Meteo identification
RINEXCLI -i and related options demonstrations.
Meteo Data is very similar to Observation Data, we expect a similar behavior.
When identification meteo data, you want to know:
- the physics being measured (temperature, pressure..)
- the time frame of the observations
- ideally: what sensors were in use
- ideally: the location of the sensors
-
rinex-cli(latest) - local git repo
We'll run the identification process on POTS00DEU hosted in this repo.
The following applies as long as at least one Meteo file is present in the pool.
rinex-cli -f ./test_resources/MET/V3/POTS00DEU_R_20232540000_01D_05M_MM.rnx.gz -i -a
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%% Meteo Data %%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
["POTS00DEU_R_20232540000_01D_05M_MM.rnx.gz"]
EpochReport {
first: "2023-09-11T00:00:00 UTC",
last: "2023-09-11T23:55:00 UTC",
system: "UTC",
sampling: {
"dt=300s": 287,
},
}
Observables: ["PR", "TD", "HR"]
TD sensor:
model: "Vaisala"
type: "HMP45A-P"
PR sensor:
model: "Vaisala"
type: "PTU200"
altitude above sea: 132.8177m
HR sensor:
model: "Vaisala"
type: "HMP45A-P"The time frame tells you that the observations are expressed in UTC as all METEO RINEX.
The sampling of 6 observations per hour is strictly steady, no gaps were detected.
We only have three types of observations: Temperature, Pressure and Moisture.
Some information about the sensors are given. The altitude above sea level of the sensor is given.
This is interesting to know for atmospheric models.
