Intensity Forecasts Research Articles

Exploring the typhoon intensity forecasting through integrating AI weather forecasting with regional numerical weather model

Recent advancements in artificial intelligence (AI) have notably enhanced global weather forecasting, yet accurately predicting typhoon intensity remains challenging. This is largely due to constraints inherent in regression algorithm properties including deep neural networks and inability of coarse resolution to capture the finer-scale weather processes. To address these insufficiencies in typhoon intensity forecasting, we propose an attractive approach by initiating regional Weather Research and Forecasting (WRF) model with Pangu-weather, a state-of-the-art AI weather forecasting system (AI-Driven WRF), whose forecasting power can be further augmented by the implementation of dynamic vortex initialization. The results highlight limitations in Pangu-Weather’s capability to accurately forecast typhoon intensity. In contrast, the AI-Driven WRF model demonstrated notable advancements over Pangu-Weather, achieving more reliable and accurate predictions of typhoon intensity. Furthermore, the AI-Driven WRF model demonstrated promising results in predicting typhoon intensity and wind details, showing commendable performance to traditional global numerical model-driven WRF models. Our analysis underscores the potential of AI weather forecasting models as a viable alternative for driving regional models, suggesting a promising avenue for future research in meteorology.

Are Rapidly Intensifying Tropical Cyclones Associated with Unique Vortex and Convective Characteristics?

Abstract The largest tropical cyclone (TC) intensity forecast errors are typically associated with episodes of rapid intensification (RI). Here, we explore whether TCs that undergo RI are associated with different vortex and convective characteristics compared to TCs that are either slowly intensifying (SI) or nonintensifying (NI). Because characteristics of TC structure are strongly linked to intensity, a normalization technique is employed to examine how the anomalous TC structure, relative to the average structure for a given intensity, is related to TC intensity change. TC structure is assessed using a recently developed database of airborne Doppler radar analyses collected by NOAA’s WP-3D aircraft over the last three decades, primarily in the North Atlantic basin. We find that RI episodes are associated with significantly taller and narrower primary circulations than both SI and NI episodes. RI episodes also tend to occur in storms with anomalously deep overturning circulations and larger azimuthally averaged ascent in the upper troposphere than SI and NI episodes. Additionally, the inner core of RI TCs exhibits a significantly greater areal coverage of convective bursts, which are defined as locations with relatively vigorous ascent in the mid–upper troposphere. Ultimately, TC intensity change is governed by multiscale processes, and RI is found to occur preferentially in TCs that have both favorable synoptic-scale environmental conditions and favorable vortex structures for intensification. Consequently, observations of the anomalous TC structure may serve as useful predictors for TC intensity change and RI. Significance Statement Current forecasts of tropical cyclone (TC) intensity have the largest errors when storms experience periods of rapid intensification (RI). This study explores whether TCs that undergo RI have unique vortex and convective characteristics compared to TCs that either slowly intensify or those that fail to intensify altogether. It is found that TCs which undergo RI tend to have taller and narrower vortex structures than non-RI storms. TCs that undergo RI also display stronger ascent in the upper troposphere than non-RI storms. These characteristics may serve as useful predictors for TC intensity change and forecasting whether RI will occur.

A review of integrated use of machine learning algorithms, GIS and remote sensing techniques in the prediction of rainfall patterns and floods in the U. S.

This paper systematically reviews the integrated use of machine learning algorithms, Geographic Information Systems (GIS), and Remote Sensing (RS) techniques in the prediction of rainfall patterns and flood events in the U.S. With increasing climate variability, accurate forecasting of rainfall and flood risks has become critical to safeguarding communities and infrastructure. GIS enables spatial analysis and mapping of flood-prone areas, supporting risk assessment and disaster preparedness. RS contributes real-time satellite imagery and environmental data, essential for tracking rainfall patterns and assessing surface conditions. Machine learning algorithms enhance these technologies by providing predictive modeling capabilities, allowing for more precise forecasts of rainfall intensity and flood potential. This paper explores the synergy between GIS, RS, and machine learning, emphasizing their combined impact on improving flood prediction accuracy and decision-making in disaster management. Key challenges, including data heterogeneity, computational demands, and the integration of diverse datasets, are discussed. Additionally, the paper reviews current U.S. policies on data-sharing and technology adoption, highlighting the need for regulatory frameworks that support innovation while ensuring data privacy and accuracy. Through an analysis of recent studies, this paper presents a comprehensive overview of the advantages and constraints of using these integrated technologies for flood prediction, offering insights into future directions and recommendations for enhancing flood management systems. The review concludes that advancing integrated GIS, RS, and machine learning applications will require addressing data-related challenges and fostering collaborative efforts across agencies to strengthen flood prediction and resilience capabilities in the U.S.

Improving tropical cyclone rapid intensification forecasts with satellite measurements of sea surface salinity and calibrated machine learning

Abstract Forecasting rapid intensification (RI) of tropical cyclones (TC) is a mission known for large errors. One under-researched factor that affects TC intensification is salinity, which is important for density stratification in certain ocean regions and can affect the surface enthalpy flux under a strengthening hurricane. To investigate the impact and efficacy of using salinity information in state-of-the-art forecasting, we use a statistical model consisting of a variety of machine learning (ML) methods. For salinity data, we use satellite measurements of pre-storm sea surface salinity (SSS) as a proxy for the salinity stratification. We train and test the model on various ocean basins, including the Atlantic, northern east Pacific, northern west Pacific. A calibrator was trained on top of the ML models to correct and enhance probability forecasts. The calibrator significantly improves probability forecasts relative to recent works. The ML model performance is improved with the addition of SSS in the Eastern North Pacific, western North Pacific, and the Caribbean subregion of the North Atlantic, and the overall model performance is better than previous studies. SSS decreases model skill for a model trained on the full Atlantic basin. In the Indian Ocean, SSS is also notably correlated with RI occurrence, but the TC samples are not sufficient to train ML models.

The Direct Assimilation of Radar Reflectivity Data with a Two-Moment Microphysics Scheme for a Landfalling Typhoon in an OSSE Framework

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

A New Frontier in Wind Shear Intensity Forecasting: Stacked Temporal Convolutional Networks and Tree-Based Models Framework

Wind shear presents a considerable hazard to aviation safety, especially during the critical phases of takeoff and landing. Accurate forecasting of wind shear events is essential to mitigate these risks and improve both flight safety and operational efficiency. This paper introduces a hybrid Temporal Convolutional Networks and Tree-Based Models (TCNs-TBMs) framework specifically designed for time series modeling and the prediction of wind shear intensity. The framework utilizes the ability of TCNs to capture intricate temporal patterns and integrates it with the predictive strengths of TBMs, such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Categorical Boosting (CatBoost), resulting in robust forecast. To ensure optimal performance, hyperparameter tuning was performed using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), enhancing predictive accuracy. The effectiveness of the framework is validated through comparative analyses with standalone machine learning models such as XGBoost, RF, and CatBoost. The proposed TCN-XGBoost model outperformed these alternatives, achieving a lower Root Mean Squared Error (RMSE: 1.95 for training, 1.97 for testing), Mean Absolute Error (MAE: 1.41 for training, 1.39 for testing), and Mean Absolute Percentage Error (MAPE: 7.90% for training, 7.89% for testing). Furthermore, the uncertainty analysis demonstrated the model’s reliability, with a lower mean uncertainty (7.14 × 10−8) and standard deviation of uncertainty (6.48 × 10−8) compared to other models. These results highlight the potential of the TCNs-TBMs framework to significantly enhance the accuracy of wind shear intensity predictions, emphasizing the value of advanced time series modeling techniques for risk management and decision-making in the aviation industry. This study highlights the framework’s broader applicability to other meteorological forecasting tasks, contributing to aviation safety worldwide.

Intensification of an Autumn Tropical Cyclone by Offshore Wind Farms in the Northern South China Sea

AbstractThe rapid development of the wind industry is accompanied by increasing environmental impacts. Currently, there is a lack of research on the impacts of offshore wind farm (OWF) on tropical cyclone (TC) intensity, including the mechanisms involved. This research is carried out by using a coupled and an uncoupled numerical model to investigate the impact of OWF on an autumn TC in the northeastern South China Sea. The results show that the wind speed deficit caused by OWF leads to an increase in surface pressure on the inflow side. This causes the surface pressure in the TC periphery to increase by advection, even if the TC is some distance away from the OWF. The increase in pressure gradient from the periphery to the TC center enhances the TC secondary circulation, thereby intensifying the TC. When the TC enters the OWF, the above mechanisms weaken and the ocean dominates the TC intensification. This is because the reduction in wind speed caused by the OWF results in a weaker sea surface current velocity, which weakens the flow of upstream cold water into the OWF, warming the sea surface temperature (SST) within the OWF. This implies that the horizontal gradient of the local SST is an important factor to be considered in the development of OWF. Sensitivity experiments indicate that OWF can also intensify other types of TC, and that higher cut‐out wind speeds lead to stronger intensification effects. These results also provide a new perspective on TC intensity forecasts.

Influence of CyGNSS L2 wind data on tropical cyclone analysis and forecasts in the coupled HAFS/HYCOM system

This study examines the influence of NASA Cyclone Global Navigation Satellite System (CyGNSS) Level 2-derived 10 m (near-surface) wind speed over the ocean on analyses and forecasts within the NOAA operational Hurricane Analysis and Forecast System (HAFS). HAFS is coupled with a regional configuration of the HYCOM ocean model. The primary advantages of data from the CyGNSS constellation of satellites include higher revisit frequency compared to polar-orbiting satellites, and the availability of reliable wind observations over the ocean surface during convective precipitation. CyGNSS data are available early in the life cycle of tropical cyclones (TCs) when aerial reconnaissance observations are not available. We focus on TCs whose forecasts were initialized when the TC was a depression or tropical storm. In the present study, we find first, that assimilation of CyGNSS near-surface winds improves storm track, intensity, and structure statistics in the analysis and early in the forecast, for many cases. Second, we find that assimilation of CyGNSS observations provides additional insights into the evolution of air-sea interaction in intensifying TCs: In effect, the ocean responds in the coupled model to modifications in the initial 10 m wind field, thereby impacting forecasts of intensity, storm structure, and sea surface height, as demonstrated by two case studies. We also discuss some forecasts where assimilating CYGNSS appears to degrade performance for either intensity or structure.

Impact of Directly Assimilating Radar Reflectivity Using a Reflectivity Operator Based on a Double-Moment Microphysics Scheme on the Analysis and Forecast of Typhoon Lekima (1909)

In previous studies, radar reflectivity is often directly assimilated using reflectivity operators based on a single-moment (SM) microphysics scheme, though the forecast model uses a double-moment (DM) microphysics scheme. With the fixed number concentrations, only the mixing ratios of hydrometeors are directly updated during the assimilation, which leads to a mismatch between the analyzed microphysical state variables and the microphysics scheme of the forecast model. In this study, the radar reflectivity is directly assimilated through an observation operator consistent with the DM Thompson microphysics scheme used in numerical integrations, and the impact of reflectivity operators based on SM and DM schemes on the analysis performance of the ensemble Kalman filter for typhoon Lekima on 9 August 2019 is evaluated. Reflectivity observations from a single operational weather radar in Wenzhou City, Zhejiang Province of China are assimilated. In addition, the dual-polarization observations from the same radar are used to evaluate the quality of the analysis. The analyzed reflectivity and dual-polarization characteristics obtained by different reflectivity operators are examined in detail. Compared with the experiments applying the reflectivity operator based on the SM Lin scheme, the use of a reflectivity operator consistent with the DM Thompson scheme adopted in the forecast model results in analyzed reflectivity and polarization characteristics that are more consistent with the observed characteristics in terms of general structure, location, and intensity. Forecasted reflectivity, 3 h accumulated precipitation, and typhoon intensity and track are also evaluated. The application of the reflectivity operator based on the DM scheme makes better forecasts of typhoon intensity, precipitation, and reflectivity, which also improves the forecast skills on typhoon tracks to a certain extent.

An Integrated Approach for Development of a Decision Support System for Early Surveillance of Japanese Encephalitis: a Remote Sensing-based study in Conjunction with Epidemiological Scenario in Assam, India

Abstract. Japanese Encephalitis (JE) is one of the serious mosquito borne viral diseases mostly prevalent in India and many of the South Asian countries. A comprehensive study was conducted in Dibrugarh, a JE endemic district of Assam taking into the account of the environmental and social factors that determine the outbreak of the disease. Effect of environmental and climatic factors on mosquito vector densities was investigated using geospatial tools and techniques. Wetlands were found to be the most preferred habitat type for most of the JE-transmitting mosquitoes followed by paddy-growing areas and ponds. The villages abundant with these habitats or located nearby were found to be more vulnerable to JE risk. Based on the spatial distribution of habitat types, villages in the district were divided into three categories of vector abundance and hence the risk of JE. It is found that rainfall (intensity as well as duration) is the most significant determinant in the modulation of mosquito density followed by temperature. It is also observed that the derived models could explain about 61% to 73% variation in mosquito density due to change in weather variables as indicated by R2 values of the Models. Among socio-economic determinants, pig rearing habit of the villagers was found to be most significant. JE risk map was prepared by integrating vector abundance map with the map of host abundance in GIS domain. Attempts were also made to forecast the intensity of JE cases based on a time series analysis of historical morbidity pattern, whereas forecast for onset of the disease was based on the modulation of weather variables in the study area. A web-based decision support system (DSS) developed by integrating the forecasts of JE onset, intensity and JE prone villages has been provided to the concerned health authorities to take timely intervention measures. The JE early warning system developed for Dibrugarh district was extended to two more JE prone districts viz, Sibsagar and Tinsukia as validation sites and observed that the accuracy of forecast was within the range of 69% to 77%. DSS on JE has opened up new opportunities to study similar vector-borne diseases of the JE serogroup viz., West Nile.

Experimental Comparison of Two Main Paradigms for Day-Ahead Average Carbon Intensity Forecasting in Power Grids: A Case Study in Australia

Accurate carbon intensity forecasts enable consumers to adjust their electricity use, reducing it during high fossil-fuel generation and increasing it when renewables dominate. Existing methods for carbon intensity forecasting can be categorized into a source-disaggregated approach (SDA), focused on delivering individual generation forecasts for each potential source (e.g., wind, brown-coal, etc.), and a source-aggregated approach (SAA), attempting to produce a single carbon intensity forecast for the entire system. This research aims to conduct a thorough comparison between SDA and SAA for carbon intensity forecasting, investigating the factors that contribute to variations in performance across two distinct real-world generation scenarios. By employing contemporary machine learning time-series forecasting models, and analyzing data from representative locations with varying fuel mixes and renewable penetration levels, this study provides insights into the key factors that differentiate the performance of each approach in a real-world setting. The results indicate the SAA proves to be more advantageous in scenarios involving increased renewable energy generation, with greater proportions and instances when renewable energy generation faces curtailment or atypical/peaking generation is brought online. While the SDA offers better model interpretability and outperforms in scenarios with increased niche energy generation types, in our experiments, it struggles to produce accurate forecasts when renewable outputs approach zero.

A Dynamic Smagorinsky Model for Horizontal Turbulence Parameterization in Tropical Cyclone Simulation

AbstractThe horizontal turbulence parameterization is vital for the intensity and structure forecasting of tropical cyclone (TC) in numerical weather prediction (NWP) models. The default two‐dimensional (2D) standard Smagorinsky model with a single universal constant in Weather and Research Forecasting (WRF) model has been proven to be over dissipative for TC, leading to underprediction of TC intensity. This study provides the first attempt to implement the physically based 2D dynamic Smagorinsky model (DSM) for horizontal turbulence parameterization in WRF model for TC forecasts. The DSM dynamically computes the Smagorinsky coefficient as a function of the resolved flow during the simulation, avoiding the need to prescribe the coefficient a prior. The test results of the DSM in a TC NWP model show that the DSM can significantly improve the wind intensity forecasts compared to the standard Smagorinsky model.

Variational All‐Sky Assimilation Framework for MWHS‐II With Hydrometeors Control Variables and Its Impacts on Analysis and Forecast of Typhoon Cases

AbstractAll‐sky radiance assimilation has been extensively developed to provide additional information for numerical weather prediction under cloudy conditions. Microwave radiances are particularly sensitive to hydrometeors, which can be used to initialize hydrometeor directly if the hydrometeor control variables (HCVs) are available. However, the effects of HCVs statistical structure and their multivariate correlation on all‐sky radiance assimilation remain unclear. In this study, five HCVs are introduced into the variational assimilation system. The characteristics of hydrometeor background errors are analyzed, and the combined effect with the observation operator is discussed. Then a 3D Variational all‐sky assimilation framework with HCVs is modified to assimilate Fengyun‐3C/D Microwave Humidity Sounder‐II radiance. It is shown that hydrometeors are initialized by radiance directly, and the thermodynamic fields are adjusted accordingly. The characteristics of multi‐variables increments are associated with both the characteristics of HCVs in background error and the Jacobians in observation operator. Furthermore, cycle assimilation and forecast experiments for three typhoon cases are conducted. It is found that the difference between observed and analyzed brightness temperatures decreases when HCVs are activated, and the hydrometeors analysis fields are more consistent with observations. Additionally, the typhoon intensity forecasts are improved with enhanced double warm‐core and the secondary circulation. This paper analyzes the characteristics of variational all‐sky assimilation framework with HCVs, and demonstrates the potential value of HCVs for variational all‐sky radiance assimilation.

Effects of Surface Layer Physics Schemes on the Simulated Intensity and Structure of Typhoon Rai (2021)

The influences of surface layer (SL) physics schemes on the simulated intensity and structure of Typhoon Rai (2021) are investigated using the WRF model. Numerical experiments using different SL physics schemes—revised MM5 scheme (MM5), Eta similarity scheme (CTL), and Mellor–Yamada–Nakanishi–Niino scheme (MYNN)—are conducted. The results show that the intensity forecast of Typhoon Rai is largely influenced by SL physics schemes, while its track forecast is not significantly affected. All three experiments can successfully capture the movement of Rai, while CTL provides better intensity simulation compared to the other two experiments. The higher ratio of enthalpy exchange coefficient to drag coefficient (CK/CD) in CTL than MM5 and MYNN leads to significantly increased surface enthalpy fluxes, which are crucial for the typhoon intensification of the former. To explore the influence of SL physics on the structural evolution of the typhoon, the azimuthal-mean angular momentum (AM) budget is utilized. The results indicate that asymmetric eddy terms may also largely contribute to the AM tendencies, which are relatively more comparable in the weaker TC for MM5, compared to the stronger TC with the dominant symmetric mean terms for CTL. Furthermore, the extended Sawyer–Eliassen (SE) equation is solved to quantify the transverse circulations of the typhoon induced by different forcing sources for CTL and MM5. The SE solution indicates that the transverse circulation above and within the boundary layer is predominantly induced by diabatic heating and turbulent friction, respectively, for both CTL and MM5, while all other physical forcing terms are relatively insignificant for the induced transverse circulation for CTL, except for the large contribution from the eddy forcing in the upper-tropospheric outflow for MM5. With the stronger connective heating in the eyewall and boundary-layer radial inflow, the linear SE analysis agrees much better with the nonlinear simulation for CTL than MM5.

CNN vs. LSTM: A Comparative Study of Hourly Precipitation Intensity Prediction as a Key Factor in Flood Forecasting Frameworks

Accurate precipitation intensity forecasting is crucial for effective flood management and early warning systems. This study evaluates the performances of convolutional neural network (CNN) and long short-term memory (LSTM) models in predicting hourly precipitation intensity using data from Sainte Catherine de la Jacques Cartier station near Québec City. The models predict precipitation levels from one to six hours ahead, which are categorized into slight, moderate, heavy, and very heavy precipitation intensities. Our methodology involved gathering hourly precipitation data, defining input combinations for multistep ahead forecasting, and employing CNN and LSTM models. The performances of these models were assessed through qualitative and quantitative evaluations. The key findings reveal that the LSTM model excelled in the short-term (1HA to 2HA) and long-term (3HA to 6HA) forecasting, with higher R2 (up to 0.999) and NSE values (up to 0.999), while the CNN model was more computationally efficient, with lower AICc values (e.g., −16,041.1 for 1HA). The error analysis shows that the CNN demonstrated higher precision in the heavy and very heavy categories, with a lower relative error, whereas the LSTM performed better for the slight and moderate categories. The LSTM outperformed the CNN in minor- and high-intensity events, but the CNN exhibited a better performance for significant precipitation events with shorter lead times. Overall, both models were adequate, with the LSTM providing better accuracy for extended forecasts and the CNN offering efficiency for immediate predictions, highlighting their complementary roles in enhancing early warning systems and flood management strategies.

The impact of coupling a dynamic ocean in the Hurricane Analysis and Forecast System

Coupling a three-dimensional ocean circulation model to an atmospheric model can significantly improve forecasting of tropical cyclones (TCs). This is particularly true of forecasts for TC intensity (maximum sustained surface wind and minimum central pressure), but also for structure (e.g., surface wind-field sizes). This study seeks to explore the physical mechanisms by which a dynamic ocean influences TC evolution, using an operational TC model. The authors evaluated impacts of ocean-coupling on TC intensity and structure forecasts from NOAA’s Hurricane Analysis and Forecast System v1.0 B (HFSB), which became operational at the NOAA National Weather Service in 2023. The study compared existing HFSB coupled simulations with simulations using an identical model configuration in which the dynamic ocean coupling was replaced by a simple diurnally varying sea surface temperature model. The authors analyzed TCs of interest from the 2020–2022 Atlantic hurricane seasons, selecting forecast cycles with small coupled track-forecast errors for detailed analysis. The results show the link between the dynamic, coupled ocean response to TCs and coincident TC structural changes directly related to changing intensity and surface wind-field size. These results show the importance of coupling in forecasting slower-moving TCs and those with larger surface wind fields. However, there are unexpected instances where coupling impacts the near-TC atmospheric environment (e.g., mid-level moisture intrusion), ultimately affecting intensity forecasts. These results suggest that, even for more rapidly moving and smaller TCs, the influence of the ocean response to the wind field in the near-TC atmospheric environment is important for TC forecasting. The authors also examined cases where coupling degrades forecast performance. Statistical comparisons of coupled versus uncoupled HFSB further show an interesting tendency: high biases in peak surface winds for the uncoupled forecasts contrast with corresponding low biases, contrary to expectations, in coupled forecasts; the coupled forecasts also show a significant negative bias in the radii of 34 kt winds relative to National Hurricane Center best track estimates. By contrast, coupled forecasts show very small bias in minimum central pressure compared with a strong negative bias in uncoupled. Possible explanations for these discrepancies are discussed. The ultimate goal of this work will be to enable better evaluation and forecast improvement of TC models in future work.

Understanding the initial conditions contributing to the rapid intensification of typhoons through ensemble sensitivity analysis

While steady improvements have been achieved for the track forecasts of typhoons, there has been a lack of improvement for intensity forecasts. One challenge for intensity forecasts is to capture the rapid intensification (RI), whose nonlinear characteristics impose great difficulties for numerical models. The ensemble sensitivity analysis (ESA) method is used here to analyze the initial conditions that contribute to typhoon intensity forecasts, especially with RI. Six RI processes from five typhoons (Chaba, Haima, Meranti, Sarika, and Songda) in 2016, are applied with ESA, which also gives a composite initial condition that favors subsequent RI. Results from individual cases have generally similar patterns of ESA, but with different magnitudes, when various cumulus parameterization schemes are applied. To draw the initial conditions with statistical significance, sample-mean azimuthal components of ESA are obtained. Results of the composite sensitivity show that typhoons that experience RI in 24 h favor enhanced primary circulation from low to high levels, intensified secondary circulation with increased radial inflow at lower levels and increased radial outflow at upper levels, a prominent warm core at around 300 hPa, and increased humidity at low levels. As the forecast lead time increases, the patterns of ESA are retained, while the sensitivity magnitudes decay. Given the general and quantitative composite sensitivity along with associated uncertainties for different cumulus parameterization schemes, appropriate sampling of the composite sensitivity in numerical models could be beneficial to capturing the RI and improving the forecasting of typhoon intensity.摘要针对台风强度特别是快速增强 (RI) 的预报难点, 本文对2016年5个台风的6次RI过程应用集合敏感性分析 (ESA), 以分析有利于RI的初始条件. 对于不同的个例和积云参数化方案, ESA获得的有利于台风RI的集合敏感性相似但量级存在差异. 复合敏感性揭示了经历RI的台风所需的初始条件, 其有更强的主环流, 更显著的暖心及增加的低层湿度. ESA估计的集合敏感性随预报时长增加而减弱. 通过采样复合敏感性可有望改进集合初始条件及台风强度预报.

Impacts of the all-sky assimilation of FY-3D and FY-3E MWHS-2 radiances on analyses and forecasts of Typhoon Muifa (2022)

The newly launched Fengyun-3E (FY-3E) satellite in the early morning contributes to forming a complete three-orbit observation system, comprehensively improving the spatial coverage of satellite data. This study highlights the importance of applying the Microwave Humidity Sounder-II (MWHS-2) data from the FY-3E satellite by exploring the impact of jointly assimilating FY-3D and FY-3E MWHS-2 data under all-sky conditions on regional typhoon forecasting. Firstly, the impact of the different observation error statistics is studied based on FY-3D all-sky MWHS-2 radiance data assimilation for Typhoon Muifa (2022). The results show that the features of adaptive observation error statistics are rather consistent based on observational and meteorological information samples over different periods. Appropriate observation errors have the potential to improve typhoon track and intensity predictions, especially for rainfall forecasts. Consistent positive benefits of the typhoon intensity forecasts are obtained with the assimilation of the cloud-affected radiances from FY-3D MWHS-2. Moreover, joint assimilation experiments using FY-3D and FY-3E MWHS-2 radiances are conducted to compare the relative impact of each sensor for both clear-sky and all-sky. Results indicate improvements in the analyses of 500 hPa geopotential height and humidity with the introduction of cloud-affected radiance in joint assimilation experiments. The forecast performance of the joint assimilation method is significantly better than that with only FY-3E MWHS-2 observations in terms of the typhoon track, intensity, and precipitation. This superiority is particularly obvious in all-sky data assimilation experiments compared to clear-sky conditions.

Artificial neural networks as a tool for seasonal forecast of attack intensity of Spodoptera spp. in Bt soybean.

Soybean (Glycine max) is the world's most cultivated legume; currently, most of its varieties are Bt. Spodoptera spp. (Lepidoptera: Noctuidae) are important pests of soybean. An artificial neural network (ANN) is an artificial intelligence tool that can be used in the study of spatiotemporal dynamics of pest populations. Thus, this work aims to determine ANN to identify population regulation factors of Spodoptera spp. and predict its density in Bt soybean. For two years, the density of Spodoptera spp. caterpillars, predators, and parasitoids, climate data, and plant age was evaluated in commercial soybean fields. The selected ANN was the one with the weather data from 25 days before the pest's density evaluation. ANN forecasting and pest densities in soybean fields presented a correlation of 0.863. It was found that higher densities of the pest occurred in dry seasons, with less wind, higher atmospheric pressure and with increasing plant age. Pest density increased with the increase in temperature until this curve reached its maximum value. ANN forecasting and pest densities in soybean fields in different years, seasons, and stages of plant development were similar. Therefore, this ANN is promising to be implemented into integrated pest management programs in soybean fields.

Impacts of free tropospheric turbulence parametrisation on a sheared tropical cyclone

AbstractThe turbulent transport of momentum, heat, and moisture can impact tropical cyclone intensity. However, representing subgrid‐scale turbulence accurately in numerical weather prediction models is challenging due to a lack of observational data. To address this issue, a case study of Hurricane Maria was conducted to analyse the influence of different free tropospheric turbulence parametrisations on sheared tropical cyclones. The study used the current Met Office Unified Model (MetUM) parametrisation, as well as a parametrisation scheme with significantly reduced free tropospheric mixing length. Convection‐permitting ensemble simulations were performed for both mixing schemes at two initialisation times (four 18‐member ensembles in total), revealing an improvement in the intensity forecasts of Hurricane Maria when the mixing length was decreased in the free troposphere. By implementing this change, the less diffuse simulations presented a drier mid‐level. The resolved downward transport of drier air from the mid‐levels into the inflow layer (so‐called “downdraft ventilation”) was thus more effective in reducing the storm's intensity. In contrast to earlier studies, where decreasing the diffusivity in the boundary layer intensified the storm, we show that decreasing the free tropospheric diffusivity can weaken the storm by enhancing shear‐related weakening processes. While this study was performed using the MetUM, the findings highlight the general importance of considering turbulence parametrisation, and show that changes in diffusivity can have different impacts on storm intensity depending on the environment and where the changes are applied.