Three Toolboxes.
One Platform.
Practical tools for NMHS leaders to measure value, assess AI readiness, and plan infrastructure investment.
Measure, Build & Communicate
NMHS Value
Five tools grounded in the Six Capitals framework (IIRC) — helping NMHS leaders assess performance, build funding cases, and report value to governments, donors and partners.
Score your NMHS against 21 KPIs across all six capital dimensions. Produces a radar chart benchmarked against WMO standards.
Build a structured investment case for government and donors. Live cost-benefit calculator with export to PDF or plain text.
Map how weather intelligence creates value across 12 pre-built sectors — agriculture, aviation, DRM, energy, health and more — plus 3 custom chains.
Complete the Integrated Reporting worksheet across all 8 IIRC content elements. Produces a structured narrative for your annual report.
Country Hydromet Diagnostics self-assessment across 10 elements. Benchmarks NMHS maturity on the 1–5 CHD scale used by the Alliance for Hydromet Development.
NMHS Value Creation
Toolbox
A practical guide to measuring, demonstrating and maximizing value for National Meteorological and Hydrological Services — using the Six Capitals framework.
The Problem
Why Value Creation Matters
Many NMHSs struggle to demonstrate their full worth, secure sustainable funding, and justify investment. Traditional cost-benefit analysis fails to capture the complete picture.
Funding Shortfalls
Government allocations are often inadequate, leaving critical observation networks under-maintained and service quality poor.
Hidden Value
The true economic and social value of weather services — avoided losses, informed decisions — is rarely captured in standard accounts.
Weak Stakeholder Links
NMHSs frequently lack formal partnerships with the sectors they serve, limiting co-creation of value and service relevance.
Limited Reporting
Most NMHSs report only financial performance, missing the broader story of value across human, intellectual and social capitals.
The Framework
The Six Capitals Model
NMHSs depend on and impact six forms of capital. Sustainable performance requires attending to all of them — not just finances.
Financial Capital
Funds available for NMHS operations, including government allocations, cost recovery from aviation and commercial services, and donor grants.
5 KPIsManufactured Capital
Physical infrastructure: observation networks, ICT systems, data centres, vehicles and buildings used in service production.
3 KPIsIntellectual Capital
Knowledge-based intangibles: data records, software, forecast procedures, research outputs and tacit expertise of staff.
4 KPIsHuman Capital
Staff competencies, motivation, training programmes and alignment of individuals with the organisation's strategic direction.
5 KPIsSocial & Relational Capital
Stakeholder relationships, trust, partnerships, legal frameworks and brand reputation that underpin the NMHS's licence to operate.
5 KPIsNatural Capital
Environmental resources and processes that support NMHS operations — including access to sites for weather observation.
QualitativeThe Chain
The Value Creation Process
Capitals flow through the NMHS business model to produce services and outcomes. Value is created, preserved or eroded at every stage.
KPI Measurement Framework
21 Key Performance Indicators rated on a 0–5 scale across all six capitals
| KPI | Name | Description | Benchmark |
|---|---|---|---|
| F1 | Govt financing adequacy | Percentage of NMHS budget adequately covered by government allocation | 100% of approved budget |
| F2 | Aviation cost recovery | Percentage of aeronautical service costs recovered through user charges | Full cost recovery (ICAO) |
| F3 | Return on Capital Employed | Net operating surplus as a percentage of total capital employed | Positive and growing |
| F4 | Commercial services income | Revenue from tailored sector services and data licensing | Growing commercial base |
| F5 | Donor grant income | Number and value of active development grants | Active donor pipeline |
| KPI | Name | Description | Benchmark |
|---|---|---|---|
| M1 | Observation network operationality | % of observation network operational at any time (requires TCO model) | >80% operational |
| M2 | ICT system operationality | % of ICT and data systems functioning (servers, telecoms, workstations) | >95% uptime |
| M3 | Buildings fitness for purpose | % of NMHS buildings rated fit for meteorological/hydrological operations | 100% fit for purpose |
| KPI | Name | Description | Benchmark |
|---|---|---|---|
| I1 | Internal research proportion | % of R&D conducted internally by own staff and infrastructure | Growing internal capacity |
| I2 | External research partnerships | % of research through external partnerships (universities, WMO, etc.) | Active regional/intl links |
| I3 | Open data availability | % of NMHS data made freely and openly available to all | 100% (WMO data policy) |
| I4 | Electronic service delivery | % of forecasts, warnings and analyses available electronically | 100% digital delivery |
| KPI | Name | Description | Benchmark |
|---|---|---|---|
| H1 | Staff trust in leadership | Result of anonymous annual staff survey on trust in leadership | >80% trust score |
| H2 | Formal training plan | Existence and quality of a multi-year training and development plan | Documented plan in place |
| H3 | Competency development | % of staff with access to structured competency development opportunities | 100% access |
| H4 | Staff attachments/exchanges | Number of staff in external attachments, exchanges or study visits per year | Regular opportunities |
| H5 | Objective-setting & appraisal | % of staff with documented objectives and completed annual appraisal | 100% completion |
| KPI | Name | Description | Benchmark |
|---|---|---|---|
| S1 | Legal framework conformance | Degree to which NMHS legal framework conforms to WMO and national norms | Full conformance |
| S2 | Reporting quality | Quality of annual reports (financial, performance, integrated) on 0–5 scale | Integrated report at L4+ |
| S3 | Formal stakeholder partnerships | Number and quality of formal agreements (MoUs) with key user sectors | MoUs with all key sectors |
| S4 | Communication plans | Existence of stakeholder-specific communication plans for different user groups | Plans for all key groups |
| S5 | Brand reputation survey | Results of external stakeholder survey on NMHS reputation and trustworthiness | >50% strongly positive |
KPI Scoring Guide
Rate current performance on the 0–5 scale and set improvement targets
Building the Business Case
Articulating NMHS value to governments, donors and development partners
Identify Revenue Streams
Quantify Avoided Losses
Build the Narrative
Sector Value Chain Analysis
Mapping how weather intelligence creates value in priority sectors
NMHSs create value by embedding weather intelligence into sectoral decision-making. Map these chains to demonstrate relevance and engage sector stakeholders.
🌾 Agriculture & Food Security
✈ Civil Aviation
🚗 Road Transport
⚠ Disaster Risk Management
Integrated Thinking & Reporting
Communicating the full value story to government, donors and stakeholders
Based on the International Integrated Reporting Council (IIRC) framework, integrated reporting enables NMHSs to move beyond financial statements and demonstrate performance across all six capitals.
7 Guiding Principles
- ✓Strategic focus & future orientation
- ✓Connectivity of information
- ✓Stakeholder relationships
- ✓Materiality — report on what matters most
- ✓Conciseness — brief and substantive
- ✓Reliability & completeness
- ✓Consistency & comparability
8 Content Elements
- 1Organisational overview & external environment
- 2Governance
- 3Business model
- 4Risks & opportunities
- 5Strategy & resource allocation
- 6Performance across all six capitals
- 7Outlook — future challenges & uncertainties
- 8Basis of preparation & presentation
Country Hydromet Diagnostics
A structured peer-to-peer assessment to benchmark NMHS maturity across 10 elements
The CHD assesses NMHS performance across 10 elements through peer review. It complements the Six Capitals KPIs and is applicable to any NMHS worldwide.
Getting Started
Your Value Creation Journey
A practical four-phase roadmap for NMHS leaders. Adapt the pace to your context and capacity.
Our Services
How BoW Supports Value Creation
Business of Weather provides practical support to help NMHSs implement this toolbox and build lasting value creation capacity.
BoW Team Advisory
Expert guidance on applying the Six Capitals framework, KPI assessment and integrated reporting — direct support for Directors-General and their teams.
Peer Learning Network
Learn from NMHSs that have completed CHD reviews and KPI assessments. Share experiences, tools and templates with peers worldwide through the BoW network.
Diagnostic Tools
Access the BoW integrated thinking and reporting tool. Structured assessment worksheets for all 21 KPIs and 10 CHD elements — ready to use out of the box.
Donor & Stakeholder Engagement
BoW helps NMHSs make the case to government owners and development partners using the value creation framework and evidence-based reporting.
Sector Linkage Support
Facilitation of partnerships with aviation, agriculture, transport and disaster risk sectors — identifying co-creation opportunities and new revenue streams.
Capacity Building
Training workshops on integrated thinking, KPI measurement, business model development and communicating value — for both technical and leadership staff.
How ready is your NMHS
for Artificial Intelligence?
Part A of the BoW AI Readiness assessment helps NMHS leaders understand their organisation’s current capability, data infrastructure, and strategic alignment before adopting AI-based forecasting and decision-support tools.
Score each item 0–3. The tool calculates your weighted maturity score automatically. Complete as a facilitated workshop with NMHS technical and management staff (allow 2–3 hours).
Data is the fuel for all AI/ML methods — gaps here are the most common reason AI pilots fail. This pillar assesses whether your NMHS has the data foundations needed to train, validate and deploy AI applications.
- The NMHS maintains a continuous, digitised archive of historical meteorological data (temperature, precipitation, wind, pressure,
- The NMHS maintains a continuous, digitised archive of historical hydrological data (river levels, discharge, soil moisture) coveri
- Data from automatic weather stations (AWS) and hydrological gauges is transmitted and stored in near real-time with minimal gaps (
- A formal data quality control (QC) process is in place and applied consistently to both incoming real-time and archived historical
- A Climate Data Management System (CDMS) or equivalent platform is operational and used for data storage, retrieval, and product ge
- Metadata standards are applied to datasets (e.g., WMO WIGOS standards), enabling interoperability with regional and global systems
- A formal data governance policy exists, covering data ownership, access rights, sharing protocols, and privacy obligations.
- Data sharing agreements are in place with key national partners (Ministry of Agriculture, Emergency Management, water authorities,
- The NMHS participates in regional and international data exchange mechanisms (e.g., WMO Global Telecommunication System, EUMETNET
Many gaps in this pillar can be addressed with modest investment in cloud access. This pillar rarely needs to be fully resolved before starting AI pilots — a score of 2 across key items is usually sufficient to begin.
- The NMHS has dedicated servers or computing infrastructure capable of running numerical weather prediction (NWP) or post-processin
- The NMHS has access to cloud computing resources (government cloud, commercial cloud, or a regional shared infrastructure such as
- Staff workstations are modern enough to run data science tools (Python, R, Jupyter notebooks, xarray, etc.) without significant pe
- The NMHS has reliable, high-speed internet connectivity at its main operational centre (sufficient for downloading ECMWF open data
- The NMHS uses open-source or licensed software platforms for data analysis and visualisation (e.g., QGIS, Metview, Python scientif
- A data backup and disaster recovery system is in place for critical operational data and systems.
- The NMHS has access to Doppler weather radar data in a format suitable for digital processing and potential ML-based analysis (e.g
- IT infrastructure is maintained by dedicated IT staff or through a formal support arrangement with a reliable provider.
- The NMHS has or can access GPU-enabled computing resources for training machine learning models (on-premises or via cloud).
- Cybersecurity measures are in place to protect operational systems and data (firewalls, access controls, software update policies,
Human capacity is the hardest bottleneck to address and the most important. No AI tool deploys itself — someone must understand it, adapt it, and maintain it. Checklist items 3.1, 3.2, 3.4 and 3.7 together define whether
- At least one staff member has working knowledge of a programming language used in data science (Python, R, or equivalent) and uses
- At least one staff member has experience with machine learning concepts or has completed relevant training (e.g., ECMWF ML course,
- Operational forecasters have basic digital literacy and are comfortable using software-based forecasting tools (decision support s
- The NMHS has a documented training plan or learning pathway for staff to develop data science and AI skills over the next 1–2 year
- Staff have participated in WMO, EUMETNET, ECMWF, or equivalent training programmes on numerical modelling or data-driven methods w
- NMHS leadership (Director, heads of department) understands the potential and realistic limitations of AI for hydrometeorological
- The NMHS has a strategy or plan to attract and retain technically skilled staff (data scientists, IT specialists), including appro
- Staff turnover in technical roles is low enough to sustain institutional knowledge and continuity of AI initiatives (key roles sta
- The NMHS collaborates with local universities or research institutions to access data science expertise and to provide opportuniti
- Staff are aware of at least one concrete AI application directly relevant to their work (e.g., ML-based bias correction of NWP out
AI tools that cannot plug into operational workflows have no impact. This pillar assesses whether your current systems and processes are positioned to receive and use AI outputs. A structured forecasting workflow is the
- The NMHS uses a structured, documented forecasting workflow with defined steps from data ingestion to product dissemination, enabl
- Standard Operating Procedures (SOPs) exist for key operational tasks (severe weather warning issuance, flood forecasting, seasonal
- The NMHS uses probabilistic or ensemble-based forecasting methods in its operational workflow (either from global models or throug
- The NMHS has implemented impact-based forecasting (IBF) or is actively working towards it in line with WMO guidelines.
- A nowcasting system is operational and integrated into the daily forecasting workflow (even if using simple extrapolation methods)
- The NMHS has a hydrological forecasting capability for key river catchments that could be enhanced with ML-based methods for disch
- The NMHS has a mechanism to verify and validate forecast performance against observations (skill scores, verification reports shar
- The NMHS has experience integrating outputs from external NWP models (e.g., ECMWF IFS, regional NWP partners) into its own product
- Forecasters are open to using AI-generated guidance as decision-support (rather than viewing AI as a threat to professional judgme
- The NMHS has a product dissemination system (website, mobile app, API, or similar) capable of delivering AI-enhanced products to e
Without leadership endorsement and clear accountability frameworks, AI pilots rarely transition to operational products. This pillar rarely blocks early pilots but matters significantly for sustained progress beyond Phas
- NMHS leadership has formally endorsed or expressed support for exploring AI applications and has communicated this to staff.
- The NMHS has or is developing internal guidelines on the responsible use of AI (human oversight requirements, QA protocols for AI
- The NMHS is aware of and has engaged with the national digital transformation strategy and any relevant AI or technology policy fr
- The NMHS has a budget line or access to funding specifically for technology modernisation and innovation (including potential acce
- The NMHS has a process for evaluating and adopting new tools or methods (a technical committee, innovation working group, or equiv
- Accountability for AI-generated products is clearly defined: a qualified human forecaster remains responsible for all issued warni
- The NMHS has a mechanism to communicate AI-related risks, errors, or failures to management and relevant external stakeholders in
No national service needs to build AI capability in isolation. The partnerships described in Part B (ECMWF, MeteoSwiss, Oxford, EUMETNET) provide a practical starting point. This pillar assesses your current connectivity
- The NMHS has active collaboration with at least one other NMHS or regional centre that could extend to sharing AI/ML methods and t
- The NMHS has explored or established partnerships with private sector technology providers for AI tools, cloud services, or data s
- The NMHS participates in the relevant WMO Regional Association activities where AI and digital transformation are discussed, and i
- The NMHS has participated in, or is aware of, AI pilot projects in the hydrometeorological sector at the regional or global level
- The NMHS has an active relationship with national universities and research institutes that could generate internships or research
- NMHS leadership has directly engaged EUMETNET, EUMETSAT, or ECMWF on participation in training or collaborative programmes.
- The NMHS is aware of and aligned with the EUMETNET E-AI programme, its workshop schedule, and the EUMETSAT bursary mechanism for a
- The NMHS has engaged or considered engaging leading research universities or international research groups on applied climate or w
- The NMHS has a mechanism for capturing lessons from external partnerships and embedding them into internal workflows and instituti
To complete the assessment, open the interactive tool, score each item, add observations, and export your maturity report.
Plan the full cost of your
observation infrastructure
Based on the World Bank & Grimes et al. (2022) methodology, this calculator helps NMHSs and their partners estimate the Total Cost of Ownership for hydromet observation networks — covering capital investment, operations, maintenance and lifecycle replacement over any planning horizon.
Total Cost of Ownership reveals the true long-run cost of operating an observation network — beyond the procurement price. Based on Charting a Course for Sustainable Hydromet Observation Networks (World Bank, 2022).
+ Total Annual O&M × System Lifetime (L6–14)
+ Life-Cycle Replacements (L17)
Useful for comparing systems of different durations
WB benchmark: <15% is considered sustainable
Reference: Grimes et al. (2022). Charting a Course for Sustainable Hydrological and Meteorological Observation Networks. World Bank. CC BY 3.0 IGO.