Cloud Detection 1) Optimised CI Microwindowscnc 2) Singular Vector Decomposition 3) Comparison of Methods fffffffffff
1) CI Microwindow Optimisation
Aim: Find a better pair of MWs, and/or a better threshold value, using objective criteria based on simulated spectra with known cloud amounts Currently: MW1 = [788.2, ] cm -1 MW2 = [832.3, 834.4] cm -1 CI = L MW1 / L MW2 If CI < threshold → cloud If CI > threshold → clear operational threshold = 1.8 CRISTA experiment
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
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 Effective Fraction CEF: ‘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, ] [832.3, 834.4]0.181 Optimised MWs[ , 775.0] [ , ]0.157
Monte-Carlo approach: Randomly-selecting MWs from the domain (specified by mid-point and width) and iterating from these to adjust the boundaries different MW pairs randomly selected from the entire 750–970 cm -1. Select region of lowest RMSE and do another iterations. Repeat. MW1 = [777, 779] cm -1 MW2 = [819, 820] cm -1 RMSE = 0.156
Another criterion: Best MWs will have large relative distance between clear and cloudy distributions of CI RelDist = (mean CI clear – mean CI cloudy ) / (stddev clear + stddev cloud )
Current MWs have RelDist = 2.03 MW1 = [800, 802] cm -1 MW2 = [831, 832] cm -1 RelDist = 2.77
Summary and Future Work MW1 MW2 RMSERelDist Current MWs [788.2, ] [832.3, 834.4] Desmond MWs [ , 775.0] [ , ]0.157 na M.C. RMSE MWs [777.0, 779.0] [819.0, 820.0] na M.C. RelDist MWs [800.0, 802.0] [831.0, 832.0] na 2.77 In future: 1)Iterate within M.C MWs to find exact location of min/maximum 2)See how the two agree 3)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 D ii weights the U j 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 SV clear
Calculate M cloudy singular vectors SV cloudy
15km 12km 9km 6km Use SV clear and SV cloud to do a Least Squares Fit of arbitrary signal L( ϑ ) = ∑ N i c i SV clear i + ∑ M j d j SV clear j
Chi-Squared Ratio Test:, and then threshold 1, then clear >1, then cloud
Integrated Radiance Ratio Test:, and then threshold total 0, then clear 1, then cloud
Summary and Future Work: 1)Have successfully calculated SVs to represent atmospheric constituent variability (SV clear ) and SVs to capture variability in cloud spectra (SV cloud ) 2)Have implemented two detection methods and have defined thresholds using simulated and real MIPAS data 3)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 1)Std. Deviations in VMRs from climatological means for retrieved well-mixed trace gases from MORSE should give measure of strength of each detection method 2)No clear ‘winner’ yet Continue testing and comparing … CIRA climatology??