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Abstract
This study explores the use of nonuniform fast spherical Fourier transforms on meteorological data that are arbitrarily distributed on the sphere. The applicability of this methodology in the atmospheric sciences is demonstrated by estimating spectral coefficients for nontrivial subsets of reanalysis data on a uniformly spaced latitude–longitude grid, a global cloud resolving model on an icosahedral mesh with 3-km horizontal grid spacing, and for temperature anomalies from arbitrarily distributed weather stations over the United States. A spectral correction technique is developed that can be used in conjunction with the inverse transform to yield data interpolated onto a uniformly spaced grid, with optional triangular truncation, at reduced computational cost compared to other variance conserving interpolation methods, such as kriging or natural spline interpolation. The spectral correction yields information that can be used to deduce gridded observational biases not directly available from other methods.
Abstract
This study explores the use of nonuniform fast spherical Fourier transforms on meteorological data that are arbitrarily distributed on the sphere. The applicability of this methodology in the atmospheric sciences is demonstrated by estimating spectral coefficients for nontrivial subsets of reanalysis data on a uniformly spaced latitude–longitude grid, a global cloud resolving model on an icosahedral mesh with 3-km horizontal grid spacing, and for temperature anomalies from arbitrarily distributed weather stations over the United States. A spectral correction technique is developed that can be used in conjunction with the inverse transform to yield data interpolated onto a uniformly spaced grid, with optional triangular truncation, at reduced computational cost compared to other variance conserving interpolation methods, such as kriging or natural spline interpolation. The spectral correction yields information that can be used to deduce gridded observational biases not directly available from other methods.
Abstract
The initial analysis of the water vapor flux measurements taken onboard a NOAA DC-6 during the Barbados Oceanographic and Meteorological Experiment (BOMEX) is presented. The flux of water vapor seems to be constant in the lower subcloud layer. Day-to-day variations, as well as variations within a day are apparent in the evaporation data. Spatial variations of evaporation also seem to be present. The average value of water vapor flux for the experimental period is ∼0.5 cm day−1. Spectra of the instantaneous flux reveal significant alongwind-crosswind differences. Height variation of the wavelength of maximum spectral density for crosswind runs is confirmed. The instantaneous flux is intermittent in nature. Consideration of the cross spectra and time series signatures allows some speculation upon models which may be responsible for a major portion of the water vapor transport in the lower subcloud layer during BOMEX.
Abstract
The initial analysis of the water vapor flux measurements taken onboard a NOAA DC-6 during the Barbados Oceanographic and Meteorological Experiment (BOMEX) is presented. The flux of water vapor seems to be constant in the lower subcloud layer. Day-to-day variations, as well as variations within a day are apparent in the evaporation data. Spatial variations of evaporation also seem to be present. The average value of water vapor flux for the experimental period is ∼0.5 cm day−1. Spectra of the instantaneous flux reveal significant alongwind-crosswind differences. Height variation of the wavelength of maximum spectral density for crosswind runs is confirmed. The instantaneous flux is intermittent in nature. Consideration of the cross spectra and time series signatures allows some speculation upon models which may be responsible for a major portion of the water vapor transport in the lower subcloud layer during BOMEX.
Abstract
The local, linear, least squares derivative (LLSD) approach to radar analysis is a method of quantifying gradients in radar data by fitting a least squares plane to a neighborhood of range bins and finding its slope. When applied to radial velocity fields, for example, LLSD yields part of the azimuthal (rotational) and radial (divergent) components of horizontal shear, which, under certain geometric assumptions, estimate one-half of the two-dimensional vertical vorticity and horizontal divergence equations, respectively. Recent advances in computational capacity as well as increased usage of LLSD products by the meteorological community have motivated an overhaul of the LLSD methodology’s application to radar data. This paper documents the mathematical foundation of the updated LLSD approach, including a complete derivation of its equation set, discussion of its limitations, and considerations for other types of implementation. In addition, updated azimuthal shear calculations are validated against theoretical vorticity using simulated circulations. Applications to nontraditional radar data and new applications to nonvelocity radar data including reflectivity at horizontal polarization, spectrum width, and polarimetric moments are also explored. These LLSD gradient calculations may be leveraged to identify and interrogate a wide variety of severe weather phenomena, either directly by operational forecasters or indirectly as part of future automated algorithms.
Abstract
The local, linear, least squares derivative (LLSD) approach to radar analysis is a method of quantifying gradients in radar data by fitting a least squares plane to a neighborhood of range bins and finding its slope. When applied to radial velocity fields, for example, LLSD yields part of the azimuthal (rotational) and radial (divergent) components of horizontal shear, which, under certain geometric assumptions, estimate one-half of the two-dimensional vertical vorticity and horizontal divergence equations, respectively. Recent advances in computational capacity as well as increased usage of LLSD products by the meteorological community have motivated an overhaul of the LLSD methodology’s application to radar data. This paper documents the mathematical foundation of the updated LLSD approach, including a complete derivation of its equation set, discussion of its limitations, and considerations for other types of implementation. In addition, updated azimuthal shear calculations are validated against theoretical vorticity using simulated circulations. Applications to nontraditional radar data and new applications to nonvelocity radar data including reflectivity at horizontal polarization, spectrum width, and polarimetric moments are also explored. These LLSD gradient calculations may be leveraged to identify and interrogate a wide variety of severe weather phenomena, either directly by operational forecasters or indirectly as part of future automated algorithms.
Abstract
This study explores gulf-breeze circulations (GBCs) and bay-breeze circulations (BBCs) in Houston–Galveston, investigating their characteristics, large-scale weather influences, and impacts on surface properties, boundary layer updrafts, and convective clouds. The results are derived from a combination of datasets, including satellite observations, ground-based measurements, and reanalysis datasets, using machine learning, changepoint detection method, and Lagrangian cell tracking. We find that anticyclonic synoptic patterns during the summer months (June–September) favor GBC/BBC formation and the associated convective cloud development, representing 74% of cases. The main Tracking Aerosol Convection Interactions Experiment (TRACER) site located close to the Galveston Bay is influenced by both GBC and BBC, with nearly half of the cases showing evident BBC features. The site experiences early frontal passages ranging from 1040 to 1630 local time (LT), with 1300 LT being the most frequent. These fronts are stronger than those observed at the ancillary site which is located further inland from the Galveston Bay, including larger changes in surface temperature, moisture, and wind speed. Furthermore, these fronts trigger boundary layer updrafts, likely promoting isolated convective precipitating cores that are short lived (average convective lifetime of 63 min) and slow moving (average propagation speed of 5 m s−1), primarily within 20–40 km from the coast.
Abstract
This study explores gulf-breeze circulations (GBCs) and bay-breeze circulations (BBCs) in Houston–Galveston, investigating their characteristics, large-scale weather influences, and impacts on surface properties, boundary layer updrafts, and convective clouds. The results are derived from a combination of datasets, including satellite observations, ground-based measurements, and reanalysis datasets, using machine learning, changepoint detection method, and Lagrangian cell tracking. We find that anticyclonic synoptic patterns during the summer months (June–September) favor GBC/BBC formation and the associated convective cloud development, representing 74% of cases. The main Tracking Aerosol Convection Interactions Experiment (TRACER) site located close to the Galveston Bay is influenced by both GBC and BBC, with nearly half of the cases showing evident BBC features. The site experiences early frontal passages ranging from 1040 to 1630 local time (LT), with 1300 LT being the most frequent. These fronts are stronger than those observed at the ancillary site which is located further inland from the Galveston Bay, including larger changes in surface temperature, moisture, and wind speed. Furthermore, these fronts trigger boundary layer updrafts, likely promoting isolated convective precipitating cores that are short lived (average convective lifetime of 63 min) and slow moving (average propagation speed of 5 m s−1), primarily within 20–40 km from the coast.
Conceptualizing the Impact of Dust-Contaminated Infrared Radiances on Data Assimilation for Numerical Weather PredictionAbstract
Numerical weather prediction systems depend on Hyperspectral Infrared Sounder (HIS) data, yet the impacts of dust-contaminated HIS radiances on weather forecasts has not been quantified. To determine the impact of dust aerosol on HIS radiance assimilation, we use a modified radiance assimilation system employing a one-dimensional variational assimilation system (1DVAR) developed under the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Numerical Weather Prediction–Satellite Application Facility (NWP-SAF) project, which uses the Radiative Transfer for TOVS (RTTOV). Dust aerosol impacts on analyzed temperature and moisture fields are quantified using synthetic HIS observations from rawinsonde, Micropulse Lidar Network (MPLNET), and Aerosol Robotic Network (AERONET). Specifically, a unit dust aerosol optical depth (AOD) contamination at 550 nm can introduce larger than 2.4 and 8.6 K peak biases in analyzed temperature and dewpoint, respectively, over our test domain. We hypothesize that aerosol observations, or even possibly forecasts from aerosol predication models, may be used operationally to mitigate dust induced temperature and moisture analysis biases through forward radiative transfer modeling.
Abstract
Numerical weather prediction systems depend on Hyperspectral Infrared Sounder (HIS) data, yet the impacts of dust-contaminated HIS radiances on weather forecasts has not been quantified. To determine the impact of dust aerosol on HIS radiance assimilation, we use a modified radiance assimilation system employing a one-dimensional variational assimilation system (1DVAR) developed under the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Numerical Weather Prediction–Satellite Application Facility (NWP-SAF) project, which uses the Radiative Transfer for TOVS (RTTOV). Dust aerosol impacts on analyzed temperature and moisture fields are quantified using synthetic HIS observations from rawinsonde, Micropulse Lidar Network (MPLNET), and Aerosol Robotic Network (AERONET). Specifically, a unit dust aerosol optical depth (AOD) contamination at 550 nm can introduce larger than 2.4 and 8.6 K peak biases in analyzed temperature and dewpoint, respectively, over our test domain. We hypothesize that aerosol observations, or even possibly forecasts from aerosol predication models, may be used operationally to mitigate dust induced temperature and moisture analysis biases through forward radiative transfer modeling.
Abstract
A series of meteorological measurements with a small uncrewed aircraft system (sUAS) was collected at Oliver Springs Airport in Tennessee. The sUAS provides a unique observing system capable of obtaining vertical profiles of meteorological data within the lowest few hundred meters of the boundary layer. The measurements benefit simulated plume predictions by providing more accurate meteorological data to a dispersion model. The sUAS profiles can be used directly to drive HYSPLIT dispersion simulations. When using sUAS data covering a small domain near a release and meteorological model fields covering a larger domain, simulated pollutants may be artificially increased or decreased near the domain boundary because of inconsistencies in the wind fields between the two meteorological inputs. Numerical experiments using the Weather Research and Forecasting (WRF) Model with observational nudging reveal that incorporating sUAS data improves simulated wind fields and can significantly affect mixing characteristics of the boundary layer, especially during the morning transition period of the planetary boundary layer. We conducted HYSPLIT dispersion simulations for hypothetical releases for three case study periods using WRF meteorological fields with and without assimilating sUAS measurements. The comparison of dispersion results on 15 and 16 December 2021 shows that using sUAS observational nudging is more significant under weak synoptic conditions than under strong influences from regional weather. Very different dispersion results were introduced by the meteorological fields used. The observational nudging produced not just an sUAS-nudged wind flow but also adjusted meteorological fields that further impacted the mixing calculation in HYSPLIT.
Abstract
A series of meteorological measurements with a small uncrewed aircraft system (sUAS) was collected at Oliver Springs Airport in Tennessee. The sUAS provides a unique observing system capable of obtaining vertical profiles of meteorological data within the lowest few hundred meters of the boundary layer. The measurements benefit simulated plume predictions by providing more accurate meteorological data to a dispersion model. The sUAS profiles can be used directly to drive HYSPLIT dispersion simulations. When using sUAS data covering a small domain near a release and meteorological model fields covering a larger domain, simulated pollutants may be artificially increased or decreased near the domain boundary because of inconsistencies in the wind fields between the two meteorological inputs. Numerical experiments using the Weather Research and Forecasting (WRF) Model with observational nudging reveal that incorporating sUAS data improves simulated wind fields and can significantly affect mixing characteristics of the boundary layer, especially during the morning transition period of the planetary boundary layer. We conducted HYSPLIT dispersion simulations for hypothetical releases for three case study periods using WRF meteorological fields with and without assimilating sUAS measurements. The comparison of dispersion results on 15 and 16 December 2021 shows that using sUAS observational nudging is more significant under weak synoptic conditions than under strong influences from regional weather. Very different dispersion results were introduced by the meteorological fields used. The observational nudging produced not just an sUAS-nudged wind flow but also adjusted meteorological fields that further impacted the mixing calculation in HYSPLIT.
Abstract
Providing advance warning for impending severe convective weather events (i.e., tornadoes, hail, wind) fundamentally requires an ability to predict and/or detect these hazards and subsequently communicate their potential threat in real time. The National Weather Service (NWS) provides advance warning for severe convective weather through the issuance of tornado and severe thunderstorm warnings, a system that has remained relatively unchanged for approximately the past 65 years. Forecasting a Continuum of Environmental Threats (FACETs) proposes a reinvention of this system, transitioning from a deterministic product-centric paradigm to one based on probabilistic hazard information (PHI) for hazardous weather events. Four years of iterative development and rapid prototyping in the National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) with NWS forecasters and partners has yielded insights into this new paradigm by discovering efficient ways to generate, inform, and utilize a continuous flow of information through the development of a human–machine mix. Forecasters conditionally used automated object-based guidance within four levels of automation to issue deterministic products containing PHI. Forecasters accomplished this task in a timely manner while focusing on communication and conveying forecast confidence, elements considered necessary by emergency managers. Observed annual increases in the usage of first-guess probabilistic guidance by forecasters were related to improvements made to the prototyped software, guidance, and techniques. However, increasing usage of automation requires improvements in guidance, data integration, and data visualization to garner trust more effectively. Additional opportunities exist to address limitations in procedures for motion derivation and geospatial mapping of subjective probability.
Abstract
Providing advance warning for impending severe convective weather events (i.e., tornadoes, hail, wind) fundamentally requires an ability to predict and/or detect these hazards and subsequently communicate their potential threat in real time. The National Weather Service (NWS) provides advance warning for severe convective weather through the issuance of tornado and severe thunderstorm warnings, a system that has remained relatively unchanged for approximately the past 65 years. Forecasting a Continuum of Environmental Threats (FACETs) proposes a reinvention of this system, transitioning from a deterministic product-centric paradigm to one based on probabilistic hazard information (PHI) for hazardous weather events. Four years of iterative development and rapid prototyping in the National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) with NWS forecasters and partners has yielded insights into this new paradigm by discovering efficient ways to generate, inform, and utilize a continuous flow of information through the development of a human–machine mix. Forecasters conditionally used automated object-based guidance within four levels of automation to issue deterministic products containing PHI. Forecasters accomplished this task in a timely manner while focusing on communication and conveying forecast confidence, elements considered necessary by emergency managers. Observed annual increases in the usage of first-guess probabilistic guidance by forecasters were related to improvements made to the prototyped software, guidance, and techniques. However, increasing usage of automation requires improvements in guidance, data integration, and data visualization to garner trust more effectively. Additional opportunities exist to address limitations in procedures for motion derivation and geospatial mapping of subjective probability.
Abstract
The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.
Abstract
The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.
