Forecast Parameters Research Articles

Unfulfilled water impact forecasts, plans and projects

The article presents a retrospective review of some forecasts, plans and projects of anthropogenic impact on water resources that did not materialize, were not implemented by the planned date or were incomplete. Values of full (water intake) and irrevocable water consumption for 2000, predicted in the 1960s and 1970s by well-known domestic and foreign researchers, are compared with the actual water consumption in 2000 in the world, the USA, and our country. It is shown that most of water consumption forecast parameters turned out to be significantly higher than the actual one, which gives reason to consider these forecasts were not borne out. In the Water Strategy of the Russian Federation, developed in 2009 for the period up to 2020, these values were also significantly overestimated (by tens of percent). Incomplete implementation of various programs did not lead to the expected significant improvement in the water quality of rivers, including the Volga, and reservoirs in Russia. Such failed projects as NAWAPA in the USA and Canada, projects of interzonal redistribution of water resources in the USSR, projects of Nizhneobskaya and Turukhanskaya (Evenkijskaya) hydroelectric power plants, partially implemented projects of Cheboksary and Nizhnekamsk hydroelectric power plants, as well as a number of others are considered. Among the main reasons for unsuccessful forecasts, unfulfilled plans and projects are the lack of reliable data, incomplete knowledge about laws of nature and society development, financial problems, environmental demands, and the dramatically changed economic and political situation.

Machine learning approaches for assessing rechargeable battery state-of-charge in unmanned aircraft vehicle-eVTOL

The long stability of electric vertical take-off and landing (eVTOL) aircraft is majorly associated with energy storage devices like batteries. Lithium-ion battery (LIB) is frequently used battery in most of eVTOL because they have high charge storage capacity, good health of battery and long-life cycles. To maintain the health of battery, the state-of-charge (SoC) and state-of-health (SoH) are the most important parameters. This study demonstrates the SoC evaluation of batteries in eVTOL aircrafts and then forecasts SoC of batteries using different machine learning (ML) approaches such as Support Vector Regression, Random Forest, Linear Regression. The experimental dataset was collected by an open portal at Carnegie Mellon University wherein over 15 million records including a hundred charge/discharge cycles, and several working conditions are available. SoC of batteries was first calculated by using collected dataset. Input parameters for SoC forecasting by ML models were prepared with different features such as voltage, current, charging/discharging energy and temperature. By feature selection analysis, EDischarge and voltage were found to be the most effective features for SoC of battery. The experimental dataset was first split into 80 % of training and 20 % of testing and then applied for three ML models (Support Vector Regression, Random Forest, Linear Regression). As compared to other ML models, Random Forest presented the best performance having the lowest error values (RMSE ≈ 0.000985, R2= 0.9996) due to non-linear relationship between every feature and SoC. The studies suggested that ML approach for battery’s SoC forecasting would provide promising methods to manage the health of battery for eVTOL aircraft.

Optimization of Double Exponential Smoothing Using Particle Swarm Optimization Algorithm in Electricity Load

Electricity load forecasting plays a critical role in ensuring the efficient allocation of resources, maintenance optimization, and uninterrupted power supply. The double exponential smoothing (DES) method is widely used in forecasting time series data due to its adaptability and robustness, particularly in handling linear trends without seasonal patterns. However, determining the optimal value of the alpha parameter in DES is crucial for accurate forecasting results. This study proposes the use of the Particle Swarm Optimization (PSO) algorithm to optimize the alpha parameter in DES for electricity load forecasting. PSO is a computational method that iteratively improves candidate solutions by moving particles in the search space based on simple mathematical formulas. By optimizing the alpha parameter using PSO, we aim to enhance the accuracy of short-term electricity load forecasts. Our results demonstrate that the PSO-optimized DES approach achieves a Mean Absolute Percentage Error (MAPE) of 2.89% and an accuracy of 97.11%, indicating significant improvements in forecasting performance. While the PSO algorithm provides promising results, future research may explore the application of other metaheuristic algorithms, such as the whale or orca algorithms, to further enhance the optimization of DES parameters for electricity load forecasting. This study contributes to the advancement of forecasting techniques in the power industry, facilitating more efficient power generation and distribution planning.

Prediction of Air Pressure using Support Vector Machine Compare with Multi Layer Perception Model in an Area of North Twenty-four Parganas, West Bengal

Lifesaving requires the capacity to foresee the weather. Applications for weather prediction in agriculture, transportation, floods, and other natural phenomena are significant. The prediction of Air Pressure is crucial for weather forecasting. Multiple linear regression is one of the techniques available for Air Pressure prediction. model. To determine how one independent variable and one dependent variable are related to one another In statistics, the linear regression model is an extremely helpful tool. Multiple linear regression is an extension of the linear regression model. We utilized MATLAB to add the variables to our experiment. Weather forecasting relies heavily on Air Pressure prediction. For the purpose of forecasting rainfall, we have chosen Kolkata, or more specifically, the Dum Dum Metropolitan region in North 24 Parganas. We have investigated additional atmospheric forecasting parameters. applying a multilayer perceptron to predict Air Pressure. Once we get the coefficient, we have the intended and calculated Air Pressure. We have identified the error. Now we are applying Support Vector Machine over Multi Linear Preceptron to get better results.

Proposal of model for prediction of grape processing and spraying time by using IoT smart agriculture sensor data

The grape industry's impact on agriculture and the economy requires precise forecasting for processing and spraying schedules to optimize production. This article introduces an innovative IoT-based model for predicting optimal timings in grape processing and spraying. By integrating real-time environmental and viticultural data, the model improves decision-making, enhancing product quality, reducing energy consumption, and increasing operational efficiency. Crucially, SARIMA predictive algorithms forecast parameters like temperature, humidity, wind speed, and air pressure. This comprehensive model transforms the grape industry, offering advanced decision support and promoting sustainable, resource-efficient production. The research signals a potential shift to precision agriculture, balancing economic viability with environmental stewardship in grapes.

New well water cut prediction using machine learning

Background: The drilling of new wells is one of the most effective geological and technical activities. In mature fields characterized by high production of reserves and high water availability, the selection of design points for drilling is a difficult task. Forecasting the parameters of new wells is possible by using geological and hydrodynamic models or analytical methods. In this paper, the authors propose the use of machine learning algorithms to predict the initial parameters of new wells based on an extensive set of geological and field data.
 Aim: The article describes the process of developing machine learning algorithms and demonstrates the performance indicators of a complex model. As part of this work, testing of machine learning algorithms was performed to predict the start-up water cut of potential candidates.
 Materials and methods: Within the framework of this work, various machine learning methods were applied on geological and technical field data.
 Results: The developed complex model showed acceptable convergence results based on classification and regression metrics, which indicates its applicability for predicting the start-up water cut of project wells.
 Conclusion: This method of predicting indicators is an alternative tool for predicting the start-up water cut of new wells, which makes it possible to clarify and supplement the forecast parameters calculated using a geological and hydrodynamic model or empirical dependencies of the initial water cut of new wells on geological parameters.

Evaluating the performance of WRF in simulating winds and surface meteorology during a Southern California wildfire event

The intensity and frequency of wildfires in California (CA) have increased in recent years, causing significant damage to human health and property. In October 2007, a number of small fire events, collectively referred to as the Witch Creek Fire or Witch Fire started in Southern CA and intensified under strong Santa Ana winds. As a test of current mesoscale modeling capabilities, we use the Weather Research and Forecasting (WRF) model to simulate the 2007 wildfire event in terms of meteorological conditions. The main objectives of the present study are to investigate the impact of horizontal grid resolution and planetary boundary layer (PBL) scheme on the model simulation of meteorological conditions associated with a Mega fire. We evaluate the predictive capability of the WRF model to simulate key meteorological and fire-weather forecast parameters such as wind, moisture, and temperature. Results of this study suggest that more accurate predictions of temperature and wind speed relevant for better prediction of wildfire spread can be achieved by downscaling regional numerical weather prediction products to 1 km resolution. Furthermore, accurate prediction of near-surface conditions depends on the choice of the planetary boundary layer parameterization. The MYNN parameterization yields more accurate prediction as compared to the YSU parameterization. WRF simulations at 1 km resolution result in better predictions of temperature and wind speed than relative humidity during the 2007 Witch Fire. In summary, the MYNN PBL parameterization scheme with finer grid resolution simulations improves the prediction of near-surface meteorological conditions during a wildfire event.

Determining the prospects of the development of the cooperative of fruit and vegetable products

In this article, the economic importance of the fruit-vegetable cooperative and its importance in providing the country's population with food products comes from its relevance. The main goal of the research is to supply industrial enterprises with raw materials by using the system of cooperation in the production of fruit and vegetable products. In the conducted scientific research, the mechanisms of state support for fruit and vegetable growers in the Republic of Uzbekistan, the development trends of fruit and vegetable cooperation in the Kashkadarya region to ensure food safety by improving the fruit and vegetable network as a result of the stable growth of cooperation, and to provide the population with ecologically clean products On the basis of economic-mathematical modeling of directions for determining and improving the efficiency of fruit and vegetable enterprises, forecast parameters until 2026 have been developed.

Using the Arima model to forecast the share of railways in the industry

The relevance of the studied topic is related to the statistical study of the contribution of using the Arima model to forecast the share of railways in the industry to the socio-economic development of the country, the analysis of the dynamics of their economic growth, and the need to develop and evaluate the most appropriate economic-mathematical models for managing the type of activity. Taking into account that various econometric models can be used in the analysis of problems related to small business activity, we found it necessary to use some of these models. The purpose of the study is to model the share of small business in the industry and develop forecast parameters. Using the Arima model to forecast the share of railways in the industry and private entrepreneurship activities were chosen as the object of research. The scientific works of the republican and foreign economists, as well as the data of the republican and regional statistical offices serve as a source of information. The expected result of the research is to reveal the modern laws of the studied economic processes, to justify their situation using the ARIMA model. As a result of the conducted research, the forecast parameters related to the share of small business in the industry until 2028 were determined.

Machine Learning Techniques for Sugarcane Yield Prediction Using Weather Variables

Weather has a profound influence on crop growth, development and yield. The present study deals with the use of weather parameters for sugarcane yield forecasting. Machine learning techniques like K- Nearest Neighbors (KNN) and Random Forest model have been used for sugarcane yield forecasting. Weather parameters namely maximum temperature and minimum temperature, rainfall, relative humidity in the morning and evening, sunshine hours, evaporation along with sugarcane yield have been used as inputs variables. The performance metrics like R2, Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) have been used to select the best model for predicting the yield of the crop. Among the models, Random Forest algorithm is selected as the best fit based on the high R2 and minimum error values. The results indicate that among the weather variables, rainfall and relative humidity in the evening have significant influence on sugarcane yield.

ЭКОНОМИКОМАТЕМАТИЧЕСКАЯ МОДЕЛЬ ЭФФЕКТИВНОГО УПРАВЛЕНИЯ ПРОМЫСЛОМ

One of the most pressing problems of sustainable development of territories is the management of the number and composition of populations. Populations are characterized by certain interrelated quantitative and qualitative characteristics, so they form the basis of evolutionary development. Self-organization mechanisms possessed by individual individuals contribute to the continuous reproduction of the numerical composition of populations and optimization of their distribution in the natural environment. In this regard, the economic problem of managing the dynamic composition of a population is extremely important and promising for research. The use of mathematical methods of study and the construction of economic and mathematical models improves the accuracy of forecast parameters. The article presents mathematical considerations leading to a biomechanical model of a single population subject to fishing, based on differential equations of dynamic balance. These equations are onedimensional and have a visual biomechanical interpretation. Based on these equations, economic conditions for the effective management of this industry were found and studied.

Forecasting Development of Medical Services Market in the Context of Model-Based Innovation Economy

The relevance of the article is due to the current stage of worldwide medical services development. The global nature of innovation development and digital technologies have affected all spheres of human activity, including medicine. On the other hand, the increasing role of social aspects in economic development forms new tasks in the development of healthcare. It should become of a better quality and more accessible. The paper used general scientific and special research methods. Among the special research methods, we used mathematical methods: statistical research, correlation and regression analysis. This article presents the results of forecasting the development of the medical services market in the Khorezm region of the Republic of Uzbekistan in the mid-term perspective. The mathematical model of forecasting the development of the medical services market took into account such factors as the number of private clinics, the number of employees of medical organizations, the real cost of medical services in constant prices, the number of operating hospital facilities, real total income per capita in constant prices of organizations, total income per capita in constant prices. Correlation analysis showed a high, stable relationship between the indicators. Regression analysis allowed to determine the forecast parameters of the market of medical services in the Khorezm region of the Republic of Uzbekistan. The reliability of calculations was checked by Student’s t-test and Durbin-Watson statistic test. Increasing the quality of life of the population in the region is the basis for increasing demand for quality health services and will lead to the growth of the private sector of health services.

Operational Storm Surge Forecasting at the National Hurricane Center: The Case for Probabilistic Guidance and the Evaluation of Improved Storm Size Forecasts Used to Define the Wind Forcing

Abstract The primary source of guidance used by the Storm Surge Unit (SSU) at the National Hurricane Center (NHC) for issuing storm surge watches and warnings is the Probabilistic Tropical Storm Surge model (P-Surge). P-Surge is an ensemble of Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model forecasts that is generated based on historical error distributions from NHC official forecasts. A probabilistic framework is used for operational storm surge forecasting to account for uncertainty related to the tropical cyclone track and wind forcing. Previous studies have shown that the size of a storm’s wind field is an important factor that can affect storm surge. A simple radius of maximum wind (RMW) prediction scheme was developed to forecast RMW based on NHC forecast parameters. Verification results indicate this scheme is an improvement over the RMW forecasts used by previous versions of P-Surge. To test the impact of the updated RMW forecasts in P-Surge, retrospective cases were selected from 25 storms from 2008 to 2020 that had an adequate number of observations. Evaluation of P-Surge forecasts using these improved RMW forecasts shows that the probability of detection is higher for most probability of exceedance thresholds. In addition, the forecast reliability is improved, and there is an increase in the number of high probability forecasts for extreme events at longer lead times. The improved RMW forecasts were recently incorporated into the operational version of P-Surge (v2.9), and serve as an important step toward extending the lead time of skillful and reliable storm surge forecasts.

A new accurate retrieval algorithm of bromine monoxide columns inside minor volcanic plumes from Sentinel-5P TROPOMI observations

Abstract. Bromine monoxide (BrO) is a key radical in the atmosphere, influencing the chemical state of the atmosphere, most notably the abundance of ozone (O3). O3 depletion caused by the release of bromine has been observed and modeled in polar regions, salt pans, and in particular inside volcanic plumes. Furthermore, the molar ratio of BrO and SO2 – which can be detected simultaneously via spectroscopic measurements using the differential optical absorption spectroscopy (DOAS) method – is a proxy for the magmatic composition of a volcano and potentially an eruption forecast parameter. The detection of BrO in volcanic plumes from satellite spectroscopic observations is limited by the precision and sensitivity of the retrieval, which so far only allowed for the detection of BrO during major eruptions. The unprecedented spatial resolution of up to 3.5 km×5.5 km and the high signal-to-noise ratio of the TROPOspheric Monitoring Instrument (TROPOMI) on board Sentinel-5 Precursor (S-5P) enable observing and monitoring volcanic bromine release globally even for minor eruptions or even quiescent degassing. In this study, we investigate how far the BrO retrieval can be improved using TROPOMI data and how well BrO can be detected, even in small eruptions and during quiescent volcanic degassing. There are two steps for which improvements in accuracy are investigated and applied: the improvement and quantitative determination of (1) the detection limit of the DOAS BrO column retrieval and (2) the correction of the non-volcanic background BrO signal. First, the DOAS retrieval settings are varied, and their influence on accuracy and precision is investigated with respect to the detection limit and potential systematic influences. Based on these results, we propose a dedicated DOAS evaluation scheme optimized for the detection of BrO in volcanic plumes. For the DOAS retrieval, we propose the use of a large fit window from 323–360 nm, yielding a statistical uncertainty lower by a factor of 1.8 compared to previous BrO DOAS algorithms while not enhancing systematic influences. Second, the effect of the background BrO is reduced by a latitude-dependent empirical correction scheme correlated to cloud information as well as information on the O3 column. Via these improvements, the combined statistical and systematic uncertainties in the resulting BrO vertical column density is on the order of 7×1012moleculescm-2. We present a new and accurate retrieval algorithm of BrO columns from TROPOMI observations which allows for the detection of even slightly enhanced BrO amounts inside minor eruptive plumes of bromine-rich volcanoes. While designed specifically for TROPOMI observations, the retrieval algorithm is in general also applicable to other hyperspectral satellite observations. However, some parts might require adaptation.

Barents-2.5km v2.0: an operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard

Abstract. An operational ocean and sea ice forecast model, Barents-2.5, is implemented for short-term forecasting at the coast off northern Norway, the Barents Sea, and the waters around Svalbard. Primary forecast parameters are sea ice concentration (SIC), sea surface temperature (SST), and ocean currents. The model also provides input data for drift modeling of pollutants, icebergs, and search-and-rescue applications in the Arctic domain. Barents-2.5 has recently been upgraded to include an ensemble prediction system with 24 daily realizations of the model state. SIC, SST, and in situ hydrography are constrained through the ensemble Kalman filter (EnKF) data assimilation scheme executed in daily forecast cycles with a lead time up to 66 h. Here, we present the model setup and validation in terms of SIC, SST, in situ hydrography, and ocean and ice velocities. In addition to the model's forecast capabilities for SIC and SST, the performance of the ensemble in representing the model's uncertainty and the performance of the EnKF in constraining the model state are discussed.

Enhancing Sales Forecasting Accuracy Through Optimized Holt-Winters Exponential Smoothing with Modified Improved Particle Swarm Optimization

The Holt-Winters Exponential Smoothing method utilizes three smoothing parameters, namely alpha (α), beta (β), and gamma (γ), which have a significant impact on the accuracy of the forecasting process. One of the main challenges in the Holt-Winters Exponential Smoothing method is to find the best combination of the smoothing parameters, α, β, and γ, to achieve optimal forecasting accuracy. In this research, the MIPSO optimization method is used to find the optimal combination of values for α, β, and γ. The sales data used in the study covers the period from January 2021 to May 2023. The research results indicate the best accuracy achieved by combining the Holt-Winters Exponential Smoothing algorithm with the MIPSO optimization algorithm during the data period from January 2021 to May 2023, with a MAPE value of 9.1717%. Therefore, the use of the MIPSO algorithm helps discover the optimal combination of α, β, and γ parameters for forecasting.

A Practical Approach to Select Representative Deterministic Models Using Multiobjective Optimization from an Integrated Uncertainty Quantification Workflow

Summary Selecting a set of deterministic (e.g., P10, P50, and P90) models is an important and difficult step in any uncertainty quantification workflow. In this paper, we propose to use multiobjective optimization to find a reasonable balance between the often conflicting features that must be captured by these models. We embed this approach into a streamlined uncertainty quantification workflow that seamlessly integrates multirealization history matching, production forecasting with uncertainty ranges, and representative deterministic model selection. Some uncertain parameters strongly impact simulated responses representing historic (production) data and are selected as active parameters for history matching, whereas others are important only for forecasting. An ensemble of conditional realizations of active history-matching parameters is generated in the multirealization history-matching stage using a distributed optimizer that is integrated with either a randomized maximum likelihood (RML) or a Gaussian mixture model (GMM). This ensemble is extended with unconditional realizations of forecast parameters generated by sampling from their prior distribution. Next, the petroleum engineer must select primary and secondary key performance indicators and identify models from this ensemble that optimally generate P10, P50, and P90 values for these indicators. In addition to matching target values of these key performance indicators (e.g., cumulative oil/gas/water production and recovery factor), selected representative models (RMs) typically must satisfy regulatory or management-imposed requirements or constraints (e.g., the value of some key parameters must be within a user-specified tight range). It can be quite difficult to find a set of RMs that satisfy all requirements. Even more challenging, some requirements may conflict with others, such that no single model can satisfy all requirements. To overcome these technical difficulties, we propose in this paper to formulate different requirements and constraints as objectives and develop a novel two-stage multiobjective optimization strategy to find a set of Pareto optimal solutions based on the concept of dominance. In the first stage, we propose selecting P10, P50, and P90 candidates by minimizing the indicator mismatch function and constraints violation function. In the second stage, we propose selecting combinations of P10, P50, and P90 candidates from the previously generated posterior ensemble, obtained in the first stage by optimizing other objectives. One or more sets of RMs can then be selected from the set of optimal solutions according to case-dependent preferences or requirements. Because the number of P10, P50, and P90 candidates selected in the first stage is much smaller than the number of all samples, the proposed two-stage approach performs much more efficiently than directly applying the traditional multiobjective optimization approach or clustering-based approaches. The proposed method is tested and validated against a realistic example. Our results confirm that the proposed method is robust and efficient and finds acceptable solutions with no or minimal violations of constraints. These results suggest that our advanced multiobjective optimization technique can select high-quality RMs by striking a balance between conflicting constraints. Thus, a better decision can be made while running much fewer simulations than would be required with traditional methods.

Study and Development of IOT based Smart Irrigation System using Soil Moisture and Weather Prediction

Abstract: The smart irrigation system was developed to optimize water use of crops. The system has a distributed wireless network of soil-moisture temperature sensors placed in the root zone of the plants. Also, a gateway unit handles sensor information, triggers, actuators, and transmits data to the web application. The intelligence of The proposed system is based on a smart algorithm, which considers sensed data along with the weather forecast parameters like precipitation, air temperature, humidity, and UV for the near future. The complete system has been developed and deployed on a pilot scale, where the sensor node data is wirelessly collected over the cloud using web- services and a web-based information visualization and decision support system provides real-time information insights based on the analysis of sensor dataand weather forecast data. The paper describes the system and discusses in detail the information processing results of three weeks data based on the proposed algorithm. The system is fully functional and the prediction results are very encouraging

Simplified Method for Predicting Hourly Global Solar Radiation Using Extraterrestrial Radiation and Limited Weather Forecast Parameters

Solar radiation has important impacts on buildings such as for cooling/heating load forecasting, energy consumption forecasting, and multi-energy complementary optimization. Two types of solar radiation data are commonly used in buildings: radiation data in typical meteorological years and measured radiation data from meteorological stations, both of which are types of historical data. However, it is difficult to predict the hourly global solar radiation, which affects the application of relevant prediction models in practical engineering. Most existing methods for predicting hourly global solar radiation have issues such as difficulty in obtaining input parameters or complex data processing, which limits their practical engineering applications. This study proposed a simplified method to accurately predict the hourly horizontal solar radiation using extraterrestrial solar radiation, weather types, cloud cover, air temperature, relative humidity, and time as the input parameters. The back-propagation network, support vector machine, and light gradient boosting machine (LightGBM) models were used to establish the prediction model, and Shapley additive explanations were used to analyze the relationship between the input variables and the prediction results to simplify the structure of the prediction model. Taking Lanzhou New District in Gansu Province as an example, the results showed that the LightGBM model performed the best, with the root mean square error of 126.1 W/m2. Shapley additive explanations analysis showed that weather type was not a significant factor in the LightGBM model. Therefore, the weather type was removed from the LightGBM model and the root mean square error was 135.2 W/m2. The results showed that extra-terrestrial radiation and limited weather forecast parameters can be used to predict hourly global solar radiation with satisfactory prediction results.

Cloud-resolving time-lagged rainfall ensemble forecasts for typhoons in Taiwan: Examples of Saola (2012), Soulik (2013), and Soudelor (2015)

As high resolution is required for numerical models to adequately simulate convective storms and thus produce quantitative precipitation forecasts (QPFs), a time-lagged ensemble out to 8 days at 6-h intervals using a 2.5-km cloud-resolving model is applied to three rainy typhoons that made landfall in Taiwan in recent years: Saola (2012), Soulik (2013), and Soudelor (2015), following an earlier study.For the three typhoons where the worst-case rainfall scenario turned out to happen in Taiwan, the system was able to predict this particular scenario with high accuracy (with a rainfall pattern similar to the observed) at an earliest lead time of about 162 h, 79 h, and 164 h before landfall, and thus provided key information for early preparation. Within the short range (≤72 h), as the predicted tracks converged toward the best track, high-quality QPFs were consistently generated starting at about 36 h, 55 h, and 80 h prior to landfall, respectively, with derived probabilities in good to excellent agreement with the observations, even at extreme thresholds (≥500 and 700 mm in 24 h). Leading up to the track convergence, the probabilities across various rainfall thresholds increased markedly, so their time evolution also provided useful information for decision makers and hazard preparation.The underlying reason behind our results is the high predictability of topographic rainfall in Taiwan produced by the typhoon circulation, which cannot be properly captured without high model resolution. Thus, without compromising the resolution, we demonstrate the advantages of the time-lagged strategy for ensemble forecasting, assuming QPF as the key target forecast parameter.