A Successful Practical Experience with Dedicated Geostationary Operational Environmental Satellites GOES-10 and -12 Supporting Brazil

Costa, Susanga; Negri, Renato Galante; Ferreira, Nelson Jesus; Schmit, Timothy J.; Arai, Nelson; Flauber, Wagner; Ceballos, Juan Carlos; Vila, Daniel; Rodrigues, Jurandir Ventura; Machado, Luiz A. T.; Pereira, S.; Bottino, Marcus Jorge; Sismanoglu, Raffi Agop; Langden, Pedro

doi:10.1175/bams-d-16-0029.1

Cited by 9 publications

(5 citation statements)

References 42 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For spatial cloud field property analysis, the Geostationary Operational Environmental Satellite (GOES) 10.4 µm brightness temperature data acquired over a 10 • × 10 • box centered on T3 were used to verify the occurrence of cold cloud tops to indicate the presence of precipitating clouds around the studied region. GOES data were received and processed operationally by CPTEC/INPE (Centre for Weather Forecasting and Climate Research/National Institute for Space Research) (Costa et al, 2018).…”

Section: Datamentioning

confidence: 99%

What drives daily precipitation over the central Amazon? Differences observed between wet and dry seasons

et al. 2021

View full text Add to dashboard Cite

Abstract. This study offers an alternative presentation regarding how diurnal precipitation is modulated by convective events that developed over the central Amazon during the preceding nighttime period. We use data collected during the Observations and Modelling of the Green Ocean Amazon (GoAmazon 2014/2015) field campaign that took place from 1 January 2014 to 30 November 2015 in the central Amazon. Local surface-based observations of cloud occurrence, soil temperature, surface fluxes, and planetary boundary layer characteristics are coupled with satellite data to identify the physical mechanisms that control the diurnal rainfall in central Amazon during the wet and dry seasons. This is accomplished through evaluation of the atmospheric properties during the nocturnal periods preceding raining and non-raining events. Comparisons between these non-raining and raining transitions are presented for the wet (January to April) and dry (June to September) seasons. The results suggest that wet-season diurnal precipitation is modulated by nighttime cloud coverage and local influences such as heating induced turbulence, whereas the dry-season rain events are controlled by large-scale circulations.

show abstract

Section: Datamentioning

confidence: 99%

What drives daily precipitation over the central Amazon? Differences observed between wet and dry seasons

et al. 2021

View full text Add to dashboard Cite

show abstract

“…GL data for the entire year of 2016 was generated using GOES-13 satellite VIS imagery (spectral range 0.53-0.71 µm), positioned at 75 • W over the Equator. The specific scan adopted by GOES-13 for South American region is described by Costa et al [32]. The VIS imagery has a temporal sampling interval of 30 min and a spatial resolution of 1 km at nadir, which is resampled to a grid size of 0.04 • .…”

Section: Gl Satellite Productmentioning

confidence: 99%

“…The authors reported daily mean errors generally within ±10 W m −2 with standard deviations lower than 20 W m −2 . The GL1.2 database has contributed to the validation of numerical weather prediction models and data assimilation systems [29,30], to climatological and environmental studies [31][32][33], to hydrological modeling [34], to potential evapotranspiration estimation [35] and, last but not least, to solar resource assessment [36].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Global Solar Irradiance Estimates from GL1.2 Satellite-Based Model over Brazil Using an Extended Radiometric Network

et al. 2020

Self Cite

View full text Add to dashboard Cite

The GL (GLobal radiation) physical model was developed to compute global solar irradiance at ground level from (VIS) visible channel imagery of geostationary satellites. Currently, its version 1.2 (GL1.2) runs at Brazilian Center for Weather Forecast and Climate Studies/National Institute for Space Research (CPTEC/INPE) based on GOES-East VIS imagery. This study presents an extensive validation of GL1.2 global solar irradiance estimates using ground-based measurements from 409 stations belonging to the Brazilian National Institute of Meteorology (INMET) over Brazil for the year 2016. The INMET reasonably dense network allows characterizing the spatial distribution of GL1.2 data uncertainties. It is found that the GL1.2 estimates have a tendency to overestimate the ground data, but the magnitude varies according to region. On a daily basis, the best performances are observed for the Northeast, Southeast, and South regions, with a mean bias error (MBE) between 2.5 and 4.9 W m−2 (1.2% and 2.1%) and a root mean square error (RMSE) between 21.1 and 26.7 W m−2 (10.8% and 11.8%). However, larger differences occur in the North and Midwest regions, with MBE between 12.7 and 23.5 W m−2 (5.9% and 11.7%) and RMSE between 27 and 33.4 W m−2 (12.7% and 16.7%). These errors are most likely due to the simplified assumptions adopted by the GL1.2 algorithm for clear sky reflectance (Rmin) and aerosols as well as the uncertainty of the water vapor data. Further improvements in determining these parameters are needed. Additionally, the results also indicate that the GL1.2 operational product can help to improve the quality control of radiometric data from a large network, such as INMET's. Overall, the GL1.2 data are suitable for use in various regional applications.

show abstract

“…It may be noted that the denominator in equation (13) is never zero and hence the algorithm is free of singularity. Finally, we compute…”

Section: Efficient Onboard Algorithm Onboard Algorithmmentioning

confidence: 99%

“…Also, the extent of compensation restricts that the inclination be less. 13 This limitation is also applicable to methods handled by AOCS. To overcome this limitation, we need additional processing in the ground and do resampling 9 with help of advanced navigation sensors.…”

Section: Introductionmentioning

confidence: 99%

Efficient onboard image motion compensation for orbital inclination and eccentricity of geostationary weather satellites

Ramachandran

Agarwal

Daniel

2020

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

Image registration is important in geostationary weather satellites. Achieving consistent registration of the images with respect to the geographical locations on the Earth is here of interest. The consistency in the registration between the images is affected whenever the orbital inclination and eccentricity are not zero. The imaging payload has a two-axis scanning mirror to capture the Earth image. The above orbital effects together with scan mirror pointing direction are the factors that cause the misregistration. This paper presents an onboard algorithm that provides the scan compensation angles due to the above factors and achieves consistent registration. The compensation varies every second, which is the time taken for each scan. Hence it is preferred to have computations onboard than to have ground based bulk uplinks for the scan compensation. The paper presents an algorithm that is useful, say, when (i) the onboard computing capabilities are limited, (ii) the navigation accuracies are coarse and (iii) the image resampling is not preferred on the ground and the payload data are directly used for weather applications. The paper also discusses the tests that were carried on the onboard software in order to validate its performance in achieving the consistent registration before launch. This is done by using another independent software tool which is also described in detail. Image motion algorithm was invoked for a couple of days in INSAT 3DR. The atmospheric wind vector deduced directly from the satellite images is given at the end.

show abstract

A Successful Practical Experience with Dedicated Geostationary Operational Environmental Satellites GOES-10 and -12 Supporting Brazil

Cited by 9 publications

References 42 publications

What drives daily precipitation over the central Amazon? Differences observed between wet and dry seasons

What drives daily precipitation over the central Amazon? Differences observed between wet and dry seasons

Evaluation of Global Solar Irradiance Estimates from GL1.2 Satellite-Based Model over Brazil Using an Extended Radiometric Network

Efficient onboard image motion compensation for orbital inclination and eccentricity of geostationary weather satellites

Contact Info

Product

Resources

About