Designing a First-Snowfall Scatterplot in Power BI: Data, DAX, and Winter Visuals

Table of Contents

1. Key Highlights
2. Introduction
3. Where to get the data and what it looks like
4. Establishing the season: how to define "first snowfall of the season"
5. From daily records to a "first-snow month" table
6. DAX measures you’ll want in the report
7. Building the scatterplot in Power BI core visuals
8. Elevating the visual with custom visuals: Deneb (Vega-Lite) and HTML Content
9. Designing for readability and comprehension
10. Interaction and tooltip best practices
11. Quality control and troubleshooting
12. Real-world interpretations and use cases
13. Packaging and sharing the report
14. Ethics and limitations
15. Troubleshooting checklist before publishing
16. Practical implementation checklist (step-by-step)
17. FAQ

Key Highlights

• Practical workflow to extract first-snowfall months from seasonal weather data, compute corresponding snow amounts, and plot them in a readable scatterplot.
• Step-by-step DAX for defining seasons, identifying the first-snow month per season, and creating measures for visual size and tooltips; plus options for custom visuals (Deneb/Vega-Lite, HTML Content) to add snowflake markers and subtle animation.
• Guidance on design, interactivity, and accessibility to produce an informative visualization that balances aesthetics and clarity.

Introduction

A single data point—when the first measurable snow of a season fell—can tell a compact story about climate variability, seasonal shifts, and local weather patterns. Visualized across decades, first-snow dates reveal trends, outliers, and the variability that matters to planners, commuters, and outdoor industries. Turning that data into an evocative, accurate chart requires careful definition of "season," correct extraction of first-snow events, and design choices that respect both aesthetics and accessibility.

This guide translates raw seasonal-snowfall records into a clear scatterplot: season/year on one axis, month of first snowfall on the other, and point size proportional to the amount of snow in that month. It walks through data preparation (for daily or monthly inputs), DAX formulas to compute first-month measures, options for the visual itself (Power BI core visuals, Deneb/Vega-Lite, or HTML Content), and design patterns to make the chart legible and engaging while remaining accessible.

The workflow is data-driven, reproducible, and adaptable to any location with at least ~40 years of data—Denver’s seasonalsnowfall dataset is one example; the methods apply equally to municipal, state, or national snow records.

Where to get the data and what it looks like

Reliable sources:

• US National Weather Service seasonal snowfall pages (e.g., https://www.weather.gov/bou/seasonalsnowfall for Denver).
• NOAA/NCDC archives, local climate stations, state climatology offices.
• City or airport meteorological station datasets.

Typical formats:

• Daily records: date, snowfall (inches), precipitation flags.
• Monthly aggregates: year, month, total snowfall for that month.
• Seasonal tables: total snowfall per season, often already grouped by the meteorological "season" used by the provider.

Which format to use depends on availability. Daily data gives the most precise first-snow detection. Monthly data reduces resolution (you'll detect the first month containing any snowfall rather than the first date). Both are valid; the techniques below handle either.

Establishing the season: how to define "first snowfall of the season"

A critical step is choosing a season definition. Visual consistency and defensibility hinge on this decision.

Common choices:

• October–September season: if your region typically sees snowfall beginning in October, define a season as Oct 1 through Sep 30 (season label "YYYY–YYYY+1" where Oct–Dec use the lower year).
• July–June or September–August: useful if you prefer a “water year” or a local administrative standard.
• Calendar year (Jan–Dec): sometimes used when working with datasets that already aggregate by calendar year.

Recommendation:

• Use October 1 through September 30 for Northern Hemisphere locations where fall-to-spring snowfall matters. That aligns the first snow with the fall that begins the following winter.
• Be explicit: create a season label like "2019–2020" and store a season numeric key for sorting.

DAX formula example for season (Oct–Sep logic):

SeasonLabel =
VAR YearNum = YEAR( 'Date'[Date] )
VAR MonthNum = MONTH( 'Date'[Date] )
RETURN
IF(
    MonthNum >= 10,
    FORMAT(YearNum, "0000") & "–" & FORMAT(YearNum + 1, "0000"),
    FORMAT(YearNum - 1, "0000") & "–" & FORMAT(YearNum, "0000")
)

Create a SeasonSort numeric column to keep the seasons in chronological order:

SeasonSort =
VAR YearNum = YEAR( 'Date'[Date] )
VAR MonthNum = MONTH( 'Date'[Date] )
RETURN
IF(
    MonthNum >= 10,
    YearNum * 100 + MonthNum,   -- October of YearNum onward
    (YearNum - 1) * 100 + MonthNum
)

If you work with monthly aggregated records, apply the same mapping logic to the Year and Month fields.

From daily records to a "first-snow month" table

Daily data gives the best fidelity. The goal: for each season, find the earliest date with measurable snowfall, then extract the month and snowfall amount of that date (or amount aggregated for the month if you prefer month totals).

Workflow summary:

1. Ensure data quality: confirm units (inches or cm), missing value handling, and station homogenization.
2. Add Date table and relate it to the weather table on Date.
3. Add SeasonLabel and SeasonSort columns as shown above.
4. Determine the first snowfall date (or first date where snowfall >= threshold) per season.
5. Extract month and snowfall amount corresponding to that first date.
6. Build an aggregated table or measures that the visuals will use.

Thresholds:

• "Measurable snowfall" typically means 0.1 inches (0.254 cm) or 0.01 inches depending on station reporting. Use the same threshold consistently.
• For automated detection, create a boolean column: FirstSnowCandidate = IF('Weather'[Snowfall] >= 0.1, 1, 0).

DAX patterns for daily data If you want a calculated table with one row per season showing first-snow date:

FirstSnowBySeason = 
ADDCOLUMNS(
    SUMMARIZE('Date', 'Date'[SeasonLabel], 'Date'[SeasonSort]),
    "FirstSnowDate",
        CALCULATE(
            MIN( 'Weather'[Date] ),
            FILTER(
                'Weather',
                'Weather'[Snowfall] >= 0.1
                && 'Weather'[SeasonLabel] = EARLIER( 'Date'[SeasonLabel] )
            )
        )
)

Note: EARLIER with Date table may require adjusting relationships; an alternative safer pattern uses SUMMARIZE over season labels pulled from the Weather table itself:

FirstSnowBySeason =
SUMMARIZE(
    FILTER( 'Weather', 'Weather'[Snowfall] >= 0.1 ),
    'Weather'[SeasonLabel],
    'Weather'[SeasonSort],
    "FirstSnowDate", MIN( 'Weather'[Date] )
)

Then add columns for the month and snowfall amount on that date:

FirstSnowBySeason = 
ADDCOLUMNS(
    FirstSnowBySeason,
    "FirstSnowMonthNum", MONTH( [FirstSnowDate] ),
    "FirstSnowMonthName", FORMAT( [FirstSnowDate], "MMMM" ),
    "FirstSnowAmount", 
        CALCULATE(
            SUM( 'Weather'[Snowfall] ),
            FILTER( 'Weather', 'Weather'[Date] = [FirstSnowDate] )
        )
)

If the station reports zero on the first date but the first measurable was later, the MIN over filtered rows handles this.

If you want the amount to be the total snow for that month rather than the single date, replace the Last calculation with a monthly aggregation:

"FirstSnowMonthAmount",
CALCULATE(
    SUM( 'Weather'[Snowfall] ),
    FILTER(
        ALL( 'Weather' ),
        YEAR( 'Weather'[Date] ) = YEAR( [FirstSnowDate] )
        && MONTH( 'Weather'[Date] ) = MONTH( [FirstSnowDate] )
    )
)

This yields the snowfall total in that month.

Monthly aggregated datasets If your source is already monthly, the task is simpler: within each season, find the earliest month where the monthly snow total is >= threshold. A single table scan with MINX works:

FirstSnowMonthMonthly =
SUMMARIZE(
    'MonthlyWeather',
    'MonthlyWeather'[SeasonLabel],
    "FirstSnowMonthNum",
        MINX(
            FILTER(
                'MonthlyWeather',
                'MonthlyWeather'[SeasonLabel] = EARLIER('MonthlyWeather'[SeasonLabel])
                && 'MonthlyWeather'[Snowfall] >= 0.1
            ),
            'MonthlyWeather'[MonthNum]
        ),
    "FirstSnowAmount",
        MINX(
            FILTER(
                'MonthlyWeather',
                'MonthlyWeather'[SeasonLabel] = EARLIER('MonthlyWeather'[SeasonLabel])
                && 'MonthlyWeather'[Snowfall] >= 0.1
            ),
            'MonthlyWeather'[Snowfall]
        )
)

Note: If no snow occurred in a season, handle nulls explicitly and decide how to display those seasons (e.g., skip, plot at “No snow”, or mark as missing).

DAX measures you’ll want in the report

Rather than relying exclusively on calculated tables, create measures for dynamic visuals and tooltips. Here are recommended measures:

1. Season count (for axis ordering; often unnecessary if using a Season table)
2. First Snow Month Number (for plotting on the Y axis)
3. First Snow Month Label (for tooltip)
4. Snow Amount at First Month (for bubble size)
5. Rank or anomaly measures (optional): rank seasons by earliest or latest first-snow month, compute deviations from long-term median/mean.

Example measures assuming you have a Season table connected appropriately:

Measure: FirstSnowDate (per selected Season)

FirstSnowDate =
VAR SelectedSeason = SELECTEDVALUE( 'Season'[SeasonLabel] )
RETURN
CALCULATE(
    MIN( 'Weather'[Date] ),
    FILTER(
        ALL( 'Weather' ),
        'Weather'[SeasonLabel] = SelectedSeason
        && 'Weather'[Snowfall] >= 0.1
    )
)

Measure: FirstSnowMonthNum

FirstSnowMonthNum =
VAR d = [FirstSnowDate]
RETURN IF( ISBLANK(d), BLANK(), MONTH(d) )

Measure: FirstSnowMonthName

FirstSnowMonthName =
VAR d = [FirstSnowDate]
RETURN IF( ISBLANK(d), "No snow", FORMAT( d, "MMMM" ) )

Measure: FirstSnowMonthAmount

FirstSnowMonthAmount =
VAR d = [FirstSnowDate]
RETURN
IF(
    ISBLANK( d ),
    BLANK(),
    CALCULATE(
        SUM( 'Weather'[Snowfall] ),
        FILTER( ALL( 'Weather' ), 'Weather'[Date] = d )
    )
)

If using monthly aggregated input, replace date logic with the minimal month number and then sum where month equals that month and season equals the selected season.

Point-sizing measure for bubble chart Bubble charts often expect a non-negative size metric. Scale the snow amount into a reasonable point size range to avoid overly tiny or huge bubbles:

BubbleSize =
VAR amt = [FirstSnowMonthAmount]
RETURN
IF( ISBLANK( amt ), 0, LOG10( amt + 1 ) * 10 )

Adjust constants (log base and multiplier) to tune visual scaling.

Building the scatterplot in Power BI core visuals

Basic bubble scatter:

• Axis: Season (categorical). Use SeasonSort for axis sorting or use Season numeric key on the X-axis and format labels with SeasonLabel.
• Y-axis: FirstSnowMonthNum (1..12) or FirstSnowMonthName with an ordering column so months sort correctly from Jan to Dec or Oct to Sep depending on season definition. If you prefer a vertically intuitive axis, map months to numeric positions and show month names as data labels.
• Details: SeasonLabel (helps ensure one point per season).
• Size: BubbleSize measure or [FirstSnowMonthAmount] scaled.
• Tooltip: Include [FirstSnowMonthName], [FirstSnowMonthAmount], [FirstSnowDate], maybe the long-term median and rank.

Styling tips for a readable scatter:

• Use clear tick marks on the Y-axis (Months). Label months succinctly (Oct, Nov, Dec, Jan…).
• If plotting months across a fall-to-spring season, show the seasonal order rather than calendar order: Oct, Nov, Dec, Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep—if that’s how you define the season.
• Avoid overlapping points: jitter slightly in X or Y or enable categorical spacing that separates seasons. For years where first-snow months fall in the same month across multiple seasons, jitter can prevent overplotting. Jitter can be simulated via a small random offset in a custom visual or by mapping season to a numeric axis with tiny fractional offsets in the measure used for X.

Handling seasons with no snowfall

• Do not plot a value for seasons where no measurable snow fell; leave the point absent and annotate the year in a small table below or in the tooltip.
• Alternatively, plot a special glyph at the bottom of the chart with a different color and a clear legend label "No measurable snowfall."

Elevating the visual with custom visuals: Deneb (Vega-Lite) and HTML Content

If you want snowflake-shaped markers, glow animation, and falling-into-place effects, the core scatter lacks that flexibility. Two robust options:

1. Deneb (Vega-Lite) Deneb exposes Vega-Lite and Vega for advanced charting inside Power BI. You can map data fields to custom marks, use images/SVG symbols, and add entrance transitions.

Basic Vega-Lite idea:

• Use point marks with shape set to an image URL (snowflake SVG) or use a custom symbol by encoding a path.
• Use size encoding to map to FirstSnowMonthAmount.
• Add a "delay" transform for each mark based on SeasonSort to orchestrate falling/appear animations.
• Use y encoding to map month number; x encoding to season numeric key.

Example Vega-Lite skeleton (abbreviated):

{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "data": { "name": "dataset" },
  "mark": {
    "type": "image",
    "width": 20,
    "height": 20
  },
  "encoding": {
    "x": { "field": "SeasonSort", "type": "quantitative", "axis": {"title":"Season"} },
    "y": { "field": "FirstSnowMonthNum", "type": "quantitative", "axis": {"title":"First Snow Month", "values":[10,11,12,1,2,3,4]} },
    "url": { "field": "SnowflakeURL", "type": "nominal" },
    "size": { "field": "FirstSnowMonthAmount", "type": "quantitative" },
    "tooltip": [
      {"field":"SeasonLabel","type":"nominal","title":"Season"},
      {"field":"FirstSnowMonthName","type":"nominal","title":"Month"},
      {"field":"FirstSnowMonthAmount","type":"quantitative","title":"Snow (in)"}
    ]
  },
  "config": {
    "mark": { "opacity": 0.95 }
  }
}

Deneb also supports Vega-level signals and custom transitions for animated entry. Be careful to keep animation subtle and accessible; don’t rely on motion alone to convey information.

2. HTML Content visual The HTML Content custom visual can render arbitrary HTML/CSS/JS and accept fields as inputs. It is well-suited for decorative elements:

• Render SVG snowflakes as image or inline SVG for crisp scaling.
• Use CSS animations (keyframes) for gentle falling and a glow on hover.
• Accept Power BI binding fields for Season, Month, Size, Tooltip text.
• Use ARIA attributes and fallback text for accessibility.

Caveats:

• The HTML Content visual runs scripts in an embedded iframe and has size/performance constraints. Keep animation lightweight.
• Embedding large background images will increase report size; use compressed images and set CSS background-size to cover with appropriate compression.

SVG snowflake example Inline SVG lets you tune color and glow via CSS filters:

<svg width="40" height="40" viewBox="0 0 24 24" role="img" aria-label="Snowflake">
  <g fill="none" stroke="#e6f4ff" stroke-width="1.2" stroke-linecap="round" stroke-linejoin="round">
    <!-- simplified snowflake path -->
    <path d="M12 2v20M2 12h20M4 4l16 16M4 20L20 4" />
  </g>
</svg>

Add CSS for a glow:

svg:hover g { filter: drop-shadow(0 0 6px rgba(176, 224, 255, 0.9)); transform: scale(1.05); }

Animate entry using CSS transforms and delays based on SeasonSort.

Designing for readability and comprehension

Design choices should make patterns easier to interpret without sacrificing aesthetics.

Axes and labels:

• Use explicit month ticks and labels rather than expecting viewers to infer from positions.
• Show year labels sparsely on the X-axis if there are many seasons (every 2–5 years).
• Add light gridlines keyed to months for easy horizontal reading across seasons.

Color, contrast, and legends:

• Use a limited palette: cool blues and whites for snow; contrasting accent for outliers (e.g., a late first-snow colored orange).
• Ensure color contrast meets WCAG AA for text and legend elements.
• Do not rely on color alone—use shape or border differences for accessibility.

Sizing:

• Map bubble sizes to a perceptually linear scale where possible. Log scaling often helps compress a few huge snowfall months so that smaller but meaningful values remain visible.
• Include a size legend with example bubbles and numeric labels.

Annotations:

• Label notable seasons (extremely early or very late first-snow months).
• Display median first-snow month line and an interquartile band to express central tendency and dispersion.
• Add hover tooltips that show raw counts and contextual statistics (median, percentiles, station elevation).

Background and decorative elements:

• Use a subtle snowy background image or gradient at low opacity to set tone but keep it from competing with data marks.
• Place the background at the report page level, not inside the visual, so it will not be clipped or cause performance issues.
• Use a semi-transparent white/blue panel behind the chart to ensure axis text is readable over the background.

Animation: tasteful, not distracting

• Avoid continuous motion that distracts from analysis.
• Use a one-time entrance animation (marks ease into position when the report opens).
• Enable a glow on hover to provide micro-interaction feedback.

Interaction and tooltip best practices

Tooltips should give context without overwhelming.

Essential tooltip fields:

• Season label (e.g., 2019–2020)
• First-snow date (exact date if daily data)
• First-snow month (name and number)
• Snow amount in that month (with unit)
• Station and data source (e.g., NWS Denver)
• Link or callout to raw data or "view more" if your environment supports it

Interactivity:

• Allow cross-filtering: clicking a point should highlight the season and filter other visuals (e.g., a small table of daily events).
• Add a tooltip page for longer narratives or small multiples that show the full season’s chronology.
• Consider a Play Axis or time-slider to animate season-by-season comparisons over several decades.

Keyboard accessibility:

• Not all custom visuals provide keyboard access. If accessibility is required, supplement the visual with a table/stats card accessible to keyboard and screen-reader users.
• Add data labels or an accessible data table export option.

Export and printing:

• Consider how the visual will export to PDF. Avoid heavy backgrounds that print poorly. Provide a "print-friendly" button that toggles a neutral background and removes animation.

Quality control and troubleshooting

Common issues and fixes:

1. Duplicate points for a season

• Cause: Multiple records qualify as the “first” due to identical dates or monthly aggregation producing multiple rows.
• Fix: Aggregate first-snow detection to a single canonical date (MIN) per season, then join back to get snow amount.

2. Missing seasons or nulls

• Cause: No measurable snow according to threshold.
• Fix: Decide whether to show a special glyph for "no snow" or leave blank and list missing seasons in a table. Use BLANK-safe DAX.

3. Overlapping bubbles

• Cause: Several seasons have first-snow in the same month.
• Fix: Apply slight jitter in X or Y for distribution or use transparency and stroke to reveal overlap.

4. Inconsistent season definitions

• Cause: Mixed use of calendar year vs. seasonal year in source data.
• Fix: Normalize before calculating first-snow using a consistent SeasonLabel expression applied to every row.

5. Performance problems with Deneb or HTML

• Cause: Large number of marks, heavy images, or complex animations.
• Fix: Pre-aggregate to one mark per season; compress SVGs; limit animation; use vector shapes instead of multiple images.

Real-world interpretations and use cases

This visualization supports multiple practical uses:

• City planning and road maintenance: Track trends in first-snow timing to adjust seasonal staffing and treatment schedules.
• Retail and supply-chain planning: Retailers selling winter goods can correlate early/late first snow with inventory timing.
• Public communications: Newsrooms can create an intuitive graphic to explain an unusually late or early first-snow to the public.
• Climate research: Combine first-snow month series with temperature anomalies to investigate relationships between warming and seasonal onset.
• Recreation and tourism: Ski resorts can use long-term patterns of first-snow to inform season-opening predictions.

Case example (conceptual) A city’s report shows a clustering of first-snow months in November, with occasional January outliers. Annotating outliers with additional context—such as altitude of the station, influence of El Niño/La Niña years, or urban heat island effects—helps stakeholders evaluate whether the outlier signals operational risk or an isolated meteorological event.

Packaging and sharing the report

• Publish the report to Power BI Service with clear page-level descriptions and alt text on visuals where supported.
• Include a data dictionary page: define season logic, threshold for measurability, units, and data source with retrieval date.
• If distributing outside Power BI (PNG or PDF), provide a CSV export of the FirstSnowBySeason table for reproducibility.

Licensing and provenance:

• Note data licensing and attribution; for NWS/NOAA data, include the attribution line and date of download.
• Encourage users to cite the station ID and elevation for regional analyses.

Ethics and limitations

• Be transparent about station changes (relocation, instrumentation changes) that can create artificial shifts in first-snow timing.
• Avoid overinterpreting short-term trends. Long-term shifts require rigorous statistical methods beyond simple scatterplots.
• Document the threshold chosen for measurable snowfall. Small changes in threshold can change which month is detected as first-snow.

Troubleshooting checklist before publishing

• Verify season labels and order across the whole dataset.
• Confirm units and convert consistently (inches vs. cm).
• Spot-check a handful of seasons by inspecting original daily logs.
• Check for duplicate or missing data that would affect the MIN calculation.
• Validate bubble size scaling across small and large snowfall events.

Practical implementation checklist (step-by-step)

1. Obtain and inspect the data (daily preferred).
2. Create or import a comprehensive Date table.
3. Add SeasonLabel and SeasonSort columns (consistent across tables).
4. Create measures or a calculated table for the first-snow date per season.
5. Derive month number, month name, and snowfall amount for the first-snow month.
6. Create BubbleSize measure (apply a perceptually scaled transform).
7. Build a scatterplot with Season (X) and MonthNum (Y); size by BubbleSize and tooltip populated.
8. Choose core or custom visual. Use Deneb or HTML Content to add snowflake markers and animation if desired.
9. Tune design: axis labels, size legend, gridlines, background, and annotation.
10. Run accessibility checks: color contrast, tooltip clarity, keyboard alternatives.
11. Validate with spot comparisons to raw data and export a reproducible data table.
12. Publish and document data provenance.

FAQ

Q: Which “season” should I use? A: Choose the season boundary that best reflects the local climatology. For many Northern Hemisphere sites, Oct 1–Sep 30 is sensible because snowfall commonly begins in October. Use the same definition consistently across all rows and document your choice. Provide a SeasonLabel field like “2019–2020” and a SeasonSort numeric for correct ordering.

Q: My data is monthly. Can I still find the “first snowfall”? A: Yes. With monthly data you detect the first month that contains measurable snowfall rather than the first date. Use the earliest month within the season where the monthly snow total meets or exceeds your measurability threshold. Be explicit in the report that you’re using monthly resolution.

Q: How should I handle seasons with no measurable snow? A: Options include leaving those seasons blank on the scatter, plotting a special glyph (e.g., hollow circle labeled “No snowfall”), or listing them in a companion table. Avoid plotting zero on the month axis as it conflates no-snow with an earlier month.

Q: What threshold should I use to define measurable snowfall? A: Meteorological practice often uses 0.1 inch (0.254 cm) as a cutoff for measurable snowfall when station sensors report to the hundredth of an inch. Choose a threshold consistent with the station’s reporting precision and document it.

Q: How do I scale bubble sizes so both small and large snowfall months are visible? A: Use a transform such as logarithmic scaling or a square-root transform on the snowfall amount before mapping to bubble size. Add a legend illustrating what specific bubble sizes represent. Example: BubbleSize = LOG10( FirstSnowMonthAmount + 1 ) * 10, then tune the multiplier.

Q: Can I animate the points so snowflakes “fall into place”? A: Yes with Deneb (Vega/Vega-Lite) or the HTML Content custom visual. Implement entry transitions for the marks. Keep animations subtle, short, and optional. Do not use continuous motion; prefer an initial entrance animation. Provide a non-animated alternative for users with motion sensitivity.

Q: How do I ensure the visualization is accessible? A: Use sufficient color contrast, supply text alternatives or an accessible data table, avoid reliance on color alone, and ensure tooltips and page descriptions provide the same information available visually. For custom visuals, check keyboard navigation and provide an alternate tabular view for screen reader users.

Q: Should I annotate statistical context like median or trends? A: Yes. Add a horizontal line showing the median first-snow month plus a shaded interquartile range to communicate dispersion. Consider a secondary visualization (small multiple or line chart) showing median first-snow month over rolling periods to illustrate trends statistically.

Q: Where should I store and publish the underlying data? A: Archive the raw data and processing steps in a reproducible repository (internal server, GitHub, or dataset library). In the Power BI report, provide a data source note with retrieval URL and date. For government data, include the standard attribution line from the provider.

Q: Can I use multiple stations? A: Yes. You can create a multi-series scatter or small multiples for stations. Ensure consistent season definitions and processing across all stations. Use facets (small multiples) for clean comparisons rather than overlaying many points in a single chart.

Q: Any final tips? A: Keep the visualization tightly focused: the scatter should answer “when did the first snow occur?” not try to tell the whole seasonal snowfall story alone. Pair it with a supporting table or small multiples for deeper analysis. Document assumptions and choices, and validate the pipeline with spot checks against raw data.