analytics provider sits in the seam between raw geospatial hazard data and a client-facing portfolio score. The score is the visible artefact. The defensibility of the score is what determines whether the next research call goes smoothly or turns into a three-week methodology rework. Asset locations come in messy, with coordinates missing on a tail of subsidiaries; parent linkage shifts when a corporate action goes through; NGFS phase IV scenarios refresh and the prior crosswalk no longer reconciles; CMIP6 downscaling choice matters more for some hazards than others, and the wrong choice surfaces only when a regional client asks about a specific city. Transition risk piles on, with sector pathways, PACTA-style alignment, carbon price assumptions, and stranded-asset adjustments that have to live in the same pipeline as the physical layer. The methodology pack a client reads is the artefact that has to make all of this auditable, and writing that pack while running the next refresh is what turns a forty-hour week into sixty.

What you walk away with

Ship an asset-level hazard pipeline with documented lineage from raw tile to issuer score.
Defend NGFS phase IV scenario choice and CMIP6 downscaling layer choice in a written methodology pack.
Reconcile parent issuer linkage through corporate actions without silent score drift across refreshes.
Integrate transition pathway and physical hazard layers under a single auditable join model.
Run regression tests that prove last quarter's score moved for a reason a client can read.

The 12 modules

Module 1. The Methodology Pack Reader's Walkthrough

Start from the artefact a client actually challenges. Walk through a real climate risk methodology pack section by section, naming the sentences that auditors and analysts redline most often. Identify which decisions need a written defence (scenario selection, downscaling layer, hazard return period, asset-to-issuer rollup) and which can stay as engineering notes. By the end of the module you have a methodology pack outline keyed to the questions you actually receive.

Module 2. Asset Geolocation and Parent Issuer Linkage

The hardest unglamorous problem in the pipeline. Cover coordinate quality tiers, polygon versus point representations, address parsing for the long tail of subsidiaries, and the linkage layer that maps every asset to a parent issuer through ownership chains. Build a corporate-actions calendar so M&A and divestitures do not silently change scores at refresh. Includes the imputation rules you write down so a client can audit them.

Module 3. NGFS Phase IV Scenarios and the Crosswalk to Prior Vintages

Phase IV refreshes assumptions, variables, and naming. A client reading a year-over-year comparison wants to know which moves are scenario refresh and which are model refresh. Build the crosswalk table that maps phase III to phase IV variables, identify the seven or eight scenario choices that drive most of the score variance, and write the defence for the orderly versus disorderly versus hot-house-world choice you make per use case.

Module 4. CMIP6 Downscaling Choices Per Hazard Type

Heat needs different spatial resolution than flood; flood needs different resolution than wildfire. Walk through the trade-offs between statistical and dynamical downscaling, the bias-correction techniques that matter for tail risk, and the resolution choices defensible for asset-level rollups versus country-level rollups. Includes the table you publish naming which downscaling source backs which hazard layer in your pipeline.

Module 5. Physical Hazard Layer Engineering

Concrete builds for the flood, wildfire, heat, drought, and tropical cyclone hazard layers. Cover the public and licensed sources, the return-period choices, the change-over-time methodology, and the join keys to assets. Includes the regression tests that catch when a hazard layer vendor updates their underlying tiles without notice and quietly changes your scores.

Module 6. Transition Pathway and Carbon Price Integration

The transition layer that has to share a pipeline with the physical layer. Cover sector pathway selection, carbon price scenarios across the NGFS variants, stranded-asset adjustments for fossil-fuel-heavy issuers, and the cost-pass-through assumptions that drive the impact estimates. Walk through how the transition score reconciles to the physical score under a single issuer-level number a client can interpret.

Module 7. Sector Alignment and PACTA-Style Comparisons

Clients want to know how their portfolio stacks against a sector benchmark and a Paris-aligned reference. Build the alignment layer that compares issuer trajectories to sector pathways, surface the named outliers, and write the methodology defence for the reference scenario choice. Includes the visualisation pack a research team uses in client decks without your manual reformatting.

Module 8. Data Quality Tiers and Imputation Rules

Not every issuer has clean asset data. Build the data quality tiering that classifies issuers into reliable, modelled, and proxied buckets, write the imputation rules that lift coverage without inventing precision, and publish the tier on every issuer-level score so a client can see what they are reading. Includes the threshold rule for when an issuer is marked unreliable rather than scored.

Module 9. Refresh Discipline and Regression Testing

Quarterly refreshes are where silent drift happens. Build the regression test pack that compares issuer scores quarter-over-quarter, attributes the delta to data refresh versus methodology refresh versus corporate actions, and flags the issuers that moved more than threshold for engineering review. Includes the release notes template a client reads on the morning of every refresh.

Module 10. Client-Facing Methodology Documentation

The artefact that decides whether the next research call goes smoothly. Write the methodology pack that names every choice: scenario, downscaling layer, hazard source, return period, rollup logic, imputation rule, refresh cadence. Includes the appendix structure that survives a buy-side methodology committee review and the FAQ that pre-answers the questions you have received twice already.

Module 11. Handling the Methodology Challenge Call

When a client analyst escalates a methodology question, the call is a structured exchange. Cover the preparation pack you bring to the call, the questions you answer in writing afterwards, the source-data artefacts you offer to share, and the boundary between disclosing methodology and disclosing proprietary licensed inputs. Includes the post-call follow-up template that closes the issue in the client's internal record.

Module 12. Shipping the Pipeline Into Production

Tie the build together. Cover the orchestration layer that runs ingestion, hazard layering, transition layering, rollup, and regression tests as a single pipeline; the monitoring that catches a silent vendor change before clients do; the run-book a teammate uses to ship the refresh while you are out; and the methodology version log that ties every published score to the exact pipeline version that produced it. Ends with the production-readiness checklist before the next quarterly refresh.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Use modules 1, 10, and 11 when a client redlines the methodology pack.

Use modules 2, 5, and 8 when a refresh moves scores and the asset linkage layer is suspect.

Use modules 3, 4, and 6 when an analyst asks why the orderly-scenario number changed.

Use modules 9 and 12 when the next refresh is two weeks out and the regression pack is not ready.

What you get with this course

Twelve written modules in the Art of Service learning environment.
Downloadable methodology pack template with the appendix structure pre-built.
NGFS phase III to phase IV crosswalk table template.
Asset-to-issuer linkage data quality tier rubric.
Quarterly refresh regression test pack template.
The hand-built implementation playbook keyed to a portfolio you actually run.

What you will have in hand by Day 1, Week 1, Month 1

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

Modules are self-paced; a working engineer typically clears all twelve in three to five weeks alongside live work.

The implementation playbook is keyed to your actual portfolio composition so the first methodology pack draft is ready before the next quarterly refresh.

Before and after

Before

Methodology questions land mid-refresh and turn into multi-week reworks. The score is shippable but the chain behind it lives in scattered notebooks. A client analyst on the next call asks why the orderly-scenario number moved and the answer takes three days to reconstruct.

After

The methodology pack is the canonical artefact and every score traces back to it. Refresh deltas are attributed before the score ships. Methodology challenge calls are short because the written defence is already in the client's hands.

What happens if you do not address this

The next phase of client review is going to be tighter, not looser. A pipeline that cannot defend its scenario choice, its downscaling layer, and its asset linkage in writing becomes a methodology rework cycle that crowds out the build work. The team that ships methodology packs cleanly is the team that gets the next mandate.

Who it is for

Built for the associate or mid-level climate risk engineer at an index, analytics, ratings, or data provider whose job is to keep the physical and transition risk pipeline running, defend the methodology under client redline, and ship clean refreshes without silent score drift. Most useful when you sit between the data engineering team that owns ingestion and the research team that owns scenario design, and the methodology questions land on your desk.

Who this is NOT for. Not for portfolio managers using climate scores as an input, not for ESG analysts writing qualitative narratives, not for catastrophe risk modellers in primary insurance pricing. The build assumes you are inside the pipeline, not consuming its output.

How it arrives

Text-based course in the Art of Service learning environment, plus downloadable templates and worked examples for every module, plus the hand-built implementation playbook delivered alongside course access.

Time investment. Roughly twenty to thirty hours of reading and template work, plus the time to apply the playbook to your own pipeline. Built to be done alongside a full client-refresh workload, not in a dedicated training week.

Why $199 is the right number

Internal methodology wikis usually stop at scenario summaries and do not cover the asset-to-issuer linkage discipline. NGFS and CMIP6 documentation is the source material but does not walk through how to write the defence pack a buy-side analyst will read. Vendor training from hazard-data providers covers their layer in isolation, not the join model that ties physical and transition into a single auditable pipeline. This course is the join.

FAQ

Does the course assume a specific vendor stack?

No. The templates are written so they apply to any combination of licensed hazard data, NGFS scenario inputs, and internal asset databases. The playbook keys to your actual stack on delivery.

Is there a coding component?

The templates assume a Python pipeline. The methodology and documentation modules apply regardless of language; the engineering modules show worked examples in Python.

How current are the NGFS and CMIP6 references?

Phase IV NGFS variables and the latest CMIP6 downscaling sources are covered. Updates are pushed to the learning environment when reference releases ship.

Can the implementation playbook reference a portfolio under NDA?

Yes. The playbook is delivered after you specify the portfolio shape; the worked examples generalise the structure without naming holdings.

Is there a refund?

Thirty-day refund if the course is not what you needed.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.