Colour Index Microwindow Position Optimisation Harry Desmond and Anu Dudhia

Overview Present Colour Index (CI): Two microwindows (MWs): MW1 = 788 – 796 cm -1 MW2 = 832 – 834 cm -1 CI = L MW1 / L MW2 Placements of these should be optimised … but how?

3 different extinction coefficients (0.001 km -1, 0.01 km -1, and 0.1 km -1 ) 4 standard RFM atmospheres (day, polar summer, polar winter and tropical) and their 1 σ variants (*_var) 9 different cloud top heights within the MIPAS FOV (-2.0 km + n0.5 km) 6 different tangent heights (6 km, 9 km,12 km, 15 km, 18 km, 21 km) A TOTAL OF 1296 CLOUDY ATMOSPHERES REPRESENTED Ensemble of Simulated Cloud Spectra

Test #1: Correlation Between CI and CEF Cloud Effective Fraction CEF is the ‘effective blocking power’ of the cloud in the FOV. Pair of MWs with highest correlation between CI and CEF is best log(CEF) = a + b log(CI)

Trying all possible A band combinations of 1-3 cm -1 -wide MWs and then iteratively solving for exact boundaries, a RMSE minimum was located when: Original MWs: RMSE = MW1 = – cm -1 MW2 = – cm -1 RMSE = % better

Test #2: Separation Between Clear and Cloudy States Part A) How many clear spectra are lost when threshold = max(CI cloudy )? Original MWs: ~20% Minimum in all 1 cm -1 MW combinations lost 0.7% (96.5% better) of clear spectra: MW1 = 788 – 789 cm -1 MW2 = 819 – 820 cm -1 threshold

Part B) Relative distance between CI clear and threshold = max(CI cloudy ) as compared to spread of the CI clear as measured by standard deviation σ clear ? Original MWs: 1.17 Maximum distance for all 1 cm -1 MW combinations is 1.97 (68.9% better): MW1 = 773 – 774 cm -1 MW2 = 819 – 820 cm -1 relative distance = [ CI clear – max(CI cloudy ) ] / σ clear threshold CI clear

Original MWs: 2.03 Maximum distance for all 1 cm -1 MW combinations is 1.58 (worse): MW1 = 756 – 757 cm -1 MW2 = 818 – 820 cm -1 Part C) Relative distance between CI clear and CI cloudy as compared to spread of the CI clear and CI cloudy as measured by standard deviation σ clear and σ cloudy ? relative distance = [ CI clear – CI cloudy ] / [σ clear + σ cloudy ]most general test CI cloudy CI clear

Conclusions and Further Work If want information about cloud itself, use Test #1’s results: MW1 = – cm -1 and MW2 = – cm -1 If want to distinguish most reliably between clear and cloudy spectra, use Test #2’s results and use original MWs: MW1 = – cm -1 and MW2 = – cm -1 Try other function types to get optimal RMSE Better iterative-extreme-locating method – Simulated Annealing? Use MWs of >1 cm -1 in Test #2 Try with non-homogeneous cloud

Macroscopic Cloud Parameter Retrieval Jane Hurley, Anu Dudhia, Don Grainger

Aim to retrieve most obvious macrophysical cloud properties: Cloud Top Height CTH (relative to instrument pointing) Cloud Top Temperature CTT Cloud Extinction Coefficient k ext

Cloud Forward Model (CFM): Radiance in MIPAS FOV Assume that: 1. a cloud in the MIPAS FOV is horizontally homogeneous – that is, has a constant cloud top height across the FOV and can be characterized by a single extinction coefficient. 2.the temperature structure within the cloud can be determined by the wet adiabatic lapse rate estimated downwards from the cloud top temperature.

Total radiance measured within two MIPAS FOVs (the FOV containing the cloud top and the FOV immediately below) calculated by the CFM for varying cloud top heights and extinction coefficients.

The radiance is considered in the clearest microwindow of the MIPAS A band: 960 cm -1 – 961 cm -1 O 3 & CO 2 O 3 O3O3 O 3 & NH 3 O 3 & CO 2 O 3

Gas Correction and Validation with Simulations Real MIPAS measurements R m will include significant gaseous radiation contributions R g, while the CFM calculates only the radiation contribution by the cloud itself R c. It is thus necessary to deduce what portion of the measured signal is due to the cloud. Assume that the cloud has a continuum signal and that the gaseous contribution has emission/absorption lines.

Optimal Estimations retrieval of form with state vector and using: Real Measurements: 2 radiance measurements from MIPAS spectrum – the first sweep flagged as cloudy and the one immediately below DIRECT Pseudo-Measurements: T ret = temperature corresponding to first flagged cloudy sweep EF = Cloud effective fraction, as estimated from CI RELATE

Application to MIPAS Spectra “Hot spot” of high cloud over Indonesian toga core, mountainous regions such as the Southern Andes and Rockies, Amazon Basin and the Congo; Increasing cloud top height towards the tropics; Retrieved CTT is nearly fully correlated with CTH; Retrieved log(kext) is more or less constant over the globe.

If there IS a cloud in the FOV at a certain latitude, this shows the probability that it will occur at a given altitude / temperature / extinction …

Future Work Check retrieval against other retrievals of macroscopic properties: McClouds etc Run over larger MIPAS dataset to get a high cloud climatology Compare high cloud climatology with others