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Optimal Channel Selection

Optimal Channel Selection. Redundancy. “Information Content” vs. “On the diagnosis of the strength of the measurements in an observing system through the use of metrics that measure the amount of information contained within its observations”. Prior State Space. 0.64 μ m (H=1.20).

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Optimal Channel Selection

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  1. Optimal Channel Selection

  2. Redundancy “Information Content” vs. “On the diagnosis of the strength of the measurements in an observing system through the use of metrics that measure the amount of information contained within its observations”

  3. Prior State Space 0.64 μm (H=1.20) LWP (gm-3) 0.64 & 2.13 μm (H=2.51) 17 Channels (H=3.53) LWP (gm-3) Re (μm) Re (μm) Recall the Liquid Cloud Problem • Blue  a priori state space • Green  state space that also matches MODIS visible channel (0.64 μm) • Red  state space that matches both 0.64 and 2.13 μm channels • Yellow  state space that matches all 17 MODIS channels (only a factor of 2 better resolution)

  4. IWP = 100 gm-2 Re = 16 μm Ctop = 9 km Measurement Redundancy • Using multiple channels with similar sensitivities to the parameters of interest merely adds redundant information to the retrieval. • While this can have the benefit of reducing random noise, it cannot remove biases introduced by forward model assumptions that often impact both channels in similar ways as well.

  5. Channel Selection • The information content of individual channels in an observing system can be assessed via: where kj is the row of K corresponding to channel j. • The channels providing the greatest amount of information can then be sequentially selected by adjusting the covariance matrix via:

  6. Method • Evaluate Sy • Compute K • Establish prior information • Evaluate the information content of each channel, Hj, with respect to the a priori, Sa • Select the channel that provides the most information and update the covariance matrix using the appropriate row of K • Recompute the information content of all remaining channels with respect to this new error covariance, S1 • Select the channel that provides the most additional information • Repeat this procedure until the signal-to-noise ratio of all remaining channels is less than 1:

  7. Channel 1: Biggest resolution increase  H1 = 2.58 • Channel 2: Most improvement relative to new resolution  H2 = 1.58 • Channel 3: Refinement to final resolution  H3 = 1, Hf = 5.16 3 2 1 Measuring Stick Analogy • The first channel selected provides the greatest number of divisions of the measuring stick. • Each subsequent channel further refines the resolution of the observing system until none of the remaining channels contains enough information to further resolve state space. Full range of a priori solutions

  8. Optimizing Retrieval Algorithms • GOAL: Select optimal channel configuration that maximizes retrieval information content for the least possible computational cost by limiting the amount of redundancy in the observations • APPROACH: Use Jacobian of the forward model combined with appropriate error statistics to determine the set of measurements that provides the most information concerning the geophysical parameters of interest for the least computational cost

  9. IWP = 100 gm-2 Re = 16 μm Ctop = 9 km MODIS Cloud Retrievals Revisited x = (IWP, Tc, Re, α)

  10. Information Spectra • Relative to the a priori, the 11 μm channel provides the most information due to its sensitivity to cloud height and its lower uncertainty relative to the visible channels. • Once the information this channel carries is added to the retrieval, the I.C. of the remaining IR channels is greatly reduced and two visible channels are chosen next. IWP = 100 gm-2 Re = 16 μm Ctop = 9 km

  11. Unrealistic Errors • When a uniform 10% measurement uncertainty is assumed, the visible/near-IR channels are weighted unrealistically strongly relative to the IR. IWP = 100 gm-2 Re = 16 μm Ctop = 9 km IWP = 100 gm-2 Re = 16 μm Ctop = 9 km 10 %

  12. Thin Cloud (IWP = 10 gm-2) • For very thin clouds, the improved accuracy of IR channels relative to those in the visible increases their utility in the retrieval. IWP = 100 gm-2 Re = 16 μm Ctop = 9 km IWP = 10 gm-2 Re = 16 μm Ctop = 9 km

  13. Larger Crystals (Re = 40 μm) • At large effective radii, both the visible and IR channels lose sensitivity to effective radius. Two IR channels are chosen primarily for retrieving cloud height and optical depth. IWP = 100 gm-2 Re = 16 μm Ctop = 9 km IWP = 100 gm-2 Re = 40 μm Ctop = 9 km

  14. High Cloud (Ctop = 14 km) • The enhanced contrast between cloud top temperature and the surface increases the signal to noise ratio of the IR channels. IWP = 100 gm-2 Re = 16 μm Ctop = 9 km IWP = 100 gm-2 Re = 16 μm Ctop = 14 km

  15. Ancillary Cloud Boundary Info. • With CloudSat cloud boundaries, the information content of the IR channels is much lower and the optimal channels reduce to those in the Nakajima and King (1990) algorithm. IWP = 100 gm-2 Re = 16 μm Ctop = 9 km IWP = 100 gm-2 Re = 16 μm Ctop = 9 km CloudSat

  16. Channel Selection Histogram • Dark blue – conservative scattering visible channels • Light blue – absorbing visible/near IR channels • Green – water vapor • Yellow – 3.78/4.05 μm • Orange – IR window • Red – CO2 slicing

  17. With CloudSat Cloud Boundaries • With ancillary cloud boundary information, fewer MODIS channels provide information above the level of background noise. • IR channels are selected less often due to the much better a priori cloud placement.

  18. 11 μm 0.646 μm 4.05 μm 2.13 μm 13.34 μm Total The Five Channel Algorithm • When the complete ensemble of cases is examined and a list of all channels selected are compiled, a five channel retrieval algorithm emerges as optimal for the widest range of scenes.

  19. Split-window (IR) Nakajima and King (Vis) 11 μm 11.92 μm 0.646 μm 2.13 μm 0.646 μm 11 μm 4.05 μm 2.13 μm Total 4.05 μm 13.34 μm Total Traditional Approaches • The Nakajima and King (1990) and split-window (Inoue, 1985) approaches each exhibit information content over part of the solution space but the combination of the two is required to provide maximum information over the full range considered.

  20. Without CloudSat With CloudSat 11 μm 0.646 μm 4.05 μm 11 μm 0.646 μm 4.05 μm 2.13 μm 13.34 μm Total 2.13 μm 13.34 μm Total CloudSat’s Impact • In the absence of cloud boundary information from CloudSat, infrared radiances from MODIS add significant additional information concerning cloud top height.

  21. Model IWP Sensitivity to a Doubling of CO2 Optimizing A New Observing System • This distribution of IWP represents one of the largest uncertainties in climate models and a gap in current observing systems.

  22. Infrared – Thin cloudsSolar – Top of clouds SIRICE – Ice Water Path Microwave – Liquid Water Path Microwave - Precipitation The SIRICE Concept • The Sub-millimeter & Infrared Ice Cloud Experiment (SIRICE) offers the unique capability to measure a component of the water cycle that we know little about – the amount of ice in the atmosphere.

  23. De = 50 μm IWP = 150.4 gm-2 Tc = 223 K The Infrared Cloud Ice Radiometer Goal: Design a new IR radiometer with two or three optimal bands between 7-14 μm for retrieving IWP and effective diameter.

  24. Total Water Vapor SFC Properties Error (K) Cloud Thickness Crystal Properties Measurements Wavelength (μm) Error Characterization • Error analyses include the following assumptions: • PSD shape • Crystal habit • Surface T • Surface ε • Cloud geometric thickness • q profile • Measurement error of 0.5 K also assumed on each channel

  25. Information (bits) Information (bits) Wavelength (μm) Wavelength (μm) Importance of Error Analysis (AGAIN!) Measurement Error Only (σ = 0.5 K)

  26. IWP Dme Frequency Frequency IWP (gm-2) Dme (μm) ARM Cases • To establish the optimal channels for global retrievals 10,000 high cloud scenes are generated from statistics acquired at the ARM TWP and SGP sites to establish approximate global cloud statistics. • Cloud properties are retrieved from radar and lidar measurements. While there are uncertainties in these products, it is hoped that they provide a realistic representation of the observed distribution.

  27. 13.6 μm Optimal Channels Selected First Selected Anytime 10.9 μm Wavelength (μm) Wavelength (μm)

  28. Synthetic Retrievals • Modeled radiances for all 141 channels are employed to test a number of possible wavelength combinations in an optimal estimation inversion framework: • Retrieval vector x = (LWP, De); • xa = (120 gm-2, 75 μm), σa = (2000 gm-2, 200 μm); • xtruth = (75 gm-2, 50 μm); • y = various combinations of channels between 7 and 14 μm.

  29. Synthetic Retrievals • xa = (120 gm-2, 75 μm), xtruth = (75 gm-2, 50 μm), σa = (2000, 200) * Time to process a 100x100 array or 10,000 pixels on a 1.4 GHz Linux PC.

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