Multidimensional Modeling of Atmospheric Effects and Surface Heterogeneities on Remote Sensing

The overall goal of this project is to establish a modeling capability that allows a quantitative determination of atmospheric effects on remote sensing including the effects of surface heterogeneities. This includes an improved understanding of aerosol and haze effects in connection with structural, angular, and spatial surface heterogeneities. One important objective of the research is the possible identification of intrinsic surface or canopy characteristics that might be invariant to atmospheric perturbations so that they could be used for scene identification. Conversely, an equally important objective is to find a correction algorithm for atmospheric effects in satellite-sensed surface reflectances. The technical approach is centered around a systematic model and code development effort based on existing, highly advanced computer codes that were originally developed for nuclear radiation shielding applications. Computational techniques for the numerical solution of the radiative transfer equation are adapted on the basis of the discrete-ordinates finite-element method which proved highly successful for one and two-dimensional radiative transfer problems with fully resolved angular representation of the radiation field

Evaluation of radar multiple scattering effects in Cloudsat configuration

International audienceMonteCarlo simulations have been performed to evaluate the importance of multiple scattering effects in co- and cross-polar radar returns for 94 GHz radars in Cloudsat and airborne configurations. Thousands of vertically structured profiles derived from some different cloud resolving models are used as a test-bed. Mie theory is used to derive the single scattering properties of the atmospheric hydrometeors. Multiple scattering effects in the co-polar channel (reflectivity enhancement) are particularly elusive, especially in airborne configuration. They can be quite consistent in satellite configurations, like CloudSat, especially in regions of high attenuation and in the presence of highly forward scattering layers associated with snow and graupel particles. When the cross polar returns are analysed [but note that CloudSat does not measure any linear depolarization ratio (LDR hereafter)], high LDR values appear both in space and in airborne configurations. The LDR signatures are footprints of multiple scattering effects; although depolarization values as high as ?5 dB can be generated including non-spherical particles in single scattering modelling, multiple scattering computations can produce values close to complete depolarization (i.e. LDR=0 dB). Our simulated LDR profiles from an air-borne platform well reproduce, in a simple frame, some experimental observations collected during the Wakasa Bay experiment. Since LDR instrumental uncertainties were not positively accounted for during that experiment, more focused campaigns with air-borne polarimetric radar are recommended. Multiple scattering effects can be important for CloudSat applications like rainfall and snowfall retrievals since single scattering based algorithms will be otherwise burdened by positive biases

Two adaptive radiative transfer schemes for numerical weather prediction models

Radiative transfer calculations in atmospheric models are computationally expensive, even if based on simplifications such as the δ-two-stream approximation. In most weather prediction models these parameterisation schemes are therefore called infrequently, accepting additional model error due to the persistence assumption between calls. This paper presents two so-called adaptive parameterisation schemes for radiative transfer in a limited area model: A perturbation scheme that exploits temporal correlations and a local-search scheme that mainly takes advantage of spatial correlations. Utilising these correlations and with similar computational resources, the schemes are able to predict the surface net radiative fluxes more accurately than a scheme based on the persistence assumption. An important property of these adaptive schemes is that their accuracy does not decrease much in case of strong reductions in the number of calls to the δ-two-stream scheme. It is hypothesised that the core idea can also be employed in parameterisation schemes for other processes and in other dynamical models

Statistical characteristics of surrogate data based on geophysical measurements

In this study, the statistical properties of a range of measurements are compared with those of their surrogate time series. Seven different records are studied, amongst others, historical time series of mean daily temperature, daily rain sums and runoff from two rivers, and cloud measurements. Seven different algorithms are used to generate the surrogate time series. The best-known method is the iterative amplitude adjusted Fourier transform (IAAFT) algorithm, which is able to reproduce the measured distribution as well as the power spectrum. Using this setup, the measurements and their surrogates are compared with respect to their power spectrum, increment distribution, structure functions, annual percentiles and return values. It is found that the surrogates that reproduce the power spectrum and the distribution of the measurements are able to closely match the increment distributions and the structure functions of the measurements, but this often does not hold for surrogates that only mimic the power spectrum of the measurement. However, even the best performing surrogates do not have asymmetric increment distributions, i.e., they cannot reproduce nonlinear dynamical processes that are asymmetric in time. Furthermore, we have found deviations of the structure functions on small scales

Three-dimensional effects in polarization signatures as observed from precipitating clouds by low frequency ground-based microwave radiometers

International audienceConsistent negative polarization differences (i.e. differences between the vertical and the horizontal brightness temperature) are observed when looking at precipitating systems by ground-based radiometers at slant angles. These signatures can be partially explained by one-dimensional radiative transfer computations that include oriented non-spherical raindrops. However some cases are characterized by polarization values that exceed differences expected from one-dimensional radiative transfer. A three-dimensional fully polarized Monte Carlo model has been used to evaluate the impact of the horizontal finiteness of rain shafts with different rain rates at 10, 19, and 30 GHz. The results show that because of the reduced slant optical thickness in finite clouds, the polarization signal can strongly differ from its one-dimensional counterpart. At the higher frequencies and when the radiometer is positioned underneath the cloud, significantly higher negative values for the polarization are found which are also consistent with some observations. When the observation point is located outside of the precipitating cloud, typical polarization patterns (with troughs and peaks) as a function of the observation angle are predicted. An approximate 1-D slant path radiative transfer model is considered as well and results are compared with the full 3-D simulations to investigate whether or not three-dimensional effects can be explained by geometry effects alone. The study has strong relevance for low-frequency passive microwave polarimetric studies

Estimation of the systematic error of precipitation and humidity in the MM5 model

To comprehensively diagnose model capabilities in simulating atmospheric flow including the relevant microphysical processes, the main prognostic fields of the MM5 model are compared with ERA40 reanalysis data. This approach allows to identify and compare meaningful features of model parameterization schemes and to quantify model errors. Various combinations of schemes for cumulus convection, planetary boundary layer (PBL), microphysics and radiative transfer are used in order to identify those combinations which produce the closest resemblance between model state and reanalysis. The spatial structure of systematic errors, both horizontal and vertical will be described and geographical regions and synoptic situations will be identified, which are associated with pronounced systematic model deviations. The study focused on precipitation and humidity fields as well as on the main thermodynamic atmospheric variables on a coarse resolution grid (about 80 km) over the North Atlantic - Europe region. Our results identify advantages and shortcomings of the various parameterization schemes. They also indicate that, in general, the combination of best schemes does not result in optimal simulations of a particular variable

Surrogate cloud fields generated with the Iterative Amplitude Adapted Fourier Transform algorithm

A new method of generating two-dimensional and three-dimensional cloud fields is presented, which share several important statistical properties with real measured cloud fields.Well-known algorithms such as the Fourier method and the Bounded Cascade method generate fields with a specified Fourier spectrum. The new iterative method allows for the specification of both the power spectrum and the amplitude distribution of the parameter of interest, e.g. the liquid water content or liquid water path. As such, the method is well suited to generate cloud fields based on measured data, and it is able to generate broken cloud fields. Important applications of such cloud fields are e.g. closure studies. The algorithm can be supplied with additional spatial constraints which can reduce the number of measured cases needed for such studies. In this study the suitability of the algorithm for radiative questions is evaluated by comparing the radiative properties of cloud fields from cloud resolving models of cumulus and stratocumulus with their surrogate fields at nadir, and for a solar zenith angle of 0◦ and 60◦. The cumulus surrogate clouds ended up to be identical to the large eddy simulation (LES) clouds on which they are based, except for translations and reflections. The root mean square differences of the stratocumulus transmittance and reflectance fields are less than 0.03% of the radiative budget. The radiances and mean actinic fluxes fit better than 2%. These results demonstrate that these LES clouds are well described from a radiative point of view, using only a power spectrum together with an amplitude distribution