Introduction

Some incidents arrive like a thunderclap. Most, though, creep in like a change in the weather.

Logs looked a bit noisier than usual. Someone mentioned, “Search feels slow today.” A deploy was rolled back “just to be safe.” Tickets drifted in about timeouts from a region you don’t usually think about.

Individually, these are anecdotes. Together, they’re weak signals of a front moving in: a future outage.

This is where the idea of an Analog Incident Weather Station comes in—a daily, low‑tech “paper barometer” that helps you notice those weak signals early, talk about them consistently, and act before you’re paged at 3 a.m.

In this post, we’ll explore how Site Reliability Engineering (SRE) practices combine with a simple, analog ritual to forecast tomorrow’s incidents from today’s operational weather.

SRE and the Art of Forecasting Failure

Site Reliability Engineering (SRE) is about more than uptime dashboards and on‑call rotations. At its core, SRE is concerned with:

• Availability – Is the service up when users need it?
• Performance – Is it fast enough to be usable and delightful?
• Resilience – How gracefully does it handle failures and recover from them?

SREs combine software engineering, operations, and systems thinking to make reliability a feature—not an afterthought. To do this well, they need to:

• Detect emerging risk early
• Quantify reliability and risk
• Respond quickly and consistently
• Learn from every incident

The challenge is that the earliest signs of trouble are rarely obvious. They show up as weak signals.

From Gut Feeling to Early Warning System

Most teams already sense when something is “off.” The trouble is that these impressions stay trapped in:

• hallway conversations
• Slack side threads
• individual intuition (“I’ve seen this before…”)

Because they’re anecdotal, they are easy to dismiss. But weak signals can be transformed into structured early warning indicators. Examples include:

• A slow but steady rise in error budgets being burned
• More frequent “minor” rollbacks
• Increasing support tickets tagged with “timeouts” or “slow search”
• A growing backlog of flaky tests related to the same subsystem

Instead of relying on memory and scattered chat logs, the Analog Incident Weather Station gives these weak signals a visible, shared home, so the whole team can see the “weather” changing.

Introducing the Analog Incident Weather Station

Think of your Incident Weather Station as a literal, physical barometer for your systems.

At its simplest, it’s:

• A whiteboard or poster in a common space
• A daily ritual (5–10 minutes) where the team updates it
• A small storyboard and taxonomy for classifying weak signals

This is deliberately low‑tech. You already have dashboards, metrics, and alerting systems. The point here is to:

1. Make weak signals impossible to ignore.
2. Create a shared language and habit around risk.
3. Encourage proactive, human‑centered discussion before the pager goes off.

The Daily “Paper Barometer” Ritual

Once a day (or per shift), the on‑call engineer or a designated “weather reporter” updates the station. The ritual can be as simple as:

1. Set the overall weather

   • Sunny: Everything is stable; no notable weak signals.
   • Cloudy: Some concerns; watching a couple of areas.
   • Storm Warning: Strong signals; probable incident if unaddressed.

2. Add signal cards Use sticky notes or index cards for each notable weak signal:

   • Short title (e.g., “Search latency spike in EU”)
   • Category (from your signal taxonomy, see below)
   • Date and source (who noticed, where)
   • Optional severity (e.g., 1–3)

3. Discuss for 5–10 minutes

   • What’s new since yesterday?
   • Are any patterns emerging?
   • Do we need to act now (e.g., create a ticket, add an SLO, improve a runbook)?

This small investment turns weak signals into a living map of operational risk.

Building a Signal Taxonomy and Storyboard

To keep this useful and not just decorative, you need structure.

A Simple Signal Taxonomy

Start with a few high‑level categories that reflect your systems and workflows. For example:

• User Experience Signals

  • Increased complaints, churn indicators, UX reports (“It feels slow”).

• Performance & Capacity Signals

  • Latency drifts, CPU/memory trends, queue depth growth.

• Stability & Quality Signals

  • Flaky tests, frequent rollbacks, noisy alerts, dependency churn.

• Operational Workflow Signals

  • On‑call burnout, long incident response times, overloaded teams.

• Security & Compliance Signals

  • Access anomalies, delayed patches, audit findings.

Each sticky note is tagged with one category. Over time, you’ll see clusters: “We’ve had three performance-related signals in the last two weeks—all in the same service.” That’s your storm front.

Storyboarding: From Signal to Story

Beside the taxonomy, create a simple storyboard to structure conversations:

1. Once upon a time…
   What is normal behavior or baseline reliability?

2. But then…
   What weak signal did we observe, and where?

3. Which meant…
   What potential risk or impact could this imply if it continues?

4. So we decided…
   What action are we taking (if any) and how will we know if it worked?

This storyboard helps teams move from “vibes” to explicit risk narratives that can be documented, prioritized, and tracked.

Connecting the Weather Station to SLOs

A weather station without instruments is just pretty art. Your instruments are your SLOs (Service Level Objectives).

SLOs are measurable reliability targets—for example:

• “99.9% of successful checkout requests in a rolling 30‑day window.”
• “95% of search queries respond under 300 ms over 7 days.”

These objectives guide decisions about:

• When to slow or stop feature releases
• When to prioritize reliability work over new functionality
• Whether a weak signal is just noise, or evidence of a trend

In your daily weather check, ask:

• Are any weak signals aligned with SLOs that are close to or breaching their error budgets?
• Do we need a new SLO because the weak signal points to a gap in what we measure?

This bridges gut feeling with quantitative risk management. A cloudy barometer plus shrinking error budget is a strong hint that a storm is coming.

Runbooks: What to Do When the Storm Hits

Recognizing a storm is only half the story. You also need a plan.

Runbooks are predefined, actionable steps for investigating and mitigating specific failure modes. Good runbooks:

• Reduce confusion during incidents
• Shorten mean time to recovery (MTTR)
• Help less experienced engineers contribute effectively

As your weather station surfaces recurring weak signals (“Kafka lag creeping up again”), you can:

• Create or refine runbooks for those areas
• Add “early action” steps—what to do before an SLO page fires

For example:

If search latency in EU increases by 20% for > 15 minutes without a corresponding traffic spike, follow the “Search Latency Early Investigation” runbook.

The analog weather station tells you where to invest in runbooks; the runbooks tell you what to do when the forecast looks bad.

Post‑Mortems: Turning Storms Into Better Forecasts

No matter how good your forecasting gets, incidents will still happen. The question is: do they teach you anything?

Post‑mortems (or incident reviews) transform outages into learning opportunities by asking:

• What happened, and why?
• Which signals did we miss, ignore, or not even measure?
• Where did our runbooks or SLOs help—or fail us?

The outputs should feed directly back into your weather station:

• New signals to watch for next time
• Refined taxonomies (maybe you need a “third‑party dependency” category)
• Improved SLOs that better reflect user impact
• Updated runbooks with steps learned from the incident

Over time, your station stops being just a barometer; it becomes a climate record of your system’s reliability journey.

Making It Cultural, Not Just Procedural

The real power of the Analog Incident Weather Station is cultural. It:

• Normalizes talking about risk and uncertainty openly
• Encourages engineers to surface concerns without needing a hard metric first
• Shifts the mindset from reactive firefighting to proactive risk management

To make it stick:

• Tie the ritual to existing ceremonies (stand‑up, shift handover).
• Rotate who plays “weather reporter” to share ownership.
• Celebrate when storms are avoided thanks to early detection.

When leadership cares about the weather station updates as much as a new feature demo, you know reliability has become a first‑class concern.

Conclusion

You don’t need a new monitoring vendor to forecast reliability. You need a way to:

• Spot weak signals early
• Give them shared language and visibility
• Connect them to SLOs, runbooks, and post‑mortems

An Analog Incident Weather Station—a literal, daily “paper barometer” for operational health—does exactly that. It turns scattered gut feelings into structured, actionable intelligence.

Over time, your team will find that the biggest incidents no longer arrive out of a clear blue sky. The clouds were forming. The pressure was dropping. The barometer—if you had one—was already warning you.

So put up the board. Grab the sticky notes. Start reading the weather of your systems—before the storm hits.