Colour Index Microwindow Position Optimisation Macroscopic Cloud Parameter Retrieval

Colour Index Microwindow Position Optimisation Macroscopic Cloud Parameter Retrieval

Colour Index Microwindow Position Optimisation Macroscopic Cloud Parameter Retrieval

Orr, Barry, Executive Sports Producer has reference to this Academic Journal, PHwiki organized this Journal Colour Index Microwindow Position Optimisation Harry Desmond in addition to Anu Dudhia Overview Present Colour Index (CI): Two microwindows (MWs): MW1 = 788 – 796 cm-1 MW2 = 832 – 834 cm-1 CI = LMW1 / LMW2 Placements of these should be optimised but how 3 different extinction coefficients (0.001 km-1, 0.01 km-1, in addition to 0.1 km-1) 4 st in addition to ard RFM atmospheres (day, polar summer, polar winter in addition to tropical) in addition to their 1 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

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Test 1: Correlation Between CI in addition to CEF Cloud Effective Fraction CEF is the ‘effective blocking power’ of the cloud in the FOV. Pair of MWs with highest correlation between CI in addition to CEF is best log(CEF) = a + b log(CI) Trying all possible A b in addition to combinations of 1-3 cm-1-wide MWs in addition to then iteratively solving as long as exact boundaries, a RMSE minimum was located when: Original MWs: RMSE = 0.181 MW1 = 774.075 – 775.0 cm-1 MW2 = 819.175 – 819.95 cm-1 RMSE = 0.157 13.6% better Test 2: Separation Between Clear in addition to Cloudy States Part A) How many clear spectra are lost when threshold = max(CIcloudy) 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

Original MWs: 1.17 Maximum distance as long as all 1 cm-1 MW combinations is 1.97 (68.9% better): MW1 = 773 – 774 cm-1 MW2 = 819 – 820 cm-1 Original MWs: 2.03 Maximum distance as long as all 1 cm-1 MW combinations is 1.58 (worse): MW1 = 756 – 757 cm-1 MW2 = 818 – 820 cm-1 most general test Conclusions in addition to Further Work If want in as long as mation about cloud itself, use Test 1’s results: MW1 = 774.075 – 775.0 cm-1 in addition to MW2 = 819.175 – 819.95 cm-1 If want to distinguish most reliably between clear in addition to cloudy spectra, use Test 2’s results in addition to use original MWs: MW1 = 788.0 – 796.0 cm-1 in addition to MW2 = 832.0 – 834.0 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 kext

Cloud Forward Model (CFM): Radiance in MIPAS FOV Assume that: a cloud in the MIPAS FOV is horizontally homogeneous – that is, has a constant cloud top height across the FOV in addition to can be characterized by a single extinction coefficient. 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 in addition to the FOV immediately below) calculated by the CFM as long as varying cloud top heights in addition to extinction coefficients. The radiance is considered in the clearest microwindow of the MIPAS A b in addition to : 960 cm-1 – 961 cm-1 O3 & CO2 O3 O3 O3 O3 & NH3 O3 & CO2 O3

Gas Correction in addition to Validation with Simulations Real MIPAS measurements Rm will include significant gaseous radiation contributions Rg, while the CFM calculates only the radiation contribution by the cloud itself Rc. 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 in addition to that the gaseous contribution has emission/absorption lines. Optimal Estimations retrieval of as long as m with state vector in addition to using: Real Measurements: 2 radiance measurements from MIPAS spectrum – the first sweep flagged as cloudy in addition to the one immediately below DIRECT Pseudo-Measurements: Tret = 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 in addition to Rockies, Amazon Basin in addition to 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

Orr, Barry 12 News Sports Tonight - KPNX-TV Executive Sports Producer

Orr, Barry Executive Sports Producer

Orr, Barry is from United States and they belong to 12 News Sports Tonight – KPNX-TV and they are from  Phoenix, United States got related to this Particular Journal. and Orr, Barry deal with the subjects like Sports

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