Jerome Vidot

A new ice cloud parameterization for infrared radiative transfer simulation of cloudy radiances: Evaluation and optimization with IIR observations and ice cloud profile retrieval products

A new ice cloud optical property database in the thermal infrared has been parameterized for the RTTOV radiative transfer model. The Self-Consistent Scattering Model (SCSM) database is based on an ensemble model of ice crystals and a parameterization of the particle size distribution. This convolution can predict the radiative properties of cirrus without the need of a priori information on the ice particle shape and an estimate of the ice crystal effective dimension. The ice cloud optical properties are estimated through linear parameterizations of ambient temperature and ice water content. We evaluate the new parameterization against existing parameterizations used in RTTOV. We compare infrared observations from Imaging Infrared Radiometer, on board CALIPSO, against RTTOV simulations of the observations. The simulations are performed using two different products of ice cloud profiles, retrieved from the synergy between space-based radar and lidar observations. These are the 2C-ICE and DARDAR products. We optimized the parameterization by testing different SCSM databases, derived from different shapes of the particle size distribution, and weighting the volume extinction coefficient of the ensemble model. By selecting a large global data set of ice cloud profiles of visible optical depths between 0.03 and 4, we found that the simulations, based on the optimized SCSM database parameterization, reproduces the observations with a mean bias of only 0.43 K and a standard deviation of 6.85 K. The optimized SCSM database parameterization can also be applied to any other radiative transfer model.

MERIS level 2 products over land: present status and potential improvements

The detection of Dense Dark Vegetation (DDV) using the Atmospherically Resistant Vegetation Index (ARVI) and then the aerosol retrieval over DDV is the critical point of the atmospheric correction scheme over land for MERIS implemented in the level 2 processor. We present here what we can expect from the MERIS product by applying a MERIS-like land algorithm to SeaWiFS data over Europe. It is shown that the DDV cover is sufficient in summer but not in winter where an extension of the concept of DDV is needed in order to enable an operational aerosol characterisation. A linear relationship between ARVI and reflectance of the extended DDV in the red should allow the use of such grey targets for the retrieval of aerosol optical properties (aerosol optical thickness at 550 nm and Angström coefficient) throughout the year with a little loss of accuracy

Evaluation of Radiative Transfer Models With Clouds

Data from hyperspectral infrared sounders are routinely ingested worldwide by the National Weather Centers. The cloud-free fraction of this data is used for initializing forecasts which include temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from these sounders are sensitive to the vertical distribution of ice and liquid water in clouds, this information is not fully utilized. In the future, this information could be used for validating clouds in National Weather Center models and for initializing forecasts. We evaluate how well the calculated radiances from hyperspectral Radiative Transfer Models (RTMs) compare to cloudy radiances observed by AIRS and to one another. Vertical profiles of the clouds, temperature, and water vapor from the European Center for Medium-Range Weather Forecasting were used as input for the RTMs. For nonfrozen ocean day and night data, the histograms derived from the calculations by several RTMs at 900 cm 1 have a better than 0.95 correlation with the histogram derived from the AIRS observations, with a bias relative to AIRS of typically less than 2 K. Differences in the cloud physics and cloud overlap assumptions result in little bias between the RTMs, but the standard deviation of the differences ranges from 6 to 12 K. Results at 2,616 cm 1 at night are reasonably consistent with results at 900 cm . Except for RTMs which use full scattering calculations, the bias and histogram correlations at 2,616 cm 1 are inferior to those at 900 cm 1 for daytime calculations. Plain Language Summary Getting the right clouds of the right type, at the right time and location in Global Circulation Models, is key to getting the local energy balance right. This is key to an accurate forecast. If the clouds are of the wrong type or at the wrong location or time, the accuracy of the forecast is degraded. We evaluate the accuracy of the best currently available cloud description (produced by the European Center for Medium-Range Weather Forecasting) by comparing the radiances calculated using Radiative Transfer Models (RTMs) from six major development teams to cloudy radiances observed by the Atmospheric Infrared Sounder at the same location and time. The better RTMs fit statistically reasonably well in the 11-μm atmospheric window area, with little latitude (zonal) and day/night cloud-type related bias. None of the RTMs fit well in the 4-μm atmospheric window area during daytime, unless the calculations use full scattering. With the current state of art, all major RTMs would be suitable to start the validation of cloud effects in the National Weather Center models using just one 11-μm atmospheric window channel.

The VIS/NIR Land and Snow BRDF Atlas for RTTOV: Comparison between MODIS MCD43C1 C5 and C6

A monthly mean land and snow Bidirectional Reflectance Distribution Function (BRDF) atlas for visible and near infrared parts of the spectrum has been developed for Radiative Transfer for Television Infrared Observation Satellite (TIROS) Operational Vertical sounder (TOVS) (RTTOV). The atlas follows the methodology of the RTTOV University of Wisconsin infrared land surface emissivity (UWIREMIS) atlas, i.e., it combines satellite retrievals and a principal component analysis on a dataset of hyper-spectral surface hemispherical reflectance or albedo. The current version of the BRDF atlas is based on the Collection 5 of the Moderate Resolution Imaging (MODIS) MCD43C1 Climate Modeling Grid BRDF kernel-driven model parameters product. The MCD43C1 product combines both Terra and Aqua satellites over a 16-day period of acquisition and is provided globally at 0.05° of spatial resolution. We have improved the RTTOV land surface BRDF atlas by using the last Collection 6 of MODIS product MCD43C1. We firstly found that the MODIS C6 product improved the quality index of the BRDF model as compared with that of C5. When compared with clear-sky top of atmosphere (TOA) reflectance of Spinning Enhanced Visible and InfraRed Imagers (SEVIRI) solar channels over snow-free land surfaces, we showed that the reflectances are simulated with an absolute accuracy of 3% to 5% (i.e., 0.03–0.05 in reflectance units) when either the satellite zenith angle or the solar zenith angle is below 70°, regardless of the MODIS collection. For snow-covered surfaces, we showed that the comparison with in situ snow spectral albedo is improved with C6 with an underestimation of 0.05 in the near infrared.

Comparison of the RTTOV-12 ice cloud models for hyperspectral IR instruments using the A-Train

The fast radiative transfer model RTTOV offers different ice cloud models to simulate observations from hyperspectral infrared instruments such as AIRS, IASI or CrIS. Since the last RTTOV version 12 there are two ice cloud models: the first one is an ice water content and temperature parameterization of a large dataset of ice cloud optical properties coming from the Met Office, and the second one is based on the ice crystal size dependent ice cloud optical properties database provided by SSEC. In order to compare these two ice cloud models, we used collocated data from the A-Train during two weeks of global observations. The ice cloud profiles description (ice water content and effective size of ice crystals) used as inputs for RTTOV is provided by the synergetic retrieval from active sensors (CloudSat and CALIOP/CALIPSO) named the 2C-ICE product. The RTTOV top of atmosphere simulations using these ice cloud profiles are then compared with collocated infrared observations from the IIR radiometer that is onboard CALISPO and has three infrared window channels. We found that RTTOV-12 is able to reproduce observations with nearly no biases and standard deviation below 7 K in window channels. The results show also the strong impact of the knowledge of the ice crystal size that is not currently information predicted by NWP models. By using hyperspectral infrared instruments, the spectral consistency of these two ice cloud models is also preliminary discussed.

An update on the RTTOV fast radiative transfer model (currently at version 12)

This paper gives an update of the RTTOV (Radiative Transfer for TOVS) fast radiative transfer model, which is widely used in the satellite retrieval and data assimilation communities. RTTOV is a fast radiative transfer model for simulating top-of-atmosphere radiances from passive visible, infrared and microwave downward-viewing satellite radiometers. In addition to the forward model, it also optionally computes the tangent linear, adjoint and Jacobian matrix providing changes in radiances for profile variable perturbations assuming a linear relationship about a given atmospheric state. This makes it a useful tool for developing physical retrievals from satellite radiances, for direct radiance assimilation in NWP models, for simulating future instruments, and for training or teaching with a graphical user interface. An overview of the RTTOV model is given, highlighting the updates and increased capability of the latest versions, and it gives some examples of its current performance when compared with more accurate line-by-line radiative transfer models and a few selected observations. The improvement over the original version of the model released in 1999 is demonstrated.

Aerosol remote sensing over land: comparison of two methods

Aerosol remote sensing over land requires knowing the surface reflectance in some spectral bands. Dense dark vegetation can be used in the blue and in the red based on ground based measurements of their reflectances or even space measurements from a statistical analysis for clear days. An aerosol remote sensing algorithm based on DDV is available on MERIS data (Santer et al., 1999). An other alternative is to derive the surface reflectances from space as far as you have ground based characterization of the aerosols to perform suitable atmospheric correction, at least on a representative time series (Borde and Verdebout, 2001). The two algorithms, applied on SeaWiFS images, are compared over three sites (Toulouse, Ispra, Adriatic) for which ground based measurements are available.