Saastamoinen tropospheric delay, in brief
A Saastamoinen-style model estimates the neutral-atmosphere signal delay using meteorological quantities and a mapping from zenith to the satellite direction. Pressure, temperature, water-vapour pressure, height and angular units need explicit conventions. The model does not eliminate all atmospheric uncertainty.
Learning objectives
- Separate hydrostatic and wet delay components.
- Estimate pressure, temperature, and water vapor pressure.
- Map zenith delay to the line of sight.
Delay structure
The neutral atmosphere delays GNSS code and phase measurements. Saastamoinen separates the delay into a stable hydrostatic part and a more variable wet part.
In the zenith direction, the total is usually around 2 to 2.5 m. At lower elevation angles, mapping to the slant path can multiply the delay.
Example inputs
| Parameter | Value |
|---|---|
| Station height | 1200 m |
| Latitude | 35.7 deg = 0.623 rad |
| Relative humidity | 0.40 |
| Satellite elevation | 30 deg |
Step 1
Standard atmosphere variables
When local meteorological observations are unavailable, estimate pressure, temperature, and water vapor pressure from height and humidity.
Step 2
Zenith hydrostatic delay
The hydrostatic term dominates the total delay and depends mainly on surface pressure.
Step 3
Zenith wet delay
The wet term is smaller here because the example uses relatively dry conditions.
Step 4
Map to the satellite line of sight
With a 30 degree elevation, the zenith angle is 60 degrees and the simple mapping factor is 1/cos z = 2.
Typical ranges
| Condition | Typical delay |
|---|---|
| Zenith | 2 to 2.5 m |
| Elevation 30 deg | 4 to 5 m |
| Elevation below 15 deg | 10 to 20 m |
| Wet component | 3 to 20 cm |
Result
Computed result
4.1312 m
For this Tehran example and 30 degree satellite elevation, the Saastamoinen slant delay is about 4.13 m.
What you should understand now
- The hydrostatic delay is large and predictable.
- The wet delay is smaller but can be more variable.
- Elevation angle can dominate the slant delay through the mapping function.
Related tutorials
Corrections
Klobuchar Ionospheric Correction
A numerical Klobuchar walkthrough from receiver/satellite geometry to L1 delay in meters.
Positioning algorithms
SPP from Scratch
Compute GPS single point positioning from RINEX observations: transmission time, satellite clocks, broadcast orbits, Sagnac and atmospheric corrections, iterative WLS, residuals and covariance.
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Point out numerical ambiguity, missing prerequisites, or a step that needs a fuller derivation.