Satellite and radar meteorology are game-changers in weather forecasting. These tools give us a bird's-eye view of the atmosphere, helping meteorologists track storms, predict severe weather, and understand global weather patterns.

From geostationary satellites watching entire hemispheres to Doppler radar detecting rain and wind, these technologies provide crucial data. They've revolutionized forecasting, allowing for more accurate predictions and earlier warnings of dangerous weather events.

Satellite Meteorology for Forecasting

Satellite Types and Sensing Techniques

• Satellite meteorology uses artificial satellites to observe and analyze atmospheric conditions from space, providing a global perspective on weather patterns
• Two main types of meteorological satellites
  • Geostationary satellites
    • Remain stationary relative to Earth's surface
    • Provide continuous coverage of a specific region
  • Polar-orbiting satellites
    • Orbit from pole to pole
    • Provide global coverage with higher resolution at higher latitudes
• Satellites utilize various sensors to detect electromagnetic radiation across different wavelengths
  • Visible spectrum (reflected sunlight)
  • Infrared spectrum (emitted heat)
  • Microwave spectrum (atmospheric moisture and temperature)
• Remote sensing techniques enable measurement of atmospheric parameters
  • Multispectral imaging captures data across multiple wavelength bands
  • Atmospheric sounding determines vertical profiles of temperature and humidity

Applications and Products

• Satellite data crucial for monitoring severe weather events
  • Tracking tropical cyclones (hurricanes, typhoons)
  • Detecting large-scale atmospheric circulation patterns (jet streams, frontal systems)
• Advanced satellite products contribute to numerical weather prediction models
  • Atmospheric motion vectors derived from cloud movement
  • Cloud-top heights used to estimate storm intensity
  • Sea surface temperatures for ocean-atmosphere interaction analysis
• Limitations of satellite meteorology
  • Resolution constraints (especially for smaller-scale phenomena)
  • Cloud obstruction of lower atmospheric layers
  • Complex data interpretation algorithms required

Interpreting Satellite Imagery

Cloud Analysis and Weather System Identification

• Visible satellite imagery provides information during daylight hours
  • Cloud patterns reveal atmospheric dynamics
  • Cloud types indicate atmospheric stability
  • Cloud thickness suggests precipitation potential
• Infrared imagery allows for 24-hour monitoring
  • Cloud-top temperatures indicate cloud height and potential storm intensity
  • Cold cloud tops (bright white in enhanced IR) suggest strong convection
  • Warm cloud tops (darker in enhanced IR) indicate lower-level clouds
• Water vapor imagery reveals mid to upper-level moisture content
  • Dark areas indicate dry air aloft
  • Bright areas suggest high moisture content
  • Aids in identifying jet streams and potential storm development areas
• Recognizing characteristic cloud patterns associated with specific weather systems
  • Comma-shaped clouds indicate mid-latitude cyclones
  • Spiral bands suggest tropical cyclones
  • Clustered, circular patterns often represent thunderstorm complexes

Advanced Interpretation Techniques

• Cloud top temperature gradients in infrared imagery indicate storm intensity
  • Rapid cooling of cloud tops suggests intensifying convection
  • Warming of cloud tops may indicate storm weakening
• Multispectral analysis techniques enhance feature distinction
  • Combining visible and IR bands to differentiate cloud types
  • Using water vapor and IR bands to assess atmospheric instability
• Time-lapse sequences track weather system evolution
  • Observing storm development and dissipation
  • Analyzing frontal movement and interaction
  • Monitoring large-scale circulation pattern changes
• Limitations in satellite interpretation
  • Difficulty distinguishing cloud layers in visible imagery
  • Potential misinterpretation of low clouds in IR imagery (similar temperature to surface)
  • Challenges in estimating precipitation rates solely from satellite data

Radar Meteorology for Forecasting

Radar Principles and Technologies

• Radar (Radio Detection and Ranging) emits and detects reflected radio waves
  • Measures distance and intensity of precipitation and other atmospheric targets
  • Operates in various wavelengths (S-band, C-band, X-band)
• Doppler effect in radar systems measures radial velocity
  • Detects movement towards or away from the radar
  • Provides information on wind speeds and directions within storms
• Dual-polarization radar technology enhances hydrometeor classification
  • Transmits and receives both horizontal and vertical polarizations
  • Distinguishes between different types of precipitation (rain, snow, hail)
  • Improves estimation of precipitation rates and totals
• Radar reflectivity measured in dBZ (decibels relative to Z)
  • Correlates with precipitation intensity
  • Typical values: light rain (20-30 dBZ), heavy rain (50+ dBZ), hail (60+ dBZ)
• Beam propagation characteristics influence data interpretation
  • Atmospheric refraction bends radar beam
  • Attenuation reduces signal strength over distance
  • Beam blockage by terrain or structures creates data voids

Radar Networks and Limitations

• Weather radar networks provide comprehensive coverage
  • NEXRAD (Next-Generation Radar) in the United States
  • Similar networks in other countries (UK, Australia, Japan)
• Real-time monitoring of precipitation and severe weather events
  • Continuous scanning at multiple elevation angles
  • Data updates typically every 5-10 minutes
• Limitations of radar meteorology
  • Beam blockage by terrain creates blind spots
  • Range-dependent resolution (lower resolution at greater distances)
  • Non-meteorological echoes can cause misinterpretation
    • Ground clutter (buildings, mountains)
    • Biological targets (birds, insects)
    • Anomalous propagation in certain atmospheric conditions

Analyzing Radar Data

Reflectivity and Storm Structure Analysis

• Reflectivity patterns in plan position indicator (PPI) displays
  • Reveal spatial distribution of precipitation
  • Higher dBZ values indicate stronger echoes and potentially more intense precipitation
  • Typical patterns: stratiform rain (uniform), convective (cellular), linear (squall lines)
• Vertical cross-sections provide storm structure information
  • Identify features like bright bands (melting layer)
  • Detect hail cores and updraft regions
• Volume scans offer three-dimensional storm analysis
  • Crucial for identifying severe weather features
    • Hook echoes (potential tornado signature)
    • Bounded weak echo regions (intense updrafts)
    • Mesocyclones (rotating thunderstorms)
• Dual-polarization products enhance precipitation classification
  • Differential reflectivity (ZDR) indicates particle shape
  • Correlation coefficient (CC) helps distinguish meteorological from non-meteorological targets
  • Specific differential phase (KDP) improves heavy rain estimation

Velocity Data and Precipitation Estimation

• Doppler velocity data reveals storm dynamics
  • Identifies mesocyclones (rotating updrafts in supercells)
  • Detects divergence patterns (potential downbursts)
  • Measures wind shear associated with severe thunderstorms and tornadoes
• Precipitation accumulation algorithms estimate rainfall totals
  • Utilize reflectivity-rainfall (Z-R) relationships
  • Account for variations in precipitation types
  • Essential for hydrological forecasting and flash flood warnings
• Recognition of specific radar signatures aids in severe weather anticipation
  • Bow echoes (potential for damaging straight-line winds)
  • TVS (Tornado Vortex Signature) indicates possible tornado formation
  • BWER (Bounded Weak Echo Region) suggests strong updraft and potential hail
• Integration of radar data with other sources enhances analysis
  • Combining radar with satellite imagery for comprehensive storm structure
  • Incorporating surface observations to verify precipitation types
  • Using numerical model data to anticipate storm evolution and movement