Why the rain 'spikes'?

Ξ

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Sep 20, 2023, 2:29:55 PM9/20/23

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Hello,

What do you think is the cause for the database adding values for rain right after what I'm pretty sure are temp power cuts?

If you look at the graphs you see there's an interruption before 21:00

In the db the data looks like this:

dateTime rainRate rain
---------- ---------- ----------
2023-09-04 00:15:00 0.0 0.0
2023-09-04 00:30:00 0.0 0.0
2023-09-04 00:45:00 0.0 0.0
2023-09-04 01:00:00 0.0 0.0
2023-09-04 01:15:00 0.0 0.0
2023-09-04 01:30:00 0.0 0.0
2023-09-04 01:45:00 0.0 0.0
2023-09-04 02:00:00 0.0 0.0
2023-09-04 02:15:00 0.0 0.0
2023-09-04 02:30:00 0.0 0.0
2023-09-04 02:45:00 0.21052631 0.4
2023-09-04 03:00:00 0.39999999 0.0
2023-09-04 03:15:00 0.39999999 0.0
2023-09-04 03:30:00 0.39999999 0.0
2023-09-04 03:45:00 0.15999999 0.0
2023-09-04 04:00:00 0.34210526 0.4
2023-09-04 04:15:00 0.39999999 0.0
2023-09-04 04:30:00 0.39999999 0.0
2023-09-04 04:45:00 0.39999999 0.0
2023-09-04 05:00:00 0.05789473 0.0
2023-09-04 05:15:00 0.0 0.0
2023-09-04 05:30:00 0.0 0.0
2023-09-04 05:45:00 0.0 0.0
2023-09-04 06:00:00 0.0 0.0
2023-09-04 06:15:00 0.0 0.0
2023-09-04 06:30:00 0.0 0.0
2023-09-04 06:45:00 0.0 0.0
2023-09-04 07:00:00 0.0 0.0
2023-09-04 07:15:00 0.0 0.0
2023-09-04 07:30:00 0.0 0.0
2023-09-04 07:45:00 0.0 0.0
2023-09-04 08:00:00 0.0 0.0
2023-09-04 08:15:00 0.0 0.0
2023-09-04 08:30:00 0.0 0.0
2023-09-04 08:45:00 0.0 0.0
2023-09-04 09:00:00 0.0 0.0
2023-09-04 09:15:00 0.0 0.0
2023-09-04 09:30:00 0.0 0.0
2023-09-04 09:45:00 0.0 0.0
2023-09-04 10:00:00 0.0 0.0
2023-09-04 10:15:00 0.0 0.0
2023-09-04 10:30:00 0.0 0.0
2023-09-04 10:45:00 0.0 0.0
2023-09-04 11:00:00 0.0 0.0
2023-09-04 11:15:00 0.0 0.0
2023-09-04 11:30:00 0.0 0.0
2023-09-04 11:45:00 0.0 0.0
2023-09-04 12:00:00 0.0 0.0
2023-09-04 12:15:00 0.0 0.0
2023-09-04 12:30:00 0.0 0.0
2023-09-04 12:45:00 0.0 0.0
2023-09-04 13:00:00 0.0 0.0
2023-09-04 13:15:00 0.0 0.0
2023-09-04 13:30:00 0.41621621 0.8
2023-09-04 13:45:00 1.03999999 0.8
2023-09-04 14:00:00 1.73157894 0.4
2023-09-04 14:15:00 2.52631578 1.2
2023-09-04 14:30:00 2.98666666 0.8
2023-09-04 14:45:00 3.14736841 0.4
2023-09-04 15:00:00 2.93157894 0.4
2023-09-04 15:15:00 2.32631578 0.4
2023-09-04 15:30:00 1.79199999 0.0
2023-09-04 15:45:00 1.30526315 0.4
2023-09-04 16:00:00 1.06842105 0.4
2023-09-04 16:15:00 1.33157894 0.4
2023-09-04 16:30:00 1.56842104 0.8
2023-09-04 16:45:00 1.76315789 0.4
2023-09-04 17:00:00 1.86666666 0.0
2023-09-04 17:15:00 1.49473683 0.4
2023-09-04 17:30:00 1.41621621 0.39999999
2023-09-04 17:45:00 1.51578947 0.4
2023-09-04 18:00:00 1.54133333 0.4
2023-09-04 18:15:00 1.88799999 0.80000000
2023-09-04 18:30:00 1.81333332 0.0
2023-09-04 18:45:00 1.57297296 0.39999999
2023-09-04 19:00:00 1.25789473 0.0
2023-09-04 19:15:00 0.83157894 0.0
2023-09-04 19:30:00 0.39999999 0.0
2023-09-04 19:45:00 0.05866666 0.0
2023-09-04 20:15:00 0.0 11.2
2023-09-04 20:30:00 0.0 0.0
2023-09-04 20:45:00 0.0 0.0
2023-09-04 21:00:00 0.0 0.0
2023-09-04 21:15:00 0.0 0.0
2023-09-04 21:30:00 0.0 0.0
2023-09-04 21:45:00 0.0 0.0
2023-09-04 22:00:00 0.0 0.0
2023-09-04 22:15:00 0.0 0.0
2023-09-04 22:30:00 0.55135135 0.80000000
2023-09-04 22:45:00 0.87466666 0.80000000
2023-09-04 23:00:00 1.80540540 0.39999999
2023-09-04 23:15:00 1.99999999 0.0
2023-09-04 23:30:00 1.44736841 0.0
2023-09-04 23:45:00 1.53599999 0.80000000

This totals to 25.6 mm, but on Wunderground and the other sites where my station uploads weather data it's 13.97 mm: https://www.wunderground.com/dashboard/pws/I5302/graph/2023-09-4/2023-09-4/daily

If I remove the 11.2 value from 20:15 (after power was restored) the total drops to more or less what's on Wunderground and it's a lot more realistic.

Regards,

Ivo

Ξ

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Sep 20, 2023, 2:36:14 PM9/20/23

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Same story for the 16th of the month:

Wunderground shows total of 9mm of rain: https://www.wunderground.com/dashboard/pws/I5302/table/2023-09-16/2023-09-16/daily

dateTime rainRate rain
-------------------- ---------------- ----------
2023-09-16 03:15:00 3.91466665868075 0.4
2023-09-16 03:30:00 3.89473683416 0.0
2023-09-16 03:45:00 1.568421049432 0.0
2023-09-16 04:00:00 0.77368420894800 0.4
2023-09-16 04:30:00 0.399999999184 4.4
2023-09-16 04:45:00 0.399999999184 0.0
2023-09-16 05:15:00 0.0 4.4
2023-09-16 05:30:00 0.0 0.0
2023-09-16 05:45:00 0.0 0.0
2023-09-16 06:15:00 0.0 4.4
2023-09-16 06:30:00 0.0 0.0
2023-09-16 06:45:00 0.0 0.0
2023-09-16 07:00:00 0.0 0.0
2023-09-16 07:15:00 0.1315789471 0.4
2023-09-16 07:30:00 0.689473682804 0.4
2023-09-16 07:45:00 1.11351351124194 0.4
2023-09-16 08:00:00 1.199999997552 0.0
2023-09-16 08:15:00 1.357894734072 0.4
2023-09-16 08:30:00 0.989473682192 0.4
2023-09-16 08:45:00 1.042105261032 0.4
2023-09-16 09:00:00 1.199999997552 0.0
2023-09-16 09:45:00 1.00952380746438 8.4
2023-09-16 10:45:00 1.64067796275471 9.2
2023-09-16 11:00:00 0.42105263072000 0.0
2023-09-16 11:30:00 0.0 9.2
2023-09-16 11:45:00 0.0 0.0
2023-09-16 12:00:00 0.0 0.0

2023-09-16 12:15:00 0.0 0.0

2023-09-16 12:30:00 0.0 0.0
2023-09-16 12:45:00 0.0 0.0
2023-09-16 13:00:00 0.0 0.0
2023-09-16 13:15:00 0.0 0.0
2023-09-16 13:30:00 0.0 0.0
2023-09-16 13:45:00 0.0 0.0
2023-09-16 14:00:00 0.0 0.0
2023-09-16 14:15:00 0.0 0.0
2023-09-16 14:30:00 0.0 0.0
2023-09-16 14:45:00 0.0 0.0
2023-09-16 15:00:00 0.0 0.0
2023-09-16 15:15:00 0.0 0.0
2023-09-16 15:30:00 0.0 0.0
2023-09-16 15:45:00 0.0 0.0
2023-09-16 16:00:00 0.0 0.0
2023-09-16 16:15:00 0.0 0.0
2023-09-16 16:30:00 0.0 0.0
2023-09-16 16:45:00 0.0 0.0
2023-09-16 17:00:00 0.0 0.0
2023-09-16 17:15:00 0.0 0.0
2023-09-16 17:30:00 0.0 0.0
2023-09-16 17:45:00 0.0 0.0
2023-09-16 18:00:00 0.0 0.0
2023-09-16 18:15:00 0.0 0.0
2023-09-16 18:30:00 0.0 0.0
2023-09-16 18:45:00 0.0 0.0
2023-09-16 19:00:00 0.0 0.0
2023-09-16 19:15:00 0.0 0.0
2023-09-16 19:30:00 0.0 0.0
2023-09-16 19:45:00 0.0 0.0
2023-09-16 20:15:00 0.0 9.2
2023-09-16 20:30:00 0.0 0.0
2023-09-16 20:45:00 0.0 0.0
2023-09-16 21:00:00 0.0 0.0

Those values with 9.2mm of rain are especially suspicious.

Tom Keffer

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Sep 20, 2023, 4:10:51 PM9/20/23

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Perhaps the problem is in your weather station or its driver...?

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Ξ

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Sep 20, 2023, 7:21:03 PM9/20/23

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If the wrong data is only in the db but not reported to the likes of PWS Weather, Wunderground etc is it possible to narrow it down to the station or the driver?!

Tom Keffer

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Sep 20, 2023, 11:45:20 PM9/20/23

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No. That's not enough clues to narrow it down.

But, you say the "spike" is in the database, but not being uploaded to PWS or Wunderground? Can you post your station ID for either/both?

On Wed, Sep 20, 2023 at 12:21 PM Ξ <fcky...@gmail.com> wrote:

If the wrong data is only in the db but not reported to the likes of PWS Weather, Wunderground etc is it possible to narrow it down to the station or the driver?!

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Ξ

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Jan 7, 2024, 2:32:09 AM1/7/24

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Hello Tom,

I've posted above a link to the exact date I was comparing: https://www.wunderground.com/dashboard/pws/I5302/table/2023-09-16/2023-09-16/daily

You can see there on September 16, 2023 the total precipitation is 9.14 mm whereas WeeWx is showing "Today's Rain: 59,2mm"

Here's also the PWS link for the same date: https://www.pwsweather.com/station/pws/dolniokol?timespan=day&date=2023-09-16

It's showing a total of 9.1mm

If we look for the month of December:

PWS shows total of 41.7mm https://www.pwsweather.com/station/pws/dolniokol?timespan=month&date=2023-12-01

WU shows a total of 41.67mm https://www.wunderground.com/dashboard/pws/I5302/graph/2023-12-7/2023-12-7/monthly

However if I look at the weewx report it's over 100mm:

MONTHLY CLIMATOLOGICAL SUMMARY for Dec 2023
NAME: Dolni Okol
ELEV: 1010 meters LAT: 42-29.40 N LONG: 023-29.70 E

TEMPERATURE (C), RAIN (mm), WIND SPEED (m/s)

HEAT COOL AVG
MEAN DEG DEG WIND DOM
DAY TEMP HIGH TIME LOW TIME DAYS DAYS RAIN SPEED HIGH TIME DIR
----------------------------------------------------------------------------------------
01 9.4 14.7 13:30 -1.1 00:18 8.6 0.0 0.0 0.6 6.6 11:58 326
02 7.6 17.5 15:23 2.7 20:58 10.4 0.0 0.0 0.1 3.3 14:38 325
03 4.9 12.0 00:26 -2.0 23:56 13.1 0.0 13.2 0.1 3.3 13:02 33
04 -2.3 11.7 15:39 -6.3 07:48 20.3 0.0 0.0 0.0 2.3 03:53 117
05 -0.8 1.4 14:51 -2.5 07:23 18.8 0.0 0.0 0.0 1.4 11:35 303
06 0.7 1.3 14:03 -0.1 00:00 17.3 0.0 0.4 0.0 0.6 02:03 307
07 0.3 3.7 11:02 -2.0 23:16 17.7 0.0 0.0 0.1 3.0 13:48 56
08 -2.3 -0.9 13:18 -3.8 22:07 20.3 0.0 0.0 0.0 0.0 00:00 90
09 -2.1 1.0 14:38 -4.4 05:04 20.1 0.0 0.0 0.0 0.9 14:42 161
10 -0.8 10.6 15:17 -4.0 05:44 18.8 0.0 4.0 0.0 2.3 13:02 316
11 0.3 7.2 14:56 -3.6 07:52 17.7 0.0 0.8 0.0 2.0 12:04 138
12 3.8 13.6 15:35 0.1 21:58 14.2 0.0 0.8 0.0 3.0 05:10 14
13 3.2 11.1 14:28 0.4 04:02 14.8 0.0 0.0 0.0 1.2 15:36 302
14 5.7 11.7 10:36 0.6 02:20 12.3 0.0 7.2 0.1 3.1 04:20 41
15 1.1 2.1 03:06 -0.7 23:56 16.9 0.0 42.0 0.0 2.8 05:17 56
16 -2.0 -0.5 13:54 -3.7 23:59 20.0 0.0 0.0 0.0 0.0 00:00 90
17 -5.7 0.5 13:58 -10.5 07:32 23.7 0.0 0.0 0.0 0.0 00:00 90
18 -3.0 9.9 15:34 -8.1 01:40 21.0 0.0 18.8 0.0 0.0 00:00 90
19 -0.9 12.1 15:20 -4.6 07:04 18.9 0.0 4.8 0.0 0.0 00:00 90
20 1.4 9.2 15:04 -5.2 06:31 16.6 0.0 5.2 0.1 2.6 16:48 315
21 1.0 10.6 15:13 -3.5 20:04 17.0 0.0 0.8 0.1 2.5 22:09 47
22 1.9 3.7 03:57 0.1 23:01 16.1 0.0 4.0 0.2 4.4 00:41 320
23 2.4 5.3 11:23 -0.5 17:43 15.6 0.0 0.8 0.3 4.5 21:58 348
24 6.4 13.5 15:33 3.1 06:08 11.6 0.0 0.0 0.2 4.2 16:06 337
25 8.7 17.6 15:33 2.6 23:45 9.3 0.0 0.0 0.2 3.2 14:08 331
26 6.8 16.6 15:18 -0.3 05:13 11.2 0.0 0.0 0.1 2.2 09:18 330
27 4.3 16.0 15:26 -1.4 23:57 13.7 0.0 0.0 0.0 2.8 01:21 51
28 2.6 18.6 15:55 -2.7 07:19 15.4 0.0 0.0 0.1 2.3 14:41 169
29 2.7 15.1 15:49 -2.0 05:40 15.3 0.0 0.0 0.0 2.0 13:05 162
30 1.9 8.7 13:26 -2.4 04:34 16.1 0.0 0.0 0.1 2.7 14:40 35
31 2.3 15.5 15:20 -1.2 08:30 15.7 0.0 0.0 0.0 1.8 11:30 177
----------------------------------------------------------------------------------------
2.0 18.6 28 -10.5 17 498.6 0.0 102.8 0.1 6.6 01 342

Even though I know nothing I have the gut feeling the WS6in1 driver for Bresser doesn't handle well buffered data or the station itself passes on buffered data wrongly (but in either case PWS and WU discard those values)?!

Also so sorry for the late reply here as well.

Thanks,

Ivo

Tom Keffer

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Jan 7, 2024, 3:32:31 PM1/7/24

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It's possible the errant values are in the daily summaries, but not the main archive table. The former is used for the NOAA reports, the latter for the WU posts.

I don't know your timezone, but I imagine something like this:

select dateTime, datetime(dateTime, 'unixepoch', 'localtime'), rain from archive where dateTime > 1694822400 and dateTime <= 1694908800;

select dateTime, datetime(dateTime, 'unixepoch', 'localtime'), sum from archive_day_rain where dateTime > 1694822400 and dateTime <= 1694908800;

-tk

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Ξ

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Jan 7, 2024, 9:11:22 PM1/7/24

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I can pull data for December (tomorrow), but in the the first two posts I've also posted excerpts from the db itself:

https://groups.google.com/g/weewx-user/c/rIbaSfEgpFs/m/_HcbPFtXAQAJ

2023-09-04 19:30:00 0.39999999 0.0
2023-09-04 19:45:00 0.05866666 0.0
2023-09-04 20:15:00 0.0 11.2

2023-09-04 20:30:00 0.0 0.0
2023-09-04 20:45:00 0.0 0.0

https://groups.google.com/g/weewx-user/c/rIbaSfEgpFs/m/sUmMTLNXAQAJ

2023-09-16 09:45:00 1.00952380746438 8.4
2023-09-16 10:45:00 1.64067796275471 9.2

2023-09-16 11:00:00 0.42105263072000 0.0

[....]

2023-09-16 19:30:00 0.0 0.0
2023-09-16 19:45:00 0.0 0.0
2023-09-16 20:15:00 0.0 9.2
2023-09-16 20:30:00 0.0 0.0

Please notice in the last one how there's a sudden 9.2 value after hours of no rain, in my view it's those bogus values that are discarded by WU/PWS but they stay in the db.

Tom Keffer

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Jan 7, 2024, 9:13:32 PM1/7/24

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Yes, I know you posted those, but we need to see the values in the daily summary for the same time periods.

To view this discussion on the web visit https://groups.google.com/d/msgid/weewx-user/7c4f63d8-6694-4b1b-9bc4-ddf0a2066001n%40googlegroups.com.

Ξ

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Jan 8, 2024, 6:23:29 PM1/8/24

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alright, I guess I misunderstood :)

Here's the output adjusted for the local time zone:

sqlite> select dateTime, datetime(dateTime, 'unixepoch', 'localtime'), rain from archive where dateTime > 1694811638 and dateTime <= 1694898038;

1694812500|2023-09-16 00:15:00|0.0
1694813400|2023-09-16 00:30:00|0.0
1694814300|2023-09-16 00:45:00|0.0
1694815200|2023-09-16 01:00:00|0.0
1694816100|2023-09-16 01:15:00|0.0
1694817000|2023-09-16 01:30:00|0.0
1694817900|2023-09-16 01:45:00|0.0
1694818800|2023-09-16 02:00:00|0.0
1694819700|2023-09-16 02:15:00|0.0
1694820600|2023-09-16 02:30:00|0.8
1694821500|2023-09-16 02:45:00|2.4
1694822400|2023-09-16 03:00:00|3.6
1694823300|2023-09-16 03:15:00|0.4
1694824200|2023-09-16 03:30:00|0.0
1694825100|2023-09-16 03:45:00|0.0
1694826000|2023-09-16 04:00:00|0.4
1694827800|2023-09-16 04:30:00|4.4
1694828700|2023-09-16 04:45:00|0.0
1694830500|2023-09-16 05:15:00|4.4
1694831400|2023-09-16 05:30:00|0.0
1694832300|2023-09-16 05:45:00|0.0
1694834100|2023-09-16 06:15:00|4.4
1694835000|2023-09-16 06:30:00|0.0
1694835900|2023-09-16 06:45:00|0.0
1694836800|2023-09-16 07:00:00|0.0
1694837700|2023-09-16 07:15:00|0.4
1694838600|2023-09-16 07:30:00|0.4
1694839500|2023-09-16 07:45:00|0.4
1694840400|2023-09-16 08:00:00|0.0
1694841300|2023-09-16 08:15:00|0.4
1694842200|2023-09-16 08:30:00|0.4
1694843100|2023-09-16 08:45:00|0.4
1694844000|2023-09-16 09:00:00|0.0
1694846700|2023-09-16 09:45:00|8.4
1694850300|2023-09-16 10:45:00|9.2
1694851200|2023-09-16 11:00:00|0.0
1694853000|2023-09-16 11:30:00|9.2
1694853900|2023-09-16 11:45:00|0.0
1694854800|2023-09-16 12:00:00|0.0
1694855700|2023-09-16 12:15:00|0.0
1694856600|2023-09-16 12:30:00|0.0
1694857500|2023-09-16 12:45:00|0.0
1694858400|2023-09-16 13:00:00|0.0
1694859300|2023-09-16 13:15:00|0.0
1694860200|2023-09-16 13:30:00|0.0
1694861100|2023-09-16 13:45:00|0.0
1694862000|2023-09-16 14:00:00|0.0
1694862900|2023-09-16 14:15:00|0.0
1694863800|2023-09-16 14:30:00|0.0
1694864700|2023-09-16 14:45:00|0.0
1694865600|2023-09-16 15:00:00|0.0
1694866500|2023-09-16 15:15:00|0.0
1694867400|2023-09-16 15:30:00|0.0
1694868300|2023-09-16 15:45:00|0.0
1694869200|2023-09-16 16:00:00|0.0
1694870100|2023-09-16 16:15:00|0.0
1694871000|2023-09-16 16:30:00|0.0
1694871900|2023-09-16 16:45:00|0.0
1694872800|2023-09-16 17:00:00|0.0
1694873700|2023-09-16 17:15:00|0.0
1694874600|2023-09-16 17:30:00|0.0
1694875500|2023-09-16 17:45:00|0.0
1694876400|2023-09-16 18:00:00|0.0
1694877300|2023-09-16 18:15:00|0.0
1694878200|2023-09-16 18:30:00|0.0
1694879100|2023-09-16 18:45:00|0.0
1694880000|2023-09-16 19:00:00|0.0
1694880900|2023-09-16 19:15:00|0.0
1694881800|2023-09-16 19:30:00|0.0
1694882700|2023-09-16 19:45:00|0.0
1694884500|2023-09-16 20:15:00|9.2
1694885400|2023-09-16 20:30:00|0.0
1694886300|2023-09-16 20:45:00|0.0
1694887200|2023-09-16 21:00:00|0.0
1694888100|2023-09-16 21:15:00|0.0
1694889000|2023-09-16 21:30:00|0.0
1694889900|2023-09-16 21:45:00|0.0
1694890800|2023-09-16 22:00:00|0.0
1694891700|2023-09-16 22:15:00|0.0
1694892600|2023-09-16 22:30:00|0.0
1694893500|2023-09-16 22:45:00|0.0
1694894400|2023-09-16 23:00:00|0.0
1694895300|2023-09-16 23:15:00|0.0
1694896200|2023-09-16 23:30:00|0.0
1694897100|2023-09-16 23:45:00|0.0
1694898000|2023-09-17 00:00:00|0.0
sqlite>

sqlite> select dateTime, datetime(dateTime, 'unixepoch', 'localtime'), sum from archive_day_rain where dateTime > 1694811638 and dateTime <= 1694898038;
1694898000|2023-09-17 00:00:00|0.8

sqlite> select dateTime, datetime(dateTime, 'unixepoch', 'localtime'), sum from archive_day_rain where dateTime > 1694822400 and dateTime <= 1694908800;
1694898000|2023-09-17 00:00:00|0.8

Tom Keffer

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Jan 8, 2024, 11:30:00 PM1/8/24

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Unfortunately, the daily summary total is for 2023-09-17. We need 2023-09-16.

Rather than mess with unix epoch times, here is a simpler way to do it:

select dateTime, date(dateTime,'unixepoch','localtime'), sum(rain) from archive where date(dateTime,'unixepoch','localtime')=='2023-09-16';

select dateTime, date(dateTime,'unixepoch','localtime') as dt, sum from archive_day_rain where dt=='2023-09-16';

The first query is not totally accurate because the record stamped 2023-09-16 00:00 is included, although it is actually part of the previous day. But, your totals are so far off, it should be good enough.

To view this discussion on the web visit https://groups.google.com/d/msgid/weewx-user/723d0041-45b2-4a1a-8414-0f6a02487cc5n%40googlegroups.com.

Ξ

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Jan 14, 2024, 6:41:45 PM1/14/24

to weewx-user

Many thanks for the pointers:

sqlite> select dateTime, date(dateTime,'unixepoch','localtime'), sum(rain) from archive where date(dateTime,'unixepoch','localtime')=='2023-09-16';
1694811600|2023-09-16|59.2
sqlite> select dateTime, date(dateTime,'unixepoch','localtime') as dt, sum from archive_day_rain where dt=='2023-09-16';
1694811600|2023-09-16|59.2
sqlite>

Ξ

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Jan 14, 2024, 6:56:02 PM1/14/24

to weewx-user

and the NOAA reports show it too:

MONTHLY CLIMATOLOGICAL SUMMARY for Sep 2023

NAME: Dolni Okol
ELEV: 1010 meters LAT: 42-29.40 N LONG: 023-29.70 E

TEMPERATURE (C), RAIN (mm), WIND SPEED (m/s)

HEAT COOL AVG

MEAN DEG DEG WIND DOM INSIDE INS
DAY TEMP HIGH TIME LOW TIME DAYS DAYS RAIN SPEED HIGH TIME DIR HIGH LOW
-----------------------------------------------------------------------------------------------------
01 16.7 28.7 15:30 8.5 07:30 1.3 0.0 0.0 0.1 2.7 14:30 165 22.8 20.3
02 18.6 33.0 16:15 10.7 07:30 0.0 0.6 0.0 0.0 2.1 16:30 186 23.1 20.7
03 18.8 32.7 16:15 10.0 07:30 0.0 0.8 0.0 0.1 2.2 10:45 168 23.2 20.6
04 12.3 16.5 00:15 11.1 20:15 5.7 0.0 25.6 0.1 4.2 07:30 95 22.5 19.0
05 13.3 20.0 15:45 11.5 07:30 4.7 0.0 11.6 0.0 2.0 02:00 282 19.0 18.3
06 15.7 25.1 16:07 10.5 04:40 2.3 0.0 0.0 0.0 2.1 14:02 161 18.8 17.6
07 17.2 25.7 16:02 14.0 07:19 0.8 0.0 0.8 0.0 2.6 12:09 152 19.4 18.3
08 17.3 30.3 15:36 10.3 07:18 0.7 0.0 0.8 0.0 2.5 10:30 161 20.4 18.4
09 16.9 32.8 16:19 9.2 07:33 1.1 0.0 0.0 0.1 2.2 15:13 188 21.0 18.7
10 14.9 29.4 16:24 6.1 06:52 3.1 0.0 0.0 0.1 2.2 14:40 184 21.1 17.9
11 15.2 32.1 15:30 6.5 06:01 2.8 0.0 0.0 0.0 1.9 15:10 163 21.2 18.0
12 15.5 34.3 17:10 5.3 07:07 2.5 0.0 0.0 0.0 2.0 12:38 155 20.9 17.7
13 16.0 36.0 17:17 7.0 07:08 2.0 0.0 0.0 0.0 2.1 15:20 168 21.5 18.1
14 18.0 32.7 15:41 10.1 03:42 0.0 0.0 0.0 0.1 2.2 14:23 86 21.9 19.3
15 18.0 29.2 16:13 11.1 07:04 0.0 0.0 0.0 0.0 2.0 11:07 185 22.6 20.0
16 17.5 28.7 17:15 13.9 23:45 0.5 0.0 59.2 0.0 1.7 15:02 188 22.0 20.7
17 16.0 29.0 16:20 11.2 23:54 2.0 0.0 0.8 0.0 1.1 16:16 172 20.9 19.6
18 16.4 29.9 15:55 9.8 23:53 1.6 0.0 0.0 0.0 2.0 15:16 182 20.4 19.0
19 14.6 29.6 16:43 7.1 06:42 3.4 0.0 0.4 0.0 1.8 23:47 146 20.0 17.8
20 16.6 31.8 17:33 9.3 07:27 1.4 0.0 9.6 0.1 2.5 00:04 164 21.2 18.3
21 16.8 33.2 17:23 10.6 02:41 1.2 0.0 0.4 0.0 2.0 20:39 295 20.6 19.1
22 19.7 33.1 16:33 11.2 07:13 0.0 1.7 0.0 0.1 4.2 15:00 290 22.3 19.2
23 21.2 36.6 15:47 11.7 07:30 0.0 3.2 0.0 0.0 2.2 12:23 289 23.6 20.5
24 17.8 24.7 15:31 12.8 04:53 0.2 0.0 0.0 0.0 1.8 10:08 243 23.4 21.5
25 17.9 28.8 16:26 14.7 05:33 0.1 0.0 0.0 0.1 2.3 10:52 168 22.3 21.2
26 16.2 27.2 16:20 9.9 07:32 1.8 0.0 0.0 0.1 3.6 14:22 168 21.6 19.6
27 15.5 26.6 15:14 9.4 06:00 2.5 0.0 0.0 0.1 3.0 11:47 169 21.5 19.1
28 14.7 26.1 15:53 7.8 05:53 3.3 0.0 0.0 0.0 2.1 13:01 216 20.1 18.6
29 15.2 26.7 15:29 9.8 23:51 2.8 0.0 0.0 0.1 2.8 13:59 205 20.1 18.3
30 14.5 26.9 17:13 8.7 07:55 3.5 0.0 0.0 0.1 2.3 12:57 201 20.5 17.8
-----------------------------------------------------------------------------------------------------
16.5 36.6 23 5.3 12 51.2 6.3 109.2 0.0 4.2 04 176 23.6 17.6

=====================================================================================================

For December

MONTHLY CLIMATOLOGICAL SUMMARY for Dec 2023
NAME: Dolni Okol
ELEV: 1010 meters LAT: 42-29.40 N LONG: 023-29.70 E

TEMPERATURE (C), RAIN (mm), WIND SPEED (m/s)

HEAT COOL AVG

MEAN DEG DEG WIND DOM INSIDE INS
DAY TEMP HIGH TIME LOW TIME DAYS DAYS RAIN SPEED HIGH TIME DIR HIGH LOW
-----------------------------------------------------------------------------------------------------
01 9.4 14.7 13:30 -1.1 00:18 8.6 0.0 0.0 0.6 6.6 11:58 326 7.8 5.0
02 7.6 17.5 15:23 2.7 20:58 10.4 0.0 0.0 0.1 3.3 14:38 325 7.9 7.2
03 4.9 12.0 00:26 -2.0 23:56 13.1 0.0 13.2 0.1 3.3 13:02 33 7.9 7.1
04 -2.3 11.7 15:39 -6.3 07:48 20.3 0.0 0.0 0.0 2.3 03:53 117 8.7 6.1
05 -0.8 1.4 14:51 -2.5 07:23 18.8 0.0 0.0 0.0 1.4 11:35 303 6.4 4.8
06 0.7 1.3 14:03 -0.1 00:00 17.3 0.0 0.4 0.0 0.6 02:03 307 4.8 4.2
07 0.3 3.7 11:02 -2.0 23:16 17.7 0.0 0.0 0.1 3.0 13:48 56 4.2 3.7
08 -2.3 -0.9 13:18 -3.8 22:07 20.3 0.0 0.0 0.0 0.0 00:00 90 3.7 2.9
09 -2.1 1.0 14:38 -4.4 05:04 20.1 0.0 0.0 0.0 0.9 14:42 161 2.9 2.4
10 -0.8 10.6 15:17 -4.0 05:44 18.8 0.0 4.0 0.0 2.3 13:02 316 4.4 2.1
11 0.3 7.2 14:56 -3.6 07:52 17.7 0.0 0.8 0.0 2.0 12:04 138 5.7 3.0
12 3.8 13.6 15:35 0.1 21:58 14.2 0.0 0.8 0.0 3.0 05:10 14 6.2 4.1
13 3.2 11.1 14:28 0.4 04:02 14.8 0.0 0.0 0.0 1.2 15:36 302 5.5 4.7
14 5.7 11.7 10:36 0.6 02:20 12.3 0.0 7.2 0.1 3.1 04:20 41 5.6 4.9
15 1.1 2.1 03:06 -0.7 23:56 16.9 0.0 42.0 0.0 2.8 05:17 56 5.5 4.7
16 -2.0 -0.5 13:54 -3.7 23:59 20.0 0.0 0.0 0.0 0.0 00:00 90 4.8 3.7
17 -5.7 0.5 13:58 -10.5 07:32 23.7 0.0 0.0 0.0 0.0 00:00 90 5.4 2.7
18 -3.0 9.9 15:34 -8.1 01:40 21.0 0.0 18.8 0.0 0.0 00:00 90 5.4 2.3
19 -0.9 12.1 15:20 -4.6 07:04 18.9 0.0 4.8 0.0 0.0 00:00 90 6.4 3.1
20 1.4 9.2 15:04 -5.2 06:31 16.6 0.0 5.2 0.1 2.6 16:48 315 6.2 3.8
21 1.0 10.6 15:13 -3.5 20:04 17.0 0.0 0.8 0.1 2.5 22:09 47 7.5 4.3
22 1.9 3.7 03:57 0.1 23:01 16.1 0.0 4.0 0.2 4.4 00:41 320 5.6 4.6
23 2.4 5.3 11:23 -0.5 17:43 15.6 0.0 0.8 0.3 4.5 21:58 348 5.2 4.3
24 6.4 13.5 15:33 3.1 06:08 11.6 0.0 0.0 0.2 4.2 16:06 337 7.2 4.3
25 8.7 17.6 15:33 2.6 23:45 9.3 0.0 0.0 0.2 3.2 14:08 331 9.2 5.9
26 6.8 16.6 15:18 -0.3 05:13 11.2 0.0 0.0 0.1 2.2 09:18 330 10.7 7.4
27 4.3 16.0 15:26 -1.4 23:57 13.7 0.0 0.0 0.0 2.8 01:21 51 11.7 8.7
28 2.6 18.6 15:55 -2.7 07:19 15.4 0.0 0.0 0.1 2.3 14:41 169 11.4 8.4
29 2.7 15.1 15:49 -2.0 05:40 15.3 0.0 0.0 0.0 2.0 13:05 162 11.4 8.4
30 1.9 8.7 13:26 -2.4 04:34 16.1 0.0 0.0 0.1 2.7 14:40 35 10.7 8.2
31 2.3 15.5 15:20 -1.2 08:30 15.7 0.0 0.0 0.0 1.8 11:30 177 10.7 7.9
-----------------------------------------------------------------------------------------------------
2.0 18.6 28 -10.5 17 498.6 0.0 102.8 0.1 6.6 01 342 11.7 2.1

sqlite> select dateTime, date(dateTime,'unixepoch','localtime'), sum(rain) from archive where date(dateTime,'unixepoch','localtime')=='2023-12-03';
1701554400|2023-12-03|13.2

whereas 4.32 mm on Wunderground for 03/12/2023 https://www.wunderground.com/dashboard/pws/I5302/graph/2023-12-3/2023-12-3/daily

sqlite> select dateTime, date(dateTime,'unixepoch','localtime'), sum(rain) from archive where date(dateTime,'unixepoch','localtime')=='2023-12-15';
1702591200|2023-12-15|42.4

whereas 13.97 mm on Wunderground for 15/12/2023 https://www.wunderground.com/dashboard/pws/I5302/graph/2023-12-15/2023-12-15/daily

sqlite> select dateTime, date(dateTime,'unixepoch','localtime'), sum(rain) from archive where date(dateTime,'unixepoch','localtime')=='2023-12-18';
1702850400|2023-12-18|18.8

whereas 4.83 mm on Wunderground for 18/12/2023 https://www.wunderground.com/dashboard/pws/I5302/graph/2023-12-18/2023-12-18/daily

Tom Keffer

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Jan 14, 2024, 11:03:41 PM1/14/24

to weewx...@googlegroups.com

I think I'm getting close to figuring this out. Can you confirm the following:

- You are using the "ws6in1" driver, right? This one.

- What unit system are you using in your database? This is the setting for option "target_unit".

-tk

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Ξ

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Jan 17, 2024, 9:22:26 AM1/17/24

to weewx-user

Hi Tom,

Yes, that's exactly the ws6in1 driver I'm using, ever since July 2022.

target_unit is set to METRICWX and that's how I've always had it since I started running weewx 5 years ago on this R-Pi initially with an Acurite station.

Thanks a bunch!

Tom Keffer

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Jan 18, 2024, 2:26:05 AM1/18/24

to weewx...@googlegroups.com

I would guess that the driver is not calculating "rain" correctly.

WeeWX uses "rain" for the values in the archive table and daily summaries. The WU uses some mysterious combination of "dayRain" and "hourRain" (they don't say) for rain totals.

The driver calculates "rain" as the difference in some rain total (I think differences in "dayRain", but it's hard to tell from the code). This is as it should be.

You say that the "spikes" occur after power outages. I'm thinking the value for "dayRain" also changes.

I hate to "pass the buck," but I've taken this as far as I can. I don't think there's a problem in the WeeWX code --- after all, it works correctly for thousands of users and many different drivers. The only unusual circumstances in your case is the use of metric units in the database, and the use of the ws6in1 driver.

Please ask the author of the driver to double check the calculations for rain. Ask him to try a power failure and see what happens to "dayRain".

-tk

To view this discussion on the web visit https://groups.google.com/d/msgid/weewx-user/36d9c7e5-59a8-4442-9d63-1be15ed6393en%40googlegroups.com.

Ξ

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Jan 18, 2024, 9:09:05 AM1/18/24

to weewx-user

Tom,

It's much appreciated you've spent the time to look into this, if there's nothing wrong with WeeWX - there's nothing wrong with WeeWX :)
I'll ask Bob if he can have a gander at this.

I have to admit though I find it a bit stranger using metric would be considered an unusual circumstances, it's what the rest of the world uses after all.

Again, thanks a lot!

Ivo

michael.k...@gmx.at

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Jan 18, 2024, 10:14:34 AM1/18/24

to weewx-user

I think from the perspective that

the metric system for storing values in the database is not default
the "normal user" shouldn't get in touch with database values und "normal circumstances"
hence, the vast majority of installations will not use the metric system in the db

it is unusual.

Ξ

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Jan 18, 2024, 12:28:35 PM1/18/24

to weewx-user

OK thanks, but that's been set from the very beginning that is to say when weewx was set up when the db was empty.

Personally, I see no reason why I should use anything non-metric, that's just a US thing.

Ξ

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Jan 18, 2024, 1:09:34 PM1/18/24

to weewx-user

I'd like to post some more details about December

-------------------------------------------------------------------------------------------------------------------------------------------------------------

The graphs with the sudden out of nowhere rain values again match what appear to the power outages

-------------------------------------------------------------------------------------------------------------------------------------------------------------

1701555300|2023-12-03 00:15:00|0.0
1701556200|2023-12-03 00:30:00|0.0
1701557100|2023-12-03 00:45:00|0.0
1701558000|2023-12-03 01:00:00|0.0
1701558900|2023-12-03 01:15:00|0.0
1701559800|2023-12-03 01:30:00|0.0
1701560700|2023-12-03 01:45:00|0.0
1701561600|2023-12-03 02:00:00|0.0
1701562500|2023-12-03 02:15:00|0.0
1701563400|2023-12-03 02:30:00|0.0
1701564300|2023-12-03 02:45:00|0.0
1701565200|2023-12-03 03:00:00|0.0
1701566100|2023-12-03 03:15:00|0.0
1701567000|2023-12-03 03:30:00|0.0
1701567900|2023-12-03 03:45:00|0.0
1701568800|2023-12-03 04:00:00|0.0
1701569700|2023-12-03 04:15:00|0.0
1701570600|2023-12-03 04:30:00|0.0
1701571500|2023-12-03 04:45:00|0.0
1701572400|2023-12-03 05:00:00|0.0
1701573300|2023-12-03 05:15:00|0.0
1701574200|2023-12-03 05:30:00|0.0
1701575100|2023-12-03 05:45:00|0.0
1701576000|2023-12-03 06:00:00|0.0
1701576900|2023-12-03 06:15:00|0.0
1701577800|2023-12-03 06:30:00|0.0
1701578700|2023-12-03 06:45:00|0.0
1701579600|2023-12-03 07:00:00|0.0
1701580500|2023-12-03 07:15:00|0.0
1701581400|2023-12-03 07:30:00|0.0
1701582300|2023-12-03 07:45:00|0.0
1701583200|2023-12-03 08:00:00|0.0
1701584100|2023-12-03 08:15:00|0.0
1701585000|2023-12-03 08:30:00|0.0
1701585900|2023-12-03 08:45:00|0.0
1701586800|2023-12-03 09:00:00|0.0
1701587700|2023-12-03 09:15:00|0.0
1701588600|2023-12-03 09:30:00|0.0
1701589500|2023-12-03 09:45:00|0.0
1701590400|2023-12-03 10:00:00|0.0
1701591300|2023-12-03 10:15:00|0.0
1701592200|2023-12-03 10:30:00|0.0
1701593100|2023-12-03 10:45:00|0.0
1701594000|2023-12-03 11:00:00|0.0
1701594900|2023-12-03 11:15:00|0.0
1701595800|2023-12-03 11:30:00|0.0
1701596700|2023-12-03 11:45:00|0.0
1701597600|2023-12-03 12:00:00|0.0
1701598500|2023-12-03 12:15:00|0.0
1701599400|2023-12-03 12:30:00|0.4
1701600300|2023-12-03 12:45:00|0.0
1701601200|2023-12-03 13:00:00|0.4
1701602100|2023-12-03 13:15:00|0.4
1701603000|2023-12-03 13:30:00|0.0
1701603900|2023-12-03 13:45:00|0.4
1701604800|2023-12-03 14:00:00|0.4
1701605700|2023-12-03 14:15:00|0.0
1701606600|2023-12-03 14:30:00|0.0
1701607500|2023-12-03 14:45:00|0.4
1701608400|2023-12-03 15:00:00|0.0
1701609300|2023-12-03 15:15:00|0.4
1701610200|2023-12-03 15:30:00|0.4
1701611100|2023-12-03 15:45:00|0.0
1701612000|2023-12-03 16:00:00|0.0
1701612900|2023-12-03 16:15:00|0.4
1701613800|2023-12-03 16:30:00|0.0
1701614700|2023-12-03 16:45:00|0.4
1701615600|2023-12-03 17:00:00|0.4
1701616500|2023-12-03 17:15:00|0.0
1701617400|2023-12-03 17:30:00|0.0
1701618300|2023-12-03 17:45:00|0.0
1701619200|2023-12-03 18:00:00|0.0
1701620100|2023-12-03 18:15:00|0.0
1701621000|2023-12-03 18:30:00|0.0
1701621900|2023-12-03 18:45:00|0.0
1701623700|2023-12-03 19:15:00|4.4
1701624600|2023-12-03 19:30:00|0.0
1701625500|2023-12-03 19:45:00|0.0
1701627300|2023-12-03 20:15:00|4.4
1701628200|2023-12-03 20:30:00|0.0
1701629100|2023-12-03 20:45:00|0.0
1701630000|2023-12-03 21:00:00|0.0
1701630900|2023-12-03 21:15:00|0.0
1701631800|2023-12-03 21:30:00|0.0
1701632700|2023-12-03 21:45:00|0.0
1701633600|2023-12-03 22:00:00|0.0
1701634500|2023-12-03 22:15:00|0.0
1701635400|2023-12-03 22:30:00|0.0
1701636300|2023-12-03 22:45:00|0.0
1701637200|2023-12-03 23:00:00|0.0
1701638100|2023-12-03 23:15:00|0.0
1701639000|2023-12-03 23:30:00|0.0
1701639900|2023-12-03 23:45:00|0.0

============================================================================================================================

1702592100|2023-12-15 00:15:00|0.8
1702593000|2023-12-15 00:30:00|0.8
1702593900|2023-12-15 00:45:00|0.8
1702594800|2023-12-15 01:00:00|0.4
1702595700|2023-12-15 01:15:00|0.4
1702596600|2023-12-15 01:30:00|0.8
1702597500|2023-12-15 01:45:00|0.8
1702598400|2023-12-15 02:00:00|0.8
1702599300|2023-12-15 02:15:00|0.0
1702600200|2023-12-15 02:30:00|0.4
1702601100|2023-12-15 02:45:00|0.4
1702602000|2023-12-15 03:00:00|0.0
1702602900|2023-12-15 03:15:00|0.4
1702603800|2023-12-15 03:30:00|0.0
1702604700|2023-12-15 03:45:00|0.4
1702605600|2023-12-15 04:00:00|0.0
1702606500|2023-12-15 04:15:00|0.0
1702607400|2023-12-15 04:30:00|0.0
1702608300|2023-12-15 04:45:00|0.4
1702609200|2023-12-15 05:00:00|0.0
1702610100|2023-12-15 05:15:00|0.4
1702611000|2023-12-15 05:30:00|0.0
1702611900|2023-12-15 05:45:00|0.4
1702612800|2023-12-15 06:00:00|0.0
1702613700|2023-12-15 06:15:00|0.4
1702614600|2023-12-15 06:30:00|0.0
1702615500|2023-12-15 06:45:00|0.0
1702616400|2023-12-15 07:00:00|0.399999999999999
1702617300|2023-12-15 07:15:00|0.0
1702618200|2023-12-15 07:30:00|0.4
1702619100|2023-12-15 07:45:00|0.0
1702620000|2023-12-15 08:00:00|0.0
1702620900|2023-12-15 08:15:00|0.4
1702621800|2023-12-15 08:30:00|0.4
1702622700|2023-12-15 08:45:00|0.0
1702623600|2023-12-15 09:00:00|0.4
1702624500|2023-12-15 09:15:00|0.0
1702625400|2023-12-15 09:30:00|0.399999999999999
1702626300|2023-12-15 09:45:00|0.4
1702627200|2023-12-15 10:00:00|0.4
1702628100|2023-12-15 10:15:00|0.0
1702629000|2023-12-15 10:30:00|0.4
1702629900|2023-12-15 10:45:00|0.4
1702630800|2023-12-15 11:00:00|0.399999999999999
1702631700|2023-12-15 11:15:00|0.4
1702632600|2023-12-15 11:30:00|0.0
1702633500|2023-12-15 11:45:00|0.4
1702634400|2023-12-15 12:00:00|0.0
1702635300|2023-12-15 12:15:00|0.0
1702636200|2023-12-15 12:30:00|0.0
1702637100|2023-12-15 12:45:00|0.0
1702638000|2023-12-15 13:00:00|0.0
1702638900|2023-12-15 13:15:00|0.0
1702639800|2023-12-15 13:30:00|0.0
1702640700|2023-12-15 13:45:00|0.0
1702641600|2023-12-15 14:00:00|0.0
1702642500|2023-12-15 14:15:00|0.0
1702643400|2023-12-15 14:30:00|0.0
1702644300|2023-12-15 14:45:00|0.0
1702645200|2023-12-15 15:00:00|0.0
1702646100|2023-12-15 15:15:00|0.0
1702647000|2023-12-15 15:30:00|0.0
1702647900|2023-12-15 15:45:00|0.0
1702648800|2023-12-15 16:00:00|0.0
1702649700|2023-12-15 16:15:00|0.0
1702650600|2023-12-15 16:30:00|0.0
1702651500|2023-12-15 16:45:00|0.0
1702652400|2023-12-15 17:00:00|0.0
1702653300|2023-12-15 17:15:00|0.0
1702654200|2023-12-15 17:30:00|0.0
1702655100|2023-12-15 17:45:00|0.0
1702656000|2023-12-15 18:00:00|0.0
1702656900|2023-12-15 18:15:00|0.0
1702657800|2023-12-15 18:30:00|0.0
1702659600|2023-12-15 19:00:00|14.0
1702671300|2023-12-15 22:15:00|14.0
1702672200|2023-12-15 22:30:00|0.0
1702673100|2023-12-15 22:45:00|0.0
1702674000|2023-12-15 23:00:00|0.0
1702674900|2023-12-15 23:15:00|0.0
1702675800|2023-12-15 23:30:00|0.0
1702676700|2023-12-15 23:45:00|0.0

Tom Keffer

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Jan 18, 2024, 1:16:16 PM1/18/24

to weewx...@googlegroups.com

Again, I would look to the driver. It is calculating rain from differences of dayRain, which, I suspect, is prone to unpredictable values after a power outage.

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Ξ

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Jan 18, 2024, 2:57:24 PM1/18/24

to weewx-user

And thanks again, I've pinged Bob if he can look at it.

All the best!

Bob Atchley

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Jan 18, 2024, 8:30:56 PM1/18/24

to weewx-user

Sorry I'm picking this thread up a bit late.

Looking at the output of the SQL commands I'm seeing unexpected behaviour (apart from the rain spikes). The Bresser weather station has a buffer that with the 15 minute interval you are using should allow up to 150 days of data to be recovered. But there are gaps which presumably align with the power outages - this should not happen.

This probably means that the buffer is full. Unfortunately the buffer is not circular, once its full no more data is stored. The interface to the weather station from the WS6in1 driver does not provide a way to reset this buffer - it must be done manually. To make matters worse some copies of the printed user manual is missing the details on how to do this.

If this is the case for you, this link to an on line version contains the necessary information (pages 20 and 21 under the heading Data Log)

https://www.bresser.de/out/media/9fc9f246c452a7307b473233c8057805.pdf

I personally use a 5 minute interval (50 day buffer) and reset the buffer every month after backing up the database, with a 15 minute interval I would recommend resetting the buffer every 3 months.

Having said all this I have no idea what is causing the rain spikes.. My experience is if the buffer is full the driver reads through it all but as its old data it all gets discarded. May be if the buffer has never been reset it contains data out of time sequence and this is causing the problem.

My intuition is if you reset the weather station buffer regularly the problem will go away - ultimately the data comes from the weather station not the driver. I will check the WS6in1 driver in case I can find anything else that might be causing this.

If you are already resetting the weather station buffer then I'll need to think again

Hope this helps

Bob

Ξ

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Jan 19, 2024, 1:12:41 AM1/19/24

to weewx-user

Hi Bob,

You've already explained to me about the buffer in another thread, but I haven't got around to to empty it or change the setting, I'll go there tomorrow and do just that! Thanks again!

When looking at the data for 15-Dec-23 specifically, it does appear that 14 is the sum of all the previous values for the day since midnight, I'd imagine the buffer would have more values stores, no!? Basically as Tom says these are the dayRain values aggregated and inserted into the db once it's up again:
(I've removed the rows with 0)

Bob Atchley

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Jan 19, 2024, 8:05:22 AM1/19/24

to weewx-user

Interesting, I hadn't noticed that, and I see the same pattern on the other days you have posted.

I suspect what is happening is that if the data buffer is full, the final data item is continually over written with that days accumulated rain which the WS6.in1 driver then assumes is a normal value.

I will test this - I need to wait for the buffer on my weather station to be full (late Feb) and then wait for a rainy day. Depending on what is output by the weather station to the driver I will update the driver.

But again for you the simple solution is to regularly manually reset the data buffer following the instructions in the (on line) user manual - this should ensure you have no gaps in your data and will remove the spikes - a double win.

Hope this helps

Bob

John Smith

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Jan 19, 2024, 11:27:24 AM1/19/24

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On Thu, 18 Jan 2024 at 13:26, Tom Keffer <tke...@gmail.com> wrote:

You say that the "spikes" occur after power outages. I'm thinking the value for "dayRain" also changes.

Maybe he should get a UPS and put a big battery on it so it doesn't die during blackouts.

Ξ

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Jan 24, 2024, 8:19:42 AM1/24/24

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Thanks Bob, I'll follow your advice from now on to regularly clear the console data buffer.

btw, any particular reason why you recommended (in another thread) the 5 minute setting of the buffer as opposed to any other?

Ξ

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Jan 24, 2024, 8:35:25 AM1/24/24

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One more question if I may: I tried searching but I couldn't find explanation why the loop data doesn't report the battery status?!

Bob Atchley

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Jan 24, 2024, 5:38:20 PM1/24/24

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Unfortunately the interface from the Bresser PC console doesn't include the battery status - so not provided because its not available (i wish it was, a silly oversight)

Why 5 minute intervals ... it seems a sensible compromise of providing useful data while not filling the disk up too rapidly (the console itself does 12 second updates), but recording at the same interval that weewx is putting data into the database seems a good compromise.

Hope this helps

Bob

Ξ

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Jan 24, 2024, 10:29:53 PM1/24/24

to weewx-user

Ahh, so the console shows the battery status but it doesn't send the message anywhere and it can't be read?

Alright, thanks for all the clarifications.

Bob Atchley

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Feb 18, 2024, 5:42:16 PM2/18/24

to weewx-user

After a long wait for the weather station console data buffer to be full, and a shorter wait for some rain I can now report that I have been able to repeat the rain spikes issue.

The data buffer simply stops being written when it is full, but the console has some sort of 'feature' that if the data buffer is full the first proper packet it sends includes the rain (but the logic of how much rain is reported escapes me - if it was rain since midnight it could have been useful, but it isn't) with a 0.0 rain rate the second packet has 0.0 rain and a positive rain rate.

I have put a fix in the WS6in1 driver (1.05) to suppress the first 2 packets, so the spikes are no longer present.

But the real fix is the user needs to manually empty the console data buffer periodically

Hope this helps

Bob

Ξ

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Apr 30, 2024, 10:38:16 AM4/30/24

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Hello Bob,

Many thanks for this.

All the best,

Ivo

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