🌦️Atmospheric Science Unit 18 – Atmospheric Remote Sensing Techniques

Key Concepts and Principles

• Remote sensing involves gathering information about the atmosphere from a distance using electromagnetic radiation
• Passive remote sensing detects natural radiation emitted or reflected by the atmosphere (visible light, infrared)
• Active remote sensing emits energy and detects the backscattered radiation (radar, lidar)
• Atmospheric properties (temperature, humidity, wind) can be inferred from the interaction of radiation with atmospheric constituents
• Radiative transfer theory describes how electromagnetic radiation propagates through the atmosphere
  • Accounts for absorption, emission, and scattering processes
  • Forms the basis for retrieving atmospheric properties from remote sensing measurements
• Inverse problem solving is used to estimate atmospheric parameters from the measured radiation
  • Involves complex algorithms and mathematical techniques (optimal estimation, machine learning)

Electromagnetic Spectrum Basics

• Electromagnetic radiation consists of oscillating electric and magnetic fields propagating through space
• Characterized by wavelength ($\lambda$), frequency ($\nu$), and energy ($E$)
  • Shorter wavelengths correspond to higher frequencies and higher energies ($E = h\nu$, where $h$ is Planck's constant)
• Different regions of the electromagnetic spectrum have unique properties and applications in remote sensing
  • Visible light (0.4-0.7 $\mu$m) used for imaging and detecting clouds, aerosols, and surface features
  • Infrared (0.7-100 $\mu$m) sensitive to temperature, water vapor, and greenhouse gases
  • Microwave (1 mm-1 m) penetrates clouds and provides information on precipitation, sea surface temperature, and soil moisture
• Atmospheric windows are wavelength ranges with minimal absorption, allowing radiation to pass through the atmosphere (visible, near-infrared, microwave)
• Atmospheric absorption bands are wavelength ranges where gases strongly absorb radiation (water vapor, carbon dioxide, ozone)

Remote Sensing Instruments and Platforms

• Radiometers measure the intensity of electromagnetic radiation at specific wavelengths
  • Scanning radiometers (AVHRR, MODIS) provide high spatial resolution images of the Earth's surface and atmosphere
  • Sounders (AIRS, IASI) measure vertical profiles of temperature and humidity using multiple spectral channels
• Spectrometers measure the spectrum of radiation over a wide range of wavelengths
  • Used to identify and quantify atmospheric constituents based on their unique absorption features (greenhouse gases, pollutants)
• Lidars emit laser pulses and measure the backscattered radiation to determine the vertical distribution of aerosols, clouds, and wind
• Radars emit microwave pulses and measure the backscattered radiation to detect precipitation, wind, and ocean surface properties
• Satellites provide global coverage and continuous observations of the atmosphere and Earth's surface
  • Polar-orbiting satellites (NOAA, MetOp) offer high spatial resolution but infrequent temporal sampling
  • Geostationary satellites (GOES, Himawari) provide continuous coverage over a fixed area with high temporal resolution
• Aircraft and ground-based instruments complement satellite observations with detailed measurements at specific locations

Atmospheric Absorption and Scattering

• Absorption occurs when atmospheric gases and particles convert electromagnetic energy into internal energy (heat)
  • Selective absorption by gases (water vapor, carbon dioxide, ozone) creates distinct absorption bands in the spectrum
  • Absorption depends on the gas concentration, pressure, and temperature
• Scattering redirects radiation in different directions due to interaction with atmospheric constituents
  • Rayleigh scattering by air molecules is strongest at shorter wavelengths (blue sky, red sunsets)
  • Mie scattering by aerosols and cloud droplets is more significant at longer wavelengths
  • Non-selective scattering by large particles (dust, ice crystals) affects all wavelengths equally
• Optical depth measures the attenuation of radiation as it passes through the atmosphere
  • Depends on the amount and properties of absorbing and scattering constituents
  • High optical depth indicates strong attenuation (opaque atmosphere), while low optical depth implies a more transparent atmosphere
• Radiative transfer models simulate the propagation of radiation through the atmosphere accounting for absorption and scattering processes
  • Used to interpret remote sensing measurements and retrieve atmospheric properties

Retrieval Techniques and Algorithms

• Retrieval algorithms estimate atmospheric parameters from the measured radiation using inverse problem solving
• Forward models simulate the expected radiation based on assumed atmospheric properties and instrument characteristics
  • Radiative transfer models are used to calculate the absorption and scattering of radiation
  • Instrument models account for the spectral response, viewing geometry, and noise characteristics of the sensor
• Optimization techniques adjust the assumed atmospheric properties to minimize the difference between the simulated and measured radiation
  • Least squares fitting finds the best match between the model and observations
  • Optimal estimation incorporates prior knowledge (climatology, physical constraints) to regularize the solution
• Machine learning approaches (neural networks, random forests) learn the relationship between the measured radiation and atmospheric properties from a large dataset
  • Trained on synthetic data generated by radiative transfer models or on collocated observations from other instruments
• Validation of retrieved products is essential to assess their accuracy and uncertainty
  • Comparison with independent measurements (radiosondes, ground-based instruments)
  • Analysis of retrieval residuals and sensitivity to input parameters

Data Processing and Interpretation

• Raw sensor data undergoes calibration to convert digital counts into physically meaningful radiances or reflectances
  • Accounts for sensor degradation, orbital drift, and changes in instrument settings over time
• Geolocation assigns geographical coordinates to each pixel based on the satellite orbit and attitude information
• Atmospheric correction removes the effects of absorption and scattering to retrieve surface properties (land cover, ocean color)
• Cloud detection and masking identify pixels contaminated by clouds using thresholds and statistical tests
• Data fusion combines observations from multiple sensors or platforms to create consistent and comprehensive datasets
  • Merging polar-orbiting and geostationary satellite data to improve spatial and temporal coverage
  • Assimilating satellite observations into numerical weather prediction models to initialize and constrain the simulations
• Visualization and analysis tools help users explore and interpret the remote sensing data
  • False-color composite images highlight specific features or properties (vegetation, water, snow)
  • Time series analysis reveals trends, cycles, and anomalies in atmospheric and surface parameters
  • Spatial pattern recognition identifies coherent structures and phenomena (fronts, cyclones, heat waves)

Applications in Weather and Climate Studies

• Nowcasting and short-term forecasting rely on frequent satellite imagery to monitor the development and motion of weather systems (clouds, precipitation, severe storms)
• Numerical weather prediction models assimilate satellite observations to improve the accuracy and consistency of the initial conditions
  • Radiances from infrared and microwave sounders constrain the temperature and humidity profiles
  • Atmospheric motion vectors derived from cloud and water vapor tracking provide information on the wind field
• Climate monitoring uses long-term satellite records to detect and attribute changes in the Earth system
  • Sea surface temperature, sea level rise, and ice sheet dynamics from altimeters and radiometers
  • Greenhouse gas concentrations and their spatial distribution from infrared spectrometers (AIRS, GOSAT)
  • Aerosol optical depth and type from visible and near-infrared imagers (MODIS, MISR)
• Satellite data support renewable energy applications by providing information on solar irradiance, wind speed, and cloud cover
• Agricultural monitoring benefits from remote sensing of vegetation health, soil moisture, and evapotranspiration
• Air quality assessment and forecasting rely on satellite measurements of pollutants (nitrogen dioxide, particulate matter) and their precursors

Limitations and Future Developments

• Cloud contamination affects the retrieval of atmospheric and surface properties, particularly in the visible and infrared wavelengths
  • Developing advanced cloud detection and removal techniques (multi-angle, multi-spectral, machine learning)
• Retrieval uncertainties arise from instrument noise, calibration errors, and imperfect forward models
  • Characterizing and propagating uncertainties through the retrieval process
  • Improving the accuracy and consistency of calibration across different sensors and platforms
• Limited vertical resolution of passive sounders hinders the ability to resolve fine-scale atmospheric structures
  • Developing advanced infrared and microwave sounder technologies with higher spectral resolution (IASI-NG, MTG-IRS)
• Trade-offs between spatial, temporal, and spectral resolution limit the ability to capture multi-scale processes
  • Designing constellations of small satellites (CubeSats) to provide high-resolution observations with frequent revisit times
• Integration of remote sensing data with in-situ measurements and numerical models is crucial for a comprehensive understanding of the Earth system
  • Developing data assimilation techniques that can handle the increasing volume and diversity of observations
  • Improving the representation of physical processes and feedbacks in Earth system models
• Continuity and consistency of long-term satellite records are essential for climate monitoring and trend detection
  • Ensuring the overlap and intercalibration of successive satellite missions
  • Reprocessing historical datasets using updated algorithms and ancillary information