Statistical Analysis of Massive Data Streams: Proceedings of a Workshop (2004)
Chapter: 2. Philosophy of the use of remote sensing data for climate monitoring
Abstract of Presentation
Exploratory Climate Analysis Tools for Environmental Satellite and Weather Radar Data John Bates, National Climatic Data Center
1. Introduction
Operational data from environmental satellites form the basis for a truly global climate observing system. Similarly, weather radar provides the very high spatial and rapid time sampling of precipitation required to resolve physical processes involved in extreme rainfall events. In the past, these data were primarily used to assess the current state of the atmosphere to help initialize weather forecast models and to monitor the short-term evolution of systems (called nowcasting).
The use of these data for climate analysis and monitoring is increasing rapidly. So, also, are the planning and implementation for the next generation of environmental satellite and weather radar programs. These observing systems challenge our ability to extract meaningful information on climate variability and trends. In this presentation, I will attempt only to provide a brief glimpse of applications and analysis techniques used to extract information on climate variability. First, I will describe the philosophical basis for the use of remote sensing data for climate monitoring, which involves the application of the forward and inverse forms of the radiative transfer equation. Then I will present three examples of the application of statistical analysis techniques to climate monitoring: (1) the detection of long-term climate trends, (2) the time-space analysis of very large environmental satellite and weather radar data sets, and (3) extreme event detection. Finally, a few conclusions will be given.
2. Philosophy of the use of remote sensing data for climate monitoring
Remote sensing involves the use of active or passive techniques to measure different physical properties of the electromagnetic spectrum and to relate those observations to more traditional geophysical variables such as surface temperature and precipitation. Passive techniques use upwelling radiation from the Earth-atmosphere system in discrete portions of the spectrum (e.g., visible, infrared, and microwave) to retrieve physical properties of the system. Active techniques use a series of transmitted and returned signals to retrieve such information.
This is done by using the radiative transfer equation in the so-called forward and inverse model solutions. In the forward problem, sample geophysical variables, such as surface temperature and vertical temperature and moisture profiles, are input to the forward radiative transfer model. In the model, this information is combined with specified instrument error characteristics and responsivity to produce simulated radiances. The inverse radiative transfer problem starts with satellite-observed radiances. Because the inverse radiative transfer equation involves taking the inverse of an ill-conditioned matrix, a priori information, in the form of a first guess of the solution, is required to stabilize the matrix prior to inversion. The output of this process is geophysical
