Alpine Snowmelt Timing Maps That Outpace Pack Forecasts

Spring alpine travel is a guessing game until you learn to read the snow itself. National pack forecasts give you a region-wide number, but that number melts differently on a north-facing chute at 3200 m versus a solar-stoked talus slope at 2600. I've seen parties waste three hours postholing on a slope that the map said would hold, while corn snow on an adjacent face could've been skied hours earlier. The gap between 'forecast melt date' and actual timing can be five days or more in a warm spring. That's the difference between a pristine descent and a slog.

This guide is for people who need to know not just when the snow melts, but where first, and at what rate. It's built from a decade of combining satellite imagery, weather station data, and ground-truth notes. The maps you'll make won't replace field judgment, but they'll give you a window that static models can't touch.

Who Gets Stung by Lagging Pack Forecasts

The corn-skier, the wet-slide prospector, and the June 14th ridge traverse

Back in May a friend pinned a dream line in the North Cascades—south-facing, forty degrees, perfect corn by late morning. He trusted a regional pack forecast that said “melt progressing normally.” Normal was wrong. That slope had shed its snow five days early. He post-holed through alder hell for two hours and turned around at the toe of the glacier. The corn was gone; the run was jagged ice. That’s the cost of lagging data: not just disappointment but real hazard—exhaustion, route failure, a missed weather window that doesn’t come again.

Corn skiers live and die by the melt window. So do wet-slab avalanche hunters. When the snowpack transitions from dry to isothermal, the probability of a destructive wet slide spikes. A four-day-old map showing 30% snow cover where it’s actually 10%? That’s how you pick a slope that already let go. I’ve guided parties straight into that trap—once, in the Sierra, after a model said “patchy coverage” and we found continuous wet slab at treeline. Nobody got buried, but the whumpf under our skis was louder than any forecast warning.

Then there’s the June 14th ridge traverse. Not a date—a mindset. Every year a specific week exists where north-facing ramps are still snowed in but south aspects are bare or rotten. Miss it by three days and the link-up turns into 2 km of loose scree and down-climbing gullies. Static broader forecasts can’t resolve that line—they blend basin-scale melt rates into mush. Your route collapses because the model never saw the hundred-meter aspect change that governs your crux pitch.

What a 4-day offset costs in real terms

The numbers are brutal. When satellite-derived snow-covered-area rasters update weekly—which many public products do—the typical latency hits three to six days depending on cloud cover and processing lag. A four-day offset means you're reading the snow line from last Tuesday. In high solar-radiation terrain at 40° north, alpine snow can retreat 150 meters elevation in four days. That’s not a margin—it’s a cliff. Late June on a south face? Two hundred meters. Your “safe approach” gully becomes a moat of bergschrunds and running water. The thin snowbridge you counted on? Gone.

What usually breaks first is judgement: you commit to a line that the pack no longer supports. The consequence isn’t just a longer day—it’s a bivy, a rescue, or a fall through a concealed stream channel. I lost a pair of Dynafit bindings that way—post-holing across a “continuous snowfield” that was actually a crust over a torrent. The map showed 80% cover. On the ground: 30% floating islands of death.

Why static model resolution is your enemy

Most publicly available snow models run at 1 km or coarser. That’s roughly one data point per square kilometer. A typical valley in the Coast Range contains five distinct aspects, three elevation bands, and a wind-loaded lee zone. The model smears them into one number—75% snow cover—and calls it done. For a ridge walker or ski mountaineer that number is useless. The north-facing bowl might hold solid snow; the south-facing exit ramp might be bare and talus-covered. You can’t see the ramp until you’re standing above it, and by then the route is baked in.

A 1-km snow pixel doesn't know about your couloir. It knows about the whole mountainside.

— field observation, Sierra Nevada, late June

The fix isn’t “better forecasts”—it’s making your own map at the resolution that matches your terrain. That starts with pulling raw rasters, not trusting a smoothed bulletin. The catch: most people stop at the pretty picture. They never check the pixel age or the cloud mask. That’s what we fix next.

What You Need Before Making Maps

Satellite data access you can actually trust

You need a reliable feed of snow-covered-area rasters. MODIS delivers daily 500 m resolution—good enough for regional melt timing, but the catch is cloud cover. Sentinel-2 gives you 10 m pixels, sharp enough to see a single basin, yet revisit intervals stretch 5 days. Landsat 8 and 9 sit at 30 m, every 8 days, but the Collection 2 surface reflectance products are cleaner than anything from five years ago. Most teams skip this: register for NASA Earthdata Login before you hit the field season. That takes 48 hours to clear if your institution is new. I have seen people block a whole weekend because they assumed MODIS order-by-date was instant. It's not. Also grab a Copernicus Open Access Hub account for Sentinel downloads—free, no nonsense, but the API throttles after 20 concurrent pulls. Quick reality check—if you chase 10 m resolution across a 100 km mountain range, you will drown in gigabytes before the first melt.

A GIS tool or Earth Engine account—pick one

You have two paths. Desktop GIS: QGIS with the Semi-Automatic Classification Plugin works, but you manually stack 90–120 rasters per season. That hurts. Google Earth Engine lets you script the whole pull in 60 lines of JavaScript—no downloads, no local storage fights. The trade-off is learning the Earth Engine API syntax and debugging server-side errors with zero local logs. What usually breaks first is the date filter: people write filterDate('2023-03-01', '2023-06-30') and forget MODIS year-doy format. Wrong order. Or they skip the cloud-mask band and get 40 % of their scene as white noise. Most beginners should start with Earth Engine because you can kill a bad computation without trashing your hard drive. That said, if your home range sits near a border or in a data-sparse zone, you will still need local weather station feeds to ground-truth the satellite signal.

Weather station data feeds for your zone

Pack forecasts lag because they generalize across elevation bands. Your map needs point obs from SNOTEL or CoCoRaHS to calibrate melt onset. Grab at least three stations inside or adjacent to your watershed—one low, one mid, one high. The problem: SNOTEL stations have sensor drift after heavy snow-load years. Not yet in the archive? Check the daily SWE values against the satellite-derived snow-covered-area. If your MODIS scene says 80 % snow while the station shows zero SWE on June 1, you have a timing mismatch—likely the satellite over-predicts persistence on north-facing slopes. That is why your map becomes useless. A single rhetorical question worth asking: can your workflow ingest station data automatically, or are you copying CSV columns into a spreadsheet every Tuesday? Copy-paste is the pitfall that kills repeatability.

'The hardest part is not the code—it's knowing which pixels to trust and which are still hiding under afternoon clouds.'

— backcountry skier who rebuilt their map monthly, then switched to automated feeds

We fixed this by building a simple Earth Engine script that pulls MODIS NDSI, masks clouds with QA band 1, and writes a CSV of snow-covered fraction per elevation band. Then we overlay SNOTEL melt dates as point validation. The first time you see your own map contradict the regional forecast—and the station confirms the map—you will never go back to pack forecasts. That feeling is worth the setup headache. Start with accounts now. The rasters will wait.

Reality check: name the recreation owner or stop.

Pull Snow-Covered-Area Rasters Step by Step

Selecting scenes by window and cloud cover

You want the week before your usual melt-out date, plus three weeks prior. That window catches the transition from continuous snow to patchy exposure. I open the Sentinel-2 browser, draw a polygon around my target basin, and filter by date range. Cloud cover—under 20% or the scene is worthless. The catch: low cloud cover often means no recent scenes at all, especially in maritime ranges where storms roll through weekly. You lose a day waiting for a clear pass. Better to accept 30% cloud cover and mask those pixels later.

Filtering for your specific alpine basin

Most people grab the full tile—wrong move. That tile includes valleys, forests, shaded north faces, and lakes that never hold snow. Clip to your drainage boundary or a buffered ridge line. We fixed this by burning a watershed shapefile into the processing chain early—before any band math runs. The result: a raster that only covers your terrain, not the flat farmland 20 kilometers south. Does this matter? Yes, because valley pixels bias the snow-covered-area statistic downward, making you think melt is slower than it's. Slower means you arrive on a wet slope holding afternoon slush.

A quick aside—if your basin spans two satellite tiles, merge them before clipping. The seam blows out if you clip first and merge later. I have seen people stitch four tiles and end up with a checkerboard of nodata gaps. That hurts. Use gdal_merge.py with the same resolution and projection; don't let the software auto-resample unless you want 10-meter pixels next to 20-meter ones.

Exporting binary snow/no-snow GeoTIFFs

You have a few cloud-masked, clipped scenes. Now you need the snow line as a hard boundary. Band 3 (green) and Band 8 (near-infrared) give you the normalized difference snow index—NDSI = (Green – SWIR) / (Green + SWIR). That formula is standard but the threshold is not. For alpine basins with mixed rock and ice, 0.4 works; for forested edges you push to 0.6. We set the threshold after inspecting three clear scenes manually—plotting histograms until the separation looked right. Export as 1-bit GeoTIFF: 1 = snow, 0 = no snow, and –9999 for cloud-mask nodata. That file is small, fast to load, and you can stack multiple dates into a single 3D array.

Pro tip: name the files with ISO date strings—2025-05-12_sca.tif—not "scene42_final_v2.tif". You will thank yourself three weeks later when you automate the snapshot. Wrong order: downloading first, then deciding what to clip. Not yet. Define your basin polygon before you touch any API. Do it once, do it right.

“The first time I ran this workflow I used full tiles for ten basins—the stack weighed 12 GB and the snow-covered-area curve flatlined for two weeks. Clipping fixed everything.”

— backcountry partner who now preps his own melt maps for the Sierra

That sounds fine until you realize your clipped raster still has cloud shadows misclassified as snow. Shadows on steep east faces drop NDSI values below your threshold—suddenly a shaded snowfield disappears from the binary. The fix: apply a topographical mask—exclude slopes steeper than 50 degrees for north aspects, where shadows dominate. Or use a multi-temporal filter: if a pixel was snow in the previous two scenes and disappears in one, flag it as suspect. Not perfect, but better than believing a single date that says your basin is suddenly bare.

Tools and Setup That Actually Work

Google Earth Engine vs. QGIS vs. a custom Python script

Most people start with QGIS. It's free, familiar, and you can load a snow-cover raster in under a minute. That sounds fine until you try to stitch together a full season of daily MODIS scenes—then QGIS chokes, your laptop fan screams, and you're waiting 20 minutes for a single reproject. I have seen three different backcountry teams abandon QGIS mid-winter because they couldn't batch-process 90 days of data without the program crashing. The catch is that QGIS is still the best option if you only need one or two snapshots per month—say, for a single basin you know well. For anything more, you need the cloud.

Google Earth Engine handles a 500 MB scene like it's a postage stamp. The trade-off is a steeper learning curve: you write JavaScript or Python snippets, not click buttons. Most people give up after the first failed export. But once you get a script running, you can pull snow-covered-area rasters for an entire mountain range in under five minutes. Quick reality check—Earth Engine is free for non-commercial use, but the export quotas are real. If you need daily data for three basins across two states, you will hit the limits within a month. A free-tier hobby account won't sustain that. Paid tiers start around $100 per month; worth it if you're making maps for a guide service or a ski patrol.

Not every outdoor checklist earns its ink.

Custom Python script—that's for the person who already writes code. You lose the visual interface entirely but gain full control over reprojection, clipping, and the exact snow-cover threshold (MODIS band 3 vs. band 6—they differ). What usually breaks first is the local storage. You download 500 MB scenes, process them, and suddenly your laptop is out of space. I have a friend who runs his pipeline on a $50 Raspberry Pi with an external SSD; it works, but it's slow. One rhetorical question: do you want a fast, reliable map this weekend or do you want to tinker with code for three days?

Not every outdoor checklist earns its ink.

Free vs paid data tiers

MODIS is free. Sentinel-2 is free. Landsat 8 is free. That's enough for 95% of snowmelt timing work. The pitfall is resolution: MODIS at 500 m per pixel will blur small drainages into nothing. Sentinel-2 at 10 m is sharp enough to see individual gullies, but the revisit time is worse—five days vs. MODIS's daily pass. If your target basin is narrow and north-facing, Sentinel-2 might miss the melt window entirely because of cloud cover. That hurts.

Not every outdoor checklist earns its ink.

Not every outdoor checklist earns its ink.

Not every outdoor checklist earns its ink.

Paid data—think PlanetScope or Maxar—gives you daily sub-meter imagery. The cost is prohibitive for a personal project: $500–$2,000 per square kilometer per year. I have only seen paid data used by avalanche forecasters working for resorts or DOT. For a home-range map, free data works if you composite multiple scenes and accept a bit of fuzziness. Trade-off: you lose precision but save your wallet.

Hardware that won't choke on a 500 MB scene

We tried running MODIS on a 2015 MacBook Air. It took 40 minutes to load one layer. That was the moment we bought a refurbished workstation.

— Backcountry guide, Colorado Front Range, winter 2023

That quote is not exaggerated. A 500 MB GeoTIFF with 30+ bands will cripple a machine with 8 GB of RAM. Minimum specs that we have tested: 16 GB RAM, a quad-core processor (Intel i5 or Apple M1), and an SSD—not a spinning hard drive. Even then, QGIS will lag on large mosaics. The workaround is to pre-clip the raster to your AOI before loading it into QGIS; that shrinks the file by 60–80%. Earth Engine users can ignore hardware entirely—all processing happens server-side. The only bottleneck is your internet speed. If your connection drops mid-export, you lose the job and have to restart. That's maddening. We fixed this by running Earth Engine exports overnight, then downloading in the morning. Wrong order? Not if you value your sanity.

Adapting the Workflow for Different Constraints

Timing windows: early vs late meltout zones

The same workflow that nails melt timing on a south-facing talus slope can fail hard on a north-facing cirque that holds snow into July. I’ve watched people run the standard MODIS-based method across a whole mountain range and then wonder why their "snow-free" map still shows white patches in mid-summer. The catch is simple: meltout timing isn’t uniform—it’s a spectrum. Early zones (low elevation, exposed ridges) might shed snow by late May; late zones (shaded bowls, high plateaus) can linger until August. You can adapt by clipping your study area to a single aspect or elevation band first. That sounds fine until you realize a 500-meter elevation range still includes deep gullies that trap snow. So cut your raster stack into 250-meter elevation slices or by aspect quadrants—whatever your data resolution supports. Then run the pixel-by-pixel threshold check separately for each slice. Wrong order? That burns time, but the trade-off is accuracy: one threshold for the whole basin underestimates melt timing in cold pockets and overestimates it on hot slopes.

Data gaps: handling persistent cloud or steep terrain

The biggest frustration I encounter is not the algorithm—it’s missing pixels. Cloud cover in spring can block half your scene for weeks. MODIS passes daily, but a single persistent cloudbank over a valley floor leaves you with no clean observation for a critical window. One fix: composite across a 7-day moving window instead of a single day, accepting lower temporal resolution for higher spatial coverage. That works, but it blurs the melt date by ±3 days. Another approach: switch to Landsat 8/9 (30-meter pixels) for cloudy regions—it has fewer overpasses but better cloud masking. Steep terrain adds its own headache. Shadows in deep canyons get flagged as snow cover by early algorithms; I have seen false melt dates two weeks late because a shaded pixel never showed as "snow-free." The fix is a terrain-shadow mask from a DEM—burn those pixels out of your analysis entirely. A blunt tool, but better than letting shadows corrupt your map.

"Used a single cloud-filter threshold for a whole basin? You just mapped dirt temperature, not snowmelt timing."

— Field hydrologist, Sierra Nevada survey

User types: I'm a phone-based scout vs. a desk mapper

If you’re standing on a talus slope pulling up a map on your phone, you need a different adaptation than someone sitting at a workstation with QGIS open. The phone scout can’t rerun the full Python script in the field. So you precompute a "meltout date grid" as a single GeoTIFF, upload it to a mobile GIS app (like Avenza or Gaia GPS), and query it with a tap. That works for a single watershed—but you lose temporal nuance. The desk mapper, by contrast, can afford to iterate: try three different cloud-mask settings, check the melt curves, and adjust the threshold manually. I’ve done both: the phone map gets me to the right ridge, but the desk version catches the cold patch that held snow an extra five days, saving me from postholing through wet slush. The pitfall for the phone scout is oversimplification—a single threshold might be off by a week in mixed terrain. The desk mapper’s trap is overfitting—spending hours tweaking parameters instead of finishing one usable map. Pick your adaptation based on how much flexibility you can actually execute, not how much you wish you had.

Most teams skip is the base constraint: what satellite data actually reaches your region. In the Alps, Sentinel-2 is dense; in the Alaska Range, MODIS’s daily revisit beats Landsat’s 16-day gap despite coarser resolution. Adapt to what you can reliably get, not what the tutorial used. That harsh reality—data availability dictates method choice—should guide every decision, not the other way around.

Common Pitfalls That Make Your Map Useless

Cloud shadow misclassified as snow

The most common failure I see in automated snowmaps is also the sneakiest. A band of dark pixels draping across a south-facing slope—cloud shadow—gets tagged as snow because the NDSI value barely crosses the threshold. On a Sentinel-2 scene from early May, shadows can drop reflectance in the green band enough that the ratio flips. You pull what looks like clean snow-covered area, and the melt curve includes a phantom patch that never existed. Fixing this means layering a cloud mask that differentiates shadow from water or snow by using the shortwave infrared band (band 11 or 12) and a local cosine correction. But even that fails if the shadow falls over bright granite or talus. The real check: overlay a hillshade from the same acquisition and look for abrupt brightness changes that align with cloud direction. Not glamorous. It saves your season.

Static NDSI thresholds that don't fit your lithology

That default NDSI cutoff of 0.4 works fine on forested slopes in the Cascades. On the volcanic lithology of the Oregon high desert? It drowns. Pumice and light-colored ash deposits routinely hit NDSI values above 0.35 in summer, triggering false snow persistence. Conversely, dirty firn on an alpine glacier might read 0.38 and you discard usable data. The pitfall is assuming a single value applies across your entire study area. I have seen teams process 3,000 km² of the Sierra Nevada and end up with a melt map that shows snow lasting two weeks longer on the east side—simply because rhyolite outcrops there raise the false-positive rate. Corrective check: sample NDSI over known snow-free peaks for your region in early August. If the median exceeds 0.3, adjust your threshold up by 0.05 increments until those pixels drop out. Painful. Worth it.

Odd bit about recreation: the dull step fails first.

Overlooking aspect-driven melt heterogeneity

Snow doesn't melt south-facing first, north-facing last like the textbook says—not when you have complex terrain. A single raster of snow-covered area averaged over a week can smooth out the fact that on June 15 the north bowl still holds 80 percent coverage while the south-facing chute is bare. If your map collapses that into one snow/no-snow binary at a coarse temporal step, you miss the fine-scale recession that matters for route planning or runoff timing. The fix is to generate separate melt curves for four or eight aspect classes and only merge them after you verify that each class transitions independently. Quick reality check—plot your snow-depletion date against aspect for a single sub-basin. If all aspects show identical timing, your threshold or mask is wrong. That seam blows out your weekly snapshot.

Odd bit about recreation: the dull step fails first.

Odd bit about recreation: the dull step fails first.

‘A single threshold works across 90 percent of scenes until it doesn’t—and the ten percent will break your trust in the map.’

— Field notes from a Cascades snow-survey season; the author now runs per-scene histograms before batch processing

Odd bit about recreation: the dull step fails first.

Odd bit about recreation: the dull step fails first.

Frequently Asked Questions on Snowmelt Timing

How often does satellite data update?

Every day, in theory. MODIS and VIIRS sensors sweep the whole planet daily, but cloud cover is the real throttle. On the Pacific Crest, I have seen weeks where only two clear-ish scenes emerge for a single basin. Sentinel-2 revisits every five days at mid-latitudes, yet shadows in tight valleys can ruin half the pixels. The catch is—fresh doesn't mean usable. A scene from three days ago under clear skies beats a “today” scene that's 80% cloud. Most teams skip this: they pull the latest mosaic and wonder why their melt line jumps backward. Check the cloud mask first. Pull the last usable scene, not the last scene.

What's the smallest basin I can map reliably?

Around 10 square kilometers. MODIS native resolution is 500 meters per pixel, so a basin that's five pixels wide gives you a ragged boundary. I have tried it on a 4 km² cirque—the result was a checkerboard of noise. Sentinel-2 at 10 meters can resolve smaller features, but it trades temporal density: five-day revisit means you might miss the melt window entirely in a fast spring. A good rule: if your watershed fits inside a single hiking hour, satellite timing will frustrate you. The trade-off is clear—fine spatial scale kills temporal frequency. You can combine both sensors, but then you fight different projection systems and spectral bands. That hurts.

What usually breaks first is the difference between a basin’s mean elevation and the point where snow actually clings. A 12 km² basin dropping from 4,000 to 2,800 meters can still have a distinct melt front. Below 5 km², the edge becomes a statistical noise bath. Quick reality check—open your DEM, count the pixels that fall inside your basin at MODIS scale. Under 20 pixels? Find a different approach.

How do I ground-truth a pixel in the field?

Stand at the center of a map pixel. If your boots sink into wet snow and your GPS says the snow depth exceeds 10 cm, that pixel is still snow-covered. If you see bare ground and rock, it’s gone. Simple—but nobody does this systematically.

— anecdote from a Sierra guide who spent three springs hiking the same transect every week

You don't need a spectrometer. Print a 1:24,000 map with the pixel grid overlaid. Hike to the center of three to five pixels per basin—target one high, one mid, one low in elevation. Take a photo with a timestamped GPS. Record two things: is the ground >50% snow, and how deep is the melt layer? A wet, patchy pixel that's 40% bare is already melted for your purposes—packing a camp on wet moss is different from snow camping. The critical pitfall: people claim a pixel is “accurate” because they saw a snow patch from the trail. Don’t do that. You need to walk to the pixel center, not eyeball from half a mile away.

One more thing—timing. Ground-truth within 48 hours of the satellite pass. A warm afternoon can turn a firm snowfield into slush, but the satellite already captured the morning reflectance. If you check at 4 p.m., you're validating a different condition. I fixed this by scheduling field days on the same afternoon the satellite overpass happens—local noon for Sentinel-2, late morning for MODIS. Write the pass time on your map. Stick to it.

Next Step: Automate a Weekly Snapshot for Your Home Range

Setting up a recurring Earth Engine script

You have the rasters flowing — now make them come to you. The trick is turning a one-off pull into a Monday-morning ritual. I use Earth Engine’s Export.table.toDrive wrapped in a simple ui.Chart.image.series that emails me the latest snow-covered-area polygon when it finishes. Set a trigger via Earth Engine Apps or a cron job on a cheap VM that hits the REST API every Tuesday at 6 AM. The catch: Google’s free tier throttles concurrent exports, so stagger your regions. One team I know scripts three overlapping tiles and merges them locally — that keeps each export under the 16-hour wall-clock limit.

You also need a fail-safe. What happens when the satellite source changes — say, MODIS gets decommissioned and VIIRS takes over? Hard-code a fallback image collection ID and a date check. Most people skip this; their pipeline dies silently. Instead, wrap the export in a try-catch that logs to a public sheet. A short if (collectionSize === 0) { Export. … } saves your season.

Exporting to a field-friendly format

GeoTIFFs are useless on a phone in a canyon with no signal. Export your snow-free-date raster as a KML polygon with a time_start attribute. Earth Engine can reduce the raster to contour lines or polygonized zones — use reduceToVectors() with a 15-day interval. Sure, you lose precision, but a 10 MB KML loads on a Garmin inReach. That beats a 200 MB TIFF that crashes Gaia GPS. Quick reality check — vectorizing too coarse a threshold (like 30 days) masks early-melt pockets that kill a traverse. Trade off detail for portability, but test the boundary at 10 days first.

For mobile maps, export a GeoJSON to Google Drive and serve it via a simple web app that your phone’s browser can fetch. I have seen groups push the KML into Avenza’s custom map format using a free converter. That path works until the coordinate system shifts — always export in EPSG:4326 with WGS84. Wrong projection? The seam blows out and your route line floats 500 meters off.

Pairing with local obs networks for calibration

Your map shows bare ground on June 5. But the local SNOTEL station still reads 10 cm of snow. That mismatch isn't an error — it’s a signal. Pull SNOTEL daily SWE data from the NRCS API and overlay it as a time series on your Earth Engine chart. The snow-persistence raster is a model; the ground truth is a dirty pressure sensor. When the two diverge by more than a week, your snowCover threshold in the ee.Image.updateMask() is too aggressive. Crank it from 15% to 30% and re-run. I once chased a phantom early melt for three weeks — turned out the band I chose (NDSI_Snow_Cover) had a cloud-shadow bug in that tile. Pairing with a webcam snapshot from a nearby trailhead confirmed the snow was still there.

“Our auto-generated melt map said the ridge was dry. The local ranger’s photo showed the opposite — we hiked anyway and post-holed up to our thighs.”

— comment from a Pacific Crest section-hiker, summer 2024

That anecdote stings because it’s avoidable. Build a slackbot or SMS alert that fires when the map’s predicted snow-free date differs more than 7 days from the nearest obs point. Not a fancy dashboard — just a curl posting to a channel. Then you adjust the threshold mid-season, not after a rescue. That’s the loop: automate the snapshot, format it for your device, and cross-check with a sensor you trust. Do that weekly, and by July you’ll own your range’s melt timing better than any regional forecast.