Did the power go out after this storm — and where?
A Before-After-Control-Impact natural experiment on real satellite data — the affected area vs. a comparable unaffected control, before vs. after. The change has to clear an empirical noise floor to count as a real signal; a genuine null is reported as a null. Computed on Google Earth Engine — not yet scientist-verified.
Augusta, Georgia
Night-time lights over Augusta, Georgia changed by -44% after the event, control-corrected vs. a nearby less-affected city. A clear drop in light, consistent with widespread power loss.
Observational analog (a natural experiment over comparable places) — evidence, not a prediction for any one spot. Each bar shows the change versus the natural jitter between undisturbed control pixels; it must clear the ┊ tick to count as a real signal.
Provenance & full trace — reproducible
Every number came from this exact query on Google Earth Engine. Same query → same number.
Night-time lights over Augusta, Georgia changed by -44% after the event, control-corrected vs. a nearby less-affected city. A clear drop in light, consistent with widespread power loss.
San Juan, Puerto Rico
Night-time lights over San Juan, Puerto Rico changed by -33% after the event, control-corrected vs. a nearby less-affected city. A clear drop in light, consistent with widespread power loss.
Observational analog (a natural experiment over comparable places) — evidence, not a prediction for any one spot. Each bar shows the change versus the natural jitter between undisturbed control pixels; it must clear the ┊ tick to count as a real signal.
Provenance & full trace — reproducible
Every number came from this exact query on Google Earth Engine. Same query → same number.
Night-time lights over San Juan, Puerto Rico changed by -33% after the event, control-corrected vs. a nearby less-affected city. A clear drop in light, consistent with widespread power loss.
New Orleans, Louisiana
Night-time lights over New Orleans, Louisiana changed by -26% after the event, control-corrected vs. a nearby less-affected city. A clear drop in light, consistent with widespread power loss.
Observational analog (a natural experiment over comparable places) — evidence, not a prediction for any one spot. Each bar shows the change versus the natural jitter between undisturbed control pixels; it must clear the ┊ tick to count as a real signal.
Provenance & full trace — reproducible
Every number came from this exact query on Google Earth Engine. Same query → same number.
Night-time lights over New Orleans, Louisiana changed by -26% after the event, control-corrected vs. a nearby less-affected city. A clear drop in light, consistent with widespread power loss.
Fort Myers, Florida
Not measurable for Fort Myers, Florida: too few cloud-free Black Marble nights (clouds and the storm obscure the surface), so an outage can't be separated from noise.
Observational analog (a natural experiment over comparable places) — evidence, not a prediction for any one spot. Each bar shows the change versus the natural jitter between undisturbed control pixels; it must clear the ┊ tick to count as a real signal.
Provenance & full trace — reproducible
Every number came from this exact query on Google Earth Engine. Same query → same number.
Not measurable for Fort Myers, Florida: too few cloud-free Black Marble nights (clouds and the storm obscure the surface), so an outage can't be separated from noise.
Houston, Texas
Not measurable for Houston, Texas: too few cloud-free Black Marble nights (clouds and the storm obscure the surface), so an outage can't be separated from noise.
Observational analog (a natural experiment over comparable places) — evidence, not a prediction for any one spot. Each bar shows the change versus the natural jitter between undisturbed control pixels; it must clear the ┊ tick to count as a real signal.
Provenance & full trace — reproducible
Every number came from this exact query on Google Earth Engine. Same query → same number.
Not measurable for Houston, Texas: too few cloud-free Black Marble nights (clouds and the storm obscure the surface), so an outage can't be separated from noise.
Analysis-ready products for actual events that this question maps to — open each in the catalog, or browse them on the NASA Disasters Portal.
Draw a rectangle to pick your area of interest, then see what NASA data covers it (live, here in your browser) or download a ready-to-run notebook with your AOI pre-filled. The notebook runs in any Python environment — it needs a free Earthdata Login to fetch the data.
-82.15, 33.36 → -81.9, 33.55 (Augusta, Georgia)When a hurricane knocks out the grid, the lights themselves become the data. NASA's **Black Marble** measures how much light each place emits at night — so a city going dark after a storm is a visible, mappable signal of where power was lost. This is the analysis behind the Disasters Portal's Hurricane-Helene power-outage maps.
When a hurricane knocks out the grid, the lights themselves become the data. NASA’s Black Marble measures how much light each place emits at night — so a city going dark after a storm is a visible, mappable signal of where power was lost. This is the analysis behind the Disasters Portal’s Hurricane-Helene power-outage maps.
What you can answer
- Where the lights went out. Compare a clear pre-storm night to a post-storm night: neighbourhoods that dimmed or vanished are the likely outage areas.
- How recovery progresses — track the lights coming back over the following nights.
- Relative severity — bigger radiance drops over more people = higher-priority areas.
What you can NOT answer (be careful)
- It’s a proxy, not a meter. Black Marble measures light, not the grid directly. Clouds, moonlight, snow cover, and even wildfire smoke distort nighttime radiance — which is why the product is BRDF-corrected and why you compare clear nights and read it cautiously.
- Exact customer counts — light loss correlates with outages but isn’t a utility outage feed.
- Under thick cloud — a storm’s own clouds can blind the sensor; wait for the first clear night.
How you’d approach it
Take VNP46A2 for a clear night before and after, difference the radiance over your AOI, mask cloud-flagged pixels, and overlay population to rank affected areas. Supports the Respond phase of the NASA Disasters program. The interactive nightlights guide explains the method.
Make it yours → Set your AOI and pick clear pre-storm and post-storm dates, then overlay population to prioritize the hardest-hit areas.
The Before-After-Control-Impact vs a noise floor at the heart of this question — runnable on synthetic data, right here. The full earthaccess code template further down does it on real NASA data (needs an Earthdata login).