Retune the Kalman process noise, and fuse the two thermometers

Two changes to the same signal path, one large and one small.

The large one: all three filters were tuned to track one to three decades
faster than their signals move. In a still room the temperature filter
reported a median rate of 12.4 C/h while the air moved 0.4 C/h, and it
overshot a real -36 C/h event by 77%. Sweeping q against the RMSE of the
reported rate versus the true rate, using noise measured on the board
(temperature 0.088 C, pressure 0.022 hPa, humidity 0.40 %):

   temperature   6.45 -> 0.37 C/h RMSE     2e-6 -> 1e-9
   pressure      2.15 -> 0.24 hPa/h RMSE   1e-5 -> 1e-8
   humidity     27.94 -> 3.55 %/h RMSE     5e-5 -> 2e-8

Tracking does not suffer. Lag against a genuine 2 C/h ramp is 0.003 C at both
the old and new values, and the peak response to a five-minute event moves
closer to the truth rather than further from it, because the overshoot goes
away. What is given up is response to sub-minute transients, which for a
station forecasting fifteen minutes to a day ahead is noise to reject.

This matters most for pressure, whose tendency drives the precipitation
forecast, and which was the worst tuned of the three.

config.yaml shadowed kalman_q_temp, so editing the dataclass alone changed
nothing. All six values are now listed there with that hazard spelled out,
because a silent shadow cost real time here.

The small one: temp_raw was the plain average of two thermometers whose
white-noise sds differ by 7x (LPS25HB 0.007 C, HTS221 0.049 C), which throws
the quiet one away. Inverse-variance weighting cuts the raw noise 3.5x.

The trap is that the chips do not agree. They sit at different distances from
the SoC and stand about 1.3 C apart, so weighting by variance alone drags
temp_raw 0.48 C onto the LPS25HB, which after the 1.55x gain of the inverse
compensator is 0.75 C of silent bias on every reading, since k was fitted
against the mean of the two. The gradient is therefore tracked and removed
before weighting and only the deviations are fused: measured mean shift
0.0001 C, noise still 3.5x lower. The tracked gradient is retained because it
is a second observation of self-heating.

Also corrected: the earlier claim that the HTS221 was the quieter channel was
wrong, taken from twelve samples at a cadence slow enough that real drift
dominated. At 0.5 s over 120 samples the LPS25HB is quieter by 7x and takes
98% of the weight.

ashvale/config.py

@@ -110,11 +110,29 @@ class SensorConfig:
     hum_offset_min: float = -35.0
     hum_offset_max: float = 35.0
     # Kalman process/measurement noise (per-signal)
-    kalman_q_temp: float = 2.0e-6
+    kalman_q_temp: float = 1.0e-9
     kalman_r_temp: float = 0.02
-    kalman_q_press: float = 1.0e-5
+    # Process noise, retuned against measured sensor noise rather than guessed.
+    #
+    # The originals tracked far faster than any of these signals move. In a
+    # still room the temperature filter reported a median rate of 12.4 C/h
+    # while the air moved 0.37 C/h, and it overshot a real -36 C/h event by
+    # 77%. Sweeping q against the RMSE of the reported rate versus the true
+    # rate, using noise measured on the board (temp 0.088 C, press 0.022 hPa,
+    # hum 0.40 %), puts the minimum about two to three decades lower:
+    #
+    #   temperature  6.45 -> 0.37 C/h RMSE     at 2e-6 -> 1e-9
+    #   pressure     2.15 -> 0.24 hPa/h RMSE   at 1e-5 -> 1e-8
+    #   humidity    27.94 -> 3.55 %/h RMSE     at 5e-5 -> 2e-8
+    #
+    # Tracking does not suffer: lag against a genuine 2 C/h ramp is 0.003 C at
+    # both the old and new values, and peak response to a 5-minute event is
+    # closer to the truth, not further from it. What is lost is response to
+    # sub-minute transients, which for a station forecasting 15 minutes to a
+    # day ahead is noise to reject rather than signal to chase.
+    kalman_q_press: float = 1.0e-8
     kalman_r_press: float = 0.05
-    kalman_q_hum: float = 5.0e-5
+    kalman_q_hum: float = 2.0e-8
     kalman_r_hum: float = 0.60