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The influence of the thermodynamic forcing of the Tibetan Plateau (TP) on the Asian summer monsoon remains controversial because the role of elevated heating across the TP remains unclear at multiple time scales. At the extended-range scale, the boundary forcing is more important than the initial field in the forecast process. In this study, we investigated the role of subdaily thermodynamic forcing across the TP in generating 30-day predictions of precipitation in East Asia by conducting a series of hindcast experiments. The surface potential vorticity forcing was used to identify typical years when the TP forcings were extremely strong or weak. The results indicated that the subdaily thermal forcing of the TP was very important for improving the East Asian precipitation forecast accuracy, especially for predictions longer than 14 days in June 2022, when diffusion heating is very strong and can develop over the TP. In such a case, the corrected TP heating could not only correct for low-level water vapor transport but also modular uplevel circulation, which could propagate downstream, thus favoring the correct prediction of precipitation over East Asia. However, in the other cases, the individual influences of thermal perturbations across the TP are not the only important factors. These findings reveal ways to improve the extended-range forecast skill over East Asia. [ABSTRACT FROM AUTHOR]

This study examines the complexities of climate modeling, specifically in the Panj River Basin (PRB) in Central Asia, to evaluate the transition from CMIP5 to CMIP6 models. The research aimed to identify differences in historical simulations and future predictions of rainfall and temperature, examining the accuracy of eight General Circulation Models (GCMs) used in both CMIP5 (RCP4.5 and 8.5) and CMIP6 (SSP2–4.5 and 5–8.5). The evaluation metrics demonstrated that the GCMs have a high level of accuracy in reproducing maximum temperature (Tmax) with a correlation coefficient of 0.96. The models also performed well in replicating minimum temperature (Tmin) with a correlation coefficient of 0.94. This suggests that the models have improved modeling capabilities in both CMIPs. The performance of Max Plank Institute (MPI) across all variables in CMIP6 models was exceptional. Within the CMIP5 domain, Geophysical Fluid Dynamics (GFDL) demonstrated outstanding skill in reproducing maximum temperature (Tmax) and precipitation (KGE 0.58 and 0.34, respectively), while (Institute for Numerical Mathematics) INMCM excelled in replicating minimum temperature (Tmin) (KGE 0.28). The uncertainty analysis revealed a significant improvement in the CMIP6 precipitation bias bands, resulting in a more precise depiction of diverse climate zones compared to CMIP5. Both CMIPs consistently tended to underestimate Tmax in the Csa zone and overestimate it in the Bwk zone throughout all months. Nevertheless, the CMIP6 models demonstrated a significant decrease in uncertainty, especially in ensemble simulations, suggesting improvements in forecasting PRB climate dynamics. The projections revealed a complex story, as the CMIP6 models predict a relatively small increase in temperature and a simultaneous drop in precipitation. This indicates a trend towards more uniform temperature patterns across different areas. Nevertheless, the precipitation forecasts exhibited increased variability, highlighting the intricate interaction of climate dynamics in the PRB area under the impact of global warming scenarios. Hydrological components in global climate models can be further improved and developed with the theoretical reference provided by this study. [ABSTRACT FROM AUTHOR]

Traditional deep learning-based prediction methods predominantly rely on weather radar data to quantify precipitation, often neglecting the integration of the thermal processes involved in the formation and dissipation of precipitation, which leads to reduced prediction accuracy. To address this limitation, we introduce the Dual-Attention Recurrent Neural Network (DA-RNN), a model that combines satellite infrared (IR) data with radar-derived vertically integrated liquid (VIL) content. This model leverages the fundamental physical relationship between temperature and precipitation in a predictive framework that captures thermal and water vapor dynamics, thereby enhancing prediction accuracy. The results of experimental evaluations on the SEVIR dataset demonstrate that the DA-RNN model surpasses traditional methods on the test set. Notably, the DA-TrajGRU model achieves reductions in mean squared error (MSE) and mean absolute error (MAE) of 30 ( 9.3 % ) and 89 ( 6.4 % ), respectively, compared with those of the conventional TrajGRU model. Furthermore, our DA-RNN exhibits robust false alarm rates (FAR) for various thresholds, with only slight decreases in the critical success index (CSI) and Heidke skill score (HSS) when increasing the threshold. Additionally, we present a visualization of precipitation nowcasting, illustrating that the integration of multiple data sources effectively avoids overestimation of VIL values, further increasing the precision of precipitation forecasts. [ABSTRACT FROM AUTHOR]

ABSTRACT Development and dissemination of seasonal forecasts are integral components of the climate services provided by numerous meteorological services worldwide, offering estimates of meteorological variables on a seasonal time scale to aid local warning systems and decision‐making processes. The World Meteorological Organization (WMO) recommends that operational seasonal forecasts be objective and that the process be traceable and reproducible, including the selection and calibration of models. Following these guidelines, the Chilean Meteorological Service (Dirección Meteorológica de Chile, DMC) has implemented the next generation of seasonal forecasts, NextGen‐Chile. This new forecast system is based on a multi‐model ensemble using state‐of‐the‐art general circulation models (GCMs) from the calibrated North American Multi‐Model Ensemble (NMME) project. The forecasts from the GCMs are calibrated using a canonical correlation analysis‐based regression with a homogenised dataset of ground stations. The system is completed with two statistical models built using canonical correlation analysis on sea surface temperature (SST) in the ENSO and the Southwestern Pacific regions. Individually calibrated GCMs and statistical models are combined by weighing their hindcast skill to construct the final calibrated multi‐model ensemble (CMME) prediction. A verification analysis of probabilistic re‐forecasts during 2019–2021 has been performed, adding an average‐based ensemble forecast (CMME‐Mean). The CMME models outperformed the individual models in discrimination and showed less seasonal variability in performance than the individual models, adding consistency to the forecast. All metrics analysed during the verification process were maximised in the central region of Chile, which could be attributed to the high concentration of ground stations in the central region and the definition of a central region‐centred domain for the CCA calculation. Looking into the near future of NextGen‐Chile, a Flexible Seasonal Forecast is introduced as a more comprehensive approach for seasonal forecasts, allowing users and stakeholders to access information beyond the tercile seasonal forecast approach. [ABSTRACT FROM AUTHOR]

Accurate seasonal precipitation forecasts, especially for extreme events, are crucial to preventing meteorological hazards and their potential impacts on national development, social activity, and security. However, the intensity of summer precipitation is often largely underestimated in many current dynamic models. This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks (CycleGAN) to improve the seasonal forecasts for June-July-August precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System (NUIST-CFS 1.0). The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping (QM) method. Using the unpaired bias-correction model, we can also obtain advanced forecasts of the frequency, intensity, and duration of extreme precipitation events over the dynamic model predictions. This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts. [ABSTRACT FROM AUTHOR]

Global warming has profound effects on precipitation patterns, leading to more frequent and extreme precipitation events over the world. These changes pose significant challenges to the sustainable development of socio-economic and ecological environments. This study evaluated the performance of the new generation of the mesoscale Weather Research and Forecasting (WRF) model in simulating long-term extreme precipitation events over the Minjiang River Basin (MRB) of China from 1981 to 2020. We calculated 12 extreme precipitation indices from the WRF simulations and compared them with observations. The spatio-temporal variations of extreme precipitation were further analyzed in terms of intensity, frequency, and duration. The results indicated that the WRF model can appropriately reproduce the spatial distribution of extreme precipitation indices with acceptable biases. The performance is significantly better for intensity and frequency indices compared to duration indices. Except for PRCPTOT and R10mm, WRF accurately captures the interannual variations of extreme precipitation. Meanwhile, the results of the pre-whitening Mann-Kendall (PWMK) test suggested that WRF can identify significant increasing trends in extreme precipitation, particularly for R95p, R99p, and R50mm. This study provides valuable insights for extreme precipitation forecasting and warning in other mountainous regions. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Although rainfall-triggered landslides are initiated by subsurface hydro-mechanical processes related to the loading, weakening, and eventual failure of slope materials, most landslide early warning systems (LEWSs) have relied solely on rainfall event information. In previous decades, several studies demonstrated the value of integrating proxies for subsurface hydrologic information to improve rainfall-based forecasting of shallow landslides. More recently, broader access to commercial sensors and telemetry for real-time data transmission has invigorated new research into hydrometeorological thresholds for LEWSs. Given the increasing number of studies across the globe using hydrologic monitoring, mathematical modeling, or both in combination, it is now possible to make some insights into the advantages versus limitations of this approach. The extensive progress demonstrates the value of in situ hydrologic information for reducing both failed and false alarms through the ability to characterize infiltration during – as well as the drainage and drying processes between – major storm events. There are also some areas for caution surrounding the long-term sustainability of subsurface monitoring in landslide-prone terrain, as well as unresolved questions in hillslope hydrologic modeling, which relies heavily on the assumptions of diffuse flow and vertical infiltration but often ignores preferential flow and lateral drainage. Here, we share a collective perspective based on our previous collaborative work across Europe, North America, Africa, and Asia to discuss these challenges and provide some guidelines for integrating knowledge of hydrology and climate into the next generation of LEWSs. We propose that the greatest opportunity for improvement is through a measure-and-model approach to develop an understanding of landslide hydro-climatology that accounts for local controls on subsurface storage dynamics. Additionally, new efforts focused on the subsurface hydrology are complementary to existing rainfall-based methods, so leveraging these with near-term precipitation forecasts is a priority for increasing lead times. [ABSTRACT FROM AUTHOR]

In this study, quantitative precipitation forecasts (QPFs) for 24-h Mei-yu rainfall at the short range (days 1–3) during May-June of 2012–2014 by a cloud model at two different grid sizes of 2.5 and 5 km are compared using point-to-point categorical measures. With strong topographic control and enhancement, abundant Mei-yu rainfall in Taiwan allows for the use of very high thresholds up to 500 mm (per 24 h), and classification based on observations is also performed to isolate the larger 16% (group A) and the largest 4% of events (group A+) from all samples. Our results show clear improvements in threat scores in heavy rainfall, with the greatest gain (by 0.16) on day 1 at the highest threshold adopted (500 mm) in the largest events of group A+, when a finer grid is used. Improvements are seen at thresholds ≥ 200 mm on day 1, ≥ 100 mm on day 2, and over 50–350 mm on day 3, mainly due to a better capability of the finer model to simulate heavy rainfall in larger events over and near the terrain. The present work provides new insights into the importance and usefulness of increasing model resolution, when and if QPFs of heavy rainfall at precise locations are crucial for hazard mitigation. Similar benefits are not as evident in the literature, likely because the thresholds used were not high enough, the larger events were not isolated, or the impact of topography on rainfall is not as strong and apparent as in Taiwan. [ABSTRACT FROM AUTHOR]

This study focuses on developing an hourly water level prediction model for small- and medium-sized agricultural reservoirs using the Tree-based Pipeline Optimization Tool (TPOT), an automated machine learning (AutoML) technique. The study area is the Baekhak Reservoir in South Korea, and various precipitation-related and reservoir water storage data were collected. Using these collected data, we compared widely used individual machine learning and deep learning models with the pipeline models generated by TPOT. The comparison showed that pipeline models, which included various preprocessing and ensemble techniques, exhibited higher predictive accuracy than individual machine learning and even deep learning models. The optimal pipeline model was evaluated for its performance in predicting water levels during an extreme rainfall event, demonstrating its effectiveness for hourly water level prediction. However, issues such as the overprediction of peak water levels and delays in predicting sudden water level changes were observed, likely due to inaccuracies in the ultra-short-term forecast precipitation data and the lack of information on reservoir operations (e.g., gate openings and drainage plans for agriculture). This study highlights the potential of AutoML techniques for use in hydrological modeling, and demonstrates their contribution to more efficient water management and flood prevention strategies in agricultural reservoirs. [ABSTRACT FROM AUTHOR]

Accurate forecast precipitation is crucial for hydropower generation, drought and flood warning, and hydrological forecasting. However, raw forecast precipitation often suffers from systematic errors due to inaccurate initial conditions in numerical weather prediction (NWP) models. In this study, we develop a deep-learning-based post-processing method to correct forecast precipitation. Our method leverages convolutional neural networks (CNN) to analyze spatial features and long short-term memory networks (LSTM) to capture temporal dynamics, effectively modeling the local spatiotemporal characteristics (e.g., mean sea level pressure and elevation) of precipitation. Crucially, we also consider the impact of large-scale weather patterns (e.g., high-latitude blockings, the Meiyu trough) on precipitation by extracting relevant features through a CNN model and integrating this information with the local spatiotemporal data to improve forecast accuracy. Results indicate that the proposed CNN-CNN-LSTM method outperforms the three baselines (i.e., CNN-LSTM, CNN, LSTM) for all seasons and lead times (15 days) in the Huaihe River basin of China. Specifically, for the summer precipitation with a one-day lead time, the CNN-CNN-LSTM model achieves a 4.7% reduction in root mean square error and a 30.5% reduction in relative bias compared to CNN-LSTM alone. Furthermore, the relative importance of large-scale predictors is constantly increasing with the extension of lead times. By effectively integrating large-scale weather information and local-scale spatiotemporal information, the proposed CNN-CNN-LSTM method offers a novel approach to enhance the correction effect, providing significant valuable for hydrometeorological applications. [ABSTRACT FROM AUTHOR]

Precipitation forecasting is vital for managing disasters, urban traffic, and agriculture. This study develops an improved model for short-term precipitation forecasting by combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Gated Recurrent Unit (GRU). Using precipitation data from January 1, 2019, to December 31, 2022, as a sample, the model capitalizes on CEEMDAN's superior signal decomposition capabilities and GRU's ability to capture nonlinear dynamic patterns in time series. To assess the model's effectiveness, comparisons were conducted with 12 benchmark models, including CEEMDAN-LSTM, EMD-GRU, EMD-LSTM, BI-LSTM, GRU, LSTM, and TCN. The results demonstrate that the CEEMDAN-GRU model achieves higher accuracy and stability in short-term precipitation forecasting. Leveraging an Adam optimizer with adaptive learning rate reduction enhances convergence and ensures reliable predictions, achieving an R²of 0.7915, MAE of 0.05382, and MSE of 0.09081. [ABSTRACT FROM AUTHOR]

We carried out the first Mobile Field Observation Campaign of Atmospheric Profiles (MFOCAP) in the southeast Tibet and the Three‐River Source Region (TRSR) of the Tibetan Plateau (TP) by adopting two vehicle‐mounted integrated mobile observations (MO) system from July 18 to 30, 2021. Reliable MO data sets of air temperature (Ta), water vapor density (WVD) and relative humidity (RH) with high spatio‐temporal resolution over the TP were obtained and assimilated to improve precipitation forecast using the four‐dimensional variational (4DVAR) data assimilation (DA) method. The results show that Ta, WVD and RH profile data retrieved with the mobile microwave radiometer (MR) are credible over the TP. The atmospheric vertical structure measured by the mobile MR can reproduce the spatio‐temporal evolution characteristics of water vapor transport, temperature stratification and cloud structure. The distribution pattern of 24‐hr accumulated rainfall prediction with Ta profile DA was closer to measurements, and 6–12 hr forecasts for low to moderate rainfall in the central and western regions of Qinghai province were improved significantly. Data assimilation with air temperature retrievals from mobile MR observations were found beneficial for accurate simulation of water vapor transport, convergence and divergence of wind field, and upward motion associated with precipitation events. The finding of this study highlights the value of MR remote sensing observations in improving the rainfall monitoring and forecasts over the TP and downstream regions. Plain Language Summary: The dynamic and thermal effects generated by Tibetan Plateau (TP) have an important impact on weather and climate. Due to sparse and low time‐frequency of conventional radiosonde observation (Raob) over the TP, understanding of the atmospheric dynamic and thermal structure is insufficient, and forecasting errors of precipitation are larger than other regions. To improve the precipitation forecast over the TP, we implemented the first Mobile Field Observation Campaign of Atmospheric Profiles in the southeast Tibet and the Three‐River Source Region using two MO systems and assimilated retrieved Ta and RH profiles data into the Global Forecast System of China Meteorological Administration (CMA_GFS) model, and evaluated the impacts on rainfall prediction over the TP. Mobile MR observations can be used to retrieve reliable products of Ta, water vapor density and RH profiles over the TP. DA with retrieved Ta profile from mobile MR can effectively improve short‐term forecasts of low to moderate precipitation in the central and western regions of Qinghai province, and is beneficial for accurate simulation of water vapor transport, convergence and divergence of wind field, and upward motion associated with precipitation events. This study provides an effective pathway to investigate the formation mechanism and influence of small‐scale and meso‐scale weather system such as TP vortex and shear line. Key Points: Mobile MR observation can be used to retrieve the creditable air temperature, water vapor density and relative humidity profiles over the TPData assimilation with air temperate profiles from Mobile MR can effectively improve the model forecasts of low to moderate precipitationMore accurate representations of water vapor and wind fields with data assimilation of MR retrievals can improve precipitation forecast [ABSTRACT FROM AUTHOR]

The shape of ice crystals affects their radiative properties, growth rate, fall speed, and collision efficiency; thus, it plays a significant role in cloud optical properties and precipitation formation. Ambient conditions, like temperature and humidity, determine the basic habit of ice crystals, while microphysical processes, such as riming and aggregation, further shape them, resulting in a diverse set of ice crystal shapes and effective densities. Current classification algorithms face two major challenges: (1) ice crystals are often classified as a whole (at the image scale), necessitating identification of the dominant component of aggregated ice crystals, and (2) single-label classifications lead to information loss because of the compromise between basic habit and microphysical process information. To address these limitations, we present a two-pronged solution here: (1) a rotated object detection algorithm (IceDetectNet) that classifies each component of an aggregated ice crystal individually and (2) a multi-label classification scheme that considers both basic habits and physical processes simultaneously. IceDetectNet was trained and tested on two independent datasets obtained by a holographic imager during the NASCENT campaign in Ny-Ålesund, Svalbard, in November 2019 and April 2020. The algorithm correctly classified 92 % of the ice crystals as either aggregate or non-aggregate and achieved an overall accuracy of 86 % for basic habits and 82 % for microphysical process classification. At the component scale, IceDetectNet demonstrated high detection and classification accuracy across all sizes, indicating its ability to effectively classify individual components of aggregated ice crystals. Furthermore, the algorithm demonstrated a good generalization ability by classifying ice crystals from an independent generalization dataset with overall accuracies above 70 %. IceDetectNet can provide a deeper understanding of ice crystal shapes, leading to better estimates of ice crystal mass, fall velocity, and radiative properties; therefore, it has the potential to improve precipitation forecasts and climate projections. [ABSTRACT FROM AUTHOR]

In July 2023, an extreme precipitation event (EPE) caused severe damage and nearly 100 fatalities in the Hai River basin, North China. Using a Lagrangian approach, we investigated the moisture transport regime and identified the circulation pattern responsible for the EPE. The primary moisture sources were the northwest Pacific (24%), southeast China (21%), and the South China Sea (20%). The circulation pattern, featuring the western Pacific subtropical high to the northeast, tropical cyclone KHANUN in the northwest Pacific, and a low‐pressure system southwest of the Hai River Basin, predominantly controlled the moisture transport. We discovered that an unprecedented EPE in 1963 exhibited a similar circulation pattern. By classifying historical EPEs into three categories based on seven atmospheric predictors, we identified circulation patterns associated with disaster‐prone events. The most frequent category is characterized by a circulation pattern with positive geopotential height anomalies in northeast Asia and negative anomalies to the south, extending from the northwest Pacific to the Hai River Basin. This classification provides insights into the atmospheric conditions conducive to severe EPEs in the region. Plain Language Summary: In July 2023, the Hai River Basin in North China experienced extreme heavy rainfall, leading to a massive flood and severe consequences for the local population. Our investigation reveals that the primary moisture sources for this extreme precipitation event are the northwest Pacific, southeast China, and the South China Sea. Moreover, we identified a distinctive circulation pattern featuring a positive geopotential height anomaly in northeast Asia and a negative geopotential height anomaly to southwest of Hai River Basin and in the northwest Pacific, which significantly contributed to the occurrence of extreme precipitation. This research enhances our understanding of moisture transport regimes and offers valuable insights for forecasting extreme precipitation events over the Hai River Basin and other regions. Key Points: The northwest Pacific, southeast China, and South China Sea are the key moisture sources for the extreme precipitation event in July 2023The western Pacific subtropical high, tropical cyclone KHANUN, and low‐pressure system DPKSURI collectively converge moisture of 2023 eventHigh pressure in northeast Asia and low pressure in northwest Pacific and south Hai River Basin drive most extreme precipitation [ABSTRACT FROM AUTHOR]

Weather forecasting is a classical problem in remote sensing, in which precipitation is difficult to predict accurately because of its complex physical motion. Precipitation significantly impacts human life, work, and the ecological environment. Precise precipitation forecasting is vital for water resource management, ecological protection, and disaster mitigation through precise precipitation forecasting. This study introduces an innovative deep learning-based precipitation-forecasting method DFMM-Precip that integrates reanalysis of precipitation data and satellite data using a multi-modal fusion layer and predicts future precipitation details through a global–local joint temporal-spatial attention mechanism. By effectively combining satellite infrared data with reanalysis data, the approach enhances the accuracy of precipitation forecasting. Experimental results for 24 h precipitation forecasts show that DFMM-Precip's multi-modal fusion layer successfully integrates multi-modal data related to precipitation, leading to improved forecast accuracy. In particular, the global–local joint temporal-spatial attention mechanism provides precise, detailed forecasting of spatial and temporal precipitation patterns, outperforming other state-of-the-art models. The MSE of the forecasting results is 10 times lower than that of the advanced RNN model and 2.4 times lower than that of the advanced CNN model with single-modal data input. The probability of successful rainfall prediction is improved by more than 10%. [ABSTRACT FROM AUTHOR]

Precipitation prediction is crucial for various sectors, including agriculture, water resource management, and transportation. This paper addresses the gap in rainfall prediction studies that seldom separate spatial and temporal scales. The spatial distribution of rainfall in Zhejiang Province in 2015 is analyzed using multiple interpolation methods: Inverse Distance to a Power (IDP), Kriging Interpolation, Minimum Curvature Interpolation (MC), Modified Shepard's Method (MSM), Nearest Neighbour Interpolation (NeaN), and Natural Neighbor(NN). Additionally, daily and monthly rainfall are simulated using a 30-year historical precipitation dataset (1990-2020) of an observation station from Zhejiang Province, employing Radial Basis Function Network (RBFN) and Back Propagation (BP) neural networks.Our results confirm the accuracy of these methods in both simulation and interpolation. The benefits of Artificial Neural Networks (ANNs) over traditional interpolation techniques in predicting rainfall are underscored, while the challenges ANNs face, such as nonlinear rainfall patterns, training time, computational complexity, and the configuration of meteorological stations are also acknowledged. Notably, RBFN outperformed the BP model in simulating rainfall, especially for longer forecast periods.In conclusion, the potential applications and future directions of ANNs in rainfall prediction are discussed, their utility across different spatial and temporal scales are emphasized. By comparing ANNs with classical interpolation methods, their respective strengths are highlighted, providing scientific insights for future precipitation forecasting at provincial administrative levels. [ABSTRACT FROM AUTHOR]

The accuracy of the Weather Research and Forecasting (WRF) model's predictions for air temperature and precipitation in northwestern Greece varies under different weather conditions. However, there is a lack of understanding regarding how well the model performs for specific Weather Types (WTs), especially in regions with a complex topography like NW Greece. This study evaluates the WRF model's ability to predict 2 m air temperature and precipitation for 10 objectively defined WTs. Forecasts are validated against observations from the station network of the National Observatory of Athens, focusing on biases and skill variation across WTs. The results indicate that anticyclonic WTs lead to a significant overestimation of early morning air temperatures, especially for inland stations. The precipitation forecast skill varies depending on the threshold and characteristics of each WT, showing optimal results for WTs where precipitation is associated with a combination of depression activity, and orographic effects. These findings indicate the need for adjustments based on WT in operational forecasting systems for regions with similar topographical complexities. [ABSTRACT FROM AUTHOR]

In this study, a latent heat nudging lightning data assimilation (LDA) method independent of the flash rate was developed and tested with data from the Lightning Mapping Imager (LMI) onboard the Feng-Yun-4A (FY-4A) satellite based on the Weather Research and Forecasting (WRF) model. In this LDA method, the positive temperature perturbations at the lightning location are first calculated by the difference between the moist adiabatic temperature of a lifted air parcel and the model temperature. The positive temperature perturbations in the mixed-phase region are then assimilated by a nudging method to adjust the latent heat within the convective system. Meanwhile, the water vapor mixing ratio is adapted to the temperature perturbations accordingly to constrain the relative humidity to remain unchanged. This method considers the physical nature of the convective system, in contrast with other LDA methods that establish an empirical or statistical relationship between the lightning flash rates and model variables. The impact of this LDA method on short-term (⩽6 h) forecasts was evaluated using two severe convective events in eastern China: a multi-region heavy rainfall event and a thunderstorm high-wind event. The results showed that LDA could add thermodynamic information associated with the convective system to the WRF model during the nudging period, leading to a more reasonable storm environment. In the forecast fields, the simulations with LDA produced more realistic convective structures, resulting in an improvement in forecasts of precipitation and high winds. [ABSTRACT FROM AUTHOR]

The ever-increasing capacity of numerical weather prediction (NWP) models requires accurate flow information at higher spatial and temporal resolutions. The atmospheric motion vectors (AMVs) extracted from the Advanced Geostationary Radiation Imager (AGRI) mounted on the Fengyun-4A (FY-4A) satellite can provide information about atmospheric flow fields on small scales. This study focused on the assimilation of FY-4A AMVs and its impact on forecasts in the regional NWP system of the China Meteorological Administration—Beijing (CAM-BJ). The statistical characterization of FY-4A AMVs was firstly analyzed, and an optimal observation error in each vertical level was obtained. Three groups of retrospective runs over a one-month period were conducted, and the impact of assimilating the AMVs with different strategies on the forecasts of the CMA-BJ system were compared and evaluated. The results suggested that the optimal observation errors reduced the standard deviation of the background departures for U and V wind, leading to an improvement in the standard deviation in the corresponding analysis departures of about 8.3% for U wind and 7.3% for V wind. Assimilating FY-4A AMV data with a quality indicator (QI) above 80 and the optimal observation errors reduced the error of upper wind forecast in the CMA-BJ system. A benefit was also obtained in the error of surface wind forecast after 6 h of the forecasts, although it was not significant. For rainfall forecast with different thresholds, the score skills increased slightly after 6 h of the forecasts. There was an overall improvement for the overprediction of 24 h accumulated precipitation forecast including the AMVs, even when conventional observations were relatively rich. The application of FY-4A AMVs with a QI > 80 and adjustment to observation errors has a positive impact on the upper wind forecast in the CMA-BJ system, improving the score skill of rainfall forecasting. [ABSTRACT FROM AUTHOR]

Reliable forecast precipitation can support disaster prevention and mitigation and sustainable socio-economic development. Improving forecast precipitation accuracy remains a challenge. Therefore, a novel method for multi-model forecast precipitation integration considering long lead times was proposed based on deep learning. First, the accuracy of numerical forecast precipitation was evaluated under different lead times. Secondly, an integrated model was built by coupling the attention mechanism and a long short-term memory neural network (LSTM). Finally, integrated forecast precipitation was obtained by taking high-precision numerical forecast precipitation as an input and examining its accuracy and applicability. Considering the example of the Yalong River, the results showed the following: (1) numerical forecast precipitation fails to forecast precipitation of a ≥10 mm/d intensity well, and is less applicable in streamflow forecast; (2) traditional machine learning methods for integrating multi-model forecast precipitation fail to forecast precipitation of a ≥25 mm/d intensity; (3) the LSTM-A integration model formed by attention weighting after the LSTM output can combine the advantages of numerical forecast precipitation under different intensities and improve the forecast precipitation accuracy for 7-day lead times; and (4) the LSTM-A integrated forecast precipitation has the best applicability in streamflow forecast, with an NSE above 0.82 and an MRE below 30% with 7-day lead times. These findings contribute to improving precipitation forecast accuracy at different intensities and enhancing defense against extreme weather events. [ABSTRACT FROM AUTHOR]

To enhance the precision of precipitation forecasting in the Qinghai–Tibet Plateau region, a comprehensive study of both macro- and micro-characteristics of local precipitation is imperative. In this study, we investigated the particle size distribution, droplet velocity, droplet number density, Z – I (radar reflectivity–rainfall intensity) relationship, and gamma distribution of precipitation droplet spectra with a single precipitation duration of at least 20 min and precipitation of 5 mm or more at four stations (Nyalam, Lhasa, Shigatse, and Naqu) in Tibet during recent years from June to August. The results are as follows: (1) in the fitting relationship curve between precipitation raindrop spectral particle size and falling speed at the four stations in Tibet, when the particle size was less than 1.5 mm, the four lines essentially coincided. When the particle size exceeded 1.5 mm, the speed in Nyalam was the highest, followed by Naqu, and the speed in Lhasa was the lowest. The falling speed of particles correlated with altitude. (2) The five microphysical characteristics (mean diameter (Dm), average volume diameter (Dv), mode diameter (Dd), dominant diameter (Dp), and median diameter (Dnd)) at the four stations have different correlation relationships with altitude under different rainfall intensities. Dm exhibits a negative correlation with altitude at the same rainfall intensity; in contrast, Dv shows a positive correlation with altitude. For microphysical parameters such as Dd and Dp , a rainfall intensity of 10 mm h−1 serves as the boundary line, and they have different correlation relationships with altitude under the same rainfall intensity level. (3) The Z – I relationships at the four stations exhibited variations. Owing to the proximity in altitude between Lhasa and Shigatse, as well as between Nyalam and Nagqu, the coefficient a and index b in the Z – I relationships of the two groups of sites were relatively similar. (4) The fitting curves of the exponential and gamma distributions of the precipitation particle size at the aforementioned four stations are largely comparable. The exponential distribution fitting exhibits a slightly better effect. The parameter μ in the gamma distribution decreases with an increase in altitude, while N0 and λ in the exponential distribution show a clear upward trend with altitude. [ABSTRACT FROM AUTHOR]

In real-time operations, the minutely/hourly updated high-resolution rapid refresh (HRRR) system is one of the most expensive numerical weather prediction (NWP) models. Based on a twenty-member HRRR-time-lagged-ensemble (HRRR-TLE) system developed from two real-time convection-permitting HRRR models, CMA-GD(R3) and CMA-SH3, from the China Meteorological Administration (CMA), this study proposes an optimized probability-matching (OPM) technique to improve 0-12 h quantitative precipitation forecasts (QPFs) based on the correlation and error relationships between ensemble forecasts and observations during the training window. Then, a series of sensitivity experiments using different cost functions and optimized ratios was conducted to further improve OPM predictions. The results indicate that: (1) In the HRRR-TLE system, there is no always optimal member in both weak rain and severe rain forecasts, as measured by the equitable threat score (ETS) and bias extent (BE) at four thresholds (1+, 5+, 10+, and 20+ mm h-1; e.g., "1+" means ≥ 1). (2) Compared with the HRRR-TLE system, the QPFs generated by the traditional PM technique showed a notable increase in ETS and a decrease in BE at all of the above thresholds. Compared with the traditional probability-matching method (PM), OPM can generate more skillful forecasts on both spatial representations and rain rates by using the sliding-weight method and optimized ensembles, respectively. (3) In particular, in the 20+ mm h-1 forecasts, which are often difficult to predict, the ETS of the optimal OPM test, with a 20% optimization ratio and symmetric mean absolute percentage error cost function, increased by 64.6%, and the BE decreased by 5.7%, relative to PM. Moreover, OPM shows good stability in both daytime and nighttime periods. [ABSTRACT FROM AUTHOR]

Business Finland 6G Visible project's objective is the development of 6G-era service and architecture solutions utilizing autonomous and semi-autonomous driving, with both physical and logical computational elements and with use cases for real-life verification and validation. Finnish Meteorological Institute is focusing especially on weather- and safety-related services for autonomous vehicles. We are tailoring our road weather services for the special needs of autonomous driving, keeping in mind that autonomous vehicles are more sensitive to the harsh winter weather conditions and benefit from more accurate weather information considering the sensor systems of each vehicle. Employing weather radar-based nowcasting of more accurate short-term precipitation forecasting benefits autonomous traffic, especially in cases of heavy local precipitation by re-routing/route planning and avoiding heaviest precipitation. Evaluation of autonomous vehicles' sensor systems' sensitivity to harsh weather conditions allows for weather forecasting based on the real vulnerability of each vehicle. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The World Food Programme, in collaboration with the Mozambique National Meteorology Institute, is partnering with several governmental and non-governmental organizations to establish an advanced early warning system for droughts in pilot districts across Mozambique. The "Ready, Set & Go!" system is operational in Mozambique for activating anticipatory action (AA) against droughts based on predefined thresholds, triggers and pre-allocated financing. The system uses bias-corrected and downscaled seasonal forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) as core information to anticipate severe reductions in rainfall during the rainy season. This information guides the implementation of actions to reduce the impacts of rainfall deficits in the critical window between a forecast and the onset of the drought event. Within this window of opportunity, the system releases an alert for readiness (Ready) and activation (Set) preceding the mobilization of anticipatory action on the ground (Go). With the recent adoption of the Southern African Development Community "Maputo Declaration on Bridging the Gap between Early Warning and Early Action", member states have committed to enhancing the reach of early warning system by leaving no one behind. Therefore, there is a need to assess the opportunities and limitations of the Ready, Set & Go! system to scale up drought AA information to all districts in Mozambique. This study describes the Ready, Set & Go! system, which uses ensemble forecasts of the Standardized Precipitation Index to trigger anticipatory action against droughts on a seasonal timescale. The Ready, Set & Go! optimizes the use of seasonal forecast information by choosing triggers for anticipatory action based on verification statistics and on a double-confirmation process, which combines longer lead times with shorter lead time forecasts for issuing drought alerts. In this study, we show the strengths of the system by benchmarking it against three simpler triggering approaches. Our findings indicate that the Ready, Set & Go! system has significant potential to scale up AA activities against severe droughts throughout the entire rainy season, covering on average 76 % of the Mozambican districts. This approach outperforms the three benchmarked methods, demonstrating higher hit rates, extended lead times and a lower false alarm. If efforts are concentrated on the first part of the rainy season, national coverage against severe droughts could be expanded to 87 % of all districts. By aligning with the objectives outlined in the "Maputo Declaration" and the "Early Warning for All" initiative, this research contributes to safeguarding communities against the adverse impacts of climate-related events, aligning with the ambitious goal of universal protection by 2027. [ABSTRACT FROM AUTHOR]

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The Multi‐Radar Multi‐Sensor (MRMS) product incorporates radar, quantitative precipitation forecasts, and gage data at a high spatiotemporal resolution for the United States and southern Canada. MRMS is subject to various sources of measurement error, especially in complex terrain. The goal of this study is to provide a framework for understanding the uncertainty of MRMS in mountainous areas with limited observations. We evaluate 8‐hr time series samples of MRMS 15‐min intensity through a comparison to 204 gages located in the mountains of Colorado. This analysis shows that the MRMS surface precipitation rate product tends to overestimate rainfall with a median normalized root mean squared error (RMSE) of 42% of the maximum MRMS 15‐min intensity. For each time series sample, various features related to the physical characteristics influencing MRMS performance are calculated from the topography, surrounding storms, and rainfall observed at the gage location. A gradient‐boosting regressor is trained on these features and is optimized with quantile loss, using the RMSE as a target, to model nonlinear patterns in the features that relate to a range of error. This model was used to predict a range of error throughout the mountains of Colorado during warm months, spanning 6 years, resulting in a spatiotemporally varying error model of MRMS for sub‐hourly precipitation rates. Mapping of this data set by aggregating normalized RMSE over time reveals that areas further from radar sites in higher elevation terrain show consistently greater error. However, the model predicts larger performance variability in these regions compared to alternative error assessments. Plain Language Summary: Storms in mountainous regions can develop quickly and cause significant flooding. The lack of precipitation gages in mountainous remote areas inhibits detailed monitoring of these hazardous events. Radar estimates of precipitation can fill the gaps in areas where gages are sparse, but the signal can be blocked by mountains, depending on where the storm is relative to the radar site. Because the error of radar estimates of precipitation can change based on where the storm is located in relation to the surrounding terrain and location of the radar, the reliability of these precipitation estimates is variable, adding to the difficulty of monitoring storms in mountains. Here we develop a novel method of identifying where and when the radar estimates of precipitation are reliable, based on attributes of the region, rainfall, and storm events. The results can assist in deciding when to trust radar estimates of precipitation and in determining where more gages or radar sites are necessary. Unsurprisingly, precipitation estimates in areas that are far from radar sites and in more complex mountainous terrain have less reliable radar precipitation estimates. However, reliability in these areas is variable and error is not always high. Key Points: The error of radar quantitative precipitation estimates (QPEs) is highly variable, especially in remote areas with complex terrain where QPEs are generally assumed unreliableLonger lasting precipitation tends to have lower QPE errorA spatiotemporally varying error model of sub‐hourly radar QPEs is developed for the mountains of Colorado [ABSTRACT FROM AUTHOR]

This work explores the detailed study of Bangladeshi precipitation patterns, with a particular emphasis on modeling annual rainfall changes in six coastal cities using Markov chains. To create a robust Markov chain model with four distinct precipitation states and provide insight into the transition probabilities between these states, the study integrates historical rainfall data spanning nearly three decades (1994-2023). The stationary test statistic (X²) was computed for a selected number of coastal stations, and transition probabilities between distinct rainfall states were predicted using this historical data. The findings reveal that the observed values of the test statistic, X², are significant for all coastal stations, indicating a reliable model fit. These results underscore the importance of understanding the temporal evolution of precipitation patterns, which is crucial for effective water resource management, agricultural planning, and disaster preparedness in the region. The study highlights the dynamic nature of rainfall patterns and the necessity for adaptive strategies to mitigate the impacts of climate variability. Furthermore, this research emphasizes the interconnectedness of climate studies and the critical need for enhanced data-gathering methods and international collaboration to bridge knowledge gaps regarding climate variability. By referencing a comprehensive range of scholarly works on climate change, extreme rainfall events, and variability in precipitation patterns, the study provides a thorough overview of the current research landscape in this field. In conclusion, this study not only contributes to the understanding of precipitation dynamics in Bangladeshi coastal cities but also offers valuable insights for policymakers and stakeholders involved in climate adaptation and resilience planning. The integration of Markov chain models with extensive historical data sets serves as a powerful tool for predicting future rainfall trends and developing informed strategies to address the challenges posed by changing precipitation patterns. [ABSTRACT FROM AUTHOR]

Copyright of LHB: Hydroscience Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The forecast of torrential precipitation caused by vortex is a crucial and challenging aspect of rainstorm forecasting in North China. This paper analyzes the characteristics of atmospheric anomalies during the occurrence and development of two rainstorm events caused by vortex, "Case-20120721" and "Case-20160720". The results show that if the temperature anomaly front was steep, the falling area of rainstorm was well corresponding with the intersection of temperature anomaly front and ground, and also with the bottom position of gradient zone of anomalous specific humidity. In cases with relatively gentle temperature anomaly front areas, the falling area of the rainstorm corresponded to the intersection of the negative axis of anomalous geopotential height with the ground and the center of specific humidity anomaly. Both cases were accompanied by strong vertical shear of horizontal anomalous wind, and the location where the negative central axis of the anomalous V wind field intersected the ground indicates that the cold air brought by the anomalous northerly wind corresponded well with the heavy rainstorm area at the top of the vortex. Additionally, the areas where the anomaly axes of moist vorticity and moist divergence column connect to the ground always corresponded to heavy precipitation areas. It is worth noting that initially, these axes of anomaly were vertical, but they began to tilt after the precipitation developed strongly. [ABSTRACT FROM AUTHOR]

Deep learning (DL) approaches to forecast precipitation and inundation areas in the short-term forecast horizon have up until now been treated as independent research problems from the model development perspective. However, for the urban hydrology area, the coupling of these models is necessary in order to forecast the upcoming inundation area maps and is, therefore, of the utmost importance for successful flood risk management. In this paper, three deep-learning-based models are coupled in a systematic modular approach with the aim to analyze the performance of this model chain in an operative setup for urban pluvial flooding nowcast: precipitation nowcasting with an adapted version of the NowcastNet model, the forecast of manhole overflow hydrographs with a Seq2Seq model, and the generation of a spatiotemporal sequence of inundation areas in an urban catchment for the upcoming hour with an encoder–decoder model. It can be concluded that the forecast quality still largely depends on the accuracy of the precipitation nowcasting model. With the increasing development of DL models for both precipitation and flood nowcasting, the presented modular approach for model coupling enables the substitution of individual blocks for better and newer models in the model chain without jeopardizing the operation of the flooding forecast system. [ABSTRACT FROM AUTHOR]

Accurate and reliable precipitation forecasting is vital for effective water resource management and disaster mitigation, especially in geographically diverse and climatically complex regions like China. This study proposes an advanced methodology for medium- and long-term hydrological forecasting by integrating multiple machine learning models through a modified stacking ensemble strategy (MSES). We developed and compared five deterministic precipitation forecasting models, including elastic net regression (ENR), support vector regression, random forest, extreme gradient boosting, and light gradient boosting to provide forecasts with lead times ranging from 0 to 5 months at a spatial resolution of 0.5$^\circ$. The MSES was then evaluated against the traditional Bayesian model averaging (BMA) approach. Our comprehensive evaluation, based on deterministic forecasting metrics such as the anomaly correlation coefficient (ACC), mean squared skill score (MSSS), and Graded Precipitation Score (Pg), demonstrated the MSES outperformed individual models and the BMA method. The MSES achieved ACC scores between 0.6 and 0.9 for lead time (LT) $= 0$ month, with an average of around 0.8 for LT $= 2$ months. The MSSS for MSES was above 0.5 in more than half of the evaluations, and the Pg score was consistently above 80, indicating high accuracy in precipitation magnitude prediction. These findings highlight the promise of advanced machine learning strategies like MSES in improving the accuracy and robustness of precipitation forecasts, addressing critical needs in water resource management and disaster mitigation in China.

River regulation in mountainous and high-precipitation regions with hydropower dams often struggles to find the right balance between hydropower generation while ensuring flood protection for downstream inhabitants. The goal of hydropower generation is to keep reservoirs at the maximum pool as often as possible while for flood control, it is to maintain sufficient cushion in available storage to absorb an incoming flood wave. Using weather forecasts to proactively manage reservoir operations for such conflicting goals is now a well-known solution. However, this challenge of applying forecast-informed reservoir operations is magnified in developing regions where there is a paucity of ground data to track reservoir dynamics. In this study, we explore the utility of using publicly available precipitation forecasts from the Global Ensemble Forecasting System (GEFS) with a fully satellite-based reservoir tracking framework called reservoir assessment tool (RAT) to understand the potential of forecast-informed operations in highly mountainous and high-precipitation regions that are mostly ungauged. We apply our investigation to the case of damaging floods that took place in 2018 in the Southern Indian state of Kerala where river regulation is carried out with a fleet of hydropower dams. Our results show that the precipitation forecast from GEFS has sufficient skill, if focused on trends and bias adjustment, to predict reservoir inflow peaks up to a week ahead of time where the trend for the timing of the peak and rate of rise match well. Using our satellite-based RAT framework, we explore the range of actionable scenarios for dam operators that could potentially minimize downstream flood risk with this forecast-informed reservoir operations scheme. [ABSTRACT FROM AUTHOR]

The development of subseasonal forecasts has seen significant advancements, transforming our ability to predict weather patterns and climate variability on intermediate timescales ranging from 2 weeks to 2 months. Motivated by the need to enhance our understanding of subseasonal precipitation forecasts and their applicability to the hydrology forecast, this study retrospectively analysed precipitation ensemble forecasts from subseasonal prediction models in the Uruguay River basin nearby Salto Grande dam. Three models were considered: two from the S2S project (ECMWF and CNRM) and one from the SubX project (GEFS). Model forecasts were analysed on a weekly time scale using both deterministic and probabilistic approaches. Multimodel probabilistic forecasts combining the three different models were built to increase forecast skill. Individual models have a skill larger than or equal to the climatological forecast until 2 weeks in advance. Particularly, ECMWF shows better skill in both ensemble mean and probabilistic forecast. Multimodel probabilistic forecast improves the skill of the forecast throughout the year, with the skill even surpassing the climatological forecast by up to 4 weeks in advance during the summer. In addition, model skill was analysed considering the state of the El Niño–Southern Oscillation (ENSO) on a weekly and monthly basis. On weekly time scales the ENSO state modifies model skill differently depending on the sub‐basin and season considered. However, the influence of ENSO on forecast skill is more clearly observed on monthly time scales, with largest improvement in the lower basin during springtime. The results of this work suggest that subseasonal models are a promising tool to bridge the gap between weather and climate forecast in the Uruguay River basin and have the potential to be utilized for hydrological forecasting in the study region. [ABSTRACT FROM AUTHOR]

Despite the well-known importance of radar data assimilation, there are limited studies on landfalling typhoons in terms of directly assimilating radar reflectivity data, especially using a reflectivity operator based on double-moment microphysics. In this study, radar reflectivity data assimilation experiments are conducted with an ensemble Kalman filter (EnKF), using simulated observations in an Observing System Simulation Experiment (OSSE) framework for the landfalling typhoon In-Fa. With an OSSE, it is convenient to analyze the impact of assimilation of radar reflectivity on analysis and forecast for various state variables, especially for hydrometeors. Our results show that the direct assimilation of radar reflectivity with EnKF does not introduce non-physical hydrometeors and is able to adjust well, not only to hydrometers, but also to some large-scale variables which are not directly related to reflectivity, especially in terms of temperature and vertical velocity. Though the most notable reduction in the Root Mean Square Errors (RMSEs) is observed through mixing the ratio of rainwater and snow, the analysis of other variables is also significantly improved with the accumulation of assimilation cycles. The correlation analysis reveals the strongest correlation between radar reflectivity data and hydrometeor-related variables as well as the correlation with certain large-scale variables, indicating that these cross-variables are updated well through the reliable multivariate ensemble covariance in the EnKF. As a result, an obvious improvement in typhoon intensity and precipitation forecast is obtained in the data assimilation experiment. The impact of assimilation on radar reflectivity can last for up to 15–16 h. [ABSTRACT FROM AUTHOR]

Quantitative precipitation forecasts (QPF) from numerical weather prediction models need systematic verification to enable rigorous assessment and informed use, as well as model improvements. The United States (US) National Oceanic and Atmospheric Administration (NOAA) recently made a major update to its regional tropical cyclone modeling capabilities, introducing two new operational configurations of the Hurricane Analysis and Forecast System (HAFS). NOAA performed multi-season retrospective forecasts using the HAFS configurations during the period that the Hurricane Weather and Forecasting (HWRF) model was operational, which was used to assess HAFS performance for key tropical cyclone forecast metrics. However, systematic QPF verification was not an integral part of the initial evaluation. The first systematic QPF evaluation of the operational HAFS version 1 configurations is presented here for the 2021 and 2022 season re-forecasts as well as the first HAFS operational season, 2023. A suite of techniques, tools, and metrics within the enhanced Model Evaluation Tools (METplus) software suite are used. This includes shifting forecasts to mitigate track errors, regridding model and observed fields to a storm relative coordinate system, as well as object oriented verification. The HAFS configurations have better performance than HWRF for equitable threat score (ETS), but larger over forecast biases than HWRF. Storm relative and object oriented verification show the HAFS configurations have larger precipitation areas and less intense precipitation near the TC center as compared to observations and HWRF. HAFS QPF performance decreased for the 2023 season, but the general spatial patterns of the model QPF were very similar to 2021-2022. [ABSTRACT FROM AUTHOR]

Forecasting rain is essential for the alleviation and management of floods, environmental flows and water demand in different sectors. Precipitation is affected by various meteorological factors and has strong nonlinear characteristics, which significantly hinders its ability to be predicted. To improve the accuracy and robustness of prediction results, this paper proposes a precipitation ensemble forecasting model (CEEMDAN-BMA model) based on complete ensemble empirical modal decomposition (CEEMDAN) and Bayesian model averaging (BMA) methods using monthly precipitation data from Beijing and Guangzhou stations from January 1950 to December 2020 to explore the model's validity. The ensemble prediction results of the CEEMDAN-BMA model were analysed based on six evaluation indices. The results show that the CEEMDAN-BMA model performs well in terms of monthly precipitation prediction for both the Beijing and Guangzhou stations. The RMSE, MAE, and R2 values of the monthly precipitation prediction results for the Beijing station are 22.355 mm, 14.973 mm, and 0.897, respectively, and the RMSE, MAE, and R2 values of the monthly precipitation prediction results for the Guangzhou station are 35.86 mm, 28.371 mm, and 0.932, respectively. In addition, the CEEMDAN-BMA model provides a 90% confidence interval (CI) to quantify the uncertainty of the prediction results. The coverage of the 90% CI of the CEEMDAN-BMA model for the Beijing station is 91.67%, the average width is 82.76 mm, and the average offset is 0.009 mm; the coverage of the 90% CI for the Guangzhou station is 96.67%, the average width is 143.84 mm, and the average offset is 0.059 mm. Compared with those of the other models, the prediction results of the CEEMDAN-BMA model are superior. [ABSTRACT FROM AUTHOR]

Seasonal precipitation forecasting (SPF) is critical for effective water resource management and risk mitigation. Large-scale climate drivers significantly influence regional climatic patterns and forecast accuracy. This study establishes relationships between key climate drivers—El Niño–Southern Oscillation (ENSO), Southern Oscillation Index (SOI), Indian Ocean Dipole (IOD), Real-time Multivariate Madden–Julian Oscillation (MJO), and Multivariate ENSO Index (MEI)—and seasonal precipitation anomalies (rainy, summer, and winter) in Eastern Thailand, utilizing Pearson's correlation coefficient. Following the establishment of these correlations, the most influential drivers were incorporated into the forecasting models. This study proposed an advanced SPF methodology for Eastern Thailand through a Seasonal WaveNet-LSTM model, which integrates Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs) with Wavelet Transformation (WT). By integrating large-scale climate drivers alongside key meteorological variables, the model achieves superior predictive accuracy compared to traditional LSTM models across all seasons. During the rainy season, the WaveNet-LSTM model (SPF-3) achieved a coefficient of determination (R2) of 0.91, a normalized root mean square error (NRMSE) of 8.68%, a false alarm rate (FAR) of 0.03, and a critical success index (CSI) of 0.97, indicating minimal error and exceptional event detection capabilities. In contrast, traditional LSTM models yielded an R2 of 0.85, an NRMSE of 10.28%, a FAR of 0.20, and a CSI of 0.80. For the summer season, the WaveNet-LSTM model (SPF-1) outperformed the traditional model with an R2 of 0.87 (compared to 0.50 for the traditional model), an NRMSE of 12.01% (versus 25.37%), a FAR of 0.09 (versus 0.30), and a CSI of 0.83 (versus 0.60). In the winter season, the WaveNet-LSTM model demonstrated similar improvements, achieving an R2 of 0.79 and an NRMSE of 13.69%, with a FAR of 0.23, compared to the traditional LSTM's R2 of 0.20 and NRMSE of 41.46%. These results highlight the superior reliability and accuracy of the WaveNet-LSTM model for operational seasonal precipitation forecasting (SPF). The integration of large-scale climate drivers and wavelet-decomposed features significantly enhances forecasting performance, underscoring the importance of selecting appropriate predictors for climatological and hydrological studies. [ABSTRACT FROM AUTHOR]

The optimal spatial layout for a radar wind profiler (RWP) network in rainfall forecasting, especially over complex terrain, remains uncertain. This study explores the benefits of assimilating vertical wind measurements from various RWP network layouts into convective-scale numerical weather prediction (NWP) through observing system simulation experiments (OSSEs). Synthetic RWP data were assimilated into the Weather Research and Forecasting (WRF) model using the National Severe Storms Laboratory three-dimensional variational data assimilation (DA) system for three southwest (SW)-type heavy rainfall events in the Beijing-Tianjin-Hebei region. Four types of DA experiments were conducted and compared: a control experiment (CTL) that assimilates data solely from the operational RWP network, and three additional experiments incorporating foothill (FH), ridge (RD), and combined foothill-ridge (FH_RD) RWP network layouts. A detailed examination of the 21 July 2023 case reveals that the FH_RD experiment generally exhibits more skillful storm forecasts in terms of areal coverage, storm mode, and orientation, benifiting from refined mesoscale wind analysis. Particularly, in the RD experiment, RWP data assimilation notably reduces wind errors and enhances mesoscale dynamics near the Taihang Mountains upstream of Beijing, crucial for convective initiation (CI). Aggregated score metrics across all cases also indicate that both FH and RD experiments offer substantial added value over the operational network alone. Further sensitivity experiments on vertical resolution and maximum detection height indicate that the RWP system configuration with the highest detection height achieves the best performance, while lower detection height degrades forecast quality. These findings highlight the importance of strategic RWP network placement along the Taihang Mountains' ridge and foothill for short-term quantitative precipitation forecast in the Beijing-Tianjin-Hebei region. [ABSTRACT FROM AUTHOR]

Quantitative precipitation forecast (QPF) verification stands out as one of the most formidable endeavors in the realm of forecast verification. Traditional verification methods are not suitable for high-resolution forecasting products in some cases. Therefore, the SAL (structure, amplitude and location) method was proposed as a method of object-based spatial verification that studies precipitation verification in a certain range, which is combined with factors including structure, amplitude and location of the targets. However, the setting of the precipitation threshold would affect the result of the verification. This paper presented an improved method for determining the precipitation threshold using the QPF from ECMWF, which is an ensemble forecast model and three-source fusion product that was used in China from 1 July to 31 August 2020, and then the results obtained with this method were compared with the other two traditional methods. Furthermore, the SAL and the traditional verification methods were carried out for geometric, simulated and real cases, respectively. The results showed the following: (1) The proposed method in this paper for determining the threshold was more accurate at identifying the precipitation objects. (2) The verification area size was critical for SAL calculation. If the area selected was too large, the calculated SAL value had little significance. (3) ME (Mean Error) could not identify the displacement between prediction and observation, while HSS (Heidke Skill Score) was sensitive to the displacement of the prediction field. (4) Compared with the traditional verification methods, the SAL method was more straight forward and simple, and it could give a better representation of prediction ability. Therefore, forecasters can better understand the model prediction effect and what needs to be improved. [ABSTRACT FROM AUTHOR]

Our study investigates the use of machine learning models for daily precipitation prediction using data from 56 meteorological stations in Jilin Province, China. We evaluate Stacked Long Short-Term Memory (LSTM), Transformer, and Support Vector Regression (SVR) models, with Stacked-LSTM showing the best performance in terms of accuracy and stability, as measured by the Root Mean Square Error (RMSE). To improve robustness, Gaussian noise was introduced, particularly enhancing predictions for zero-precipitation days. Key predictors identified through variable attribution analysis include temperature, dew point, prior precipitation, and air pressure. Additionally, we demonstrate the practical benefits of precipitation forecasts in optimizing water resource allocation. A prediction-based strategy outperforms equal distribution in managing resources efficiently, as shown in a case study using 2022 Beidahu data. Overall, our research advances precipitation forecasting through deep learning and offers valuable insights for water resource management. [ABSTRACT FROM AUTHOR]

This paper conducts an observing system simulation experiment (OSSE) to assess the impact of assimilating traditional sounding and surface data, along with dropsonde observations over the northern South China Sea (SCS) on heavy rain forecasts in Taiwan. Utilizing the hybrid ensemble transform Kalman filter (ETKF) and the three-dimensional variational (3DVAR) data assimilation (DA) system, this study focuses on an extreme precipitation event near Taiwan on 22 May 2020. The event was mainly influenced by strong southwesterly flow associated with an eastward-moving southwest vortex (SWV) from South China to the north of Taiwan. A nature run (NR) serves as the basis, generating virtual observations for radiosonde, surface, and dropsonde data. Three experiments—NODA (no DA), CTL (traditional observation DA), and T5D24 (additional dropsonde DA)—are configured for comparative analyses. The NODA experiment shows premature and weaker precipitation events across all regions compared with NR. The CTL experiment improved upon NODA's forecasting capabilities, albeit with delayed onset but prolonged precipitation duration, particularly noticeable in southern Taiwan. The inclusion of dropsonde DA in the T5D24 experiment further enhanced precipitation forecasting, aligning more closely with NR, particularly in southern Taiwan. Investigations of DA impact reveal that assimilating traditional observations significantly enhances the SWV structure and wind fields, as well as the location of frontal systems, with improvements persisting for 40 to 65 h. However, low-level moisture field enhancements are moderate, leading to insufficient precipitation forecasts in southern Taiwan. Additional dropsonde DA over the northern SCS further refines low-level moisture and wind fields over the northern SCS, as well as the occurrence of frontal systems, extending positive impacts beyond 35 h and thus improving the rain forecast. [ABSTRACT FROM AUTHOR]

This study examines the effectiveness of adaptive observation experiments using the ensemble transformation sensitivity (ETS) method to improve precipitation forecasts during heavy rainfall events in South China and the Sichuan Basin. High-resolution numerical models are employed to simulate adaptive observations. By identifying the sensitive areas of key weather system positions 42 hours before heavy rainfall events, the adaptive observations improve the prediction of jet streams, strong winds, and shear lines, which are essential for accurate heavy rainfall forecasting. This improvement is reflected in both the precipitation structure and location accuracy within the verification region. In South China, targeted observations enhance rainfall predictions by improving water vapor transport. In the Sichuan Basin, adaptive observations refine water vapor transport and adjust vortex dynamics. This research highlights the importance of accurately predicting shear lines and jet streams for forecasting heavy rainfall in these areas. Overall, this study found that adaptive observation enhances the precipitation forecast skills of the structure and location for heavy rainfall in South China and the Sichuan Basin, emphasizing their potential utility in operational numerical weather prediction. [ABSTRACT FROM AUTHOR]

This study demonstrates the advantages of scale‐ and variable‐dependent localization (SDL and VDL) on three‐dimensional ensemble variational data assimilation of the hourly‐updated high‐resolution regional forecast system, the Rapid Refresh Forecast System (RRFS). SDL and VDL apply different localization radii for each spatial scale and variable, respectively, by extended control vectors. Single‐observation assimilation tests and cycling experiments with RRFS indicated that SDL can enlarge the localization radius without increasing the sampling error caused by the small ensemble size and decreased associated imbalance of the analysis field, which was effective at decreasing the bias of temperature and humidity forecasts. Moreover, simultaneous assimilation of conventional and radar reflectivity data with VDL, where a smaller localization radius was applied only for hydrometeors and vertical wind, improved precipitation forecasts without introducing noisy analysis increments. Statistical verification showed that these impacts contributed to forecast error reduction, especially for low‐level temperature and heavy precipitation. Plain Language Summary: In atmospheric data assimilation based on ensemble forecasts, the analysis increment is limited to the vicinity of each observation by spatial localization to prevent spurious analysis increments due to sampling error caused by the small ensemble size. Scale‐ and variable‐dependent localization (SDL and VDL) make it possible to set optimal localization radii separately for each spatial scale and variable. Sensitivity experiments in this study with a high‐resolution forecast system showed that SDL could decrease the bias of temperature and humidity forecasts and that VDL could improve precipitation forecasts without introducing noisy analysis increments. Key Points: This study implements scale‐ and variable‐dependent localization (SDL and VDL) for data assimilation of the Rapid Refresh Forecast SystemSDL decreases the imbalance of the analysis field and the bias of temperature and humidity forecasts by the larger localization radiusVDL enables simultaneous assimilation of conventional and radar reflectivity data without introducing noisy analysis increments [ABSTRACT FROM AUTHOR]

Satellite data play an irreplaceable role in global observation data systems. Effective comprehensive application of satellite products will inevitably improve numerical weather prediction. FengYun-4 (FY-4) series satellites can provide not only radiance data but also retrieval data with high temporal and spatial resolutions. To evaluate the potential benefits of the combination application of FY-4 Advanced Geostationary Radiance Imager (AGRI) products on Typhoon Saola analysis and forecast, two group of experiments are set up with the Weather Research and Forecasting model (WRF). Compared with the benchmark experiment, whose sea surface temperature (SST) is from the National Centers for Environmental Prediction (NCEP) reanalysis data, the SST replacement experiments with FY-4 A/B SST products significantly improve the track and precipitation forecast, especially with the FY-4B SST product. Based on the above results, AGRI clear-sky and all-sky assimilations with FY-4B SST are implemented with a self-constructed AGRI assimilation module. The results show that the AGRI all-sky assimilation experiment can obtain better analyses and forecasts. Furthermore, it is proven that the combination application of AGRI radiance and SST products is beneficial for typhoon prediction. [ABSTRACT FROM AUTHOR]

This research investigates the correlation between Precipitable Water Vapor (PWV), derived from Global Navigation Satellite System (GNSS) measurements, and rainfall during flood events in Chanthaburi, Surat Thani, and Songkhla, Thailand. Utilizing GPS-PWV and meteorological data collected from 2007 to 2016, the study reveals a significant increase in average PWV during flood events at CHAN, SRTN, and SOKA stations, suggesting its potential as an anticipatory indicator for impending rainfall. The robust correlation between PWV and rainfall patterns underscores the pivotal role of meteorological parameters in shaping PWV distribution. Categorized by flood events, consistent correlations were observed, with Case 1 (2009) showing correlation coefficients of 0.78 for CHAN, Case 2 (2010) displaying correlation coefficients of 0.70 for SRTN and 0.27 for SOKA, and Case 3 (2011) exhibiting correlation coefficients of 0.64 for SRTN and 0.71 for SOKA. These findings contribute valuable insights for precipitation forecasting and flood management, emphasizing the utility of PWV as a reliable tool. Future studies incorporating an expanded network of GNSS CORS stations aim to refine PWV distribution understanding for enhanced monitoring and prediction of precipitation events in the region. [ABSTRACT FROM AUTHOR]

Forecasting precipitation accurately poses significant challenges due to various factors affecting its distribution and intensity, including but not limited to subgrid variability. Although higher resolution simulations are often considered to improve precipitation forecasts, it is crucial to note that simply increasing resolution may not suffice without appropriate adjustments to parameterization schemes or tuning. Traditionally, ensembles of simulations are used to generate uncertainty predictions associated with precipitation forecasts, but this approach can be computationally intensive. As an alternative, there is a growing trend towards leveraging neural networks for precipitation prediction, which offers potential computational advantages. We propose a new approach to generating ensemble weather predictions for high‐resolution precipitation without requiring high‐resolution training data. The method uses generative adversarial networks to learn the complex patterns of precipitation and produce diverse and realistic precipitation fields, allowing to generate realistic precipitation ensemble members using only the available control forecast. We demonstrate the feasibility of generating realistic precipitation ensemble members on unseen higher resolutions. We use evaluation metrics such as RMSE, CRPS, rank histogram and ROC curves to demonstrate that our generated ensemble is almost identical to the ECMWF IFS ensemble, on which our model was trained on. [ABSTRACT FROM AUTHOR]