wp 2000 improved identification of clouds jane hurley, anu dudhia, don grainger university of oxford
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WP 2000 Improved Identification of Clouds
Jane Hurley, Anu Dudhia, Don Grainger
University of Oxford
Current Cloud Detection
Colour Index (CI) Method is now used to detect cloud …
A couple of caveats … however …
• microwindows have not been optimized – would be useful if CI ~ EF
• fails to detect cloud with cloud fraction < 30%
Objective:
To create and analyze RFM-simulated cloudy spectrum of varying cloud effective fraction EF to formulate a new cloud detection method using Singular Vector Decomposition SVD.
Singular Vector Decomposition SVD
• is statistical technique used for finding patterns in high dimensional data;
• transforms a number of potentially correlated variables into a smaller number of uncorrelated variables (SINGULAR VECTORS)
• first SV captures the most variance … and each successive SV captures increasingly less variance
Idea is to find singular vectors that describe clear and cloudy atmospheres and use them in cloud detection
Use 2nd half of A band because more sensitive to cloud presence
RFM-simulated spectrum with EF = 0 and 9.0km tangent height and the corresponding first 8 Clear Singular Vectors SVclear
Clear Singular Vectors
Cloudy Singular Vectors•Subtract off mean spectral radiance from Original signal;
•Use SVclear to do a Least Squares Fit (LSF) on Original-Mean signal;
•Subtract LSF from Original signal to get Cloud-Only signal.
Compare with Aerosol signature with same EF in the FOV:
• SVD-calculated Cloud-only signal
• Aerosol signal
Do SVD on Cloud-only signal to get Cloudy Singular Vectors SVcloud
Use SVclear and SVcloud for given tangent height to do a LSF to mean-subtracted Original signal
15 km
12 km
9 km
6 km
EF ≠ 0 → Non-zero fit coefficient to SVcloud!
Application to MIPAS data
Cloud DetectionMethod 1: χ2 Ratio
Use SVclear and SVcloud to do LSF of arbitrary spectrum.
Use χ2 error to measure goodness of fit.
Method 2: Ratio of Integrated Reconstructed Radiances
Use SVclear and SVcloud to do LSF of arbitrary spectrum.
Reconstruct cloudy and total radiance using LSF.
Comparison of Methods4 Methods of Cloud Detection:
• Radiance Thresholding
• Colour Index
• SVD χ2 Ratio
• SVD Ratio of Integrated Reconstructed Radiances
Methods applied to RFM Data: Percent that method gets prognosis right
Methods applied to MIPAS 2003 data: Percent agreement between methods
Future Work
Finish selecting optimal microwindows for use with Colour Index Method – those that best correlate with EF.
Finalize choice of thresholds for SVD Methods.
Compare methods of cloud detection on large MIPAS dataset against known databases (ISCCP) etc to see what difference this makes.
Do SVD analysis of ice clouds and implement this into an identification scheme. Hopefully will then have a cloud type identification scheme.
For MIPAS data, not so sharp a distribution, obviously … but clearly a bimodal distribution
Can fit a Gaussian to ‘clear’ peak and set a threshold:
thr = peak + 3st.dev.
Should pick up 99.5% of cloud, if truly Gaussian
Cloud Detection
Method 1: χ2 Ratio
Use SVclear and SVcloud to do LSF of arbitrary spectrum.
Use χ2 error to measure goodness of fit.
Fit given spectrum with SVclear → χ2clear
Fit given spectrum with SVclear and SVcloud → χ2clear+cloud
Consider ratio of χ2clear / χ2
clear+cloud:
• χ2clear / χ2
clear+cloud > 1 for cloudy spectra
• χ2clear / χ2
clear+cloud ≈ 1 for clear spectra
Method 2: Ratio of Integrated Reconstructed Radiances
Use SVclear and SVcloud to do LSF of arbitrary spectrum.
Radiance of cloud LSVcloud = mean(Σi (fit coeff)i SVcloud i), where i ranges over the cloudy SVs only and average over spectral points.
LSVcloud = 0 for clear spectra
LSVcloud > 0 for cloudy spectra
Total radiance LSVall = mean(Σi (fit coeff)i SVi), where i ranges over all SVs and average over spectral points.
Consider ratio LSVcloud / LSVall:
LSVcloud / LSVall = 0 for clear spectra
LSVcloud / LSVall > 0 for cloudy spectra