Rain Forecasts Research Articles

Dropsonde Data Impact on Rain Forecasts in Taiwan Under Southwesterly Flow Conditions with Observing System Simulation Experiments

Dropsonde Data Impact on Rain Forecasts in Taiwan Under Southwesterly Flow Conditions with Observing System Simulation Experiments

Precipitation Proxies for Flash Flooding: A Seven-Year Analysis over the Contiguous United States

Abstract Flash flooding remains a challenging prediction problem, which is exacerbated by the lack of a universally accepted definition of the phenomenon. In this article, we extend prior analysis to examine the correspondence of various combinations of quantitative precipitation estimates (QPEs) and precipitation thresholds to observed occurrences of flash floods, additionally considering short-term quantitative precipitation forecasts from a convection-allowing model. Consistent with previous studies, there is large variability between QPE datasets in the frequency of “heavy” precipitation events. There is also large regional variability in the best thresholds for correspondence with reported flash floods. In general, flash flood guidance (FFG) exceedances provide the best correspondence with observed flash floods, although the best correspondence is often found for exceedances of ratios of FFG above or below unity. In the interior western United States, NOAA Atlas 14 derived recurrence interval thresholds (for the southwestern United States) and static thresholds (for the northern and central Rockies) provide better correspondence. The 6-h QPE provides better correspondence with observed flash floods than 1-h QPE in all regions except the West Coast and southwestern United States. Exceedances of precipitation thresholds in forecasts from the operational High-Resolution Rapid Refresh (HRRR) generally do not correspond with observed flash flood events as well as QPE datasets, but they outperform QPE datasets in some regions of complex terrain and sparse observational coverage such as the southwestern United States. These results can provide context for forecasters seeking to identify potential flash flood events based on QPE or forecast-based exceedances of precipitation thresholds. Significance Statement Flash floods result from heavy rainfall, but it is difficult to know exactly how much rain will cause a flash flood in a particular location. Furthermore, different precipitation datasets can show very different amounts of precipitation, even from the same storm. This study examines how well different precipitation datasets and model forecasts, used by forecasters to warn the public of flash flooding, represent heavy rainfall leading to flash flooding around the United States. We found that different datasets have dramatically different numbers of heavy rainfall events and that high-resolution model forecasts of heavy rain correspond with observed flash flood events about as well as precipitation datasets based on rain gauge and radar in some regions of the country with few observations.

Global convection-permitting model improves subseasonal forecast of plum rain around Japan

Abstract In 2020 early summer, a historically severe rainy season struck East Asia, causing extensive damage to life and property. Subseasonal forecast of this event challenges the limits of rainy season predictability. Employing the integrated Atmospheric Model Across Scales (iAMAS) and the Sunway supercomputer, we conducted ensemble one-month forecasts at global 3 km, variable 4-60 km, and global 60 km resolutions. The global convection-permitting forecast accurately captures the rainband, while other forecasts exhibited northward and weaker shifts due to the northward shifts of the atmospheric rivers (ARs) over Japan, attributed to intensified Western North Pacific Subtropical High (WNPSH). Further, the double-ITCZ-like tropical rainfall pattern in Western Pacific (D-ITCZ-WP) in global convection-permitting forecast contributes to a more accurate WNPSH and rainband. In contrast, other forecasts show a single-ITCZ-like pattern in Western Pacific (S-ITCZ-WP), leading to a northward-shifted WNPSH and rainband, advocating the importance of accurately representing tropical convections, as they can significantly affect mid-/high-latitude weather and climate.

A New Post-Processing Method for Improving Track and Rainfall Ensemble Forecasts for Typhoons over Eastern China

This paper proposes a new post-processing method for model data in order to improve typhoon track and rainfall forecasts. The model data used in the article include low-resolution ensemble forecasts and high-resolution forecasts. The entire improvement method contains the following three steps. The first step is to correct the typhoon track forecast: three ensemble member optimization methods are applied to the low-resolution ensemble forecasts, and then the best optimization method is selected with the principle of the smallest average distance error. The results of rainfall forecasts show that the corrected rainfall forecast performs better than the original forecasts. The second step is to derive the high-resolution probability rainfall forecast: the neighborhood method is applied to the deterministic high-resolution rainfall forecast. The last step is to correct the typhoon rainfall forecast: the low- and high-resolution forecasts are blended using the probability-matching method with two different schemes. The results show that the forecasts of the two schemes perform better than the original forecast under all rainfall thresholds and all forecast lead times. In terms of bias score, a rain forecast from one scheme corrects the rainfall deviation from observation better for light and moderate rainfall, whereas a rain forecast from another scheme corrects the rainfall deviation better for heavy and torrential rainfall. The better performance of corrected rain forecasts in the case of Typhoon Lekima and Rumbia over eastern China is demonstrated.

Red-Wattled Lapwing (Vanellus indicus): A Traditional Rain Forecaster for Tribal Farmers of Southwestern Rajasthan

Abstract An intensive survey was carried out over the past 8 years in tribal-dominated areas of southwestern Rajasthan to document red-wattled lapwing (Vanellus indicus) behavior as a rain forecaster, relevant to farm planning and better crop production. The present study included tribal farmers, agriculture scientists, extension workers, ornithologists, and farm workers. The precision of their observations was compared with a contemporaneous study of 10–15 red-wattled lapwing nests across the 46-ha operational area of the Agricultural Research Sub-Station (ARSS) and Adaptive Trial Center (ATC), Sumerpur, Pali, Rajasthan (25.1526°N latitude, 73.0823°E longitude). Local knowledge was gathered through surveys and focus group discussions with the respondents to discuss lapwing behavior as a rain prediction indicator. Meteorological data were gathered from the ATC farm observatory. The findings revealed that the older tribal farmers have an assortment of knowledge about the behavior of red-wattled lapwing as rain prediction indicators such as nesting sites, egg position, egg arrangement, nesting material, numbers of eggs, and egg-laying patterns. These predictions have a direct correlation with modern meteorological observations. It was also noted that the elder tribal farmers planned their farms using lapwing indicators in order to increase crop production, whereas younger generations overlook the traditional rain prediction indicators and have significant knowledge about meteorological data-based weather forecasting. Both the elder and younger tribal farmers were equally interested in meteorological weather forecast announcements, but due to network connectivity challenges, residents in some remote places were unable to access modern meteorological information. Long-term accurate rain forecasting is needed for better agricultural planning and to secure crop production. Rain forecasting can be improved by blending modern meteorological information with traditional proven indications. Farmers who have considerable traditional knowledge along with modern technological updates are capable of using both sources of information in suitable and successful farm planning for better crop production. Significance Statement The purpose of this study is to examine red-wattled lapwing behavior as rain measurements and compare its reliability with modern rain forecasting methods. We also develop a theoretical framework for understanding traditional and modern rain forecasting methods for disaster management in remote and accessible areas, to assist resource management and to minimize costs of cultivation. Our results provide a guideline for blending both types of knowledge and for providing relevant information to remote tribal areas at appropriate times for better farm planning and crop production.

Improved freezing rain forecast using machine learning

Freezing rain is one of the most damaging weather phenomena in winter or early spring in many parts of the world, affecting traffic, power lines and agriculture. Thus, reliable and computationally efficient prediction of its occurrence is urgently needed in weather forecast operations. However, there are different thermodynamic processes that can lead to freezing rain, resulting in unsatisfactory forecasting performance of the state-of-the-art Numerical Weather Prediction (NWP) models. Here a data-driven forecasting method for freezing rain using machine learning technologies is proposed. Observations of weather phenomenon collected from 2 515 national weather stations of China for winter of 2016–2019 and the corresponding atmospheric predictors derived from ERA5 reanalysis are used. The prediction function is constructed based on the classification and regression tree, and the predicting variables include temporal and vertical profiles of fundamental thermodynamic and kinematic parameters from 500 hPa to 1000 hPa, with a total dimension of 2 304. The LightGBM (Light Gradient Boosting Machine) framework is adopted to train our prediction model and an algorithm-level approach of modifying the loss function is used to address the imbalance of classes to improve forecasting skill. Results show that the data-driven prediction model, namely DDFR (data driven forecast of freezing rain), out-performs the benchmark NWP, i.e., ECMWF IFS product. It's improvements in terms of TS score range from 120% to 258% depending on different forecast leading times, which range from 0 to 12 h. In addition, DDFR is applied in an operational NWP model of China. The problem of domain adaptation is tackled and transfer learning method is employed to adapt the original DDFR to this NWP model. The effectiveness of such adaptation has been demonstrated by its performance on both training and testing datasets.

Optimal control strategies for stormwater detention ponds

Stormwater detention ponds are essential stormwater management solutions that regulate the urban catchment discharge towards streams. Their purposes are to reduce the hydraulic load to avoid stream erosion, as well as to minimize the degradation of the natural waterbody by direct discharge of pollutants. Currently, static controllers are widely implemented for detention pond outflow regulation in engineering practice, i.e., the outflow discharge is capped at a fixed value. Such a passive discharge setting fails to exploit the full potential of the overall water system, hence further improvements are needed. We apply formal methods to synthesize (i.e., derive automatically) optimal active controllers. We model the stormwater detention pond, including the urban catchment area and the rain forecasts with its uncertainty, as hybrid Markov decision processes. Subsequently, we use the tool UppaalStratego to synthesize using Q-learning a control strategy maximizing the retention time for pollutant sedimentation (optimality) while also minimizing the duration of emergency overflow in the detention pond (safety). These strategies are synthesized for both an off-line and on-line settings. Simulation results for an existing pond show that UppaalStratego can learn optimal strategies that significantly reduce emergency overflows. For off-line controllers, a scenario with low rain periods shows a 26% improvement of pollutant sedimentation with respect to static control, and a scenario with high rain periods shows a reduction of overflow probability of 10%–19% for static control to lower than 5%, while pollutant sedimentation has only declined by 7% compared to static-control. For on-line controllers, one scenario with heavy rain shows a 95% overflow duration reduction and a 29% pollutant sedimentation improvement compared to static control.

Implementing a digital twin for optimized real-time control of Gothenburg's regional sewage system

Abstract The emerging challenges facing the City of Gothenburg and surrounding municipalities include the impacts of a variable climate, rapid growth and urbanization, aging infrastructure and changing community values and customer expectations. A digital twin (DT), based on the Future City Flow (FCF) platform, was created for the catchment to address many of these challenges in terms of improved real-time sewerage performance and predictability in the face of storms, improved DT visualization of the regional sewage transport system for operators and greater responsiveness to avoid service interruption. The FCF-based DT provides a sound base for modelling, simulating, forecasting and controlling the catchment. The versatility of the FCF DT makes it useful for gaining insight into catchment dynamics and the simulator can be used to study the effects of different scenarios or changes in the catchment. The predictive control imbedded in the FCF DT recommends setpoints for controlling the catchment that can be used by the operators for decision support or even be directly applied in the control system as model-based predictive control. A DT requires good quality online data from the catchment and, perhaps most importantly, high-quality rain forecasts, especially when prognosing long time horizons.

Assessing Nowcast Models in the Central Mexico Region Using Radar and GOES-16 Satellite Data

In this study, the nowcast models provided by the Python pySTEPS library were evaluated using radar derived rain rate data and the satellite product Split-Window Difference (SWD) based on GOES-16 data, focusing on central Mexico. Initially, we obtained a characterization of the rainfall that occurred in the region using the radar rain rate and the SWD. Subsequently the nowcasts were evaluated using both variables. Two nowcast models were employed from pySTEPS: Extrapolation and S-PROG. The results indicate that average SWD is below 2.5 K, 90 min before the onset of rainfall events, and, on average, the SWD is 2 K during rainfall events. The results from both nowcast models were accurate and produced similar results. The nowcasts performed better when SWD data were used as input, having an average Probability of Detection (PoD) above 70% and a False Alarm Rate (FAR) reaching 40% for the 15-min prediction. The nowcasts were less accurate using the radar rain rate as input for the 15-min forecast, where the PoD was maximum 70% and FAR reaching 40%. However, these nowcasts were more reliable during well-organized precipitation events. In this work, it was determined that the nowcast models provided by pySTEPS can provide valuable rain forecasts using GOES-16 satellite and radar data for the central Mexico region.

ARTIFICIAL NEURAL NETWORK FOR PREDICTION OF LAND SUBSIDENCE IN MUDSLIDES REGION THROUGH INSAR AND RAIN DATA

Abstract. Mudslides are powerful and fast-moving mass movements that pose significant risks to human lives, infrastructure, and natural environments. They are commonly triggered by intense rainfall and their impact is particularly severe in mountainous regions. Synthetic Aperture Radar (SAR) technology can be used to calculate the subsidence of the territory over time by means of a temporal series of SAR images through the Persistent Scatterer Interferometry (PSI) technique. In some research Interferometric SAR (InSAR PSI) data were used to train Long Short-Term Memory (LSTM) based Artificial Neural Network (ANN) to provide movements forecasting. This paper proposes a new LSTM based ANN to forecast future territory movements considering both the past InSAR PSI data, the rain forecasting of the next acquisition and the past cumulative amount of rain since the movements of mudslides are strictly dependent to the quantity of rainfall accumulated in the terrain. The results of the proposed ANN are shown in terms of Mean Square Error (MSE) and Mean Absolute error (MAE) by comparing them with a LSTM-based ANN trained with only the InSAR PSI data.

Probabilistic Intraday Wastewater Treatment Plant Inflow Forecast Utilizing Rain Forecast Data and Sewer Network Sensor Data

AbstractForecasting of wastewater treatment plant inflow dynamics constitutes an enabler technology for wastewater treatment process optimization using model predictive control. However, accurate inflow prediction is still challenging, especially for strong rainfall events, where complex system dynamics and missing information on future rainfall represent limiting factors. We propose a seasonal probabilistic time series model for modeling the short‐term wastewater inflow accurately while providing quantification of forecast uncertainty. To ensure suitability for practical implementation, the unconstrained parameters of the predictive distribution are modeled as linear functions of the input variables in the framework of Generalized Additive Models for Location Scale and Shape. Non‐linear effects are approximated by Rectified Linear Units, accounting for buffering within the sewer network and flow‐dependent catchment response time. In addition to water level measurements from within the sewer network and rain rate measurements, rain forecasts are incorporated as exogenous regressors, where historical rain forecasts are used for model calibration. The model performance is evaluated on historical data from a German wastewater treatment plant using deterministic and probabilistic scoring rules. We benchmark against an autoregressive time series model and a long short‐term memory artificial neural network. Our results show that the proposed model unites the benefits of high prediction accuracy of the neural network and enhanced intelligibility of the autoregressive model, but accurate real‐time rain forecasts are mandatory for successful real‐world implementation.

Pacemod: parametric contour-based modifications for glyph generation

Metaphoric glyphs are intuitively related to the underlying problem domain and enhance the readability and learnability of the resulting visualization. Their construction, however, implies an appropriate modification of the base icon, which is a predominantly manual process. In this paper, we introduce the parametric contour-based modification (PACEMOD) approach that lays the foundations of automated, controllable icon manipulations. Technically, the PACEMOD parametric representation utilizes diffusion curves, enriched with new degrees of freedom in arc-length parameterization, which allows for manipulation of the icon contours’ geometry and the related color attributes. Moreover, we propose an implementation of our generic approach for a specific, automated design of metaphoric glyphs, based on periodic, wave-like contour modifications. Finally, the practicality of such periodic contour modifications is demonstrated by two visualization examples, which comprise uncertainty visualization of a rain forecast and gradient glyphs applied to COVID-19 data. In summary, with the PACEMOD approach we introduce an instrument that facilitates a user-centered design of metaphoric glyphs and provides a generic basis for potential further implementations according to specific applications.

Implementation of Rainfall Forecasting using ANN

Prediction of rainfall is essential for many sectors, including flood mitigation, disaster prevention, agriculture production, and water resource management. Rain forecasts are used to warn of natural disasters such as floods or to plan planting activities to improve the quality of crop yields. An accurate rainfall prediction remains a challenging task due to the uncertainty of natural phenomena considering that rainfall is a non-linear and dynamic process. Various climatic variables such as temperature, relative humidity, wind speed and direction, are among those that affect the dynamic process of rainfall. This study suggests a machine learning technique based on Artificial Neural Network (ANN) model to forecast rainfall by using two-years historical local rain rate and others meteorological data. Evaluation metrics such as mean absolute error, root mean square error, and correlation coefficient, are used to evaluate model’s performance. The results show good performance and sensible prediction accuracy. The comparison with existing model clearly depicted that ANN model predicts rainfall better than statistical models particularly at lower and moderate rain rate.

Incorporating Rainfall Forecast Data in X-SLIP Platform to Predict the Triggering of Rainfall-Induced Shallow Landslides in Real Time

Extreme and prolonged rainfall resulting from global warming determines a growing need for reliable Landslide Early Warning Systems (LEWS) to manage the risk of rainfall-induced shallow landslides (also called soil slips). Regional LEWS are typically based on data-driven methods because of their greater computational effectiveness, which is greater than the ones of physically based models (PBMs); however, the latter reproduces the physical mechanism of the modelled phenomena, and their modelling is more accurate. The purpose of this research is to investigate the prediction quality of the simplified PBM SLIP (implemented in the X-SLIP platform) when applied on a regional scale by analysing the stability of rain forecasts. X-SLIP was updated to handle the GRIB files (format for weather forecast). Four real-time predictions were simulated on some towns of the Emilia Apennines (northern Italy) involved in widespread soil slips on 5 April 2013; specifically, maps of factors of safety related to this event were derived assuming that X-SLIP had run 72 h, 48 h, 24 h and 12 h in advance. The results indicated that the predictions with forecasts (depending on the forecast quality) are as accurate as the ones derived with rainfall recordings only (benchmark). Moreover, the proposed method provides a reduced number of false alarms when no landslide was reported to occur in the whole area. X-SLIP with rain forecasts can, therefore, represent an important tool to predict the occurrence of future soil slips at a regional scale.

Using Deep Learning to Identify Circulation Patterns of Intense Rainfall in the Beijing–Tianjing–Hebei Region

The properties and distributions of precipitation are often determined by specific synoptic patterns. Hence, the objective identification of corresponding impact patterns is an important field of research for improving rain forecasting. However, the identification of the weather patterns producing intense rainfall is much more challenging. Since they are violent and local, impact patterns tend to be meso- or smaller-scale systems and are often incompletely presented or only presented in limited regions. In this paper, a deep learning network with a feature cross-fusion module, FConvNeXt, was proposed to address this difficulty and showed great potential. Four major patterns corresponding to intense rainfall in the Beijing–Tianjing–Hebei Region were studied. Statistical testing showed that FConvNeXt performed better than ConvNeXt and ResNet and that the model could identify the weak synoptic forcing type, the subtropical high-pressure type, and the low-vortex pattern with high accuracy. Furthermore, a strictly independent 2021 dataset was tested, and FConvNeXt maintained an equal if not even slightly better performance in spite of a decrease in the subtropical high-pressure type. Meanwhile, the study showed that the accuracy in identifying the upper-level trough type is the lowest for the three deep learning methods, which may be because the northeast vortex was intercepted in the limited region, making it difficult to distinguish from the shallow upper-level trough type. This study is useful for improving the fine objective of forecasting intense rainfall.

Coupled model biases and extended‐range prediction skill during the onset phase of the Indian summer monsoon with different initializations related to land surface and number of observations

AbstractIn this study, we have analyzed the role of the initialization in the forecast of the monsoon onset phase beyond 10 days lead time (i.e., in the extended range) to understand the impact of displacement or shift in initial conditions in the extended‐range forecast. Two displacement errors are considered: (a) the initial error arising due to a change in land surface initial conditions and (b) the initial error arising due to a change in the number of observations. For the first part (a), we have analyzed and compared the difference in prediction skills in the United Kingdom Met Office (UKMO) GloSea5 forecasts run with two different land surface initial conditions (IC) configurations. In one configuration, the IC is prepared using a monthly land surface climatology; in the other configuration, it is based on daily land surface reanalysis. In the other part (b), we used the Indian Institute of Tropical Meteorology's Climate Forecast System (IITM_CFS) model with two different ICs (NCEP and NCMRWF differing in the number of observations over the Indian land region). Both runs indicate a shift in the initial condition, which manifests as displacement error. UKMO and IITM_CFS runs have the same land surface model when the corresponding twin experiments are compared in (a) and (b). Analysis of the initial displacement errors from these runs indicates that improving the realism in the land surface initial conditions can effectively modulate or change the surface meteorological fields in the prediction model during the onset phase. The result shows that the rotational and divergence components of the surface winds differ in the two sets of runs. Results also indicate that the difference in surface initialization manifests as differences in rotational and divergent kinetic energy. This could lead to a difference in the forecast of monsoon onset rain. Further analysis also suggests that the local land surface initial condition error, in addition to an error in large‐scale teleconnections, affects the monsoon onset forecast and its prediction skill in the extended‐range time‐scale.

Rain forecasts and computer modelling provides sharper water estimates

Rain forecasts and computer modelling provides sharper water estimates

Forecasting Precipitation from Radar Wind Profiler Mesonet and Reanalysis Using the Random Forest Algorithm

Data-driven machine learning technology can learn and extract features, a factor which is well recognized to be powerful in the warning and prediction of severe weather. With the large-scale deployment of the radar wind profile (RWP) observational network in China, dynamical variables with higher temporal and spatial resolution in the vertical become strong supports for machine-learning-based severe convection prediction. Based on the RWP mesonet that has been deployed in Beijing, this study uses the measurements from four triangles composed of six RWP stations to determine the profiles of divergence, vorticity, and vertical velocity before rainfall onsets. These dynamic feature variables, combined with cloud properties from Himawari-8 and ERA-5 reanalysis, serve as key input parameters for two rainfall forecast models based on the random forest (RF) classification algorithm. One is for the rainfall/non-rainfall forecast and another for the rainfall grade forecast. The roles of dynamic features such as divergence, vorticity, and vertical velocity are examined from ERA-5 reanalysis data and RWP measurements. The contribution of each feature variable to the performance of the RF model in independent tests is also discussed here. The results show that the usage of RWP observational data as the RF model input tends to result in better performance in rainfall/non-rainfall forecast 30 min in advance of rainfall onset than using the ERA-5 data as inputs. For the rainfall grade forecast, the divergence and vorticity that were estimated from the RWP measurements at 800 hPa show importance in improving the model performance in heavy and moderate rain forecasts. This indicates that the atmospheric dynamic variable measurements from RWP have great potential to improve the prediction skill of convection with the aid of a machine learning model.

Possibilities and human reasoning

This article describes how human reasoning differs from standard logics. It tells the story of three sorts of inference, for example: the possibility of rain implies the possibility of no rain; a forecast of rain or snow implies the possibility of rain and the possibility of snow; and a forecast of frost does not imply a forecast of frost or snow or both. People accept these inferences, which each violate the semantics of standard logics. But, they are predictions from the theory that assertions refer to mental models of possibilities that each hold in default of knowledge to the contrary. The moral of the story is that inconsistencies in human reasoning, unlike those in standard logics, have only local consequences.

An Intelligent Early Flood Forecasting and Prediction Leveraging Machine and Deep Learning Algorithms with Advanced Alert System

Flood disasters are a natural occurrence around the world, resulting in numerous casualties. It is vital to develop an accurate flood forecasting and prediction model in order to curb damages and limit the number of victims. Water resource allocation, management, planning, flood warning and forecasting, and flood damage mitigation all benefit from rain forecasting. Prior to recent decades’ worth of research, this domain demonstrated to be promising prospects in time series prediction tasks. Therefore, the main aim of this study is to build a forecasting model based on the exponential smoothing-long-short term memory (ES-LSTM) structure and recurrent neural networks (RNNs) for predicting hourly precipitation seasons; and classify the precipitation using an artificial neural network (ANN) model and decision tree (DT) algorithm. We employ the dataset from the Australian commonwealth office of meteorology named Historical Daily Weather dataset to test the effectiveness of the proposed model. The findings showed that the ES-LSTM and RNN had achieved 3.17 and 6.42 in terms of mean absolute percentage error (MAPE), respectively. Meanwhile, the ANN and DT models obtained a prediction accuracy rate of 96.65% and 84.0%, respectively. Finally, the outcomes revealed that ES-LSTM and ANN had achieved the best results compared to other models.