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NASA NTRS2020

Multi-Objective Flight Control for Ride Quality Improvement for Flexible Aircraft

This paper describes a multi-objective flight control system design for ride quality improvement for flexible aircraft using multi-functional distributed flight control surfaces. A multi-objective optimal control design is developed to provide an acceleration suppression capability in conjunction with a gust load alleviation in order to provide ride quality improvement. A gust estimation is developed to estimate the gust load using a recursive least-squares algorithm. A ride quality assessment study is conducted using a flexible wing generic transport model. Six different flight control designs are implemented. The study shows that ride quality can be significantly improved with the acceleration suppression control.

Nguyen, Nhan↗

NASA NTRS2019

An Approach to Retrieve BRDF from Satellite and Airborne Measurements of Surface-Reflected Radiance Based on Decoupling of Atmospheric Radiative Transfer and Surface Reflection

Bi-directional Reflection Distribution Function (BRDF) defines anisotropy of the surface reflection. It is required to specify the boundary condition for radiative transfer (RT) modeling. Measurements of reflected radiance by satellite- and air-borne sensors provide information about anisotropy of surface reflection. Atmospheric correction needs to be performed to derive BRDF from the reflected radiance. Common approach for BRDF retrievals consists of the use of kernel-based BRDF and RT modeling that needs to be done anew at every step of the iterative process. The kernels’ weights are obtained by minimization of the difference between measured and modeled radiance. This study develops a new method of retrieving kernel-based BRDF that requires RT calculations to be done only once. The method employs the exact analytical expression of radiance at any atmospheric level through the solutions of two auxiliary atmosphere-only RT problems and the surface-reflected radiance at the surface level. The latter is related to BRDF and solutions of the auxiliary RT problems by a Fredholm integral equation of the second kind. The approach requires to perform RT calculations one time before the iterations. It can use observations taken at different atmospheric conditions assuming that surface conditions remain unchanged during the time span of observations. The algorithm accurately catches zero weights of the kernels that may be a concern if the number of kernels is greater than 3 in current mainstream approaches. The study presents numerical tests of the BRDF retrieval algorithm for various surface and atmospheric conditions.

Radkevich, Alexander↗

NASA NTRSDate not supplied

TPSAS-NF1676L-16988-DND

There has been renewed interest to uniformly recalibrate historical geostationary (GEO) data records to aid in climate monitoring. GEO sensors have annual repeatable angular sampling over a given location. The view angle is fixed and the imaging schedule is usually constant through its lifetime. Given the fact that colocated GEOs are always share the same sub-satellite point and maintain their imaging schedules provides repeatable angular sampling over decades. One of the biggest challenges in transferring a reference sensor calibration using invariant desert targets to another sensor is the accuracy of the bidirectional reflectance distribution function (BDRF). A well-calibrated GEO can be used to predict the daily exoatmospheric radiance model (DERM) over a desert target for a given GMT that is valid for any GEO sensor at the same location. The advantage of this method is that a BRDF is not needed. Another challenge of invariant target calibration is the unique spectra signature of the desert. However, since most GEOs are built in batches, the spectral response functions (SRF) are very similar for most historical GEOs, the spectral band adjustment factor (SBAF) between GEO sensors is much smaller than for MODIS and GEO sensors. Since the water vapor burden over the desert is seasonal, both the TOA and desert surface can be considered invariant for a given day of the year. The reference GEO can be inter-calibrated with MODIS or VIIRS, which have onboard visible calibration using solar diffusers, using other methods, such as ray-matching or deep convective clouds. Also the next generation GEOs will have onboard visible calibration, which will increase the accuracy of this method. Three Meteosats over the Libyan desert will be used to illustrate the DERM method. The reference Meteosat will be inter-calibrated against Aqua-MODIS. The reference GEO DERM will be constructed and used to calibrate the remaining Meteosats. The DERM calibration will be validated by comparing the calibration using Aqua-MODIS ray-matching. Similarly, two GOES sensors using the Sonoran desert will also be highlighted. An uncertainty analysis will also be performed with emphasis on the SBAF, derived over the desert targets using both SCIAMACHY and Hyperion hyper-spectral radiances.

David Doelling↗

NASA NTRS2020

Electron Energy Partition Across Interplanetary Shocks. III. Analysis

An analysis of model fit results of 15,210 electron velocity distribution functions (VDFs), observed within ±2 hr of 52 interplanetary (IP) shocks by the Wind spacecraft near 1 au, is presented as the third and final part on electron VDFs near IP shocks. The core electrons and protons dominate in the magnitude and change in the partialto-total thermal pressure ratio, with the core electrons often gaining as much or more than the protons. Only a moderate positive correlation is observed between the electron temperature and the kinetic energy change across the shock, while weaker, if any, correlations were found with any other macroscopic shock parameter. No VDF parameter correlated with the shock normal angle. The electron VDF evolves from a narrowly peaked core with flaring suprathermal tails in the upstream to either a slightly hotter core with steeper tails or much hotter flattop core with even steeper tails downstream of the weaker and strongest shocks, respectively. Both quasi-static and fluctuating fields are examined as possible mechanisms modifying the VDF, but neither is sufficient alone. For instance, flattop VDFs can be generated by nonlinear ion acoustic wave stochastic acceleration (i.e., inelastic collisions), while other work suggested they result from the combination of quasi-static and fluctuating fields. This three-part study shows that not only are these systems not thermodynamic in nature; even kinetic models may require modification to include things like inelastic collision operators to properly model electron VDF evolution across shocks or in the solar wind.

Solar wind↗

NASA NTRS2020

Neural Network Repair of Lossy Compression Artifacts in the Sept 2015 – March 2016 Duration of the MMS/FPI Dataset

During the Sept 2015 –March 2016 duration (sometimes referred to as Phase 1A) of the Magnetospheric Multiscale Mission (MMS), the Dual Electron Spectrometers (DES) were configured to generously utilize lossy compression. While this maximized the number of velocity distribution functions downlinked, it cameat the expense of lost information content for a fractionof the frames. Following this period of lossy compression, the DES was re-configured in a way that allowed for 95% of the framesto arrive to the ground without loss. Using this high-quality set offrameson-orbit observations, we compressed and decompressed the frameson the ground to create a side-by-side record of the compression effect. This record was used to drive an optimization method that (a) derived basis functions capable of approximating the lossless sample space and with non-negative coefficients and (b) fitted a function which maps the lossy framesto basis weights that recreate the framewithout compression artifacts. This methodis introduced and evaluated in this paper.Data users should expect a higher level of confidence in the absolute scale of density/temperature measurements andnotice less sinusoidal bias in the velocity X and Y components(GSE).

Magnetosphere, Neural Networks, Image Processing, ↗

NASA NTRS2020

Assimilation of SMAP and ASCAT Soil Moisture Retrievals into the JULES Land Surface Model Using the Local Ensemble Transform Kalman Filter

A land data assimilation system is developed to merge satellite soil moisture retrievals into the Joint U.K. Land Environment Simulator (JULES) land surface model (LSM) using the Local Ensemble Transform Kalman Filter (LETKF). The system assimilates microwave soil moisture retrievals from the Soil Moisture Active Passive (SMAP) radiometer and the Advanced Scatterometer (ASCAT) after bias correction based on cumulative distribution function fitting. The soil moisture assimilation estimates are evaluated with ground-based soil moisture measurements over the continental U.S. for five consecutive warm seasons (May–September of 2015–2019). The result shows that both SMAP and ASCAT retrievals improve the accuracy of soil moisture estimates. Especially, the SMAP single-sensor assimilation experiment shows the best performance with the increase of temporal anomaly correlation by ΔR ~ 0.05 for surface soil moisture and ΔR ~ 0.03 for root-zone soil moisture compared with the LSM simulation without satellite data assimilation. SMAP assimilation is more skillful than ASCAT assimilation primarily because of the greater skill of the assimilated SMAP retrievals compared to the ASCAT retrievals. The skill improvement also depends significantly on the region; the higher skill improvement in the western U.S. compared to the eastern U.S. is explained by the Kalman gain in the two experiments. Additionally, the regional skill differences in the single-sensor assimilation experiments are attributed to the number of assimilated observations. Finally, the soil moisture assimilation estimates provide more realistic land surface information than model-only simulations for the 2015 and the 2016 western U.S. droughts, suggesting the advantage of using satellite soil moisture retrievals in the current drought monitoring system.

SMAP↗

NASA NTRS2020

Assessing the Impact of Soil Layer Depth Specification on the Observability of Modeled Soil Moisture and Brightness Temperature

The utility of hydrologic land surface models (LSMs) can be enhanced by using information from observational platforms, but mismatches between the two are common.This study assesses the degree to which model agreement with observations (observability) is affected by two mechanisms in particular: 1) physical incongruities between the support volumes being characterized and 2) inadequateor inconsistent parameterizations of physical processes. The Noah and Noah-MP LSMs by default characterize surface soil moisture (SSM) in the top 10 cm of the soil column. This depth may be reasonable when comparing against soil moisture from in situ probes centered at 5 cm,but it is notably different from the 5 cm (or less) sensing depth of NASA’s Soil Moisture Active Passive (SMAP) satellite mission. These depth inconsistencies are examined by using thinner model layers in the Noah and Noah-MP LSMs and comparing resultant simulations to in situ and SMAP soil moisture. In addition, a forward radiative transfer model to simulate microwave brightness temperatures (Tbs) is used to facilitate direct comparisons of LSM-based and SMAP-based L-band Tb retrievals. Observability is quantified using Kolmogorov-Smirnov distance values, calculated from empirical cumulative distribution functions of SSM and Tb time series. Experiment results depend on the particular subspace being analyzed (SSM or Tb). This study concludes that therole of increasedsoil layer discretizationon LSM observability is secondary to the influence of component parameterizations, the effects of which dominate systematic differences with observations

SMAP↗

NASA NTRSDate not supplied

Tools for Performing SBG hyperspectral Observing System Simulation Experiment

One of NASA’s Decadal Survey mission, Surface Biology and Geology (SBG), will include a hyperspectral remote sensing imager, which has a very high spatial resolution and a wide spectral coverage (from UV to Near IR). Unprecedented large data volumes will be generated by the SBG hyperspectral instrument. Before the launch of the new satellite, an Observing System Simulation Experiment (OSSE) can be used to study different designs of the new satellite system. One of the key components in an OSSE study is a radiative transfer model (RTM) or forward model. In this presentation, we will describe a Principal Component-based Radiative Transfer Model (PCRTM) which is capable of simulating atmospheric (TOA) radiance or reflectance spectra from far IR to visible and UV spectral regions (50 wavenumber to 30000 wavenumber) quickly and accurately. Multiple scattering from multiple layers of clouds/aerosols are included in the model. The PCRTM has a very good accuracy relative to reference line-by-line radiative transfer models (LBLRTM), and it saves 3-4 orders of magnitude computational time relative to LBLRTM or MODTRAN. The PCRTM model has been successfully used to analyze large volumes of data from hyperspectral sensors such as AIRS, CrIS, and IASI. It has also been used to perform OSSE studies for the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission. Another useful tool for the OSSE is surface Bidirectional Reflectance Distribution Function (BRDF) database. It is very crucial for the SBG OSSE to include realistic BRDF spectra. Currently, most of the surface reflectance spectra such as those in the ECOSIS and ECOSTRESS are measured at specific observation geometries. We have developed a hyperspectral bidirectional reflectance (HSBR) model which combines Ross-Li BRDF model with the existing reflectance spectral libraries using a principal component analysis. This HSBR model can provide realistic BRDF spectra under various observation conditions. It can also be used to generate realistic BRDF spectra using measurements from multi-band imagers or spectrometers such as MODIS or VIIRS.

Xu Liu↗

NASA NTRS2020

N20 VIIRS RSB Calibration Algorithms and Results: Collection 2.0

The NOAA-20 (N20) satellite, previously the Joint Polar Satellite System-1 satellite, was launched on November 18, 2017. One of the five major scientific instruments aboard the satellite is the Visible Infrared Imaging Radiometer Suite (VIIRS). VIIRS scans the Earth’s surface in 22 spectral bands, 14 of which are the reflective solar bands (RSBs) with band center wavelengths from 0.412 to 2.25μm. VIIRS regularly performs on-orbit radiometric calibration of its RSBs, primarily through the observations of the onboard solar diffuser (SD). The on-orbit change of the SD’s bidirectional reflectance distribution function, known as the H-factor, is determined by the onboard SD stability monitor (SDSM). Since the H-factor exhibits angular dependence, obtaining the H-factor along the SD to the telescope direction is a challenge for the NOAA-20 VIIRS. Recently, Collection 2.0 of the NASA Land Science Investigator-led Processing Systems (SIPS) products were released. As a part of this reprocessing effort, we made two major improvements in the N20 VIIRS RSB radiometric calibration. One is the improved SD and SDSM attenuation screen transmittance functions, obtained by using calibration data collected during both the yaw maneuver and a small portion of regular orbits, resulting in a higher quality H-factor for the SDSM view. Another is the use of the H-factor for the telescope view, derived from the H-factor for the SDSM view, by using the results for the SNPP VIIRS. In June 2019, we delivered a set of mission-long N20 VIIRS Collection 2.0 RSB radiometric calibration look-up-tables. These tables have been employed by the NASA Land SIPS group to reprocess the entire time series of the NOAA-20 VIIRS products. In this paper, we discuss the Collection 2.0 NOAA-20 VIIRS RSB calibration algorithms and results.

N20 VIIRS↗

NASA NTRS2020

Assessments of S-NPP and N20 VIIRS DNB and M bands calibration stability and consistency using a homogeneous ground target

The S-NPP and N20 VIIRS Day-Night band (DNB) and M bands top-of-atmosphere (TOA) radiance and reflectance are calculated and a kernel-driven Bidirectional Reflectance Distribution Function (BRDF) correction model is used to correct the surface and atmosphere combined BRDF influence. Due to degradation in the DNB modulated Relative Spectral Response (RSR), the S-NPP VIIRS observed TOA DNB reflectance indicates a decrease of 1.89% and the SCIAMACHY spectra derived TOA DNB reflectance has a decrease of 1.63% for the past 8.5 years. The N20 VIIRSTOA DNB reflectance decreased 0.50% in the past 2.5 years. The DNB radiance is also compared with the integral of the M bands radiance from M4, M5, and M7. The fitting trends of DNB to the integral of M bands ratios indicate a0.48% decrease for S-NPP VIIRS and 0.14% increase for N20 VIIRS. The N20 VIIRS DNB to integral M bands ratios are closer to 1 than the S-NPP VIIRS data. The BRDF corrected reflectance comparisons show that the N20 VIIRS data is slightly lower than those of S-NPP VIIRS. The averages of the linear fit values of the N20 to S-NPP VIIRS from 2018to 2020 June are -1.97%, -4.99%, -3.36%, and -0.85% for M4, M5, M7, and DNB, respectively. Our results indicate that the S-NPP and N20 VIIRS DNB and M bands calibration have been stable. The cross-sensor differences in DNB and M bands are generally consistent with other independent studies using similar and different approaches.

Libya 4↗

NASA NTRS2020

SNPP VIIRS RSB on-orbit radiometric calibration algorithms Version 2.0 and the performances. Part II: The performances

In Part I (Lei et. al, submitted to J. of Appl. Rem. Sens.),we gave detailed reviews of the algorithms Version 2.0 used for the on-orbit radiometric calibration of the reflective solar bands (RSBs) of the first Visible Infrared Imaging Radiometer Suite instrument. These algorithms improve the accuracy of the measured on-orbit change factor of the solar diffuser bidirectional reflectance distribution function, the H-factor, and reveal that the H-factor is angle dependent. With the help of lunar observations and the improved H-factor, the algorithms give more accurate values for the RSB detector on-orbit F-factor changes (F-factor is a correction factor to the initially retrieved scene spectral radiance). In this paper we review the RSB radiometric calibration performances. We show the H-factor temporal trend, the estimated uncertainty of the retrieved H-factor, and the F-factor temporal trend. Additionally we show the detector signal-to-noise ratio (SNR), the estimated uncertainty of the top-of-the-atmosphere solar spectral reflectance, the reflectance temporal trend for the Libya 4 desert, and the differences in the reflectances among the SNPP and the NOAA-20 VIIRS, and the Aqua MODIS. Our results show that although the SNRs trend downwards, they exceed the requirements by large margins. The reflectances from the Libya 4 desert show that the SNPP VIIRS’ reflectance is higher than those of the Aqua MODIS and the NOAA-20 VIIRS.

on-orbit calibration↗

NASA NTRS2020

SNPP VIIRS RSB on-orbit radiometric calibration algorithms Version 2.0 and the performances. Part I: The algorithms

The first VIIRS instrument is aboard the Suomi National Polar-orbiting Partnership satellite. The instrument has 14 reflective solar bands (RSBs) to passively collect photons reflected from the Earth surface in the design wavelengths from 412 to 2250 nm. The instrument uses a solar diffuser (SD) to radiometrically calibrate its RSBs. When lit by the Sun through an attenuation screen (the SD screen), the SD diffusely reflects off the incident sunlight to act as a radiance source for the calibration. An onboard solar diffuser stability monitor (SDSM) yields the on-orbit change of the SD bidirectional reflectance distribution function (BRDF) by comparing the signal strength from the SD with that from the Sun attenuated by another attenuation screen (the SDSM screen). Complications arise due to the discovery that the on-orbit change of the BRDF is angle dependent. Additionally, the SDSM does not cover the wavelengths for the short-wave infrared bands in the RSBs. Furthermore, satellite yaw maneuvers were performed in the early mission to yield data for refining the prelaunch SDSM screen relative effective transmittance and the relative product of the SD screen transmittance and the BRDF at the mission start. But the yaw maneuver data are coarse in the solar azimuth angles and thus are unable to yield accurate values between the measurement angles. Over the years of performing on-orbit radiometric calibration through the SD for the VIIRSRSBs, we have developed several highly effective calibration algorithms to address the issues mentioned above. This paper reviews these algorithms.

on-orbit cailbration↗

NASA NTRS2020

SNPP VIIRS RSB on-orbit radiometric calibration algorithms Version 2.0 and the performances. Part I: The algorithms

Ning Lei↗

NASA NTRS2020

SNPP VIIRS RSB on-orbit radiometric calibration algorithms Version 2.0 and the performances. Part II: The performances

Ning Lei↗

NASA NTRS2020

Performance of NOAA-20 VIIRS Solar Diffuser and Solar Diffuser Stability Monitor

Visible Infrared Imaging Radiometer Suite (VIIRS) radiometrically calibrates its reflective solar bands (RSBs) primarily through a sunlit onboard solar diffuser (SD). The sunlit SD provides a known radiance under the condition that the absolute product of the SD screen transmittance and the bidirectional reflectance distribution function (BRDF) along the SD-to-telescope direction is accurately known. The BRDF changes due to solar exposure. The change, referred to as the H-factor, is monitored by the onboard SD stability monitor (SDSM). The accuracy of the retrieved H-factor propagates to the retrieved F-factor which corrects the scene spectral radiance. High accuracy of the retrieved H-factor relies on high accuracies in the SDSM screen relative effective transmittance and the relative product of the SD screen effective transmittance and the BRDF at the mission start, and a high SDSM detector signal-to-noise ratio (SNR). This article briefly reviews the algorithms used for the NOAA-20 (N20) VIIRS RSB on-orbit radiometric calibration. Additionally, we show the performance of the N20 VIIRS SDSM, giving the SDSM detector SNRs and the SDSM detector gain temporal changes. We develop a model for the SNRs. The model shows that the decreased SNRs in time are due to the detector gain decreases. We also show the N20 VIIRS SD on-orbit performance, measured by the retrieved H-factor and the estimated standard deviation of its error. The H-factor for the telescope view is obtained from the H-factor for the SDSM view, multiplied by an H-factor angular dependence term. We use an innovative method to determine the angular dependence, using the dependence obtained for the Suomi National Polar-orbiting Partnership (SNPP) VIIRS.

VIIRS↗

NASA NTRS2021

Positional Dependence of SNPP VIIRS Solar Diffuser BRDF Change Factor: An Empirical Approach

The Earth-observing Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership satellite regularly performs on-orbit radiometric calibration of its reflective solar bands (RSBs), primarily through observations of an onboard sunlit solar diffuser (SD). The on-orbit change of the SD bidirectional reflectance distribution function (BRDF) value, quantified by a numerical factor called the H-factor, is determined by the onboard SD stability monitor. Our previous study showed that the H-factor is solar angle and view direction dependent. In this study, we determine the dependence of the H-factor on the detector SD view footprint location. We fit an empirical model to the NASA Collection 1 SNPP VIIRS Level 1B (L1B) spectral reflectance difference across the detectors in an RSB over uniform Earth scenes of the Libya 4 desert and deep convective clouds. We apply the model predicted SD positional dependent H-factor to calibrate the RSBs. Under this new calibration scheme, the original unreal striping is removed from the homogeneous Libya 4 desert and the deep convective cloud images, as well as the original unreal striping from the Dunhuang desert image. The SD positional dependent H-factor has been used to calculate the SNPP VIIRS RSB radiometric correction factor for the NASA Collection 2.0 SNPP VIIRS L1B products.

SNPP VIIRS↗

NASA NTRS1993

Cassini Power Management and Distribution

The cassini Power Subsystem provides power generation, management and distribution functions for the Saturn Orbiter Spacecraft. Power is derived from three parallel Radioisotope Thermoelectric Generators.

Cassin↗

NASA NTRS2001

Laboratory characterization of silicon avalanche photodiodes (APD) for pulse position modulation (PPM) detection

Two commercially available large area silicon avalanche photodiodes (APD) were characterized in the laboratory. Specifically, the response of the APD’s to a sequence of 8-bit pulse position modulated (256-PPM) laser pulses, with and without background noise, was recorded and stored for post analysis. Empirical probability distribution functions (pdf) were constructed from the signal and noise slot data and compared to pdf’s predicted by an analytical model based on Webb/Gaussian statistics. The limited pulse sequence was also used to generate bit-error rate (BER) versus signal photons per pulse values, albeit with large error bars. These BER measurements were also compared to results predicted using the Gaussian and Webb/Gaussian models for APD channel statistics. While the measurements qualitatively reflect features predicted by theory, significant quantitative deviations are displayed between the measurements and theory. The source of these discrepancies is not currently well understood, but it is surmised that inaccurate knowledge of detector parameters such as gain and noise equivalent temperature models may explain the discrepancies.

Biswas, A.↗