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Atmospheric turbulence often limits the performance of long-range imaging systems in applications. Realistic turbulence simulations provide means to evaluate this effect and assess turbulence mitigation algorithms. Current methods typically use phase screens or turbulent point spread functions (PSFs) to simulate the image distortion and blur due to turbulence. While the first method takes long computation times, the latter requires empirical models or libraries of PSF shapes and their associated tip and tilt motion, which might be overly simplistic for some applications. In this work, an approach is evaluated which tries to avoid these issues. Generative neural networks models are able to generate extremely realistic imitations of real (image) data with a short calculation time. To treat anisoplanatic imaging for the considered application, the model output is an imitation PSF-grid that has to be applied to the input image to yield the turbulent image. Certain shape features of the model outcome can be controlled by traversing within subsets of the model input space or latent space. The use of a conditional variational autoencoder (cVAE) appears very promising to yield fast computation times and realistic PSFs and is therefore examined in this work. The cVAE is trained on field trial camera images of a remote LED array. These images are considered as grids of real PSFs. First the images are pre-processed and their PSFs properties are determined for each frame. Main goal of the cVAE is the generation of PSF-grids under conditional properties, e.g. moments of PSFs. Different approaches are discussed and employed for a qualitative evaluation of the realism of the PSF-grids generated by the trained models. A comparison of required simulation computing time is presented and further considerations regarding the simulation method are discussed.

The performance of tomographic adaptive optics systems will depend on the vertical profile of turbulence in the atmosphere. Since the profile is changing over time, to maintain optimal correction the tomographic reconstructor must be continually updated with new profile information. Several reconstructor parameters must then be chosen to optimise performance given the constraints of real-time computing resources: the number of reconstructed layers, reoptimisation period and averaging time. We analyse the effect of changing these parameters by coupling fast Fourier-domain AO simulation with a large database of over 10,000 high resolution turbulence profiles measured by the Stereo-SCIDAR at Paranal.

The Learn and Apply reconstruction scheme uses the knowledge of atmospheric turbulence to generate a tomographic reconstructor, and its performance is enhanced by the real-time identification of the atmosphere and the wind profile. In this paper we propose a turbulence profiling method that is driven by the atmospheric model. The vertical intensity distribution of turbulence, wind speed and wind direction can be simultaneously estimated from the Laser Guide Star measurements. We introduce the implementation of such a method on a GPU accelerated non-linear least-squares solver, which significantly increases the computation efficiency. Finally, we present simulation results to demonstrate the convergence quality from numerically generated telemetry, the end-to-end Adaptive Optics simulation results, and a time-to-solution analysis, all based on the MAVIS system design.

Taean is a small peninsular along the west coast of the mainland of Korean peninsula. It faces with the yellow sea to the west, while it is bordered with the mainland Korea to the east. The area is well known to have 1) heavy mid and high altitude air traffic, 2) busy traffic routes of ocean liners between Korea and neighboring countries and 3) complex and rapid changes in its atmospheric characteristics that tends to reduce the accuracy of regional weather forecast and therefore influencing the local industry including ocean liners. In this conference, we report a progress on customization of atmospheric transmission model suitable for direct solar irradiance measurement in the region. A large volume of direct solar irradiance and atmospheric variable data were collected over the period of 12 months in 2018. The data were obtained not only from the ground stations for low altitude, but also from the radiosonde measurement for high altitude. Initial analysis using MODTRAN showed some portions of the direct solar irradiance data that disagree with the predictions from the MODTRAN standard atmospheric models. Further in-depth study including sensitivity analysis were carried out and resulted in construction of a customized MODTRAN atmospheric model offering more accurate prediction to the measured direct solar irradiance data of the local area.

As so far, studies based on remote sensing to explore ozone column concentration keep a watchful eye on the stratosphere or troposphere, while few focus on the near-surface, though it directly correlative to human health. In this paper, the regional near-surface total column ozone was inversed based on the moderate-resolution imaging spectroradiometer (MODIS) for its extraordinary spatial resolution. First, the statistical synthetic regression algorithm was utilized to retrieve the first guess. A nonlinear physical iterative method was then employed to acquire final ozone profiles. Finally, after creating a unique database, the ozone column concentration was obtained by using the multivariable linear regression model. Compared with the measurements of ground monitoring sites, the retrieval results were over 95% accurate and its distribution consists with the actual situation. The method proposed in this paper can be applied to monitor air pollution.

In the previous chapter, we described the basics of calculating the spectral transmittance though the Earth’s atmosphere using PcModWin©. Besides transmittance, another popular atmospheric modeling parameter is the resulting scattered radiance created within a path from some point in sky or space, or from the Earth’s surface, to a remote sensor, or both the transmittance and resulting radiance from this path. This chapter will discuss how to calculate the total and component radiances that can be created by the atmosphere and the Earth’s surface, as well as the losses they may experience. This material applies to both solar and lunar illumination.

The software has been developed for thematic processing of data of Russian satellite systems. It is based on the algorithm of atmospheric correction developed by IAO SB RAS. The software allows retrieval of the reflection coefficients of the Earth’s surface with the RTM atmospheric correction algorithm in the visible and near-IR range on the assumption of a uniform surface. The software includes converters for reading and unpacking input formats of satellite data and related metadata, an atmospheric correction module, and an auxiliary module for formation of the atmospheric model. The atmospheric correction includes procedures for statistical simulation of the Earth’s surface illumination and for calculation of the spherical albedo of the atmosphere, intensity of radiation noninteracting with the surface, and intensity of radiation reflected by the surface.

The threshold wind speed is a useful criterion in determining whether strong turbulence is generated within the Stable Boundary Layer (SBL), the layer where all surface emissions remain confined during the night. Confidence turbulence estimates are extremely important for atmospheric transport and dispersion simulations, although due to its complex dinamics many aspects of the SBL are neglected by numerical models that, in turn, are the inputs and boundary conditions for the transport and dispersion simulations. Turbulence is especially important during severe episodies like hazardous material accidental releases, for example. Turbulence intensity can affect the dispersion speed, released material concentration, and its reach. For many decades, remote sensing has been an important tool in filling the gap of information and providing advances in the atmospheric sciences. The doppler lidar is increasingly being used for micrometeoroly and Planetary Boundary Layer (PBL) studies because of its autonomy and long range capability, in contrast with traditional techniques as radioprobes and captive balloons. After 1 year of continuous measurements with a doppler lidar, it was possible to determine the threshold wind speed for Ipero, Sao Paulo, Brazil. Besides threshold wind speed, it was observed that the SBL turbulence has a straight relationship with the Low-Level Jets (LLJs) that frequently occur over the region. The vertical turbulence distribution depends greatly on the LLJ characteristics, which in turn is highly variable during its life cycle. The strong turbulence regime is associated to the stronger LLJs, that presents a more defined pattern. In contrast, weak LLJs (that generate weaker SBL turbulence) present more dispersive characteristics in respect to the entire dataset. These differences are seen both for the LLJ height as for the turbulence vertical profile. These results will contribute for the atmospheric modeling and dispersion simulations, as well for the environmental studies at Ipero.

We analyzed clouds macrophysics and their radiative effects according to COSMO mesoscale atmospheric model and measurements at Lindenberg Observatory (Germany). Different cloud parameters simulated by the model and derived from measurements of ground-based remote sounding systems as well as by measurements of the BSRN station have been compared for snow-free conditions during March-October, 2016. We show that simulated water vapor content (QV) values were higher than the observed ones in the middle and upper troposphere. Furthermore, the modeled cloud ice water content (QI) is significantly higher at these altitudes and the ice cloud water path (TQI) is ten times higher than the observed value. Typical values of TQI and of TQC (liquid cloud water path) lie within 3*10-13-1.1 kg/m2 and 0.0017-2.9 kg/m2 , respectively with average values of 0.015 and 0.079 kg/m2 correspondingly. We also found a positive bias with the measurements for the TQC greater than 0.1 kg/m2 and a negative bias for other values. A clear nonlinear dependence of solar irradiance at ground against TQC values was obtained both from measurements and model experiments. We showed a significant (29%) underestimation of model solar irradiance for the same TQC values in overcast cloud conditions compared with the observations.

An accurate forecast of atmospheric turbulence is needed to characterize the performance of free-space electro-optical systems. Atmospheric turbulence physics is intricate and stochastic and relies on an assortment of assumptions, making modeling difficult to formulate in a rigorous first principles manner. Machine learning (ML) techniques have been shown to parameterize complex relationships in large datasets and are able to more accurately predict response variables than standard regression methods. This study applies machine learning techniques to develop a model that forecasts the refractive index structure parameter, 𝐶𝑛 2. Measurements of 𝐶𝑛 2 were obtained from a field experiment along with meteorological observations. Several machine learning models were created and compared to optical scintillometer output and current atmospheric turbulence models. The ML based models are shown to generate predictions of 𝐶𝑛 2 that are more highly correlated to observed 𝐶𝑛 2 than physics-based formulations.

Atmospheric correction is a necessary step in image processing data and spectra recorded by spaceborne sensors for pure cloudless atmosphere, primarily in the visible and near-IR spectral range. We have present a fast and sufficiently accurate method of atmospheric correction based on the proposed analytical solutions describing with high accuracy the spectrum of outgoing radiation at the top boundary of the cloudless atmosphere. This technique includes the model of the atmosphere and its optical parameters that are important in terms of radiation transfer. The solution of the inverse problem for finding unknown parameters of the model is carried out by the method of non-linear least squares (Levenberg-Marquardt algorithm) for an individual selected pixel of the image (its spectrum), taking into account the adjacency effects. Using the found parameters of the atmosphere and the average surface reflectance, assuming homogeneity of the atmosphere within a certain area of the hyperspectral image (around dark pixel), the spectral reflectance at the Earth's surface is calculated for all other pixels. It is essential that the procedure of the numerical simulation with non-linear least squares of the direct transfer problem is based on using analytical solutions, which provides a very short calculation time of the atmospheric parameters (seconds or less) and the ability to perform atmospheric correction "on- y." The developed technique SHARC (Sattelite Hypercube Atmosheric Rapid Correction) was performed using the synthetic outgoing radiation spectra at the top of atmosphere (TOA), obtained by exact radiative transfer calculations (COART and LibRadTran codes), as well as the spectra of real space images Hyperion hyper- spectrometer.

Accurate retrieval of surface emissivity from long-wave infrared (LWIR) hyperspectral imaging data is necessary for many scientific and defense applications. Emissivity estimation consists of two interwoven steps: atmospheric compensation (AC) and temperature-emissivity separation (TES). AC uses an atmospheric estimate to convert the at-aperture radiance to ground radiance. Using the ground radiance, TES produces a temperature and emissivity estimate. TES algorithms require an accurate atmospheric model, and assumes that emissivity spectra for solids are smooth, compared to atmospheric features. A high-resolution atmospheric model is band-averaged to the sensor's spectral response function (SRF). Characterization and maintenance of the SRF is difficult, and errors cause rough emissivity estimates. We propose a method where spectra with smooth reflective emissivities are used to correct errors from the SRF. In-Scene AC (ISAC) methods can be used to find accurate estimates of the band-averaged atmospheric upwelling and transmission, but not the downwelling radiance which is needed for TES. Typical TES methods use a model for the downwelling radiance and an assumed SRF, which will differ from the true SRF causing unnaturally rough emissivity estimates. While ISAC estimates include the true SRF it is difficult to separate the SRF from these measurements. Instead of estimating the SRF directly, our method uses smooth low emissivity materials to produce a correction for the downwelling radiance that matches the true band-averaged values. We demonstrate this technique using simulated data.

The objective of this research is to model anisoplanatic atmospheric effects for synthetically generated Synthetic Aperture Ladar (SAL) data, and to evaluate the impact of these effects on target classification in SAL imagery. Although SAL wavelengths present key advantages in target exploitation, they are heavily susceptible to atmo- spheric turbulence; hence the inclusion of atmospheric effects in simulation is crucial. With utilization of an atmospheric modeling toolbox, we introduce the ability to model anisoplanatic atmospheric effects into a ray tracing simulation tool. We show image classification results as a function of atmospheric severity.

Accurate estimation of surface emissivity from long-wave infrared (LWIR) hyperspectral imaging data acquired by airborne or space-borne sensors is necessary for many scientific and defense applications. This process consists of two interwoven steps: atmospheric compensation (AC) and temperature-emissivity separation (TES). AC uses an estimated atmospheric model to estimate the ground radiance from the at-aperture radiance, and then TES produces a temperature and emissivity estimate using this ground radiance. Hyperspectral TES algorithms assume that the emissivity spectra for solids are smooth compared to the atmosphere. The quality of TES estimates depend on the accuracy of the atmospheric estimates and their band-averaging to the sensor spectral resolution. The objective of this paper is to study the sensitivity of TES results to errors in atmospheric estimates. There are several errors associated with TES output including temperature errors, and shape, bias, and smoothness errors in the emissivity. The effects of atmospheric errors are studied by using MODTRAN to generate models with varying temperature and water vapor amounts. One model is used to simulate at-aperture radiances while the others are used for AC and TES on the simulated spectra. The mismatch between the models affects the TES results. While most errors cause TES to fail providing rough emissivity estimates, magnitude and shape errors provide smooth but incorrect emissivities. Understanding how atmospheric errors affect the TES estimates and what is required of atmospheric estimates to provide smooth emissivity estimates will help in developing algorithms to find the correct atmosphere for a scene.

Monitoring the concentration of greenhouse gases from space is an important need. It can be achieved via a precise analysis of the chemical gaseous species (CO2, CH4, CO, etc.) signature in the spectrum of the reflected sunlight. The MICROCARB project aims to reach a very high quality measurement from on board a small volume Microsat platform, in 4 different spectral bands (O2 spectral band, 2 CO2 spectral bands and a CH4 spectral band), in order to locate and characterize the CO2 sinks and sources and to have a better understanding of the carbon cycle. But this is a very tricky measurement especially for the CO2 monitoring as its concentration in the atmosphere is about 380ppm and we need to measure it with accuracy better than p/− 1ppm. Another presentation is fully dedicated to the MICROCARB instrument design (see [1]).

NASA LaRC is developing and integrating a double-Pulsed 2-micron direct detection IPDA lidar for CO2 column measurement from an airborne platform. The presentation will describe the development of the 2-micrometers IPDA lidar system and present the airborne measurement of column CO2 and will compare to in-situ measurement for various ground target of different reflectivity.

Hyperspectral imaging (HSI) in the Mid-Wave InfraRed (MWIR, 3-5 microns) can provide information on a variety of science applications from determining the chemical composition of lava lakes on Jupiter’s moon Io, to investigating the amount of carbon liberated into the Earth’s atmosphere during a wildfire. The limited signal available in the MWIR presents technical challenges to achieving high signal-to-noise ratios, and therefore it is typically necessary to cryogenically cool MWIR instruments. With recent improvements in microbolometer technology and emerging interferometric techniques, we have shown that uncooled microbolometers coupled with a Sagnac interferometer can achieve high signal-to-noise ratios for long-wave infrared HSI. To explore if this technique can be applied to the MWIR, this project, with funding from NASA, has built the Miniaturized Infrared Detector of Atmospheric Species (MIDAS). Standard characterization tests are used to compare MIDAS against a cryogenically cooled photon detector to evaluate the MIDAS instruments’ ability to quantify gas concentrations. Atmospheric radiative transfer codes are in development to explore the limitations of MIDAS and identify the range of science objectives that MIDAS will most likely excel at. We will simulate science applications with gas cells filled with varying gas concentrations and varying source temperatures to verify our results from lab characterization and our atmospheric modeling code.

The Electro-Optical Signal Transmission and Ranging (EOSTAR) model is an image-based Tactical Decision Aid (TDA) for thermal imaging systems (MWIR/LWIR) developed for a sea environment with an extensive atmosphere model. The Triangle Orientation Discrimination (TOD) Target Acquisition model calculates the sensor and signal processing effects on a set of input triangle test pattern images, judges their orientation using humans or a Human Visual System (HVS) model and derives the system image quality and operational field performance from the correctness of the responses. Combination of the TOD model and EOSTAR, basically provides the possibility to model Target Acquisition (TA) performance over the exact path from scene to observer. In this method ship representative TOD test patterns are placed at the position of the real target, subsequently the combined effects of the environment (atmosphere, background, etc.), sensor and signal processing on the image are calculated using EOSTAR and finally the results are judged by humans. The thresholds are converted into Detection-Recognition-Identification (DRI) ranges of the real target. In experiments is shown that combination of the TOD model and the EOSTAR model is indeed possible. The resulting images look natural and provide insight in the possibilities of combining the two models. The TOD observation task can be done well by humans, and the measured TOD is consistent with analytical TOD predictions for the same camera that was modeled in the ECOMOS project. © 2017 SPIE. The Society of Photo-Optical Instrumentation Engineers (SPIE)

Hyperspectral imaging for gas detection applications is an under-researched topic. The same gas model is used in most of the gas detection studies in the literature. This model aims to formulate the scene covering the gas emission as well as the background and the atmosphere. Therefore, the model requires prior knowledge on transmittance, emissivity, and temperature values of the components in the scene. The commonly used approaches to estimate these parameters include atmospheric modeling and statistical inference. However, accessing such information is costly in remote detection applications. Some studies avoid background characterization by decomposing the scene using spectral-spatial information. There are several studies in the literature using this model. They aim to detect various types of gases on different parts of electromagnetic spectrum. Most of these studies use hyperspectral radiance information regarding the scene. However, using brightness temperature map of the data instead of radiance data is more suitable for direct analysis. For this reason, we used brightness temperature spectrum in this study. On the other hand, the detection algorithms are generally based on pixel based investigation. Since the emission of the gas is sourced by a pipe or a chimney, investigating the emission region at the segment level increases detection accuracy. In this study, we used an iterative spectral feature based pixel clustering algorithm followed by spatial segmentation.

Two Doppler lidars were recently used to collect data from the planetary boundary layer (PBL) in Sao Paulo city (23°32’S, 46°38’W). The measurement campaign was carried out from December-2015 to February-2016, during the summer, which is the rainy season. Although Sao Paulo is the main city of a huge metropolitan region with more than 11 million of inhabitants and 7 millions of vehicles, according to the government agencies, the lack of PBL observational data is still a limitation for the atmospheric dispersion studies. Therefore, this work should contribute to the comprehension of PBL mechanisms and also for future atmospheric modeling studies. The data revealed that the nocturnal low-level jets (LLJs) frequently occurred along those 3 months, but its height is highly variable, from 100 m up to 650 m. It was also seen that the nocturnal LLJs can extend for several hours, right before the sunset until sunrise. This work aims to investigate the turbulence production by the nocturnal LLJs and its influence into the stable boundary layer (SBL).

An accurate model and parameterization of fog is needed to increase the reliability and usefulness of electro-optical systems in all relevant environments. Current models vary widely in their ability to accurately predict the size distribution and subsequent optical properties of fog. The Advanced Navy Aerosol Model (ANAM), developed to model the distribution of aerosols in the maritime environment, does not currently include a model for fog. One of the more prevalent methods for modeling particle size spectra consists of fitting a modified gamma function to fog measurement data. This limits the fog distribution to a single mode. Here we establish an empirical model for predicting complicated multimodal fog droplet size spectra using machine learning techniques. This is accomplished through careful measurements of fog in a controlled laboratory environment and measuring fog particle size distributions during outdoor fog events.

Space Situational Awareness (SSA) is of utmost importance in today's congested and contested space environment. Satellites must perform orbital corrections for station keeping, devices like high efficiency electric propulsion systems such as a Hall effect thrusters (HETs) to accomplish this are on the rise. The health of this system is extremely important to ensure the satellite can maintain proper position and perform its intended mission. Electron temperature is a commonly used diagnostic to determine the efficiency of a hall thruster. Recent papers have coordinated near infrared (NIR) spectral measurements of emission lines in xenon and krypton to electron temperature measurements. Ground based observations of these spectral lines could allow the health of the thruster to be determined while the satellite is in operation. Another issue worth considering is the availability of SSA assets for ground-based observations. The current SSA architecture is limited and task saturated. If smaller telescopes, like those at universities, could successfully detect these signatures they could augment data collection for the SSA network. To facilitate this, precise atmospheric modeling must be used to pull out the signature. Within the atmosphere, the NIR has a higher transmission ratio and typical HET propellants are approximately 3x the intensity in the NIR versus the visible spectrum making it ideal for ground based observations. The proposed research will focus on developing a model to determine xenon and krypton signatures through the atmosphere and estimate the efficacy through ground-based observations. The model will take power modes, orbit geometries, and satellite altitudes into consideration and be correlated with lab and field observations.

The daytime sky has been recently demonstrated as a useful calibration tool for deriving polarization cross-talk properties of large astronomical telescopes. The Daniel K Inouye Solar Telescope (DKIST) and other large telescopes under construction can benefit from precise polarimetric calibration of large mirrors. Several atmospheric phenomena and instrumental errors potentially limit the techniques accuracy. At the 3.67m AEOS telescope on Haleakala, we have performed a large observing campaign with the HiVIS spectropolarimeter to identify limitations and develop algorithms for extracting consistent calibrations. Effective sampling of the telescope optical configurations and filtering of data for several derived parameters provide robustness to the derived Mueller matrix calibrations. Second-order scattering models of the sky show that this method is relatively insensitive to multiple-scattering in the sky provided calibration observations are done in regions of high polarization degree. The technique is also insensitive to assumptions about telescope induced polarization provided the mirror coatings are highly reflective. Zemax-derived polarization models show agreement between the functional dependence of polarization predictions and the corresponding on-sky calibrations., Comment: Accepted for publication in SPIE JATIS

Short wave radiation (SWR) and its attenuation with depth have a major impact on the vertical distribution of the oceanic water temperature, dynamical processes, and ocean-atmosphere interactions. In numerical modeling of oceanic processes, the SWR usually comes from the atmospheric model predictions, while the short wave attenuation schemes are internally prescribed (estimated) inside the oceanic dynamical model. It has been reported that atmospheric models show a tendency to overestimate the shortwave radiation due to underestimation of predicted low-level clouds. Most existing schemes to specify the attenuation of SWR with depth in numerical models are based on: the Jerlov (1976) water-types classification; climatological estimates of attenuation coefficients or from the biological model predictions of light-absorbing and scattering water constituents. All of the above attenuation schemes are prone to introducing errors in the attenuation of short wave radiation with depth. As a result, we have to deal with two types of errors in the oceanic modeling: those due to the incorrect specification of the magnitude of SWR at the surface (from the atmospheric model), and those due to inaccurate vertical attenuation of SWR (prescribed in the oceanic model). We have developed an approach for estimating errors in the oceanic model heat budget due to errors in surface values of SWR and in its attenuation with depth. Based on this approach, we present examples illustrating sensitivities of the heat budget of the water column to the changes in specification of surface SWR and its attenuation.

Water level was estimated, using AltiKa radar altimeter onboard the SARAL satellite, over the Ukai reservoir using modified algorithms specifically for inland water bodies. The methodology was based on waveform classification, waveform retracking, and dedicated inland range corrections algorithms. The 40-Hz waveforms were classified based on linear discriminant analysis and Bayesian classifier. Waveforms were retracked using Brown, Ice-2, threshold, and offset center of gravity methods. Retracking algorithms were implemented on full waveform and subwaveforms (only one leading edge) for estimating the improvement in the retrieved range. European Centre for Medium-Range Weather Forecasts (ECMWF) operational, ECMWF re-analysis pressure fields, and global ionosphere maps were used to exactly estimate the range corrections. The microwave and optical images were used for estimating the extent of the water body and altimeter track location. Four global positioning system (GPS) field trips were conducted on same day as the SARAL pass using two dual frequency GPS. One GPS was mounted close to the dam in static mode and the other was used on a moving vehicle within the reservoir in Kinematic mode. In situ gauge dataset was provided by the Ukai dam authority for the time period January 1972 to March 2015. The altimeter retrieved water level results were then validated with the GPS survey and in situ gauge dataset. With good selection of virtual station (waveform classification, back scattering coefficient), Ice-2 retracker and subwaveform retracker both work better with an overall root-mean-square error

In this paper, we draw attention to the requirements for obtaining measurement data essential to accurate modeling and prediction of atmospheric laser beam propagation at heights 10 meters or less above the ocean surface. This is an atmospheric zone we call the marine boundary layer . There are numerous models and applications for optical beam propagation in the upper atmosphere that based on the Kolmogorov Theory of Turbulence. However, close to the ocean surface standard procedures for measuring and modeling the effects of boundary layer turbulence may no longer apply. In addition to classical measurement procedures, we suggest a new methodology and measurement procedures for exploration of beam propagation and the ensuing dynamics within the marine boundary layer. [1,2]

In this study we investigated the impact of land surface surface process & land-atmospheric interaction on weather and surface hydrology. The ultimate goal is to integrate remote sense data into numerical mesoscale weather prediction and regional climate model in order to improve prediction of the impacts of land-atmosphere interactions and land-surface processes on regional weather, and hydrology. We have used climatology based green vegetation fraction and 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) based green vegetation fraction and integrated in the Land Information System to conduct uncoupled simulation to understand the impact on surface and hydrological parameters in the summer season. The vegetation response is also realized through coupled regional climate simulation in which climatological based greenness and 8-days varying vegetation is investigated and quantify the impact of vegetation on summertime precipitation process. This study has bought following findings (a) Satellite based vegetation indices captures vegetation temporal patterns more realistic than climatological vegetation data and detects early/late spring signature through vegetation indices, (b) Integrated satellite vegetation greenness input data in regional weather model resolved much better soil moisture and soil temperature including the diurnal cycle of surface heat fluxes and surface temperature in the simulation. Secondly, integration of the TRMM based satellite rainfall product into coupled hydrological and Atmospheric model and results shows better resolved soil moisture patterns in the remote regions of the Asia Mountain regions.

Oil spills are one of the major environmental concerns, especially in the coastal zones of the ocean. Satellite remote sensing imagery has proved to be a useful tool for monitoring oil spills in the marine environment. With its two daily acquisitions and the possibility to obtain near-real-time data free of charge, the Moderate Resolution Imaging Spectroradiometer (MODIS) shows interesting potential as such a cost-effective supplementary tool. Several researches on oil spill detection in MODIS imagery has been carried out for the past few years. Basically, oil spills were manually detected from MODIS imagery [1,2]. The disadvantage of the manual detection method is inefficient and subjective. Shi et al. proposed an oil spill detection method from MODIS imagery by using fuzzy cluster and texture feature extraction [3]. It works in an automatic manner and does not require any priori knowledge of occurrence or the spectral attributes of spills. But its efficiency in very near shore regions is limited. Chen and Zhao detected oil spills from the oil-water contrast ratio image by using a thresholding method [4].They found that the oil-water contrast ratio can be enhanced by replacing the original image with the ratio image of two different band ones in 400-800 nm. To obtain the oil-water contrast ratio image from the MODIS imagery, they selected the oil spill area and the background sea area and then calculated the mean radiance or emissivity value in those areas. By doing so, the automation and the accuracy of the method were reduced. Adamo et al. [5] and Kudryavtsev et al. [6] proposed physical methods for oil spill detection from MODIS imagery acquired in sunglint conditions. These two methods take imaging geometry into consideration and have the aid of other models or functions such as the Cox and Munk (1954) model [7],the CMOD4 model [8,9], the ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric model, and the transfer function, which increase the algorithm complexity and rely on some assumptions.

The paper presents an approach to specify initial and boundary conditions from the output data of global model SLAV for mesoscale modelling of atmospheric processes in areas not covered by meteorological observations. From the data and the model equations for a homogeneous atmospheric boundary layer the meteorological and turbulent characteristics of the atmospheric boundary layer are calculated. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

In the fields of early warning, one is depending on reliable image exploitation: Only if the applied detection and tracking algorithms work efficiently, the threat approach alert can be given fast enough to ensure an automatic initiation of the countermeasure. In order to evaluate the performance of those algorithms for a certain electro-optical (EO) sensor system, test sequences need to be created as realistic and comprehensive as possible. Since both, background and target signature, depend on the environmental conditions, a detailed knowledge of the meteorology and climatology is necessary. Trials for measuring these environmental characteristics serve as a solid basis, but might only constitute the conditions during a rather short period of time. To represent the entire variation of meteorology and climatology that the future system will be exposed to, the application of comprehensive atmospheric modelling tools is essential. This paper gives an introduction of the atmospheric modelling tools that are currently used at Fraunhofer IOSB to simulate spectral background signatures in the infrared (IR) range. It is also demonstrated, how those signatures are affected by changing atmospheric and climatic conditions. In conclusion – and with a special focus on the modelling of different cloud types - sources of error and limits are discussed.

Some X-ray instruments require the utilization of large-area windows to provide vacuum barriers. The necessary attributes of the window material include transparency to X-rays, low scattering, and possession of suitable mechanical properties for reliable long-term performance. Kapton is one such material except that it is a polymer and a large window made from Kapton with a pressure differential of one atmosphere across it can undergo substantial deformation at room temperature. In this paper, we report on the mechanical testing of Kapton samples including creep measurements, and comparison with published data. We use of these data together with analytical / numerical models to predict the changes in the profile of Kapton vacuum windows over time, and show good agreement with experimental measurements.

This study investigates laser beam propagation through an atmospheric boundary layer near the ocean surface. Objectives of this research are to ascertain feasibility limits for achieving maximum energy efficiency at extended ranges in the face of atmospheric and other distortions as the laser beam penetrates through transitional (anisotropic) and turbulent (isotropic) boundary layer regimes. Various aspects of turbulence modeling of laser beam propagation near the ocean surface are discussed including: Kolmogorov's model of atmospheric turbulence, parameterized structure functions (e.g., velocity and temperature gradients, gradients in refractive index) and other important factors affecting near surface propagation such as humidity, aerosols, and wave slap . Various preliminary modeled propagation results are shown, and a new methodology is proposed for improving existing model estimates with new time domain measurement procedures.

This study investigates laser beam propagation through an atmospheric boundary layer near the ocean surface. Objectives of this research are to ascertain feasibility limits for achieving maximum energy efficiency at extended ranges in the face of atmospheric and other distortions as the laser beam penetrates through transitional (anisotropic) and turbulent (isotropic) boundary layer regimes. Various aspects of turbulence modeling of laser beam propagation near the ocean surface are discussed including: Kolmogorov’s model of atmospheric turbulence, parameterized structure functions (e.g., velocity and temperature gradients, gradients in refractive index) and other important factors affecting near surface propagation such as humidity, aerosols, and wave slap. Various preliminary modeled propagation results are shown, and a new methodology is proposed for improving existing model estimates with new time domain measurement procedures.

The infrared (IR) energy radiated from any source passes through the atmosphere before reaching the sensor. As a result, the total signature captured by the IR sensor is significantly modified by the atmospheric effects. The dominant physical quantities that constitute the mentioned atmospheric effects are the atmospheric transmittance and the atmospheric path radiance. The incoming IR radiation is attenuated by the transmittance and path radiance is added on top of the attenuated radiation. In IR scene simulations OpenGL is widely used for rendering purposes. In the literature there are studies, which model the atmospheric effects in an IR band using OpenGLs exponential fog model as suggested by Beers law. In the standard pipeline of OpenGL, the related fog model needs single equivalent OpenGL variables for the transmittance and path radiance, which actually depend on both the distance between the source and the sensor and also on the wavelength of interest. However, in the conditions where the range dependency cannot be modeled as an exponential function, it is not accurate to replace the atmospheric quantities with a single parameter. The introduction of OpenGL Shading Language (GLSL) has enabled the developers to use the GPU more flexible. In this paper, a novel method is proposed for the atmospheric effects modeling using the least squares estimation with polynomial fitting by programmable OpenGL shader programs built with GLSL. In this context, a radiative transfer model code is used to obtain the transmittance and path radiance data. Then, polynomial fits are computed for the range dependency of these variables. Hence, the atmospheric effects model data that will be uploaded in the GPU memory is significantly reduced. Moreover, the error because of fitting is negligible as long as narrow IR bands are used.

Fast and accurate computation of light path deviation due to atmospheric refraction is an important requirement for real-time simulation of optical imaging sensor systems. A large body of existing literature covers various methods for application of Snell’s Law to the light path ray tracing problem. This paper provides a discussion of the adaptation to real time simulation of atmospheric refraction ray tracing techniques used in mid-1980's LOWTRAN releases. The refraction ray trace algorithm published in a LOWTRAN-6 technical report by Kneizys (et. al.) has been coded in MATLAB for development, and in C-language for simulation use. To this published algorithm we have added tuning parameters for variable path segment lengths, and extensions for Earth grazing and exoatmospheric "near Earth" ray paths. Model atmosphere properties used to exercise the refraction algorithm were obtained from tables published in another LOWTRAN-6 related report. The LOWTRAN-6 based refraction model is applicable to atmospheric propagation at wavelengths in the IR and visible bands of the electromagnetic spectrum. It has been used during the past two years by engineers at the U.S. Army Aviation and Missile Research, Development and Engineering Center (AMRDEC) in support of several advanced imaging sensor simulations. Recently, a faster (but sufficiently accurate) method using Gauss-Chebyshev Quadrature integration for evaluating the refraction integral was adopted.

The Optical Payload for Lasercomm Science (OPALS) Flight System on-board the International Space Station uses a charge coupled device (CCD) camera to detect a beacon laser from Earth. Relative measurements of the background contributed by upwelling radiance under diverse illumination conditions and varying surface terrain is presented. In some cases clouds in the field-of-view allowed a comparison of terrestrial and cloud-top upwelling radiance. In this paper we will report these measurements and examine the extent of agreement with atmospheric model predictions.

The authors have studied the radiation transfer in multilayer atmosphere. The analytical formulae for the calculation of the transmission coefficient, reflectance and absorption of dispersion media consisting of three plane layers were obtained. It was shown that absorption of dispersed media depends strongly on absorption layer’s position in dispersed media. The lowest value is marked when the layer takes place below of the media the light falls from above. Investigation of the radiation balance of the atmosphere is usually conducted on the basis of the theory of radiative transfer and numerical methods [1]. In conducting research using various models of the atmosphere [2-4]. Accuracy of the results depends on the accuracy of the approximation and taking into account all the effects that significantly affect the results, such as the effect of the spatial limitations of the dispersion medium [4-6].

The total zenith tropospheric delay (ZTD) is an important parameter of the atmosphere and directly or indirectly reflects the weather processes and variations. This paper presents a hardware and software complex for continuous measurements and prediction of atmospheric thermodynamics and radiowaves refraction index. The main part is a network of ground-based spatially separated GPS-GLONASS receivers, which allows the remote sensing zenith tropospheric delay. GPS-Derived Zenith Tropospheric Delay shows the day to day variation and mesoscale spatial and temporal variability. Comparison with the numerical weather reanalysis fields and solar photometer measurements showed agreement with the relative deviation of less than 10%. Hardware-software complex includes the numerical model of the atmosphere on a computational cluster. A variational assimilation system was used to examine the comparative impact of including satellite derived total zenith tropospheric delay from GPS and GLONASS ground observations. Preliminary results show that the initial field of radiowaves refraction index was improved by assimilating the satellite derived ZTD.

A simple meteorological model considering turbulence, moisture microphysics and radiation heat transfer in the atmosphere has been developed for a prompt forecasting of processes taking place in the atmospheric boundary layer. Numerical simulation outcomes obtained with the using of this model will help to quickly forecast a daily variation of such meteorological parameters as rain precipitations, cloud cover and humidity.

The efficient particle measurement methods are very important for the assessment of ambient air quality. Compared with other methods, the light scattering method has the advantages of non-destructive and real time detection, and has been used in the detection and analysis of particle composition and concentration. Different from non-polarized light scattering detection, polarized light scattering is suitable for the measurement of fine particles, such as PM 1 and PM 2.5 . In this paper, we establish a three-component model to describe three typical particle elements in atmospheric suspended matters, and also set several pollution state changes according to some reported real measured data. By our primary polarization simulation under weak scattering approximation, we analyze Mueller matrix elements fluctuation with the different changes of particle concentration and distribution, and then extract two characteristic elements to follow the status particle composition. The results imply the potential of the backscattered polarization characterization to distinguish and evaluate the parameter variation in an atmospheric particulate model, and also show the possible sensitivity higher than the non-polarization characteristic.

This paper will focus on the effect of atmospheric conditions on EO sensor performance using computer models. We have shown the importance of accurately modeling atmospheric effects for predicting the performance of an EO sensor. A simple example will demonstrated how real conditions for several sites in China will significantly impact on image correction, hyperspectral imaging, and remote sensing. The current state-of-the-art model for computing atmospheric transmission and radiance is, MODTRAN® 5, developed by the US Air Force Research Laboratory and Spectral Science, Inc. Research by the US Air Force, Navy and Army resulted in the public release of LOWTRAN 2 in the early 1970’s. Subsequent releases of LOWTRAN and MODTRAN® have continued until the present. Please verify that (1) all pages are present, (2) all figures are correct, (3) all fonts and special characters are correct, and (4) all text and figures fit within the red margin lines shown on this review document. Complete formatting information is available at http://SPIE.org/manuscripts Return to the Manage Active Submissions page at http://spie.org/submissions/tasks.aspx and approve or disapprove this submission. Your manuscript will not be published without this approval. Please contact author_help@spie.org with any questions or concerns. The paper will demonstrate the importance of using validated models and local measured meteorological, atmospheric and aerosol conditions to accurately simulate the atmospheric transmission and radiance. Frequently default conditions are used which can produce errors of as much as 75% in these values. This can have significant impact on remote sensing applications.

This work intends to develop an efficient algorithm for aerosol retrieval in haze episodes. The increasing emissions of anthropogenic particles provide the serious air pollutants. Although extreme concentrations of aerosols in the atmosphere can prevent aerosol monitoring with surface-level sun/sky photometers, satellites can still be used in such conditions to observe the Earth’s atmosphere from space. It is known that the precise simulation of multiple light-scattering processes within the atmosphere is necessary for aerosol remote sensing and needs a long computational time especially in such an optically thick atmosphere as aerosol episode. Accordingly efficient and practical algorithms for radiation simulations are indispensable to retrieve particle characteristics in a case of serious air pollution.

The alternative Bendersky, Kopeika, and Blaunstein (BKB) model of measuring the refractive structure index parameter, Cn² has proven to be a reliable, well-used means of quantifying and characterizing the atmospheric turbulence in a given environment. This model relies on various meteorological parameters such as temperature, wind speed, relative humidity, and time of day in order to procure the resulting Cn² quantity. Using experimentally confirmed results from a desert environment, the utility of this model may be extended to other climates by adapting temporal hour weights used within the model. The adaptation of these weighted parameters are shown to have a relationship with the unique weather conditions of a given region which are demonstrated by data points collected from two testing ranges located in Florida in addition to archived weather data. The resulting extended model is then compared to commercial scintillometer data for validation. Keywords: BKB Model, Scintillation, Scintillometer, Atmospheric Turbulence, Refractive Index Structure Parameter

The Space Optics Laboratory at Yonsei University, Korea, in cooperation with Breault Research Organization (BRO) in Tucson, Arizona, have invested significant research and development efforts into creating large scale ray tracing techniques for simulating “reflected” light from the earth with an artificial satellite. This presentation describes a complex model that combines the sun, the earth and an orbiting optical instrument combined into a real scale nonsequential ray tracing computation using BRO’s Advanced Systems Analysis Program, ASAP®. The Sun is simulated as a spherically emitting light source of 695,500 km in diameter. The earth also is simulated as a sphere with its characteristics defined as target objects to be observed and defined with appropriate optical properties. They include the atmosphere, land and ocean elements, each having distinctive optical properties expressed by single or combined characteristics of refraction, reflection and scattering. The current embodiment has an atmospheric model consisting of 33 optical layers, a land model with 6 different albedos and the ocean simulated with sun glint characteristics. A space-based optical instrument, with an actual opto-mechanical prescription, is defined in an orbit of several hundreds to thousands of miles in altitude above the earth’s surface. The model allows for almost simultaneous evaluations of the imaging and radiometric performances of the instrument. Several real-life application results are reported suggesting that this simulation approach not only provides valuable information that can greatly improve the space optical instrument performance but also provides a simulation tool for scientists to evaluate all phases of a space mission.

The advent of expensive, large-aperture telescopes and complex adaptive optics (AO) systems has strengthened the need for detailed simulation of such systems from the top of the atmosphere to control algorithms. The credibility of any simulation is underpinned by the quality of the atmosphere model used for introducing phase variations into the incident photons. Hitherto, simulations which incorporate wind layers have relied upon phase screen generation methods that tax the computation and memory capacities of the platforms on which they run. This places limits on parameters of a simulation, such as exposure time or resolution, thus compromising its utility. As aperture sizes and fields of view increase the problem will only get worse. We present an autoregressive method for evolving atmospheric phase that is efficient in its use of computation resources and allows for variability in the power contained in frozen flow or stochastic components of the atmosphere. Users have the flexibility of generating atmosphere datacubes in advance of runs where memory constraints allow to save on computation time or of computing the phase at each time step for long exposure times. Preliminary tests of model atmospheres generated using this method show power spectral density and rms phase in accordance with established metrics for Kolmogorov models.

ZEMAX provides a standard built-in atmospheric model to simulate atmospheric refraction and dispersion. This model has been compared with other ones to assess its intrinsic accuracy, critical for very demanding application like ADCs for AO-assisted extremely large telescopes. A revised simple model, based on updated published data of the air refractivity, is proposed by using the "Gradient 5" surface of Zemax. At large zenith angles (65 deg), discrepancies up to 100 mas in the differential refraction are expected near the UV atmospheric transmission cutoff. When high-accuracy modeling is required, the latter model should be preferred., Advances in Optical and Mechanical Technologies for Telescopes and Instrumentation, June 23-27, 2014, Series: Proceedings of SPIE; no. 9151

Long range imaging systems have applications in vessel traffic monitoring, border and coastal observation, and generic surveillance. Often, sign reading and identification capabilities are required, and medium or long-wave infrared systems are simply not the best solution for these tasks, because of the low scene contrast. Among reflected light imagers, the short-wave infrared has a competitive advantage over the visible and near-infrared spectrum, being less affected by path attenuation, scattering and turbulence. However, predicting a SWIR system long range performance still represents a challenge because of the need of an accurate atmospheric modelling. In this paper, we present the key limiting performance factors for long range applications, and how we used popular atmospheric models to extract the synthetic simulation parameters needed for range performance prediction. We then present a case study for a long range application, where the main requirement is to read a vessel name at distances greater than 10km. The results show a significant advantage of SWIR over visible and near-infrared solutions for long range identification tasks.

An Atmospheric radiative transfer model calculates radiative transfer of electromagnetic radiation through a planetary atmosphere. Both shortwave radiance and longwave radiance parameterizations in an atmospheric model calculate radiation fluxes and heating rates in the earth-atmospheric system. One radiative transfer model is the rapid radiative transfer model (RRTM), which calculates of longwave and shortwave atmospheric radiative fluxes and heating rates. Longwave broadband radiative transfer code for general circulation model (GCM) applications, RRTMG, is based on the single-column reference code, RRTM. The RRTMG is a validated, correlated k-distribution band model for the calculation of longwave and shortwave atmospheric radiative fluxes and heating rates. The focus of this paper is on the RRTMG long wave (RRTMG_LW) model. In order to improve computational efficiency, RRTMG_LW incorporates several modifications compared to RRTM. In RRTM_LW there are 16 g points in each of the spectral bands for a total of 256 g points. In RRTMG_LW, the number of g points in each spectral band varies from 2 to 16 depending on the absorption in each band. RRTMG_LW employs a computationally efficient correlated-k method for radiative transfer calculations. It contains 16 spectral bands with various number of quadrature points (g points) in each of the bands. In total, there are 140 g points. The radiative effects of all significant atmospheric gases are included in RRTMG_LW. Active gas absorbers include H 2 O, O 3 , CO 2 , CH 4 , N 2 O, O 2 and four types of halocarbons: CFC-11, CFC-12, CFC-22, and CCL4. RRTMG_LW also treats the absorption and scattering from liquid and ice clouds and aerosols. For cloudysky radiative transfer, a maximum-random cloud overlapping scheme is used. Small scale cloud variability, such as cloud fraction and the vertical overlap of clouds can be represented using a statistical technique in RRTMG_LW. Due to its accuracy, RRTMG_LW has been implemented operationally in many weather forecast and climate models. RRTMG_LW is in operational use in ECMWF weather forecast system, the NCEP global forecast system, the ECHAM5 climate model, Community Earth System Model (CESM) and the weather and forecasting (WRF) model. RRTMG_LW has also been evaluated for use in GFDL climate model. In this paper, we examine the feasibility of using graphics processing units (GPUs) to accelerate the RRTMG_LW as used by the WRF. GPUs can provide a substantial improvement in RRTMG speed by supporting the parallel computation of large numbers of independent radiative calculations. Furthermore, using commodity GPUs for accelerating RRTMG_LW allows getting a much higher computational performance at lower price point than traditional CPUs. Furthermore, power and cooling costs are significantly reduced by using GPUs. A GPU-compatible version of RRTMG was implemented and thorough testing was performed to ensure that the original level of accuracy is retained. Our results show that GPUs can provide significant speedup over conventional CPUs. In particular, Nvidia’s GTX 680 GPU card can provide a speedup of 69x for the compared to its single-threaded Fortran counterpart running on Intel Xeon E5-2603 CPU.

A dual wavelength micro pulse polarization lidar system is designed, in which Nd:YAG laser at 1064nm and 532nm is employed as the exciting source, to research the depolarization ratio, the lidar ratio and the angstrom coefficient of aerosols. Numerical simulations are performed with the standard atmospheric model to verify the detection ability of the system. The simulation results show that the lidar system is capable of detecting the depolarization ratio of aerosols up to a height larger than 6 km at night and 2 km in the daytime with a sigal to noise ratio of 10. In the meantime, the lidar system can also be used to detect the angstrom coefficient, and can provide important scientific data for studies of the relastionship among these parameters.