Context: This page directly addresses claims made on Twitter/X that global cloud measurements are "difficult or even impossible" and that satellites don't measure key factors affecting solar radiation. The evidence below demonstrates that these claims are factually incorrect.
"I have never seen figures for global cloud cover. I suspect such a thing is difficult or even impossible to measure, given the effect will vary with cloud thickness and altitude, as well as amount of cover, season and time of day. It blows CO2 correlation arguments out of the water"
View original tweet →"What matters is how much solar radiation gets through the cloud, which depends on thickness, time of day, season etc. Yes, I know we have satellites, but I doubt they include all of those factors in the measurements, let alone have trends for them"
View original tweet →Reality: Global cloud cover has been continuously measured by satellites since 1983.
Reality: Satellites measure both cloud thickness and altitude directly.
Reality: This is precisely what satellites measure.
Reality: Every factor mentioned is measured and tracked.
What this shows: How much energy from the sun clouds are blocking, measured directly in watts per square meter. This is the exact measurement claimed to be impossible - "how much solar radiation gets through the cloud."
Key finding: Clouds block approximately 79 W/m² of solar radiation globally (shortwave cloud radiative effect). This measurement accounts for cloud thickness, altitude, and solar angle.
📊 View CERES Data →What this shows: 34 years of measurements showing how cloud thickness (and therefore solar radiation blocking) has changed over time. Includes seasonal cycles and long-term trends.
Key finding: Cloud optical thickness increased by +21% from 1983 to 2017, with clear seasonal patterns and regional variations all measured and documented.
📊 View ISCCP Trends →What this shows: Maps showing where clouds are thickest, highest, and blocking the most solar radiation. Demonstrates that satellites measure all the factors claimed to be unmeasured: thickness, altitude, seasonal variation, and regional patterns.
📊 July 1983 Snapshot → 📊 January 2000 Snapshot → 📊 June 2017 Snapshot → 📊 November 1991 Snapshot → 📊 July 2008 Snapshot →What this shows: How cloud properties differ between tropics, mid-latitudes, and polar regions. Shows that satellites provide consistent global coverage across all climate zones.
Key finding: Each region has distinct cloud patterns that are continuously measured and tracked over 34 years.
📊 View Regional Analysis →What this shows: Direct comparison of winter vs summer cloud patterns globally. Addresses the claim that seasonal variations aren't measured.
Key finding: Seasonal patterns are clearly visible and measurable, with distinct differences between winter and summer cloud cover and thickness.
📊 View Seasonal Comparison →What this shows: How cloud measurements deviated from the 34-year average during extreme periods. Demonstrates measurement precision.
Key finding: Satellites can detect and quantify unusual cloud patterns, showing the measurements are precise enough to identify anomalies.
📊 View Anomaly Maps →What this shows: Relationships between different cloud measurements (cover, thickness, altitude, water content). Shows that multiple independent measurements validate each other.
Key finding: The correlations make physical sense and demonstrate data consistency and reliability.
📊 View Correlation Analysis →What this shows: How cloud properties vary with latitude, from equator to poles. Shows systematic patterns that demonstrate global coverage.
Key finding: Clear latitude-dependent patterns show satellites provide consistent measurements across all latitudes.
📊 View Zonal Averages →What this shows: The highest and lowest cloud cover and thickness periods over 34 years. Demonstrates the range of variability that satellites can measure.
Key finding: Extreme events are identifiable and quantifiable, showing measurement precision over decades.
📊 View Extreme Events →What this shows: How many observations satellites make at each location, and how complete the global coverage is.
Key finding: Global coverage is consistently >95%, with multiple observations per location providing statistical reliability.
📊 View Data Quality →Every factor claimed to be unmeasured or impossible to measure is, in fact, measured:
The data is publicly accessible, peer-reviewed, and has been used in thousands of scientific publications.