You’ve probably used a weather app. Maybe you’ve even built one. But do you actually know what a radar map is really telling you – or hiding from you? As a developer and founder of Rain Viewer, I’ve spent the last decade parsing radar feeds, filtering out noise, and making chaotic atmospheric data digestible for humans – and APIs. If you’re building anything that touches geolocation, logistics, drones, or weather-dependent automation, understanding radar is more than curiosity – it’s the base. Here’s your crash course in reading radar like a dev, not a tourist. Radar ≠ Rain: Understand What You’re Seeing Radar ≠ Rain: Understand What You’re Seeing First myth to bust: radar doesn’t “see” rain.It measures reflectivity (DBZH) – radio waves bouncing offsomething. That “something” could be: something rain
hail
snow
birds & insects
dust or pollen
even aluminum chaff from planes rain hail snow birds & insects dust or pollen even aluminum chaff from planes Most apps simplify this into colored blobs. But that abstraction can hide a lot. Red doesn’t always mean danger, green doesn’t always mean a light shower. How to tell what’s real: How to tell what’s real: Check RHOHV (correlation coefficient): 0.9? likely precipitation.
<0.7? could be birds, bugs, or chaff. 0.9? likely precipitation. <0.7? could be birds, bugs, or chaff. Check VRAD (radial velocity): erratic jumps? probably biological. erratic jumps? probably biological. Check ZDR (differential reflectivity): high ZDR? flat raindrops. near zero? hail or clutter. high ZDR? flat raindrops. near zero? hail or clutter. Example: if you see a blob with low RHOHV and chaotic VRAD, congratulations – you’ve spotted a swarm of birds. Radar Is Always Delayed Radar Is Always Delayed Radars don’t stream live video – they scan in rotation, upload in batches, then mosaic into frames. Expect 5–10 minutes of latency at best.For drones or real-time route optimization, factor this in – or you’re chasing ghosts. Dev tip: Always read the metadata timestamps.
Don’t combine frames from mismatched scans.
Add a buffer window or fallback logic in latency-critical systems. Always read the metadata timestamps. Don’t combine frames from mismatched scans. Add a buffer window or fallback logic in latency-critical systems. Motion Matters: Watch the Loop Motion Matters: Watch the Loop A single frame is just a snapshot. But storms move fast.That cell 20km away, moving at 60 km/h, could be overhead in 20 minutes. To build smarter UX: To build smarter UX: Animate loops with directional arrows.
Compute storm vectors by comparing successive frames.
Use interpolation between frames for smoother animations. Animate loops with directional arrows. Compute storm vectors by comparing successive frames. Use interpolation between frames for smoother animations. In Rain Viewer, we invested months optimizing storm tracking and arrow overlays – because pattern velocity beats position every time. velocity Signal ≠ Truth: Beware Artifacts Signal ≠ Truth: Beware Artifacts Radars don’t just pick up weather.Mountains, buildings, planes, wildlife, and temperature inversions all generate false echoes. Here are the common culprits – and how to catch them: Artifact

What it looks like

How to detect/filter



Ground Clutter

Persistent blob at low elevation

Static masks, Gabella filter



Death Rings (AP)

Concentric rings expanding outward

Only at low elevation, disappears at higher scans



Birds/Insects

Smudges that jump in VRAD

Low RHOHV + erratic velocity



Chaff (military countermeasure)

“Snowflakes” in DBZH, no motion

Very low RHOHV



Dust/Pollen

Weak streaks moving with wind

Low intensity & high correlation Artifact

What it looks like

How to detect/filter



Ground Clutter

Persistent blob at low elevation

Static masks, Gabella filter



Death Rings (AP)

Concentric rings expanding outward

Only at low elevation, disappears at higher scans



Birds/Insects

Smudges that jump in VRAD

Low RHOHV + erratic velocity



Chaff (military countermeasure)

“Snowflakes” in DBZH, no motion

Very low RHOHV



Dust/Pollen

Weak streaks moving with wind

Low intensity & high correlation Artifact

What it looks like

How to detect/filter Artifact Artifact Artifact What it looks like What it looks like What it looks like How to detect/filter How to detect/filter How to detect/filter Ground Clutter

Persistent blob at low elevation

Static masks, Gabella filter Ground Clutter Ground Clutter Ground Clutter Persistent blob at low elevation Persistent blob at low elevation Persistent blob at low elevation Static masks, Gabella filter Static masks, Gabella filter Static masks, Gabella filter Death Rings (AP)

Concentric rings expanding outward

Only at low elevation, disappears at higher scans Death Rings (AP) Death Rings (AP) Death Rings (AP) Concentric rings expanding outward Concentric rings expanding outward Concentric rings expanding outward Only at low elevation, disappears at higher scans Only at low elevation, disappears at higher scans Only at low elevation, disappears at higher scans Birds/Insects

Smudges that jump in VRAD

Low RHOHV + erratic velocity Birds/Insects Birds/Insects Birds/Insects Smudges that jump in VRAD Smudges that jump in VRAD Smudges that jump in VRAD Low RHOHV + erratic velocity Low RHOHV + erratic velocity Low RHOHV + erratic velocity Chaff (military countermeasure)

“Snowflakes” in DBZH, no motion

Very low RHOHV Chaff (military countermeasure) Chaff (military countermeasure) Chaff (military countermeasure) “Snowflakes” in DBZH, no motion “Snowflakes” in DBZH, no motion “Snowflakes” in DBZH, no motion Very low RHOHV Very low RHOHV Very low RHOHV Dust/Pollen

Weak streaks moving with wind

Low intensity & high correlation Dust/Pollen Dust/Pollen Dust/Pollen Weak streaks moving with wind Weak streaks moving with wind Weak streaks moving with wind Low intensity & high correlation Low intensity & high correlation Low intensity & high correlation How to clean your data: How to clean your data: Use multi-product checks (DBZH + RHOHV + VRAD + ZDR).
Run Gabella filter (gradient + morphological analysis).
Compare scans at different elevation angles. Use multi-product checks (DBZH + RHOHV + VRAD + ZDR). Run Gabella filter (gradient + morphological analysis). Compare scans at different elevation angles. Pro tip: The more products you combine, the better your noise filtering. Where to Get Radar Data Where to Get Radar Data If you want to experiment yourself, here are reliable open datasets: NOAA – US raw & MRMS (cleaned composite)
EUMETNET OPERA – European composite
opendata.fmi.fi – Finland
opendata.dwd.de – Germany
data.europa.eu – various EU datasets NOAA – US raw & MRMS (cleaned composite) EUMETNET OPERA – European composite opendata.fmi.fi – Finland opendata.fmi.fi opendata.fmi.fi opendata.dwd.de – Germany opendata.dwd.de opendata.dwd.de data.europa.eu – various EU datasets data.europa.eu data.europa.eu Most raw data comes in HDF5, BUFR, or netCDF formats –  so be ready to parse. Why It Matters Why It Matters Whether you’re building a delivery app, an autonomous drone, or just love hacking on weather data, radar literacy is crucial. You’ll stop treating those colorful blobs as gospel – and start seeing the patterns, pitfalls, and possibilities underneath. Next time you look at a radar map, don’t just check if it’s raining. Read it.

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