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The accurate prediction of high‐impact weather systems using cloud‐resolving models is still a challenge among researchers. This study evaluates the consistency of the combination of the three‐dimensional variational technique within the Gridpoint Statistical Interpolation assimilation system (GSI‐3DVAR) and nudging in the same modelling system on short‐range forecasts of three heavy rainfall events from the southwest monsoon season of 2021 over the Indian subcontinent. Three experiments have been conducted (i) control (CNTL): assimilated conventional and satellite observations; (ii) radar and lightning data assimilation (RLDA): assimilated radar reflectivity and lightning proxy reflectivity data along with all observations used in CNTL; and (iii) lightning data assimilation (LDA): same as RLDA but without the assimilation of radar data; particularly done to test the impact of assimilation of only lightning data. The model‐simulated rainfall is evaluated by using the Integrated Multi‐Satellite Retrievals (IMERG) for Global Precipitation Measurement (GPM IMERG) rainfall data. The intercomparison of LDA and RLDA for event 1 highlighted that both represent the convective regions reasonably better than CNTL, but RLDA outperforms LDA and thus further assimilation experiments are done with RLDA. RLDA provided reasonably accurate forecasts compared to CNTL, which is evident in the spatial distribution of rainfall and area‐averaged three‐hourly accumulated rainfall. Verification metrics for the three selected heavy rainfall events reveal that an optimal forecast performance (especially in the first six hours of free forecast) is obtained by the simulation with assimilation of radar and lightning data during the pre‐forecast period, through correcting the position and timing of convective centres. The probability of detection (POD) values are higher for light rainfall categories than for the heavy rain categories. POD values were higher in RLDA than CNTL throughout simulation for all three events. For all these three selected events, fractions skill scores (FSS) of RLDA are always better than CNTL with different neighbourhood sizes for different threshold values throughout the forecast period. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting in real-time is a challenging task that demands accurate and current data from multiple sources. Despite various approaches proposed by researchers to address this challenge, models such as the interaction-based dual attention LSTM (IDA-LSTM) face limitations, particularly in radar echo extrapolation. These limitations include higher computational costs and resource requirements. Moreover, the fixed kernel size across layers in these models restricts their ability to extract global features, focusing more on local representations. To address these issues, this study introduces an enhanced convolutional long short-term 2D (ConvLSTM2D) based architecture for precipitation nowcasting. The proposed approach includes time-distributed layers that enable parallel Conv2D operations on each image input, enabling effective analysis of spatial patterns. Following this, ConvLSTM2D is applied to capture spatiotemporal features, which improves the model's forecasting skills and computational efficacy. The performance evaluation employs a real-world weather dataset benchmarked against established techniques, with metrics including the Heidke skill score (HSS), critical success index (CSI), mean absolute error (MAE), and structural similarity index (SSIM). ConvLSTM2D demonstrates superior performance, achieving an HSS of 0.5493, a CSI of 0.5035, and an SSIM of 0.3847. Notably, a lower MAE of 11.16 further indicates the model's precision in predicting precipitation. [ABSTRACT FROM AUTHOR]

This study used a Weather Research and Forecasting (WRF)‐based Ensemble Kalman Filter (EnKF) system to assimilate reflectivity (Z) and radial velocity (Vr) data in precipitating and clear‐air regions from the Beijing Daxing International Airport C‐band phased‐array radar (C‐PAR) to improve the forecasts of a convective initiation (CI) case occurred on 18 June 2020. The results showed that high‐frequency assimilating the C‐PAR Vr in clear‐air region is conducive to increase the forecast lead time of CI by significantly improving the initial dynamic and thermodynamic fields, which creates a more accurate pre‐CI environment. After assimilating the C‐PAR clear‐air Vr, the CI case can be accurately predicted with a 20 min forecast lead time in the best‐case scenario. This is the first real‐case study to demonstrate the benefits of assimilating high spatiotemporal resolution PAR clear‐air radial velocity data for the CI process. Plain Language Summary: Convection initiation is the beginning stage of severe convective weather. Accurate prediction of its location and timing is very important for monitoring and early warning of severe convective weather. However, CI forecast is still a great challenge worldwide. The good news is that newly deployed phased‐array radar (PAR) with high spatiotemporal resolution can capture the much needed small‐scale information of the preconvective environment and the whole CI process. This study combines the new PAR observation and numerical weather prediction model through Ensemble Kalman Filter (EnKF), one of the most popular data assimilation techniques, to improve forecast of a CI event. With the improved data, we successfully forecast CI process mainly thanks to the PAR that help improved three‐dimensional winds, temperature, and humidity analysis, particularly in the clear‐air regions. Key Points: First real‐case assimilation of C‐band phased‐array radar clear‐air data on convection initiation using Ensemble Kalman FilterAssimilating clear‐air radial velocity plays a key role in successful forecast of convective initiation (CI)Assimilating clear‐air radial velocity has the potential to accurately forecast CI with a lead time up to 20 min [ABSTRACT FROM AUTHOR]

Radar reflectivity images have the potential to provide vital information on the development of convective cloud interiors, which can play a critical role in precipitation prediction. However, traditional prediction methods face challenges in preserving the high-frequency component, leading to blurred prediction results. To address this issue and accurately estimate radar reflectivity intensity, this paper proposes a novel reflectivity image prediction approach based on the Spatial Memory in Memory (Spatial MIM) networks and the Pix2Pix networks. Firstly, a rough radar reflectivity image prediction is made using the Spatial MIM network. Secondly, the prediction results from the Spatial MIM network are fed into the Pix2pix network, which improves the high-frequency component of the predicted image and solves the image blurring issue. Finally, the proposed approach is evaluated using data from Oklahoma in the United States during the second and third quarters of 2021. The experimental results demonstrate that the proposed approach yields more accurate radar reflectivity prediction images. [ABSTRACT FROM AUTHOR]

Dual-polarization radar data are useful for numerical models to improve precipitation forecasts. For an extremely heavy precipitation event that occurred in Central China on 11 August 2021, the hydrometeor concentration and water vapor content used in the initial field of the Weather Research and Forecasting (version 4.1) model are retrieved by the statistical relationship of relative humidity with dual-polarization radar reflectivity in Suizhou City of Central China. Three experiments are conducted, and the simulation results are compared after assimilating the radar data. The results indicate that the multiple factors contributing to this extreme heavy precipitation event included the divergence of upper-level airflows, the middle- and low-level low vortex/shear, the easterly jet stream in front of the low vortex, and the continuous intrusion of cold air on the ground. In addition, with the retrieval of the hydrometeor concentration and water vapor content, the composite reflectivity forecast results are more similar to the observations. Also, the location and intensity of the short-term extremely heavy precipitation event are less different from the observations. In addition, by cyclically adjusting the hydrometeor concentration and water vapor content in the initial field, we can obtain better forecasts of the reflectivity and short-term extremely heavy precipitation, and this improvement can be maintained for approximately 3 h. [ABSTRACT FROM AUTHOR]

Multiscale valid time shifting (VTS) was explored for a real-time convection-allowing ensemble (CAE) data assimilation (DA) system featuring hourly assimilation of conventional in situ and radar reflectivity observations, developed by the Multiscale Data Assimilation and Predictability Laboratory. VTS triples the base ensemble size using two subensembles containing member forecast output before and after the analysis time. Three configurations were tested with 108-member VTS-expanded ensembles: VTS for individual mesoscale conventional DA (ConVTS) or storm-scale radar DA (RadVTS) and VTS integrated to both DA components (BothVTS). Systematic verification demonstrated that BothVTS matched the DA spread and accuracy of the best-performing individual component VTS. The 10-member forecasts showed BothVTS performs similarly to ConVTS, with RadVTS having better skill in 1-h precipitation at forecast hours 1–6, while Both/ConVTS had better skill at later hours 7–15. An objective splitting of cases by 2-m temperature cold bias revealed RadVTS was more skillful than Both/ConVTS out to hour 10 for cold-biased cases, while BothVTS performed best at most hours for less-biased cases. A sensitivity experiment demonstrated improved performance of BothVTS when reducing the underlying model cold bias. Diagnostics revealed enhanced spurious convection of BothVTS for cold-biased cases was tied to larger analysis increments in temperature than moisture, resulting in erroneously high convective instability. This study is the first to examine the benefits of a multiscale VTS implementation, showing that BothVTS can be utilized to improve the overall performance of a multiscale CAE system. Further, these results underscore the need to limit biases within a DA and forecast system to best take advantage of VTS analysis benefits. [ABSTRACT FROM AUTHOR]

Wind retrievals from high‐frequency radars (HFRs) provide high‐density, hourly wind estimates over the ocean near the coast. These wind retrievals are a promising new source of near‐surface wind estimates in coastal regions where it is difficult to deploy large networks of buoys and where scatterometers cannot observe because of land contamination. In addition to improving ocean monitoring, these wind retrievals are also useful in numerical weather prediction. The wind estimates are retrieved from the HFR Doppler spectra in conjunction with the Simulating WAves Nearshore (SWAN) model, HFR forward model, and HFR adjoint model. In this work, wind retrievals were generated from three Coastal Ocean Dynamics Applications Radar (CODAR) SeaSonde HFR sites located near the mouth of the Chesapeake Bay in August 2017 and assimilated in the Coupled Ocean/Atmosphere Mesoscale Prediction System® (COAMPS) model. The impact of the assimilated HFR wind retrievals on near‐surface weather forecasts is measured with adjoint‐derived forecast sensitivity observation impact (FSOI) and a comparison to forecasts from a data‐denial observing system experiment (OSE). The FSOI measurement indicates the HFR winds had neutral impact on the 12‐h forecast while the OSE comparison suggests a small improvement in the 10‐m u and v winds at lead times up to 36 h. Compared to a similar study in the Southern California Bight, the small impact on the forecast seen in this Chesapeake Bay study could be related to there being a smaller number of wind retrievals in a smaller region during a period where the HFR wind retrievals were already similar to the background wind field. We suggest that HFR wind retrievals used for numerical weather prediction may be more beneficial when generated for long swaths of coastlines rather than small, isolated areas. [ABSTRACT FROM AUTHOR]

Worldwide, wildlife–aircraft strikes cost more than US$1.2 billion in aircraft damage and downtime and jeopardize the safety of aircrews, passengers, and animals. Radar has long been used to monitor flying animal movements and can be a useful tool for strike mitigation. In the USA, the Avian Hazard Advisory System (AHAS) is an early‐warning system that integrates data from next‐generation weather radar (NEXRAD) weather surveillance radars (WSRs) with historic bird occurrence data to quantify avian activity and forecast the relative bird risk within a ~9.3‐km radius of military and civilian airfields. Bird detection radars (BDRs) with both horizontal‐surveillance and vertical‐scanning components are also available for monitoring local avian activity at airports, but we have little information regarding the congruence of broad‐scale warnings and local avian activity where WSRs and BDRs overlap. We quantified trends in biological activity recorded at hourly intervals by a BDR at an airfield in Texas, USA, and in the most frequently assigned AHAS risk forecasts for that site during the same intervals. We then examined the strength of association between these datasets by season and time of day to determine when information from BDRs might best complement forecasts from the broad‐scale AHAS system. We found a strong overall association between the datasets but weak or moderate agreement during daylight periods, when most strikes occur. NEXRAD WSRs see only limited bird activity near the Earth's surface, where the majority of damaging strikes take place and, not surprisingly, AHAS warnings during our study were best predicted by the BDR at higher altitudes. Our results suggest BDRs might best complement early‐warning systems, like AHAS, as part of integrated strike mitigation plans at airfields with large numbers of hazardous birds flying at low altitudes during daylight hours, especially in late afternoon. [ABSTRACT FROM AUTHOR]

A radar echo sequence which includes N frames plays a crucial role in monitoring precipitation clouds and serves as the foundation for accurate precipitation forecasting. To predict future frames, spatiotemporal models are used to leverage historical radar echo sequences. The spatiotemporal information combining both temporal and spatial information is derived from radar echo sequence. The spatiotemporal information reveals the changing trend of intensity in the echo region over time. Dynamic variation information extracted in radar echo maps mainly consists of nonstationary information and spatiotemporal information. However, the changing trends at different locations within the precipitation cloud are different, so the significance of the spatiotemporal information should be different. The current precipitation forecasting model, Memory In Memory (MIM), has the capability to store the nonstationary information derived from radar echo maps. However, the MIM falls short in discerning the significance of the spatiotemporal information extracted from these maps. To address this limitation, we propose a novel model, SAMM-MIM (self-attention memory module-MIM), which regulates the generation of hidden states and spatiotemporal memory states using a SAMM. The proposed SAMM uses the self-attention mechanism and a series of gate mechanisms to concentrate on significant spatiotemporal information, learn changing trends in the echo region, and output predictive information. The predictive information which is stored in hidden states contains predictions of the changing trends of dynamic variation information. Experimental evaluation on a dataset of radar data from Qingdao, China, demonstrates that SAMM-MIM achieves superior prediction performance compared with other spatiotemporal sequence models, as indicated by improved scores on mean squared error, critical success index, and missing alarm rate metrics. [ABSTRACT FROM AUTHOR]

Millimetre‐wave radar offers high ranging accuracy and can capture subtle vibration information of the human heart. This study proposes a heartbeat prediction method based on the transformer model using millimetre‐wave radar. Firstly, the millimetre‐wave radar was used to collect the heartbeat data and conduct normalisation processing. Secondly, a position coding was introduced to assign sine or cosine variables to input data and extract their relative position relationship. Subsequently, the transformer encoder was adopted to allocate attention to input data through the multi‐head attention mechanism, using a mask layer before the decoding layer to prevent the leakage of future information. Finally, we employ the fully connected layer was employed in the linear decoder for regression and output the predicted results. Our experimental results demonstrate that the proposed transformer model achieves nearly 30% higher prediction accuracy than traditional long short‐term memory models while improving both the prediction accuracy and convergence rate. The proposed method has great potential in predicting the heartbeat state of elderly and sick patients. [ABSTRACT FROM AUTHOR]

Due to their periodic nature, metasurfaces used to perform anomalous reflection raise parasitic harmonic reflections. We show a classical synthesis example of such a structure and highlight its limitations. Floquet analysis and its associated simulation environment are exploited to understand the origin of these parasitic reflections and to mitigate them. The proposed method is based on the optimization of the metasurface periodic pattern: the supercell. A predictive method is built to calculate radar cross-section patterns from supercell Floquet simulation, avoiding dealing with heavy simulations. The proposed model, the optimization outputs, and the general results are exposed in details. Different cases are also discussed to prove the repeatability of the proposed method. An earlier version of this paper was presented at the European Microwave Conference and was published in its proceedings. [ABSTRACT FROM AUTHOR]

Accurate and timely precipitation forecasts can help people and organizations make informed decisions, plan for potential weather-related disruptions, and protect lives and property. Instead of using physics-based numerical forecasts, which can be computationally prohibitive, there has been a growing interest in using deep learning techniques for precipitation prediction in recent years due to the success of these approaches in various other fields. These deep learning approaches generally use historical composite reflectivity (CR) at the surface level to predict future time steps. However, other relevant factors related to the potential motion and vertical structure of the storm have not been considered. To address this issue, this research proposes a multisource ConvLSTM (MS-ConvLSTM) model to improve the accuracy of precipitation forecasting by incorporating multiple data sources into the prediction process. The model was trained on a dataset of radar echo features, which includes not only composite reflectivity (CR), but also echo top (ET), vertically integrated liquid (VIL) water, and radar-retrieved wind field data at different elevations. Experiment results showed that the proposed model outperformed traditional methods in terms of various evaluation metrics, such as mean absolute error (MAE), mean squared error (MSE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). [ABSTRACT FROM AUTHOR]

In order to explore the improvement of radar radial velocity data assimilation on the initial and forecast fields of typhoons, this study assimilates the quality-controlled radial velocity data in the case of Typhoon Linfa (2015) using the three-dimensional variational data assimilation system of the weather research and forecasting model (WRF-3DVAR), and then conducts several sensitivity experiments with different horizontal length scale factors. The results show that reducing the horizontal length scale factor of the background error covariance can effectively assimilate the micro- and meso-scale information from radar data and improve the forecasting effect of Linfa. Following the optimization of the horizontal length scale factor, the radial velocity data assimilation can improve the typhoon wind field structure, produce reasonable cyclonic wind field increments, and further improve the dynamic and thermal structure of the inner core area of the typhoon. Then, we can obtain a better initial field of model forecasting, and thus typhoon track and intensity forecasting are improved. [ABSTRACT FROM AUTHOR]

Radar reflectivity assimilation is often used to initialize hydrometeors, to which Numerical Weather Prediction (NWP) is highly sensitive. To better initialize hydrometeors, this study further developed the background error covariance (BEC) with vertical and multivariable correlations of hydrometeor control variables (H-BEC) in the WRF three-dimensional variational data assimilation system (WRFDA-3DVar). The impacts of the H-BEC are discussed using single radar reflectivity tests and series of cycling data assimilation and forecasting experiments for five multi-type convective rainfall cases. The conclusions are summarized as follows: (1) The vertical correlations can speed up the minimization of the cost function, whereas the multivariable correlations further accelerate this minimization; (2) The vertical correlations slightly improve the precipitation forecasting and only in the first hour, while multivariate correlations lead to a larger improvement and persist into the third hour; (3) The application of H-BEC leads to a more reasonable thermodynamic and dynamical structure of the initial field, thereby improving the capability of short-term precipitation forecasting. [ABSTRACT FROM AUTHOR]

The sensitivity of convection-allowing forecasts over the continental United States to radar reflectivity data assimilation (DA) frequency is explored within the Gridpoint Statistical Interpolation (GSI)-based ensemble–variational (EnVar) system. Experiments with reflectivity DA intervals of 60, 20, and 5 min (RAIN60, RAIN20, and RAIN5, respectively) are conducted using 10 diverse cases. Quantitative verification indicates that the degree of sensitivity depends on storm features during the radar DA period. Five developing storms show high sensitivity, whereas five mature or decaying storms do not. The 20-min interval is the most reliable given its best overall performance compared to the 5- and 60-min intervals. Diagnostics suggest that the differences in analyzed cold pools (ACPs) among RAIN60, RAIN20, and RAIN5 vary by storm features during the radar DA period. Such ACP differences result in different forecast skills. In the case where RAIN20 outperforms RAIN60 and the case where RAIN5 outperforms RAIN20, assimilation of reflectivity with a higher frequency commonly produces enhanced and widespread ACPs, promoting broader storms that match better with reality than a lower frequency. In the case where RAIN5 performs worse than RAIN20, the model imbalance of RAIN5 overwhelms information gain associated with frequent assimilation, producing overestimated and spuriously fast-moving ACPs. In the cases where little sensitivity to the reflectivity DA frequency is found, similar ACPs are produced. [ABSTRACT FROM AUTHOR]

The study is based on the Weather Research and Forecasting Model (WRF) and the three-dimensional variational (3DVAR) data assimilation system. Based on two different control variables, the effects of radar radial velocity assimilation in forecasting of the tropical cyclone (TC) Kompasu were evaluated. The single observation experiment showed that DA_ψχ produces cyclonic increments, while DA_UV only produces increments in the same direction as the observation. DA_ψχ significantly enhances the wind field at 850 hPa with a large number of unphysical cyclonic increments. On the other hand, DA_UV produces reasonable cyclonic increments to enhance the TC. The assimilation of DA_UV makes the surface wind enhanced and the sea level pressure at the TC center reduced. The circular structure of the DA_ψχ wind field is not clear and neither is the large wind area concentrated. In addition, the DA_ψχ shows spurious convection at the high altitude of the vertical cross section, while the DA_UV presents enhanced large wind area at the bottom. The RMSE of the radial velocity is smaller during the circular assimilation in DA_UV. DA_ψχ does not improve the track forecast of Kompasu, while DA_UV shows a significant improvement by the track forecast. [ABSTRACT FROM AUTHOR]

Currently, most deep learning (DL)-based models for precipitation forecasting face two conspicuous issues: the smoothing effect in the precipitation field and the degenerate effect of forecasting precipitation intensity. Therefore, this study proposes "time series residual convolution (TSRC)", a DL-based convolutional neural network for precipitation nowcasting over China with a lead time of 3 h. The core idea of TSRC is it compensates the current local cues with previous local cues during convolution processes, so more contextual information and less uncertain features would remain in deep networks. We use four years' radar echo reflectivity data from 2017 to 2020 for model training and one year's data from 2021 for model testing and compare it with two commonly used nowcasting models: optical flow model (OF) and UNet. Results show that TSRC obtains better forecasting performances than OF and UNet with a relatively high probability of detection (POD), low false alarm rate (FAR), small mean absolute error (MAE) and high structural similarity index (SSIM), especially at longer lead times. Meanwhile, the results of two case studies suggest that TSRC still introduces smoothing effects and slightly outperforms UNet at longer lead times. The most considerable result is that our model can forecast high-intensity radar echoes even for typhoon rainfall systems, suggesting that the degenerate effect of forecasting precipitation intensity can be improved by our model. Future works will focus on the combination of multi-source data and the design of the model's architecture to gain further improvements in precipitation short-term forecasting. [ABSTRACT FROM AUTHOR]

Accurate short-term precipitation forecast is extremely important for urban flood warning and natural disaster prevention. In this paper, we present an innovative deep learning model named ISA-PredRNN (improved self-attention PredRNN) for precipitation nowcasting based on radar echoes on the basis of the advanced PredRNN-V2. We introduce the self-attention mechanism and the long-term memory state into the model and design a new set of gating mechanisms. To better capture different intensities of precipitation, the loss function with weights was designed. We further train the model using a combination of reverse scheduled sampling and scheduled sampling to learn the long-term dynamics from the radar echo sequences. Experimental results show that the new model (ISA-PredRNN) can effectively extract the spatiotemporal features of radar echo maps and obtain radar echo prediction results with a small gap from the ground truths. From the comparison with the other six models, the new ISA-PredRNN model has the most accurate prediction results with a critical success index (CSI) of 0.7001, 0.5812 and 0.3052 under the radar echo thresholds of 10 dBZ, 20 dBZ and 30 dBZ, respectively. [ABSTRACT FROM AUTHOR]

The skillful anticipation of tornadoes produced by quasi-linear convective systems (QLCSs) is a well-known forecasting challenge. This study was motivated by the possibility that warning accuracy of QLCS tornadoes depends on the processes leading to tornadogenesis, namely, one that is dominated by an apparent release of horizontal shearing instability [shearing instability dominant (SID)] and one by a pre-tornadic mesocyclone [pre-tornadic mesocyclone dominant (PMD)] and its associated generative mechanisms. The manual classification of the genesis of 530 QLCS tornadoes as either SID or PMD was performed using heuristic, yet process-driven criteria based on single-Doppler radar (WSR-88D) data. This included 214, 213, and 103 tornadoes that occurred during 2019, 2017, and 2016, respectively. As a function of tornadogenesis process, 36% were classified as SID, and 60% were classified as PMD; the remaining 4% could not be classified. Approximately 30% of the SID cases were operationally warned prior to tornadogenesis, compared to 44% of the PMD cases. PMD tornadoes were also more common during the warm season and displayed a diurnal, midafternoon peak in frequency. Finally, SID cases were more likely to be associated with QLCS tornado outbreaks but tended to be slightly shorter lived. A complementary effort to investigate environmental characteristics of QLCS tornadogenesis revealed differences between SID and PMD cases. MLCAPE was relatively larger for warm-season SID cases, and 0–3-km SRH was relatively larger in warm-season PMD cases. Additionally, pre-tornadic frontogenesis was more prominent for cool-season SID cases, suggestive of a more significant role of the larger-scale meteorological forcing in vertical vorticity that fosters tornadogenesis through SID processes. [ABSTRACT FROM AUTHOR]

We investigate the impact of directly assimilating radar reflectivity data using an ensemble Kalman filter (EnKF) based on a double-moment (DM) microphysics parameterization (MP) scheme in GSI-EnKF data assimilation (DA) framework and WRF model for a landfall typhoon Lekima (2019). Observations from a single operational coastal Doppler are quality-controlled and assimilated. Compared with the baseline experiment initialized by GFS analysis, the reflectivity data assimilation experiment (Z-DA) resulted in an obvious improvement in both structural analysis and typhoon forecast skills in terms of intensity, precipitation, and track. Sensitivity experiments were conducted to evaluate the ability of EnKF to update certain state variables considering that the degree of freedom of analytical variables increased with a DM MP scheme. When either the total number concentration or other large-scale state variables that are not directly linked to reflectivity observations via the observation operator are not updated, the tendency of RMSIs and PS to be imbalanced is significantly increased during DA cycles compared to those of Z-DA with updating a full set of state variables, resulting in increased intensity and track forecast errors. These results indicate that the reliable ensemble covariance could handle the underconstraint issue associated with the DM scheme, and helps in obtaining more physically balanced analytical fields. [ABSTRACT FROM AUTHOR]

A technique for model initialization using three-dimensional radar reflectivity data has been developed and applied within the NOAA 13-km Rapid Refresh (RAP) and 3-km High-Resolution Rapid Refresh (HRRR) regional forecast systems. This technique enabled the first assimilation of radar reflectivity data for operational NOAA forecast models, critical especially for more accurate short-range prediction of convective storms. For the RAP, the technique uses a diabatic digital filter initialization (DFI) procedure originally deployed to control initial inertial gravity wave noise. Within the forward-model integration portion of diabatic DFI, temperature tendencies obtained from the model cloud/precipitation processes are replaced by specified latent heating-based temperature tendencies derived from the three-dimensional radar reflectivity data, where available. To further refine initial conditions for the convection-allowing HRRR model, a similar procedure is used in the HRRR, but without DFI. Both of these procedures, together called the "Radar-LHI" (latent heating initialization) technique, have been essential for initialization of ongoing precipitation systems, especially convective systems, within all NOAA operational versions of the 13-km RAP and 3-km HRRR models extending through the latest implementation upgrade at NCEP in 2020. Application of the latent heat-derived temperature tendency induces a vertical circulation with low-level convergence and upper-level divergence in precipitation systems. Retrospective tests of the Radar-LHI technique show significant improvement in short-range (0-6 h) precipitation system forecasts, as revealed by reflectivity verification scores. Results presented document the impact on HRRR reflectivity forecasts of the radar reflectivity initialization technique applied to the RAP alone, HRRR alone, and both the RAP and HRRR. SIGNIFICANCE STATEMENT: The large forecast uncertainty of convective situations, even at short lead times, coupled with the hazardous weather they produce, makes convective storm prediction one of the most significant shortrange forecast challenges confronting the operational numerical weather prediction community. Prediction of heavy precipitation events also requires accurate initialization of precipitation systems. An innovative assimilation technique using radar reflectivity data to initialize NOAA operational weather prediction models is described. This technique, which uses latent heating specified from radar reflectivity (and can accommodate lightning data and other convection/precipitation indicators), was first implemented in 2009 at NOAA/NCEP and continues to be used in 2022 in the NCEP-operational RAP and HRRR models, making it a backbone of the NOAA rapidly updated numerical weather prediction capability. [ABSTRACT FROM AUTHOR]

This study quantitatively examines the contribution of assimilating observations in the regions with different dynamic instabilities to the analysis and prediction of an extreme rainstorm event in Fujian Province of China. The wind profiling radar (WPR) observations are classified into two groups, i.e., strong and weak instability areas (SIA and WIA), according to their local dynamic instability identified by the ensemble spread. Their performance of assimilation and prediction in terms of the wind and precipitation are evaluated and compared in detail. The results show that the wind analysis error by assimilating all of the WPR observations can be reduced by about 30%. In particular, the wind analysis errors by only assimilating the observations in the SIA are about 12% lower than those in the WIA. They are related to the existence of the low-level horizontal wind shear with strong instability in the SIA. The case study shows that the assimilation of observations in the SIA can effectively correct the wind fields on the two sides of the wind shear line, producing an improved precipitation forecast compared to observation assimilation in the WIA. [ABSTRACT FROM AUTHOR]

Based on the Weather Research and Forecasting (WRF) Model and the three-dimensional variational (3DVAR) data assimilation system, this study investigates the effects of assimilation of radar reflectivity and radial velocity under different momentum control variables on the forecast of Southwest China Vortex precipitation. It is shown that the U−V control variable strengthens the wind speed and vorticity to be better matching the observation, while using ψ−χ as the control variable will produce too large increments which are unphysical. The root mean square errors (RMSE) of radar radial velocity are around 2.4 m/s in the experiment using ψ−χ control variables, while the RMSE are below 2 m/s in the experiment with U−V control variables. The composite reflectivity from the analysis of the U−V control variables matches better with the observation than that from the analysis of the ψ−χ control variables, i.e., the forecast rain band location under U−V control variables is more accurate. ψ−χ control variable enhances the cold high-pressure system in near surface, while the U−V control variable is not significant. The water vapor flux convergence in the lower layers of the ψ−χ control variable is overestimated leading excessive precipitation in the forecast. The Equitable Threat Score (ETS) of the U−V control variable is about 0.1 higher than ψ−χ control variable. In summary, the U−V control variable is superior to the ψ−χ control variable in terms of analysis and forecasting about Southwest China Vortex precipitation. [ABSTRACT FROM AUTHOR]

Polarimetric radar data and total lightning data are used to develop lightning cessation guidance for isolated cells in the Washington, D.C. area. A total of 23 non-severe thunderstorms during the 2015–2017 warm seasons are analyzed. Radar and lightning data are superimposed using the Warning Decision Support System–Integrated Information software to develop cessation algorithms. This includes using the hydrometeor classification algorithm to locate graupel for each convective cell. Results show that the three best-performing cessation algorithms use thresholds of (1) ZH ≥ 40 dBZ at −5 °C, (2) ZH ≥ 35 dBZ at −10 °C, and (3) graupel at −15 °C. Lightning is not expected 15 min after the threshold is no longer met for each algorithm. These algorithms are recommended only for isolated cells in the Washington, D.C. area. Further study needs to be completed to draw conclusions for other convective cell types and different geographic regions. [ABSTRACT FROM AUTHOR]

ow squalls are sudden snow events that last less than 1 h, are characterized by low visibility and gusty winds, and can result in notable societal impacts. This analysis develops a climatology of non-lake-effect snow squall events in southern New England for 1994–2018 and investigates the synoptic environment and mesoscale factors conducive to their formation. National Weather Service surface observations were used to identify events; sea level pressure maps, composite radar charts, and a cell-tracking algorithm were used to determine their organization and movement; and ERA5 hourly reanalysis data were used to analyze the associated synoptic and infer mesoscale features, as well as convective and symmetric instability. A total of 100 events were identified and categorized into four distinct types on the basis of the direction of movement of the associated radar echoes, which is closely linked to characteristic synoptic structures and mesoscale factors. The four types are Classic (squall movement from the northwest; 72 events), Atlantic (from the southwest; 15 events), Northern (from the north; 9 events), and Special (varying; 4 events). All types have a 500-hPa trough over the Northeast but differ in the structure of the trough and its relation to lower-level flow, which accounts for the differences in movement of the squalls. The snow events occur in shallow, convective squall lines, and the ingredients for convection were present in all cases. Both upright and symmetric instability are typically present, all cases had at least one lower-tropospheric layer with cyclonic differential vorticity advection, and many cases were also associated with frontogenesis. [ABSTRACT FROM AUTHOR]

The Center for Analysis and Prediction of Storms has recently developed capabilities to directly assimilate radar reflectivity and radial velocity data within the GSI-based ensemble Kalman filter (EnKF) and hybrid ensemble three-dimensional variational (En3DVar) system for initializing convective-scale forecasts. To assess the performance of EnKF and hybrid En3DVar with different hybrid weights (with 100%, 20%, and 0% of static background error covariance corresponding to pure 3DVar, hybrid En3DVar, and pure En3DVar) for assimilating radar data in a Warn-on-Forecast framework, a set of data assimilation and forecast experiments using the WRF Model are conducted for six convective storm cases of May 2017. Using an object-based verification approach, forecast objects of composite reflectivity and 30-min updraft helicity swaths are verified against reflectivity and rotation track objects in Multi-Radar Multi-Sensor data on space and time scales typical of National Weather Service warnings. Forecasts initialized by En3DVar or the best performing EnKF ensemble member produce the highest object-based verification scores, while forecasts from 3DVar and the worst EnKF member produce the lowest scores. Averaged across six cases, hybrid En3DVar using 20% static background error covariance does not improve forecasts over pure En3DVar, although improvements are seen in some individual cases. The false alarm ratios of EnKF members for both composite reflectivity and updraft helicity at the initial time are lower than those from variational methods, suggesting that EnKF analysis reduces spurious reflectivity and mesocyclone objects more effectively. [ABSTRACT FROM AUTHOR]

In this study, hourly data assimilation (DA) cycling is performed during a 24-h time period for Hurricane Matthew (2016), assimilating ground-based (GBR) and tail-Doppler radar (TDR) observations together, as well as separately using HWRF and its Hybrid 3DEnVar DA system. The objective is to examine the impacts of assimilating such data on the analysis and prediction of the weakening and re-intensification stages of the eyewall replacement cycle (ERC) of Matthew. Experiments assimilating GBR observations make quicker corrections to the initially inconsistent storm structure than does the TDR experiment, resulting in the primary and secondary eyewalls being realistically represented during the DA cycling period. The TDR experiment analyses show less-realistic concentric eyewall structure before, during, and after TDR observations become available. The forecasts from experiments assimilating GBR observations show more-realistic structural and point intensity changes for the ERC consistently throughout the cycling period when compared with the experiments assimilating TDR observations. Combined assimilation of GBR and TDR observations show similar ERC forecasts, on average, to the GBR experiment. The superior performance of the GBR experiments is shown to be tied to its earlier and longer availability despite its limited low-level coverage especially at the early stage of the cycling. The inferior performance of the TDR experiments even during the availability of TDR is hypothesized to be a result of rapidly changing 3D observational coverage during the high-frequency cycling. Brief mechanism diagnostics additionally suggest the need of properly initializing the TC concentric eyewalls to capture the ERC during the forecasts. [ABSTRACT FROM AUTHOR]

Several data assimilation (DA) approaches exist to generate consistent and continuous precipitation fields valuable for hydrometeorological applications and land data assimilation. Usually, DAs are based on either static or dynamic approaches. Static methods rely on deterministic forecasts to estimate background error covariance matrices, while dynamic ones use ensemble forecasts. Associating the two methods is known as hybrid DA and has proven beneficial for different applications as it combines the advantages of both approaches. The present study intends to explore hybrid DA for the 6-hour Canadian Precipitation Analysis (CaPA). Based on optimal interpolations (OI), CaPA blends forecasts and observations from surface stations and ground-based radar datasets to provide precipitation fields over the North American domain. The application of hybrid DA to CaPA consisted of finding the optimal linear combination between i) an OI based on the Regional Deterministic Prediction System (RDPS) and ii) an Ensemble Kalman Filter (EnKF) based on the 20-member Regional Ensemble Prediction System (REPS). The results confirmed the known effectiveness of the hybrid approach when low-density observation networks 10 assimilated. Indeed, the experiments conducted for the summer without radar datasets and for the winter (characterized by very few observations in CaPA) showed that attributing a relatively high weight of the EnKF (50 and 70% for summer and winter, respectively) gave better analysis skills and a reduction of false alarms than the OI method. A deterioration of the moderate to high-intensity precipitation bias was, however, observed during summer. Reducing to 30% the weight attributed to the EnKF permitted alleviating the bias deterioration while improving skill compared to the OI-based CaPA. [ABSTRACT FROM AUTHOR]

A valid time shifting (VTS) method is explored for the GSI-based ensemble variational (EnVar) system modified to directly assimilate radar reflectivity at convective scales. VTS is a cost-efficient method to increase ensemble size by including subensembles before and after the central analysis time. Additionally, VTS addresses common time and phase model error uncertainties within the ensemble. VTS is examined here for assimilating radar reflectivity in a continuous hourly analysis system for a case study of 1–2 May 2019. The VTS implementation is compared against a 36-member control experiment (ENS-36), to increase ensemble size (3 × 36 VTS), and as a cost-savings method (3 × 12 VTS), with time-shifting intervals τ between 15 and 120 min. The 3 × 36 VTS experiments increased the ensemble spread, with largest subjective benefits in early cycle analyses during convective development. The 3 × 12 VTS experiments captured analysis with similar accuracy as ENS-36 by the third hourly analysis. Control forecasts launched from hourly EnVar analyses show significant skill increases in 1-h precipitation over ENS-36 out to hour 12 for 3 × 36 VTS experiments, subjectively attributable to more accurate placement of the convective line. For 3 × 12 VTS, experiments with τ ≥ 60 min met and exceeded the skill of ENS-36 out to forecast hour 15, with VTS-3 × 12τ90 maximizing skill. Sensitivity results demonstrate preference to τ = 30–60 min for 3 × 36 VTS and 60–120 min for 3 × 12 VTS. The best 3 × 36 VTS experiments add a computational cost of 45%–67%, compared to the near tripling of costs when directly increasing ensemble size, while best 3 × 12 VTS experiments save about 24%–41% costs over ENS-36. Significance Statement: The purpose of this work is to study a valid time shifting method to improve the prediction of severe convective storm systems over the continental United States. This method improves ensemble-based radar reflectivity analyses by including ensemble member information at times before and after the analysis time, thereby increasing the ensemble size at just a fractional added computational cost. The results show the method can boost the accuracy of high-resolution convection prediction out to at least 12 h. This case study motivates future systematic testing in a real-time setting and potential implementation to enhance the U.S. operational ensemble-based convection-allowing forecast model and data assimilation system. [ABSTRACT FROM AUTHOR]

The current study utilizes the operational data assimilation system of Deutscher Wetterdienst (DWD) to investigate the impacts of improved radar forward operator. First of all, it is shown that for experiments in which both conventional and radar data are assimilated and the latent heat nudging (LHN) is applied, the one with improved operator (i.e., with improved Mie-scattering scheme and accounting for beam broadening effect and etc.) exhibits neutral impacts on short-term forecasts. However, in subsequent experiments, in which conventional data are not assimilated and the LHN is switched off, the one with improved operator not just reduces representation error during data assimilation cycles but also enhances the short-term forecast skills. In addition, it is found that the subsequent experiments result in much shorter observation error correlation length scales for radar reflectivity data, indicating that the application of the LHN or assimilation of conventional data may increase the length scales. • Improved radar forward operator configurations reduce representation error but exhibit neutral impact on short-term forecasts • However, in the pure radar data assimilation experiment, improved operator configurations enhance forecast skills • The observation error statistics estimated by the Desroziers method exhibit clear dependency on the data assimilation [ABSTRACT FROM AUTHOR]

Based on the C-band Doppler radar data and the hourly precipitation data of heavy precipitation in Ulanqab from 2018 to 2019, the Z-Z relationship of local convective precipitation and the correlation between vertically integrated liquid water (VIL) and precipitation were studied. The heavy precipitation was divided into cumulus precipitation and cumulus mixed cloud precipitation, and different types of Z-Jrelationship was established to compare it with the traditional and unclassified overall optimal Z-Zrelationship. It shows that the estimation of different types of precipitation by different Z-Z relationships was obviously better than the other two. Cumulus precipitation had a certain lag correlation with VIL1 and the last 4 scanning VIL values within an hour had no indicative significance for precipitation; mixed precipitation was basically synchronized with\?L; the correlation decreased with the increase of the distance from radar, the corresponding degree of VIL and precipitation in stations within 30 km was significantly higher than that of other regional stations. A continuous non-zero VIL sequence close to a certain place can be used as a forecast indicator. Once a zero value appeared in the VlL time series, even it occurred only once, the two sequences before and after the zero value should be distinguished. [ABSTRACT FROM AUTHOR]

The impact of assimilating China's operational X‐band Phased‐Array radar's (X‐PAR) data on the analysis and warning forecast of the vortex structure and intensity of the June 8, 2018 Foshan, Guangdong province, tornadic storm was investigated for the first time using an Ensemble Kalman Filter (EnKF) data assimilation system. Both radar radial velocity (Vr) and reflectivity (Z) from two S‐band operational radars and one X‐PAR were assimilated. Deterministic forecasts were launched every 6 min from 05:42 UTC (20 min before the tornado touched down) to 06:00 UTC from the EnKF mean analysis field. Five experiments were conducted to examine the added capability of Z assimilation of the EnKF system, and to investigate the impact of assimilating X‐PAR data on the analysis and prediction of the tornadic storm. Compared to the experiment without Z assimilation, the assimilation of Z reduced the analysis error and greatly reduced the forecast error of Z. The assimilation of X‐PAR data greatly improved the vortex structure of the tornadic storm at low levels, and improved the intensity of the rear inflow of the tornadic storm, especially with a higher assimilation frequency. Compared to the experiments without X‐PAR data assimilation, assimilating X‐PAR data improved the predictability of tornadic storm. Plain Language Summary: Tornadoes are difficult to predict worldwide. However, the scan speed of current operational S‐band (10 cm wavelength) radar is too slow to capture the evolution of tornadoes. The newly built X‐band (3 cm wavelength) Phase‐Array radar (X‐PAR) network in Guangdong Province, China, are hopeful to fill in the gap with their higher scan frequency and higher spatial resolution, which can capture the fine‐scale structure and rapid evolution of small scale systems such as tornadic storms. This study focuses on a case where a tornado embedded within the typhoon Ewiniar was well captured by the X‐PAR network. It demonstrated for the first time the use of X‐PAR data on the prediction of a tornadic storm in China. We used ensemble‐based data assimilation techniques named Ensemble Kalman Filter to utilize the information provided by the X‐PAR data. With the improved information, the analyzed tornadic storm vortex structure at low levels was greatly improved, especially with a higher assimilation frequency. Compared to the experiments without X‐PAR data, the experiment using X‐PAR data successfully predicted the tornadic vortex a few minutes in advance. Key Points: The impact of assimilating China's X‐band Phased‐Array radar (X‐PAR) radar data on the analysis and forecast of a tornadic storm was first investigated using an Ensemble Kalman Filter systemThe assimilation of X‐PAR data helps to produce a more realistic‐looking tornadic storm structure, especially at low levelsIncreasing assimilation frequency of X‐PAR data reduces the analysis error and subsequently improves the 8‐min tornadic storm forecast [ABSTRACT FROM AUTHOR]

The Central Weather Bureau in Taiwan successfully implemented a version of WRF coined as TWRF (Typhoon WRF) as the operational typhoon prediction system. The TWRF has two nested domains with 15/3 km resolution covering large areas over the western North Pacific, which has led to significant improvements of typhoon predictions over the previous version with coarser resolutions. Built upon this success, the WRF Four-Dimensional Data Assimilation (FDDA) system has been implemented in TWRF. This study investigates the impact of assimilating dual-Doppler radar retrieval winds from eight sets of dual-Doppler radars using FDDA on the prediction of Typhoon Nesat (2017) that passed over Taiwan. The wind field retrieved with dual-Doppler radars has a vertical extent from 1 to 10 km and the horizontal resolution is 1 km. After quality control and data thinning, the radar retrieval winds are assimilated using FDDA for two update cycles in addition to the existing hybrid 3DEnVar for all other observations in TWRF. Furthermore, we assimilate the additional radar data that become available during the 4-hour waiting period for the completion of the global model prediction to be used as the lateral boundary condition for TWRF. For Typhoon Nesat, the wind structure and rainfall forecasts are improved with the assimilation of radar-retrieval winds. The overall improvements demonstrated by this case study suggest potentially high impacts for improving the prediction of typhoon-related rainfall with assimilation of dual-Doppler radar data. [ABSTRACT FROM AUTHOR]

Polarimetric radar data and total lightning data are used to develop lightning cessation guidance for isolated cells in the Washington, D.C. area. A total of 23 non-severe thunderstorms during the 2015–2017 warm seasons are analyzed. Radar and lightning data are superimposed using the Warning Decision Support System–Integrated Information software to develop cessation algorithms. This includes using the hydrometeor classification algorithm to locate graupel for each convective cell. Results show that the three best-performing cessation algorithms use thresholds of (1) ZH ≥ 40 dBZ at −5 °C, (2) ZH ≥ 35 dBZ at −10 °C, and (3) graupel at −15 °C. Lightning is not expected 15 min after the threshold is no longer met for each algorithm. These algorithms are recommended only for isolated cells in the Washington, D.C. area. Further study needs to be completed to draw conclusions for other convective cell types and different geographic regions.

Objective: Reported rainfall data from multiple rain gauges and its corresponding estimate from Dual-Polarization (Dual-Pol) radar is presented here. The ordered set of data pairs were collected from multiple peer reviewed publications spanning across the last decade. Data description: Taken from multiple sources, the data set represents several radar sites and rain gauge sites combined for 12,734 data points. The data is relevant in various applications of hydrometeorology and engineering as well as weather forecasting. Further, the importance of accuracy in radar precipitation estimates continues to increase, necessitating the incorporation of as much data as possible. [ABSTRACT FROM AUTHOR]

The assimilation of wind profiler (profiler) observations has the potential of improving the model initial condition of dynamical field with its high spatial and temporal wind field information. In this study, the impacts of two kinds of wind observation operators, i.e., eastward and northward velocities (u and v ; uv_scheme) and wind speed and direction (spd and dir ; sd_scheme), on analyses and forecasts are discussed to determine the optimal observation operator for profiler data assimilation. To compare the performance of the two observation operators on profiler data assimilation, we carry out a series of single wind tests and a two-week period cycling assimilation and forecasting experiments. The single wind tests show that the adjustment of the two observation operators to the analyses of wind can be significantly different with large innovation (OMB) of dir. The uv_scheme adjusts the corresponding model variables by ingesting profiler information of u and v winds, so that the analyzed spd is indirectly affected by the observed dir. By contrast, the changes in observed dir will not affect the analyzed spd in sd_scheme , as the observed spd and dir are independently assimilated. The case diagnoses illustrate that the sd_scheme generates the best analysis of wind in RMSE when validating against either the profiler or the pilot observations. The improved analysis of winds in turn leads to better wind forecasts and precipitation prediction, confirming the advantage of sd_scheme. Results from the cycling experiments further demonstrate that, compared with the uv_scheme , the sd_scheme shows decent improvements in wind forecasts, thus leading to better precipitation forecast skills. • Two wind observation operators (uv_scheme and sd_scheme) for profiler data assimilation were compared and evaluated. • Employing two observation operators to assimilate profiler data has significantly different effects on the analyzed wind. • Comparisons of wind forecasts and precipitation forecast scores from the two observation operators favor sd_scheme. [ABSTRACT FROM AUTHOR]

In this study, a lightning data assimilation (LDA) method adjusting dynamical fields was examined in the rapid cycle with Weather Research and Forecasting (WRF) model and WRF Data Assimilation (WRFDA). This method retrieves pseudo vertical velocity profiles from total lightning observations and promotes wind convergence over lightning regions. This newly developed LDA scheme is compared with radar data assimilation (RDA) that has been routinely applied in severe storm nowcasting models. Depending on whether the radar or lightning data is assimilated, four experiments were designed to evaluate the positive impacts of dynamical adjustment from LDA. Using a typical severe mesoscale convection system occurred in Beijing, we found that the effect of RDA and LDA are different. The assimilation of radar radial velocity mainly improves the forecast in a longer time, while assimilation of pseudo-vertical-velocity from lightning data mainly corrects intensity and location of the forecasted precipitation. When both radar and lightning data are assimilated, the small-scale wind convergence are promoted and contributes to the intensified updrafts. Thermal and water vapor fields are also adjusted indirectly. Consequently, the convective precipitation is significantly improved and the positive impacts from the combined assimilation scheme persisted over a longer period (at least 3 h). It can be concluded that a combined assimilation of convective data from multiple sources such as radar and lightning data enhance prominently the accuracy of short-term convection forecasts. • A lightning data assimilation method adjusting dynamical fields is applied in nowcasting through rapid update cycling. • Radar data and lightning data assimilation are combined to improve the accuracy of short-term precipitation forecasts. • Radar radial velocity improves forecast in a longer time, while lightning data corrects its intensity and location. [ABSTRACT FROM AUTHOR]

Based on the WRF-3DVar system, different momentum control variables in radar data assimilation are investigated for their effects on the analysis and forecast of strong convective weather systems in the study. The single observation assimilation tests are performed besides three cycling data assimilation experiments based on U-wind/ V -wind (UV) and stream-function and velocity potential (ψχ) that are scalar representations of flow fields for their irrotational and divergence components. The wind field increments for both control variables in single observation tests show that when UV are the control variables, the wind field increments have a smaller range of influence that it better reflects the characteristics of the observed information itself at the convective scale than ψχ. When ψχ are used as control variables, negative increments are generated in the analyzed wind field because ψχ have the property of maintaining the integrated value of the wind field. In addition, radar data from 5 Doppler radars in northeast China are assimilated. The cycling assimilation experiments show that CV_UV produces enhanced forecast than CV_ ψχ in terms of radar reflectivity and precipitation. Another case also proves this conclusion. In particular, CV_UV improves the dynamics field and water vapor convergence, which lead to better intensity and structure prediction of the strong convection process. In contrast, CV_ ψχ yields relative discontinuous wind field structure, which tends to reduce the organization of the forecast strong echoes. Another set of experiments are further designed with only radar reflectivity assimilated with CV_UV. The results show that assimilating radial velocity is helpful to improve the forecasting skill. • Different control variables are studied for their effects on the analysis and forecast of strong convective systems. • CV_UV is relatively more localized in terms of analysis increments and is applicable to the convective scale. • CV_UV improves the dynamics field and water vapor convergence for the strong convection process. • CV_UV produces enhanced forecast than CV_ψχ in terms of radar reflectivity and precipitation. [ABSTRACT FROM AUTHOR]

In this study, an approach is proposed to improve the numerical weather prediction (NWP) of convective event forecasts by combining very short-term extrapolated reflectivity (0−1 h) with a numerical model. After direct assimilation of real radar observations, the pseudo-water vapor estimated from future extrapolated reflectivity is assimilated using a cycled three-dimensional variational assimilation (3DVAR) system. To filter extrapolated reflectivity values with large errors and extract reliable and useful convection information, a proposed regional filtering procedure is performed on the extrapolated reflectivity. In addition, regional filters of different scales and the reflectivity of different extrapolation times are examined. The assimilation results for two convection events with different characteristics show that assimilation of the pseudo-water vapor estimated from extrapolated reflectivity can improve the reflectivity and accumulated precipitation forecasting. The improvement effect is more significant in case 1, which is a well-organized squall line event, and less effective in case 2, which is a faster developing and evolving local convection event. In both cases, the regional filtering method can limit the wet bias in the analysis field and suppress spurious forecasts of reflectivity and precipitation caused by assimilating the pseudo-water vapor estimated from extrapolated reflectivity without regional filters. Additionally, the assimilation of a shorter (30 min) time window may prevent the introduction of more error into the analysis field, but the improvement of the forecasting skill may be limited. • An approach to improve convective events forecast by combining extrapolated reflectivity with numerical model is proposed. • The pseudo-water vapor derived by extrapolated reflectivity is assimilated using cycled 3DVAR system. • A regional filtering method was performed to filter out small-scale noise in extrapolated reflectivity [ABSTRACT FROM AUTHOR]

• Precipitation up to 3-hour is predicted based on radar echo images of Wuhan station. • ST-LSTM-SA is proposed by 3D convolution, spatiotemporal LSTM and self-attention. • ST-LSTM-SA has a better response to medium and heavy rain events. • ST-LSTM-SA outperforms ConvLSTM and machine learning methods at 1–3 h leads. Accurate and timely short-term forecasting services of precipitation variable are significant for people's lives and property security. The data-driven approaches demonstrate promising performance in the extrapolation of precipitation. In this paper, we proposed a spatiotemporal prediction model, namely the Spatial Temporal Long Short-Term Memory based on the self-attentive mechanism (ST-LSTM-SA). This model enables better aggregation of sequence features inspired by proposed improvements. The encapsulated 3D convolution is developed to fully exploit the short-term spatiotemporal information, and the channel correlation is modeled by self-attention mechanism to further improve representations in the long-term interaction, the effectiveness of which is validated in the ablation study. Comprehensive experiments have been conducted on the radar echo sequence, we successfully predicted future radar reflectivity images for next 3 h with data for previous three hours as inputs in Wuhan, China. Three machine learning methods: multiple linear regression (MLR), support vector regression (SVR) and artificial neural networks (ANN) and deep learning model—ConvLSTM have been chosen as comparative groups to corroborate the nowcasting availability of this model for radar echo extrapolation. Moreover, the supplementary experiment on minute dataset also illustrated the superiority of ST-LSTM-SA. The studies analyzed the forecasting performance in terms of image quality and rainfall error. The experimental results demonstrated the better versatility and performance of ST-LSTM-SA. These conclusions and attempts may provide efficient guidance for precipitation nowcasting in urban areas. [ABSTRACT FROM AUTHOR]

• A real-time rolling prediction system of urban river water level is proposed. • System is based on radar rainfall and hydrological and hydrodynamic coupling model. • The real rolling prediction of review time is restored. • Detailed long series analysis and case analysis. Urban river network plays an important role in the process of drainage. The real-time prediction system of river water level can provide early warning information to the decision-makers of relevant departments in time, which plays an important role in reducing flood risk. Aiming at the rapid and strong response of urban river to rainfall, a real-time rolling prediction system of urban river water level is proposed based on radar rainfall prediction products and coupling model of hydrology and hydrodynamics. In the system, each simulation is divided into two parts: the review period and the prediction period. In the review period, the river model is driven by the observed rainfall to obtain the latest simulation state to avoid cumulative error, while in the prediction period, the radar rainfall is used to drive in the short-term prediction of river water level. Taking Fuzhou as the research area, a long series review of the process from April 1, 2020 to October 1, 2020 was carried out. The accuracy of the river model, radar-rainfall and prediction system during the study period are evaluated and discussed in detail. The results show that the river model can simulate the process of water level change in rainfall and rain free period. The performance of radar rainfall to total rainfall prediction is better than that of process prediction. The real-time prediction system can give the water level risk according to each radar forecast rainfall process, and has good performance in judging the peak water level and peak time, which can provide reference for urban river dispatching decision-makers. [ABSTRACT FROM AUTHOR]

Heavy and localized summer events are very hard to predict and, at the same time, potentially dangerous for people and properties. This paper focuses on an event occurred on 15 July 2020 in Palermo, the largest city of Sicily, causing about 120 mm of rainfall in 3 h. The aim is to investigate the event predictability and a potential way to improve the precipitation forecast. To reach this aim, lightning (LDA) and radar reflectivity data assimilation (RDA) was applied. LDA was able to trigger deep convection over Palermo, with high precision, whereas the RDA had a key role in the prediction of the amount of rainfall. The simultaneous assimilation of both data sources gave the best results. An alert for a moderate–intense forecast could have been issued one hour and a half before the storm developed over the city, even if predicting only half of the total rainfall. A satisfactory prediction of the amount of rainfall could have been issued at 14:30 UTC, when precipitation was already affecting the city. Although the study is centered on a single event, it highlights the need for rapidly updated forecast cycles with data assimilation at the local scale, for a better prediction of similar events. [ABSTRACT FROM AUTHOR]