Cloud Detection 1) Optimised CI Microwindowscnc 2) Singular Vector Decomposition 3) Comparison of Methods fffffffffff
1) CI Microwindow Optimisation
Currently: MW1 = [788.2, 796.25] cm-1 MW2 = [832.3, 834.4] cm-1 CI = LMW1 / LMW2 If CI < threshold → cloud If CI > threshold → clear operational threshold = 1.8 CRISTA experiment Aim: Find a better pair of MWs, and/or a better threshold value, using objective criteria based on simulated spectra with known cloud amounts
Spectral database: Tangent Height: 6, 9, 12, 15, 18, 21 km Cloud-Top Height: -2,-1.5,-1,-0.5,0,0.5,1.0,1.5,2.0 Cloud extinction: 0.1, 0.01, 0.001/km Atmospheres: mid-lat night, equatorial day, polar winter (night) and polar summer (day), plus these perturbed by 1-sigma climatological variations (Remedios, 2001) A TOTAL OF 1296 CLOUDY ATMOSPHERES REPRESENTED
Cloud Effective Fraction CEF: where k is the cloud extinction (/km), x is the integrated distance along a pencil beam within the cloud, is the normalised field-of-view response function, z is the tangent height ‘CLOUD DETECTION’ REDUCED TO PARTICULAR THRESHOLD VALUE OF CEF
Best MWs are those which best correlate CI with CEF … Current MWs show ~ linear relationship: for a,b minimumizing
Iterative approach (Desmond): Search through MWs with integer wavenumber boundaries and then, for each 'coarse' MW, iterate moving each boundary one grid point at a time. MW1 MW2 RMSE Current MWs [788.2, 796.25] [832.3, 834.4] 0.181 Optimised MWs [774.075, 775.0] [819.175, 819.95] 0.157
MW1 = [777, 779] cm-1 MW2 = [819, 820] cm-1 RMSE = 0.156 Monte-Carlo approach: Randomly-selecting MWs from the domain (specified by mid-point and width) and iterating from these to adjust the boundaries 10000 different MW pairs randomly selected from the entire 750–970 cm-1. Select region of lowest RMSE and do another 10000 iterations. Repeat.
Another criterion: Best MWs will have large relative distance between clear and cloudy distributions of CI RelDist = (mean CIclear – mean CIcloudy) / (stddevclear + stddevcloud)
MW1 = [800, 802] cm-1 MW2 = [831, 832] cm-1 RelDist = 2.77 Current MWs have RelDist = 2.03
Summary and Future Work MW1 MW2 RMSE RelDist Current MWs [788.2, 796.25] [832.3, 834.4] 0.181 2.03 Desmond MWs [774.075, 775.0] [819.175, 819.95] 0.157 na M.C. RMSE MWs [777.0, 779.0] [819.0, 820.0] 0.156 na M.C. RelDist MWs [800.0, 802.0] [831.0, 832.0] na 2.77 • In future: • Iterate within M.C MWs to find exact location of min/maximum • See how the two agree • Test to see how rigorous each set of MWs is at cloud detection and EF estimation
2) Singular Vector Decomposition
Singular Vector Decomposition SVD: • is statistical technique used for finding patterns in high dimensional data: • m×n matrix A can be decomposed into • A=V DU • V m×m left-singular vectors • U m×n right-singular vectors • D m×m singular values • transforms a number of potentially correlated variables into a smaller number of uncorrelated variables (SINGULAR VECTORS) orthonormal matrices diagonal matrix
In this case: A is a set of m spectra each of length n Each row of U is a singular vector with n ‘spectral points’ Singular value Dii weights the Uj singular vector. Idea is to find singular vectors that describe clear and cloudy atmospheres and use them in cloud detection
Calculate N clear singular vectors SVclear
Calculate M cloudy singular vectors SVcloudy
Use SVclear and SVcloud to do a Least Squares Fit of arbitrary signal L(ϑ) = ∑Ni ci SVclear i + ∑Mj dj SVclear j 15km 12km 9km 6km
1, then clear >1, then cloud Chi-Squared Ratio Test: , and then threshold
0, then clear 1, then cloud , and then threshold total Integrated Radiance Ratio Test:
Summary and Future Work: • Have successfully calculated SVs to represent atmospheric constituent variability (SVclear) and SVs to capture variability in cloud spectra (SVcloud) • Have implemented two detection methods and have defined thresholds using simulated and real MIPAS data • Have tested proficiency using simulated data • Complete full comparison of different cloud detection methods used to date.
3) Comparison of Detection Methods
Comparison of Detection Methods: 1. Current Operational CI 2. Optimised CI microwindows 3. SVD chi-squared ratio 4. SVD integrated radiance ratio 5. Simple radiance threshold Idea: Compare retrievals (using MORSE) of 'well-mixed' gases assuming that using spectra with residual cloud will result in retrievals which deviate significantly from climatology
Analysis done on cases where: Different cloud-detection methods disagree over whether it is clear/cloudy – and only use the clear cases
Summary and Future Work • Std. Deviations in VMRs from climatological means for retrieved well-mixed trace gases from MORSE should give measure of strength of each detection method • No clear ‘winner’ yet • Continue testing and comparing … CIRA climatology??
