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??