Cloud top pressure/altezza

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Cloud top pressure/altezza. Temperatura Necessita il profilo, giorno/notte, emissivita’ Temperatura corretta Necessita il profilo, stima optical thickness, relazione optical thickness emissivita’ A partire dal tipo (soggettiva e storica) - PowerPoint PPT Presentation

Transcript of Cloud top pressure/altezza

  • Cloud top pressure/altezzaTemperatura Necessita il profilo, giorno/notte, emissivitaTemperatura corretta Necessita il profilo, stima optical thickness, relazione optical thickness emissivitaA partire dal tipo (soggettiva e storica)Ombra (illuminazione, cloud detection, shadow detection, calcolo). Solo di giorno, sole allo zenith, solo bordi, no calibrazione, si risoluzione spaziale (OK per VIS), complessita nel riconoscimento di forme, solo su superfici riflettenti, nubi fine e senza contorni definiti (cirri)Stereoscopia. >1 osservazione contemporanee (vento) (geostazionari: perfettamente in fase, multiviewing: (A)ATSR (2), MISR (9), POLDER (14)), geometria (no calibrazione), cloud detection, navigazione, cloud recognition (difficile, limitato ai bordi), risoluzione spaziale, nubi fini e senza contorniLimb sounding + vede nubi fine, no calibrazione, problemi a scendere (

  • Stereoscopia

  • Stereoscopia

  • Hasler, BAMS 1981

  • Hasler, BAMS 1981

  • Hasler, BAMS 1981

  • IR-WVCurva precalcolataMisura clear skyMisura broken cloudyStima Tb fully cloudy

  • MLEV

  • CO2SlicingInputTemperature and Water Vapor profiles (representative of the FOV under consideration)Observations for, at least, two channels in the CO2 absorption band

  • CO2Slicing: TheorySolving Equation:Iob(n1)-Iclear(n1)Iob(n2)-Iclear(n2)Icloud(n1,pc)-Iclear(n1)Icloud(n2,pc)-Iclear(n2)=The solution is given by the value of pc that minimizes the difference between the right and left side

  • Pair SelectionBroad Band Spectrometer:


  • Example spectra

  • CO2Slicing: weighting function spaceMODIS CO2 channelsInterferometer CO2 channels

  • CO2Slicing ResultsCloud top height differences from lidar (CLS) (1 km intervals).

  • IR Retrieval Scheme for CloudsTemperature andwater vapor retrievalsin clear sky FOVsCalibrateddataCloud maskDeterminationof cloud altitude, thickness and temperatureDeterminationof cloud emissivityRetrieval of microphysical properties (optical thickness, ice water path, particle size and shape) Validation of Products

  • Cloud Emissivity

  • Minimum Local Emissivity Variance (MLEV)Observations between 750 and 900 cm-1

  • Retrieved cloud at 9.5 km, lidar indicates single layer cloud between 7.5 and 9.8 km.

  • CO2Slicing and MLEV ResultsCloud top height differences from lidar (CLS) (1 km intervals).Cloud top height differences from lidar (CLS).CO2 SlicingMLEV

  • Cloud Top Retrieval ConclusionsThe high-spectral CO2Slicing seems to be more accurate than the broadband versionMLEV can be used to compliment the CO2Slicing.The different approaches seem to agree better in presence of optically thick cloudsUse images to assist in analysis

  • lidar

  • The fact that the depth of solar Fraunhofer lines in scattered light is less than those observed in direct sunlight, was discovered by Shefov [1959] [17] and Grainger and Ring [1962] [6] and is known as the Ring Effect or Filling-in. Several publications analysed this effect and its origins, showing that rotational Raman scattering provides the dominant contribution to the Ring Effect [1, 10, 4, 5, 8, 3, 18]. The majority of these studies however concentrated on cloud-free conditions.

  • Cloud radar


  • Cloud top pressure/HeightStereoscopiaAnalisi dellombraAssorbimento differenziale: O2 A-bandMolecular scatteringRaman Scattering (UV)Limb SoundingTemperaturaCO2 15 micron slicingMLEV

  • Cloud top pressure, temperature, effective emissivity Retrieved for every 5x5 box of 1 km FOVs, when at least 5 FOVs are cloudy, day & nightCO2 Slicing technique (5 bands, 12.0-14.2 m) retrieve pc; Tc from temperature profileratio of cloud forcing in 2 nearby bands most accurate for high and mid-level cloudsPreviously applied to HIRS (NOAA POES, 20 km), GOES sounder (~ 30 km)Accuracy of technique ~ 50 mb MODIS 1st satellite sensor capable of CO2 slicingat high spatial resolutionCloud top properties (P. Menzel, R. Frey, K. Strabala, L. Gumley, et al. NOAA NESDIS, U. Wisc./CIMSS)S. Platnick, ISSAOS 02

  • CO2 slicing: theorySolving Equation:Iob(n1)-Iclear(n1)Iob(n2)-Iclear(n2)Icloud(n1,pc)-Iclear(n1)Icloud(n2,pc)-Iclear(n2)=solution given by the value of pc that minimizes the difference between the right and left side

  • CO2slicing: weighting functions

    Bands w/greater CO2 absorptionhave weighting functions moresensitive to high cloudsS. Platnick, ISSAOS 02

  • BT in and out of clouds for MODIS CO2 bands - demonstrate weighting functions and cloud top algorithmS. Platnick, ISSAOS 02

    CO2 slicing, well documented approach, applied since the 1970s. How to pick paris for CO2 slicing? Is more better?Example spectral region for HIS.Weighting function, the higher spectral resolution instrument is better.Results, validation against lidar observations.Determining emissivity.Formula and error.Calculations of emissivity uncertainty plotted against cloud emissivity and wavenumber at 225K shows the potential error in magnitude as well as by percent (next slide). The uncertainty in the calculation is a function of many variables including emissivity, wavelength, cloud representative temperature, and measurement uncertainties of clear radiance, cloudy radiance, and cloud temperature. For these figures, measurement uncertainties are parameterized to characteristic values leaving the others as variables. As you can see the errors are a strong function of emissivity, becoming very high (several times the measured values) for low emissivity cases. In fact, the errors become even greater for warmer cloud layers, with errors nearly doubling for cloud temperatures of 255K. As a result, this method can only be used reliably for higher emissivity cases, say above 0.1 where errors are generally less than 30%. Scatter plot of calculated emissivity vs. cloud thickness using cloud mean temperature is represented by the blue dots. The red error bars represent the error analysis as discussed above. Here you can see the generally smaller errors associated with high emissivity cases and the relatively large errors associated with smaller emissivities and thicker clouds. The green Xs represent the emissivity calculated using the cloud top temperature instead of layer mean temperature. The difference between the two emissivities shows the importance of properly calculating the cloud representative temperature especially for optically thick (high emissivity) clouds where differences can be quite large.MLEV, and new approach to get cloud temperature and emissivity.Graphical example of MLEV. Emissivity of a cloud should not show spectral lines.Comparison of CO2 and MLEV.A comparison of IR retrieval and lidar.Validation of GOES CO2 and ground base lidar/radar combination.Platnick_LAquila