Precomputed Lookup Tables Research Articles

Atmospheric correction based on inherent optical properties of sea water at NIR wavelengths combined with an automated aerosol spectra determination (ASD) technique

Atmospheric correction is the process whereby atmospheric effects on sensor-recorded radiance are removed and the surface radiance is estimated. Atmospheric effects due to gaseous absorption, molecular scattering – and their seasonal as well as latitudinal variations – can be adequately accounted for by using pre-computed look-up tables. However, scattering by aerosol particles is difficult to correct. At-sensor radiance at near-infrared (NIR) wavelengths, after being corrected for gaseous absorption and molecular scattering, was assumed (in standard atmospheric correction) to have been entirely due to aerosol scattering and was used to calculate the aerosol parameters. This assumption, although valid for open ocean clear waters, is not valid for turbid waters due to scattering by suspended particles in the water, which results in an appreciable amount of water-leaving radiance in the NIR region. A new turbid water atmospheric correction scheme is described here for Oceansat-2 Ocean Colour Monitor (OCM-2) data based on inherent optical properties (IOPs) of sea water at NIR, and obtaining an accurate spectral profile of aerosol radiance.

Probabilistic approach to cloud and snow detection on Advanced Very High Resolution Radiometer (AVHRR) imagery

Abstract. Derivation of probability estimates complementary to geophysical data sets has gained special attention over the last years. Information about a confidence level of provided physical quantities is required to construct an error budget of higher-level products and to correctly interpret final results of a particular analysis. Regarding the generation of products based on satellite data a common input consists of a cloud mask which allows discrimination between surface and cloud signals. Further the surface information is divided between snow and snow-free components. At any step of this discrimination process a misclassification in a cloud/snow mask propagates to higher-level products and may alter their usability. Within this scope a novel probabilistic cloud mask (PCM) algorithm suited for the 1 km × 1 km Advanced Very High Resolution Radiometer (AVHRR) data is proposed which provides three types of probability estimates between: cloudy/clear-sky, cloudy/snow and clear-sky/snow conditions. As opposed to the majority of available techniques which are usually based on the decision-tree approach in the PCM algorithm all spectral, angular and ancillary information is used in a single step to retrieve probability estimates from the precomputed look-up tables (LUTs). Moreover, the issue of derivation of a single threshold value for a spectral test was overcome by the concept of multidimensional information space which is divided into small bins by an extensive set of intervals. The discrimination between snow and ice clouds and detection of broken, thin clouds was enhanced by means of the invariant coordinate system (ICS) transformation. The study area covers a wide range of environmental conditions spanning from Iceland through central Europe to northern parts of Africa which exhibit diverse difficulties for cloud/snow masking algorithms. The retrieved PCM cloud classification was compared to the Polar Platform System (PPS) version 2012 and Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 cloud masks, SYNOP (surface synoptic observations) weather reports, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical feature mask version 3 and to MODIS collection 5 snow mask. The outcomes of conducted analyses proved fine detection skills of the PCM method with results comparable to or better than the reference PPS algorithm.

Retrieval of Cloud Properties Using CALIPSO Imaging Infrared Radiometer. Part II: Effective Diameter and Ice Water Path

AbstractThis paper describes the version-3 level-2 operational analysis of the Imaging Infrared Radiometer (IIR) data collected in the framework of the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission to retrieve cirrus cloud effective diameter and ice water path in synergy with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) collocated observations. The analysis uses a multisensor split-window technique relying on the concept of microphysical index applied to the two pairs of channels (12.05, 10.6 μm) and (12.05, 8.65 μm) to retrieve cirrus microphysical properties (effective diameter, ice water path) at 1-km pixel resolution. Retrievals are performed for three crystal families selected from precomputed lookup tables identified as representative of the main relationships between the microphysical indices. The uncertainties in the microphysical indices are detailed and quantified, and the impact on the retrievals is simulated. The possible biases have been assessed through consistency checks that are based on effective emissivity difference. It has been shown that particle effective diameters of single-layered cirrus clouds can be retrieved, for the first time, down to effective emissivities close to 0.05 when accurate measured background radiances can be used and up to 0.95 over ocean and land, as well as over low opaque clouds. The retrieval of the ice water path from the IIR effective optical depth and the effective diameter is discussed. Taking advantage of the cloud boundaries retrieved by CALIOP, an IIR power-law relationship between ice water content and extinction is established for four temperature ranges and shown to be consistent with previous results on average for the chosen dataset.

TRECO: Dynamic Technology Remapping for Timing Engineering Change Orders

Due to increasing integrated circuit design complexity, engineering change orders (ECOs) have become a necessary technique to resolve late-found functional errors and/or performance deficiencies. To fix timing violations, gate sizing and buffer insertion are commonly used in postmask ECO. These techniques, however, may not be powerful enough, especially when spare cells are inserted to balance between functional and timing repair capabilities. We propose a postmask ECO technique, called TRECO, to remedy timing violations based on technology remapping, which also supports functional ECO. Unlike conventional technology mapping, TRECO performs technology mapping with respect to a limited set of spare cells and confronts dynamic changes of wiring cost incurred by selection of different spare cells. With a precomputed lookup table of representative circuit templates, TRECO iteratively performs technology remapping to restructure timing critical subcircuits until no timing violation can be further removed. Experimental results on five industrial designs show the effectiveness of TRECO in ECO timing optimization and in timing-aware functional ECO.

Soil Moisture Retrieval Using Time-Series Radar Observations Over Bare Surfaces

A time-series algorithm is proposed to retrieve bare surface soil moisture and rms height using two copolarized (HH and VV) L-band backscattering coefficients (σ0). The retrieval approach inverts a forward model for radar scattering from an isotropic bare surface. Because real-time inversion of a complex forward model is often computationally impractical, the inversion is implemented using a precomputed lookup table representation of σ0 obtained from numerical Maxwell model in 3-D simulations. The retrieval process assumes that surface roughness properties are constant during the time-series interval, so that only a single rms height estimate is produced for the entire time series. The use of this rms height estimate as a constraint simplifies the associated soil moisture retrievals at each time step. A Monte-Carlo simulation of this algorithm with 0.7 dB radar measurement error (1-sigma) shows that retrievals using six time steps outperform a “snapshot” method (which retrieves rms height and soil moisture at each time step) by a factor of about two in rms soil moisture error. A second study using measured data having 6 to 11 time steps shows an rms error of 0.044 cm3/cm3 for soil moisture with a correlation coefficient of 0.89 between retrieved and in situ data. Surface rms height estimates are also found accurate to 10 to 30% of in situ measurements. It is also shown that retrieval performance is not sensitive to errors in knowledge of the surface roughness correlation length for most of the bare surface conditions examined.

Retrieval of Ice Cloud Optical Thickness and Effective Particle Size Using a Fast Infrared Radiative Transfer Model

AbstractA computationally efficient radiative transfer model (RTM) is developed for the inference of ice cloud optical thickness and effective particle size from satellite-based infrared (IR) measurements and is aimed at potential use in operational cloud-property retrievals from multispectral satellite imagery. The RTM employs precomputed lookup tables to simulate the top-of-the-atmosphere (TOA) radiances (or brightness temperatures) at 8.5-, 11-, and 12-μm bands. For the clear-sky atmosphere, the optical thickness of each atmospheric layer resulting from gaseous absorption is derived from the correlated-k-distribution method. The cloud reflectance, transmittance, emissivity, and effective temperature are precomputed using the Discrete Ordinate Radiative Transfer model (DISORT). For an atmosphere containing a semitransparent ice cloud layer with a visible optical thickness τ smaller than 5, the TOA brightness temperature differences (BTDs) between the fast model and the more rigorous DISORT results are less than 0.1 K, whereas the BTDs are less than 0.01 K if τ is larger than 10. With the proposed RTM, the cloud optical and microphysical properties are retrieved from collocated observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) in conjunction with the Modern Era Retrospective-Analysis for Research and Applications (MERRA) data. Comparisons between the retrieved ice cloud properties (optical thickness and effective particle size) based on the present IR fast model and those from the Aqua/MODIS operational collection-5 cloud products indicate that the IR retrievals are smaller. A comparison between the IR-retrieved ice water path (IWP) and CALIOP-retrieved IWP shows robust agreement over most of the IWP range.

A high performance hardware architecture for multi-frame hierarchical motion estimation

This paper presents the architecture design and FPGA implementation of a multi-frame hierarchical motion estimation (MFHME) circuit. The target application of the circuit is high quality motion-compensated video frame rate up-conversion that requires dense motion fields (MF) and accurate motion trajectories. To obtain accurate motion trajectories, the circuit uses two frames as references and calculates the block matching errors for both the luminance and chrominance components of the images. In addition, the sum of squared pixel differences, instead of the sum of the absolute pixel differences, is used as the metric of the block matching errors in order to further improve the accuracy of the estimated motion trajectories. To achieve low computation complexity, the circuit has been designed based on a hierarchical structure and a pre-computed lookup table is used to provide the squared pixel differences. The implementation result shows that the circuit is able to support the frame rate up-conversion of high definition video (1080P format) from 30 to 60 frames per second at a clock frequency of 55 MHz.

Origins of ambiguity in the inversion of remote sensing reflectance signals by spectral matching in optically complex shelf seas

The reported occurrence of multiple solutions in the inversion of remote sensing reflectance, Rrs, signals is of considerable significance for attempts to recover the concentrations of optically significant materials (OSMs) in shelf seas. The severity of this problem was assessed by quantifying the number of matches between individual multi wavelength remote sensing “observations” and the entries in a pre-computed look-up table (LUT) spanning the range of possible observations. As a simplifying step, radiative transfer modeling was used to confirm the existence of a linear relationship between Rrs in the visible wavebands and the ratio of the backscattering to absorption coefficients, bb/a over the full range of OSM concentrations likely to be found in shelf seas. This meant that an LUT of appropriate size and resolution could be constructed from bb/a vectors rather than Rrs spectra, with a considerable saving of computational effort. The number of matches in the LUT was then determined as a function of the degree of noise present in the observation and the strictness of the matching criterion (a simple least-squares fitting routine). Perfect matching for the six visible wavebands of the SeaWiFs satellite radiometer was achieved only when the “observed” bb/a vector was represented exactly in the library. The introduction of noise representing observational errors rapidly led to multiple matches, as did relaxation of the matching criterion. As an example of the sensitivity of OSM recovery to errors of observation, it was found that an average error of 0.1%, statistically distributed across all wavebands, led to an average recovery error of 6.4% in CHL for 3000 randomly selected observations.

An embedded upward flame spread model using 2D direct numerical simulations

A fully coupled 2D fluid–solid direct numerical simulation (DNS) approach is used to simulate co-flow flame spread over poly(methyl methacrylate) (PMMA) at different angles of inclination. Comparison of simulations and experimental measurements are conducted over a range of flame spread rates. Results show that the heat flux to the preheating region varies considerably in time — contradicting often employed assumptions used in established flame spread theories. Accounting for the time dependent behavior is essential in accurate predictions of flame spread, however, a universal characterization in terms of easily defined parameters is not found. Alternatively, a reaction progress variable based embedded flame model is developed using mixture fraction, total enthalpy and surface temperature. State maps of the gas-phase properties and surface heat flux are constructed and stored in pre-computed lookup tables. The resulting model provides a computationally efficient and a local formulation to determine the flame heat flux to the surface resulting in excellent agreement to DNS and experiments for predictions of flame spread rate and position of the pyrolysis front.

Statistical estimation of stratospheric particle size distribution by combining optical modelling and lidar scattering measurements

Abstract. A method for estimating the stratospheric particle size distribution from multiwavelength lidar measurements is presented. It is based on matching measured and model-simulated backscatter coefficients. The lidar backscatter coefficients measured at the three commonly used wavelengths 355, 532 and 1064 nm are compared to a precomputed look-up table of model-calculated values. The optical model assumes that particles are spherical and that their size distribution is unimodal. This inverse problem is not trivial because the optical model is highly non-linear with a strong sensitivity to the size distribution parameters in some cases. The errors in the lidar backscatter coefficients are explicitly taken into account in the estimation. The method takes advantage of the statistical properties of the possible solution cluster to identify the most probable size distribution parameters. In order to discard model-simulated outliers resulting from the strong non-linearity of the model, solutions farther than one standard deviation of the median values of the solution cluster are filtered out, because the most probable solution is expected to be in the densest part of the cluster. Within the filtered solution cluster, the estimation algorithm minimizes a cost function of the misfit between measurements and model simulations. Two validation cases are presented on Polar Stratospheric Cloud (PSC) events detected above the ALOMAR observatory (69° N – Norway). A first validation is performed against optical particle counter measurements carried out in January 1996. In non-depolarizing regions of the cloud (i.e. spherical particles), the parameters of an unimodal size distribution and those of the optically dominant mode of a bimodal size distribution are quite successfully retrieved, especially for the median radius and the geometrical standard deviation. As expected, the algorithm performs poorly when solid particles drive the backscatter coefficient. A small bias is identified in modelling the refractive index when compared to previous works that inferred PSC type Ib refractive indices. The accuracy of the size distribution retrieval is improved when the refractive index is set to the value inferred in the reference paper. Our results are then compared to values retrieved with another similar method that does not account for the effect of the measurements errors and the non-linearity of the optical model on the likelihood of the solution. The case considered is a liquid PSC observed over northern Scandinavia on January 2005. An excellent agreement is found between the two methods when our algorithm is applied without any statistical filtering of the solution cluster. However, the solution for the geometrical standard deviation appears to be rather unlikely with a value close to unity (σ≈1.04). When our algorithm is applied with solution filtering, a more realistic value of the standard deviation (σ≈1.27) is found. This highlights the importance of taking into account the non linearity of the model together with the lidar errors, when estimating particle size distribution parameters from lidar measurements.

Adaptive Hybrid ARQ Systems With BCJR Decoding

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We propose and evaluate a novel method of constructing Hybrid Automatic Repeat reQuest (HARQ) systems using the specific properties of the Bahl, Cocke, Jelinek, and Raviv (BCJR) error-correcting algorithm. Because the convergence to the actual codeword is not always guaranteed with the BCJR approach, we propose a system in which two different types of Negative AcKnowledgement messages (NAKs) are employed. The first type is the conventional 1-bit NAK, and the second type specifies retransmission pattern in such a way that the additional parity bits are concentrated on the parts of the code trellis that did not converge to a valid sequence. This is different from the traditional construction of rate-compatible punctured codes (RCPCs), which attempts to obtain the optimal weight distance properties of the codes without taking the convergence properties of the BCJR decoder into account. We demonstrate the performance of the algorithm using RCPCs, and we show that our system outperforms the best known conventional HARQ scheme in terms of the throughput and the average length of retransmitted blocks on practical Gaussian, Rayleigh, and thresholded Rayleigh channels. Moreover, as opposed to other adaptive HARQ algorithms, our solution requires no precomputed lookup tables, and it is robust to changes in the channel characteristics and only introduces moderate increase in feedback link throughput requirements. </para>

Composite finite elements for 3D image based computing

We present an algorithmical concept for modeling and simulation with partial differential equations (PDEs) in image based computing where the computational geometry is defined through previously segmented image data. Such problems occur in applications from biology and medicine where the underlying image data has been acquired through, e.g. computed tomography (CT), magnetic resonance imaging (MRI) or electron microscopy (EM). Based on a level-set description of the computational domain, our approach is capable of automatically providing suitable composite finite element functions that resolve the complicated shapes in the medical/biological data set. It is efficient in the sense that the traversal of the grid (and thus assembling matrices for finite element computations) inherits the efficiency of uniform grids away from complicated structures. The method’s efficiency heavily depends on precomputed lookup tables in the vicinity of the domain boundary or interface. A suitable multigrid method is used for an efficient solution of the systems of equations resulting from the composite finite element discretization. The paper focuses on both algorithmical and implementational details. Scalar and vector valued model problems as well as real applications underline the usability of our approach.

FLUTE: Fast Lookup Table Based Rectilinear Steiner Minimal Tree Algorithm for VLSI Design

In this paper, we present a very fast and accurate rectilinear Steiner minimal tree (RSMT) algorithm called fast lookup table estimation (FLUTE). FLUTE is based on a precomputed lookup table to make RSMT construction very fast and very accurate for low-degreeThe degree of a net is the number of pins in the net. nets. For high-degree nets, a net-breaking technique is proposed to reduce the net size until the table can be used. A scheme is also presented to allow users to control the tradeoff between accuracy and runtime. FLUTE is optimal for low-degree nets (up to degree 9 in our current implementation) and is still very accurate for nets up to degree 100. Therefore, it is particularly suitable for very large scale integration applications in which most nets have a degree of 30 or less. We show experimentally that, over 18 industrial circuits in the ISPD98 benchmark suite, FLUTE with default accuracy is more accurate than the Batched 1-Steiner heuristic and is almost as fast as a very efficient implementation of Prim's rectilinear minimum spanning tree algorithm.

Image Processing for a High-Resolution Optoelectronic Retinal Prosthesis

In an effort to restore visual perception in retinal diseases such as age-related macular degeneration or retinitis pigmentosa, a design was recently presented for a high-resolution optoelectronic retinal prosthesis having thousands of electrodes. This system requires real-time image processing fast enough to convert a video stream of images into electrical stimulus patterns that can be properly interpreted by the brain. Here, we present image-processing and tracking algorithms for a subretinal implant designed to stimulate the second neuron in the visual pathway, bypassing the degenerated first synaptic layer. For this task, we have developed and implemented: 1) A tracking algorithm that determines the implant's position in each frame. 2) Image cropping outside of the implant boundaries. 3) A geometrical transformation that distorts the image appropriate to the geometry of the fovea. 4) Spatio-temporal image filtering to reproduce the visual processing normally occurring in photoceptors and at the photoreceptor-bipolar cell synapse. 5) Conversion of the filtered visual information into a pattern of electrical current. Methods to accelerate real-time transformations include the exploitation of data redundancy in the time domain, and the use of precomputed lookup tables that are adjustable to retinal physiology and allow flexible control of stimulation parameters. A software implementation of these algorithms processes natural visual scenes with sufficient speed for real-time operation. This computationally efficient algorithm resembles, in some aspects, biological strategies of efficient coding in the retina and could provide a refresh rate higher than fifty frames per second on our system.

The Sensitivity of Ice Cloud Optical and Microphysical Passive Satellite Retrievals to Cloud Geometrical Thickness

Most satellite-based ice cloud retrieval algorithms rely on precomputed lookup libraries for inferring the ice cloud optical thickness (tau) and effective particle size ( D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> ). However, this retrieval methodology does not account for the case where cloud geometrical thickness may vary by several kilometers. In this paper, we investigate the effect of the ice cloud geometrical thickness on the retrieval of tau and D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> for algorithms using the Moderate Resolution Imaging Spectroradiometer infrared (IR) bands at 8.5 and 11 mum (or 12 mum) or solar bands at 0.65 and 1.64 mum (or 2.13 mum). We use a rigorous radiative transfer package to simulate the IR brightness temperatures and solar reflectances, assuming that the ice cloud top height is fixed at 12 or 15 km with a variation of cloud geometrical thickness from 0.5 to 5 km. The simulated brightness temperatures and reflectances are then used to investigate the errors of cloud tau and D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> inferred from the precomputed lookup tables developed with a specific geometrical thickness. It is found that the retrieval errors in tau and D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> increase with increasing tau for the IR and solar methods. In both cases, cloud tau and D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> may be underestimated and overestimated, respectively, if the effect of the cloud geometrical thickness is not taken into account. The effect of the cloud geometrical thickness on the retrieval of cloud optical and microphysical properties is much larger for the IR algorithm than for the solar-band-based algorithm. This paper demonstrates that the inclusion of the information about the cloud geometrical thickness may improve the accuracy of the retrieval of the cloud properties on the basis of the precomputed lookup libraries

A fast infrared radiative transfer model based on the adding–doubling method for hyperspectral remote-sensing applications

A fast infrared radiative transfer (RT) model is developed on the basis of the adding–doubling principle, hereafter referred to as FIRTM-AD, to facilitate the forward RT simulations involved in hyperspectral remote-sensing applications under cloudy-sky conditions. A pre-computed look-up table (LUT) of the bidirectional reflection and transmission functions and emissivities of ice clouds in conjunction with efficient interpolation schemes is used in FIRTM-AD to alleviate the computational burden of the doubling process. FIRTM-AD is applicable to a variety of cloud conditions, including vertically inhomogeneous or multilayered clouds. In particular, this RT model is suitable for the computation of high-spectral-resolution radiance and brightness temperature (BT) spectra at both the top-of-atmosphere and surface, and thus is useful for satellite and ground-based hyperspectral sensors. In terms of computer CPU time, FIRTM-AD is approximately 100–250 times faster than the well-known discrete-ordinate (DISORT) RT model for the same conditions. The errors of FIRTM-AD, specified as root-mean-square (RMS) BT differences with respect to their DISORT counterparts, are generally smaller than 0.1 K.

Parameterized code SHARM-3D for radiative transfer over inhomogeneous surfaces

The code SHARM-3D, developed for fast and accurate simulations of the monochromatic radiance at the top of the atmosphere over spatially variable surfaces with Lambertian or anisotropic reflectance, is described. The atmosphere is assumed to be laterally uniform across the image and to consist of two layers with aerosols contained in the bottom layer. The SHARM-3D code performs simultaneous calculations for all specified incidence-view geometries and multiple wavelengths in one run. The numerical efficiency of the current version of code is close to its potential limit and is achieved by means of two innovations. The first is the development of a comprehensive precomputed lookup table of the three-dimensional atmospheric optical transfer function for various atmospheric conditions. The second is the use of a linear kernel model of the land surface bidirectional reflectance factor (BRF) in our algorithm that has led to a fully parameterized solution in terms of the surface BRF parameters. The code is also able to model inland lakes and rivers. The water pixels are described with the Nakajima-Tanaka BRF model of wind-roughened water surface with a Lambertian offset, which is designed to model approximately the reflectance of suspended matter and of a shallow lake or river bottom.

Analysis of linear combination algorithms in cryptography

Several cryptosystems rely on fast calculations of linear combinations in groups. One way to achieve this is to use joint signed binary digit expansions of small “weight.” We study two algorithms, one based on nonadjacent forms of the coefficients of the linear combination, the other based on a certain joint sparse form specifically adapted to this problem. Both methods are sped up using the sliding windows approach combined with precomputed lookup tables. We give explicit and asymptotic results for the number of group operations needed, assuming uniform distribution of the coefficients. Expected values, variances and a central limit theorem are proved using generating functions.Furthermore, we provide a new algorithm that calculates the digits of an optimal expansion of pairs of integers from left to right. This avoids storing the whole expansion, which is needed with the previously known right-to-left methods, and allows an online computation.

Dynamic power management for nonstationary service requests

Dynamic power management (DPM) is a design methodology aimed at reducing power consumption of electronic systems by performing selective shutdown of idle system resources. The effectiveness of a power management scheme depends critically on accurate modeling of service requests and on computation of the control policy. In this work, we present an online adaptive DPM scheme for systems that can be modeled as finite-state Markov chains. Online adaptation is required to deal with initially unknown or nonstationary workloads, which are very common in real-life systems. Our approach moves from exact policy optimization techniques in a known and stationary stochastic environment and extends optimum stationary control policies to handle the unknown and nonstationary stochastic environment for practical applications. We introduce two workload learning techniques based on sliding windows and study their properties. Furthermore, a two-dimensional interpolation technique is introduced to obtain adaptive policies from a precomputed look-up table of optimum stationary policies. The effectiveness of our approach is demonstrated by a complete DPM implementation on a laptop computer with a power-manageable hard disk that compares very favorably with existing DPM schemes.

GENSPECT: a line-by-line code with selectable interpolation error tolerance

Current line-by-line radiative transfer codes accelerate calculations by interpolating the line function where it varies slowly. This can increase calculation performance by a factor of 10 or more but causes a reduction in calculation accuracy. We present a new line-by-line algorithm that computes absorption coefficients to a specified percentage-error tolerance in a near minimal number of calculations. The algorithm employs a novel binary division of a calculation's spectral interval, coupled with a pre-computed lookup table that predicts where it is appropriate to reduce the resolution of a particular line without exceeding the required error tolerance. Line contributions are computed piecewise across a cascaded series of grids which are then interpolated and summed to derive the absorption coefficient. The algorithm is coded in MATLAB as part of a toolbox of radiative transfer functions for the analysis of planetary atmospheres and laboratory experiments.