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This article summarizes research and risk reduction that will inform acquisition decisions regarding NOAA’s future national operational weather radar network. A key alternative being evaluated is polarimetric phased-array radar (PAR). Research indicates PAR can plausibly achieve fast, adaptive volumetric scanning, with associated benefits for severe-weather warning performance. We assess these benefits using storm observations and analyses, observing system simulation experiments, and real radar-data assimilation studies. Changes in the number and/or locations of radars in the future network could improve coverage at low altitude. Analysis of benefits that might be so realized indicates the possibility for additional improvement in severe-weather and flash-flood warning performance, with associated reduction in casualties. Simulations are used to evaluate techniques for rapid volumetric scanning and assess data quality characteristics of PAR. Finally, we describe progress in developing methods to compensate for polarimetric variable estimate biases introduced by electronic beam-steering. A research-to-operations (R2O) strategy for the PAR alternative for the WSR-88D replacement network is presented.

After decades of research and development, the WSR-88D (NEXRAD) network in the United States was upgraded with dual-polarization capability, providing polarimetric radar data (PRD) that have the potential to improve weather observations, quantification, forecasting, and warnings. The weather radar networks in China and other countries are also being upgraded with dual-polarization capability. Now, with radar polarimetry technology having matured, and PRD available both nationally and globally, it is important to understand the current status and future challenges and opportunities. The potential impact of PRD has been limited by their oftentimes subjective and empirical use. More importantly, the community has not begun to regularly derive from PRD the state parameters, such as water mixing ratios and number concentrations, used in numerical weather prediction (NWP) models. In this review, we summarize the current status of weather radar polarimetry, discuss the issues and limitations of PRD usage, and explore potential approaches to more efficiently use PRD for quantitative precipitation estimation and forecasting based on statistical retrieval with physical constraints where prior information is used and observation error is included. This approach aligns the observation-based retrievals favored by the radar meteorology community with the model-based analysis of the NWP community. We also examine the challenges and opportunities of polarimetric phased array radar research and development for future weather observation.

The National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Administration (NOAA) have a long and successful history of weather radar research. The NOAA ground-based radars—WSR-88D network—provide nationwide precipitation observations and estimates with advanced polarimetric capability. As a counterpart, the NASA–JAXA spaceborne radar—the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM DPR)—has global coverage and higher vertical resolution than ground-based radars. While significant advances from both NOAA’s WSR-88D network and NASA–JAXA’s spaceborne radar DPR have been made, no systematic comparisons between the WSR-88D network and the DPR have been done. This study for the first time generates nationwide comprehensive comparisons at 136 WSR-88D radar sites from 2014 to 2020. Systematic differences in reflectivity are found, with ground radar reflectivity on average 2.4 dB smaller than that of the DPR (DPR version 6). This research found the discrepancies between WSR-88D and DPR arise from different calibration standards, signal attenuation correction, and differences in the ground and spaceborne scattering volumes. The recently updated DPR version 7 product improves rain detection and attenuation corrections, effectively reducing the overall average WSR-88D and DPR reflectivity differences to 1.0 dB. The goal of this study is to examine the systematic differences of radar reflectivity between the NOAA WSR-88D network and the NASA–JAXA DPR and to draw attention to radar-application users in recognizing their differences. Further investigation into understanding and alleviating the systematic bias between the two platforms is needed.

We present an integrated framework that leverages multiple weather radar calibration and monitoring techniques to provide real-time diagnostics on reflectivity calibration, antenna pointing, and dual-polarization moments. This framework uses a volume-matching technique to track the absolute calibration of radar reflectivity with respect to the Global Precipitation Measurement (GPM) spaceborne radar, the relative calibration adjustment (RCA) technique to track relative changes in the radar calibration constant, the solar calibration technique to track daily change in solar power and antenna pointing error, and techniques that track properties of light-rain medium to monitor the differential reflectivity and dual-polarization moments. This framework allows for an evaluation of various calibration and monitoring techniques. For example, we found that a change in the RCA is highly correlated to a change in absolute calibration, with respect to GPM, if a change in antenna pointing can first be ruled out. It is currently monitoring 67+ radars from the Australian radar network. Because of the diverse and evolving nature of the Australian radar network, flexibility and modularity are at the core of the calibration framework. The framework can tailor its diagnostics to the specific characteristics of a radar (band, beamwidth, etc.). Because of its modularity, it can be expanded with new techniques to provide additional diagnostics (e.g., monitoring of radar sensitivity). The results are presented in an interactive dashboard at different level of details for a wide and diverse audience (radar engineers, researchers, forecasters, and management), and it is operational at the Australian Bureau of Meteorology.

The FY‐3G Precipitation Measurement Radar (PMR), the world's second dual‐frequency satellite precipitation radar (SR), provides three‐dimensional precipitation structure data in mid‐ and low‐latitude regions, with performance comparable to Global Precipitation Measurement Dual‐frequency PR (GPM DPR). Ground‐based weather radar (GR) data are used to perform a comparative analysis of the reflectivity consistency between PMR and DPR satellite‐ground radar observations. The results indicate that PMR and DPR reflectivity factors are systematically higher than GR. PMR and DPR are 0.94 and 1.43 dB higher than CINRAD reflectivity respectively, while 1.19 and 1.98 dB higher than NEXRAD with uncertainty around 2 dB. Stratiform samples exhibit the smallest biases, with reflectivity differences further reduced below the bright band. PMR precipitation classification result aligns well with DPR. Through ground‐based comparisons with CINRAD and NEXRAD, the FY‐3G PMR exhibits relatively small differences. This makes it well‐suited for joint global precipitation observations alongside the DPR. Plain Language Summary We compare the consistency of FY‐3G PMR and GPM DPR with GR, using 23 CINRAD and 18 NEXRAD radars to analyze effective precipitation cross‐observation events during the summer of 2024. We separate the matching samples into different precipitation types (Stratiform and convective) and height using SR level‐2 products. Stratiform precipitation samples, which accounted for a larger proportion of the all precipitation samples, exhibited better correlation and smaller bias due to being weaker and more homogeneous. In contrast, convective precipitation samples, characterized by intense variability, showed larger biases, primarily influenced by differences in SR attenuation correction algorithms. We believe that, beyond external calibration discrepancies, factors such as attenuation correction, observation geometry, and frequency differences significantly affect the consistency of reflectivity between SR and GR. This study does not aim to determine which system is more accurate but aims to improve SR algorithms to enhance the reflectivity consistency between SR and GR. This study evaluates the consistency between PMR and GR, providing insights for building a validation network for FY‐3G. Key Points First evaluation of precipitation radar performance on FY‐3G precipitation measurement satellite based on ground‐based radar reflectivity First research on the consistency of reflectivity between satellite PR and ground radar on FY‐3G and GPM First evaluation of the precipitation classification and attenuation correction algorithms for the FY‐3G Precipitation Measurement Radar

The S-bandWSR-88D weather radar is sensitive enough to observe biological scatterers like birds and insects. However, their non-spherical shapes and frequent collocation in the radar resolution volume create challenges in identifying their echoes. We propose a method of extracting bird (or insect) features by coherently averaging dual polarization measurements from multiple radar scans, containing bird (insect) migration. Additional features are also computed to capture aspect and range dependence, and the variation of these echoes over local regions. Next, ridge classifier and decision tree machine learning algorithms are trained, first only with the averaged dual pol inputs and then different combinations of the remaining features are added. The performance of all models for both methods, are analyzed using metrics computed from the test data. Further studies on different patterns of birds/insects, including roosting birds, bird migration and insect migration cases, are used to further investigate the generality of our models. Overall, the ridge classifier using only dual polarization variables was found to perform consistently well across all these tests. Our recommendation is that this classifier can be used operationally on the US Next-Generation Radars (NEXRAD), as a first step in classifying biological echoes. It would be used in conjunction with the existing Hydrometeor Classification Algorithm (HCA), where the HCA would first separate biological from non-biological echoes, then our algorithm would be applied to further separate biological echoes into birds and insects. To the best of our knowledge, this study is the first to train a machine learning classifier that is capable of detecting diverse patterns of bird and insect echoes, based on dual polarization variables at each range gate.

We present the first ever real‐time numerical weather prediction system with 30‐s update cycles at a 500‐m grid spacing for the prediction of convective precipitation in the subsequent 30 min using a new‐generation multi‐parameter phased array weather radar. The system comprises a regional atmospheric model known as the SCALE and the local ensemble transform Kalman filter (LETKF). To accelerate the SCALE‐LETKF system, data transfer between the two aforementioned components is performed using a memory copy instead of a file I/O. A complete real‐time workflow including domain nesting and observational data transfer is constructed. A real‐time test in July and August 2020 showed that the system is fast enough for a real‐time application of 30‐s forecast‐analysis cycles and 30‐min prediction. The development includes a new thinning method considering the spatially correlated observation errors in the dense radar data. This new thinning method is effective in two past case studies in the summer of 2019. Plain Language Summary We developed the first ever real‐time weather prediction system that refreshes forecasts every 30 s using frequent observations for the forecasting of sudden heavy rainfall, a major risk in the warm season. The system takes advantage of state‐of‐the‐art radar observations and performs predictions for the succeeding 30 min at 500‐m horizontal grid spacing. A real‐time test in July and August 2020 showed that the system is fast enough to update forecasts every 30 s in real time. We evaluated the system using past case data and found that it accurately predicted small‐scale rainfalls. Key Points A complete real‐time workflow for 30‐s‐update 500‐m‐mesh numerical weather prediction for convective precipitation was developed The system assimilates data from a phased array weather radar every 30 s and performs 30‐min extended forecasts in real time The forecast accuracy is improved by applying a new thinning method to the dense radar data

The displaced phased center antenna (DPCA) method of clutter cancellation for ground moving target detection from airborne platforms has been in use for a number of decades. Application of the DPCA method for spaceborne Doppler weather radar velocity estimation was suggested in 2007. The initial description and analysis of the technique was followed several years ago by demonstration using a multiantenna airborne radar. Recent reviews of methods and technology for spaceborne cloud and precipitation radar have also mentioned possible use of DPCA. However, to date, analyses of the application of DPCA to spaceborne Doppler weather radar have assumed that the two channels and antennas are identical, including perfect alignment, and that the DPCA condition is well-satisfied. This study uses simulation to examine the effects of relaxing these assumptions. The simulation method and its validation are discussed, with companion analytical calculations in the appendix. Next, simulations are used to show the effects on the Doppler estimates from errors in pointing and positioning relative to the ideal DPCA. The DPCA technique is relatively robust to possible errors, indicating that a practical DPCA radar system can provide precise Doppler measurements from space.

We propose a simulation framework that can be used to design and evaluate the performance of adaptive scanning algorithms on different phased-array weather radar designs. The simulator is proposed as tool to 1) compare the performance of different adaptive scanning algorithms on the same weather event, 2) evaluate the performance of a given adaptive scanning algorithm on several weather events, and 3) evaluate the performance of a given adaptive scanning algorithm on a given weather event using different radar designs. We illustrate the capabilities of the proposed framework to design and evaluate the performance of adaptive algorithms aimed at reducing the update time using adaptive scanning. The example concept of operations is based on a fast low-fidelity surveillance scan and a high-fidelity adaptive scan. The flexibility of the proposed simulation framework is tested using two phased-array-radar designs and three complementary adaptive scanning algorithms: focused observations, beam clustering, and dwell tailoring. Based on a significant weather event observed by an operational NEXRAD radar, our experimental results consist of radar data that were simulated as if the same event had been observed by arbitrary combinations of radar systems and adaptive scanning configurations. Results show that simulated fields of radar data capture the main data-quality impacts from the use of adaptive scanning and can be used to obtain quantitative metrics and for qualitative comparison and evaluation by forecasters. That is, the proposed simulator could provide an effective interface with meteorologists and could support the development of concepts of operations that are based on adaptive scanning to meet the evolutionary observational needs of the U.S. National Weather Service.

Continental coverage and year-round operation of the weather radar networks provide an unprecedented opportunity for studying large-scale airborne migration. The broad and local-scale airborne information collected by these infrastructures can answer many ecological questions. However, extracting and interpreting the biological information from such massive weather radar data remains an intractable problem. Recently, many big-data problems have been solved using the deep learning technology. In this study, the biological information in the weather radar data is identified using the advanced deep learning method. The proposed method consists of two main parts, i.e., a rendering and casting procedure and an image segmentation procedure based on a convolutional neural network. The biological data are automatically extracted by rendering and mapping, image segmentation, and result masking. By analyzing the typical radar data from single and multiple stations, we partly reveal the intensity and speed of the migration pattern. We present the first feasibility study of the extraction of local and large-scale biological phenomena from the Chinese weather radar network data.