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At least 163 records · Page 9

Estimation Filter for Alignment of the Spitzer Space Telescope

A document presents a summary of an onboard estimation algorithm now being used to calibrate the alignment of the Spitzer Space Telescope (formerly known as the Space Infrared Telescope Facility). The algorithm, denoted the S2P calibration filter, recursively generates estimates of the alignment angles between a telescope reference frame and a star-tracker reference frame. At several discrete times during the day, the filter accepts, as input, attitude estimates from the star tracker and observations taken by the Pointing Control Reference Sensor (a sensor in the field of view of the telescope). The output of the filter is a calibrated quaternion that represents the best current mean-square estimate of the alignment angles between the telescope and the star tracker. The S2P calibration filter incorporates a Kalman filter that tracks six states - two for each of three orthogonal coordinate axes. Although, in principle, one state per axis is sufficient, the use of two states per axis makes it possible to model both short- and long-term behaviors. Specifically, the filter properly models transient learning, characteristic times and bounds of thermomechanical drift, and long-term steady-state statistics, whether calibration measurements are taken frequently or infrequently. These properties ensure that the S2P filter performance is optimal over a broad range of flight conditions, and can be confidently run autonomously over several years of in-flight operation without human intervention.

Bayard, David↗

A TRMM-Calibrated Infrared Technique for Global Rainfall Estimation

This paper presents the development of a satellite infrared (IR) technique for estimating convective and stratiform rainfall and its application in studying the diurnal variability of rainfall on a global scale. The Convective-Stratiform Technique (CST), calibrated by coincident, physically retrieved rain rates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), is applied over the global tropics during summer 2001. The technique is calibrated separately over land and ocean, making ingenious use of the IR data from the TRMM Visible/Infrared Scanner (VIRS) before application to global geosynchronous satellite data. The low sampling rate of TRMM PR imposes limitations on calibrating IR- based techniques; however, our research shows that PR observations can be applied to improve IR-based techniques significantly by selecting adequate calibration areas and calibration length. The diurnal cycle of rainfall, as well as the division between convective and ~ t i f m rainfall will be presented. The technique is validated using available data sets and compared to other global rainfall products such as Global Precipitation Climatology Project (GPCP) IR product, calibrated with TRMM Microwave Imager (TMI) data. The calibrated CST technique has the advantages of high spatial resolution (4 km), filtering of non-raining cirrus clouds, and the stratification of the rainfall into its convective and stratiform components, the latter being important for the calculation of vertical profiles of latent heating.

Negri, Andrew J.↗

Calibration and postlaunch performance of the Meteor 3/TOMS instrument

Prelaunch and postlaunch calibration results for the Meteor 3/total ozone mapping spectrometer (TOMS) instrument are presented here. Ozone amounts are retrieved from measurements of Earth albedo in the 312- to 380-nm range. The accuracy of albedo measurements is primarily tied to knowledge of the reflective properties of diffusers used in the calibrations and to the instrument's wavelength selection. These and other important prelaunch calibrations are presented. Their estimated accuracies are within the bounds necessary to determine column ozone to better than 1%. However, postlaunch validation results indicate some prelaunch calibration uncertainties may be larger than originally estimated. Instrument calibrations have been maintained postlaunch to within a corresponding 1% error in retrieved ozone. Onboard calibrations, including wavelength monitoring and a three-diffuser solar measurement system, are described and specific results are presented. Other issues, such as the effects of orbital precession on calibration and recent chopper wheel malfunctions, are also discussed.

Jaross, G.↗

A TRMM-Calibrated Infrared Technique for Global Rainfall Estimation

The development of a satellite infrared (IR) technique for estimating convective and stratiform rainfall and its application in studying the diurnal variability of rainfall on a global scale is presented. The Convective-Stratiform Technique (CST), calibrated by coincident, physically retrieved rain rates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), is applied over the global tropics during 2001. The technique is calibrated separately over land and ocean, making ingenious use of the IR data from the TRMM Visible/Infrared Scanner (VIRS) before application to global geosynchronous satellite data. The low sampling rate of TRMM PR imposes limitations on calibrating IR-based techniques; however, our research shows that PR observations can be applied to improve IR-based techniques significantly by selecting adequate calibration areas and calibration length. The diurnal cycle of rainfall, as well as the division between convective and stratiform rainfall will be presented. The technique is validated using available data sets and compared to other global rainfall products such as Global Precipitation Climatology Project (GPCP) IR product, calibrated with TRMM Microwave Imager (TMI) data. The calibrated CST technique has the advantages of high spatial resolution (4 km), filtering of non-raining cirrus clouds, and the stratification of the rainfall into its convective and stratiform components, the latter being important for the calculation of vertical profiles of latent heating.

Negri, Andrew J.↗

MIRO Continuum Calibration for Asteroid Mode

MIRO (Microwave Instrument for the Rosetta Orbiter) is a lightweight, uncooled, dual-frequency heterodyne radiometer. The MIRO encountered asteroid Steins in 2008, and during the flyby, MIRO used the Asteroid Mode to measure the emission spectrum of Steins. The Asteroid Mode is one of the seven modes of the MIRO operation, and is designed to increase the length of time that a spectral line is in the MIRO pass-band during a flyby of an object. This software is used to calibrate the continuum measurement of Steins emission power during the asteroid flyby. The MIRO raw measurement data need to be calibrated in order to obtain physically meaningful data. This software calibrates the MIRO raw measurements in digital units to the brightness temperature in Kelvin. The software uses two calibration sequences that are included in the Asteroid Mode. One sequence is at the beginning of the mode, and the other at the end. The first six frames contain the measurement of a cold calibration target, while the last six frames measure a warm calibration target. The targets have known temperatures and are used to provide reference power and gain, which can be used to convert MIRO measurements into brightness temperature. The software was developed to calibrate MIRO continuum measurements from Asteroid Mode. The software determines the relationship between the raw digital unit measured by MIRO and the equivalent brightness temperature by analyzing data from calibration frames. The found relationship is applied to non-calibration frames, which are the measurements of an object of interest such as asteroids and other planetary objects that MIRO encounters during its operation. This software characterizes the gain fluctuations statistically and determines which method to estimate gain between calibration frames. For example, if the fluctuation is lower than a statistically significant level, the averaging method is used to estimate the gain between the calibration frames. If the fluctuation is found to be statistically significant, a linear interpolation of gain and reference power is used to estimate the gain between the calibration frames.

Lee, Seungwon↗

Assessing High Resolution Thermal Monitoring of Complex Intertidal Environments From Space: the Case of ECOSTRESS at Rias Baixas, NW Iberia

Currently, thermal sensors on satellites and other orbital platforms with high spatial resolution designed to monitor the oceans are often insufficient to assess the surface temperature of small bodies of water or within the coastal boundary layer. As the accuracy and sensitivity of remote sensing satellites improve, the demand for more accurate and up-to-date basic data sets for calibration increases. The quality of the thermal data collected by ECOSTRESS on the International Space Station allows the characterization of thermal stress levels in coastal ecosystems with a high spatial resolution of 70 m and a return time from hours to 5 days. This study focused on the calibration of ECOSTRESS estimates with in situ surface temperature data from sensors installed at 3 cm depth in the sediment on the intertidal muddy sandflats of three of the Rías Baixas in Galicia, NW Iberian Peninsula, from 2019 to 2021. A final number of 45 ECOSTRESS temperature images were analyzed. From these, 20% contained substantial georeferencing errors which had to be corrected manually with GIS software tools. We applied the Fourier's law of Heat Conduction to derive surface estimates from loggers sub-surface measurements that could be directly compared with ECOSTRESS data. Overall, a good calibration which explained more than 80% of ECOSTRESS temperature estimates for the whole dataset was obtained, but with an intrinsic cold bias around 1.39 °C. When temperatures at a depth of 3 cm were used, the linear fit became worse and the negative bias increased to 1.49 °C. Closer inspection revealed that night measurements were responsible for this larger deviation, as ECOSTRESS estimates became much colder compared to within sediment measurements because of the combined effect of the instrument intrinsic bias and nocturnal surface cooling. The best calibration was obtained when surface estimates were calculated just for the nighttime, as the cold bias decreased to 0.93 °C. More importantly, during hot daytime conditions in emersion above 20 °C, ECOSTRESS data matched surface temperature estimates, probably because of a better performance of ECOSTRESS algorithm at dry surfaces with lower emissivity. Thus, during the most ecologically relevant periods when high temperatures could drive thermal stress in many commercially-important bivalve species, ECOSTRESS provides accurate surface estimates that can be used to derive sub surface temperatures at the depths at which the different burrowing organisms live. We thus conclude that this instrument constitutes an important global tool to examine thermal stress at an unprecedented spatial scale for complex sea-land boundary systems.

ECOSTRESS↗

SeaWiFS Postlaunch Calibration and Validation Analyses

The effort to resolve data quality issues and improve on the initial data evaluation methodologies of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Project was an extensive one. These evaluations have resulted, to date, in three major reprocessings of the entire data set where each reprocessing addressed the data quality issues that could be identified up to the time of each reprocessing. The number of chapters (21) needed to document this extensive work in the SeaWiFS Postlaunch Technical Report Series requires three volumes. The chapters in Volumes 9, 10, and 11 are in a logical order sequencing through sensor calibration, atmospheric correction, masks and flags, product evaluations, and bio-optical algorithms. The first chapter of Volume 9 is an overview of the calibration and validation program, including a table of activities from the inception of the SeaWiFS Project. Chapter 2 describes the fine adjustments of sensor detector knee radiances, i.e., radiance levels where three of the four detectors in each SeaWiFS band saturate. Chapters 3 and 4 describe the analyses of the lunar and solar calibration time series, respectively, which are used to track the temporal changes in radiometric sensitivity in each band. Chapter 5 outlines the procedure used to adjust band 7 relative to band 8 to derive reasonable aerosol radiances in band 7 as compared to those in band 8 in the vicinity of Lanai, Hawaii, the vicarious calibration site. Chapter 6 presents the procedure used to estimate the vicarious calibration gain adjustment factors for bands 1-6 using the waterleaving radiances from the Marine Optical Buoy (MOBY) offshore of Lanai. Chapter 7 provides the adjustments to the coccolithophore flag algorithm which were required for improved performance over the prelaunch version. Chapter 8 is an overview of the numerous modifications to the atmospheric correction algorithm that have been implemented. Chapter 9 describes the methodology used to remove artifacts of sun glint contamination for portions of the imagery outside the sun glint mask. Finally, Chapter 10 explains a modification to the ozone interpolation method to account for actual time differences between the SeaWiFS and Total Ozone Mapping Spectrometer (TOMS) orbits.

Hooker, Stanford B.↗

Zero-Point Calibration for AGN Black-Hole Mass Estimates

We discuss the measurement and associated uncertainties of AGN reverberation-based black-hole masses, since these provide the zero-point calibration for scaling relationships that allow black-hole mass estimates for quasars. We find that reverberation-based mass estimates appear to be accurate to within a factor of about 3.

Peterson, B. M.↗

Some tests of wet tropospheric calibration for the CASA Uno Global Positioning System experiment

Wet tropospheric path delay can be a major error source for Global Positioning System (GPS) geodetic experiments. Strategies for minimizing this error are investigted using data from CASA Uno, the first major GPS experiment in Central and South America, where wet path delays may be both high and variable. Wet path delay calibration using water vapor radiometers (WVRs) and residual delay estimation is compared with strategies where the entire wet path delay is estimated stochastically without prior calibration, using data from a 270-km test baseline in Costa Rica. Both approaches yield centimeter-level baseline repeatability and similar tropospheric estimates, suggesting that WVR calibration is not critical for obtaining high precision results with GPS in the CASA region.

Dixon, T. H.↗

Filtering flight data prior to aerodynamic system identification

An algorithm for processing flight-test data to provide state estimates and instrument calibrations for aerodynamic or hydrodynamic system identification by the equation error estimation method is developed and demonstrated on synthesized data. The extended-Kalman-filter algorithm employs a locally level, north-pointing frame of reference, accounts for rotating ellipsoidal earth effects, and estimates sensor bias, scale factors, wind components, and process noise levels by maximum-likelihood parameters. The method is found to be most effective with navigation quality inertial input data. The algorithm is applied to data from a six-degree-of-freedom F-4 aircraft simulation and shown to produce state estimates in good agreement with the simulation values.

Trankle, T. L.↗

Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data

We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from the MODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about vegetation structure and chlorophyll content at both high temporal (daily to monthly) and spatial (1 km) resolution. We use satellite-derived solar-induced fluorescence (SIF) to identify regions of high productivity crops and also evaluate the use of downscaled SIF to estimate GPP. We calibrate a set of our satellite-based models with GPP estimates from a subset of distributed eddy covariance flux towers (FLUXNET 2015). The results of the trained models are evaluated using an independent subset of FLUXNET 2015 GPP data. We show that variations in light-use efficiency (LUE) with incident PAR are important and can be easily incorporated into the models. Unlike many LUE-based models, our satellite-based GPP estimates do not use an explicit parameterization of LUE that reduces its value from the potential maximum under limiting conditions such as temperature and water stress. Even without the parameterized downward regulation, our simplified models are shown to perform as well as or better than state-of-the-art satellite data-driven products that incorporate such parameterizations. A significant fraction of both spatial and temporal variability in GPP across plant functional types can be accounted for using our satellite-based models. Our results provide an annual GPP value of 140 Pg C year 1 for 2007 that is within the range of a compilation of observation-based, model, and hybrid results, but is higher than some previous satellite observation-based estimates

CO2↗

The Next-Generation Version 3 GPCP Products

The long-standing Global Precipitation Climatology Project (GPCP) recently introduced its next-generation Version 3.1 Monthly product, , which covers the period 1983-2019. Notable improvements include higher spatial resolution (0.5°x0.5°), geosynchronous IR estimates extended to the latitude band 60°N-S and based on the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) algorithm, and high-latitude (outside 60ºN-S) precipitation estimates based on improved calibrations of Television-Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS) and Advanced Infrared Sounder (AIRS) data. The satellite-only estimate is adjusted to the best available monthly climatologies, namely the Tropical Combined Climatology (TCC) at lower latitudes and the Merged CloudSat, TRMM, and GPM (MCTG) climatology at higher latitudes. Finally, V3.1 merges these climatologically-adjusted satellite-only estimates with the Global Precipitation Climatology Centre (GPCC) gauge analyses at 1°x1°. In addition to the V3.1 Monthly product, the GPCP team is working towards a global Version 3 Daily product based on Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) Final Run V06 estimates, where available, calibrated to the GPCP V3.1 Monthly estimate. Rescaled TOVS/AIRS data are used in high-latitude areas that lack IMERG estimates, which occur over snowy/icy surfaces outside 60°N-S. Since IMERG currently extends back to June 2000, daily PERSIANN-CDR data are used for the period January 1983–May 2000 to complete the record. This presentation will provide early results for, and the latest status of, the Monthly and Daily GPCP products as a function of time and region. Key points include examining homogeneity over time and across boundaries between input datasets. One goal is to determine the suitability of the V3 products while we continue to produce the Version 2 GPCP products for on-going use.

precipitation↗

Pseudo Linear Gyro Calibration

Previous high fidelity onboard attitude algorithms estimated only the spacecraft attitude and gyro bias. The desire to promote spacecraft and ground autonomy and improvements in onboard computing power has spurred development of more sophisticated calibration algorithms. Namely, there is a desire to provide for sensor calibration through calibration parameter estimation onboard the spacecraft as well as autonomous estimation on the ground. Gyro calibration is a particularly challenging area of research. There are a variety of gyro devices available for any prospective mission ranging from inexpensive low fidelity gyros with potentially unstable scale factors to much more expensive extremely stable high fidelity units. Much research has been devoted to designing dedicated estimators such as particular Extended Kalman Filter (EKF) algorithms or Square Root Information Filters. This paper builds upon previous attitude, rate, and specialized gyro parameter estimation work performed with Pseudo Linear Kalman Filter (PSELIKA). The PSELIKA advantage is the use of the standard linear Kalman Filter algorithm. A PSELIKA algorithm for an orthogonal gyro set which includes estimates of attitude, rate, gyro misalignments, gyro scale factors, and gyro bias is developed and tested using simulated and flight data. The measurements PSELIKA uses include gyro and quaternion tracker data.

Harman, Richard↗

Robust ab initio predictions for dimensionless ratios of 𝐸⁢2 and radius observables. II. Estimation of 𝐸⁢2 transition strengths by calibration to the charge radius

Converged results for 𝐸⁢2 observables are notoriously challenging to obtain in ab initio no-core configuration interaction approaches. Matrix elements of the 𝐸⁢2 operator are sensitive to the large-distance tails of the nuclear wave function, which converge slowly in an oscillator basis expansion. Similar convergence challenges beset ab initio prediction of the nuclear charge radius. However, we exploit systematic correlations between the calculated 𝐸⁢2 and radius observables to yield meaningful predictions for relations among these observables. In particular, we examine ab initio predictions for dimensionless ratios of the form 𝐵⁡(𝐸⁢2)/(𝑒 2 ⁢𝑟 4 ) for nuclei throughout the 𝑝 shell. Finally, meaningful predictions for 𝐸⁢2 transition strengths may then be made by calibrating to the ground-state charge radius if experimentally known.

ab initio calculations↗

Optical See-Through Head Mounted Display Direct Linear Transformation Calibration Robustness in the Presence of User Alignment Noise

Augmented Reality (AR) is a technique by which computer generated signals synthesize impressions that are made to coexist with the surrounding real world as perceived by the user. Human smell, taste, touch and hearing can all be augmented, but most commonly AR refers to the human vision being overlaid with information otherwise not readily available to the user. A correct calibration is important on an application level, ensuring that e.g. data labels are presented at correct locations, but also on a system level to enable display techniques such as stereoscopy to function properly [SOURCE]. Thus, vital to AR, calibration methodology is an important research area. While great achievements already have been made, there are some properties in current calibration methods for augmenting vision which do not translate from its traditional use in automated cameras calibration to its use with a human operator. This paper uses a Monte Carlo simulation of a standard direct linear transformation camera calibration to investigate how user introduced head orientation noise affects the parameter estimation during a calibration procedure of an optical see-through head mounted display.

Axholt, Magnus↗

Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado

Abstract. Deep learning (DL)-assisted inverse mapping has shown promise in hydrological model calibration by directly estimating parameters from observations. However, the increasing computational demand for running the state-of-the-art hydrological model limits sufficient ensemble runs for its calibration. In this work, we present a novel knowledge-informed deep learning method that can efficiently conduct the calibration using a few hundred realizations. The method involves two steps. First, we determine decisive model parameters from a complete parameter set based on the mutual information (MI) between model responses and each parameter computed by a limited number of realizations (∼50). Second, we perform more ensemble runs (e.g., several hundred) to generate the training sets for the inverse mapping, which selects informative model responses for estimating each parameter using MI-based parameter sensitivity. We applied this new DL-based method to calibrate a process-based integrated hydrological model, the Advanced Terrestrial Simulator (ATS), at Coal Creek Watershed, CO. The calibration is performed against observed stream discharge (Q) and remotely sensed evapotranspiration (ET) from the water year 2017 to 2019. Preliminary MI analysis on 50 realizations resulted in a down-selection of 7 out of 14 ATS model parameters. Then, we performed a complete MI analysis on 396 realizations and constructed the inverse mapping from informative responses to each of the selected parameters using a deep neural network. Compared with calibration using observations covering all time steps, the new inverse mapping improves parameter estimations, thus enhancing the performance of ATS forward model runs. The Nash–Sutcliffe efficiency (NSE) of streamflow predictions increases from 0.53 to 0.8 when calibrating against Q alone. Using ET observations, on the other hand, does not show much improvement on the performance of ATS modeling mainly due to both the uncertainty of the remotely sensed product and the insufficient coverage of the model ET ensemble in capturing the observation. By using observed Q only, we further performed a multiyear analysis and show that Q is best simulated (NSE > 0.8) by including in the calibration the dry-year flow dynamics that show more sensitivity to subsurface characteristics than the other wet years. Moreover, when continuing the forward runs till the end of 2021, the calibrated models show similar simulation performances during this evaluation period as the calibration period, demonstrating the ability of the estimated parameters in capturing climate sensitivity. Our success highlights the importance of leveraging data-driven knowledge in DL-assisted hydrological model calibration.

54 ENVIRONMENTAL SCIENCES↗

XCal: model-based approach to X-ray CT spectral calibration

Transmission X-ray computed tomography (CT) is widely used to quantitatively reconstruct 3D objects composed of multiple materials. However, accurate CT reconstruction requires the system to be calibrated to account for the effective X-ray spectrum. Unfortunately, measurement of the effective spectrum is ill-posed, and existing calibration methods require that the system be recalibrated when the system parameters are changed. In this paper, we propose XCal, a multi-energy model-based spectral calibration approach for X-ray CT. The XCal approach models the effective spectrum using a separable physics-based model of the CT system. The model parameters are then estimated by fitting calibration data with known objects at multiple energies. An important advantage of XCal is that it allows the user to change scanner settings, such as the source voltage or X-ray filters, without the need for recalibration. Evaluations on simulated and measured datasets demonstrate that XCal significantly improves the accuracy of the estimated spectrum as compared to existing calibration methods.

Li, Wenrui [Purdue Univ., West Lafayette, IN (Unit↗

Calibration of the SNPP and NOAA 20 VIIRS Sensors for Continuity of the MODIS Climate Data Records

Accurate long-term sensor calibration and periodic re-processing to ensure consistency and continuity of atmospheric, land and ocean geophysical retrievals from space within the mission period and across different missions is a major requirement of climate data records. In this work, we applied the Multi-Angle Implementation of Atmospheric Correction (MAIAC)-based vicarious calibration technique over Libya-4 desert site to perform calibration analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites. For both VIIRS sensors we characterized residual linear calibration trends and cross-calibrated both sensors to MODerate resolution Imaging Spectroradiometer (MODIS) Aqua regarded as a calibration standard. The relative spectral response (RSR) differences were accounted for using the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) hyperspectral surface reflectance data. Our results agree with independent vicarious calibration results of both the MODIS/VIIRS Characterization Support Team as well as the CERES Imager and Geostationary Calibration Group within estimated uncertainty of 1–2%. Analysis of MAIAC geophysical products with the new calibration shows a high level of agreement of MAIAC aerosol, surface reflectance and NDVI records between MODIS and VIIRS. Excluding high aerosol optical depth (AOD), all three sensors agree in AOD with mean difference (MD) less than 0.01 and residual mean squared difference rmsd ∼ 0.04. Spectral geometrically normalized surface reflectance agrees within rmsd of 0.003–0.005 in the visible and 0.01–0.012 at longer wavelengths. The residual surface reflectance differences are fully explained by differences in spectral filter functions. Finally, difference in NDVI is characterized by rmsd ∼ 0.02 and MD less than 0.003 for NDVI based on VIIRS imagery bands I1/I2 and less than 0.01 for NDVI based on VIIRS radiometric bands M5/M7. In practical sense, these numbers indicate consistency and continuity in MAIAC records ensuring the smooth transition from MODIS to VIIRS.

MAIAC↗