Oceanography Data Processing Online Using Internet

2017 ◽  
Vol 862 ◽  
pp. 61-66 ◽  
Author(s):  
Suryadhi ◽  
Engki Andri Kisnarti

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.

2018 ◽  
Author(s):  
Jeffrey A. Geddes ◽  
Randall V. Martin ◽  
Eric J. Bucsela ◽  
Chris A. McLinden ◽  
Daniel J. M. Cunningham

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.


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

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.


2017 ◽  
pp. 239-262
Author(s):  
Miyuru Dayarathna ◽  
Paul Fremantle ◽  
Srinath Perera ◽  
Sriskandarajah Suhothayan

2020 ◽  
Vol 156 (1) ◽  
Author(s):  
Santiago E. Alvarez ◽  
Sarah M. Lein

Abstract Using online data for prices and real-time debit card transaction data on changes in expenditures for Switzerland allows us to track inflation on a daily basis. While the daily price index fluctuates around the official price index in normal times, it drops immediately after the lockdown related to the COVID19 pandemic. Official statistics reflect this drop only with a lag, specifically because data collection takes time and is impeded by lockdown conditions. Such daily real-time information can be useful to gauge the relative importance of demand and supply shocks and thus inform policymakers who need to determine appropriate policy measures.


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