Geostationary Satellite Imagery Research Articles

A Method for Object-oriented Detection of Deep Convection from Geostationary Satellite Imagery Using Machine Learning

A Method for Object-oriented Detection of Deep Convection from Geostationary Satellite Imagery Using Machine Learning

Geostationary Satellite–Derived Positioning of a Tropical Cyclone Center Using Artificial Intelligence Algorithms over the Western North Pacific

Abstract Artificial intelligence (AI) models were developed to determine the center of tropical cyclones (TCs) in the western North Pacific. These models integrated information from six channels of geostationary satellite imagery: the brightness temperature of four infrared (IR) and one shortwave IR channels, as well as the reflectivity of one visible channel. The first model is a convolutional neural network designed for spatial data processing, and the second is a convolutional long short-term memory model that effectively captures spatiotemporal information. For training, verification, and testing purposes, spatial images from six channels were obtained from the Japanese Himawari-8 satellite from 2016 to 2021. The position of the European Centre for Medium-Range Weather Forecasts 6- or 12-h prediction was assigned as an initial value to the AI models. Errors in the initial value were 20–50 km compared to the Joint Typhoon Warning Center best track, depending on TC intensity. Weak (strong) TCs exhibited large (small) errors. This error dependency was found in Automated Rotational Center Hurricane Eye Retrieval (ARCHER) product, which is currently used by several operational organizations. ARCHER errors were typically small when observations from both geostationary and polar-orbiting satellites were included. Significant errors remained in the absence of microwave channel information from polar-orbiting satellites. This study successfully developed two AI models that consistently determined the location of the TC center using only six-channel images from geostationary satellites. These models exhibited comparable or better performance than the ARCHER products. The newly developed AI models can potentially be implemented for operational use.

Cloud-Top Motion Variation of a Landfall Typhoon Observed by Geostationary Satellite Imagery

Cloud-Top Motion Variation of a Landfall Typhoon Observed by Geostationary Satellite Imagery

The Development and Initial Capabilities of ThunderCast, a Deep Learning Model for Thunderstorm Nowcasting in the United States

Abstract This paper presents the Thunderstorm Nowcasting Tool (ThunderCast), a 24-h, year-round model for predicting the location of convection that is likely to initiate or remain a thunderstorm in the next 0–60 min in the continental United States, adapted from existing deep learning convection applications. ThunderCast utilizes a U-Net convolutional neural network for semantic segmentation trained on 320 km × 320 km data patches with four inputs and one target dataset. The inputs are satellite bands from the Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) in the visible, shortwave infrared, and longwave infrared spectra, and the target is Multi-Radar Multi-Sensor (MRMS) radar reflectivity at the −10°C isotherm in the atmosphere. On a pixel-by-pixel basis, ThunderCast has high accuracy, recall, and specificity but is subject to false-positive predictions resulting in low precision. However, the number of false positives decreases when buffering the target values with a 15 km × 15 km centered window, indicating ThunderCast’s predictions are useful within a buffered area. To demonstrate the initial prediction capabilities of ThunderCast, three case studies are presented: a mesoscale convective vortex, sea-breeze convection, and monsoonal convection in the southwestern United States. The case studies illustrate that the ThunderCast model effectively nowcasts the location of newly initiated and ongoing active convection, within the next 60 min, under a variety of geographical and meteorological conditions. Significance Statement In this research, a machine learning model is developed for short-term (0–60 min) forecasting of thunderstorms in the continental United States using geostationary satellite imagery as inputs for predicting active convection based on radar thresholds. Pending additional testing, the model may be able to provide decision-support services for thunderstorm forecasting. The case studies presented here indicate the model is able to nowcast convective initiation with 5–35 min of lead time in areas without radar coverage and anticipate future locations of storms without additional environmental context.

Structural Forecasting for Short-Term Tropical Cyclone Intensity Guidance

Abstract Because geostationary satellite (Geo) imagery provides a high temporal resolution window into tropical cyclone (TC) behavior, we investigate the viability of its application to short-term probabilistic forecasts of TC convective structure to subsequently predict TC intensity. Here, we present a prototype model that is trained solely on two inputs: Geo infrared imagery leading up to the synoptic time of interest and intensity estimates up to 6 h prior to that time. To estimate future TC structure, we compute cloud-top temperature radial profiles from infrared imagery and then simulate the evolution of an ensemble of those profiles over the subsequent 12 h by applying a deep autoregressive generative model (PixelSNAIL). To forecast TC intensities at hours 6 and 12, we input operational intensity estimates up to the current time (0 h) and simulated future radial profiles up to +12 h into a “nowcasting” convolutional neural network. We limit our inputs to demonstrate the viability of our approach and to enable quantification of value added by the observed and simulated future radial profiles beyond operational intensity estimates alone. Our prototype model achieves a marginally higher error than the National Hurricane Center’s official forecasts despite excluding environmental factors, such as vertical wind shear and sea surface temperature. We also demonstrate that it is possible to reasonably predict short-term evolution of TC convective structure via radial profiles from Geo infrared imagery, resulting in interpretable structural forecasts that may be valuable for TC operational guidance. Significance Statement This work presents a new method of short-term probabilistic forecasting for tropical cyclone (TC) convective structure and intensity using infrared geostationary satellite observations. Our prototype model’s performance indicates that there is some value in observed and simulated future cloud-top temperature radial profiles for short-term intensity forecasting. The nonlinear nature of machine learning tools can pose an interpretation challenge, but structural forecasts produced by our model can be directly evaluated and, thus, may offer helpful guidance to forecasters regarding short-term TC evolution. Since forecasters are time limited in producing each advisory package despite a growing wealth of satellite observations, a tool that captures recent TC convective evolution and potential future changes may support their assessment of TC behavior in crafting their forecasts.

A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations

Abstract. Automated methods for the detection and tracking of deep convective clouds in geostationary satellite imagery have a vital role in both the forecasting of severe storms and research into their behaviour. Studying the interactions and feedbacks between multiple deep convective clouds (DCC), however, poses a challenge for existing algorithms due to the necessary compromise between false detection and missed detection errors. We utilise an optical flow method to determine the motion of deep convective clouds in GOES-16 ABI imagery in order to construct a semi-Lagrangian framework for the motion of the cloud field, independently of the detection and tracking of cloud objects. The semi-Lagrangian framework allows severe storms to be simultaneously detected and tracked in both spatial and temporal dimensions. For the purpose of this framework we have developed a novel Lagrangian convolution method and a number of novel implementations of morphological image operations that account for the motion of observed objects. These novel methods allow the accurate extension of computer vision techniques to the temporal domain for moving objects such as DCCs. By combining this framework with existing methods for detecting DCCs (including detection of growing cores through cloud top cooling and detection of anvil clouds using brightness temperature), we show that the novel framework enables reductions in errors due to both false and missed detections compared to any of the individual methods, reducing the need to compromise when compared with existing frameworks. The novel framework enables the continuous tracking of anvil clouds associated with detected deep convection after convective activity has stopped, enabling the study of the entire life cycle of DCCs and their associated anvils. Furthermore, we expect this framework to be applicable to a wide range of cases including the detection and tracking of low-level clouds and other atmospheric phenomena. In addition, this framework may be used to combine observations from multiple sources, including satellite observations, weather radar and reanalysis model data.

Comparison of Optical Flow Derivation Techniques for Retrieving Tropospheric Winds from Satellite Image Sequences

Abstract This study introduces a validation technique for quantitative comparison of algorithms that retrieve winds from passive detection of cloud- and water vapor–drift motions, also known as atmospheric motion vectors (AMVs). The technique leverages airborne wind-profiling lidar data collected in tandem with 1-min refresh-rate geostationary satellite imagery. AMVs derived with different approaches are used with accompanying numerical weather prediction model data to estimate the full profiles of lidar-sampled winds, which enables ranking of feature tracking, quality control, and height-assignment accuracy and encourages mesoscale, multilayer, multiband wind retrieval solutions. The technique is used to compare the performance of two brightness motion, or “optical flow,” retrieval algorithms used within AMVs, 1) patch matching (PM; used within operational AMVs) and 2) an advanced variational optical flow (VOF) method enabled for most atmospheric motions by new-generation imagers. The VOF AMVs produce more accurate wind retrievals than the PM method within the benchmark in all imager bands explored. It is further shown that image regions with low texture and multilayer-cloud scenes in visible and infrared bands are tracked significantly better with the VOF approach, implying VOF produces representative AMVs where PM typically breaks down. It is also demonstrated that VOF AMVs have reduced accuracy where the brightness texture does not advect with the mean wind (e.g., gravity waves), where the image temporal noise exceeds the natural variability, and when the height assignment is poor. Finally, it is found that VOF AMVs have improved performance when using fine-temporal refresh-rate imagery, such as 1- versus 10-min data.

Determining Tropical Cyclone Center and Rainband Size in Geostationary Satellite Imagery

Brightness temperature (TB) observations at an infrared channel (10.3 μm) of the Advanced Baseline Imager (ABI) on board the U. S. 16th Geostationary Operational Environmental Satellite (GOES-16) are used for determining tropical cyclone (TC) center positions and rainband sizes. Firstly, an azimuthal spectral analysis method is employed to obtain an azimuthally symmetric center of a TC. Then, inner and outer rainbands radii, denoted as RIR and ROR, respectively, are estimated based on radial gradients of TB observations at different azimuthal angles. The radius RIR describes the size of the TC inner-core region, and the radius ROR reflects the maximum radial extent of TC rainbands. Compared with the best track centers, the root mean square differences of ABI-determined centers for tropical storms and hurricanes, which totals 108 samples, are 45.35 and 29.06 km, respectively. The larger the average wavenumber-0 amplitude, the smaller the difference between the ABI-determined center and the best track center. The TB-determined RIR is close but not identical to the radius of the outermost closed isobar and usually coincides with the radius where the strongest wavenumber 1 asymmetry is located. The annulus defined by the two circles with radii of ROR and RIR is the asymmetric area of rainbands described by azimuthal wavenumbers 1–3. In general, amplitudes of wavenumber 0 component centered on the ABI-determined center are greater than or equal to those from the best track. For a case of a 60 km distance between the ABI-determined and the best track TC center, the innermost azimuthal waves of wavenumbers 1–3 are nicely distributed along or within the radial distance RIR that is determined based on the ABI-determined TC center. If RIR is determined based on the best track, the azimuthal waves of wavenumbers 1–3 are found at several radial distances that are smaller than RIR. The TC center positions, and rainband size radii are important for many applications, including specification of a bogus vortex for hurricane initialization and verification of propagation mechanism of vortex Rossby waves.

Incremental Learning with Neural Network Algorithm for the Monitoring Pre-Convective Environments Using Geostationary Imager

Early warning of severe weather caused by intense convective weather systems is challenging. To help such activities, meteorological satellites with high temporal and spatial resolution have been utilized for the monitoring of instability trends along with water vapor variation. The current study proposes a retrieval algorithm based on an artificial neural network (ANN) model to quickly and efficiently derive total precipitable water (TPW) and convective available potential energy (CAPE) from Korea’s second geostationary satellite imagery measurements (GEO-KOMPSAT-2A/Advanced Meteorological Imager (AMI)). To overcome the limitations of the traditional static (ST) learning method such as exhaustive learning, impractical, and not matching in a sequence data, we applied an ANN model with incremental (INC) learning. The INC ANN uses a dynamic dataset that begins with the existing weight information transferred from a previously learned model when new samples emerge. To prevent sudden changes in the distribution of learning data, this method uses a sliding window that moves along the data with a window of a fixed size. Through an empirical test, the update cycle and the window size of the model are set to be one day and ten days, respectively. For the preparation of learning datasets, nine infrared brightness temperatures of AMI, six dual channel differences, temporal and geographic information, and a satellite zenith angle are used as input variables, and the TPW and CAPE from ECMWF model reanalysis (ERA5) data are used as the corresponding target values over the clear-sky conditions in the Northeast Asia region for about one year. Through the accuracy tests with radiosonde observation for one year, the INC NN results demonstrate improved performance (the accuracy of TPW and CAPE decreased by approximately 26% and 26% for bias and about 13% and 12% for RMSE, respectively) when compared to the ST learning. Evaluation results using ERA5 data also reveal more stable error statistics over time and overall reduced error distribution compared with ST ANN.

The Map Data of BRDF-Adjusted Surface Reflectance from GOCI Geostationary Satellite Imagery over Korean Peninsula

In this study, the spatial maps of the BRDF (Bidirectional Reflectance Distribution Function) adjusted surface reflectance (SR) were estimated by using the Geostationary Ocean Color Imager (GOCI) mounted on the Communication, Ocean and Meteorological Satellite (COMS) over the Korean Peninsula. The BRDF-Adjusted surface Reflectance (BAR) is a more effective indicator that not only quantitatively identifies the growth characteristics of vegetation, but also corrects the bidirectional error in the time series observation characteristics, because the surface reflectance is changed according to the solar altitude during the daytime period. Therefore, this BAR products have high data utilization in various fields such as agriculture, environment, land surface information, and atmosphere. In this study, BRDF-adjusted surface reflectance maps were calculated for the Korean peninsula from April 2011 to December 2012 with hourly temporal resolution from 9 a.m. to 4 p.m. For the BAR surface reflectance, the spatial observation range is from latitude 34° N to 39 °N and longitude 125 ° E to 130 ° E, and the spatial resolution is 500 m. The semi-empirical BRDF model was used to calculate the BRDF-adjusted surface reflectance, and the radiometric characteristics of surface reflectance were decomposed into isotropic scattering, geometric scattering, and volumetric scattering. For this model simulation, at least 7 clear pixels are required to fit BRDF model. In this study, unlike the Nadir BRDF-Adjusted surface Reflectance (NBAR) calculation method which was calculated from the existing polar orbiting satellites, semi-empirical BRDF modeling was performed with a value fixed to the satellite viewing angle for each pixel since geostationary satellites of GOCI are difficult to observe in the nadir direction unlike polar satellites. It is more effective to perform BRDF correction by fixing them at the viewing angle in the case of GOCI geostationary satellite.

Data article: Full disk real-time Himawari-8/9 satellite AHI imagery from JAXA

Satellite products are essential in many research fields. Geostationary satellite imagery is rapidly updating and offers vast multi-spectral data that is useful in applications such as gridded solar forecasting and resource assessment. However, there is a high-barrier to entry for researchers in obtaining and using satellite data. A new database from the Japan Aerospace Exploration Agency (JAXA) offers near-real-time Himawari-8/9 satellite imagery by Advanced Himawari Imagers (AHIs), for free, to researchers. Typically, this type of data has a high price point; hence, it is a good opportunity for scientific researchers to work in the real real-time operational space. We present a data article complete with a Python package that: (i) downloads full disk Himawari-8/9 AHI Himawari standard data from the JAXA Himawari Monitor P-Tree system, (ii) provides example scripts showing how the data can remapped, cropped, and stored appropriately, and (iii) gives examples how to visualize the data. The Python package can be found on the Python package index at https://pypi.org/project/ftp-himawari8-hsd/.

Extrapolating shortwave geostationary satellite imagery of clouds into nighttime using longwave observations

The lack of shortwave (SW, visible, and near-infrared) geostationary satellite data at night results in degradation of many weather forecasts and real-time diagnostic products. We present a method to extrapolate SW GOES-16 advanced baseline imager data through night using nighttime longwave (LW, infrared) observations and the relationships between LW and SW data observed during the previous day. The method is not a forecast since it requires LW nighttime observations but can provide continuity through day, night, and satellite terminator hours. To provide performance statistics, the algorithm is applied during the day so the SW extrapolations can be compared to observations. Typical mean absolute errors (MAEs) range from 1.0% to 12.7% reflectance depending on the SW channel. These MAEs can be predicted using a diagnostic metric called 0-h MAE which quantifies the quality of the algorithm’s input data. In addition to quantitative error statistics, three case studies are presented, including an animation of extrapolated imagery from dusk through dawn. Considerations for future improvements include use of convolutional neural networks and/or object-based extrapolations where mesoscale features are extrapolated individually.

A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

U-High resolution network (U-HRNet): cloud detection with high-resolution representations for geostationary satellite imagery

ABSTRACT Cloud detection is one of the important tasks for remote sensing image preprocessing. In this work, multiple infrared observation channels with high time resolution are utilized to extract cloud from geostationary satellite imagery. Compared with detected cloud mask from polar satellite data with the high spatial resolution, the detection from geostationary satellite data has high timeliness and practicability, but it is challenging for the detailed segmentation ability of the network. Due to the fact that some small clouds only occupy a few pixels in geostationary satellite images with low spatial resolution, the existing network has poor segmentation ability for these small targets. To tackle this problem, a new neural network named U-High Resolution Network (U-HRNet) is proposed for cloud detection. Combing both multi-scale feature extraction using a High Resolution Network (HRNet) architecture and merging shallow information and deep information via the skip connection (SC) of a U-Net structure, the proposed U-HRNet produces strong high-resolution representations for accurate detection of details on cloud segmentation. The accuracy of the method is evaluated by manually labelled ground truth against different methods using objective evaluation indices. The results proved the proposed U-HRNet performs well on FengYun-4A (FY-4A) images and can effectively detect incorrect areas of the cloud mask products of the National Satellite Meteorological Centre (NSMC), depicting outperformance over existing methods.

Revisions to the Australian tropical cyclone best track database

The Australian tropical cyclone (TC) best track database (BT) maintained by the Bureau of Meteorology has records since 1909 of varying quality and completeness. Since 2005 a series of efforts to improve the database have included: removing internal inconsistencies, adding fixes, and identifying errors using comparisons with other datasets; upgrading intensity information since 1973 including adding maximum winds (Vm) prior to 1984–85, rederiving Dvorak Current Intensity numbers from archived material and accounting for different wind–pressure relationships used; a partial reanalysis of satellite imagery including microwave imagery using the HURSAT dataset since 1987; and considering an objective intensity dataset. The BT homogeneity is reviewed in the context of improvements in satellite technology, observational coverage, scientific developments, BT procedures and the subjective variation between analysts across time and offices. The scale of these variances is greatest in the early stages prior to 1981 in the absence of geostationary satellite imagery until 1978, satellite calibration issues from 1978–80 and prior to the introduction of the enhanced infra-red Dvorak technique in 1981. The current era since 2003 is considered to be the most accurate, comprehensive and homogeneous corresponding to the expansion of the TC database to include the current suite of fields; the application of microwave and scatterometry imagery; greater standardisation of BT practices and slight changes in the application of the Dvorak technique. These improvements have generated a more consistent dataset that could be used for weather and climate research and other TC-related work.

SIFTing through satellite imagery with the Satellite Information Familiarization Tool

The Satellite Information Familiarization Tool (SIFT) is an open-source, multi-platform graphical user interface designed to easily display spectral and temporal sequences of geostationary satellite imagery. The Advanced Baseline Imager (ABI) and Advanced Himawari Imager (AHI) on the “new generation” of geostationary satellites collect imagery with a spatial resolution four times greater than previously available. Combined with the increased number of spectral bands and more frequent imaging, the new series imagers collect approximately 60 times more data. Given the resulting large file sizes, the development of SIFT is a multiyear effort to make those satellite imagery data files accessible to the broad community of students, scientists, and operational meteorologists. To achieve the objective of releasing software that provides an intuitive user experience to complement optimum performance on consumer-grade computers, SIFT was built to leverage modern graphics processing units (GPUs) through existing open-source Python packages, and runs on the three major operating systems: Windows, Mac, and Linux. The United States National Weather Service funded the development of SIFT to help enhance the satellite meteorology acumen of their operational meteorologists. SIFT has basic image visualization capabilities and enables the fluid animation and interrogation of satellite images, creation of Red-Green-Blue (RGB) composites and algebraic combinations of multiple spectral bands, and comparison of imagery with numerical weather prediction output. Open for community development, SIFT users and features continue to grow. SIFT is freely available with short tutorials and a user guide online. The mandate for the software, its development, realized applications, and envisioned role in science and training are explained.

Unlocking GOES: A Statistical Framework for Quantifying the Evolution of Convective Structure in Tropical Cyclones

AbstractTropical cyclones (TCs) rank among the most costly natural disasters in the United States, and accurate forecasts of track and intensity are critical for emergency response. Intensity guidance has improved steadily but slowly, as processes that drive intensity change are not fully understood. Because most TCs develop far from land-based observing networks, geostationary satellite imagery is critical to monitor these storms. However, these complex data can be challenging to analyze in real time, and off-the-shelf machine-learning algorithms have limited applicability on this front because of their “black box” structure. This study presents analytic tools that quantify convective structure patterns in infrared satellite imagery for overocean TCs, yielding lower-dimensional but rich representations that support analysis and visualization of how these patterns evolve during rapid intensity change. The proposed feature suite targets the global organization, radial structure, and bulk morphology (ORB) of TCs. By combining ORB and empirical orthogonal functions, we arrive at an interpretable and rich representation of convective structure patterns that serve as inputs to machine-learning methods. This study uses the logistic lasso, a penalized generalized linear model, to relate predictors to rapid intensity change. Using ORB alone, binary classifiers identifying the presence (vs absence) of such intensity-change events can achieve accuracy comparable to classifiers using environmental predictors alone, with a combined predictor set improving classification accuracy in some settings. More complex nonlinear machine-learning methods did not perform better than the linear logistic lasso model for current data.

Mapping, Monitoring, and Prediction of Floods Due to Ice Jam and Snowmelt with Operational Weather Satellites

Among all the natural hazards throughout the world, floods occur most frequently. They occur in high latitude regions, such as: 82% of the area of North America; most of Russia; Norway, Finland, and Sweden in North Europe; China and Japan in Asia. River flooding due to ice jams may happen during the spring breakup season. The Northeast and North Central region, and some areas of the western United States, are especially harmed by floods due to ice jams and snowmelt. In this study, observations from operational satellites are used to map and monitor floods due to ice jams and snowmelt. For a coarse-to-moderate resolution sensor on board the operational satellites, like the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the National Polar-orbiting Partnership (NPP) and the Joint Polar Satellite System (JPSS) series, and the Advanced Baseline Imager (ABI) on board the GOES-R series, a pixel is usually composed of a mix of water and land. Water fraction can provide more information and can be estimated through mixed-pixel decomposition. The flood map can be derived from the water fraction difference after and before flooding. In high latitude areas, while conventional observations are usually sparse, multiple observations can be available from polar-orbiting satellites during a single day, and river forecasters can observe ice movement, snowmelt status and flood water evolution from satellite-based flood maps, which is very helpful in ice jam determination and flood prediction. The high temporal resolution of geostationary satellite imagery, like that of the ABI, can provide the greatest extent of flood signals, and multi-day composite flood products from higher spatial resolution imagery, such as VIIRS, can pinpoint areas of interest to uncover more details. One unique feature of our JPSS and GOES-R flood products is that they include not only normal flood type, but also a special flood type as the supra-snow/ice flood, and moreover, snow and ice masks. Following the demonstrations in this study, it is expected that the JPSS and GOES-R flood products, with ice and snow information, can allow dynamic monitoring and prediction of floods due to ice jams and snowmelt for wide-end users.

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.

Towards an objective historical tropical cyclone dataset for the Australian region

The appropriate design of infrastructure in tropical cyclone (TC) prone regions requires an understanding of the hazard risk profile underpinned by an accurate, homogenous long-term TC dataset. The existing Australian region TC archive, or ‘best track’ (BT), suffers from inhomogeneities and an incomplete long-term record of key TC parameters. This study assesses mostly satellite-based objective techniques for 1981–2016, the period of a geostationary satellite imagery dataset corrected for navigation and calibration issues. The satellite-based estimates of Australian-region TCs suffer from a general degradation in the 1981–1988 period owing to lower quality and availability of satellite imagery.The quality of the objective techniques for both intensity and structure is compared to the reference BT 2003–2016 estimates. For intensity the Advanced Dvorak Technique algorithm corresponds well with the BT 2003–2016, when the algorithm can use passive microwave data (PMW) as an input. For the period prior to 2003 when PMW data is unavailable, the intensity algorithm has a low bias. Systematic corrections were made to the non-PMW objective estimates to produce an extended (1989–2016) homogeneous dataset of maximum wind that has sufficient accuracy to be considered for use where a larger homogeneous sample size is valued over a shorter more accurate period of record. An associated record of central pressure using the Courtney-Knaff-Zehr wind pressure relationship was created.For size estimates, three techniques were investigated: the Deviation Angle Variance and the ‘Knaff’ techniques (IR-based), while the ‘Lok’ technique used model information (ECMWF reanalysis dataset and TC vortex specification from ACCESS-TC). However, results lacked sufficient skill to enable extension of the reliable period of record. The availability of scatterometer data makes the BT 2003–2016 dataset the most reliable and accurate. Recommendations regarding the best data source for each parameter for different periods of the record are summarised.