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Ling Liu, Kevin Garrett, Eric S. Maddy, and Sid-Ahmed Boukabara

Abstract

The National Aeronautics and Space Administration (NASA) RapidScat scatterometer on board the International Space Station (ISS) provides observations of surface winds that can be assimilated into numerical weather prediction (NWP) forecast models. In this study, the authors assess the data quality of the RapidScat Level 2B surface wind vector retrievals and the impact of those observations on the National Oceanic and Atmospheric Administration (NOAA) Global Forecast System (GFS). The RapidScat is found to provide quality measurements of surface wind speed and direction in nonprecipitating conditions and to provide observations that add both information and robustness to the global satellite observing system used in NWP models. The authors find that with an assumed uncertainty in wind speed of around 2 m s−1, the RapidScat has neutral impact on the short-range forecast of surface wind vectors in the tropics but improves both the analysis and background field of surface wind vectors. However, the deployment of RapidScat on the ISS presents some challenges for use of these wind vector observations in operational NWP, including frequent maneuvers of the spacecraft that could alter instrument performance.

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Zaizhong Ma, Eric S. Maddy, Banglin Zhang, Tong Zhu, and Sid Ahmed Boukabara

Abstract

As the first of the next-generation geostationary meteorological satellites, Himawari-8 was successfully launched in October 2014 by the Japan Meteorological Agency (JMA) and placed over the western Pacific Ocean domain at 140.7°E. It carries the Advanced Himawari Imager (AHI), which provides full-disk images of Earth at 16 bands in the visible and infrared domains every 10 min. Efforts are currently ongoing at the National Oceanic and Atmospheric Administration (NOAA)/National Environmental Satellite, Data, and Information Service (NESDIS)/Center for Satellite Applications and Research (STAR) to assimilate Himawari-8 AHI radiance measurements into the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation analysis system (GSI). All software development within the GSI to allow for assimilation of Himawari-8 AHI radiance has been completed.

This study reports on the assessment of AHI preassimilation data quality by comparing observed clear-sky ocean-only radiances to those simulated using collocated ECMWF analysis, as well as describing procedures implemented for quality control. The impact of the AHI data assimilation on the resulting analyses and forecasts is then assessed using the NCEP Global Forecast System (GFS). A preliminary assessment of the assimilation of AHI data from infrared water vapor channels and atmospheric motion vectors (AMVs) on top of the current global observing system shows neutral to marginal positive impact on analysis and forecast skill relative to an assimilation without AHI data. The main positive impact occurs for short- to medium-range forecasts of global upper-tropospheric water vapor. The results demonstrate the feasibility of direct assimilation of AHI radiances and highlight how humidity information can be extracted within the assimilation system.

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Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Jebb Q. Stewart, Eric S. Maddy, Narges Shahroudi, and Ross N. Hoffman

Abstract

Artificial intelligence (AI) techniques have had significant recent successes in multiple fields. These fields and the fields of satellite remote sensing and NWP share the same fundamental underlying needs, including signal and image processing, quality control mechanisms, pattern recognition, data fusion, forward and inverse problems, and prediction. Thus, modern AI in general and machine learning (ML) in particular can be positively disruptive and transformational change agents in the fields of satellite remote sensing and NWP by augmenting, and in some cases replacing, elements of the traditional remote sensing, assimilation, and modeling tools. And change is needed to meet the increasing challenges of Big Data, advanced models and applications, and user demands. Future developments, for example, SmallSats and the Internet of Things, will continue the explosion of new environmental data. ML models are highly efficient and in some cases more accurate because of their flexibility to accommodate nonlinearity and/or non-Gaussianity. With that efficiency, ML can help to address the demands put on environmental products for higher accuracy, for higher resolution—spatial, temporal, and vertical, for enhanced conventional medium-range forecasts, for outlooks and predictions on subseasonal to seasonal time scales, and for improvements in the process of issuing advisories and warnings. Using examples from satellite remote sensing and NWP, it is illustrated how ML can accelerate the pace of improvement in environmental data exploitation and weather prediction—first, by complementing existing systems, and second, where appropriate, as an alternative to some components of the NWP processing chain from observations to forecasts.

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Eric S. Maddy, Thomas S. King, Haibing Sun, Walter W. Wolf, Christopher D. Barnet, Andrew Heidinger, Zhaohui Cheng, Mitchell D. Goldberg, Antonia Gambacorta, Chen Zhang, and Kexin Zhang

Abstract

High spatial resolution measurements from the Advanced Very High Resolution Radiometer (AVHRR) on the Meteorological Operation (MetOp)-A satellite that are collocated to the footprints from the Infrared Atmospheric Sounding Interferometer (IASI) on the satellite are exploited to improve and quality control cloud-cleared radiances obtained from the IASI. For a partial set of mostly ocean MetOp-A orbits collected on 3 October 2010 for latitudes between 70°S and 75°N, these cloud-cleared radiances and clear-sky subpixel AVHRR measurements within the IASI footprint agree to better than 0.25-K root-mean-squared difference for AVHRR window channels with almost zero bias. For the same dataset, surface skin temperatures retrieved using the combined AVHRR, IASI, and Advanced Microwave Sounding Unit (AMSU) cloud-clearing algorithm match well with ECMWF model surface skin temperatures over ocean, yielding total uncertainties ≤1.2 K for scenes with up to 97% cloudiness.

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Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Stephen G. Penny, Jebb Q. Stewart, Amy McGovern, David Hall, John E. Ten Hoeve, Jason Hickey, Hung-Lung Allen Huang, John K. Williams, Kayo Ide, Philippe Tissot, Sue Ellen Haupt, Kenneth S. Casey, Nikunj Oza, Alan J. Geer, Eric S. Maddy, and Ross N. Hoffman

Capsule Summary

Current research applying artificial intelligence to the Earth and environmental sciences is progressing quickly, with emerging developments in terms of efficiency, accuracy, and discovery.

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