Fitting Window Research Articles

First evaluation of the GEMS glyoxal products against TROPOMI and ground-based measurements

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) on board the GEO-KOMPSAT-2B satellite is the first geostationary satellite launched to monitor the environment. GEMS conducts hourly measurements during the day over eastern and southeastern Asia. This work presents glyoxal (CHOCHO) vertical column densities (VCDs) retrieved from GEMS, with optimal settings for glyoxal retrieval based on sensitivity tests involving reference spectrum sampling and fitting window selection. We evaluated GEMS glyoxal VCDs by comparing them to the TROPOspheric Monitoring Instrument (TROPOMI) and multi-axis differential optical absorption spectroscopy (MAX-DOAS) ground-based observations. On average, GEMS and TROPOMI VCDs show a spatial correlation coefficient of 0.63, increasing to 0.87 for northeastern Asia. While GEMS and TROPOMI demonstrate similar monthly variations in the Indochinese Peninsula regions (R > 0.67), variations differ in other areas. Specifically, GEMS VCDs are higher in the winter and either lower or comparable to TROPOMI and MAX-DOAS VCDs in the summer across northeastern Asia. We attributed the discrepancies in the monthly variation to a polluted reference spectrum and high NO2 concentrations. When we correct GEMS glyoxal VCDs as a function of NO2 SCDs, the monthly correlation coefficients substantially increase from 0.16–0.40 to 0.45–0.72 in high NO2 regions. When averaged hourly, GEMS and MAX-DOAS VCDs exhibit similar diurnal variations, especially at stations in Japan (Chiba, Kasuga, and Fukue).

Tropospheric NO2 retrieval algorithm for geostationary satellite instruments: applications to GEMS

Abstract. In this study, we develop an advanced retrieval algorithm for tropospheric nitrogen dioxide (NO2) from the geostationary satellite instruments and apply it to Geostationary Environment Monitoring Spectrometer (GEMS) observations. Overall, the algorithm follows previous heritage for the polar-orbiting satellites Global Ozone Monitoring Experiment-2 (GOME-2) and Tropospheric Monitoring Instrument (TROPOMI), but several improvements are implemented to account for specific features of geostationary satellites. The DLR GEMS NO2 retrieval employs an extended fitting window compared to the current fitting window used in GEMS operational v2.0 NO2 retrieval, which results in improved spectral fit quality and lower uncertainties. For the stratosphere–troposphere separation in GEMS measurements, two methods are developed and evaluated: (1) STRatospheric Estimation Algorithm from Mainz (STREAM) as used in the DLR TROPOMI NO2 retrieval and adapted to GEMS and (2) estimation of stratospheric NO2 columns from the Copernicus Atmosphere Monitoring Service (CAMS) Integrated Forecast System (IFS) cycle 48R1 model data, which introduce full stratospheric chemistry as it will be used in the operational Sentinel-4 NO2 retrieval. While STREAM provides hourly estimates of stratospheric NO2, it has limitations in describing small-scale variations and exhibits systematic biases near the boundary of the field of view. In this respect, the use of estimated stratospheric NO2 columns from the CAMS forecast model profile demonstrates better applicability by describing not only diurnal variation but also small-scale variations. For the improved air mass factor (AMF) calculation, sensitivity tests are performed using different input data. In our algorithm, cloud fractions retrieved from the Optical Cloud Recognition Algorithm (OCRA) adapted to GEMS level 1 data are applied instead of the GEMS v2.0 cloud fraction. OCRA is used operationally in TROPOMI and Sentinel-4. Compared to the GEMS level 2 cloud fraction which is typically set to around 0.1 for clear-sky scenes, OCRA sets cloud fractions close to or at 0. The OCRA-based cloud corrections result in increased tropospheric AMFs and decreased tropospheric NO2 vertical columns, leading to better agreement with results from existing TROPOMI observations. The effects of surface albedo on GEMS tropospheric NO2 retrievals are assessed by comparing the GEMS v2.0 background surface reflectance (BSR) and TROPOMI Lambertian-equivalent reflectivity (LER) climatology v2.0 product. The differences between the two surface albedo products and their impact on tropospheric AMF are particularly pronounced over snow/ice scenes during winter. A priori NO2 profiles from the CAMS forecast model, applied in the DLR GEMS algorithm, effectively capture variations in NO2 concentrations throughout the day with high spatial resolution and the advanced chemical mechanism, which demonstrates its suitability for geostationary satellite measurements. The retrieved DLR GEMS tropospheric NO2 columns show good capability for capturing hotspot signals at the scale of city clusters and describe spatial gradients from city centres to surrounding areas. Diurnal variations of tropospheric NO2 columns over Asia are well described through hourly sampling of GEMS. Evaluation of DLR GEMS tropospheric NO2 columns against TROPOMI v2.4 and GEMS v2.0 operational products shows overall good agreement. The uncertainty of DLR GEMS tropospheric NO2 vertical columns varies based on observation scenarios. In regions with low pollution levels such as open-ocean and remote rural areas, retrieval uncertainties typically range from 10 % to 50 %, primarily due to uncertainties in slant columns. For heavily polluted regions, uncertainties in tropospheric NO2 columns are mainly driven by errors in tropospheric AMF calculations. Notably, the total uncertainty in GEMS tropospheric NO2 columns is most significant in winter, particularly over heavily polluted regions with low-level clouds below or near the NO2 peak.

Confidence intervals estimator of the kinetic parameters: do its reliability depend on the assembling method of the oxygen uptakes?

Gas exchange data acquired repeatedly under the same exercise conditions are assembled together to improve the kinetic parameters of breath-by-breath oxygen uptake. The latter are provided by the non-linear regression procedure, together with the corresponding estimators of the width of the Confidence Intervals (i.e., the Asymptotic Standard Errors; ASEs). We tested, for two different assembling procedures, whether the range of values identified by the ASE actually correspond to the 95% Confidence Interval. Ten O2 uptake responses were acquired on 10 healthy volunteers performing a square-wave moderate-intensity exercise. Kinetic parameters were estimated running the non-linear regression with a mono-exponential model on an increasingly greater number of responses (Nr, from 1 to 10), assembled together using the "stacking" and the "1-s-bins" procedures. Kinetic values obtained assembling together the 10 repetitions were assumed as "true" values. The time constant was not affected by Nr or by the assembling procedure (ANOVA; p>0.54 and p>0.16, respectively). The corresponding ASE decreased according to Nr (ANOVA; p=0.000), being significantly smaller for the "1-s-bins" procedure compared to the "stacking" one (ANOVA; p<0.001). Excluding 20s at the start of the fitting window, the range of values identified with the ASE provided by the "1-s-bins" and the "stacking" procedures included the "true" value in 85% and in 95% of cases, respectively. The "stacking" procedure should be preferred since it yielded ASEs for the time constant that provided a range of values satisfying the statistical meaning of the width of the Confidence Intervals, at the given degree of probability.

A new accurate retrieval algorithm of bromine monoxide columns inside minor volcanic plumes from Sentinel-5P TROPOMI observations

Abstract. Bromine monoxide (BrO) is a key radical in the atmosphere, influencing the chemical state of the atmosphere, most notably the abundance of ozone (O3). O3 depletion caused by the release of bromine has been observed and modeled in polar regions, salt pans, and in particular inside volcanic plumes. Furthermore, the molar ratio of BrO and SO2 – which can be detected simultaneously via spectroscopic measurements using the differential optical absorption spectroscopy (DOAS) method – is a proxy for the magmatic composition of a volcano and potentially an eruption forecast parameter. The detection of BrO in volcanic plumes from satellite spectroscopic observations is limited by the precision and sensitivity of the retrieval, which so far only allowed for the detection of BrO during major eruptions. The unprecedented spatial resolution of up to 3.5 km×5.5 km and the high signal-to-noise ratio of the TROPOspheric Monitoring Instrument (TROPOMI) on board Sentinel-5 Precursor (S-5P) enable observing and monitoring volcanic bromine release globally even for minor eruptions or even quiescent degassing. In this study, we investigate how far the BrO retrieval can be improved using TROPOMI data and how well BrO can be detected, even in small eruptions and during quiescent volcanic degassing. There are two steps for which improvements in accuracy are investigated and applied: the improvement and quantitative determination of (1) the detection limit of the DOAS BrO column retrieval and (2) the correction of the non-volcanic background BrO signal. First, the DOAS retrieval settings are varied, and their influence on accuracy and precision is investigated with respect to the detection limit and potential systematic influences. Based on these results, we propose a dedicated DOAS evaluation scheme optimized for the detection of BrO in volcanic plumes. For the DOAS retrieval, we propose the use of a large fit window from 323–360 nm, yielding a statistical uncertainty lower by a factor of 1.8 compared to previous BrO DOAS algorithms while not enhancing systematic influences. Second, the effect of the background BrO is reduced by a latitude-dependent empirical correction scheme correlated to cloud information as well as information on the O3 column. Via these improvements, the combined statistical and systematic uncertainties in the resulting BrO vertical column density is on the order of 7×1012moleculescm-2. We present a new and accurate retrieval algorithm of BrO columns from TROPOMI observations which allows for the detection of even slightly enhanced BrO amounts inside minor eruptive plumes of bromine-rich volcanoes. While designed specifically for TROPOMI observations, the retrieval algorithm is in general also applicable to other hyperspectral satellite observations. However, some parts might require adaptation.

Velocity Fluctuations Spectra in Experimental Data on Rayleigh–Taylor Mixing

Rayleigh–Taylor (RT) interfacial mixing plays an important role in nature and technology, including atmospheric flows. In this work, we identify the physics properties of Rayleigh–Taylor mixing through the analysis of unprocessed experimental data. We consider the fluctuations spectra of the specific kinetic energy of each of the velocity components, and identify their spectral shapes, by employing the group theory guided foundations and the rigorous statistical method. We find the spectral shape parameters, including their mean values and relative errors, and apply the Anderson–Darling test to inspect the residuals and the goodness-of-fit. We scrupulously study the effect of the fitting window and identify, for each velocity component, the best fit interval, where the relative errors are small and the goodness of fit is excellent. We reveal that the fluctuations spectra in RT mixing experiments can be described by a compound function, being a product of a power-law and an exponential. The data analysis results unambiguously discovered the dynamic anisotropy and the dynamic bias of RT mixing and displayed the necessity to improve the design of experiments on RT mixing.

Fitting and validation of an agent-based model for COVID-19 case forecasting in workplaces and universities.

COVID-19 forecasting models have been critical in guiding decision-making on surveillance testing, social distancing, and vaccination requirements. Beyond influencing public health policies, an accurate COVID-19 forecasting model can impact community spread by enabling employers and university leaders to adapt worksite policies and practices to contain or mitigate outbreaks. While many such models have been developed for COVID-19 forecasting at the national, state, county, or city level, only a few models have been developed for workplaces and universities. Furthermore, COVID-19 forecasting models have rarely been validated against real COVID-19 case data. Here we present the systematic parameter fitting and validation of an agent-based compartment model for the forecasting of daily COVID-19 cases in single-site workplaces and universities with real-world data. Our approaches include manual fitting, where initial model parameters are chosen based on historical data, and automated fitting, where parameters are chosen based on candidate case trajectory simulations that result in best fit to prevalence estimation data. We use a 14-day fitting window and validate our approaches on 7- and 14-day testing windows with real COVID-19 case data from one employer. Our manual and automated fitting approaches accurately predicted COVID-19 case trends and outperformed the baseline model (no parameter fitting) across multiple scenarios, including a rising case trajectory (RMSLE values: 2.627 for baseline, 0.562 for manual fitting, 0.399 for automated fitting) and a decreasing case trajectory (RMSLE values: 1.155 for baseline, 0.537 for manual fitting, 0.778 for automated fitting). Our COVID-19 case forecasting model allows decision-makers at workplaces and universities to proactively respond to case trend forecasts, mitigate outbreaks, and promote safety.

Pricing time-to-event contingent cash flows: A discrete-time survival analysis approach

Prudent management of insurance investment portfolios requires competent asset pricing of fixed-income assets with time-to-event contingent cash flows, such as consumer asset-backed securities (ABS). Current market pricing techniques for these assets either rely on a non-random time-to-event model or may not utilize detailed asset-level data that is now available with most public transactions. We first establish a framework capable of yielding estimates of the time-to-event random variable from securitization data, which is discrete and often subject to left-truncation and right-censoring. We then show that the vector of discrete-time hazard rate estimators is asymptotically multivariate normal with independent components, which has not yet been done in the statistical literature in the case of both left-truncation and right-censoring. The time-to-event distribution estimates are then fed into our cash flow model, which is capable of calculating a formulaic price of a pool of time-to-event contingent cash flows vis-á-vis calculating an expected present value with respect to the estimated time-to-event distribution. In an application to a subset of 29,845 36-month leases from the Mercedes-Benz Auto Lease Trust 2017-A (MBALT 2017-A) bond, our pricing model yields estimates closer to the actual realized future cash flows than the non-random time-to-event model, especially as the fitting window increases. Finally, in certain settings, the asymptotic properties of the hazard rate estimators allow investors to assess the potential uncertainty of the price point estimates, which we illustrate for a subset of 493 24-month leases from MBALT 2017-A.

Advances in retrieving XCH4 and XCO from Sentinel-5 Precursor: improvements in the scientific TROPOMI/WFMD algorithm

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite enables the accurate determination of atmospheric methane (CH4) and carbon monoxide (CO) abundances at high spatial resolution and global daily sampling. Due to its wide swath and sampling, the global distribution of both gases can be determined in unprecedented detail. The scientific retrieval algorithm Weighting Function Modified Differential Optical Absorption Spectroscopy (WFMD) has proven valuable in simultaneously retrieving the atmospheric column-averaged dry-air mole fractions XCH4 and XCO from TROPOMI's radiance measurements in the shortwave infrared (SWIR) spectral range. Here we present recent improvements of the algorithm which have been incorporated into the current version (v1.8) of the TROPOMI/WFMD product. This includes processing adjustments such as increasing the polynomial degree to 3 in the fitting procedure to better account for possible spectral albedo variations within the fitting window and updating the digital elevation model to minimise topography-related biases. In the post-processing, the machine-learning-based quality filter has been refined using additional data when training the random forest classifier to further reduce scenes with residual cloudiness that are incorrectly classified as good. In particular, the cloud filtering over the Arctic ocean is considerably improved. Furthermore, the machine learning calibration, addressing systematic errors due to simplifications in the forward model or instrumental issues, has been optimised. By including an additional feature associated with the fitted polynomial when training the corresponding random forest regressor, spectral albedo variations are better accounted for. To remove vertical stripes in the XCH4 and XCO data, an efficient orbit-wise destriping filter based on combined wavelet–Fourier filtering has been implemented, while optimally preserving the original spatial trace gas features. The temporal coverage of the data records has been extended to the end of April 2022, covering a total length of 4.5 years since the start of the mission, and will be further extended in the future. Validation with the ground-based Total Carbon Column Observing Network (TCCON) demonstrates that the implemented improvements reduce the pseudo-noise component of the products, resulting in an improved random error. The XCH4 and XCO products have similar spatial coverage from year to year including high latitudes and the oceans. The analysis of annual growth rates reveals accelerated growth of atmospheric methane during the covered period, in line with observations at marine surface sites of the Global Monitoring Division of NOAA's Earth System Research Laboratory, which reported consecutive annual record increases over the past 2 years of 2020 and 2021.

Fast nonlinear blind deconvolution for rotating machinery fault diagnosis

Sparse optimization based early fault diagnosis method is drawing more and more attention. In these methods, the objective function is usually a sparsity measure which can represent the impulsive signature produced by the mechanical impacts between rotating components. Therefore, the key point is the fault representation and convergence stability of the objective function. Inspired by this, fast nonlinear blind deconvolution algorithm is proposed for early fault diagnosis of rotating machinery. First, sigmoid function is developed to the generalized form to improve the fault representation ability of the objective function under noisy environment. The nonlinear mapping and sparse expression ability are discussed in detail. Then, Gaussian fitting window function and L1/2 penalty of filter are used to improve the distribution and performance of the weight vector. Finally, simulation data and early bearing fault data are employed to verify the effectiveness and determine optimal parameter setting of the proposed method. Based on the comparison results with the existing methods, the proposed method is found to be a promising method for the early-stage fault diagnosis, which can significantly improve the noise adaptability, computation effectiveness and robustness of fault diagnosis.

Precision of attenuation coefficient measurements by optical coherence tomography.

.Significance: Optical coherence tomography (OCT) is an interferometric imaging modality, which provides tomographic information on the microscopic scale. Furthermore, OCT signal analysis facilitates quantification of tissue optical properties (e.g., the attenuation coefficient), which provides information regarding the structure and organization of tissue. However, a rigorous and standardized measure of the precision of the OCT-derived optical properties, to date, is missing.Aim: We present a robust theoretical framework, which provides the Cramér –Rao lower bound for the precision of OCT-derived optical attenuation coefficients.Approach: Using a maximum likelihood approach and Fisher information, we derive an analytical solution for when the position and depth of focus are known. We validate this solution, using simulated OCT signals, for which attenuation coefficients are extracted using a least-squares fitting procedure.Results: Our analytical solution is in perfect agreement with simulated data without shot noise. When shot noise is present, we show that the analytical solution still holds for signal-to-noise ratios (SNRs) in the fitting window being above 20 dB. For other cases (, focus position not precisely known), we show that the numerical calculation of the precision agrees with the derived from simulated signals.Conclusions: Our analytical solution provides a fast, rigorous, and easy-to-use measure for OCT-derived attenuation coefficients for signals above 20 dB. The effect of uncertainties in the focal point position on the precision in the attenuation coefficient, the second assumption underlying our analytical solution, is also investigated by numerical calculation of the lower bounds. This method can be straightforwardly extended to uncertainty in other system parameters.

The TROPOSIF global sun-induced fluorescence dataset from the Sentinel-5P TROPOMI mission

Abstract. The first satellite-based global retrievals of terrestrial sun-induced chlorophyll fluorescence (SIF) were achieved in 2011. Since then, a number of global SIF datasets with different spectral, spatial, and temporal sampling characteristics have become available to the scientific community. These datasets have been useful to monitor the dynamics and productivity of a range of vegetated areas worldwide, but the coarse spatiotemporal sampling and low signal-to-noise ratio of the data hamper their application over small or fragmented ecosystems. The recent advent of the Copernicus Sentinel-5P TROPOMI mission and the high quality of its data products promise to alleviate this situation, as TROPOMI provides daily global measurements at a much denser spatial and temporal sampling than earlier satellite instruments. In this work, we present a global SIF dataset produced from TROPOMI measurements within the TROPOSIF project funded by the European Space Agency. The current version of the TROPOSIF dataset covers the time period between May 2018 and April 2021. Baseline SIF retrievals are derived from the 743–758 nm window. A secondary SIF dataset derived from an extended fitting window (735–758 nm window) is included. This provides an enhanced signal-to-noise ratio at the expense of a higher sensitivity to atmospheric effects. Spectral reflectance spectra at seven 3 nm windows devoid of atmospheric absorption within the 665–785 nm range are also included in the TROPOSIF dataset as an important ancillary variable to be used in combination with SIF. The methodology to derive SIF and ancillary data as well as results from an initial data quality assessment are presented in this work. The TROPOSIF dataset is available through the following digital object identifier (DOI): https://doi.org/10.5270/esa-s5p_innovation-sif-20180501_20210320-v2.1-202104 (Guanter et al., 2021).

Uncertainty analysis of SVD-based spaceborne far–red sun-induced chlorophyll fluorescence retrieval using TanSat satellite data

The singular value decomposition (SVD) method, a sun-induced chlorophyll fluorescence (SIF) retrieval approach, has been used widely in far-red SIF spaceborne retrievals on a global scale. However, due to its semi-empirical nature, setting different parameter values may affect its retrieval accuracy, and ultimately have a large impact on its application. Hence, in this study, we evaluated the impact of parameter selection on the far-red SIF retrieval of this approach using TanSat satellite data. We first retrieved the far-red SIF within a narrow spectral window of 757.4–759.2 nm using the first four singular vectors (SVs) that were derived from the snow and soil spectra in a globally distributed training set. The retrievals are highly consistent with TanSat mission SIF (R2 = 0.72), indicating the reliability of our retrieval results. Then, the uncertainty was executed based on the above SIF retrievals and evaluated from five metrics: the number of SVs, length of the fitting window, type of training set elements, proportion of training set elements, and spatial distribution of the training set. Results showed that an unwise selection of the number of SVs would result in large retrieval errors (R2 = 0.03). Meanwhile, a fitting window that is too short or does not include a strong Fraunhofer line would cause severe errors and a large number of negative values (R2 = 0.06). Further, failure to include training samples below the equatorial zone in the Southern Hemisphere, and high latitude samples in the Northern Hemisphere, leads to poorer outcomes (R2 = 0.57). In contrast, the other global sampling metrics had little effect on the retrieval results (generally, R2 > 0.97). Accordingly, the relevant suggestions for using this method in the future are listed in this study for reference, with the potential to improve the accuracy of SIF retrievals from ultra-high spectral resolution instruments in the far-red spectrum.

Influence of the fitting window on the O2 uptake kinetics at the onset of moderate intensity exercise.

The O2 uptake (V̇o2) data at the onset of an exercise are usually fitted with a mono-exponential function, after removal of the data pertaining to a conventional initial time period (ΔTr) lasting ∼20 s. We performed a thorough quantitative analysis on the effects of removing data pertaining to different ΔTr, aiming at identifying an objective method to establish the appropriate ΔTr. Breath-by-breath O2 uptake responses, acquired from 25 healthy adults performing a step moderate-intensity exercise, and 104 simulated biexponential responses, were analyzed. For all the responses, the kinetic parameters of a mono-exponential function and the corresponding asymptotic standard errors (ASEs) were estimated by nonlinear regression, removing the data pertaining to progressively longer initial periods (1 s each) up to 60 s. Four methods to establish objectively ΔTr were compared. The minimum estimated τ was obtained for ΔTr ≅ 35 s in both the V̇o2 and simulated data, that was about 30% lower compared with that obtained for ΔTr ≅ 0s. The average ASE values remained quite constant up to ΔTr ≅ 35 s, thereafter they increased remarkably. The τ used to generate the simulated response fell within the confidence intervals of the estimated τ in ∼85% of cases for ΔTr = 20 s ("20 s-w" method); this percentage increased to ∼92% of cases when ΔTr was established according to both the minimum τ and its narrowest confidence interval ("Mixed" method). In conclusion, the effects of removing V̇o2 data pertaining to different ΔTr are remarkable. The "Mixed" method provided estimated parameters close to those used to generate the simulated responses and is thus endorsed.NEW & NOTEWORTHY We propose a method to objectively establish the initial time period to be removed from the fitting window when, using a mono-exponential model, the kinetics of the fundamental component is determined on breath-by-breath O2 uptake data collected at the onset of a moderate-intensity exercise. Innovative statistical parameters ("Coverage" and "Concordance5%," applicable on simulated responses) were used to compare its performance with that of other three methods. The proposed method yielded the best "Coverage" and "Concordance5%."

Optimizing the Empirical Parameters of the Data-Driven Algorithm for SIF Retrieval for SIFIS Onboard TECIS-1 Satellite.

Space-based solar-induced chlorophyll fluorescence (SIF) has been widely demonstrated as a great proxy for monitoring terrestrial photosynthesis and has been successfully retrieved from satellite-based hyperspectral observations using a data-driven algorithm. As a semi-empirical algorithm, the data-driven algorithm is strongly affected by the empirical parameters in the model. Here, the influence of the data-driven algorithm’s empirical parameters, including the polynomial order (np), the number of feature vectors (nSV), the fluorescence emission spectrum function, and the fitting window used in the retrieval model, were quantitatively investigated based on the simulations of the SIF Imaging Spectrometer (SIFIS) onboard the First Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1). The results showed that the fitting window, np, and nSV were the three main factors that influenced the accuracy of retrieval. The retrieval accuracy was relatively higher for a wider fitting window; the root mean square error (RMSE) was lower than 0.7 mW m−2 sr−1 nm−1 with fitting windows wider than 735–758 nm and 682–691 nm for the far-red band and the red band, respectively. The RMSE decreased first and then increased with increases in np range from 1 to 5 and increased in nSV range from 2 to 20. According to the specifications of SIFIS onboard TECIS-1, a fitting window of 735–758 nm, a second-order polynomial, and four feature vectors are the optimal parameters for far-red SIF retrieval, resulting in an RMSE of 0.63 mW m−2 sr−1 nm−1. As for red SIF retrieval, using second-order polynomial and seven feature vectors in the fitting window of 682–697 nm was the optimal choice and resulted in an RMSE of 0.53 mW m−2 sr−1 nm−1. The optimized parameters of the data-driven algorithm can guide the retrieval of satellite-based SIF and are valuable for generating an accurate SIF product of the TECIS-1 satellite after its launch.

Scale-, time- and asset-dependence of Hawkes process estimates on high frequency price changes

The statistical estimate of the branching ratio η of the Hawkes model, when fitted to windows of mid-price changes, has been reported to approach criticality () as the fitting window becomes large. In this study – using price changes from the EUR/USD currency pair traded on the Electronic Broking Services (EBS) interbank trading platform and the S&P 500 E-mini futures contract traded at the Chicago Mercantile Exchange (CME) – it is shown that the estimated branching ratio depends little upon window size and is usually far from criticality. This is done by controlling for exogenous non-stationarities/heterogeneities at inter- and intraday scales, accomplished by using information criteria to select the degree of flexibility of the Hawkes immigration intensity, either piecewise constant or adaptive logspline, estimated using an expectation maximization (EM) algorithm. The (positive) bias incurred by keeping the immigration intensity constant is small for time scales up to two hours, but can become as high as 0.3 for windows spanning days. This emphasizes the importance of choosing an appropriate model for the immigration intensity in the application of Hawkes processes to financial data and elsewhere. The branching ratio is also found to have an intraday seasonality, where it appears to be higher during times where market activity is dominated by supposedly reflexive automated decisions and a lack of fundamental news and trading. The insights into the microstructure of the two considered markets derived from our Hawkes process fits suggest that equity futures exhibit more complex non-stationary features, are more endogenous, persistent and traded at higher speed than spot foreign exchange. We complement our point process study with EM-estimates of integer-valued autoregressive (INAR) time series models at even longer scales of months. Transferring our methodologies to the aggregate bin-count setting confirms that, even at these very long scales, criticality can be rejected.

Accurate Road Marking Detection from Noisy Point Clouds Acquired by Low-Cost Mobile LiDAR Systems

Road markings that provide instructions for unmanned driving are important elements in high-precision maps. In road information collection technology, multi-beam mobile LiDAR scanning (MLS) is currently adopted instead of traditional mono-beam LiDAR scanning because of the advantages of low cost and multiple fields of view for multi-beam laser scanners; however, the intensity information scanned by multi-beam systems is noisy and current methods designed for road marking detection from mono-beam point clouds are of low accuracy. This paper presents an accurate algorithm for detecting road markings from noisy point clouds, where most nonroad points are removed and the remaining points are organized into a set of consecutive pseudo-scan lines for parallel and/or online processing. The road surface is precisely extracted by a moving fitting window filter from each pseudo-scan line, and a marker edge detector combining an intensity gradient with an intensity statistics histogram is presented for road marking detection. Quantitative results indicate that the proposed method achieves average recall, precision, and Matthews correlation coefficient (MCC) levels of 90%, 95%, and 92%, respectively, showing excellent performance for road marking detection from multi-beam scanning point clouds.

Recommendations for spectral fitting of SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; from miniature multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements

Abstract. Fitting sulfur dioxide (SO2) differential slant column densities (dSCDs) from multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements of scattered sunlight is challenging because actinic light intensity is low in the wavelength regions where the SO2 absorption features are strongest. SO2 dSCDs were fit with different wavelength windows (λlow to λhigh) from ambient measurements with calibration cells of 2.2×1017 and 2.2×1016 molec. cm−2 inserted in the light path at different viewing elevation angles using an Ocean Optics USB2000 spectrometer in a miniature MAX-DOAS instrument. SO2 dSCDs were the least accurate, and fit errors were highest for fitting windows with λlow &lt; 307 or λlow &gt; 312 nm. The SO2 dSCDs also exhibited an inverse relationship with the depth of the differential features in the SO2 absorption cross section for fitting windows with λlow &lt; 307 nm. Spectra measured at low viewing elevation angles (i.e., α=2∘) exhibited less accurate SO2 dSCDs for the same fitting windows compared with higher angles. The use of a 400 nm short-pass filter or a polynomial to account for stray light (the offset function) increased the accuracy of the SO2 dSCDs for many different fitting windows, decreased fit errors, and decreased the dSCDs' dependence on the depth of the SO2 differential absorption features. These results suggest that the radiance at shorter wavelengths was increased by stray light. The inaccuracies at lower fitting wavelengths were increased by stray light originating from light with λ &gt; 400 nm. Deviation of the SO2 dSCD from the true value depended on the SO2 concentration for some fitting windows rather than exhibiting a consistent bias. Uncertainties in the SO2 dSCD reported by the fit algorithm were more than 50 % less than the true error for many windows, particularly for the measurements without the filter or offset function. For retrievals with the filter or offset function, increasing λhigh &gt; 320 nm tended to decrease the reported fit uncertainty but did not increase the accuracy. Based on the results of this study, a short-pass filter and a fitting window of 307.5 &lt; λ &lt; 319 nm are recommended for the retrieval of SO2 SCDs from miniature MAX-DOAS measurements. If a filter is not available or conflicts with other species to be determined (e.g., NO2 or HCHO), the offset function should be enabled, and a fit window 307.5 &lt; λ &lt; 319 nm is still recommended.

Systematic Assessment of Retrieval Methods for Canopy Far‐Red Solar‐Induced Chlorophyll Fluorescence Using High‐Frequency Automated Field Spectroscopy

AbstractRemote sensing of solar‐induced chlorophyll fluorescence (SIF) offers potential to infer photosynthesis across scales and biomes. Many retrieval methods have been developed to estimate top‐of‐canopy SIF using ground‐based spectroscopy. However, inconsistencies among methods may confound interpretation of SIF dynamics, eco‐physiological/environmental drivers, and its relationship with photosynthesis. Using high temporal‐ and spectral resolution ground‐based spectroscopy, we aimed to (1) evaluate performance of SIF retrieval methods under diverse sky conditions using continuous field measurements; (2) assess method sensitivity to fluctuating light, reflectance, and fluorescence emission spectra; and (3) inform users for optimal ground‐based SIF retrieval. Analysis included field measurements from bi‐hemispherical and hemispherical‐conical systems and synthetic upwelling radiance constructed from measured downwelling radiance, simulated reflectance, and simulated fluorescence for benchmarking. Fraunhofer‐based differential optical absorption spectroscopy (DOAS) and singular vector decomposition (SVD) retrievals exhibit convergent SIF‐PAR relationships and diurnal consistency across different sky conditions, while O2A‐based spectral fitting method (SFM), SVD, and modified Fraunhofer line discrimination (3FLD) exhibit divergent SIF‐PAR relationships across sky conditions. Such behavior holds across system configurations, though hemispherical‐conical systems diverge less across sky conditions. O2A retrieval accuracy, influenced by atmospheric distortion, improves with a narrower fitting window and when training SVD with temporally local spectra. This may impact SIF‐photosynthesis relationships interpreted by previous studies using O2A‐based retrievals with standard (759–767.76 nm) fitting windows. Fraunhofer‐based retrievals resist atmospheric impacts but are noisier and more sensitive to assumed SIF spectral shape than O2A‐based retrievals. We recommend SVD or SFM using reduced fitting window (759.5–761.5 nm) for robust far‐red SIF retrievals across sky conditions.

Scale-, time- and asset-dependence of Hawkes process estimates on high frequency price changes

The statistical estimate of the branching ratio η of the Hawkes model, when fitted to windows of mid-price changes, has been reported to approach criticality (η = 1) as the fitting window becomes large. In this study -- using price changes from the EUR/USD currency pair traded on the Electronic Broking Services (EBS) interbank trading platform and the S&P 500 E-mini futures contract traded at the Chicago Mercantile Exchange (CME) -- it is shown that the estimated branching ratio depends little upon window size and is usually far from criticality. This is done by controlling for exogenous non-stationarities/heterogeneities at inter- and intraday scales, accomplished by using information criteria to select the degree of flexibility of the Hawkes immigration intensity, either piecewise constant or adaptive logspline, estimated using an expectation maximization (EM) algorithm. The bias incurred by keeping the immigration intensity constant is small for time scales up to two hours, but can become as high as 0.3 for windows spanning days. This emphasizes the importance of choosing an appropriate model for the immigration intensity in the application of Hawkes processes to financial data and elsewhere. The branching ratio is also found to have an intraday seasonality, where it appears to be higher during times where market activity is dominated by supposedly reflexive automated decisions and a lack of fundamental news and trading. The insights into the microstructure of the two considered markets derived from our Hawkes process fits suggest that equity futures exhibit more complex non-stationary features, are more endogenous, persistent and traded at higher speed than spot foreign exchange. We complement our point process study with EM-estimates of integer-valued autoregressive (INAR) time series models at even longer scales of months. Transferring our methodologies to the aggregate bin-count setting confirms that even at these very long scales, criticality can be rejected.

Evaluation of the accuracy of the Sentinel-5 Precursor operational SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; products over China

The Sentinel-5 Precursor (S-5P) was launched into low earth orbit with a single payload TROPOspheric Monitoring Instrument (TROPOMI) on 13 October, 2017. TROPOMI is able to provide atmospheric composition observations including sulfur dioxide (SO2) daily and globally. It has a higher spatial resolution, specifically with a ground resolution of 7 km ×3.5 km in the nadir, than previous satellite instruments such as Ozone Monitoring Instrument (OMI, 13 km ×24 km) and Ozone Mapping and Profiler Suite (OMPS, 50 km ×50 km). The three types of S-5P operational SO2 products (offline (OFFL), Reprocessing (RPRO) and Near Real Time (NRTI)) are on basis of the differential optical absorption spectroscopy (DOAS) algorithm, and available from the S-5P Pre-Operations Data Hub. To evaluate these data, the results of OFFL are compared to the measurements obtained by ground-based Multi-Axis DOAS (MAX-DOAS) and China National Environmental Monitoring Center (CNEMC). We find that the S-5P operational SO2 products significantly overestimate SO2 concentrations over Northern China. Compared with four ground-based MAX-DOAS measurements in terms of the weekly average SO2 vertical column densities (VCDs), the S-5P operational SO2 products exhibit an overestimation of 61%−140%, with the correlation coefficient less than 0.5. The a priori SO2 profiles in S-5P operational products are from daily Tracer Model (TM5) simulations. SO2 surface concentrations are converted by the S-5P operational SO2 products and a priori SO2 profiles used in the retrievals. The correlation coefficient between the SO2 surface concentrations measured by CNEMC over Northern China and those from the S-5P operational products is higher than 0.5, and the S-5P operational products at these CNEMC sites show an overestimation of about 54.6% on average. In this study, the SO2 products (TROPOMI USTC SO2 products) are retrieved by the University of Science and Technology of China (USTC) with the optimal estimation algorithm (OE) from TROPOMI measurements, with the a priori SO2 profiles from monthly average GEOS-Chem simulations. The mean biases between the TROPOMI USTC SO2 products and the ground-based MAX-DOAS measurements are reduced significantly (−4.8%−22%), together with an improvement in the correlation coefficients (0.72−0.89). TROPOMI USTC SO2 surface concentrations are transformed by the TROPOMI USTC SO2 products and a priori SO2 profiles in the retrievals. Based on the evaluation statistics of the S-5P operational SO2 products, a smaller mean bias of −25.8% is found between the TROPOMI USTC SO2 surface concentrations and CNEMC measurements over Northern China. Compared to the TROPOMI USTC SO2 products in the heavily polluted area (33°−45°N, 110°−122°E), the S-5P operational SO2 products have an overestimation of around 41.3%, with a correlation coefficient of 0.73. In addition, S-5P operational SO2 products show a larger area of heavy pollution than TROPOMI USTC SO2 products. Additionally, the sensitivity analysis indicates many influential factors of the SO2 retrievals using OE algorithm, such as radiative calibration, fitting window and a priori SO2 profiles. In the fitting window of 310.5−326.0 nm, the SO2 columns retrieved by the a priori profiles from monthly GEOS-Chem simulations show better agreement with the MAX-DOAS measurements than those from daily TM5 simulations. A possible reason is that the emission inventory used in GEOS-Chem is based on a Chinese power sector database for 31 provinces supported by CMEP (Chinese Ministry of Environmental Protection), resulting in a better performance of GEOS-Chem than that of TM5 over China in simulations. And the overestimation of the S-5P operational SO2 products over Northern China may be attributed to the fitting window used.