scholarly journals Stratosphere-troposphere separation of nitrogen dioxide columns from the TEMPO geostationary satellite instrument

2018 ◽  
Author(s):  
Jeffrey A. Geddes ◽  
Randall V. Martin ◽  
Eric J. Bucsela ◽  
Chris A. McLinden ◽  
Daniel J. M. Cunningham
Keyword(s):  
Real Time ◽  
Time Of Day ◽  
Low Earth Orbit ◽  
Satellite Observations ◽  
Spatial Coverage ◽  
Tropospheric No2 ◽  
Global Coverage ◽  
Factor Ratios ◽  
Small Parts ◽  
Global Algorithm

Abstract. Separating the stratospheric and tropospheric contributions in satellite retrievals of atmospheric NO2 column abundance is a crucial step in the interpretation and application of the satellite observations. A variety of stratosphere-troposphere separation algorithms have been developed for sun-synchronous instruments in low Earth orbit (LEO) that benefit from global coverage, including broad clean regions with negligible tropospheric NO2 compared to stratospheric NO2. These global sun-synchronous algorithms need to be evaluated and refined for forthcoming geostationary instruments focused on continental regions, which lack this global context and require hourly estimates of the stratospheric column. Here we develop and assess a spatial filtering algorithm for the upcoming TEMPO geostationary instrument that will target North America. Developments include using independent satellite observations to identify likely locations of tropospheric enhancements, using independent LEO observations for spatial context, consideration of diurnally-varying partial fields of regard, and a filter based on stratospheric to tropospheric air mass factor ratios. We test the algorithm with LEO observations from the OMI instrument with an afternoon overpass, and from the GOME-2 instrument with a morning overpass. We compare our TEMPO field of regard algorithm against an identical global algorithm to investigate the penalty resulting from the limited spatial coverage in geostationary orbit, and find excellent agreement in the estimated mean daily tropospheric NO2 column densities (R2 = 0.999, slope = 1.009 for July and R2 = 0.998, slope = 0.999 for January). The algorithm performs well even when only small parts of the continent are observed by TEMPO. The algorithm is challenged the most by east coast morning retrievals in the wintertime (e.g. R2 = 0.995, slope = 1.038 at 1400 UTC). We find independent global low Earth observations (corrected for time of day) provide important context near the field-of-regard edges. We also test the performance of the TEMPO algorithm without these supporting global observations. Most of the continent is unaffected (R2 = 0.924 and slope = 0.973 for July and R2 = 0.996 and slope = 1.008 for January), with 90 % of the pixels having differences of less than ±0.2 x 1015 molecules cm−2 between the TEMPO tropospheric NO2 column density and the global algorithm. For near-real-time retrieval, even a climatological estimate of the stratospheric NO2 surrounding the field of regard would improve this agreement. In general, the additional penalty of a limited field of regard from TEMPO introduces no more error than normally expected in most global stratosphere-troposphere separation algorithms. Overall, we conclude that hourly near-real-time stratosphere-troposphere separation for the retrieval of NO2 tropospheric column densities by the TEMPO geostationary instrument is both feasible and robust, regardless of the diurnally-varying limited field of regard.

scholarly journals Stratosphere–troposphere separation of nitrogen dioxide columns from the TEMPO geostationary satellite instrument

2018 ◽  
Vol 11 (11) ◽  
pp. 6271-6287 ◽  
Author(s):  
Jeffrey A. Geddes ◽  
Randall V. Martin ◽  
Eric J. Bucsela ◽  
Chris A. McLinden ◽  
Daniel J. M. Cunningham
Keyword(s):  
Real Time ◽  
Time Of Day ◽  
Low Earth Orbit ◽  
Satellite Observations ◽  
Spatial Coverage ◽  
Tropospheric No2 ◽  
Global Coverage ◽  
Factor Ratios ◽  
Small Parts ◽  
Global Algorithm

Abstract. Separating the stratospheric and tropospheric contributions in satellite retrievals of atmospheric NO2 column abundance is a crucial step in the interpretation and application of the satellite observations. A variety of stratosphere–troposphere separation algorithms have been developed for sun-synchronous instruments in low Earth orbit (LEO) that benefit from global coverage, including broad clean regions with negligible tropospheric NO2 compared to stratospheric NO2. These global sun-synchronous algorithms need to be evaluated and refined for forthcoming geostationary instruments focused on continental regions, which lack this global context and require hourly estimates of the stratospheric column. Here we develop and assess a spatial filtering algorithm for the upcoming TEMPO geostationary instrument that will target North America. Developments include using independent satellite observations to identify likely locations of tropospheric enhancements, using independent LEO observations for spatial context, consideration of diurnally varying partial fields of regard, and a filter based on stratospheric to tropospheric air mass factor ratios. We test the algorithm with LEO observations from the OMI instrument with an afternoon overpass, and from the GOME-2 instrument with a morning overpass. We compare our TEMPO field of regard algorithm against an identical global algorithm to investigate the penalty resulting from the limited spatial coverage in geostationary orbit, and find excellent agreement in the estimated mean daily tropospheric NO2 column densities (R2=0.999, slope=1.009 for July and R2=0.998, slope=0.999 for January). The algorithm performs well even when only small parts of the continent are observed by TEMPO. The algorithm is challenged the most by east coast morning retrievals in the wintertime (e.g., R2=0.995, slope=1.038 at 14:00 UTC). We find independent global LEO observations (corrected for time of day) provide important context near the field-of-regard edges. We also test the performance of the TEMPO algorithm without these supporting global observations. Most of the continent is unaffected (R2=0.924 and slope=0.973 for July and R2=0.996 and slope=1.008 for January), with 90 % of the pixels having differences of less than ±0.2×1015 molecules cm−2 between the TEMPO tropospheric NO2 column density and the global algorithm. For near-real-time retrieval, even a climatological estimate of the stratospheric NO2 surrounding the field of regard would improve this agreement. In general, the additional penalty of a limited field of regard from TEMPO introduces no more error than normally expected in most global stratosphere–troposphere separation algorithms. Overall, we conclude that hourly near-real-time stratosphere–troposphere separation for the retrieval of NO2 tropospheric column densities by the TEMPO geostationary instrument is both feasible and robust, regardless of the diurnally varying limited field of regard.

Oceanography Data Processing Online Using Internet

2017 ◽  
Vol 862 ◽  
pp. 61-66 ◽  
Author(s):  
Suryadhi ◽  
Engki Andri Kisnarti
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Satellite Observations ◽  
The Internet ◽  
Online Data ◽  
Foreign Countries ◽  
Tidal Data ◽  
Global Coverage ◽  
Oceanographic Data ◽  
Coverage Space

The oceanographic data can be obtained by free and online websites of foreign countries. This oceanographic data are obtained from satellite observations result, but this online data is in a coarse resolution with a global coverage space, its usage in certain areas still needs to be combined and validated with the observed data locally or regionally. Thus, this oceanographic data from these local observations some be easily obtained and processed as well as easily accessible by people online, it would require equipments. In this research, the oceanographic data that need to be observed is the speed data, the direction of currents data and the tidal data. The oceanographic data obtained directly from the observed area uses is the sensors that is connected to the microcontroller and sent via a modem. In real time, these data submitted by the microcontroller via the modem that also serves as a gateway SMS directly to the server. From this server, the community can access these data online using the internet.

REAL CONDITION AND PROSPECTS OF DEVELOPMENT OF THE RADAR STATIONS OF THE COUNTER BATTERY FIGHTINGV

2019 ◽  
Vol 1 (12) ◽  
pp. 30-40
Author(s):  
V. Нolovan ◽  
V. Gerasimov ◽  
А. Нolovan ◽  
N. Maslich
Keyword(s):  
Comparative Analysis ◽  
Real Time ◽  
Weather Conditions ◽  
Time Of Day ◽  
Current Position ◽  
Real Condition ◽  
Leading Role ◽  
Artillery Shell

Fighting in the Donbas, which has been going on for more than five years, shows that a skillful counter-battery fight is an important factor in achieving success in wars of this kind. Especially in conditions where for the known reasons the use of combat aviation is minimized. With the development of technical warfare, the task of servicing the counter-battery fight began to rely on radar stations (radar) to reconnaissance the positions of artillery, which in modern terms are called counter-battery radar. The principle of counter-battery radar is based on the detection of a target (artillery shell, mortar mine or rocket) in flight at an earlier stage and making several measurements of the coordinates of the current position of the ammunition. According to these data, the trajectory of the projectile's flight is calculated and, on the basis of its prolongation and extrapolation of measurements, the probable coordinates of the artillery, as well as the places of ammunition falling, are determined. In addition, the technical capabilities of radars of this class allow you to recognize the types and caliber of artillery systems, as well as to adjust the fire of your artillery. The main advantages of these radars are:  mobility (transportability);  inspection of large tracts of terrain over long distances;  the ability to obtain target's data in near real-time;  independence from time of day and weather conditions;  relatively high fighting efficiency. The purpose of the article is to determine the leading role and place of the counter-battery radar among other artillery instrumental reconnaissance tools, to compare the combat capabilities of modern counter-battery radars, armed with Ukrainian troops and some leading countries (USA, China, Russia), and are being developed and tested in Ukraine. The method of achieving this goal is a comparative analysis of the features of construction and combat capabilities of modern models of counter-battery radar in Ukraine and in other countries. As a result of the conducted analysis, the directions of further improvement of the radar armament, increasing the capabilities of existing and promising counter-battery radar samples were determined.

scholarly journals Accuracy of Doppler Determinations of Station Positions

1972 ◽  
Vol 48 ◽  
pp. 101-103
Author(s):  
R. J. Anderle
Keyword(s):  
Elevation Angle ◽  
High Elevation ◽  
Time Of Day ◽  
Satellite Observations ◽  
Standard Errors ◽  
Lower Elevation ◽  
Self Consistent

Locations of Doppler satellite observing stations have been revised to obtain a set which is more self-consistent and more consistent with the CIO pole. Residuals of satellite observations for 1970 have been analyzed using the new coordinates to determine mean and standard errors for five days of observations of latitude versus station, time of day, and elevation angle. The accuracy of the determination of latitude is about 4 meters at moderate and high elevation angles. But since more satellite passes occur at lower elevation angles, the accuracy of determination of a component of position based on five days of observation of one satellite is only about 2 meters.

scholarly journals A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Real Time ◽  
Forest Fires ◽  
Wildland Fire ◽  
Fire Spread ◽  
Low Earth Orbit ◽  
Practical Implementation ◽  
Spatiotemporal Resolution ◽  
Active Fire

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

Using deep learning to nowcast the spatial coverage of convection from Himawari-8 satellite data

2021 ◽  
Author(s):  
Ryan Lagerquist ◽  
Jebb Q. Stewart ◽  
Imme Ebert-Uphoff ◽  
Christina Kumler
Keyword(s):  
Deep Learning ◽  
Satellite Data ◽  
Geographic Location ◽  
Ground Truth ◽  
Atmospheric Science ◽  
Skill Score ◽  
Time Of Day ◽  
Lead Times ◽  
Weather Radars ◽  
Spatial Coverage

AbstractPredicting the timing and location of thunderstorms (“convection”) allows for preventive actions that can save both lives and property. We have applied U-nets, a deep-learning-based type of neural network, to forecast convection on a grid at lead times up to 120 minutes. The goal is to make skillful forecasts with only present and past satellite data as predictors. Specifically, predictors are multispectral brightness-temperature images from the Himawari-8 satellite, while targets (ground truth) are provided by weather radars in Taiwan. U-nets are becoming popular in atmospheric science due to their advantages for gridded prediction. Furthermore, we use three novel approaches to advance U-nets in atmospheric science. First, we compare three architectures – vanilla, temporal, and U-net++ – and find that vanilla U-nets are best for this task. Second, we train U-nets with the fractions skill score, which is spatially aware, as the loss function. Third, because we do not have adequate ground truth over the full Himawari-8 domain, we train the U-nets with small radar-centered patches, then apply trained U-nets to the full domain. Also, we find that the best predictions are given by U-nets trained with satellite data from multiple lag times, not only the present. We evaluate U-nets in detail – by time of day, month, and geographic location – and compare to persistence models. The U-nets outperform persistence at lead times ≥ 60 minutes, and at all lead times the U-nets provide a more realistic climatology than persistence. Our code is available publicly.

scholarly journals A statistical approach for identifying the ionospheric footprint of magnetospheric boundaries from SuperDARN observations

2008 ◽  
Vol 26 (2) ◽  
pp. 305-314 ◽  
Author(s):  
G. Lointier ◽  
T. Dudok de Wit ◽  
C. Hanuise ◽  
X. Vallières ◽  
J.-P. Villain
Keyword(s):  
Real Time ◽  
Statistical Approach ◽  
Spectral Width ◽  
Threshold Level ◽  
Strongly Correlated ◽  
Spatial Coverage ◽  
First Results ◽  
High Latitude Ionosphere ◽  
Value Decomposition ◽  
Magnetospheric Boundaries

Abstract. Identifying and tracking the projection of magnetospheric regions on the high-latitude ionosphere is of primary importance for studying the Solar Wind-Magnetosphere-Ionosphere system and for space weather applications. By its unique spatial coverage and temporal resolution, the Super Dual Auroral Radar Network (SuperDARN) provides key parameters, such as the Doppler spectral width, which allows the monitoring of the ionospheric footprint of some magnetospheric boundaries in near real-time. In this study, we present the first results of a statistical approach for monitoring these magnetospheric boundaries. The singular value decomposition is used as a data reduction tool to describe the backscattered echoes with a small set of parameters. One of these is strongly correlated with the Doppler spectral width, and can thus be used as a proxy for it. Based on this, we propose a Bayesian classifier for identifying the spectral width boundary, which is classically associated with the Polar Cap boundary. The results are in good agreement with previous studies. Two advantages of the method are: the possibility to apply it in near real-time, and its capacity to select the appropriate threshold level for the boundary detection.

Modeling the Effects of Sleep Deprivation on Real-Time Risky Decision-Making

1998 ◽  
Vol 42 (3) ◽  
pp. 330-334
Author(s):  
Joshua B. Hurwitz
Keyword(s):  
Decision Making ◽  
Risk Perception ◽  
Real Time ◽  
Risk Taking ◽  
Time Of Day ◽  
Risky Decision Making ◽  
Risky Decision ◽  
Distance Information ◽  
Driving Task ◽  
Response Choices

Increased real-time risk-taking under sleep loss could be marked by changes in risk perception or acceptance. Risk-perception processes are those involved in estimating real-time parameters such as the speeds and distances of hazardous objects. Risk-acceptance processes relate to response choices given risk estimates. Risk-taking under fatigue was studied using a simulated intersection-crossing driving task in which subjects decided when it was safe to cross an intersection as an oncoming car approached from the cross street. The subjects performed this task at 3-hour intervals over a 36-hour period without sleep. Results were modeled using a model of real-time risky decision making that has perceptual components that process speed, time and distance information, and a decisional component for accepting risk. Results showed that varying a parameter for the decisional component across sessions best accounted for variations in performance relating to time of day.

scholarly journals Orbit Propagation and Determination of Low Earth Orbit Satellites

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Ho-Nien Shou
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Nonlinear Problems ◽  
Low Earth Orbit ◽  
Earth Orbit ◽  
Positioning Accuracy ◽  
Orbit Propagation

This paper represents orbit propagation and determination of low Earth orbit (LEO) satellites. Satellite global positioning system (GPS) configured receiver provides position and velocity measures by navigating filter to get the coordinates of the orbit propagation (OP). The main contradictions in real-time orbit which is determined by the problem are orbit positioning accuracy and the amount of calculating two indicators. This paper is dedicated to solving the problem of tradeoffs. To plan to use a nonlinear filtering method for immediate orbit tasks requires more precise satellite orbit state parameters in a short time. Although the traditional extended Kalman filter (EKF) method is widely used, its linear approximation of the drawbacks in dealing with nonlinear problems was especially evident, without compromising Kalman filter (unscented Kalman Filter, UKF). As a new nonlinear estimation method, it is measured at the estimated measurements on more and more applications. This paper will be the first study on UKF microsatellites in LEO orbit in real time, trying to explore the real-time precision orbit determination techniques. Through the preliminary simulation results, they show that, based on orbit mission requirements and conditions using UKF, they can satisfy the positioning accuracy and compute two indicators.

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