Uncertainty Management in Remote Sensing of Climate Data Summary of a Workshop (2009) / Chapter Skim
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2 Cross-Cutting Issues
2 Cross-Cutting Issues
Pages 11-23
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From page 11... ...
VALIDATION OF REMOTELY SENSED CLIMATE DATA Validation of parameters is an essential component of nearly all remote sensing-based studies and there are many considerations in performing validation. Errors in different validation techniques are complex and difficult to quantify.
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From page 12... ...
Inherent in many different techniques that are used in processing remotely sensed data is the issue of biases. A workshop participant described that bias in validation studies of some geophysical parameters occurs because of the uneven global distribution of surface cloud observations.
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From page 13... ...
In general, it does not rain very often and it is difficult to quantify rain amounts through rain gauges and compare this data with satellite measurements taken over many kilometers. The most common method for validating satellite rain estimates is to compare rain gauge data collected over a time interval during which the satellite passes over, with the satellite rain estimates.
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From page 14... ...
Caution should be taken in studies investigating interannual variability as this is often based on monthly means. The assumption that a monthly mean is based on 30 independent samples can lead to what looks like climate variation in the data, when it could actually be statistical noise.
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From page 15... ...
Validation exercises can generate spurious biases, such as trends that look like biases in the remote sensing method which are not really present in the data, but are byproducts of the analysis method. validated cloud microphysical properties, with data from multiple instruments, including satellite measurements, ground-based remote sensing measurements, and aircraft measurements.
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From page 16... ...
The primary challenge with this technique is the mismatch between spatially varying MODIS data and the temporally varying sunphotometer data so there are only select areas with coincident coverage in measurements between MODIS and AERONET. Since AERONET is a land-based network, it is difficult to match an overpass with an aerosol observation, and, as addressed earlier, the location of the ground-based observation within the satellite grid square is a consideration in validation process.
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From page 17... ...
This is in contrast, for example, to the situation in the nuclear weapons laboratories, which have already developed sophisticated methods of uncertainty quantification in order to address the policy question of whether aging warheads remain safe and functional in the absence of complete testing. With regard to testing of climate data and models, experiments are run to better understand specific physical processes, but there is no option for controlled experiments of complete systems.
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From page 18... ...
One of the tuning parameters examined was the ice crystal fall speed, and that paper concluded that it is an important parameter in a climate model, in part, because this process takes ice out of the upper troposphere where it shields the upward infrared radiation. Thus, ice fall speed turns out to be a powerful tuning knob for a climate modeler.
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From page 19... ...
CROSS-DISCIPLINARY COLLABORATIONS BETWEEN CLIMATE SCIENTISTS AND STATISTICIANS The challenge of understanding a system as complex as climate requires a partnership between geoscientists and statisticians because neither community has all of the expertise that is required. Each community is attempting to understand climate processes interacting at multiple scales and the workshop participants called for more sophisticated techniques to study these interactions that can benefit both communities.
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From page 20... ...
As described earlier, a primary goal for climate scientists is to understand the physical processes that are directly relevant to climate model, and this can be addressed through the use of statistical models. Many of the workshop participants recognized that interdisciplinary work is hard.
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From page 21... ...
The structure of the dataset needs to be analyzed to better understand the multiple physical processes that make up the climate system.
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From page 22... ...
In addition, the ability to infer details about the physical processes associated with Santa (e.g., his delivery of presents on Christmas Eve) is complicated by the fact that the only information available is through parameterizations that come from modeling efforts, much like the pa rameterizations in global climate models.
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From page 23... ...
CROSS-CUTTING ISSUES 23 FIGURE 2-2 Top: 100 variants of Santa Claus; Bottom: the average Santa Claus based on the 100 variants. Figure courtesy of Jason Salavon.
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