Short-term Precipitation Research Articles

Combined assimilation of hourly rainfall data and every 10-min AHI radiance with WRF 4DVar for the short-range heavy rainfall forecast in Eastern China

Rainfall observations and geostationary satellite water vapor radiances account for complementary humidity information over rainy areas and large-scale environments, respectively. This study investigates the simultaneous assimilation of China Hourly Merged Precipitation Analysis (CHMPA) data and clear-sky water vapor radiances provided by the Himawari-8 Advanced Himawari Imager (AHI) with Four-Dimensional Variational (4DVar) data assimilation method using Weather Research and Forecasting (WRF) model with an hourly rapid-update cycling data assimilation configuration. The rapid-update 4DVar assimilation is configured as 10-min interval for AHI radiances and 1-h interval for CHMPA rainfall data in a same 1-h assimilation time window. Generally positive impacts from assimilating CHMPA rainfall and AHI water vapor radiance observations are achieved for the 12-h short-term precipitation forecast. Error reductions are found after assimilating CHMPA rainfall and AHI water vapor radiance observations for both analyses and forecasts when compared to ERA5 reanalysis. It is found that combined assimilation of CHMPA rainfall and AHI water vapor radiance data has clearly positive impacts on heavy rainfall forecasts. A heavy rainfall case that occurred in Jiangsu Province on 28 June 2020 is examined in details. The combined assimilation of infrared radiances from AHI water vapor channels together with CHMPA rainfall data improves the vertical and horizontal transport of humidity conditions, which better adjusts the humidity distribution. The over-estimation of rainfall forecast caused by the assimilation of CHMPA is alleviated. This result can provide reference for quantitative precipitation forecasts of regional numerical weather model.

A downscaling and bias correction method for climate model ensemble simulations of local-scale hourly precipitation

Ensemble simulations of climate models are used to assess the impact of climate change on precipitation, and require downscaling at the local scale. Statistical downscaling methods have been used to estimate daily and monthly precipitation from observed and simulated data. Downscaling of short-term precipitation data is necessary for more accurate prediction of extreme precipitation events and related disasters at the regional level. In this study, we developed and investigated the performance of a downscaling method for climate model simulations of hourly precipitation. Our method was designed to recognize time-varying precipitation systems that can be represented at the same resolution as the numerical model. Downscaling improved the estimation of the spatial distribution of hourly precipitation frequency, monthly average, and 99th percentile values. The climate change in precipitation amount and frequency were shown in almost all areas by using the 50 ensemble averages of estimated precipitation, although the natural variability was too large to compare with observations. The changes in precipitation were consistent with simulations. Therefore, our downscaling method improved the evaluation of the climatic characteristics of extreme precipitation events and more comprehensively represented the influence of local factors, such as topography, which have been difficult to evaluate using previous methods.

Influence of monsoon low pressure systems on South Asian disasters and implications for disaster prediction

Transient atmospheric vortices called monsoon low pressure systems (LPS) generate a large fraction of total rainfall over South Asia and often produce extreme precipitation. Here, we assess the influence of these storms on the occurrence of disasters, using information from the Emergency Events Database (EM-DAT) that we geocoded and then associated with LPS tracks. We show that more than half of hydro-meteorological disasters over South Asia during summer are associated with these LPS events. Weaker LPS (which are called monsoon lows) occur more frequently than stronger LPS (called monsoon depressions), but the stronger LPS produce a larger number of disasters. Furthermore, although many prior studies have shown that the peak rainfall in LPS falls southwest of the vortex centre, the disasters are concentrated on the northern edge of the LPS tracks, along the Himalayas and upper basins of the Ganga and Brahmaputra rivers. Observations show a sharp peak in rainfall on the day of disasters, confirming the physical link between LPS and these hydro-meteorological disasters. A similar peak in rainfall is found in weather forecasts made up to five days before the disaster, suggesting that short-term precipitation forecasts can be useful in disaster preparation.

Prediction of calcium phosphate generation and behaviors of metals during phosphorus recovery using a modified thermodynamic model.

In this study, behaviors of metals and their effects on phosphorus recovery by calcium phosphate were investigated by the laboratory and pilot experiments as well as by the modified thermodynamic model. Batch experimental results indicated that the efficiency of phosphorus recovery decreased with the increase in metal content and more than 80% phosphorus can be recovered with a Ca/P molar ratio of 3.0 and a pH of 9.0 for the supernatant of an anaerobic tank in the A/O process with the influent containing a high metal level. The mixture of amorphous calcium phosphate (ACP) and dicalcium phosphate dihydrate (DCPD) was assumed to be the precipitated product with an experimental time of 30 min. A modified thermodynamic model was developed using ACP and DCPD as the precipitated products, and the correction equations were incorporated to simulate the short-term precipitation of calcium phosphate based on the experimental results. From the perspective of maximizing both the efficiency of phosphorus recovery and the quality or purity of the recovered product, the simulation results showed that a pH of 9.0 and a Ca/P molar ratio of 3.0 were the optimized operational condition for phosphorus recovery by calcium phosphate when the influent metal content was at the level of actual municipal sewage.

Influence of local scale and oceanic teleconnections on regional fire danger and wildfire trends

Studies and observations have pointed out that recent wildfires have been more severe and burned area is increasing in tropical regions. The current study aims at investigating the influence of oceanic climate modes and their teleconnection on global fire danger and trends in the 1980–2020 interval. Disentangling these trends demonstrates that across the extratropics they are primarily related to increases in temperature, whereas in the tropics changes in short-term precipitation distribution dominates the trends. Moreover, the environmental impact of short-term precipitation is dependent on local vegetation type and tightly related to oceanic temperatures far from the burned areas. Indeed, in the 2001–2020 period, a warmer tropical North Atlantic was associated with more fires in the Amazon and Africa, whereas ENSO has weakened the fire activity in equatorial Africa. The remarkable impact of oceanic modes of climate variability in inducing environmental conditions conducive to fires, has particular relevance for the seasonal spatiotemporal wildfire forecasts. Although local aspects are crucial for fire management, long-term predictions should take into account the behavior of potential climate drivers located far from the region of interest. Such teleconnections can be identified ahead of local weather anomalies.

Representativeness of Two Global Gridded Precipitation Data Sets in the Intensity of Surface Short-Term Precipitation over China

This study evaluates the representativeness of two widely used next-generation global satellite precipitation estimates data for short-term precipitation over China, namely the satellite data from the Climate Prediction Center morphing (CMORPH) and the satellite data from the Global Precipitation Measurement (GPM) mission. These two satellite precipitation data sets were compared with the hourly liquid in-situ precipitation from China national surface stations from 2016 to 2020. The results showed that the GPM precipitation data has better representativeness of surface short-term precipitation than that of the CMORPH data, and these two quantitative precipitation estimate (QPE) data sets underestimated extreme precipitation. Moreover, we analyzed the influence of the error between two QPE data sets and the in-situ precipitation on the classification of short-term precipitation intensity. China uses 8.1–16 mm/h as the definition of heavy precipitation, but the accuracy of the satellite QPE product was different due to the different lowest threshold of heavy rain (more than 8.1 mm/h or more than 16 mm/h). Increasing the threshold value of the QPE data for short-term strong precipitation resulted in lower accuracy for detecting such events, but higher accuracy for detecting moderate intensity rainfall. When studying short-term strong precipitation over China using precipitation grade, selecting an appropriate threshold was important to ensure accurate judgments. Additionally, it is important to account for errors caused by QPE data, which can significantly affect the accuracy of precipitation grading.

A Path Toward Short-Term Probabilistic Flash Flood Prediction

Abstract There are ongoing efforts to move beyond the current paradigm of using deterministic products driven by observation-only data to make binary warning decisions. Recent works have focused on severe thunderstorm hazards, such as hail, lightning, and tornadoes. This study discusses one of the first steps toward having probabilistic information combined with convective-scale short-term precipitation forecasts available for the prediction and warning of flash flooding. Participants in the Hydrometeorology Testbed–MRMS Hydrology (HMT-Hydro) experiment evaluated several probabilistic-based hydrologic model output from the probabilistic Flooded Locations and Simulated Hydrographs (PRO-FLASH) system during experimental real-time warning operations. Evaluation of flash flood warning performance combined with product surveys highlighted how forecasters perceived biases within the probabilistic information and how the different probabilistic approaches influenced warnings that were verified versus those that were unverified. The incorporation of the Warn-on-Forecast System (WoFS) ensemble precipitation forecasts into the PRO-FLASH product generation provided an opportunity to evaluate the first coupling of subhourly convective-scale ensemble precipitation forecasts with probabilistic hydrologic modeling at the flash flood warning time scale through archived case simulations. The addition of WoFS precipitation forecasts resulted in an increase in warning lead time, including four events with ≥29 min of additional lead time but with increased probabilities of false alarms. Additional feedback from participants provided insights into the application of WoFS forecasts into warning decisions, including how flash flood expectations and confidence evolved for verified flash flood events and how forecast probabilistic products can positively influence the communications of the potential for flash flooding.

Precipitation and Adolescent Respiratory Health in the Northeast United States.

Rationale: With more frequent and intense precipitation events across the globe due to a changing climate, there is a need to understand the relationship between precipitation and respiratory health. Precipitation may trigger asthma exacerbations, but little is known about how precipitation affects lung function and airway inflammation in early adolescents. Objectives: To determine if short-term precipitation exposure is associated with lung function and airway inflammation in early adolescents and if ever having a diagnosis of asthma modifies associations of precipitation with lung function and airway inflammation. Methods: In a prospective prebirth cohort, Project Viva, that included 1,019 early adolescents born in the northeastern United States, we evaluated associations of 1-, 2-, 3-, and 7-day moving averages of precipitation in the preceding week and forced expiratory volume in 1 second, forced vital capacity, and fractional exhaled nitric oxide (FeNO) using linear regression. We used log-transformed FeNO with effect estimates presented as percentage change. We adjusted for maternal education and household income at enrollment; any smoking in the home in early adolescence; child sex, race/ethnicity, and ever asthma diagnosis; and age, height, weight, date, and season (as sine and cosine functions of visit date) at the early adolescent visit and moving averages for mean daily temperature (same time window as exposure). Results: In fully adjusted linear models, 3- and 7-day moving averages for precipitation were positively associated with FeNO but not lung function. Every 2-mm increase in the 7-day moving average for precipitation was associated with a 4.0% (95% confidence interval, 1.1, 6.9) higher FeNO. There was evidence of effect modification by asthma status: Precipitation was associated with lower forced vital capacity and higher FeNO among adolescents with asthma. We also found that outdoor aeroallergen sensitization (immunoglobulin E against common ragweed, oak, ryegrass, or silver birch) modified associations of precipitation with FeNO, with higher FeNO in sensitized adolescents compared with nonsensitized adolescents. The associations of precipitation with FeNO were not explained by relative humidity or air pollution exposure. Conclusions: We found that greater short-term precipitation may trigger airway inflammation in adolescents, particularly among those with asthma.

Radar Reflectivity Assimilation Based on Hydrometeor Control Variables and Its Impact on Short-Term Precipitation Forecasting

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.

Contrasting relationships between plant-soil microbial diversity are driven by geographic and experimental precipitation changes

Soil microbe diversity plays a key role in dryland ecosystem function under global climate change, yet little is known about how plant-soil microbe relationships respond to climate change. Altered precipitation patterns strongly shape plant community composition in deserts and steppes, but little research has demonstrated whether plant biodiversity attributes mediate the response of soil microbial diversity to long- and short-term precipitation changes. Here we used a comparative study to explore how altered precipitation along the natural and experimental gradients affected associations of soil bacterial and fungal diversity with plant biodiversity attributes (species, functional and phylogenetic diversity) and soil properties in desert-shrub and steppe-grass communities. We found that along both gradients, increasing precipitation increased soil bacterial and fungal richness in the desert and soil fungal richness in the steppe. Soil bacterial richness in the steppe was also increased by increasing precipitation in the experiment but was decreased along the natural gradient. Plant biodiversity and soil properties explained the variations in soil bacterial and fungal richness from 43 % to 96 % along the natural gradient and from 19 to 46 % in the experiment. Overall, precipitation effects on soil bacterial or fungal richness were mediated by plant biodiversity attributes (species richness and plant height) or soil properties (soil water content) along the natural gradient but were mediated by plant biodiversity attributes (functional or phylogenetic diversity) in the experiment. These results suggest that different mechanisms are responsible for the responses of soil bacterial and fungal diversity to long- and short-term precipitation changes. Long- and short-term precipitation changes may modify plant biodiversity attribute effects on soil microbial diversity in deserts and steppes, highlighting the importance of precipitation changes in shaping relationships between plant and soil microbial diversity in water-limited areas.

Study on Short-term and Imminent Precipitation Prediction Model and Accuracy Evaluation Based on Depth Learning

In the field of weather forecasting, short-term precipitation forecast is still a global problem. The complicated mechanism of convective weather system, the medium and small scale weather system brings great difficulties to the forecast, which is difficult to detect, understand and forecast. The application of short-term precipitation forecast is still in the initial stage. At present, the optical flow method based on radar echo is widely used in short-term and imminent precipitation prediction, but the optical flow method has obvious limitations. In order to improve the forecast precision of short-term and imminent precipitation forecast model, this study introduces the method of depth learning to build the forecast model. The application of depth learning to short-term precipitation forecasting is still in its initial stage. We analyze the existing models may exist in the shortcomings of the proposed in-depth learning model combination of different ideas. By combining different network structures (such as CONVLSTM, CONVGRU, etc.), we can get a better combined forecasting model. It has certain application value.

Hybrid Scheme of Kinematic Analysis and Lagrangian Koopman Operator Analysis for Short-Term Precipitation Forecasting

With the accumulation of meteorological big data, data-driven models for short-term precipitation forecasting have shown increasing promise. We focus on Koopman operator analysis, which is a data-driven scheme to discover governing laws in observed data. We propose a method to apply this scheme to phenomena accompanying advection currents such as precipitation. The proposed method decomposes time evolutions of the phenomena between advection currents under a velocity field and changes in physical quantities under Lagrangian coordinates. The advection currents are estimated by kinematic analysis, and the changes in physical quantities are estimated by Koopman operator analysis. The proposed method is applied to actual precipitation distribution data, and the results show that the development and decay of precipitation are properly captured relative to conventional methods and that stable predictions over long periods are possible.

Comparison of the WRF-FDDA-Based Radar Reflectivity and Lightning Data Assimilation for Short-Term Precipitation and Lightning Forecasts of Severe Convection

This work evaluates and compares the performance of the radar reflectivity and lightning data assimilation schemes implemented in weather research and forecasting-four-dimensional data assimilation (WRF-FDDA) for short-term precipitation and lightning forecasts. All six mesoscale convective systems (MCSs) with a duration greater than seven hours that occurred in the Guangdong Province of China during June 2020 were included in the experiments. The results show that both the radar reflectivity data assimilation and lightning data assimilation improved the analyses and short-term forecasts of the precipitation and lightning of the MCSs. On average, for precipitation forecasts, the experiments with radar reflectivity data assimilation performed better than those with lightning data assimilation; however, for lightning forecasts, the experiments with lightning data assimilation performed better in the analysis period and 1 h forecast, and for some cases, the superiority lasted to three forecast hours. This highlights the potential of lightning data assimilation in short-term lightning forecasting.

Short-Term Rainfall Prediction Based on Radar Echo Using an Improved Self-Attention PredRNN Deep Learning Model

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.

Quantitative Short-Term Precipitation Model Using Multimodal Data Fusion Based on a Cross-Attention Mechanism

Short-term precipitation prediction through abundant observation data (ground observation station data, radar data, etc.) is an essential part of the contemporary meteorological prediction system. However, most current studies only use single-modal data, which leads to some problems, such as poor prediction accuracy and little prediction timeliness. This paper proposes a multimodal data fusion precipitation prediction model integrating station data and radar data. Specifically, our model consists of three parts. Firstly, the radar feature encoder comprises a shallow convolution neural network and a stacked convolutional long short term memory network (ConvLSTM), which is used to extract the spatio-temporal features of radar-echo data. The weather station data feature encoder is composed of a fully connected network and an LSTM, which is used to extract the sequential features of the weather station data. Then, the cross-modal feature encoder obtains cross-modal features by aligning and exchanging the feature information of the radar data and the weather station data through the cross-attention mechanism. Finally, the decoder outputs the quantitative short-term precipitation prediction value. Our model can integrate station and radar data characteristics and improve prediction accuracy and timeliness, and can flexibly add other modal features. We have verified our model on four short-term and impending rainfall datasets in South Eastern China, achieving the best performance among the algorithms.

Prediction of Radar Echo Space-Time Sequence Based on Improving TrajGRU Deep-Learning Model

Nowcasting of severe convective precipitation is of great importance in meteorological disaster prevention. Radar echo extrapolation is an effective method for short-term precipitation nowcasting. The traditional radar echo extrapolation methods lack the utilization of radar historical data as well as overlooking the nonlinear motion of small- to medium-sized convective systems in radar echoes. To solve this, we propose a deep-learning model combining CNN and RNN. The model T-UNet proposed in this paper uses an efficient convolutional neural network of UNet architecture with a residual network, where the encoder and decoder networks are connected by nested dense skip paths, while a TrajGRU recurrent neural network is added at each layer, to achieve the perceptual capability of time series. The quantitative statistical evaluation showed that the use of T-UNet could improve the nowcasting skill (CSI score, HSS score) by a maximum of 10.57% and 7.80% over a 60 min prediction cycle. Further evaluation showed that T-UNet also improved the prediction accuracy and prediction performance in the strong echo region.

The Space Conceptual Models and Water Vapor Characteristics of Typical Rainstorms during Plum Rain Season

Based on conventional observation data from the China Meteorological Administration (CMA) and reanalysis data from the American National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) between 2012 and 2021, combined with the meteorological analysis, composite synthesis, and water vapor trajectory analysis, the weather circulations of typical rainstorms during the 10 years can be divided into 4 categories: Static Front Pattern (SFP), Subtropical High Edge Pattern (SHEP), Northeast Cold Vortex Pattern (NCVP), and Low-Level Vortex and Shear Pattern (LLVSP). The SHEP and SFP rainstorms have the characteristics of long duration and wide range, while the NCVP rainstorms are characterized by mobility and disaster weather accompaniment. The daily precipitation of LLVSP cases has extremity feature. The occurrence and development of rainstorms are well coordinated with the systems on lower levels. The main water vapor channel in lower layers of the SFP cases is from the South China Sea, while it is from Bohai for the NCVP cases and the Bay of Bengal for the SHEP and LLVSP cases. The main water vapor channel in middle layers is from the Bay of Bengal because of the affection of the southwest air flow. The south boundary of the MLYRB is the most important water vapor input boundary, followed by the west boundary, while the East and North boundaries are the outflow boundaries. During the rainstorms, the low-level water vapor is exuberant with low-level water vapor convergence much stronger than the high-level divergence. Among the four types of rainstorms, the NCVP cases provide the most abundant low-level water vapor convergence, resulting in the strongest short-term precipitation among the four types. Combined with water vapor transportation and convergence, the refined spatial conceptual models of the four types of rainstorms can better judge the process intensity and falling area and provide reference for disastrous weather forecast and early warning.

Improving Short-Term Precipitation Forecasting with Radar Data Assimilation and a Multiscale Hybrid Ensemble–Variational Strategy

Abstract This paper presents a multiscale hybrid ensemble–variational (EnVar) data assimilation strategy with an hourly rapid update aiming to improve analysis of convection via radar observations and of convective environment via conventional observations. In this multiscale hybrid EnVar strategy, the ensemble members are updated by assimilating conventional data using an EnKF to provide the hybrid EnVar with flow-dependent background error covariance (BEC). A two-step approach is employed in the hybrid EnVar to achieve improved multiscale analysis by assimilating radar data and conventional data, respectively, in two successive steps. This two-step procedure enables the applications of different BEC tuning factors and different hybrid weights for radar and conventional observations. In addition, this study also examines the impacts of the flow-dependent BEC generated with and without radar data assimilation in EnKF on the performance of hybrid EnVar analysis and ensuing convective forecasting. The multiscale hybrid EnVar strategy was first evaluated through a comparison with 3DVar and EnKF using a convective rainfall case. Quantitative verifications for both precipitation and environmental variables demonstrated that the hybrid EnVar system with an optimal multiscale configuration outperformed both the 3DVar and EnKF. The multiscale hybrid EnVar strategy was then evaluated through a series of sensitivity experiments. It was shown that the two-step assimilation strategy outperformed the one-step for both the precipitation and environmental variables, and the ensemble BEC generated without radar data assimilation led to improved hybrid EnVar analysis over that with radar data assimilation by better representing uncertainties in convective environment and reducing spurious spatial and multivariate correlations.

Cause-Effect Relationship between Meso-γ-Scale Rotation and Extreme Short-Term Precipitation: Observational Analyses at Minute and Sub-Kilometer Scales

Cause-Effect Relationship between Meso-γ-Scale Rotation and Extreme Short-Term Precipitation: Observational Analyses at Minute and Sub-Kilometer Scales

Impact of Typhoons of Different Intensities on Short-Term Precipitation in the Middle and Lower Reaches of the Yangtze River in Summer

According to China’s reanalyzed meteorological dataset (CN05.1), a 6-h track intensity typhoon meteorological dataset in the Western Pacific, three types of short-term precipitation are described to study the impact of typhoons on summer rainfall of different intensities in the middle and lower reaches of the Yangtze River: short-term extreme precipitation (95% quantile), short-term heavy precipitation (75% quantile), and normal precipitation (below the lower limit of the 75% quantile threshold). The results show that the amount of short-term extreme precipitation is 1.8 and 3.7 times that of normal precipitation and short-term heavy precipitation, respectively. Considerable interannual and interdecadal fluctuations in the proportion of short-term heavy precipitation and extreme precipitation during summer are affected by typhoons, with a wide range of changes occurring between 1980 and 2000. The areas with high amounts of short-term heavy precipitation and extreme precipitation are distributed mostly in the middle and southern parts of the middle and lower reaches of the Yangtze River, whereas areas with a high amount of normal precipitation are distributed mostly in the southeastern parts of the river. The spatial distribution of the three intensities of rainfall affected by typhoons is consistent, with a gradual decrease from southeast to northwest; in addition, the spatial distribution of the proportion of total summer rainfall has similar characteristics. The three intensities of precipitation are affected by the spatial distribution of the typhoon path frequency, and the distribution of the high-value areas is essentially the same as that of precipitation. This indicates that most of the typhoons that affect summer precipitation pass through the middle and lower reaches of the Yangtze River.