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Daily Aggregation

Use to_daily when you want one row per civil day with standard daily weather summaries. This is the right tool when the model expects daily weather inputs and the source data is sub-daily.

Build A Small Hourly Series

using PlantMeteo
using Dates
using Statistics

base = DateTime(2025, 7, 1)
hourly = Weather(map(base:Hour(1):(base + Hour(47))) do t
    h = Dates.hour(t)
    Atmosphere(
        date = t,
        duration = Hour(1),
        T = 18.0 + 6.0 * sin(2pi * h / 24) + (Dates.day(t) - 1),
        Wind = 1.5,
        Rh = 0.60 - 0.10 * sin(2pi * h / 24),
        P = 101.3,
        Precipitations = h in (5, 6) ? 0.6 : 0.0,
        Ri_SW_f = h in 6:18 ? 700.0 * sin(pi * (h - 6) / 12) : 0.0
    )
end)
hourly[1:6]
TimeStepTable{Atmosphere{(:date, :duration,...}(6 x 22):
date duration T Wind P Rh Precipitations Cₐ e eₛ VPD ρ λ γ ε Δ clearness Ri_SW_f Ri_PAR_f Ri_NIR_f Ri_TIR_f Ri_custom_f
DateTime Hour Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64
1 2025-07-01T00:00:00 1 hour 18.0 1.5 101.3 0.6 0.0 400.0 1.24297 2.07162 0.828646 1.21205 2.45843e6 0.0671074 0.568495 0.130635 Inf 0.0 Inf Inf Inf Inf
2 2025-07-01T01:00:00 1 hour 19.5529 1.5 101.3 0.574118 0.0 400.0 1.3106 2.2828 0.972203 1.20562 2.45476e6 0.0672078 0.572379 0.142236 Inf 0.0 Inf Inf Inf Inf
3 2025-07-01T02:00:00 1 hour 21.0 1.5 101.3 0.55 0.0 400.0 1.37299 2.49634 1.12335 1.19969 2.45134e6 0.0673017 0.575789 0.153823 Inf 0.0 Inf Inf Inf Inf
4 2025-07-01T03:00:00 1 hour 22.2426 1.5 101.3 0.529289 0.0 400.0 1.42562 2.69347 1.26784 1.19465 2.4484e6 0.0673825 0.578543 0.164402 Inf 0.0 Inf Inf Inf Inf
5 2025-07-01T04:00:00 1 hour 23.1962 1.5 101.3 0.513397 0.0 400.0 1.46515 2.85384 1.38868 1.1908 2.44614e6 0.0674446 0.580541 0.172931 Inf 0.0 Inf Inf Inf Inf
6 2025-07-01T05:00:00 1 hour 23.7956 1.5 101.3 0.503407 0.6 400.0 1.48952 2.95887 1.46935 1.1884 2.44472e6 0.0674837 0.581742 0.178483 Inf 0.0 Inf Inf Inf Inf

Aggregate To One Row Per Day

daily = to_daily(hourly)
TimeStepTable{Atmosphere{(:date, :duration,...}(2 x 26):
date duration T Wind P Rh Precipitations Cₐ e eₛ VPD ρ λ γ ε Δ clearness Ri_SW_f Ri_PAR_f Ri_NIR_f Ri_TIR_f Ri_custom_f year dayofyear Tmin Tmax
Date Day Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Int64 Int64 Float64 Float64
1 2025-07-01 1 day 18.0 1.5 101.3 0.6 1.2 400.0 1.24239 2.13655 0.894158 1.21231 2.45843e6 0.0671086 0.567683 0.133681 Inf 19.1413 Inf Inf Inf Inf 2025 182 12.0 24.0
2 2025-07-02 1 day 19.0 1.5 101.3 0.6 1.2 400.0 1.32238 2.27357 0.951192 1.20816 2.45606e6 0.0671732 0.572507 0.141172 Inf 19.1413 Inf Inf Inf Inf 2025 183 13.0 25.0 
daily
TimeStepTable{Atmosphere{(:date, :duration,...}(2 x 26):
date duration T Wind P Rh Precipitations Cₐ e eₛ VPD ρ λ γ ε Δ clearness Ri_SW_f Ri_PAR_f Ri_NIR_f Ri_TIR_f Ri_custom_f year dayofyear Tmin Tmax
Date Day Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Int64 Int64 Float64 Float64
1 2025-07-01 1 day 18.0 1.5 101.3 0.6 1.2 400.0 1.24239 2.13655 0.894158 1.21231 2.45843e6 0.0671086 0.567683 0.133681 Inf 19.1413 Inf Inf Inf Inf 2025 182 12.0 24.0
2 2025-07-02 1 day 19.0 1.5 101.3 0.6 1.2 400.0 1.32238 2.27357 0.951192 1.20816 2.45606e6 0.0671732 0.572507 0.141172 Inf 19.1413 Inf Inf Inf Inf 2025 183 13.0 25.0

The output has one row per day and includes standard daily summaries such as mean temperature, minimum temperature, maximum temperature, summed precipitation, and integrated radiation.

daily[1].Tmin
12.0
daily[1].Tmax
24.0
daily[1].T
18.0
daily[1].Precipitations
1.2
daily[1].Ri_SW_f
19.14130036406738

Add Or Override Daily Transformations

You can request additional daily variables or override a default aggregation:

daily_custom = to_daily(
    hourly,
    :T => mean => :Tmean,
    :T => maximum => :Tpeak
)
TimeStepTable{Atmosphere{(:date, :duration,...}(2 x 28):
date duration T Wind P Rh Precipitations Cₐ e eₛ VPD ρ λ γ ε Δ clearness Ri_SW_f Ri_PAR_f Ri_NIR_f Ri_TIR_f Ri_custom_f year dayofyear Tmin Tmax Tmean Tpeak
Date Day Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Int64 Int64 Float64 Float64 Float64 Float64
1 2025-07-01 1 day 18.0 1.5 101.3 0.6 1.2 400.0 1.24239 2.13655 0.894158 1.21231 2.45843e6 0.0671086 0.567683 0.133681 Inf 19.1413 Inf Inf Inf Inf 2025 182 12.0 24.0 18.0 24.0
2 2025-07-02 1 day 19.0 1.5 101.3 0.6 1.2 400.0 1.32238 2.27357 0.951192 1.20816 2.45606e6 0.0671732 0.572507 0.141172 Inf 19.1413 Inf Inf Inf Inf 2025 183 13.0 25.0 19.0 25.0 
daily_custom[1].Tmean
18.0
daily_custom[1].Tpeak
24.0

When to_daily Is The Right Tool

Use to_daily when:

  • the source data is sub-daily
  • the model wants one row per day
  • standard daily summaries are enough

Use Weather Sampling instead when you need rolling windows, calendar windows other than simple daily reduction, custom reducers, or cached repeated queries during simulation.