🌦️Atmospheric Science Unit 18 – Atmospheric Remote Sensing Techniques
Atmospheric remote sensing techniques use electromagnetic radiation to gather information about the atmosphere from a distance. These methods involve passive detection of natural radiation and active emission of energy to measure backscattered radiation, allowing scientists to infer atmospheric properties.
Remote sensing instruments, including radiometers, spectrometers, lidars, and radars, are deployed on various platforms like satellites, aircraft, and ground stations. These tools enable the study of atmospheric absorption, scattering, and radiative transfer, providing crucial data for weather forecasting and climate monitoring.
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 (λ), frequency (ν), and energy (E)
Shorter wavelengths correspond to higher frequencies and higher energies (E=hν, 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 μm) used for imaging and detecting clouds, aerosols, and surface features
Infrared (0.7-100 μm) 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
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