Defense Meteorological Satellite Program Data Research Articles

Ionospheric Conductances Due To Electron and Ion Precipitations: A Comparison Between EISCAT and DMSP Estimates

AbstractEnergetic particle precipitation is the major source of electron production that controls the ionospheric Pedersen and Hall conductances at high latitudes. Many studies use empirical formulas to estimate conductances. The particle precipitation spectra measured by the Defense Meteorological Satellite Program (DMSP) Special Sensor J are often used as the input to the empirical formulas. In this study, we evaluate the empirical formulas of ionospheric conductances during four different types of auroral precipitation conditions based on 63 conjugate events observed by DMSP and EISCAT. The conductances calculated from the DMSP data with the empirical formulas are compared with those based on EISCAT measurements with the standard equations. The best correlation between these two is found when the empirical Robinson formulas (Robinson et al., 1987, https://doi.org/10.1029/ja092ia03p02565) are used in the presence of diffuse electron precipitation without ions. In the presence of ion precipitation, the correlation coefficients are smaller, but the correlation improves when the Galand formulas (Galand & Richmond, 2001, https://doi.org/10.1029/1999ja002001) are used to estimate the contribution of ion precipitation to the conductances. We also found that pure ion precipitation can cause the increase of conductances up to 2–7 S for Pedersen and 2.5–10 S for Hall conductances, which is positively correlated with the auroral electrojet index. Overall, the empirical formulas applied to the DMSP particle spectra underestimate the ionospheric conductances.

Association between outdoor light at night and hypertension and high-normal blood pressure: A nationwide cross-sectional study among Chinese adults.

The authors aimed to investigate the association between outdoor light at night (LAN) intensity and blood pressure. The study included 13507 participants aged 45 and above from the 2011-2012 China Health and Retirement Longitudinal Study baseline survey. Blood pressure measurements were obtained by averaging the last two readings recorded (three measurements with an interval of 45-60 s between each measurement) during the survey. Outdoor LAN intensity was assessed using Defense Meteorological Satellite Program data. The study categorized participants based on quartiles of outdoor LAN intensity and employed statistical methods like linear regression, restricted cubic splines, and logistic models to analyze the connections. After adjusting for potential confounding factors, higher levels of outdoor LAN intensity were associated with increase in systolic blood pressure (0.592mmHg/interquartile range [IQR], 95% confidence interval [CI]: 0.027,1.157), diastolic blood pressure (0.853mmHg/IQR, 95% CI: 0.525,1.180) and mean arterial pressure (0.766mmHg/IQR, 95% CI: 0.385,1.147). Interestingly, the relationship between LAN intensity and odds of hypertension followed a non-linear pattern, resembling a reverse "L" shape on cubic splines. Participants with the highest quartile of outdoor LAN intensity had 1.31-fold increased odds of hypertension (95% CI: 1.08-1.58) compared to the lowest quartile. Additionally, there was an observable trend of rising odds for high-normal blood pressure with higher levels of LAN intensity in the crude model, but no statistically significant differences were observed after adjusting for confounding factors. In conclusion, this study underscores a significant connection between outdoor LAN intensity and the prevalence of hypertension.

Topside Electron Density Modeling Using Neural Network and Empirical Model Predictions

AbstractWe model the electron density in the topside of the ionosphere with an improved machine learning (ML) model and compare it to existing empirical models, specifically the International Reference Ionosphere (IRI) and the Empirical‐Canadian High Arctic Ionospheric Model (E‐CHAIM). In prior work, an artificial neural network (NN) was developed and trained on two solar cycles worth of Defense Meteorological Satellite Program data (113 satellite‐years), along with global drivers and indices to predict topside electron density. In this paper, we highlight improvements made to this NN, and present a detailed comparison of the new model to E‐CHAIM and IRI as a function of location, geomagnetic condition, time of year, and solar local time. We discuss precision and accuracy metrics to better understand model strengths and weaknesses. The updated neural network shows improved mid‐latitude performance with absolute errors lower than the IRI by 2.5 × 109 to 2.5 × 1010 e−/m3, modestly improved performance in disturbed geomagnetic conditions with absolute errors reduced by about 2.5 × 109 e−/m3 at high Kp compared to the IRI, and high Kp percentage errors reduced by >50% when compared to E‐CHAIM.

Measurement errors in popular night lights data may bias estimated impacts of economic sanctions: Evidence from closing the Kaesong Industrial Zone

AbstractSatellite‐detected night lights data are widely used to evaluate economic impacts of sanctions. Such data should be free from political manipulation. However, measurement errors in these data, from blurring and bottom‐coding, are rarely considered. To study such errors, we use a difference‐in‐differences analysis of impacts of closing the Kaesong Industrial Zone in North Korea—a sanction South Korea imposed in 2016. Luminosity in the affected region declined by a precisely estimated 50 percent. When using the popular Defense Meteorological Satellite Program (DMSP) night lights data the apparent impacts are imprecisely estimated and far smaller. Measurement errors in DMSP data may distort evaluations of sanctions.

Remotely too equal: Popular DMSP night‐time lights data understate spatial inequality

AbstractRegional science and economics studies increasingly use the Defense Meteorological Satellite Program (DMSP) night‐time lights data to measure spatial inequality. These DMSP data are a poor proxy in this context because of their spatially mean‐reverting errors, which yield significantly lower inequality estimates than what subnational GDP data show. Inequality estimates from DMSP are also lower than what newer, research‐focused and more accurate satellites show. We demonstrate this bias using county‐level data from China and the United States. The errors in the DMSP data distort estimates of both the level of and trend in spatial inequality.

SDGSAT-1 nighttime light data improve village-scale built-up delineation

While previous urban delineation studies have been based mainly on Defense Meteorological Satellite Program (DMSP) and Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NTL) data at a 1-km or 500-m spatial resolution, the higher 10-m-spatial-resolution Sustainable Development Science Satellite-1 (SDGSAT-1) NTL data offer the possibility of sophisticated village-scale analysis. In this study, we developed the quantile method with downward heuristic rules for built-up delineation based on SDGSAT-1 NTL data and proposed a new threshold method, named the trend method framework, for urban dynamic delineation in combination with SDGSAT-1 NTL data and DMSP-VIIRS NTL data. Then, the methods developed in this study were applied to five urban agglomerations (the Beijing-Tianjin-Hebei Urban Agglomeration (BTH), Yangtze River Delta (YRD), Guangdong–Hong Kong–Macao Greater Bay Area (GHM), Chengdu–Chongqing Economic Circle (CCC), and Hainan Free Trade Port (HTP)) in China. The results showed that both the downward quantile method and the trend method can be used to delineate village-scale built-up areas. In the delineation of built-up areas based on SDGSAT-1 NTL data, the trend method provides clearer information on the built-up areas within lighted areas, leading to higher accuracy than the downward quantile method. In the built-up area delineation based on DMSP and VIIRS NTL data at 1-km resolution, the trend method combines SDGSAT-1 NTL data and accounts for information on time-series changes in urbanization, thus allowing for the more accurate extraction of built-up areas. In 2020, the trend method had higher delineation accuracies than the quantile method, with effectiveness accuracies (EAs) of 0.91, 0.77, 0.67, 0.94, and 0.65 for the YRD, BTH, CCC, GHM, and HTP, respectively, compared with the European Space Agency (ESA) land use and land cover (LULC) product. The trend method has very good application prospects for delineating built-up areas using the high-resolution SDGSAT-1 NTL dataset.

Fifty years of nightly global low-light imaging satellite observations

The history of satellite remote sensing of nighttime lights traces it is roots back the U.S. Air Force Defense Meteorological Satellite Program (DMSP) which began flying polar orbiting satellites with cloud imaging sensors in the 1970’s. The meteorologists found that they could improve weather predictions with visible and thermal band imagery. The visible band channels on early DMSP satellites worked well in the daytime—but were unable to see clouds at nights. Visible band low light imaging was added in the mid-1970’s using photomultiplier tubes to amplify the signal, enabling the detection of moonlit clouds. In 1992, NOAA established a digital archive for DMSP data at the National Geophysical Data Center in Boulder, Colorado. The Earth Observation Group (EOG) was formed in 1994 and dedicated it is efforts to making global nighttime light data products for uses by the science and policy communities. Many of the shortcomings of DMSP data were addressed with the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB), which opened a new era in nighttime light studies. In total, EOG has produced 65 annual global nighttime light products and over 650 monthly products.

Automatic Identification and New Observations of Ion Energy Dispersion Events in the Cusp Ionosphere

AbstractIn this paper we present an algorithm which identifies ion energy dispersion events in low altitude cusp observations. Such an algorithm is suitable for implementation to prioritize data for downlink, or to be run on the ground for data mining. The energy dispersion events, sometimes known as dispersed particle precipitation, have been interpreted as due to time‐of‐flight effects of energized ions from magnetopause reconnection sites. Our algorithm utilizes a novel method of integrating a scoring function across moving time windows to capture the dispersion structure. The algorithm is implemented and prototyped with Defense Meteorological Satellite Program data but specified in terms that apply to other missions for both ions and electrons. Two new types of dispersion events are uncovered with the algorithm, consisting of a cold inverse double dispersion event where one population dominates, and a V‐type dispersion event during northward interplanetary magnetic field.

Spatial allocation of anthropogenic carbon dioxide emission statistics data fusing multi-source data based on Bayesian network

A gridded social-economic data is essential for geoscience analysis and multidisciplinary application. Spatial allocation of carbon dioxide statistics data is an important issue in the context of global climate change, which involves the carbon emissions accounting and decomposition of responsibility for carbon emission reductions. In this research a new spatial allocation method for non-point source anthropogenic carbon dioxide emissions (ACDE) fusing multi-source data using Bayesian Network (BN) was introduced. In addition to common-used DMSP (Defense Meteorological Satellite Program), PD (population density) and GDP (Gross Domestic Production) data, the land cover and vegetation data was imported into the model as prior knowledge to optimize the model fitting. The prior knowledge here was based on the understanding that ACDE was dominated by human activities and has strong correlations with land cover and vegetation conditions. A 1 km gridded ACDE map integrated emissions form point-source and non-point source was generated and validated. The model predicts ACDE with high accuracies and great improvement can be observed when fusing land cover and vegetation as prior knowledge. The model can achieve successful statistics data downscaling on national scale provided adequate sample data are available, offering a novel method for ACDE accounting in China.

Mitigating geolocation errors in nighttime light satellite data and global CO2 emission gridded data

Accurate geospatial modeling of greenhouse gas (GHG) emissions is an essential part of the future of global GHG monitoring systems. Our previous work found a systematic displacement in the high-resolution carbon dioxide (CO2) emission raster data of the Open-source Data Inventory for Anthropogenic CO2 (ODIAC) emission product. It turns out this displacement is due to geolocation bias in the Defense Meteorological Satellite Program (DMSP) nighttime lights (NTL) data products, which are used as a spatial emission proxy for estimating non-point source emissions distributions in ODIAC. Mitigating such geolocation error (~1.7 km), which is on the same order of the size of the carbon observing satellites field of view, is especially critical for the spatial analysis of emissions from cities. In this paper, there is proposed a method to mitigate the geolocation bias in DMSP NTL data that can be applied to DMSP NTL-based geospatial products, such as ODIAC. To identify and characterize the geolocation bias, we used the OpenStreetMap repository to define city boundaries for a large number of global cities. Assumption is that the total emissions within the city boundaries are at the maximum if there is no displacement (geolocation bias) in NTL data. Therefore, it is necessary to find an optimal vector (distance and angle) that maximizes the ODIAC total emissions within cities by shifting the emission fields. In the process of preparing annual composites of the nighttime stable lights data, some pixels of the DMSP data corresponding to water bodies were zeroed, which due to the geolocation bias unreasonably distorted the ODIAC emission fields. Hence, an original approach for restoring data in such pixels is considered using elimination of the factor that distorted the ODIAC emission fields. It is also proposed a bias correction method for shifted high-resolution emission fields in ODIAC. The bias correction was applied to multiple cities from the different continents. It is shown that the bias correction to the emission data (elimination of geolocation error in non-point emission source fields) increases the total CO2 emissions within city boundaries by 4.76% on average, due to reduced emissions from non-urban areas to which these emissions were likely to be erroneously attributed.

Better Night Lights Data, For Longer*

AbstractNight lights data are increasingly used in applied economics, almost always from the Defense Meteorological Satellite Program (DMSP). These data are old, with production ending in 2013, and are flawed by blurring, lack of calibration and top‐coding. These inaccuracies in DMSP data cause mean‐reverting errors. This paper shows newer and better VIIRS night lights data have 80% higher predictive power for real GDP in a cross‐section of 269 European NUTS2 regions. Spatial inequality is greatly understated with DMSP data, especially for the most densely populated regions. A Pareto correction for top‐coding of DMSP data has a modest effect.

A harmonized global nighttime light dataset 1992\u20132018

Nighttime light (NTL) data from the Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership satellite provide a great opportunity for monitoring human activities from regional to global scales. Despite the valuable records of nightscape from DMSP (1992–2013) and VIIRS (2012–2018), the potential of the historical archive of NTL observations has not been fully explored because of the severe inconsistency between DMSP and VIIRS. In this study, we generated an integrated and consistent NTL dataset at the global scale by harmonizing the inter-calibrated NTL observations from the DMSP data and the simulated DMSP-like NTL observations from the VIIRS data. The generated global DMSP NTL time-series data (1992–2018) show consistent temporal trends. This temporally extended DMSP NTL dataset provides valuable support for various studies related to human activities such as electricity consumption and urban extent dynamics.

FDTD Modeling of High‐Frequency Waves Through Ionospheric Plasma Irregularities

AbstractElectromagnetic wave scattering and scintillation can occur at high latitudes of the Earth when polar cap patches are present. Polar cap patches range in size from 100 to 1,000 km and have densities on the order of 2–10 times that of the background ionosphere. Within polar cap patches, plasma irregularities caused by gradient drift instabilities are observed throughout the patch as elongated slivers. These irregularities vary in size from centimeters to tens of kilometers. Three major challenges must be overcome to thoroughly study high‐frequency electromagnetic wave interactions with polar cap patches: (1) the complex geometrical features of the irregularities within the patches must be accommodated; (2) the non‐approximated Maxwell's equations should be solved to account for all the physics of electromagnetic wave propagation, scattering, diffraction, etc.; and (3) the physics of electromagnetic wave interactions with magnetized ionospheric plasma must be included. In this paper, the finite‐difference time‐domain (FDTD) method is used to overcome the above three challenges and solve for electromagnetic wave interactions with plasma irregularities within a polar cap patch. Irregularities and patch profiles are generated using Defense Meteorological Satellite Program data and incorporated into the FDTD model. The transmission coefficients for high‐frequency electromagnetic wave propagation through perturbed and unperturbed ionospheres, as well as for magnetized and unmagnetized ionospheric plasma ionospheres are calculated. From these simulations, the effects of small‐scale irregularities on high‐frequency propagation are observed.

Long‐Term Trend of Topside Ionospheric Electron Density Derived From DMSP Data During 1995–2017

AbstractIn recent decades, significant efforts have been made to characterize and understand the global pattern of ionospheric long‐term trend. However, little attention has been paid to the topside ionosphere trend. In this study, the unique in situ data measured by series Defense Meteorological Satellite Program (DMSP) satellites were utilized to derive the long‐term trend of the topside ionosphere for the first time. We checked carefully data quality, gap, and consistency between different satellites for both electron density and ion temperature, and compared the techniques of artificial neuron network (ANN) and multiple linear regression methods for deriving the trend. The electron density (Ne) trend in the middle and low latitudes at ~860 km around 18 MLT was derived using the ANN method from 1995–2017. The trend from DMSP observations has a mean magnitude ranging from ~ − 2% to ~2% per decade, with clear seasonal, latitude and longitude variations. The derived trend was evaluated by directly comparing with the simulated trend at 500 km from the NCAR‐TIEGCM driven by realistic changes of CO2 level and geomagnetic field. The observed and simulated trends have similar geographic distribution patterns at 18 MLT. The good agreement between the observed trend around 860 km and the simulated trend near 500 km implies that the physical processes controlling the Ne trends above the peak height might be identical. Further control simulations show that the geomagnetic field secular variation is the dominant factor of the electron density trend at around 500 km, rather than the CO2 long‐term enhancement.

Investigating the Development of Abnormal Subauroral Ion Drift (ASAID) and Abnormal Subauroral Polarization Stream (ASAPS) During the Magnetically Active Times of September 2003

AbstractThis study investigates two recently reported subauroral phenomena: the abnormal subauroral ion drift (ASAID) appearing as an inverted SAID and the shielding‐E—SAID structure depicting a SAID feature on the poleward side of a small eastward or antisunward flow channel that is the shielding electric (E) field's signature. We have analyzed polar cross sections, constructed with multi‐instrument Defense Meteorological Satellite Program data, for the development of these subauroral phenomena. New results show the features of abnormal subauroral polarization stream (ASAPS) and ASAID‐ASAPS comprised by a narrow ASAID embedded in a wider ASAPS. We have identified undershielding, perfect shielding, and overshielding events. Our observational results demonstrate SAPS development during undershielding, the absence of subauroral flow channel during perfect shielding, and ASAID/ASAPS and shielding‐E—SAID/SAPS development during overshielding. The appearance of an ASAID‐ASAPS structure together with a pair of dayside‐nightside eastward auroral flow channels implies the intensification of region 2 field‐aligned currents via the westward traveling surge and thus the strengthening of overshielding conditions. From the observational results presented we conclude for the magnetically active time period studied that (i) the shielding E field drove the wider ASAPS flow channel, (ii) the ASAID‐ASAPS structure's narrow antisunward flow channel developed due to the injections of hot ring current ions in a short‐circuited system wherein the hot ring current plasma was closer to the Earth than the cold plasmaspheric plasma, and (iii) overshielding created this hot‐cold plasma configuration via the development of a plasmaspheric shoulder.

Inventorying and Monitoring Nighttime Light Distribution and Dynamics in the Mediterranean Coast Network of Southern California

Light pollution has been of increasing concern as it relates to protected areas. As such, natural resource managers need information on the distribution, intensity, and dynamics of nighttime lights in protected areas. We examine the extent of nighttime light brightness from 1992 to 2012 in the Mediterranean Coast Network (Santa Monica Mountains National Recreation Area, Channel Islands National Park, and Cabrillo National Monument) using the Defense Meteorological Satellite Program (DMSP) Operational Linescan System, which has provided global annual nighttime light imagery at 0.9 km pixel resolution. Nighttime lights appeared stable in Santa Monica Mountains National Recreation Area, decreased in Cabrillo National Monument, and are extremely low in Channel Islands National Park. However, the mean brightness values in Santa Monica Mountains National Recreation Area and Cabrillo National Monument were very high compared to all other National Parks. Indeed, both were comparable to the two National Parks in the USA with the highest mean brightness values (Cuyahoga Valley National Park, Hot Springs National Park). Monitoring night light extent, intensity, time series, and change detection using remote sensing should be a standard practice for all protected areas managed by the National Park Service due to the no-cost nature of the data and ease at which analyses can be undertaken. The DMSP data and calibrated products can be used to monitor long-term changes in light distribution and intensity while the higher-resolution Visible Infrared Imaging Radiometer Suite data can be used to show changes in light distribution and density throughout the year and can be used to test how policy or ordinance changes impact light pollution.

Inverse procedure for high‐latitude ionospheric electrodynamics: Analysis of satellite‐borne magnetometer data

AbstractThis paper presents an analysis of data from the magnetometers on board the Defense Meteorological Satellite Program (DMSP) F‐15, F‐16, F‐17, and F‐18 satellites and the Iridium satellite constellation, using an inverse procedure for high‐latitude ionospheric electrodynamics, during the period of 29–30 May 2010. The Iridium magnetometer data are made available through the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE) program. The method presented here is built upon the assimilative mapping of ionospheric electrodynamics procedure but with a more complete treatment of the prior model uncertainty to facilitate an optimal inference of complete polar maps of electrodynamic variables from irregularly distributed observational data. The procedure can provide an objective measure of uncertainty associated with the analysis. The cross‐validation analysis, in which the DMSP data are used as independent validation data sets, suggests that the procedure yields the spatial prediction of DMSP perturbation magnetic fields from AMPERE data alone with a median discrepancy of 30–50 nT. Discrepancies larger than 100 nT are seen in about 20% of total samples, whose location and magnitude are generally consistent with the previously identified discrepancy between DMSP and AMPERE data sets. Resulting field‐aligned current (FAC) patterns exhibit more distinct spatial patterns without spurious high‐frequency oscillatory features in comparison to the FAC products provided by AMPERE. Maps of the toroidal magnetic potential and FAC estimated from both AMPERE and DMSP data under four distinctive interplanetary magnetic field (IMF) conditions during a magnetic cloud event demonstrate the IMF control of high‐latitude electrodynamics and the opportunity for future scientific investigation.

Fast equatorial waves propagating at the top of the Earth's core

AbstractSince 2000, magnetic field variations originating in the core have been dominated by several pulses in the secular acceleration, leading to sharp geomagnetic “jerks” at the Earth's surface. Using models built from (i) Defense Meteorological Satellite Program data and (ii) Ørsted and Swarm satellites and ground observatory data, we show that a new pulse occurred in 2012.5, immediately following two pulses in 2006 and 2009. The three pulses can be decomposed into several equatorially symmetric modes propagating eastward and westward at 550 to 1100 km/yr, and one equatorially antisymmetric mode propagating eastward at 1650 km/yr. The characteristics of these modes are compatible to some extent with equatorial magnetic Rossby waves propagating within a 140 km thick layer at the top of the core with a density contrast of 50 ppm. This interpretation, if confirmed, would provide a new explanation for geomagnetic jerks and pulses based on stable stratification of the core.

Are dayside long-period pulsations related to the cusp?

Abstract. We compare simultaneous observations of long-period ultra-low-frequency (ULF) wave activity from a Svalbard/IMAGE fluxgate magnetometer latitudinal profile covering the expected cusp geomagnetic latitudes. Irregular Pulsations at Cusp Latitudes (IPCL) and narrowband Pc5 waves are found to be a ubiquitous element of ULF activity in the dayside high-latitude region. To identify the ionospheric projections of the cusp, we use the width of return signal of the Super Dual Auroral Radar Network (SuperDARN) radar covering the Svalbard archipelago, predictions of empirical cusp models, augmented whenever possible by Defense Meteorological Satellite Program (DMSP) identification of magnetospheric boundary domains. The meridional spatial structure of broadband dayside Pc5–6 pulsation spectral power has been found to have a localized latitudinal peak, not under the cusp proper as was previously thought, but several degrees southward from the equatorward cusp boundary. The earlier claims of the dayside monochromatic Pc5 wave association with the open–closed boundary also seems doubtful. Transient currents producing broadband Pc5–6 probably originate at the low-latitude boundary layer/central plasma sheet (LLBL/CPS) interface, though such identification with available DMSP data is not very precise. The occurrence of broadband Pc5–6 pulsations in the dayside boundary layers is a challenge to modelers because so far their mechanism has not been firmly identified.

Assessing predictive ability of three auroral precipitation models using DMSP energy flux

AbstractOur study statistically compares the total energy flux outputs of Newell et al.'s () oval variation, assessment, tracking, intensity, and online nowcasting (OVATION) Prime model, Hardy et al.'s () Kp‐based model, and a coupled Space Weather Modeling Framework ring current model to energy flux data obtained from 2198 Defense Meteorological Satellite Program (DMSP) satellite passes in the Northern Hemisphere. Our DMSP data set includes 28 days grouped into continuous 3 and 4 day periods between 2000 and 2008 and encompasses magnetic local times (MLTs) between 04:00 and 21:00. We obtain the most equatorward magnetic latitude coordinate, where a DMSP satellite energy flux measurement exceeds 0.4 erg/cm2/s, and use this point as a proxy for the equatorward boundary of the auroral oval in a particular MLT sector. We then calculate a prediction efficiency (PE) score by comparing the DMSP boundary coordinates to each model, using the same energy flux threshold to obtain a model's boundary location. We find that the PE for the OVATION Prime model is 0.55, and the PE for the Hardy Kp model is 0.51. When we accomplish the same analysis using a higher energy flux threshold equal to 0.6 erg/cm2/s, the OVATION Prime model's PE increases to 0.58, while the Hardy Kp model's score drops to 0.41. Our results indicate that more complex modeling techniques, like those used in OVATION Prime, can more accurately model the auroral oval's equatorward boundary. However, Hardy's discretized Kp model, despite its relative simplicity, is still a competitive and viable modeling option.