National Oceanic And Atmospheric Administration Research Articles

Sequential Markov chain Monte Carlo for Lagrangian data assimilation with applications to unknown data locations

AbstractWe consider a class of high‐dimensional spatial filtering problems, where the spatial locations of observations are unknown and driven by the partially observed hidden signal. This problem is exceptionally challenging, as not only is it high‐dimensional, but the model for the signal yields longer‐range time dependences through the observation locations. Motivated by this model, we revisit a lesser‐known and provably convergent computational methodology from Berzuini et al. (1997, Journal of the American Statistical Association, 92, 1403–1412); Centanniand Minozzo (2006, Journal of the American Statistical Association, 101, 1582–1597); Martin et al. (2013, Annals of the Institute of Statistical Mathematics, 65, 413–437) that uses sequential Markov Chain Monte Carlo (MCMC) chains. We extend this methodology for data filtering problems with unknown observation locations. We benchmark our algorithms on linear Gaussian state‐space models against competing ensemble methods and demonstrate a significant improvement in both execution speed and accuracy. Finally, we implement a realistic case study on a high‐dimensional rotating shallow‐water model (of about – dimensions) with real and synthetic data. The data are provided by the National Oceanic and Atmospheric Administration (NOAA) and contain observations from ocean drifters in a domain of the Atlantic Ocean restricted to the longitude and latitude intervals , , respectively.

Climate Variability of Air Temperature and Its Warming Trends in the Arabian Gulf

AbstractOver a 41-year period (1979–2019), the climate of the Arabian Gulf (Gulf) exhibited notable variability. This variation, influenced by mesoscale and globalscale meteorological and climatic determinants, showed temperature fluctuations, predominantly in the Northern Gulf (N Gulf). The N Gulf recorded an average temperature of 25.9 °C, a noticeable 3 °C higher than the broader Gulf average. This period marked extreme temperature records, with a peak at 53.8 °C in July 2016, paralleling the highest known temperatures in Asia. Data reveals a warming trend (0.1–0.6 °C per decade) in the region, with the N Gulf’s rise surpassing global trends. The years 2014–2019 were notably warmer, impacting overall trends, and 1997 was the transitional year shifting from cooler to warmer climates. The N Gulf’s seasonal dynamics are also transforming, with extended summers and shortened winters. In this study, we employed empirical orthogonal function (EOF) decomposition analysis to examine the interannual to multidecadal fluctuations in air temperature within the Gulf from 1979 to 2019, using hourly air temperature data from the Watch Forcing Dataset (WFDE5) generated from the fifth generation (ERA5) European Centre for Medium-Range Weather Fore-casts (ECMWF). Through this method, we discerned three principal EOF modes, which together accounted for 82% of the air temperature variability in the Gulf, highlighting external influences such as North Atlantic Oscillation (NAO), El Nino-Southern Oscillation (ENSO), and Atlantic Multidecadal Oscillation (AMO). The data for these external influences were sourced from the National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Laboratory. These findings stress the pressing need for detailed climate studies and mitigation strategies for a rapidly warming Gulf region, especially the N Gulf.

Climate risk and financial stability: evidence from syndicated lending

We study the impact of unexpected climate shocks on banks' individual and systemic risks. Employing climate risk measures developed using the Billion-Dollar Weather and Climate Disasters data from the National Oceanic and Atmospheric Administration (NOAA) and Dealscan syndicated lending data, we find that climate risk exposure acquired through cross-state lending increases banks' individual and systemic risks. We also find that bank profitability helps offset some of the adverse effects of climate risk. Banks reduce lending and increase loan loss reserves after the experience of an unexpected climate shock. The loan-level analysis reveals that the effect of climate risk exposure on bank risks is more pronounced for loans granted for operating and capital expenditures. We contribute to a growing literature on the impact of climate risk on financial stability and the development of robust measures of climate risk for banks.

Assessing nine devices to reduce bycatch in the pink shrimp Penaeus paulensis fisheries: Lessons from field experiments with fyke nets

The adoption of bycatch reduction devices (BRD) has been widely used to reduce bycatch in several fisheries worldwide, especially in trawl nets targeting shrimps. However, the efficiency of these devices has rarely been assessed in passive nets, such as fyke nets using light attraction. This study tested the efficiency of nine BRDs in a passive fyke net fishery performed in southern Brazil, targeting the pink shrimp Penaeus paulensis. The abundance of fishes, swimming crabs and shrimp was compared using the National Oceanic and Atmospheric Administration (NOAA) testing protocol across 1) three different light attraction colours; 2) two fisheye models; 3) two square-mesh models, and; 4) two grid models. A significant reduction in bycatch was recorded when using fisheye and square mesh, however it was also noticed an important reduction in target species. A reduction in total bycatch biomass was also recorded for both grid models (85 % for a grid angle of 50° and 61 % for a grid angle of 32°), with no significant reduction in target species when using grid angle of 32°. The extensive testing performed supports the ecological benefits of BRDs adoption and tools for management in small scale fisheries.

Homogenized gridded dataset for drought and hydrometeorological modeling for the continental United States

We present a novel data set for drought in the continental US (CONUS) built to enable computationally efficient spatio-temporal statistical and probabilistic models of drought. We converted drought data obtained from the widely-used US Drought Monitor (USDM) from its native geo-referenced polygon format to a 0.5 degree regular grid. We merged known environmental drivers of drought, including those obtained from the North American Land Data Assimilation System (NLDAS-2), US Geological Survey (USGS) streamflow data, and National Oceanic and Atmospheric Administration (NOAA) teleconnections data. The resulting data set permits statistical and probabilistic modeling of drought with explicit spatial and/or temporal dependence. Such models could be used to forecast drought at short-range, seasonal to sub-seasonal, and inter-annual timescales with uncertainty, extending the reach and value of the current US Drought Outlook from the National Weather Service Climate Prediction Center. This novel data product provides the first common gridded dataset that includes critical variables used to inform hydrological and meteorological drought.

Sublimation of Snow

Abstract Snow is a vital part of water resources, and sublimation may remove 10% to 90% of snowfall from the system. To improve our understanding of the physics that govern sublimation rates, as well as how those rates might change with the climate, we deployed an array of four towers with over 100 instruments from NCAR’s Integrated Surface Flux System from November 2022 to June 2023 in the East River Watershed, Colorado, in conjunction with the U.S. Department of Energy’s Surface Atmosphere Integrated Field Laboratory (SAIL) and the National Oceanic and Atmospheric Administration (NOAA)’s Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaigns. Mass balance observations, snow pits, particle flux sensors, and terrestrial lidar scans of the evolving snowfield demonstrated how blowing snow influences sublimation rates, which we quantified with latent heat fluxes measured by eddy covariance systems at heights 1 to 20 m above the snow surface. Detailed temperature profiles at finer resolutions highlighted the role of the stable boundary layer. Four-stream radiometers indicated the important role of changing albedo in the energy balance and its relationship to water vapor losses. Collectively, these observations span scales from seconds to seasons, from boundary layer turbulence to valley-circulation to mesoscale meteorology. We describe the field campaign, highlights in the observations, and outreach and education products we are creating to facilitate cross-disciplinary dialogue and convey relevant findings to those seeking to better understand Colorado River snow and streamflow.

Atmospheric dispersion of radioactive materials for radiological risk assessment in case of hypothetical sodium cooled fast reactor accident

The latest advancements in nuclear power, namely the Generation IV (Gen IV) reactor design, have been successfully chosen in numerous countries. These cutting-edge reactor designs boast superior safety and increased fuel efficiency compared to their predecessors. Among the Gen IV designs is the sodium-cooled fast breeder reactor, and to substantiate the safety assertions, HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) dispersion modeling evolved by the National Oceanic and Atmospheric Administration (NOAA) will be employed to scrutinize the potential consequences of an accident. In this context, the analysis focuses on the 1250 MWt/500 MWe sodium-cooled fast breeder reactor (SFR) and its partial fuel melt accident, considering the crucial radioactive reduction factor. Despite existing studies on the consequences of SFR accidents, these studies need to pay more attention to the significance of the radiation reduction factor. This factor is paramount in accurately calculating dose outcomes, as not all radioactive materials can disperse freely within structures, thereby reducing inhalation and cloud shine doses. Furthermore, this investigation underscores the impact of meteorological data and dispersion model input parameters, such as release heights of radioactive materials and measured air concentration heights, on the results of atmospheric dispersion models. Hence, uncertainties in dispersion models arise due to the variability in these factors. The HYSPLIT dispersion model estimates a cumulative dose of 1.65 rem (0.0165 Sv) for a partial fuel melt scenario. The research reveals a maximum dose of approximately 1.8 rem (0.018 Sv) within a 24-hour period, indicating compliance with NRC limitations for Indian fast reactor accident consequences.

Evaluation of Retrospective National Water Model Soil Moisture and Streamflow for Drought‐Monitoring Applications

AbstractThe National Oceanic and Atmospheric Administration (NOAA)’s National Water Model (NWM) provides analyses and predictions of hydrologic variables relevant to drought monitoring and forecasts at fine time and space scales (hourly, 0.25–1 km). We present results exploring the potential for NWM soil moisture and streamflow analyses to inform operational drought monitoring. Both agricultural and hydrologic drought monitoring rely either explicitly or implicitly on an accurate representation of anomalous soil moisture values. Much of our analysis focuses on comparisons of soil moisture anomalies in the NWM to those from in‐situ observations. To establish benchmarks for NWM soil moisture skill, we also include other gridded data sets currently used to inform the US Drought Monitor, specifically those from the North American Land Data Assimilation System phase 2 (NLDAS‐2) land surface models. We then compare NWM streamflow low flows with ∼500 stream gauges from the United States Geological Survey (USGS) Hydro‐Climatic Data Network of undisturbed basins. The NWM soil moisture simulation’s skill parallels that from NLDAS‐2. The accuracy of drought condition identification from NWM streamflow exceeds that based on soil moisture as determined by Critical Success Index scores for extreme dry percentiles. Different meteorological forcings are used in the operational NWM cycles than those used in this retrospective analysis. This forcing disconnect, together with concerns about current‐generation land surface model soil moisture‐transport schemes, inhibit its current operational use for drought monitoring.

Thermal Anomalies Observed during the Crete Earthquake on 27 September 2021

This study examines the response of the thermal channel within the Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) mechanism during the notable earthquake in Crete, Greece, on 27 September 2021. We analyze spatio-temporal profiles of Surface Latent Heat Flux (SLHF), Outgoing Longwave Radiation (OLR), and Atmospheric Chemical Potential (ACP) using reanalysis data from the National Oceanic and Atmospheric Administration (NOAA) satellite. Anomalies in these parameters are computed by removing the background profile for a non-seismic condition. Our findings reveal a substantial anomalous increase in these parameters near the earthquake’s epicenter 3 to 7 days before the main shock. The implications of these observations contribute to a deeper understanding of the LAIC mechanism’s thermal channel in seismic events.

An Evaluation of NOAA Modeled and In Situ Soil Moisture Values and Variability across the Continental United States

Abstract Estimates of soil moisture from two National Oceanic and Atmospheric Administration (NOAA) models are compared to in situ observations. The estimates are from a high-resolution atmospheric model with a land surface model [High-Resolution Rapid Refresh (HRRR) model] and a hydrologic model from the NOAA Climate Prediction Center (CPC). Both models produce wetter soils in dry regions and drier soils in wet regions, as compared to the in situ observations. These soil moisture differences occur at most soil depths but are larger at the deeper depths below the surface (100 cm). Comparisons of soil moisture variability are also assessed as a function of soil moisture regime. Both models have lower standard deviations as compared to the in situ observations for all soil moisture regimes. The HRRR model’s soil moisture is better correlated with in situ observations for drier soils as compared to wetter soils—a trend that was not present in the CPC model comparisons. In terms of seasonality, soil moisture comparisons vary depending on the metric, time of year, and soil moisture regime. Therefore, consideration of both the seasonality and soil moisture regime is needed to accurately determine model biases. These NOAA soil moisture estimates are used for a variety of forecasting and societal applications, and understanding their differences provides important context for their applications and can lead to model improvements. Significance Statement Soil moisture is an essential variable coupling the land surface to the atmosphere. Accurate estimates of soil moisture are important for forecasting near-surface temperature and moisture, predicting where clouds will form, and assessing drought and fire risks. There are multiple estimates of soil moisture available, and in this study, we compare soil moisture estimates from two different National Oceanic and Atmospheric Administration (NOAA) models to in situ observations. These comparisons include both soil moisture amount and variability and are conducted at several soil depths, in different soil moisture regimes, and for different seasons and years. This comprehensive assessment allows for an accurate assessment of biases within these models that would be missed when conducting analyses more broadly.

Assessment of Microplastics in Hanumante River of Kathmandu Valley

Plastic debris is one of the most significant organic pollutants in the aquatic environment. Researchers are currently focusing on the impact of micro and nano-scale plastic waste on aquatic systems. In this study, we investigated the distribution of plastic pellets and fragments present in the freshwater ecosystem. The goal was to assess microplastic (MP) abundance in the Hanumante River, a tributary of the Bagmati River, and analyze their properties. Sample collection involved the bottle sampling method. Filtration, wet peroxide oxidation, density separation, gravimetric analysis, and microscopic examination were performed to study the characteristics of microplastics. The study was conducted by following the guidelines of the National Oceanic and Atmospheric Administration (NOAA) protocol. Gravimetric analysis was applied to calculate the reduced mass of the sample after total organic carbon reduction. Results showed that the maximum amount of reduced sample was obtained from the sample taken from sample taken from Madhyapur Thimi area (~3.593g) and the minimum amount of reduced sample was obtained from the sample taken from the Shiva temple Jagati area (~2.130g).  Microscopic inspection showed that samples taken from different locations were composed of an average of 14–23 microplastics per liter of sample. FT-IR analysis was performed to analyze the characteristics of microplastics and the type of polymers present in the sample which showed the abundance of polymer materials like polyethylene, polypropylene, and polycarbonates. The findings imply that appropriate plastic waste management measures be implemented in the communities to safeguard the ecosystem benefits derived from the river.

Evaluation of synthetic sponge to control wave-induced currents in coastal waters

Based on National Oceanic and Atmospheric Administration (NOAA) Coral Reef Conservation Program, climate change causes coral bleaching. In response to the high roughness of coral colonies and reduced current velocity, coral reefs in near-shore regions are susceptible to accumulation of pollutants or sediments. This threatens coral reef ecosystems and causes several challenges. Controlling the wave velocity, Turbulent Kinetic Energy (TKE), and vortical structures help enhance advection/diffusion of particles from coral reef zones.In the current study, a device inspired by a marine sponge (synthetic sponge), was designed. A suction/pumping function was devised through two concentric perforated cylinders to simulate this mechanism in the synthetic sponge. Several physical and numerical experiments (using OpenFOAM software considering RANS modeling) were carried out to investigate the effects of geometrical characteristics (i.e., cylinder and perforation diameters) of synthetic sponge on wave hydrodynamics. Accordingly, wave attenuation, mean horizontal and orbital velocities, turbulent kinetic energy, and vortex structures were evaluated. In summary, the results indicated that the pumping flow can act as an energy recovery downstream of the sponge, and the horizontal velocity and TKE growing in the water column due to generation of special vortices. Therefore, the current synthetic sponge can improve momentum transfer.

A decade-long cruise time series (2008–2018) of physical and biogeochemical conditions in the southern Salish Sea, North America

Abstract. Coastal and estuarine waters of the northern California Current system and southern Salish Sea host an observational network capable of characterizing biogeochemical dynamics related to ocean acidification, hypoxia, and marine heatwaves. Here, we compiled data sets from a set of cruises conducted in estuarine waters of Puget Sound (southern Salish Sea) and its boundary waters (Strait of Juan de Fuca and Washington coast). This data product provides data from a decade of cruises with consistent formatting, extended data quality control, and multiple units for parameters such as oxygen with different end use needs and conventions. All cruises obtained high-quality temperature, salinity, inorganic carbon, nutrient, and oxygen observations to provide insight into the dynamic distribution of physical and biogeochemical conditions in this large urban estuary complex on the west coast of North America. At all sampling stations, conductivity–temperature–depth (CTD) casts included sensors for measuring temperature, conductivity, pressure, and oxygen concentrations. Laboratory analyses of discrete water samples collected at all stations throughout the water column in Niskin bottles provided measurements of dissolved inorganic carbon (DIC), dissolved oxygen, nutrient (nitrate, nitrite, ammonium, phosphate, and silicate), and total alkalinity (TA) content. This data product includes observations from 35 research cruises, including 715 oceanographic profiles, with >7490 sensor measurements of temperature, salinity, and oxygen; ≥6070 measurements of discrete oxygen and nutrient samples; and ≥4462 measurements of inorganic carbon variables (i.e., DIC and TA). The observations comprising this cruise compilation collectively characterize the spatial and temporal variability in a region with large dynamic ranges of the physical (temperature = 6.0–21.8 ∘C, salinity = 15.6–34.0) and biogeochemical (oxygen = 12–481 µmol kg−1, dissolved inorganic carbon = 1074–2362 µmol kg−1, total alkalinity = 1274–2296 µmol kg−1) parameters central to understanding ocean acidification and hypoxia in this productive estuary system with numerous interacting human impacts on its ecosystems. All observations conform to the climate-quality observing guidelines of the Global Ocean Acidification Observing Network, the US National Oceanic and Atmospheric Administration's Ocean Acidification Program, and ocean carbon community best practices. This ongoing cruise time series supports the estuarine and coastal monitoring and research objectives of the Washington Ocean Acidification Center and US National Oceanic and Atmospheric Administration (NOAA) Ocean and Atmospheric Research programs, and it provides diverse end users with the information needed to frame biological impacts research, validate numerical models, inform state and tribal water quality and fisheries management, and support decision-makers. All 2008–2018 cruise time-series measurements used in this publication are available at https://doi.org/10.25921/zgk5-ep63 (Alin et al., 2022).

Great Lakes wave forecast system on high-resolution unstructured meshes

Abstract. Wind-wave forecasts play a crucial role in the North American Great Lakes region towards ensuring the safety of communities, enhancement of the economy, and protection of property. Modeling wind waves in closed and relatively shallow basins with complex bathymetry like the Great Lakes is a challenge that is successfully tackled in part by using variable-resolution triangular unstructured meshes with no limits in terms of computational scalability and maximum resolution in the coastal areas. In this paper, we discuss recent advances in developing unstructured mesh capabilities as part of the spectral wave model WAVEWATCH III, in the context of National Oceanic and Atmospheric Administration (NOAA) operational requirements such as model robustness, efficiency, and accuracy. We revisit the history of developments leading to the transition from rectilinear to curvilinear grids and finally to an unstructured mesh version of NOAA's operational Great Lakes wave modeling system (GLWUv2.0). The article describes the development of the operational GLWUv2.0, from mesh design and scalability analysis to validation and verification for hindcast of storm cases and re-forecast using 4 months of retrospective simulations. In closed Great Lakes basins untouched by swell from distant sources, the atmospheric model's direct impact on wave behavior stands apart, showing reduced forecast accuracy over time, while maintaining consistent precision in accurately wind-hindcasted stormy conditions.

The prototype NOAA Aerosol Reanalysis version 1.0: description of the modeling system and its evaluation

Abstract. In this study, we describe the first prototype version of global aerosol reanalysis at the National Oceanic and Atmospheric Administration (NOAA), the prototype NOAA Aerosol Reanalysis version 1.0 (pNARA v1.0) that was produced for the year 2016. In pNARA v1.0, the forecast model is an early version of the operational Global Ensemble Forecast System-Aerosols (GEFS-Aerosols) model. The three-dimensional ensemble-variational (3D-EnVar) data assimilation (DA) system configuration is built using elements of the Joint Effort for Data Assimilation Integration (JEDI) framework being developed at the Joint Center for Satellite Data Assimilation (JCSDA). The Neural Network Retrievals (NNR) of aerosol optical depth (AOD) at 550 nm from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments are assimilated to provide reanalysis of aerosol mass mixing ratios. We evaluate pNARA v1.0 against a wide variety of Aerosol Robotic Network (AERONET) observations, against the National Aeronautics and Space Administration's Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA-2; Gelaro et al., 2017; Randles et al., 2017; Buchard et al., 2017) and the European Centre for Medium-Range Weather Forecasts' Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA; Inness et al., 2019), and against measurements of surface concentrations of particulate matter 2.5 (PM2.5) and aerosol species. Overall, the 3D-EnVar DA system significantly improves AOD simulations compared with observations, but the assimilation has limited impact on chemical composition and size distributions of aerosols. We also identify deficiencies in the model's representations of aerosol chemistry and their optical properties elucidated from evaluation of pNARA v1.0 against AERONET observations. A comparison of seasonal profiles of aerosol species from pNARA v1.0 with the other two reanalyses exposes significant differences among datasets. These differences reflect uncertainties in simulating aerosols in general.

Investigating the differences in calculating global mean surface CO2 abundance: the impact of analysis methodologies and site selection

Abstract. The World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) coordinates high-quality atmospheric greenhouse gas observations globally and provides these observations through the WMO World Data Centre for Greenhouse Gases (WDCGG) supported by Japan Meteorological Agency. The WDCGG and the National Oceanic and Atmospheric Administration (NOAA) analyse these measurements using different methodologies and site selection to calculate global annual mean surface CO2 and its growth rate as a headline climate indicator. This study introduces a third hybrid method named GFIT, which serves as an independent validation and open-source alternative to the methods described by NOAA and WDCGG. We apply GFIT to incorporate observations from most WMO GAW stations and 3D modelled CO2 fields from CarbonTracker Europe (CTE). We find that different observational networks (i.e. NOAA, GAW, and CTE networks) and analysis methods result in differences in the calculated global surface CO2 mole fractions equivalent to the current atmospheric growth rate over a 3-month period. However, the CO2 growth rate derived from these networks and the CTE model output shows good agreement. Over the long-term period (40 years), both networks with and without continental sites exhibit the same trend in the growth rate (0.030 ± 0.002 ppm yr−1 each year). However, a clear difference emerges in the short-term (1-month) change in the growth rate. The network that includes continental sites improves the early detection of changes in biogenic emissions.

Analysis of the GEFS-Aerosols annual budget to better understand aerosol predictions simulated in the model

Abstract. In September 2020, a global aerosol forecasting model was implemented as an ensemble member of the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Global Ensemble Forecasting System (GEFS) v12.0.1 (hereafter referred to as “GEFS-Aerosols”). In this study, GEFS-Aerosols simulation results from 1 September 2019 to 30 September 2020 were evaluated using an aerosol budget analysis. These results were compared with results from other global models as well as reanalysis data. From this analysis, the global average lifetimes of black carbon (BC), organic carbon (OC), dust, sea salt, and sulfate are 4.06, 4.29, 4.59, 0.34, and 3.3 d, respectively, with the annual average loads of 0.14, 1.29, 4.52, 6.80, and 0.51 Tg. Compared with the National Aeronautics and Space Administration (NASA) Goddard Earth Observing System–Goddard Chemistry Aerosol and Radiation Transport (GEOS4-GOCART) model, the aerosols in GEFS-Aerosols have a relatively short lifetime because of the faster removal processes in GEFS-Aerosols. Meanwhile, in GEFS-Aerosols, aerosol emissions are the determining factor for the mass and composition of aerosols in the atmosphere. The size (bin) distribution of aerosol emissions is as important as its total emissions, especially in simulations of dust and sea salt. Moreover, most importantly, the strong monthly and interannual variations in natural sources of aerosols in GEFS-Aerosols suggest that improving the accuracy of the prognostic concentrations of aerosols is important for applying aerosol feedback to weather and climate predictions.

Prediction of Hourly Airport Operational Throughput with a Multi-Branch Convolutional Neural Network

Extensive research in predicting annual passenger throughput has been conducted, aiming at providing decision support for airport construction, aircraft procurement, resource management, flight scheduling, etc. However, how airport operational throughput is affected by convective weather in the vicinity of the airport and how to predict short-term airport operational throughput have not been well studied. Convective weather near the airport could make arrivals miss their positions in the arrival stream and reduce airfield efficiency in terms of the utilization of runway capacities. This research leverages the learning-based method (MB-ResNet model) to predict airport hourly throughput and takes Hartsfield–Jackson Atlanta International Airport (ATL) as the case study to demonstrate the developed method. To indicate convective weather, this research uses Rapid Refresh model (RAP) data from the National Oceanic and Atmospheric Administration (NOAA). Although it is a comprehensive and powerful weather data product, RAP has not been widely used in aviation research. This study demonstrated that RAP data, after being carefully decoded, cleaned, and pre-processed, can play a significant role in explaining airfield efficiency variation. Applying machine learning/deep learning in air traffic management is an area worthy of the attention of aviation researchers. Such advanced artificial intelligence techniques can make use of big data from the aviation sector and improve the predictability of the national airspace system and, consequently, operational efficiency. The short-term airport operational throughput predicted in this study can be used by air traffic controllers and airport managers for the allocations of resources at airports to improve airport operations.

Cross-Calibration of HY-1D/COCTS Thermal Emissive Bands in the South China Sea

Haiyang-1D (HY-1D) is the second operational satellite in China’s Haiyang-1 series of satellites, carrying the Chinese Ocean Color and Temperature Scanner (COCTS) to provide ocean color and temperature observations. The radiometric calibration is a prerequisite to guarantee the quality of the satellite observations and the derived products, and the radiometric calibration of the thermal emissive bands of HY-1D/COCTS can effectively improve the accuracy of sea surface temperature (SST) derived from the thermal infrared data. In this paper, a study on the regional cross-calibration of the COCTS thermal emissive bands is conducted for high-accuracy SST observations in the South China Sea. The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the NOAA-20 satellite launched by the National Oceanic and Atmospheric Administration (NOAA) is selected as the calibration reference sensor, and a double-difference cross-calibration method is used for HY-1D/COCTS thermal infrared brightness temperature (BT) evaluation. The results show that the bias of the 11 µm and 12 µm thermal emissive bands of COCTS and VIIRS in the South China Sea are 0.101 K and 0.892 K, respectively, and the differences in BTs between the two sensors show temperature dependence. The cross-calibration coefficients are obtained and used to correct the BT of the COCTS thermal emissive bands. The bias of the BT of the 11 µm and 12 µm bands of COCTS are about 0.01 K after cross-calibration. To further validate the results, COCTS post-calibration data were examined using the NOAA-20 Cross-track Infrared Sounder (CrIS) data as a third-party source. The BT is calculated with the spectral response functions of the COCTS thermal emissive bands using the convolution calculation of the CrIS hyperspectral region observations. The comparison shows a small bias between the post-calibration COCTS thermal emissive band observations and CrIS, which is consistent with the comparison between VIIRS and CrIS. The accuracy of the post-calibration COCTS thermal emissive band BT data in the South China Sea has been significantly improved.

The G-IV Inner Circumnavigation: A Story of Successful Organic Interactions Between Research and Operations at NOAA

Abstract This study describes the research-to-operations process leading to a recent change in tropical cyclone (TC) reconnaissance sampling patterns as well as observing-system experiments that evaluated the impact of that change on numerical weather prediction model forecasts of TCs. A valuable part of this effort was having close, multipronged connections between the TC research and operational TC prediction communities at the National Oceanic and Atmospheric Administration (NOAA). Related to this work, NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML) and National Hurricane Center (NHC) have a long history of close collaboration to improve TC reconnaissance. Similar connections between AOML and NOAA’s Environmental Modeling Center (EMC) also laid a foundation for the observing-system experiments conducted here. More specifically, AOML and NHC collaborated in 2018 to change how NHC uses NOAA’s Gulfstream-IV (G-IV) jet during TC synoptic surveillance missions. That change added a second circumnavigation at approximately 1.5° from TC centers, when possible. Preliminary experiments suggest that the change improved track forecasts, though the intensity results are more mixed. Despite the somewhat small sample size over a 3-yr period, the track improvement does agree with prior work. This effort has led to additional work to more fully examine G-IV sampling strategies.