Radar Products Research Articles

On Discrete Convective Updrafts and Tornadoes in Quasi-Linear Convective Systems

Abstract This research attempts to use operational radar and satellite products to identify potential locations of quasi-linear convective system (QLCS) tornadogenesis, which can be difficult to predict. It is hypothesized that deep, discrete updrafts indicate portions of the QLCS capable of producing tornadoes, whereas shallower convection indicates more benign portions of the QLCS. To address this hypothesis, storm reports and storm surveys on 30–31 March 2022, during the second intensive observing period of the 2022 Propagation, Evolution, and Rotation in Linear Storms (PERiLS) field campaign, are used to identify locations of tornadoes within the QLCS. These tornado locations are then compared to representations of upper-tropospheric updrafts, namely, overshooting tops (OTs), which are identified with an algorithm using 1-min-resolution mesoscale sector data from GOES-16 Advanced Baseline Imager infrared brightness temperatures, and radar reflectivity cores aloft, identified with Multi-Radar Multi-Sensor (MRMS) 3D mosaic reflectivity products. Only a fraction (less than 30%) of tornadoes within the QLCS are associated with OTs, though over 85% of tornadoes are located near convective cores as indicated by cores of enhanced reflectivity at 9 km MSL. A numerical simulation of the event is also conducted using the Weather Research and Forecasting (WRF) Model which shows a strong relationship between simulated updraft intensity and reflectivity aloft. Given this apparent support of the hypothesis, the identification of updraft signatures within MRMS and high-resolution geostationary satellite data may ultimately help improve the identification of regions within QLCSs most likely to result in tornadoes.

Analysis of Precipitation Totals Based on Radar and Rain Gauge Data

The relationship between radar reflectivity (Z) and rainfall intensity (R) plays a crucial role in estimating precipitation and serves as a foundation for flood risk assessment. However, empirical Z–R relationships often introduce considerable uncertainty, making the correction of rainfall estimation errors a key challenge in remote-sensing-based applications. Developing an effective approach to reduce these deviations is, therefore, essential to improve the accuracy of radar-based precipitation measurements. This study aims to develop a methodology for analyzing radar-derived precipitation using dual-polarization radar measurements, with validation based on rain gauge observations. Three well-established Z–R relationships—Marshall–Palmer, Muchnik, and Joss—were applied to radar reflectivity values measured at two heights, 1 km and 1.5 km above ground level. The Marshall–Palmer relationship applied at a height of 1.5 km yielded the smallest deviations from rain gauge measurements. Both the mean absolute error (MAE) and average precipitation difference at this height were consistent, amounting to 1.99 mm, compared to 2.32 mm at 1 km. The range of deviations in all cases was 0.54–7.64 mm at 1.5 km and 0.65–7.18 mm at 1 km. Furthermore, all tested Z–R relationships demonstrated a strong linear correlation with rain gauge data, as indicated by a Pearson correlation coefficient of 0.98. These findings enable the identification of the most accurate Z–R relationships and optimal measurement heights for radar-based precipitation estimation. These results may have important implications for operational applications and the calibration of radar precipitation products.

Near real-time integration of multi-source precipitation products using a multiscale convolutional neural network

Abstract Merging multi-source precipitation data based on deep learning models to create an accurate rainfall dataset has received significant interest in recent years. This article proposes a deep learning model to produce a high-accuracy, near real-time precipitation product for the North Central region of Vietnam during the period 2019–2023, with a spatial resolution of 0.040 and a temporal resolution of 1 hour. The input multi-source data including near real-time satellite-derived precipitation products (PERSIANN-CCS, GSMaP-NRT, and IMERG-Early Run), radar precipitation, and gauge observations, and spatial features NE and POP are merged by a multiscale CNN based model with focal loss function and mean square error loss function for classification and regression tasks, respectively. Extensive experiments demonstrate that the proposed precipitation product outperforms all the input precipitation products and the post-real-time global precipitation products including GSMaP-MVK-Gauge and IMERG-Final Run. It achieves classification metrics with a CSI of 0.65 and a BIAS of 1.03, with improvements from 31.58% to 54.8% in CSI and from 17.47% to 105.82% in BIAS, compared to radar, GSMaP-MVK-Gauge, and IMERG-Final Run products. For regression metrics, it achieves an RMSE of 3.34 mm/h, and an mKGE of 0.70, with improvements from 10.18% to 100% in RMSE and from 15.71% to 71.43% in mKGE over the same reference products. These results indicate that the merged product has a greater capability to detect rainfall events and significantly better overall performance, with lower systematic and random errors compared to the same reference products. Moreover, the proposed method outperforms the other methods, including Random Forest, Long Short-Term Memory, and the original multiscale CNN.

Improving Doppler Radar Precipitation Prediction with Citizen Science Rain Gauges and Deep Learning

Accurate, real-time estimation of rainfall from Doppler radars remains a challenging problem, particularly over complex terrain where vertical beam sampling, atmospheric effects, and radar quality limitations introduce significant biases. In this work, we leverage citizen science rain gauge observations to develop a deep learning framework that corrects biases in radar-derived surface precipitation rates at high temporal resolution. A key step in our approach is the construction of piecewise-linear rainfall accumulation functions, which align gauge measurements with radar estimates and allow for the generation of high-quality instantaneous rain rate labels from rain gauge observations. After validating gauges through a two-stage temporal and spatial consistency filter, we train an adapted ResNet-101 model to classify rainfall intensity from sequences of surface precipitation rate estimates. Our model substantially improves precipitation classification accuracy relative to NOAA’s operational radar products within observed spatial regions, achieving large gains in precision, recall, and F1 score. While generalization to completely unseen regions remains more challenging, particularly for higher-intensity rainfall, modest improvements over baseline radar estimates are still observed in low-intensity rainfall. These results highlight how combining citizen science data with physically informed accumulation fitting and deep learning can meaningfully improve real-time radar-based rainfall estimation and support operational forecasting in complex environments.

Discrimination of Light Precipitation from COWVR + TEMPEST, GMI, and ATMS Passive Microwave Radiometer Observations

Abstract In the near future, the number of low-cost, SmallSat/CubeSat-sized passive microwave (MW) radiometric observations available from low-Earth-orbiting (LEO) satellites is expected to increase substantially, especially high-frequency (HF) sounders (near or exceeding 90 GHz). The number of sensors that include the constant-incidence angle, dual-polarization low-frequency (LF) channels (near or below 37 GHz) will remain steady or decline. Maintaining LF sensing capabilities in the global satellite constellation for precipitation is one of the key recommendations of the International Precipitation Working Group (IPWG). Physically, the use of HF-only sensors for precipitation skews the precipitation estimates toward indirect, ice-based (scattering) retrievals rather than the precipitation closer to the surface. Over certain Earth surfaces, a portion of light precipitation (2 mm h−1 or less) may remain undetected, which has ramifications for global satellite-based precipitation products. Observations from the SmallSat-sized Compact Ocean Wind Vector Radiometer (COWVR) and the Temporal Experiment for Storms and Tropical Systems (TEMPEST) duo are compared with the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the Advanced Technology Microwave Sounder (ATMS) for discriminating light precipitation over a variety of Earth surface conditions. Precipitation is discriminated using a surface emissivity–based transformation applied to selected channel combinations from each of these sensors using the GPM dual-frequency precipitation radar (DPR) products as precipitation references. The results presented in this study highlight the importance of the LF channels for detection of light precipitation over ocean and lightly vegetated surfaces and 50-GHz temperature sounding channels over heavily vegetated surfaces. Significance Statement In the future, the number of low-cost, SmallSat or CubeSat-sized passive microwave sensors in low-Earth orbit is expected to increase substantially, especially high-frequency sounders operating near or above 90 GHz. Over certain Earth surfaces, the exclusion of low-frequency channels below 37 GHz implies that a portion of light precipitation (less than 2 mm h−1) may remain undetected, which has ramifications for global satellite-based precipitation products. This study emphasizes the need to maintain low-frequency sensing capabilities in the global satellite constellation to represent the range of precipitation intensity. Selected combinations of low- and high-frequency channels from sensors onboard a SmallSat highlight the benefit of low-frequency channels to improve the detection of light precipitation over ocean and vegetated surfaces.

A Comparison between Tornadic and Nontornadic QLCS Mesovortices Using a Multiradar Analysis of Operational and Experimental MRMS Products

Abstract Quasilinear convective system (QLCS) tornadoes have become an active area of research in recent years. Generally weaker, shorter lived, rapidly developing, and with shallower rotation than supercell tornadoes, they present a considerable challenge to forecast and warning operations. This study tracks 121 tornadic and 153 nontornadic (null) QLCS mesovortices spanning across numerous regions of the CONUS in an effort to understand evolutionary differences in mesovortex behavior leading up to a successful or failed mode of tornadogenesis via the Multi-Radar Multi-Sensor (MRMS) system in a multiradar framework. The multiradar framework allows for a comprehensive three-dimensional analysis able to capture storm-scale characteristics both near the surface and aloft. Vertical profiles of azimuthal shear, divergent shear, and dual-polarization moments are retained along the path of each mesovortex. Tornadic mesovortices are nearly 1 km deeper in the median and often exhibit stronger cyclonic rotation compared to nulls approximately 20 min prior to tornadogenesis, a signal consistent with prior observational studies. Moreover, tornadic mesovortices tend to exhibit higher magnitudes of divergence at the near surface through 6 km above ground level (AGL) and deeper convergence extending through 4 km AGL approximately 20 min prior to tornadogenesis. Tornadic events often display higher specific differential phase KDP, while differential reflectivity ZDR is highly variable across regions of the CONUS. Environmental 0–3 km AGL storm relative helicity is higher in tornadic events 1 h prior to tornadogenesis. Close-proximity (≤75-km range) mesovortices demonstrate higher variability than those in far proximity from the nearest radar. Significance Statement Quasilinear convective system (QLCS) tornadoes are usually weaker and shorter lived relative to supercell tornadoes making it difficult to anticipate tornadogenesis within these systems and provide ample lead times to save both life and property. This study used a merging of the Next Generation Weather Radar (NEXRAD) data to analyze mesovortices occurring along the leading edge of QLCSs. It was found that tornadic mesovortices often exhibit stronger and deeper counterclockwise rotation and showed stronger divergence from the near surface through 6 km in height 20 min before the tornado. However, other Doppler radar products, such as differential reflectivity, were shown to be highly variable across regions of the United States, yet specific differential phase was higher in the tornadic cases. Therefore, signs of tornadic potential can be shown for the QLCS mesovortices in our dataset when using dual-polarized radar data along with MRMS products.

MAPPING WAVES, CURRENTS AND BATHYMETRY WITH SHORE-BASED COHERENT MARINE RADAR: NEARSHORE VALIDATION

Reliable, cost efficient, and continuous observations of nearshore hydrodynamics are often required for the design and maintenance of coastal structures as well as to understand coastal change. In the last decades, advances in digitization and computational efficiency for signal processing have led to an increased use of marine radars as a tool for hydrographic applications, such as the retrieval of bathymetry, surface currents, winds and sea state. Many marine radar products are based on a three dimensional fast Fourier transformation (3D-FFT) of the image sequences obtained from a scanning radar. These methods have been extensively validated in deep to intermediate water depths. In the nearshore, and increasingly shallow waters, validation studies are rare and the available studies mainly focus on the retrieval of bathymetry. Validated radar measurements of spatially varying wave and current fields are not yet available. The present study is thus focussed on the assessment of the limitations of radar hydrography in a nearshore environment.

Constraining the water budget of a small agricultural headwater catchment using flux tower and soil data, satellite-based evapotranspiration, and semi-distributed hydrological modelling

On a global scale, the impacts of climate change on the hydrological cycle are clearly visible and documented. Some damages to ecosystems and humanity tend to be irreversible, particularly extreme events such as floodings and droughts, which are expected to occur more frequently (IPCC 2023). Quantifying the processes at work in the hydrological cycle at the catchment scale is very complex due to landscape heterogeneity and non-linear interactions. However, it can be addressed by constraining the problem using different sources of data and modelling tools. In this study, conducted at the ORE AgrHyS agro-hydrological observatory (Brittany, NW France), we compared ground-based measurements of actual evapotranspiration (AET) and soil water content (SWC) with satellite-based AET estimates and the outputs of a semi-distributed hydrological model. AET fluxes were measured by eddy-covariance at the FR-Nzn flux tower site (part of the EUROFLUX network), located on grazed grassland at the catchment head. SWC measurements were performed with TDR (Time-Domain Reflectometry) sensors at different locations and measurement depths in the watershed (5cm depth at most sites, but also down to 50 cm at the flux tower). Satellite-based AET estimates were computed using Landsat 8 OLI-TIRS products as well as local and meteorological model data (AROME model, by Météo France). The hydrological model used multi-site daily rainfall measurements and potential evapotranspiration to simulate daily specific discharge at the outlet and further constrain soil water balance, water table depth, flow rates and AET fluxes at the catchment scale. Additionally, the SWC TDR measurements performed over the catchment were compared to estimates obtained from the Copernicus Sentinel-1 radar product. Modelled and measured AET fluxes were compared over the period 2016-2024 to explore their inter-annual variability, extreme weather events (heat waves and droughts), and to discuss discrepancies between models and measurements. The potential of spatialized AET combining local measurements and modelled outputs is also studied. Such a combination of data sources is expected to improve modelling tools and to reduce uncertainties in AET and water balance at the catchment-scale.

Lightning identification based on multiple weather radar product data

ABSTRACT Lightning, with its extremely high energy level, can cause fires and power outages and pose a significant risk to people and infrastructure. However, predicting lightning remains challenging. The occurrence and development of lightning are closely related to thunderstorms, and weather radar is a crucial tool for detecting thunderstorm characteristics. Therefore, conducting deep learning research on lightning based on radar data can reveal hidden relationships between lightning and thunderstorm characteristics, laying the foundation for lightning prediction on the basis of thunderstorm features. In this context, this paper proposes a lightning identification model, MLDYOLO, which integrates Mixed Local Channel Attention, Large Separable Kernel Attention, and Dynamic Head modules into the You Only Look Once version 8 model. Using radar-detected thunderstorm characteristic data as input, the model identifies lightning by recognizing the environment in radar data. In this model, five types of radar product data are fused into a multi-feature radar image through a Principal Component Analysis Weighted Average Fusion method, which serves as the model’s input. To validate the proposed model, it is compared with advanced models. Results demonstrate the significant potential of the proposed approach in identifying lightning and supporting future lightning predictions.

Space Surveillance with High-Frequency Radar.

High-Frequency (HF) radar is well suited to the surveillance of low-earth-orbit space. For large targets, a small deployable HF radar is able to match the detection performance of much larger space surveillance radar systems operating at higher frequencies. However, there are some unique challenges associated with the use of HF, including the range-Doppler coupling bias, coarse detection-level localisation, and the presence of meteor returns and other unwanted signals. This paper details the use of HF radar for space surveillance, including signal processing and radar product formation, tracking, ionospheric correction, and orbit determination. It is shown that by fusing measurements from multiple passes, accurate orbital estimates can be obtained. Included are results from recent SpaceFest trials of the Defence Science and Technology Group's HF space surveillance radar, achieving real-time wide-area surveillance in tracking, orbit determination, and cueing of other space surveillance sensors.

Tundra recovery post-fire in the Yukon–Kuskokwim Delta, Alaska

Abstract The extent of wildfires in tundra ecosystems has dramatically increased since the turn of the 21st century due to climate change and the resulting amplified Arctic warming. We simultaneously studied the recovery of vegetation, subsurface soil moisture, and active layer thickness (ALT) post-fire in the permafrost-underlain uplands of the Yukon–Kuskokwim Delta in southwestern Alaska to understand the interaction between these factors and their potential implications. We used a space-for-time substitution methodology with 2017 Landsat 8 imagery and synthetic aperture radar products, along with 2016 field data, to analyze tundra recovery trajectories in areas burned from 1953 to 2017. We found that spectral indices describing vegetation greenness and surface albedo in burned areas approached the unburned baseline within a decade post-fire, but ecological succession takes decades. ALT was higher in burned areas compared to unburned areas initially after the fire but negatively correlated with soil moisture. Soil moisture was significantly higher in burned areas than in unburned areas. Water table depth (WTD) was 10 cm shallower in burned areas, consistent with 10 cm of the surface organic layer burned off during fire. Soil moisture and WTD did not recover in the 46 years covered by this study and appear linked to the long recovery time of the organic layer.

JAXA Level 2 cloud and precipitation microphysics retrievals based on EarthCARE radar, lidar, and imager: the CPR_CLP, AC_CLP, and ACM_CLP products

Abstract. This study introduces the primary products and features of active-sensor-based Level 2 cloud microphysics products of the Japanese Aerospace Exploration Agency (JAXA; i.e., the cloud radar standalone cloud product (CPR_CLP), the radar–lidar synergy cloud product (AC_CLP), and the radar–lidar–imager cloud product (ACM_CLP)). Combined with the 94 GHz Doppler cloud profiling radar (CPR), 355 nm high-spectral-resolution lidar (Atmospheric Lidar, ATLID) and Multi-Spectral Imager (MSI), these products provide a detailed view of the transitions of cloud particle categories and their size distributions. Simulated EarthCARE Level 1 data mimicking actual global observations were used to assess the performance of the JAXA Level 2 cloud microphysics product. Evaluation of the product revealed that the retrievals reasonably reproduced the vertical profile of the modeled microphysics. Further validation of the products is planned for post-launch calibration and validation. Velocity-related JAXA Level 2 products (i.e., CPR_VVL, AC_VVL, and ACM_VVL) such as hydrometeor fall speed and vertical air velocity will be described in a future paper.

Deriving Coastal Sea Surface Current by Integrating a Tide Model and Hourly Ocean Color Satellite Data

Sea surface currents (SSCs) play a pivotal role in material transport, energy exchange, and ecosystem dynamics in coastal marine environments. While traditional methods to obtain wide-range SSCs, such as satellite altimetry, often struggle with limited performance in coastal regions due to waveform contamination, deriving SSCs from sequential ocean color data using maximum cross-correlation (MCC) has emerged as a promising approach. In this study, we proposed a novel SSC estimation method, called tide-restricted maximum cross-correlation (TRMCC), and implemented it on hourly ocean color data obtained from the Geostationary Ocean Color Imager II (GOCI-II) and the global tide model FES2014 to derive SSCs in coastal seas and turbid estuaries. Cross-comparison over three years with buoy data, high-frequency radar, and numerical model products shows that TRMCC is capable of obtaining high-resolution SSCs with good accuracy in coastal and estuarine areas. Both large-scale ocean circulation patterns in seas and fine-scale surface current structures in estuaries can be effectively captured. The deriving accuracy, especially in coastal and estuarine areas, can be significantly improved by integrating tidal current data into the MCC workflow, and the influence of invalid data can be minimized by using a flexible reference window size and normalized cross-correlation in the Fourier domain technique. Seasonal SSC structure in the Bohai Sea and diurnal SSC variation in the Yangtze River Estuary were depicted via the satellite method, for the first time. Our study highlights the vast potential of TRMCC to improve the understanding of current dynamics in complex coastal regions.

Sensitivity to Sea Ice Thickness Parameters in a Coupled Ice‐Ocean Data Assimilation System

Abstract Sea ice thickness (SIT) estimates derived from CryoSat‐2 radar freeboard measurements are assimilated into the Met Office's Forecast Ocean Assimilation Model. We test the sensitivity of winter simulations to the snow depth, radar freeboard product and assumed radar penetration through the snowpack in the freeboard‐to‐thickness conversion. We find that modifying the snow depth has the biggest impact on the modeled SIT, changing it by up to 0.88 m (48%), compared to 0.65 m (33%) when modifying the assumed radar penetration through the snowpack and 0.55 m (30%) when modifying the freeboard product. We find a doubling in the thermodynamic volume change over the winter season when assimilating SIT data, with the largest changes seen in the congelation ice growth. Next, we determine that the method used to calculate the observation uncertainties of the assimilated data products can change the mean daily model SIT by up to 36%. Compared to measurements collected at upward‐looking sonar moorings and during the Operation IceBridge campaign, we find an improvement in the SIT simulations' variability representation when assuming partial radar penetration through the snowpack and when improving the method used to calculate the CryoSat‐2 observation uncertainties. This paper highlights a concern for future SIT data assimilation and forecasting, with the chosen parameterization of the freeboard‐to‐thickness conversion having a substantial impact on model results.

Assessing the Impact of Radar-Rainfall Uncertainty on Streamflow Simulation

Abstract Hydrological models and quantitative precipitation estimation (QPE) are critical elements of flood forecasting systems. Both are subject to considerable uncertainties. Quantifying their relative contribution to the forecasted streamflow and flood uncertainty has remained challenging. Past work documented in the literature focused on one of these elements separately from the other. With this in mind, we present a systematic approach to assess the impact of QPE uncertainty in streamflow forecasting. Our approach explores the operational Iowa Flood Center (IFC) hydrological model performance after altering two radar-based QPE products. We ran the Hillslope Link Model (HLM) for Iowa between 2015 and 2020, altering the Multi-Radar/Multi-Sensor (MRMS) system and the specific attenuation-based (IFCA) IFC radar-derived product with a multiplicative error term. We assessed the forecasting system performance at 112 USGS streamflow gauges using the altered QPE products. Our results suggest that addressing rainfall uncertainty has the potential for much-improved flood forecasting spatially and seasonally. We identified spatial patterns linking prediction improvements to the radar’s location and the magnitude of rainfall. Also, we observed seasonal trends suggesting underestimations during the cold season (October–April). The patterns for different radar products are generally similar but also show some differences, implying that the QPE algorithm plays a role. This study’s results are a step toward separating modeling and QPE uncertainties. Future work involving larger areas and different hydrological and error models is essential to improve our understanding of the impact of QPE uncertainty. Significance Statement This study investigates the impact of radar rainfall on flood forecasting uncertainty. Previous research focused on rainfall–runoff models, ignoring the errors in rainfall estimation. We used a systematic approach to adjust two radar-rainfall products, forcing a simple hydrological model. Results show the potential improvement in streamflow prediction by correcting basinwide bias in rainfall. The optimal correction varies with basin size, location, season, and rainfall amount.

Evolution of Radar Meteorology in India and the latest trends

Weather radar is an indispensable tool in the continuous monitoring and warning of extreme events including tropical cyclones and thunderstorms. The India Meteorological Department (IMD) has been operating radars since 1949. The evolution of radar meteorology in India may be divided into three broad phases, namely, the 1950s (phase-I), then up to the year 2000 (phase-II), and thereafter (phase-III). During phase-I, radars were imported and installed in cities to aid aircraft operations. Photographs of radar scopes were analysed to provide a broad understanding of temporal evolution and spatial extent of precipitating clouds in different parts of the country and seasons. During phase-II, storm warning (X-band) and cyclone warning (S-band) radars with more power and range were installed, and some of them were indigenous. Phase-III ushered in the era of digital Doppler weather radars in India. Interfacing between numerical models and radars started in phase-III including assimilation of radar winds and model verification. Installation and operation of weather radars outside IMD also started in phase-III. Important areas where more work needs to be done include a well-trained workforce in radar meteorology, radar calibration and data standardization, radar area coverage and networking, algorithms for quantitative precipitation estimation using polarimetric products, assimilation of radar products in numerical models, research on cloud physics and dynamics, applications of AI/ML in storm and severe weather nowcasting.

Shallow Water Ice Detection From SHARAD Data in Central Utopia Planitia, Mars

Abstract One of the key scientific goals of China's first Mars mission Tianwen‐1 is to search for ground ice. This study focuses on investigating potential water ice reservoirs in the vicinity of the landing site of the Zhurong rover to provide geological context and references for data interpretation. Our study area is centered on Utopia Planitia (UP), where Shallow Radar onboard the Mars Reconnaissance Orbiter (SHARAD) previously detected subsurface echoes that could be interpreted as ice deposits. Based on the SHARAD data, we have estimated the thickness, dielectric properties, and possible material composition of the surface deposition layer. The inferred water ice volume content ranges from approximately 55%–85%, which is consistent with deposits found on the western edge of UP. Based on morphological features and radar data products, we interpret the detected sediment layer as the latitude‐dependent mantle (LDM). We have conducted a comprehensive analysis of the distribution and morphology of various periglacial landforms, including Decameter‐scale Rimmed Depressions (DRDs), polygonal landforms, and scalloped depressions on the surface of UP. The implications for the level of degradation are discussed. The radar results provide evidence that DRDs have formed as a result of the degradation of the LDM layer. Additionally, our statistical analysis of concentric crater humps (CCH) linked to subsurface pure glacial ice suggests the possible presence of an icy layer that may be as thick as a kilometer beneath the LDM unit.

Digital Marketing Strategies for Promoting Radar Product in B2B Market

In the rapidly evolving landscape of business-to-business (B2B) marketing, the integration of digital strategies has become essential for promoting advanced technologies. This paper focuses on efficient digital strategies for promoting radar products in the B2B market. This study puts forward its current trends and analyzes its challenges, pointing out that while there are extensive segmentation and needs of the radar B2B market, marketing high-tech products like radar technology involves distinct challenges that need specialized digital strategies. For one thing, the technical complexity of radar products requires specialized knowledge to convey its benefits to potential buyers in the B2B market. For another, B2B sales cycles involve multiple decision-makers, often requiring demonstrations, pilot projects, and continuous dedication to establish long-term trust. Based on these, this study identifies several key digital marketing tactics, such as content marketing, Search Engine Optimization (SEO), social media engagement, email marketing, digital advertising and real-time analytics to enhance radar products brand visibility in the B2B market. Empirically, the results provide valuable digital marketing insights for high-tech companies, especially for radar products in the B2B sector.

A Bayesian Framework for the Probabilistic Interpretation of Radar Observations and Severe Hailstorm Reports

Abstract Understanding when and where severe hailstorms occur is key to managing the serious risk they pose. Radar products, such as the maximum expected size of hail (MESH), are often used to form a severe hail climatology. However, the challenges of relating reflectivity measured aloft to severe hail at the surface mean these climatologies have unknown uncertainty. Here, we quantify the uncertainty of radar observations of severe hailstorms by deriving a probabilistic interpretation of MESH within a fully Bayesian framework calibrated using reports from Australia’s Severe Storms Archive in southeast Queensland. Moreover, our novel approach accounts for the spatially varying (under)reporting rate in the region. Despite the popularity of using MESH thresholds to distinguish severe hail events, our results suggest that these thresholds are less sharp than previously believed and question their interpretation. Furthermore, we quantify the spatial variability in the severe hail reporting rate and suggest that, even over the most densely populated areas in the region, the reporting rate may be as low as 53%. Finally, we produce a hail climatology that has a similar magnitude to existing radar-based climatologies but with smoother and more realistic spatial gradients. Our method is generalizable to many other datasets within and beyond severe weather by enabling the principled usage of reports even when the absence of a report does not necessarily indicate the event did not occur. Significance Statement Severe hailstorms are one of Australia’s costliest natural perils. This work aims to improve how we use radar to understand when and where these storms occur. Although radar is a popular tool in this pursuit, it is challenging to link reflectivity aloft to hail at the ground. Rather than using a binary threshold on a radar-based parameter to distinguish severe hail, we instead treat radar data as a predictor of the probability of severe hail, estimating this probability by concurrently estimating the reporting rate of severe hail. This work sheds new light on how we might reinterpret radar data to improve our forecasting and understanding of severe hail. Moreover, our method is applicable to other severe weather hazards and even beyond climate science.

Benchmark Dataset Applied to Quantitative Precipitation Estimation and Forecasting of Qinghai

We collected short-duration heavy precipitation events around weather radar since 2020 and get radar base data of these events. Then, we extracted radar products like Composite Reflectivity (CR), echo top height (ET) and Vertically Integrated Liquid Water (VIL) from radar base data after quality control as the input of dataset, and merge the surface precipitation of 6 minutes with background field using multi-grid variation to generate precipitation label by 6 minutes. Thus, we constructed two datasets artificial intelligence application QpefQH for short-duration heavy precipitation. QpefQH dataset has a scale of more than 15,000 images available for radar echo extrapolation and quantitative precipitation estimation tasks based on deep learning. QpefQH dataset also includes multi-source data such as satellite, numerical model, sounding, lightning, land surface temperature, pressure, wind and other observation data. QpefQH can be shared as a benchmark dataset for artificial intelligence research on short-duration heavy precipitation in Qinghai Province.