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Tong Zhu, Sid Ahmed Boukabara, and Kevin Garrett

Abstract

The impacts of both satellite data assimilation (DA) and lateral boundary conditions (LBCs) on the Hurricane Weather Research and Forecasting (HWRF) Model forecasts of Hurricane Sandy 2012 were assessed. To investigate the impact of satellite DA, experiments were run with and without satellite data assimilated, as well as with all satellite data but excluding Geostationary Operational Environmental Satellite (GOES) Sounder data. To gauge the LBC impact, these experiments were also run with a variety of outer domain (D-1) sizes. The inclusion of satellite DA resulted in analysis fields that better characterized the tropical storm structures including the warm core anomaly and wavenumber-1 asymmetry near the eyewall, and also served to reduce the forecast track errors for Hurricane Sandy. The specific impact of assimilating the GOES Sounder data showed positive impacts on forecasts of the storm minimum sea level pressure. Increasing the D-1 size resulted in increases in the day 4/5 forecast track errors when verified against the best track and the Global Forecast System (GFS) forecast, which dominated any benefits from assimilating an increased volume of satellite observations due to the larger domain. It was found that the LBCs with realistic environmental flow information could provide better constraints on smaller domain forecasts. This study demonstrated that satellite DA can improve the analysis of a hurricane asymmetry, especially in a shear environment, and then lead to a better track forecast, and also emphasized the importance of the LBCs and the challenges associated with the evaluation of satellite data impacts on regional model prediction.

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

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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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Erin E. Jones, Kevin Garrett, and Sid-Ahmed Boukabara

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The National Oceanic and Atmospheric Administration (NOAA) Global Data Assimilation System/Global Forecast System (GDAS/GFS) was extended to assimilate brightness temperatures from the Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie (SAPHIR) passive microwave water vapor sounder on board the Megha-Tropiques satellite. Quality control procedures were developed to assess the SAPHIR data quality for assimilating clear-sky observations over ocean surfaces, and to characterize observation biases and errors. A 6-week impact experiment was performed using the GDAS/GFS data assimilation system. The addition of SAPHIR observations on top of the current global observing system improved analysis and forecast humidity root-mean-square error (RMSE) results at the upper levels of the troposphere by about 6%, mostly at 100 hPa, when verified against European Centre for Medium-Range Weather Forecasts (ECMWF) analysis, though some degradation to the forecast humidity was seen at 150–200 hPa. The forecast impacts were predominant at earlier lead times between 24 and 96 h. Verification using global radiosonde observations also showed a reduction of the humidity RMSE from 4% to 6% between 500 hPa and the surface when assimilating SAPHIR, while temperature and wind speed RMSEs were reduced by up to 9% and 7% near the tropical tropopause, respectively. Other conventional forecast skill parameters including the 500-hPa geopotential height anomaly correlation showed neutral impact when assimilating SAPHIR.

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Sid-Ahmed Boukabara, Kevin Garrett, and V. Krishna Kumar

Abstract

The current constellation of environmental satellites is at risk of degrading due to several factors. This includes the following: 1) loss of secondary polar-orbiting satellites due to reaching their nominal lifetimes, 2) decrease in the density of extratropical radio-occultation (RO) observations due to a likely delayed launch of the Constellation Observing System for Meteorology, Ionosphere and Climate-2 (COSMIC-2) high inclination orbit constellation, and 3) the risk of losing afternoon polar-orbiting satellite coverage due to potential launch delays in the Joint Polar Satellite System (JPSS) programs. In this study, the impacts from these scenarios on the National Oceanic and Atmospheric Administration (NOAA) Global Forecast System skill are quantified. Performances for several metrics are assessed, but to encapsulate the results the authors introduce an overall forecast score combining metrics for all parameters, atmospheric levels, and forecast lead times. The first result suggests that removing secondary satellites results in significant degradation of the forecast. This is unexpected since it is generally assumed that secondary sensors contribute to system’s robustness but not necessarily to forecast performance. Second, losing the afternoon orbit on top of losing secondary satellites further degrades forecast performances by a significant margin. Finally, losing extratropical RO observations on top of losing secondary satellites also negatively impacts the forecast performances, but to a lesser degree. These results provide a benchmark that will allow for the assessment of the added value of projects being implemented at NOAA in support of mitigation strategies designed to alleviate the negative impacts associated with these data gaps, and additionally help NOAA to define requirements of the future global observing system architecture.

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Ting-Chi Wu, Milija Zupanski, Lewis D. Grasso, Christian D. Kummerow, and Sid-Ahmed Boukabara

Abstract

Satellite all-sky radiances from the Advanced Technology Microwave Sounder (ATMS) are assimilated into the Hurricane Weather Research and Forecasting (HWRF) Model using the hybrid Gridpoint Statistical Interpolation analysis system (GSI). To extend the all-sky capability recently developed for global applications to HWRF, some modifications in HWRF and GSI are facilitated. In particular, total condensate is added as a control variable, and six distinct hydrometeor habits are added as state variables in hybrid GSI within HWRF. That is, clear-sky together with cloudy and precipitation-affected satellite pixels are assimilated using the Community Radiative Transfer Model (CRTM) as a forward operator that includes hydrometeor information and Jacobians with respect to hydrometeor variables. A single case study with the 2014 Atlantic storm Hurricane Cristobal is used to demonstrate the methodology of extending the global all-sky capability to HWRF due to ATMS data availability. Two data assimilation experiments are carried out. One experiment uses the operational configuration and assimilates ATMS radiances under the clear-sky condition, and the other experiment uses the modified HWRF system and assimilates ATMS radiances under the all-sky condition with the inclusion of total condensate update and cycling. Observed and synthetic Geostationary Operational Environmental Satellite (GOES)-13 data along with Global Precipitation Measurement Mission (GPM) Microwave Imager (GMI) data from the two experiments are used to show that the experiment with all-sky ATMS radiances assimilation has cloud signatures that are supported by observations. In contrast, there is lack of clouds in the initial state that led to a noticeable lag of cloud development in the experiment that assimilates clear-sky radiances.

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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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Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Jebb Q. Stewart, Eric Maddy, Narges Shahroudi, and Ross N. Hoffman
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Ross N. Hoffman, V. Krishna Kumar, Sid-Ahmed Boukabara, Kayo Ide, Fanglin Yang, and Robert Atlas

Abstract

The summary assessment metric (SAM) method is applied to an array of primary assessment metrics (PAMs) for the deterministic forecasts of three leading numerical weather prediction (NWP) centers for the years 2015–17. The PAMs include anomaly correlation, RMSE, and absolute mean error (i.e., the absolute value of bias) for different forecast times, vertical levels, geographic domains, and variables. SAMs indicate that in terms of forecast skill ECMWF is better than NCEP, which is better than but approximately the same as UKMO. The use of SAMs allows a number of interesting features of the evolution of forecast skill to be observed. All three centers improve over the 3-yr period. NCEP short-term forecast skill substantially increases during the period. Quantitatively, the effect of the 11 May 2016 NCEP upgrade to the four-dimensional ensemble variational data assimilation (4DEnVar) system is a 7.37% increase in the probability of improved skill relative to a randomly chosen forecast metric from 2015 to 2017. This is the largest SAM impact during the study period. However, the observed impacts are within the context of slowly improving forecast skill for operational global NWP as compared to earlier years. Clearly, the systems lagging ECMWF can improve, and there is evidence from SAMs in addition to the 4DEnVar example that improvements in forecast and data assimilation systems are still leading to forecast skill improvements.

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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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Ross N. Hoffman, Sid-Ahmed Boukabara, V. Krishna Kumar, Kevin Garrett, Sean P. F. Casey, and Robert Atlas

Abstract

The empirical cumulative density function (ECDF) approach can be used to combine multiple, diverse assessment metrics into summary assessment metrics (SAMs) to analyze the results of impact experiments and preoperational implementation testing with numerical weather prediction (NWP) models. The main advantages of the ECDF approach are that it is amenable to statistical significance testing and produces results that are easy to interpret because the SAMs for various subsets tend to vary smoothly and in a consistent manner. In addition, the ECDF approach can be applied in various contexts thanks to the flexibility allowed in the definition of the reference sample.

The interpretations of the examples presented here of the impact of potential future data gaps are consistent with previously reported conclusions. An interesting finding is that the impact of observations decreases with increasing forecast time. This is interpreted as being caused by the masking effect of NWP model errors increasing to become the dominant source of forecast error.

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