Does Weather Actually Affect Your Mood? Track It for a Month and Find Out

“The weather affects my mood.” Almost everyone says it. Sunny days feel better, a grey week drags, a long stretch of rain pulls something down. It is one of the most widely held folk-beliefs about how a person works, and one of the least often actually checked.

This article is about checking. How to track weather alongside mood for four to six weeks, what patterns most people find, and how to read the result honestly. If you are new to mood as a tracking field, our piece on mood tracking without the pressure is a calmer place to start. If you find a pattern and want to read it well, the correlation vs causation guide covers exactly the traps you will run into.

A line about Seasonal Affective Disorder, then we move on

Weather, daylight, and mood are tangled up with Seasonal Affective Disorder, which is a clinical condition. Loggr is not a diagnostic tool, this article is not medical guidance, and the practice below is about ordinary, everyday weather sensitivity in non-clinical ranges. If you suspect a seasonal mood pattern is interfering with your life, the right next step is a clinician. Tracked data can be useful in that conversation, but it is not a self-diagnosis instrument, and we leave the topic alone for the rest of the piece.

With that established: the practical question is whether ordinary weather is moving your ordinary mood. Most people have an opinion. Few have the data.

What “weather” actually means as a tracking field

The temptation is to track weather precisely. Temperature to one decimal. Humidity. Atmospheric pressure. Cloud cover in eighths. UV index. There are APIs for all of it.

Skip that. For a four to six week experiment, the right field is categorical and short.

Pick three to five categories you can identify in two seconds from the kitchen window, without opening an app. Something like:

sunny
partly cloudy
overcast
rainy
snowy or stormy

The exact list should fit your climate. Someone in coastal Portugal does not need a snowy option. Keep the categories distinct enough that you do not hesitate at logging time. Hesitation kills the practice.

Categorical works better than precise weather data here for two reasons. First, you can actually log it on busy days. A picker tap is honest in a way that “did I remember to check the API?” is not. Second, what affects your mood is your perceived weather. The number a weather station recorded eight kilometres away is less relevant than the sky you actually looked at.

If you live somewhere with big temperature swings and you genuinely will note temperature daily, add a number field for it. Otherwise leave it out.

The mood pairing

Add a 1-to-7 scale field for mood, logged at the end of the day. Seven is wide enough to capture real differences, narrow enough to stay anchored. If you already log mood on a different scale, keep it. Switching scales mid-experiment is the worst thing you can do, and we covered the reasoning in the mood-tracking piece.

Log both fields once a day, in the evening, in under ten seconds. Weather: tap the category that best describes the day overall. Mood: tap the number. That is the whole daily ritual. It has to stay light enough to actually do for a month.

What patterns people typically find

There is no single answer to “does weather affect mood.” The honest summary, after watching a few hundred people run this experiment, is that the population splits into roughly three groups, and you do not know which one you are in until you have the data.

Some people see a clear correlation

For roughly a meaningful minority, sunny days really do average a higher mood than rainy or overcast ones, and the gap is readable. In our most-revealing pairs guide, weather and mood is included precisely because some users discover a clean effect they had assumed was there but had never quantified.

When the effect is real, the typical size is 0.5 to 1 point on a 1-to-7 mood scale. That is not life-changing on a single day, but over a week of overcast weather it adds up. People in this group often find the more useful insight is not “sunny is better” but “extended grey stretches are worse.” A single rainy Tuesday is fine. Six grey days in a row is a different kind of week.

Some people see no correlation at all

This is also common. Mood, for these people, is being driven by other things: sleep, social plans, workload, deadlines, the rhythm of the calendar. Weather is in the data but lost in the noise.

If you fall in this group, that is a real finding too. It means the next time you catch yourself blaming a low day on the rain, you can quietly check yourself. It was probably not the rain.

Some people see an inverse correlation, or a more complicated one

A surprising subset. They look at the data and find their best focus days, or their highest mood days, were actually overcast or rainy. The story is usually some combination of less fear of missing out on outdoor plans when it is raining, less pressure to “make the most of the weekend,” and a quieter atmospheric feeling that suits indoor work. Or the opposite: a heatwave is genuinely uncomfortable, and the “sunny is best” assumption breaks above 28 degrees Celsius.

The point is that you cannot guess in advance which group you are in. People who are sure they are weather-sensitive sometimes have flat data. People who are sure they are weather-resilient sometimes find a clean effect.

What the data CAN tell you

Used carefully, four to six weeks of weather plus mood data can answer a few real questions.

Whether you are weather-sensitive at all

This is the headline question. Most people have an opinion. The data has the actual answer, for your life, in this period. That is enough to update how you think about your own days.

How big the effect is relative to other inputs

Sleep almost always wins. If you log sleep alongside mood, the sleep-to-mood relationship will, for most people, be larger than the weather-to-mood one. It is easy to blame the weather when the actual driver was a five-hour night.

Which specific weather conditions move you

The patterns are sometimes more specific than “sunny good, rainy bad.” Some people only react to extended overcast stretches, not single rainy days. Some are fine with cold and grey but visibly worse on warm and humid. The categorical field captures these distinctions if you look at per-category averages, not just an overall correlation.

Whether the effect changes across seasons

If you keep logging for longer, weather sensitivity can shift across the year. The same overcast day in March and in November feels different. Six months of data, both fields logged consistently, lets you see whether your sensitivity is constant or seasonal.

What the data CANNOT tell you

This is where the correlation vs causation piece earns its keep. Even a clean pattern in your data has limits.

It cannot tell you the weather caused the mood

Weather travels with a lot of other things. Winter has shorter days, more illness, fewer social plans, less time outdoors, a different rhythm at work. Summer has more daylight, vacations, more movement, different food. If your mood data shows a winter dip, the weather is one suspect, but daylight, social rhythms, and physical activity are also suspects. The correlation does not separate them.

It cannot tell you a different climate would fix anything

The fantasy of moving somewhere sunny to feel better is older than tracking. People who move to “perfect weather” cities often discover that their mood baseline is roughly the same after the novelty wears off, because the other inputs (relationships, work, sleep, purpose) followed them. A sunny vacation is a different thing from a sunny life.

It cannot tell you a sunny vacation will repair a multi-week low

If you have been low for a month, a sunny week somewhere warm might feel better for the week. It is unlikely to reverse the pattern. Mood patterns of that length usually have inputs the weather is not.

The pair design

For the cleanest four week experiment, log:

Weather (categorical, 3 to 5 options)
Mood (scale 1 to 7)

For a slightly richer setup that disentangles weather from its usual travelling companions, add:

Sleep hours (number). This is the workhorse. Most apparent weather effects partly route through sleep (a storm wakes you up, a heatwave makes the bedroom too warm, a grey day means a longer lie-in). Logging sleep lets you ask: was the rain dragging my mood, or did I sleep badly because of the storm?
Exercised today (yes or no). On rainy days you may stay indoors more. The mood dip might be the missing walk, not the rain itself. The yes-or-no boolean is enough.

That is four fields, fifteen to twenty seconds a day. The pair design comes from the most-revealing pairs piece, and the principle is the same here: one effect, one or two inputs, and one or two controls.

How long until a pattern emerges

At least four to six weeks, ideally spanning multiple weather types. A single rainy day tells you nothing. The minimum useful read is when you have at least ten days in each of your most common weather categories.

If your local weather is unusually monotonous during your experiment window, extend it. The fields cannot pair if there is no variation in one of them.

Loggr compares your weather and mood fields automatically once there is enough data. Below the credible-sample threshold the insight is shown as locked, with a brief note on what is needed. A weather effect read off four data points is not a real read.

FAQ

Do I need a weather API for this?

No. The categorical eyeball field is fine and arguably more honest, because your perceived weather is what affects your mood. The number a weather station recorded across the city is less relevant than the sky you actually saw.

What about pressure or humidity?

Too granular for a four to six week experiment. The categorical field captures most of the signal that everyday weather has on everyday mood, and adding precise atmospheric variables tends to add more logging burden than insight. If you have a specific hypothesis (you suspect you are pressure-sensitive, for instance), you could add a single pressure number from a weather app daily, but most people will not stick with it.

Should I move to a sunnier place if my data shows weather sensitivity?

Life decisions are bigger than data. The data is one input among many. A weather correlation in your logs is real, but it is also bounded to your life as currently lived. People who move for weather often discover that the inputs they did not move (work, relationships, sleep, purpose) still drive most of their mood. The data is useful for self-knowledge, not for relocation decisions on its own.

Can I track weather retroactively?

Yes, if you remember. Weather services keep historical data, so you can fill in the categorical for the past two weeks if you trust your memory of what the sky was doing. Going further back gets unreliable. The cleaner play is to start logging today and accept that the experiment begins now.

What if my mood depends on the weather forecast, not the actual weather?

That is a real thing for some people, and worth noting separately. If you find yourself in a low mood on a sunny day after seeing a stormy forecast, log the day’s actual weather. The forecast effect is small for most people, and a categorical “what the sky was actually like” is the more durable measurement.

Key takeaways

“The weather affects my mood” is a folk-belief most people hold but few have measured. The measurement is cheap.
Use a categorical weather field with 3 to 5 options you can identify in two seconds from a window. Skip the API.
Pair it with a 1 to 7 mood scale, logged at end of day. Add sleep hours and a yes-or-no exercise field if you want to disentangle weather from its usual travelling companions.
Log honestly for four to six weeks, ideally across multiple weather types.
People split into roughly three groups: a clear positive correlation, no correlation, or an inverse or complicated one. You do not know which group you are in until you check.
The typical effect size for weather-sensitive people is 0.5 to 1 point on a 1-to-7 mood scale. Real, but smaller than sleep usually is.
The data can tell you whether you are weather-sensitive and how big the effect is. It cannot tell you that the weather caused the mood, or that a different climate would fix anything.

Try this for four weeks

Open Loggr tonight. Add a categorical weather field with five options that fit your local climate. Add a mood scale from 1 to 7 if you do not have one already. Log both at the end of every day for four weeks. Add sleep hours alongside if you want a richer read.

At the end of the four weeks, open Loggr and look at the per-category mood averages. The picture will probably surprise you in some direction. If a pattern is there, you will see it. If it is not, that is also worth knowing. Either way you will have replaced an assumption with a small piece of honest self-knowledge, which is the entire point of the practice.