Exploratory climate analysis tools for environmental satellite and weather radar data

John J. Bates, Chief

Remote Sensing Applications Division

National Climatic Data Center, NESDIS, NOAA

151 Patton Ave., Asheville, NC 28801

John.J.Bates@noaa.gov

1. Introduction

Operational data from environmental satellites form the basis for a truly global climate observing system. Similarly, weather radar provide 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 electro-magnetic 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 (Figure 1). 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 retrievals. The ultimate understanding of the satellite or radar data requires full application of the forward and inverse problems and the impact of uncertainties associated with each step in the process.

3. Detection of long-term climate trends using environmental satellite data

There are many steps in the processing of long-term satellite archive data for detection of climate trends. These include ingest and archive of the data and metadata, data characterization, pre-processing, derivation and application of retrieval algorithms, data validation, and application of trend statistical analysis and significance tests. During the ingest and archive steps, the data are first checked for gross errors and instrument stability. The data characterization is a critical and complex step involving calibration of each individual instrument and the intercalibration of multiple instruments of similar models flown on a sequence of spacecraft over decades. Both physical and empirical techniques are used to normalize a sequence of instruments to a baseline instrument and, then, to an absolute standard. Pre-processing involves additional steps, such as limb corrections for satellite viewing angle and cloudy-clear detection that are required for the specific data set and portion of the spectrum used. Next, the forward and inverse radiative transfer equations are used to develop and apply retrieval algorithms. Following validation of the retrieved data, statistical techniques are applied to the data to determine trends and the statistical significance of the trends.

Figure 2 shows an example of this process applied to the retrieval of upper tropospheric humidity (UTH); a parameter that is critical in understanding climate feedbacks. The top panel shows the mean 20-year climatology. High values of UTH are found over the monsoon regions of Africa, Austral-Asia, and South America and low values are found over the subtropical desert regions. The linear trends (middle panel) show increases in UTH over the equatorial regions and eastern Asia and decreases in the subtropics to lower mid-latitudes. The statistical significance test accounts for both the random errors of the linear fit and the red noise (lag-1 autocorrelation) of the time series at each grid point. We find that only the regions of largest trends are significant are greater than 80% confidence.

4. Time-space analysis of massive observational data sets

Satellite and radar data can be used to trace atmospheric phenomena such as clouds, precipitation, and water vapor. One technique for doing this is to reduce one of the spatial dimensions by averaging and then forming a composite by sampling along the remaining spatial dimension and the temporal dimension. Figure 3 shows an example of applying this technique to weather radar data from multiple radars in the U.S. Midwest. Data from a given latitudinal band are averaged and then the data are sampled along a given longitude for many time steps. The resulting time-space diagram can be used to infer the propagation speed of the phenomena of interest.

Additional processing of these time-space data sets has been used to reveal the nature of atmospheric wave phenomena in the global tropics. The example below uses upwelling infrared radiances (outgoing longwave radiation or OLR) to track cloud propagation. Twenty years of twice daily observations at 200 km resolution was analyzed by using fast Fourier transforms in time and space to produce a wavenumber-frequency diagram (Figure 4). In this figure, a background red noise wavenumber-frequency spectrum has been removed to reveal significant phenomena with preferred propagation direction, periodicity, and wavelength (in terms of zonal wavenumber). These wave phenomena were then compared to simple solutions of the dynamical equations of motion for equatorial phenomena (shown by the solid lines emanating from the 0, 0 coordinate). These wave phenomena include eastward propagating atmospheric Madden-Julian oscillations (MJO) and Kelvin waves and westward propagating equatorially-trapped Rossby (n=ER1) waves.

5. Extreme event detection using remotely sensed data

Another important question that climate scientists are attempting to answer using the massive amounts of remote sensing data is; are the frequency and/or intensity of extreme events changing? So far, techniques for data mining and pattern recognition for analyzing remote sensing data sets to extract such information are in their infancy. They have been largely developed for the forecasting and severe event warning communities. Figure 5 shows an example of the detection of a tornado signature in weather radar reflectivity (left panel) and Doppler radial velocity with the storm relative motion removed (right panel). A tornado vortex signature is identified as the small scale shear from positive (red) to negative (green) storm relative motion. These techniques are under development but difficult to apply as the tornado vortex signature is often ambiguous and difficult to distinguish from other phenomena.

6. Conclusions

The very high rate of data from environmental satellites and weather radars poses both major challenges and opportunities. The data rate makes it imperative to develop techniques to improve access and distribution of the data to the user community. The next generation of remote sensing data, however, will provide a quantum leap in the flow of data again. Techniques must be developed to compute meaningful and accurate quantities for climate monitoring both from past data and from future real-time observing systems. Such techniques hold great promise for improving our understanding of climate processes, particularly for long-term trend detection, time-space analysis of periodic atmospheric phenomena, and extreme event detection.

Dr. John J. Bates is the Chief of the Remote Sensing Applications Division of the U.S. National Oceanic and Atmospheric Administration National Climatic Data Center. Dr. Bates received a Ph. D. in Meteorology from the University of Wisconsin-Madison in 1986 under Professor William L. Smith on the topic of satellite remote sensing of air-sea heat fluxes. Dr. Bates then received a post-doctoral fellowship at Scripps Institution of Oceanography (1986-1988) to work jointly with the California Space Institute and the Climate Research Division. He joined the NOAA Environmental Research Laboratories in Boulder, CO in 1988 and there continued his work in applying remotely sensed data to climate applications. In 2002, Dr. Bates moved to the NOAA National Climatic Data Center in Asheville, NC.

Dr. Bates research interests are in the areas of using operational and research satellite data and weather radar data to study the global water cycle and studying interactions of the ocean and atmosphere. He has authored over 25 peer reviewed journal articles on these subjects. He served on the AMS Committee on Interaction of the Sea and Atmosphere (1987-1990) and the AMS Committee on Applied Radiation (1991-1994).

As a member of the U.S. National Research Council Global Energy and Water Cycle Experiment (GEWEX) Panel (1993-1997), Dr. Bates reviewed U.S. agency participation and plans for observing the global water cycle. He was awarded a 1998 Editors’ Citation for excellence in refereeing Geophysical Research Letters for, ‘thorough and efficient reviews of manuscripts on topics related to the measurement and climate implications of atmospheric water vapor’. He has also been a contributing author and U.S. government reviewer of the Intergovernmental Panel on Climate Change Assessment Reports. He currently serves on the International GEXEX Radiation Panel whose goal is to bring together theoretical and experimental insights into the radiative interactions and climate feedbacks associated with cloud processes, including the effects of water vapor within the atmosphere and at the Earth's surface