You’ve clicked “Create New Report” and seconds later, you have a comprehensive climate analysis document in front of you. But what exactly are you looking at, and how do you extract the insights that will shape your design decisions?

This guide walks you through every section of infrared.city’s Auto-Generated Reports, showing you not just what the data means, but how to use it to create better outdoor spaces.

Starting Point: One Click, Complete Analysis

From any project in infrared.city, clicking “Create New Report” triggers automated processing of your site and design project environmental conditions. Within seconds, the system draws the report relying on the project data and the simulation results you have previously generated—processing TMYx weather files, calculating wind speed and thermal comfort indices—and synthesizes them into a structured document with dozens of climate visualizations, key findings, environmental simulation results, and AI-powered summaries.

Important: Changes to your report are not automatically saved. Use the “Publish” button in the top-left corner to make your edits effective.

The Report Cover

Project Context

Before diving into climate data, you would eventualy customize the report metadata. This is about refining a professional document, ready for team or client presentation:

• Address: Auto-populated from your project location
• Client name: Who this analysis is for
• Issued by: Your firm or practice name
• Version: Track iterations as your design evolves
• Issue date: Automatically timestamped

Why this matters: Adjacent to these details is a 3D site visualization showing your project’s urban context—buildings, streets, and open spaces that influence microclimatic conditions. This visual helps teammates or clients understand the physical environment before encountering data, delivering a branded, contextualized analysis document that they can trust and understand.

Project Description

Comprehensive Project Documentation:

• Geographic coordinates and weather file specifications
• Site measurements (area, perimeter, coordinates)
• Urban context analysis (building density within radius)

Project Summary: AI-Generated Context

When your report generates, an AI agent has already analyzed your site conditions and produces a narrative summary. Look for the purple “Generate with AI” badge—it indicates machine-generated insights.

What the AI Summary Provides

Site Context Analysis

“The site is located at 40.4167, -3.7033 in Centro de Turismo de Sol, Madrid. It is situated in a dense urban residential area with 4,206 buildings within a 1km radius.”

This immediately tells you: you’re designing in a dense urban fabric. Expect urban heat island effects. Expect limited sky view factors.

Climate Classification

“The climate is classified as Csa (Hot-summer Mediterranean climate) under the Köppen-Geiger system.”

The system explains design implications: hot, dry summers; mild, wetter winters; specific comfort challenges tied to this climate type.

Key Climate Challenges

The AI identifies site-specific risks, like:

• Urban heat island effect intensifying summer temperatures
• Periodic droughts and water scarcity
• Extreme heat waves becoming more frequent
• Air pollution from prolonged dry periods

Temperature Ranges & Precipitation

“Temperatures ranging from -5°C to 30°C. Annual precipitation averages 800mm, distributed throughout the year.”

These are design constraints. That 35°C temperature swing means you need adaptive strategies to reach comfort. That 800mm precipitation means stormwater management matters.

Environmental Design Considerations

The AI suggests starting points—shade strategies for heat, water features for evaporative cooling, materials selection for thermal mass.

How to use this: The AI summary is synthesizing thousands of data points into actionable narrative. Read it first to orient yourself, then dive into detailed data to validate and refine your design thinking.

Climate Analysis: Core Environmental Data

Now you’re ready for the core environmental data. Each section is expandable/collapsible, letting you focus on what matters most for your project design.

Climate Summary

Climate Classification (AI-generated)

Detailed explanation of Köppen-Geiger code: “Hot-summer Mediterranean climate with temperatures from -5°C to 30°C. Warm summers with occasional hot spells, cool winters with some freezing periods.”

Key Climate Challenges are listed as bullet points—urban heat island, heat waves, stormwater runoff, air quality concerns, seasonal extremes. These directly inform your design priorities.

Climate Summary Narrative (AI-generated)

• Temperature fluctuations: “Madrid experiences hot summers with average highs around 32°C in July and August. Winters are cool with average lows near -5°C in January.”
• Wind patterns: “Prevailing winds are generally light to moderate, averaging 2-4 m/s. Wind directions vary seasonally, with northeasterly winds common in winter.”
• Solar radiation: “Madrid receives abundant sunshine, averaging about 2,800 hours of sunshine annually.”
• Thermal comfort: “Summer months often exceed comfort thresholds due to high temperatures and low humidity. Winter discomfort is primarily due to cold temperatures rather than extreme wind chill.”

How to read this: Look for extremes and patterns. The 32°C summer highs tell you shade is non-negotiable. The 2,800 sunshine hours tell you solar gain is a major design factor. The light winds (2-4 m/s) tell you natural ventilation is viable but won’t be aggressive. These numbers and values come from the relative graphs which have been generated in the climatic dashboard, here ready and explained to inform your design.

Temperature & Humidity

You’ll see two visualizations: for each metric:

1. Annual histogram showing distribution with standard deviation bands
2. Segmented monthly heatmap displaying hourly patterns throughout the year

AI-generated insight below charts:

“Climate exhibits significant seasonal variation… July highest temperatures, March lowest. Relative humidity 22% to 100%, higher in winter months.”

How to read: Look for daily patterns in segmented heatmaps. Notice predictable temperature rise and fall—this reveals passive design opportunities. The temperature range (from -8.6°C to 34.5°C) is a 43-degree swing. Your materials, systems, and spaces need to perform across this entire range. Humidity maps show nighttime peaks and daytime lows—useful for understanding condensation risk and evaporative cooling potential.

Interactive features: Click zoom icon for fullscreen view. Hover your mouse over data points to view the exact values—useful for energy modeling or comfort calculations.

Solar Analysis

Solar analysis includes four key radiation metrics, each with its own solar path visualization:

1. Global Horizontal Radiation (GHR) - Total solar energy on horizontal surfaces. This combines direct sunlight and diffuse sky radiation.
2. Direct Normal Radiation (DNR) - Solar energy arriving perpendicular to the sun’s rays. This is what causes glare and drives direct solar gain.
3. Diffuse Horizontal Radiation (DHR) - Scattered sky radiation—the light you get on overcast days. Critical for daylighting strategies.
4. Horizontal Infrared Radiation - Longwave radiation from the sky. This affects nighttime cooling and radiant heat loss.

Each is a polar diagram showing:

• Sun position throughout the year (azimuth and elevation)
• Color-coded radiation intensity at each position
• Seasonal sun paths across the sky dome

Below the solar paths, you’ll find a monthly infrared radiation heatmap showing how thermal radiation varies by month and hour.

Additional chart: Solar Radiation + Cloud Coverage comparing sky cover with solar radiation, helping evaluate daylight access and photovoltaic potential under different sky conditions.

AI-generated insight:

“Madrid receives abundant sunshine, averaging about 2,800 hours annually. Cloud coverage is minimal in summer, increasing in winter with an average of 8-10 overcast days per month.”

Seasonal variations noted:

• Summer: Intense direct solar radiation, clear skies, long days—challenges for cooling and glare
• Winter: Lower solar radiation, shorter days, increased cloud coverage—strategies for maximizing solar gain and daylighting
• Spring/Fall: Moderate radiation, variable cloud coverage—opportunities for passive design

How to read: Color intensity on solar paths shows where protection is critical. Deep red/orange on summer sun path? Peak heat gain—shade those angles. Use DNR to design shading device, DHR to evaluate daylighting potential under typical sky conditions, infrared data for nighttime cooling assessment.

Wind Analysis

Wind data appears in two formats:

Wind Roses (Three Resolutions)

You’ll see three polar diagrams showing wind direction frequency, selectable from curtain menu:

• 8-direction: Broad patterns (N, NE, E, SE, S, SW, W, NW)
• 16-direction: Medium resolution
• 36-direction: Precise directional analysis (use dropdown to select)

Each “petal” length shows wind frequency from that direction. Colors indicate speed ranges.

Wind Speed

Year-long hourly visualization showing speed variations by month and time of day. Color intensity represents magnitude (0-10+ m/s).

AI-generated insight:

“Predominant winds from west-northwest (270°-300°) and southeast (120°-150°) directions. Average wind speed is 3.05 m/s, with higher frequencies in the 2-4 m/s range.”

Additional notes: “Clear diurnal pattern, higher speeds typically afternoon and early evening. Lower during nighttime and early morning. Seasonal variation with slightly higher speeds spring and summer (March-August).”

How to read: Longest petals are the prevailing wind directions. For Madrid, the west-northwest and southeast directions dominate. This tells you:

• Orient buildings to capture these winds for natural ventilation
• Protect outdoor seating areas from these directions in winter
• Use these flows for passive cooling in summer

Notice if winds are stronger in afternoons (typical due to thermal effects) or if certain months are calmer. Madrid shows consistent 2-4 m/s winds—moderate and usable for ventilation without creating discomfort.

Design application: Use the 36-direction rose for precise building orientation. Use the wind speed heatmap to identify when outdoor spaces will be exposed to uncomfortable wind speeds (typically >5 m/s for pedestrians).

Comfort Analysis

This is where everything comes together. UTCI (Universal Thermal Climate Index) combines air temperature, humidity, wind speed, and solar radiation into a single metric representing how humans feel outdoors.

Hourly UTCI Heatmap

Choose from dropdown one of the three versions of the same annual data:

• Hourly UTCI Thermal Stress Categories - color-coded (extreme cold to extreme heat)
• Hourly UTCI Comfortable Category - Simplified comfort ranges showing comfortable vs uncomfortable periods
• Hourly UTCI Heatmap - Raw UTCI values in degrees Celsius

Each cell represents one hour of the year, color-coded by thermal sensation.

Monthly UTCI Thermal Stress Distribution

A stacked bar chart showing percentage of time in each stress category per month:

• Extreme/Very Strong/Strong/Moderate/Slight Cold Stress
• No Thermal Stress (comfort)
• Moderate/Strong/Very Strong/Extreme Heat Stress

Degree Days

Two graphs showing:

• Heating Degree Days (HDD) - Cumulative measures of heating needs
• Cooling Degree Days (CDD) - Cumulative measures of cooling needs

AI-generated insight:

“UTCI ranges from -17.4°C minimum to 41.6°C maximum, average 13.7°C. Summers experience intense heat stress (June-August), winters show moderate to strong cold stress (December-February).”

Monthly patterns: “Summer months (June-August) dominated by heat stress, July and August showing significant extreme heat stress. Winter months (December-February) display mix of thermal stress and cold conditions, with January having significant heating requirements. Total: 816 Cooling Degree Days (CDD) and 1,926 Heating Degree Days (HDD) annually—higher demand for heating than cooling, with peak cooling needs July-August and maximum heating requirements December-February.”

How to read

Hourly UTCI heatmap is your usability map:

• Blue zones: Too cold for comfortable outdoor use without protection
• Green/yellow zones: Comfortable—these are your design opportunities
• Orange/red zones: Too hot for comfortable outdoor use without cooling strategies

The monthly distribution chart shows seasonal trends at a glance. Madrid’s chart shows:

• Winter (Dec-Feb): Dominated by cold stress categories
• Spring/Fall (Mar-May, Sep-Nov): Transitional, more comfortable
• Summer (Jun-Aug): Mix of comfort and heat stress

The degree days quantify energy implications. Madrid’s 2,800 HDD vs 450 CDD tells you this is primarily a heating climate, but summer cooling cannot be ignored.

Pro tip: All visualizations are interactive and zoomable. Click fullscreen, hover for exact values at specific hours.

Environmental Analysis: Simulation Methods

This section documents the analytical methods used in your simulations. Not all projects will have all analysis types—this depends on what you’ve run in the Simulation tab.

How We Simulate

wind-speed

Displays airflow patterns and velocity distribution across the site, considering wind speed and direction inputs. Wind affects comfort, safety, energy use (ventilation), and microclimate dynamics.

pedestrian-wind-comfort (Criteria: lawson-lddc or others)

Year-round assessment of wind comfort based on standards like Lawson Criteria, accounting for frequency and speed thresholds. Spaces might feel fine occasionally but still fail comfort standards over the year. Analysis summarizes year-round comfort according to validated standards.

thermal-comfort-index

Calculates Universal Thermal Climate Index (UTCI) by integrating air temperature, wind speed, humidity, and mean radiant temperature to quantify outdoor thermal comfort at each point and hour. Provides holistic comfort indicator reflecting human thermal response under real microclimatic conditions.

thermal-comfort-statistics (Subtype: cold-stress)

Maps percentage of time UTCI drops below 9°C, increasing discomfort and exposure risk during colder periods. Even in temperate climates, early mornings, shaded areas, or winter months can bring cold stress, reducing usability.

thermal-comfort-statistics (Subtype: thermal-comfort)

Maps percentage of time UTCI remains between 9°C and 26°C—optimal range for thermal comfort outdoors. Evaluates how much time people feel thermally comfortable based on combined environmental conditions.

thermal-comfort-statistics (Subtype: heat-stress)

Maps percentage of time UTCI exceeds 26°C—threshold beyond which heat becomes physiologically stressful. Identifies heat-prone zones and duration of exposure.

Snapshots: Comparing Design Scenarios

After you’ve absorbed the climate baseline, the Snapshots section shows how different design interventions perform: snapshots are saved sets of simulations used to calculate KPIs performance metrics and enable comparison. Each snapshot includes:

Existing Urban Settlement Scenario, Snapshots Outcomes

Adding Trees Scenario, Snapshots Outcomes

3D Visualization with Thermal Overlays

Side-by-side 3D views show your site with thermal comfort color-coded spatially. Check the legend on each visualization—it shows the wind/UTCI range being displayed.

For example:

• “Existing Urban Settlement” is showing large red/orange zones (high temperatures, heat stress)
• “Adding Trees” shows those same zones shifted to yellow/green (comfortable)

This is visual proof of design impact.

How to read this:

Compare snapshots directly, side-by-side. As an example, if “Adding Trees” shows:

• Spatial Variance Reduction (Thermal Comfort Index metric) increase from 61.95% to 67.44%
• Heat Vulnerability Reduction (Thermal Comfort Statistics metric) increase from 95% to 100%

…then you have quantifiable proof that tree placement improves thermal conditions.

The side-by-side 3D views make this comparison instant and visual. Clients don’t need to understand UTCI calculations (for example)—they can see that the red zones disappeared.

Design application: Use snapshots to test multiple strategies (e.g., trees vs shade structures vs water features) and compare their performance. Export these comparisons directly into client presentations as evidence-based design validation.

AI-Powered Insights: Your Climate Interpreter

For each snapshot, AI analyzes all included simulations and generates an integrated assessment covering. The AI doesn’t just report data—it identifies problems and suggests interventions.

What AI Summaries Provide:

1. Site Context and Climate Characterization

The AI establishes baseline conditions. Complex datasets are summarized into digestible takeaways:

• “Located in Centro de Turismo de Sol, Madrid, Spain (40.4167, -3.7033). Dense urban residential area with 4,206 buildings within 1km radius. Primary land uses include retail and military.”
• “Madrid experiences a Temperate (C) climate with temperatures ranging from -5°C to 30°C. Annual precipitation is 800mm, distributed throughout the year.”

2. Thermal Comfort Variability Analysis

The AI quantifies temporal comfort patterns with specific metrics:

• “Summer heat stress is severe, with 78.28% of hours exceeding 26°C UTCI from June to September”
• “Winter cold stress is also significant, with 52.71% of hours below 9°C UTCI from December to March”
• “Annual thermal comfort (9-26°C UTCI) is achieved for 51.06% of hours, classified as ‘No thermal stress’”

3. Spatial Comfort Patterns

The AI identifies where conditions vary across the site:

• “Areas with dense tree canopy consistently show better thermal performance across seasons”
• “Open, unshaded areas experience the highest heat stress, reaching up to 84.56% of summer hours”
• “Narrow street canyons exhibit persistent cold stress in winter, while wider intersections and plazas show slightly better performance”

4. Seasonal Variations

The AI highlights when design opportunities occur:

• “Late spring to early summer (March-June) offers the best thermal comfort, with up to 67.63% of hours in the comfort range”
• “Late summer (August-September) shows severe heat stress, with UTCI values averaging 32.15°C, classified as ‘Strong Heat Stress’”

5. Integrated Design Strategy Recommendations

Based on the analysis results, the AI suggests specific interventions with spatial targeting:

• “Expand Urban Greening: Prioritize tree planting in exposed areas, particularly in the northwestern and southeastern sections”
• “Implement Cool Pavements: Introduce light-colored, reflective, and permeable materials to reduce heat absorption”
• “Optimize Building Shading: Incorporate external shading devices on east and west-facing facades”
• “Enhance Water Features: Integrate fountains or misting systems in central plazas for evaporative cooling”
• “Wind Buffering: Install strategic windbreaks in open areas to mitigate winter cold stress”
• “Solar Access Optimization: Adjust building orientations and prune vegetation to maximize winter sun exposure”

6. Performance-Based Conclusions

The AI synthesizes findings into actionable takeaways:

• “By implementing these strategies, the site can significantly improve its year-round thermal comfort, addressing both summer heat stress and winter cold stress while enhancing the overall livability of the urban environment.”

How to use AI insights: The AI cross-references all simulation results in a snapshot, citing specific analyses (noted in brackets like [Thermal Comfort Statistics (1)]) so you can trace findings back to their source visualizations. This integrated analysis saves hours of manual interpretation and provides a narrative structure for client presentations.

How to read AI insights: Think of the AI as your first-pass analyst. It has done the initial interpretation work:

1. Read the AI summary first to understand what the data is telling you
2. Examine the visualizations to verify and explore deeper
3. Use the AI language in client presentations—it’s already client-friendly

The AI isn’t perfect, but it accelerates your analysis workflow dramatically. Instead of spending 30 minutes interpreting temperature patterns, you better spend 5 minutes validating the AI’s summary and exploring nuances.

Pro tip: You can edit AI-generated sections to customize the language for your specific project or client audience. The AI gives you a starting point, not a locked-in document. But this is material for the next topic!

Making It Your Own: Editing and Customization

Reports are indeed living documents you can customize.

Hiding Sections

Sections can be hidden if not relevant to your presentation, by clicking on the “Eye” botton at the top of each section’s header. The report adapts to show only what you need.

Modifying Existing Content

All AI-generated summaries are editable:

1. Click into any text block
2. Modify, add details, or remove irrelevant points
3. Changes save automatically (but remember to publish!)

This lets you maintain automated efficiency while adding project-specific expertise. Insert your own analysis blocks:

• Project-specific observations eventually not captured by automated analysis
• Site visit findings from your team’s fieldwork
• Client requirements or sustainability goals
• Design narrative connecting climate data to proposed strategies

Collaboration & Sharing: From Draft to Publishing

Critical: Draft Mode (Default). Edit sections, add/remove content, test visualizations. Use the “Publish” button in the top-left corner to save changes and enable sharing actions.

The Publishing Workflow

Once your report content is finalized, you have multiple options for sharing and collaboration.

Report Menu

Publish Draft

• Creates locked version
• Creates a permanent snapshot of the current report state
• Generates unique public URL
• Enables sharing features with internal team members
• Prevents accidental edits to published version.

You can continue editing the draft while the published version remains stable.

Preview Published Content

Before publishing, click it to see exactly what external stakeholders will see. You can visualize it even while the download is performed. This shows:

• How the report renders on different devices
• What sections are visible
• How visualization appear in public view.

Download

Export complete report as Markdown (.md) with all images embedded. This creates an archival copy and allows offline viewing. The markdown format is also useful for:

• Converting to PDF via other tools
• Importing into documentation systems
• Version control in Git repositories

Discard Draft Changes You can remove draft changes and get back to default content (before editing). Return to draft-only mode.

Share Menu

Once published, you have multiple distribution methods:

Send Report Enter the email address where you would like to send this report. The recipient receives:

• A message with report summary
• A link to view the full report online
• Access without needing to create an account

Share link

Enter email address to send report directly with access link. In particular, it creates a shareable URL (e.g., https://preview.infrared.city/share/public/cc32...). Anyone with this link can view the report without needing an infrared.city account: anyone who has this link will be able to view this. Perfect for:

• Client review
• Stakeholder feedback
• Public presentations

Unpublish

Revoke public access by unpublishing. This:

• Invalidates all shared links
• Disables email access
• Returns the report to draft-only mode

Useful when designs change significantly or when client engagements end.

Version Control

The draft/published system creates implicit version control:

• Draft = working version you’re actively editing
• Published = locked snapshot shared externally

If you need to update a published report:

1. Make changes in draft mode
2. Click “Publish” again
3. This creates a new published version
4. All existing links update automatically to show the new version

How to read this workflow:

Think of publishing like “committing” your analysis. Draft mode is for experimentation and refinement. Publishing is for stakeholder distribution. The system prevents you from accidentally sharing incomplete work while allowing continuous improvement.

Putting It All Together: A Reading Strategy

With all these sections and features, here’s a recommended workflow for reading and using your climate report:

Phase 1: Initial Scan

1. Read the AI Project Summary to understand big-picture challenges
2. Skim the Climate Summary to confirm climate classification
3. Look at the UTCI heatmap to identify comfort patterns at a glance
4. Note the degree days to understand heating/cooling dominance

Goal: Establish baseline understanding before detailed analysis.

Phase 2: Deep Dive

1. Expand Temperature & Humidity sections and examine seasonal patterns
2. Review Solar Analysis to identify peak gain periods
3. Study Wind Roses to understand ventilation potential
4. Analyze UTCI monthly distribution to find comfort opportunities

Goal: Extract specific design requirements from detailed data.

Phase 3: Design Application

1. Compare Snapshots to test design interventions
2. Add custom sections documenting your design response
3. Use AI assistance to generate client-friendly explanations
4. Export visualizations for integration into presentations

Goal: Translate climate data into design decisions and documentation.

Phase 4: Client Presentation

1. Publish the report to create a stable version
2. Generate public link for client access
3. Highlight 3-5 key findings from the AI summaries
4. Show before/after snapshots demonstrating design impact

Goal: Communicate climate-responsive design decisions with evidence.

What Makes These Reports Useful

infrared.city reports synthesize complex climate data into structured, visual documentation.

• Interpretation - The AI summaries translate complex metrics into actionable insights.
• Spatial understanding - 3D thermal overlays show where problems exist spatially
• Interactive - Every chart zooms, every section expands, every metric is explorable.
• comparison - Snapshots let you test and validate design strategies with quantified performance metrics.
• collaborative - Publishing and sharing features turn climate analysis into team intelligence.

The reports document the analytical work you’ve already done in the platform, presenting it in a format that communicates effectively with clients and stakeholders.

Your next project deserves climate intelligence that’s as sophisticated as your design thinking.

Ready to generate your first climate report?

infrared.city climate reports combine automated weather data processing, AI-Powered Wind Simulations, UTCI thermal comfort analysis, and machine-generated interpretation to deliver outdoor thermal comfort mapping and environmental design intelligence for architects and designers. From one-click generation to collaborative sharing, every feature is built to accelerate evidence-based design.