data trove noaa ovation aurora forecast

saturn notebook · generated 2026-06-22 Report Notebook

Overview

Source: /home/coolhand/html/datavis/data_trove/data/quirky/ovation.json

Saturn profiled 71 rows across 5 columns. The stats below are deterministic and machine-readable; the prose is a language-model interpretation of those stats (opt-in, added after the fact, never sees raw rows).

[2]:

!pip install saturn-dissect
import subprocess
subprocess.run([
    "saturn", "analyze", "/home/coolhand/html/datavis/data_trove/data/quirky/ovation.json",
    "--findings", "data-trove-noaa-ovation-aurora-forecast.json",
    "--llm", "anthropic:default",
])

Summary confidence: high

This dataset captures 71 five-minute snapshots of auroral activity on January 20, 2026, with each row recording a timestamp, activity classification, intensity, and power readings for the northern and southern hemispheres. The most striking feature is that 'Storm' conditions dominate 55% of the observations, with 'Active' and 'Quiet' states making up the remainder — suggesting this day saw sustained geomagnetic disturbance. Both north and south power readings show wide, roughly uniform distributions (IQR ~110 GW) with medians well below their means, hinting that storm periods drive the upper range of power values. Intensity is similarly spread across most of its 0–1 range with near-zero skew, making the relationship between activity class and intensity worth exploring closely.

citing: row_count · column_count · activity.top_values · activity.top_rate · north_power.median · north_power.mean · north_power.iqr · south_power.median · south_power.mean · south_power.iqr · intensity.min · intensity.max · intensity.skew

Out[4]:

saturn.schema() · 5 columns

column	kind	n	null%	unique	alerts
time_tag	categorical	71	0.0%	71	long_tail
north_power	numeric	71	0.0%	60
south_power	numeric	71	0.0%	61
activity	categorical	71	0.0%	3
intensity	numeric	71	0.0%	35

Fig 1.

activity · Look for how heavily 'Storm' dominates — it accounts for over half of all observations.

Show data table

Top values for activity (3 unique shown, of 3 total).
value	count	share
Storm	39	54.9%
Active	20	28.2%
Quiet	12	16.9%

Fig 2.

intensity · Notice whether intensity values cluster at the extremes or spread evenly, which reflects the mix of storm vs. quiet periods.

Show data table

Histogram bins for intensity (median: 0.6).
bin	count
0.11 – 0.2213	20
0.2213 – 0.3325	6
0.3325 – 0.4437	4
0.4437 – 0.555	4
0.555 – 0.6663	4
0.6663 – 0.7775	1
0.7775 – 0.8888	4
0.8888 – 1	28

Fig 3.

north_power · A wide, right-skewed spread reveals that high power bursts during storm conditions pull the mean well above the median.

Show data table

Histogram bins for north_power (median: 60.0).
bin	count
11 – 33.12	26
33.12 – 55.25	8
55.25 – 77.38	5
77.38 – 99.5	6
99.5 – 121.6	5
121.6 – 143.8	6
143.8 – 165.9	8
165.9 – 188	7

Fig 4.

south_power · Compare to north_power — similar shape suggests hemispheric symmetry, but check whether peaks align in time.

Show data table

Histogram bins for south_power (median: 51.0).
bin	count
11 – 33.38	28
33.38 – 55.75	9
55.75 – 78.12	3
78.12 – 100.5	6
100.5 – 122.9	5
122.9 – 145.2	6
145.2 – 167.6	7
167.6 – 190	7

Fig 5.

activity · A simple count bar reinforces how rare 'Quiet' conditions were relative to 'Storm' and 'Active' on this day.

Show data table

Top values for activity (3 unique shown, of 3 total).
value	count	share
Storm	39	54.9%
Active	20	28.2%
Quiet	12	16.9%

Fig 6.

Per-column null rate across the corpus. Columns are ordered by input position.

Show data table

Per-column null rate across the corpus.
column	kind	null %
time_tag	categorical	0.0%
north_power	numeric	0.0%
south_power	numeric	0.0%
activity	categorical	0.0%
intensity	numeric	0.0%

Fig 7.

Pearson correlation across numeric columns (sampled, bounded).

Show data table

Pearson correlation across 3 numeric columns (values clipped to 2 decimals).
	north_power	south_power	intensity
north_power	+1.00	+1.00	+0.94
south_power	+1.00	+1.00	+0.94
intensity	+0.94	+0.94	+1.00

time_tag categorical timestamp

This column contains datetime strings representing regular 5-minute interval timestamps on 2026-01-20, making it a time-series index. All 71 values are unique (cardinality 71, null_rate 0.0, top_rate 0.014) and entropy_ratio is effectively 1.0, confirming every row maps to a distinct timestamp. The 'long_tail' alert is misleading here — the distribution is perfectly uniform (each value appears exactly once), not skewed. This column should be parsed as a proper datetime and used as a time index rather than a categorical feature.

Treatment: Parse to datetime and set as time index; do not encode as categorical.

anthropic:default · confidence high

Out[13]:

saturn.columns["time_tag"].stats

stat	value
n	71
nulls	0 (0.0%)
unique	71
top_value	2026-01-20 00:00:00
top_rate	0.01408
cardinality	71
entropy	6.15
entropy_ratio	1
alert: long_tail	71 singleton categories

Fig 8.

Top values for time_tag.

Show data table

Top values for time_tag (20 unique shown, of 71 total).
value	count	share
2026-01-20 00:00:00	1	1.4%
2026-01-20 00:05:00	1	1.4%
2026-01-20 00:10:00	1	1.4%
2026-01-20 00:15:00	1	1.4%
2026-01-20 00:20:00	1	1.4%
2026-01-20 00:25:00	1	1.4%
2026-01-20 00:30:00	1	1.4%
2026-01-20 00:35:00	1	1.4%
2026-01-20 00:40:00	1	1.4%
2026-01-20 00:45:00	1	1.4%
2026-01-20 00:50:00	1	1.4%
2026-01-20 01:00:00	1	1.4%
2026-01-20 01:05:00	1	1.4%
2026-01-20 01:10:00	1	1.4%
2026-01-20 01:15:00	1	1.4%
2026-01-20 01:20:00	1	1.4%
2026-01-20 01:25:00	1	1.4%
2026-01-20 01:30:00	1	1.4%
2026-01-20 01:35:00	1	1.4%
2026-01-20 01:40:00	1	1.4%

north_power numeric feature

This column appears to measure a directional power reading (northward component) for 71 observations, likely a physical or sensor-derived quantity given its name and continuous numeric nature. The distribution is notably platykurtic (kurtosis -1.28), meaning values are spread very flatly across the range of 11–188 with no heavy tails and no outliers detected. The IQR of 110.5 is nearly as large as the full range, and the median (60.0) sits well below the mean (77.27), suggesting a modest right skew (0.45) driven by a cluster of higher values. With 60 unique values across 71 rows and zero nulls or zeros, the data is dense and well-populated but has some repeated measurements worth investigating.

Treatment: Use as-is or apply mild log-transform to reduce right skew before regression or distance-based modelling.

anthropic:default · confidence medium

Out[16]:

saturn.columns["north_power"].stats

stat	value
n	71
nulls	0 (0.0%)
unique	60
min	11
max	188
mean	77.27
median	60
std	58.74
q1	21
q3	131.5
iqr	110.5
skew	0.4523
kurtosis	-1.28
n_outliers	0
outlier_rate	0
zero_rate	0

Fig 9.

Distribution of north_power. Vertical dash marks the median.

Show data table

Histogram bins for north_power (median: 60.0).
bin	count
11 – 33.12	26
33.12 – 55.25	8
55.25 – 77.38	5
77.38 – 99.5	6
99.5 – 121.6	5
121.6 – 143.8	6
143.8 – 165.9	8
165.9 – 188	7

south_power numeric feature

This column likely represents a power measurement (e.g., watts, kilowatts, or a similar energy metric) associated with a southern-facing sensor, panel, or zone. With a range of 11–190 and a mean of 75.77, the distribution is notably flat: an IQR of 113.0 spanning nearly the full range, combined with a platykurtic kurtosis of -1.29, indicates values are spread broadly and uniformly rather than clustering around a central tendency. The median (51.0) sits well below the mean (75.77), confirming modest right skew (0.49), but no outliers are flagged, suggesting this spread is genuine variability rather than contamination. 61 unique values across 71 rows means some repeated readings exist, which may warrant inspection for duplicate observations.

Treatment: Check for duplicate rows given 61 unique values in 71 records; apply mild log-transform or scaling before regression given right skew and wide spread.

anthropic:default · confidence medium

Out[19]:

saturn.columns["south_power"].stats

stat	value
n	71
nulls	0 (0.0%)
unique	61
min	11
max	190
mean	75.77
median	51
std	60.32
q1	19
q3	132
iqr	113
skew	0.4943
kurtosis	-1.29
n_outliers	0
outlier_rate	0
zero_rate	0

Fig 10.

Distribution of south_power. Vertical dash marks the median.

Show data table

Histogram bins for south_power (median: 51.0).
bin	count
11 – 33.38	28
33.38 – 55.75	9
55.75 – 78.12	3
78.12 – 100.5	6
100.5 – 122.9	5
122.9 – 145.2	6
145.2 – 167.6	7
167.6 – 190	7

activity categorical label

This column represents a categorical activity-level classification, likely describing geophysical or meteorological states, with exactly three levels: 'Storm', 'Active', and 'Quiet'. The dominant class is 'Storm' at 54.9% (39 of 71 rows), which is mildly surprising given that 'Storm' might intuitively be expected as a rarer, extreme condition. The distribution is moderately imbalanced — 'Quiet' accounts for only 12 observations — which may affect model performance on minority classes. Entropy ratio of 0.898 indicates the distribution is reasonably spread but not uniform.

Treatment: Ordinal-encode (Quiet < Active < Storm) or one-hot encode; monitor class imbalance if used as a target.

anthropic:default · confidence high

Out[22]:

saturn.columns["activity"].stats

stat	value
n	71
nulls	0 (0.0%)
unique	3
top_value	Storm
top_rate	0.5493
cardinality	3
entropy	1.423
entropy_ratio	0.8979

Fig 11.

Top values for activity.

Show data table

Top values for activity (3 unique shown, of 3 total).
value	count	share
Storm	39	54.9%
Active	20	28.2%
Quiet	12	16.9%

intensity numeric feature

This column represents a normalized intensity measure, bounded between 0.11 and 1.0, consistent with a scaled or clipped continuous score. The distribution is notably platykurtic (kurtosis -1.73), indicating a very flat, spread-out distribution rather than a bell curve — values are nearly uniformly scattered across the range. Despite 71 rows, there are only 35 unique values, suggesting the data originates from a discrete or quantized source (e.g., rounded measurements or a fixed rating scale). Skew is negligible (-0.07), and the IQR of 0.79 spans almost the entire range, confirming broad dispersion with no outliers.

Treatment: Use as-is or apply quantile binning given the near-uniform, flat distribution; no log-transform needed given symmetry.

anthropic:default · confidence high

Out[25]:

saturn.columns["intensity"].stats

stat	value
n	71
nulls	0 (0.0%)
unique	35
min	0.11
max	1
mean	0.6024
median	0.6
std	0.3647
q1	0.21
q3	1
iqr	0.79
skew	-0.07261
kurtosis	-1.731
n_outliers	0
outlier_rate	0
zero_rate	0

Fig 12.

Distribution of intensity. Vertical dash marks the median.

Show data table

Histogram bins for intensity (median: 0.6).
bin	count
0.11 – 0.2213	20
0.2213 – 0.3325	6
0.3325 – 0.4437	4
0.4437 – 0.555	4
0.555 – 0.6663	4
0.6663 – 0.7775	1
0.7775 – 0.8888	4
0.8888 – 1	28

How to cite

click to copy

BibTeX

@misc{saturn-data-trove-noaa-ovation-aurora-forecast-2026,
  author       = {Steuber, Luke},
  title        = {Saturn reading: data trove noaa ovation aurora forecast},
  year         ={2026},
  howpublished = {\url{https://dr.eamer.dev/saturn/view/data-trove-noaa-ovation-aurora-forecast}},
  note         = {Profiled with saturn-dissect v0.2.0, prompt saturn-insight-v2, model anthropic:default},
}

APA

Steuber, L. (2026). Saturn reading: data trove noaa ovation aurora forecast. Source: /home/coolhand/html/datavis/data_trove/data/quirky/ovation.json. Profiled with saturn-dissect v0.2.0 (saturn-insight-v2, anthropic:default). Retrieved from https://dr.eamer.dev/saturn/view/data-trove-noaa-ovation-aurora-forecast