Geostationary Operational Environmental Satellite Research Articles

On the Propagation of Satellite Precipitation Estimation Errors: From Passive Microwave to Infrared Estimates.

The launch of NOAA's latest generation of geostationary satellites known as the Geostationary Operational Environmental Satellite (GOES)-R Series has opened new opportunities in quantifying precipitation rates. Recent efforts have strived to utilize these data to improve space-based precipitation retrievals. The overall objective of the present work is to carry out a detailed error budget analysis of the improved Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for GOES-R and the passive microwave (MW) combined (MWCOMB) precipitation dataset used to calibrate it with an aim to provide insights regarding strengths and weaknesses of these products. This study systematically analyzes the errors across different climate regions and also as a function of different precipitation types over the conterminous United States. The reference precipitation dataset is Ground-Validation Multi-Radar Multi-Sensor (GV-MRMS). Overall, MWCOMB reveals smaller errors as compared to SCaMPR. However, the analysis indicated that that the major portion of error in SCaMPR is propagated from the MWCOMB calibration data. The major challenge starts with poor detection from MWCOMB, which propagates in SCaMPR. In particular, MWCOMB misses 90% of cool stratiform precipitation and the overall detection score is around 40%. The ability of the algorithms to quantify precipitation amounts for the Warm Stratiform, Cool Stratiform, and Tropical/Stratiform Mix categories is poor compared to the Convective and Tropical/Convective Mix categories with additional challenges in complex terrain regions. Further analysis showed strong similarities in systematic and random error models with both products. This suggests that the potential of high-resolution GOES-R observations remains underutilized in SCaMPR due to the errors from the calibrator MWCOMB.

Bivariate Number Distribution of Solar Soft X-Ray Flares

Abstract Conventionally, researchers investigated the number distribution of solar flares by univariate analysis using all available data. For the first time, we investigate the bivariate number distribution of peak flux (f) and duration (T) of soft X-ray flares observed by the Geostationary Operational Environmental Satellite during the past 10 years. First, the univariate number distribution of f is found to obey a power law either for each class of flares or for the B or ≥C flares in a certain range of T. But the power-law index of B-class flares is much smaller than those of other higher-class ones. For the ≥C flares, the longer the duration, the smaller the power-law index. The univariate number distribution of T deviates from the power law at shorter durations and is better fitted by a skewed Gaussian function in logarithmic coordinates for almost any given range of f, reflecting the random phenomena in solar flares. Then, based on the univariate analysis, a linear Gaussian function is proposed to describe the bivariate logarithmic number distribution of f and T, from which the local power-law index with respect to either f or T can also be analyzed. The real number distribution of B flares should be in between the observed one and the power-law distribution of stronger flares (≥C). We suggest that nanoflare activity may not be the dominant source of coronal heating.

Satellite-derived sea surface temperature fronts in a river-influenced coastal upwelling area off central–southern Chile

The variability of thermal fronts in eastern ocean boundaries has received increased attention because of being active regions of vertical fluxes of tracers and biological activity. Sea Surface Temperature (SST) images from three distinct satellite products are used to identify areas with enhanced surface thermal gradients (i.e. SST fronts) in the coastal ocean off central–southern Chile. The main objective is to evaluate their use in the study of SST frontal variability in a river-influenced continental shelf. In contrast with previous studies focused on the mesoscale structure of the upwelling front, this study highlights the importance of using high spatial resolution (i.e. 1 km) satellite products to resolve the small-scale surface thermal gradients on a relatively narrow continental shelf impacted by freshwater river outflows. An improved approach, merging Moderate Resolution Imaging Spectroradiometer (MODIS) imagery from Aqua and Terra satellites (MODIS-AT), increases the percentage of available SST data for the computation of SST gradients and frontal probability over these shelf waters. Overall, SST data from all three sources (Pathfinder, Geostationary Operational Environmental Satellite (GOES), and MODIS) resolve the major mesoscale frontal features along the offshore limit of the continental shelf. However, MODIS-AT considerably improves the detection of SST fronts over the continental shelf, especially during winter and spring when river outflows are important on the dynamics of coastal flows. A case study off the Itata River mouth reveals high spatio-temporal variability of thermal fronts over the continental shelf, which is not well detected from GOES and Pathfinder data. The analysis of MODIS-AT images is highly recommended for studies of thermal fronts over shelf waters. In contrast, the use of GOES imagery improves the monitoring of the mesoscale frontal activity farther offshore.

The Effect of Plasma Boundaries on the Dynamic Evolution of Relativistic Radiation Belt Electrons

AbstractUnderstanding the dynamic evolution of relativistic electrons in the Earth's radiation belts during both storm and nonstorm times is a challenging task. The U.S. National Science Foundation's Geospace Environment Modeling (GEM) focus group “Quantitative Assessment of Radiation Belt Modeling” has selected two storm time and two nonstorm time events that occurred during the second year of the Van Allen Probes mission for in‐depth study. Here, we perform simulations for these GEM challenge events using the 3D Versatile Electron Radiation Belt code. We set up the outer L* boundary using data from the Geostationary Operational Environmental Satellites and validate the simulation results against satellite observations from both the Geostationary Operational Environmental Satellites and Van Allen Probe missions for 0.9‐MeV electrons. Our results show that the position of the plasmapause plays a significant role in the dynamic evolution of relativistic electrons. The magnetopause shadowing effect is included by using last closed drift shell, and it is shown to significantly contribute to the dropouts of relativistic electrons at high L*. We perform simulations using four different empirical radial diffusion coefficient models for the GEM challenge events, and the results show that these simulations reproduce the general dynamic evolution of relativistic radiation belt electrons. However, in the events shown here, simulations using the radial diffusion coefficients from Brautigam and Albert (2000) produce the best agreement with satellite observations.

Radial Response of Outer Radiation Belt Relativistic Electrons During Enhancement Events at Geostationary Orbit

AbstractForecasting relativistic electron fluxes at geostationary Earth orbit (GEO) has been a long‐term goal of the scientific community, and significant advances have been made in the past, but the relation to the interior of the radiation belts, that is, to lower L‐shells, is still not clear. In this work we have identified 60 relativistic electron enhancement events at GEO to study the radial response of outer belt fluxes and the correlation between the fluxes at GEO and those at lower L‐shells. The enhancement events occurred between 1 October 2012 and 31 December 2017 and were identified using Geostationary Operational Environmental Satellite (GOES) 15 >2 MeV fluxes at GEO, which we have used to characterize the radial response of the radiation belt, by comparing to fluxes measured by the Van Allen probes Energetic Particle, Composition and Thermal Plasma Suite Relativistic Electron‐Proton Telescope (ECT‐REPT) between 2.5<L<6.0 at E=2.1 MeV. We have found that in general the response of the radiation belts during enhancement events is cohesive for L>5.0 and generally similar for L>4.5. Post‐enhancement maximum fluxes show a remarkable correlation for all L>4.0 although the magnitude of the pre‐existing fluxes on the outer belt plays a significant role and makes the ratio of pre‐enhancement to post‐enhancement fluxes less predictable in the region 4.0<L<4.5. For L<4 the fluxes are poorly correlated with geostationary orbit, but they also tend to be less variable. We have also examined SYM‐H, Kp, and AE indices and found that depending on their magnitude, the response of different parts of the outer belt can be better quantified.

Morphology of the Flare-Productive Active Region NOAA 9087

Evolution and morphological properties of the active region NOAA 9087 are analyzed based on space and ground observational data. Data on hard X-ray (HXR) and soft X-ray (SXR) were obtained from Yohkoh Telescopes (HXT and SXT) and the Geostationary Operational Environmental Satellite. Magnetograms and images in far ultraviolet were obtained from the Michelson Doppler Imager (MDI) and Extreme ultraviolet Imaging Telescope (EIT) of the Solar and Heliospheric Observatory (SOHO). White light images from the Big Bear Observatory (BBSO) and Hα-filtergrams from the Meudon Observatory were used. Data on the 2.69 GHz radio flux were taken from the World Data Center of the Learmonth Observatory (Australia). The investigated active region (AR) was observed on the solar disk from July 15 to 27, 2000, and showed a complex multipolar magnetic field configuration. A high flare activity and emissions were observed in this AR. According to Solar Geophysical Data (SGD), the 3N/M6.4 two-ribbon flare occurred on July 19, 2000, and lasted 2.5 h. The energy was released sequentially in different locations in the AR. All observational data indicate a continuous change in the structure and power of the flare at different wavelengths. HXR and type III radio bursts were observed at the initial phase of the flare. The HXR coronal source was above the line of magnetic polarity inversion of the AR. Observational evidence of magnetic reconnections during the main phase of the flare is obtained based on the analysis of sequential images of loops in the ultraviolet wavelength band. Postflare loops were observed in the 19.5 nm passband at the gradual phase, which is a manifestation of the EUV late phase. These extended loops connect the primary and secondary energy release sites of the flare. There was an additional energy transfer and heating mechanism during the main phase of the flare.

Curious Changes in Association of Complex Sunspot Groups with X-Ray Flares (≥M1) in Solar Cycles 22–24

Abstract We investigate the variations in the probabilities of complex sunspot groups (SGs)—large, asymmetric, or δ-type SGs—producing X-ray flares (≥M 1) in solar cycles (SCs) 22, 23, and 24 based on the SG data collected by the United States Air Force/Mount Wilson Observatory and the flare data recorded by Geostationary Operational Environmental Satellites. The following are the main findings. (1) For complex SGs in the whole disk as well as the northern and southern hemispheres during SC 22, their numbers and probabilities of producing flares change almost synchronously. (2) For complex SGs during SC 23, probabilities of producing flares during the early declining phase in the whole disk do not decrease with decreasing complex SG number; in the northern hemisphere, the probability of producing flares during the declining phase is significantly higher than that during the maximum phase; in the southern hemisphere, there is no definite variation trend in the probabilities of producing flares during the maximum and declining phases. (3) For complex SGs during SC 24, the probabilities of producing flares during the later rising and early declining phases are significantly higher than or comparable to those during the maximum phase no matter how their numbers change; in the northern hemisphere, the probability of producing flares during the rising phase is significantly higher than or at least comparable to that during the maximum phase; in the southern hemisphere, there is no definite variation trend in the probabilities of producing flares during the maximum and declining phases.

Characterisation of flare Soft X-ray distribution with solar magnetic activity

We analyse the 0.1 — 0.8 nm solar soft X-ray flux catalogue from Geostationary Operational Environmental Satellites (GOES), managed by NASA/NOAA, between September 1978 and September 2017, in order to investigate the possible role of solar activity and solar cycle epoch on the distribution of soft X-ray peak fluxes. We concentrate our attention on the last three solar cycles because solar activity proxies seem to indicate a decrease in the magnetic activity of our star. We know that flare soft X-ray peak fluxes are characterised by a power-law distribution with an index α ≃ 2 that shows a minor dependence on solar cycle. More in detail, we study the dependence of the power-law parameters during each single solar cycle (cycles 21-24) and during different regimes of solar activity defined using three different proxies: i) Sunspot Number (SSN), ii) Mg II core-to-wing ratio (Mg II Index), and iii) solar radio flux at 10.7 cm or 2800 MHz (F10.7). The power-law estimation analysis is performed in maximum likelihood estimation (MLE) fitting method with goodness-of-fit based on Kolmogorv- Smirnov test. Preliminary results indicate that the power-law index shows a slight decrease as solar activity decreases. This except for the F10.7 proxy. More in-depth statistical analysis is necessary to confirm our findings. A post-publication change was made to this article on 22 Jun 2020 to correct the webpage title to match the pdf.

Influence of Satellite Observation Angle to Tropical Cyclone Intensity Estimation Using the Deviation Angle Variance Technique

The deviation angle variance (DAV) method was developed to objectively estimate tropical cyclone (TC) intensity from geostationary infrared (IR) brightness temperature data. Here, we demonstrate that improvements of 25% root mean square error (RMSE) in major hurricane intensity estimation (relative to best track) can be obtained by considering the pixel-by-pixel satellite view angle in the estimation. Using data from the Chinese Fengyun 2E and 2F satellites for Super Typhoon Soudelor (2015), we demonstrate how the satellite observation angle can reduce the accuracy of intensity estimation, especially for the strongest TCs. Based on these results, an improved DAV estimator is developed using 12 years (2004–2015) of Geostationary Operational Environmental Satellite (GOES)-East satellite IR images over the North Atlantic basin.

Predicting Solar Flares with Machine Learning: Investigating Solar Cycle Dependence

Abstract A deep learning network, long short-term memory (LSTM), is used to predict whether an active region (AR) will produce a flare of class Γ in the next 24 hr. We consider Γ to be ≥M (strong flare), ≥C (medium flare), and ≥A (any flare) class. The essence of using LSTM, which is a recurrent neural network, is its ability to capture temporal information on the data samples. The input features are time sequences of 20 magnetic parameters from the space weather Helioseismic and Magnetic Imager AR patches. We analyze ARs from 2010 June to 2018 December and their associated flares identified in the Geostationary Operational Environmental Satellite X-ray flare catalogs. Our results produce skill scores consistent with recently published results using LSTMs and are better than the previous results using a single time input. The skill scores from the model show statistically significant variation when different years of data are chosen for training and testing. In particular, 2015–2018 have better true skill statistic and Heidke skill scores for predicting ≥C medium flares than 2011–2014, when the difference in flare occurrence rates is properly taken into account.

Goes-13 IR Images for Rainfall Forecasting in Hurricane Storms

Currently, it is possible to access a large amount of satellite weather information from monitoring and forecasting severe storms. However, there are no methods of employing satellite images that can improve real-time early warning systems in different regions of Mexico. The auto-estimator is the most commonly used technique that was developed for specific locations in the United States of America (32°–49° latitude) for the type of convective storms. However, the estimation of precipitation intensities for meteorological conditions in tropic latitudes, using the auto-estimator technique, needs to be re-adjusted and calibrated. It is necessary to improve this type of technique that allows decision-makers to have hydro-informatic tools capable of improving early warning systems in tropical regions (15°–25° Mexican tropic latitude). The main objective of the work is to estimate rainfall from satellite imagery in the infrared (IR) spectrum from the Geostationary Operational Environmental Satellite (GOES), validating these estimates with a network of surface rain gauges. Using the GOES-13 IR images every 15 min and using the auto-estimator, a downscaling of six hurricanes was performed from which surface precipitation events were measured. The two main difficulties were to match the satellite images taken every 15 min with the surface data measured every 10 min and to develop a program in C+ that would allow the systematic analysis of the images. The results of this work allow us to get a new adjustment of coefficients in a new equation of the auto-estimator, valid for rain produced by hurricanes, something that has not been done until now. Although no universal relationship has been found for hurricane rainfall, it is evident that the original formula of the auto-estimator technique needs to be modified according to geographical latitude.

Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI EnKF for the Analysis and Short-Term Forecast of a Mesoscale Convective System

AbstractThe recently launched Geostationary Operational Environmental Satellite “R-series” (GOES-R) satellites carry the Geostationary Lightning Mapper (GLM) that measures from space the total lightning rate in convective storms at high spatial and temporal frequencies. This study assimilates, for the first time, real GLM total lightning data in an ensemble Kalman filter (EnKF) framework. The lightning flash extent density (FED) products at 10-km pixel resolution are assimilated. The capabilities to assimilate GLM FED data are first implemented into the GSI-based EnKF data assimilation (DA) system and tested with a mesoscale convective system (MCS). FED observation operators based on graupel mass or graupel volume are used. The operators are first tuned through sensitivity experiments to determine an optimal multiplying factor to the operator, before being used in FED DA experiments FEDM and FEDV that use the graupel-mass or graupel-volume-based operator, respectively. Their results are compared to a control experiment (CTRL) that does not assimilate any FED data. Overall, both DA experiments outperform CTRL in terms of the analyses and short-term forecasts of FED and composite/3D reflectivity. The assimilation of FED is primarily effective in regions of deep moist convection, which helps improve short-term forecasts of convective threats, including heavy precipitation and lightning. Direct adjustments to graupel mass via observation operator as well as adjustments to other model state variables through flow-dependent ensemble cross covariance within EnKF are shown to work together to generate model-consistent analyses and overall improved forecasts.

Thunderstorm Cloud-Type Classification from Space-Based Lightning Imagers.

The organization and structure of thunderstorms determines the extent and severity of their hazards to the general public and their consequences for the Earth system. Distinguishing vigorous convective regions that produce heavy rain and hail from adjacent regions of stratiform or overhanging anvil clouds that produce light-to-no rainfall is valuable in operations and physical research. Cloud type algorithms that partition convection from stratiform regions have been developed for space-based radar, passive microwave, and now Geostationary Operational Environmental Satellites (GOES) Advanced Baseline Imager (ABI) multi-spectral products. However, there are limitations for each of these products including temporal availability, spatial coverage, and the degree to which they based on cloud microphysics. We have developed a cloud type algorithm for GOES Geostationary Lightning Mapper (GLM) observations that identifies convective / non-convective regions in thunderstorms based on signatures of interactions with non-convective charge structures in the lightning flash data. The GLM sensor permits a rapid (20-s) update cycle over the combined GOES-16 / GOES-17 domain across all hours of the day. Storm regions that do not produce lightning will not be classified by our algorithm, however. The GLM cloud type product is intended to provide situational awareness of electrified anvils and to complement other cloud type retrievals by providing a contemporary assessment tied to lightning physics. We propose that a future combined ABI / GLM cloud type algorithm would be a valuable product that could draw from the strengths of each instrument and approach.

Estimating the number of clear sky nights using GOES-16 images: a search for astronomical sites in Colombia

ABSTRACT Data from the Geostationary Operational Environmental Satellite 16 (GOES-16) are used to study the number of clear nights over Colombia in order to identify potential sites for astronomical observations. First, we compare data from the Advanced Baseline Imager (ABI) with in-situ observations from an all-sky camera in order to validate the cloud cover information from the ABI retrievals. Then, we extend the analysis of the GOES-16 cloud cover fields to quantify the number of hours with low values of cloud cover for each pixel and each day of 2018 for a region that includes Colombia. A clear night is defined as one with five or more consecutive hours with a low fraction of cloud cover. We select the potential sites as those with more than 80 clear nights and with an altitude higher than 2000 m above sea level (m.a.s.l.). Our analyses showed that nine sites fulfil these conditions: five are in Colombia, three are in Ecuador and one in Venezuela. From our sample, eight out of the nine sites are in the Andes Mountains. The results suggest that the most suitable site for astronomical observations in Colombia is located near to Laguna de la Cocha, in the department of Nariño, with 135 clear nights in 2018 (about 37% of the year), followed by a site near to Lago de Tota, with 117 clear nights (about 32% of the year), located near the centre of the country. Exploration of regions outside Colombia showed that the Ecuadorian Andes has locations with approximately 48% of clear nights during the studied year. We conclude that the use of GOES images to measure the number of clear nights in order to select potential astronomical site locations has a great potential.

Of Fire and Smoke Plumes, Polarimetric Radar Characteristics

Weather surveillance radars routinely detect smoke of various origin. Of particular significance to the meteorological community are wildfires in forests and/or prairies. For example, one responsibility of the National Weather Service in the USA is to forecast fire outlooks as well as to monitor wildfire evolution. Polarimetric variables have enabled relatively easy recognitions of smoke plumes in data fields of weather radars. Presented here are the fields of these variables from smoke plumes caused by grass fire, brush fire, and forest fire. Histograms of polarimetric data from plumes contrast these cases. Most of the data are from the polarimetric Weather Surveillance Radar 1988 Doppler (WSR-88D aka NEXRAD, 10 cm wavelength); hence, the wavelength does not influence these comparisons. Nevertheless, in one case, simultaneous observations of a plume by the operational Terminal Doppler Weather Radar (TDWR, 5 cm wavelength) and a WSR-88D is used to infer backscattering characteristic and, hence, sizes of dominant contributors to the returns. To interpret these measurements, Computational Electromagnetics (CEM) tools are applied. For one wildfire from Oklahoma, radar and satellite (GOES-16, Geostationary Operational Environmental Satellite) images are analyzed. The case demonstrates a potential to forecast fire intensification caused by a very rapid cold front. Finally, we suggest a possible way to extract the smoke plume return from the class of nonmeteorological scatterers.

Quasi-periodic Pulsation Detected in Lyα Emission During Solar Flares

Abstract We investigated the quasi-periodic pulsation (QPP) in Lyα, X-ray, and extreme-ultraviolet (EUV) emissions during two solar flares, i.e., an X-class (SOL2012-01-27T) and a C-class (SOL2016-02-08T). The full-disk Lyα and X-ray flux during these solar flares were recorded by the EUV Sensor and X-Ray Sensor on board the Geostationary Operational Environmental Satellite. The flare regions were located from the EUV images measured by the Atmospheric Imaging Assembly. The QPP could be identified as a series of regular and periodic peaks in the light curves, and its quasi-periodicity was determined from the global wavelet and Fourier power spectra. A quasi-periodicity at about 3 minutes is detected during the impulsive phase of the X-class flare, which could be explained as the acoustic wave in the chromosphere. Interestingly, a quasi-periodicity at roughly 1 minute is discovered during the entire evolutionary phases of solar flares, including the precursor, impulsive, and gradual phases. This is the first report of 1 minute QPP in the Lyα emission during solar flares, in particular during the flare precursor. It may be interpreted as a self-oscillatory regime of the magnetic reconnection, such as magnetic dripping.

GOES-R series image navigation and registration performance assessment tool set

An image navigation (NAV) and registration (INR) performance assessment tool set (IPATS) was developed to assess the US Geostationary Operational Environmental Satellite R-series (GOES-R) Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance. IPATS produces five INR metrics for level 1B ABI images: navigation, channel-to-channel registration, frame-to-frame registration, swath-to-swath registration, and within-frame registration. IPATS also produces one INR metric for GLM: navigation of background images. The high-precision INR metrics produced by IPATS are critical to INR performance evaluation and long-term monitoring. IPATS INR metrics also provide feedback to INR engineers for tuning the navigation algorithms and parameters to further refine INR performance. IPATS utilizes a modular algorithm design to allow the user-selectable data processing sequence and configuration parameters. We first describe the algorithmic design and the implementation of IPATS. Next, it describes the investigation of the optimization of the configuration parameters to reduce measurement errors. Finally, sample INR performance is presented, including GOES-16 and GOES-17 ABI NAV performance from postlaunch test to November 2019 and the comparison of example 24-h INR performance against the mission performance requirements. The INR assessment results show that both GOES-R ABIs are in compliance with the mission INR requirements.

GOES-16 Advanced Baseline Imager visible near-infrared channel low-light signal-to-noise ratio

Quantifying Geostationary Operational Environmental Satellite R-series (GOES-R) Advanced Baseline Imager (ABI) Channel (Ch) 2 low-light signal-to-noise ratio (SNR) is a core ABI postlaunch product test to validate prelaunch performance expectations of this parameter. For GOES-16, an SNR analysis has been performed to accomplish this goal using ABI Mode-3 MESO visible near-infrared (VNIR) channel L1b radiance product images taken every 30 s between 17:00 UTC and 17:15 UTC on May 23, 2017. Scene pixel data used in the analysis are first screened to ensure they meet the low-light criteria and to minimize scene temporal variability associated with meteorological evolution and geolocation uncertainty in highly inhomogeneous scenes. After such screening, image-to-image radiance time difference statistics are compiled and analyzed to estimate instrument SNR. Based on GOES-R ABI vendor prelaunch testing, minimum and mean expected ABI Ch 2 postlaunch SNR performance at 5% albedo was estimated to be 44.2 and 64.5, respectively. Meanwhile, the SNR computed in this analysis is found to be about 57 ± 8. Thus, low-light SNR for ABI Ch 2 is shown to meet minimum user expectations. The analysis for the other five GOES-16 ABI VNIR channels is also shown to contrast the SNR for these channels.

Towards objective identification and tracking of convective outflow boundaries in next-generation geostationary satellite imagery

Abstract. Sudden wind direction and speed shifts from outflow boundaries (OFBs) associated with deep convection significantly affect weather in the lower troposphere. Specific OFB impacts include rapid variation in wildfire spread rate and direction, the formation of convection, aviation hazards, and degradation of visibility and air quality due to mineral dust aerosol lofting. Despite their recognized importance to operational weather forecasters, OFB characterization (location, timing, intensity, etc.) in numerical models remains challenging. Thus, there remains a need for objective OFB identification algorithms to assist decision support services. With two operational next-generation geostationary satellites now providing coverage over North America, high-temporal- and high-spatial-resolution satellite imagery provides a unique resource for OFB identification. A system is conceptualized here designed around the new capabilities to objectively derive dense mesoscale motion flow fields in the Geostationary Operational Environmental Satellite 16 (GOES-16) imagery via optical flow. OFBs are identified here by isolating linear features in satellite imagery and backtracking them using optical flow to determine if they originated from a deep convection source. This “objective OFB identification” is tested with a case study of an OFB-triggered dust storm over southern Arizona. The results highlight the importance of motion discontinuity preservation, revealing that standard optical flow algorithms used with previous studies underestimate wind speeds when background pixels are included in the computation with cloud targets. The primary source of false alarms is the incorrect identification of line-like features in the initial satellite imagery. Future improvements to this process are described to ultimately provide a fully automated OFB identification algorithm.

GOES-16 Observations of Blowing Snow in Horizontal Convective Rolls on 24 February 2019

Abstract On 24 February 2019, strong winds behind an Arctic cold front led to widespread blowing snow across the northern Great Plains including areas in eastern North/South Dakota and western Minnesota. Impacts of the event ranged from blizzard conditions in northwest Minnesota to sporadic, minor reductions in visibility across the region. This study documents the event using remotely sensed observations from platforms including geostationary and polar-orbiting satellites, an S-band radar, and time-lapse images from a camera located at the University of North Dakota in Grand Forks, North Dakota. Blowing snow is observed as plumes that resemble horizontal convective rolls (HCRs). Variations in near-infrared imagery are documented, and supporting observations suggest this is due to the occurrence or absence of clouds on top of the blowing snow layer. While lack of in situ observations preclude further investigation of physical differences between plumes, the utility of the Geostationary Operational Environmental Satellite-16 (GOES-16) satellite to operational forecasters is discussed. Improvements to spatial, radiometric, and temporal resolution courtesy of the Advanced Baseline Imager (ABI) on board GOES-16 allows for daytime detection of blowing snow events that previously, was only possible with instruments on board polar-orbiting satellites. This has improved Impact-Based Decision Support Services (IDSS) at National Weather Service offices that deal with the hazard of blowing snow.