Deployement planning and measurement load
thanks
rachel
Hi there
Here are a couple of postings that may be of help:
Measurement load and data precision
System integration and telemetry
The advantage of using longer averaging interval and lower measurement load is that you get a better statistical averages. If your average time is too short, the estimate of the mean currents could be dominated by short term effects like surface waves.
Best regards, Atle Lohrmann
Atle, hi.
expanding the example cited (CS=1m, AI = 600s @ 25% load): to achieve the same predicted horizontal precision (2cm/s) with an increase to 100% load implies an AI=150s - is there really likely to be much wave bias in the current velocity estimates over 2.5 minutes of sampling?
Have you examples of continuous data (i.e. PI=AI=1s at 100% load) collected under a realistic range of coastal wavelengths and averaged in post-processing that would be able to illuminate what represents a reasonable minimum AI?
Importantly, increasing the load results in a significant reduction in battery consumption (from 157% to111% in example above), which is often the primary constraint for a given deployment (is this due to the instrument being asleep for a longer proportion of the time?)
best wishes, Jeremy
Dear Jeremy
a) Yes, you can save power by increasing the measurement load while keeping the number of pings constant. If we ping once a second for 100 ms, we will spend more power during the other 900 ms than we do in sleep mode.
b) The question about averaging time and waves can only be resolved for a given wave period. In protected areas with 3-4 s waves, AI of 60 s is fine. On the California coast line, with 10+ s waves coming in from the south seas, 60 s is not enough.
With an averaging interval of 2.5 minutes, all influence from surface waves will normally be gone. A quick Matlab simulation would proably be the best way to quantify the issue.
Best regards, Atle Lohrmann

