Post launch calibration and testing of the Advanced Baseline Imager on the GOES-R satellite

Lebair, William; Rollins, Chris; Kline, John; Todirita, Monica; Kronenwetter, J.

doi:10.1117/12.2228556

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, we believe that using the relative AOD metric as an additional input factor can make improvements in the NN approach. When GOES-R AOD retrievals, with high data latency and multispectral inversion capabilities [17,18], become available, we plan to incorporate these AOD metrics as predictors in the NN.…”

Section: Future Workmentioning

confidence: 99%

Comparing CMAQ Forecasts with a Neural Network Forecast Model for PM2.5 in New York

2017

View full text Add to dashboard Cite

Human health is strongly affected by the concentration of fine particulate matter (PM 2.5 ). The need to forecast unhealthy conditions has driven the development of Chemical Transport Models such as Community Multi-Scale Air Quality (CMAQ). These models attempt to simulate the complex dynamics of chemical transport by combined meteorology, emission inventories (EI's), and gas/particle chemistry and dynamics. Ultimately, the goal is to establish useful forecasts that could provide vulnerable members of the population with warnings. In the simplest utilization, any forecast should focus on next day pollution levels, and should be provided by the end of the business day (5 p.m. local). This paper explores the potential of different approaches in providing these forecasts. First, we assess the potential of CMAQ forecasts at the single grid cell level (12 km), and show that significant variability not encountered in the field measurements occurs. This observation motivates the exploration of other data driven approaches, in particular, a neural network (NN) approach. This approach makes use of meteorology and PM 2.5 observations as model predictors. We find that this approach generally results in a more accurate prediction of future pollution levels at the 12 km spatial resolution scale of CMAQ. Furthermore, we find that the NN is able to adjust to the sharp transitions encountered in pollution transported events, such as smoke plumes from forest fires, more accurately than CMAQ.

show abstract

Section: Future Workmentioning

confidence: 99%

Comparing CMAQ Forecasts with a Neural Network Forecast Model for PM2.5 in New York

2017

View full text Add to dashboard Cite

show abstract

“…This effort was led by the GOES-R Flight Project and L3Harris. 10 During the PLT stage of GOES-17 ABI, it was discovered that its loop heat pipe (LHP), a subsystem of the cooling system that transfers heat from the ABI electronics to the radiator, was not operating at its full capacity. Therefore, during local nighttime, the Sun may heat up the instrument faster than it can be cooled and the temperatures of the FPMs cannot be maintained at their designated levels.…”

Section: Introductionmentioning

confidence: 99%

On-orbit calibration and characterization of GOES-17 ABI IR bands under dynamic thermal condition

Wang

et al. 2020

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

The Advanced Baseline Imager (ABI) is a passive imaging radiometer on-board National Oceanic and Atmospheric Administration's (NOAA) Geostationary Operational Environmental Satellites-R (GOES-R) series. Its bands 7 to 16 are categorized as infrared (IR) bands, sampling within a spectral range of 3.9 to 13.3 μm in mid-wave infrared (MWIR) and long-wave infrared (LWIR) regions. ABI provides variable area imagery and radiometric information of Earth's surface, atmosphere, and cloud cover. All of the IR bands are calibrated on-orbit in reference to an internal blackbody. While the ABI aboard the GOES-16 satellite has been working properly, an anomaly with GOES-17 ABI's cooling system, specifically its loop heat pipe (LHP) subsystem, prevents heat from being efficiently transferred from the ABI electronics to the radiator to be dissipated into space. As a consequence, the heat accumulates inside the instrument, so the temperatures of its key components for IR calibration, including the focal plane modules (FPMs), scan mirrors, and blackbody, cannot be maintained at their designed operational levels. As an example, the temperatures of MWIR and LWIR FPMs, where IR detectors are located, are currently operated at a baseline temperature of ∼20 K warmer than the design and vary by as many as 27 K diurnally. This causes severe degradation to the data quality of ABI IR Level 1b radiance and subsequent Level 2+ products during the hot period of the day. Significant progress has been made to mitigate the effects of the LHP anomaly to optimize the IR performance of GOES-17 ABI. We summarize the efforts made by NOAA's GOES-R Calibration Working Group, working collaboratively with other teams, to evaluate and alleviate the negative impacts of warmer and floating FPM temperatures on ABI IR calibration, and assess the IR performance accordingly. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

Comparison of thermal emissive bands calibration methods of geostationary satellite sensors

Wang

Zhu

Chen

2022

2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)

View full text Add to dashboard Cite

Post launch calibration and testing of the Advanced Baseline Imager on the GOES-R satellite

Cited by 3 publications

References 0 publications

Comparing CMAQ Forecasts with a Neural Network Forecast Model for PM2.5 in New York

Comparing CMAQ Forecasts with a Neural Network Forecast Model for PM2.5 in New York

On-orbit calibration and characterization of GOES-17 ABI IR bands under dynamic thermal condition

Comparison of thermal emissive bands calibration methods of geostationary satellite sensors

Contact Info

Product

Resources

About