Tropical Cyclone Cases Research Articles

New Rankine Vortex Models Developed Based on SMAP Measurements

Abstract The L-band passive microwave radiometer on board the NASA Soil Moisture Active Passive (SMAP) satellite can measure brightness temperature to retrieve sea surface wind speed under tropical cyclone (TC) conditions without being affected by rainfall or signal saturation caused by high wind speeds. Based on this advantage, this paper used the SMAP wind products for parameterizing the key decay exponent α of the Rankine vortex model (a traditional parametric model of the TC wind field) and finally developed new Rankine models. The SMAP dataset included 67 TC cases. Through data statistics, we examined the relationship between α and the maximum wind speed Um, the relationship between α and the radius of maximum wind speed (Rm), and the relationship between Rm and the averaged radius of 17 m s−1 (R17). Results showed that the three relationships were both positive correlations, indicating that α can be parameterized in three empirical ways. The first way is to calculate solely with Um. The second way is to calculate solely with Rm. The third way is to calculate Um and Rm together. The three ways correspond to three new models: the SMAP Rankine Model-1 (SRM-1), the SMAP Rankine Model-2 (SRM-2), and the SMAP Rankine Model-3 (SRM-3). Finally, comparisons were made between the new models and three existing Rankine models, according to the model simulations and the Advanced Microwave Scanning Radiometer 2 measurements of 49 TC cases. Results showed that the SRM-3 performed best overall. Significance Statement Ocean surface wind fields are important driving factors for numerical models to guide evolution forecasting and risk assessment of tropical cyclones. The purpose of this study is to utilize the SMAP products to modify the traditional Rankine vortex model for tropical cyclone surface wind field estimation. Rankine decay exponent was found between 0.3 and 0.9 and positively dependent upon maximum wind speed and its radius. Based on these characteristics, the proposed new Rankine models could calculate the decay exponent and further the TC surface wind field symmetrically with high accuracy, simply using maximum wind and its radius. This paper shows a practical use of SMAP measurements on TC wind field monitoring, analyzing, and modeling.

A Rare Tropical Cyclone Associated With Southwest Monsoon Over the Northern Part of the South China Sea—Tropical Storm Maliksi

AbstractThis study examines the characteristics and development of Tropical Storm Maliksi, which is a special case of tropical cyclone developed in the northwestern part of the South China Sea during a southwest monsoon outbreak. Detailed analyses were conducted using observational data and forecast products. Surface observations, radar wind profilers, aircraft data, and satellite products were used to evaluate Maliksi's wind structure, revealing multiple circulation centers and gale force winds. Vertical wind profiles, warm core structure, wind waves, and the influence of sea temperatures and salinity on Maliksi's intensification were investigated. Regarding forecasting, AI‐based models outperformed conventional numerical weather prediction (NWP) models in predicting Maliksi's initial development, though both struggled to capture the persistence of strong winds as the system moved inland. High‐resolution NWP simulations were employed to examine terrain‐induced wind variability around Hong Kong International Airport, revealing the mountain wake effect and uneven wind and turbulence distribution. These findings provide insights into the challenges of forecasting and monitoring such tropical cyclones, and highlight the need for enhanced observational platforms and forecasting tools along coastlines vulnerable to these systems.

Assimilation of ground-based GNSS data using a local ensemble Kalman filter

Tropical cyclones become increasingly nonlinear and dynamically unstable in high-resolution models. The initial conditions are typically sub-optimal, leaving scope to improve the accuracy of forecasts with improved data assimilation. Simultaneously, the lack of real ground-based GNSS observations over the ocean poses significant challenges when evaluating the assimilation results in oceanic regions. In this study, an Observation System Simulation Experiment is carried out based on a tropical cyclone case. Assimilation experiments using the WRF-PDAF framework are conducted. Conventional and GNSS observation operators are implemented. A diverse array of synthetic observations, encompassing temperature (T), wind components (U and V), precipitable water (PW), and zenith total delay (ZTD), are assimilated utilizing the Local Error-Subspace Transform Kalman filter (LESTKF). The findings highlight the improvement in forecast accuracy achieved through the assimilation process over the ocean. Multiple observation types further improve the forecast accuracy. The study underscores the crucial role of GNSS data assimilation techniques. The assimilation of GNSS data presents potential for advancing weather forecasting capabilities. Thus, the construction of ground-based GNSS observation stations over the ocean is promising.

Observations and Simulations of the Winds at a Bridge in Hong Kong during Two Tropical Cyclone Events in 2023

The impact of tropical cyclones on the operation of bridges and railways are mostly dependent on the forecast wind speed trend (upward, downward or staying steady) at present in Hong Kong. There are requests to forecast the exceedance of wind speed thresholds for such operations with a lead time of many hours ahead. This study considers the technical feasibility of forecasting the wind speed along a recently built bridge in Hong Kong with a coupled mesoscale numerical weather prediction (NWP)–computational fluid dynamics (CFD) model. This bridge features numerous anemometers where the coupled model can be verified. It is found that these two tropical cyclone cases are very challenging, especially in representing the wind structure of the cyclone because of its relatively small circulation. As such, the timing of the maximum wind could be 2 to 4 h earlier than the actual observation. The maximum wind speed from CFD modeling could be higher than that from the NWP model alone by 5 to 10 m/s, which shows that CFD modeling could add value in the forecasting of wind speed exceedance, although the maximum simulated value is still lower than the actual observation by as much as 10 m/s. As a result, while the general wind speed trend may be forecast, the exceedance of definite values of wind speed limits is still practically rather challenging, given the present two cases of tropical cyclones.

Tropical cyclone tracking from geostationary infrared satellite images using deep learning techniques

ABSTRACT Accurate tracking of tropical cyclones (TCs) can provide regions of interest for intelligent forecasting of TC tracks and intensity. There have been few studies on algorithms for automatic TC tracking. This study proposes an effective TC tracking method based on deep learning combined with infrared satellite images. The study first constructed a TC tracking dataset based on the infrared images of the China Fengyun-2D geostationary satellite covering six different TC intensity levels between 2009 and 2012. This included 47 complete cases (video sequences) of TCs from generation to extinction. Based on deep learning, the visual tracking algorithm SiamRPN was used as the model framework. Combining Bi-GRU and TC cloud spatiotemporal evolution characteristics to improve the performance of the SiamRPN network, the SiamTCNet target-tracking model was designed to track TCs automatically. Considering that the shape and scale of TC changes with time, a TC is regarded as a typical non-rigid object with obvious timing characteristics, so the first frame of a TC video sequence is combined with the satellite images of the first three frames of the current frame as inputs to the proposed SiamTCNet model, which then extracts the evolution of the TC’s spatial structure and its bidirectional temporal change information. The experimental results show that the TC tracking of the proposed model is a significant improvement over the original SiamRPN model.

Forecasting of tropical cyclones ASANI (2022) and MOCHA (2023) over the Bay of Bengal - real time challenges to forecasters

This study examines the track and intensity forecasts of two typical Bay of Bengal tropical cyclones (TC) ASANI and MOCHA. The analysis of various Numerical Weather Prediction (NWP) model forecasts [ECMWF (European Centre for Medium range Weather Forecast), NCEP (National Centers for Environmental Prediction), NCUM (National Centre for Medium Range Weather Forecast-Unified Model), IMD (India Meteorological Department), HWRF (Hurricane Weather Research and Forecasting)], MME (Multi-model Ensemble), SCIP (Statistical Cyclone Intensity Prediction) model, and OFCL (Official) forecasts shows that intensity forecasts of ASANI and track forecasts of MOCHA were reasonably good, but there were large errors and wide variation in track forecasts of ASANI and in intensity forecasts of MOCHA. Among all model forecasts, the track forecast errors of IMD model and MME were least in general for ASANI and MOCHA respectively. Also, the landfall point forecast errors of IMD were least for ASANI, and the MME and OFCL forecast errors were least for MOCHA. No model is found to be consistently better for landfall time forecast for ASANI, and the errors of ECMWF, IMD and HWRF were least and of same order for MOCHA. The intensity forecast errors of OFCL and SCIP were least for ASANI, and the forecast errors of HWRF, IMD, NCEP, SCIP and OFCL were comparable and least for MOCHA up to 48 h forecast and HWRF errors were least thereafter in general. The ECMWF model forecast errors for intensity were found to be highest for both the TCs. The results also show that although there is significant improvement of track forecasts and limited or no improvement of intensity forecast in previous decades but challenges still persists in real time forecasting of both track and intensity due to wide variation and inconsistency of model forecasts for different TC cases.

Application of Ensemble Algorithm Based on the Feature-Oriented Mean in Tropical Cyclone-Related Precipitation Forecasting

Tropical cyclones (TCs) are characterized by robust vortical motion and intense thermodynamic processes, often causing damage in coastal cities as they result in landfall. Accurately estimating the ensemble mean of TC precipitation is critical for forecasting and remains a foremost global challenge. In this study, we develop an ensemble algorithm based on the feature-oriented mean (FM) suitable for spatially discrete variables in precipitation ensembles. This method can adjust the locations of ensemble precipitation fields to reduce the location-related deviations among ensemble members, ultimately enhancing the ensemble mean forecast skill for TC precipitation. To evaluate the feasibility of the FM in TC precipitation ensemble forecasting, 18 landing TC cases in China from 2019 to 2021 were selected for validation. For precipitation forecasts of the landing TCs with a varying leading time, we conducted a comprehensive quantitative evaluation and comparison of the precipitation forecast skills of the FM and arithmetic mean (AM) algorithms. The results indicate that the field adjustment algorithm in the FM can effectively align with the TC precipitation structure and the location of the ensemble mean, reducing the spatial divergence among precipitation fields. The FM method demonstrates superior performance in the equitable threat score, probability of detection, and false alarm ratio compared with the AM, exhibiting an overall improvement of around 10%. Furthermore, the FM ensemble mean shows a higher pattern of the correlation coefficient with observations and has a smaller root mean square error than the AM ensemble mean, signifying that the FM method can better preserve the characteristics of the precipitation structure. Additionally, an object-based diagnostic evaluation method was used to verify forecast results, and the results suggest that the attribute distribution of FM forecast objects more closely resembles that of observed precipitation objects (including the area, longitudinal and latitudinal centroid locations, axis angle, and aspect ratio).

The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case

Profile measurements play a crucial role in operational weather forecasting across diverse scales and latitudes. However, assimilating tropospheric wind and temperature profiles remains a challenging endeavor. This study assesses the influence of profile measurements on numerical weather prediction (NWP) using the weather research and forecasting (WRF) model coupled to the parallel data assimilation framework (PDAF) system. Utilizing the local error-subspace transform Kalman filter (LESTKF), observational temperature and wind profiles generated by WRF are assimilated into an idealized tropical cyclone. The coupled WRF-PDAF system is adopted to carry out the twin experiments, which employ varying profile densities and localization distances. The results reveal that high-resolution observations yield significant forecast improvements compared to coarser-resolution data. A cost-effective balance between observation density and benefit is further explored through the idealized tropical cyclone case. According to diminishing marginal utility and increasing marginal costs, the optimal observation densities for U and V are found around 26–27%. This may be useful information to the meteorological agencies and researchers.

Impact of precipitation mass sinks on midlatitude storms in idealized simulations across a wide range of climates

Abstract. The combination of precipitation formation and fallout affects atmospheric flows through the release of latent heat and through the removal of mass from the atmosphere, but because the mass of water vapor is only a small fraction of the total mass of Earth's atmosphere, precipitation mass sinks are often neglected in theory and models. However, a small number of modeling studies suggest that water mass sources and sinks can intensify heavily precipitating weather systems. These studies point to a need to more systematically verify the impact of neglecting precipitation mass sinks, particularly for warmer and moister climates in which precipitation rates can be much higher. In this paper, we add precipitation mass sources and sinks to an idealized general circulation model and examine their effects on steady-state midlatitude storm track statistics. The model has several idealizations, including that all condensates immediately fall out of the atmosphere, and is run across a wide range of climates, including very warm climates. We find that modifying the model to include mass sources and sinks has no detectable effect on midlatitude variability or extremes, even in climates much warmer and moister than the modern. However, we find that a 10-fold exaggeration of mass sources and sinks is sufficient to produce more intense midlatitude weather extremes and increase surface pressure variance. This result is consistent with theoretical potential vorticity analysis, which suggests that the dynamical effects of mass sources and sinks are much smaller than the dynamical effects of accompanying latent heating unless mass sinks are artificially amplified by at least a factor of 10. Finally, we use simulations of “tropical cyclone worlds” to attempt to reconcile our results with earlier work showing stronger deepening in a simulation of a tropical cyclone case study when precipitation mass sinks were included. We demonstrate that abruptly “turning on” mass sources and sinks can lead to stronger transient deepening in some individual storms (consistent with results of past work) but weaker transient deepening in other storms, without modifying the steady-state statistics of storms in equilibrium with the large-scale environment (consistent with our other results). Our results provide a firmer foundation for using general circulation models that neglect moist mass sources and sinks in climate simulations, even in climates much warmer than today, while leaving open the possibility that their inclusion might lead to short-term improvements in forecast skill.

Using Ensembles to Analyze Predictability Links in the Tropical Cyclone Flood Forecast Chain

Abstract Fluvial flooding is a major cause of death and damages from tropical cyclones (TCs), so it is important to understand the predictability of river flooding in TC cases, and the potential of global ensemble flood forecast systems to inform warning and preparedness activities. This paper demonstrates a methodology using ensemble forecasts to follow predictability and uncertainty through the forecast chain in the Global Flood Awareness System (GloFAS) to explore the connections between the skill of the TC track, intensity, precipitation, and river discharge forecasts. Using the case of Hurricane Iota, which brought severe flooding to Central America in November 2020, we assess the performance of each ensemble member at each stage of the forecast, along with the overall spread and change between forecast runs, and analyze the connections between each forecast component. Strong relationships are found between track, precipitation, and river discharge skill. Changes in TC intensity skill only result in significant improvements in discharge skill in river catchments close to the landfall location that are impacted by the heavy rains around the eyewall. The rainfall from the wider storm circulation is crucial to flood impacts in most of the affected river basins, with a stronger relationship with the post-landfall track error rather than the precise landfall location. We recommend the wider application of this technique in TC cases to investigate how this cascade of predictability varies with different forecast and geographical contexts in order to help inform flood early warning in TCs. Significance Statement This study demonstrates a methodology to analyze the cascade of predictability and uncertainty through the various stages of the tropical cyclone (TC) flood forecasting chain, illustrating how it can provide useful information to modelers interested in optimizing flood forecast skill, and to those who prepare and communicate flood forecasts with stakeholders and end-users in TC cases. The results highlight the importance of improving verification of ensemble TC precipitation forecasts, and of focusing on more than just the category of the storm and landfall location when forecasting and communicating flood impacts in TC cases.

Different Initial Condition Perturbation Methods for Convection-Permitting Ensemble Forecasting over South China during the Rainy Season

Abstract In this study, downscaling, ensemble data assimilation, time lagging, and their combination were used to generate initial condition (IC) perturbations for 12-h convection-permitting ensemble forecasting for heavy-rainfall events over South China during the rainy season in 2013–20. These events were classified as weak- and strong-forcing cases based on synoptic-scale forcing during the presummer rainy season and as landfalling tropical cyclone (TC) cases. This study investigated the impacts of various IC perturbation methods on multiscale characteristics of perturbations and the forecast performance for both nonprecipitation and precipitation variables. These perturbation methods represented different source IC uncertainties and thus differed in multiscale characteristics of perturbations in vertical structures, horizontal distributions, and time evolution. The combination of various IC perturbation methods evidently increased perturbations or spreads of precipitation in both magnitude and location and thus improved the forecast-error estimation. Such an improvement was most and least evident for TC cases during the early and late forecasts, respectively, and was more evident for strong- than weak-forcing cases beyond 6 h. The combination of various IC perturbation methods generally improved both the ensemble-mean and probabilistic forecasts with case-dependent improvements. For heavy rainfall forecasting, 1–6-h improvements were most prominent for TC cases in terms of discrimination and accuracy, while 7–12-h improvements were least prominent for weak-forcing cases in terms of reliability and accuracy. In particular, the improvements in predicting weak-forcing cases increased with spatial errors. In contrast, for strong-forcing cases, the improvements were least and most prominent before and beyond 6 h, respectively. Significance Statement Precipitation forecasting for heavy-rainfall events over South China in the rainy season is still challenging due to large uncertainties. Convection-permitting ensemble forecasting is expected to address such uncertainties to improve forecasts of heavy rainfall. However, it is not yet clear how to optimally design convection-permitting ensembles by implementing perturbations in initial conditions (ICs). This study investigates the impacts of various IC perturbation methods on convection-permitting ensemble forecasting over South China in the rainy season. Various IC perturbation methods show discrepant multiscale characteristics of perturbations, which generally complement each other when these perturbations are combined. The added values of combining various IC perturbation methods in forecasting are confirmed for most variables. However, such values are case dependent, with the largest values for tropical cyclone cases during the early forecasts and for the presummer rainy season cases with strong synoptic-scale forcing during late forecasts. Thus, it is still essential to further improve the combination of various types of IC perturbation methods.

Tropical Cyclone Modeling With the Inclusion of Wave‐Coupled Processes: Sea Spray and Wave Turbulence

AbstractWaves critically modulate the air‐sea fluxes, and upper‐ocean thermodynamics in a Tropical Cyclone (TC) system. This study improves the modeling of TC intensification by incorporating non‐breaking wave‐induced turbulence and sea spray from breaking waves into an atmosphere‐ocean‐wave coupled model. Notably, wind forecast error decreased by around 10% prior to TCs' peak intensity. The positive feedback of sea spray along with compensatory negative feedback from non‐breaking waves, overall enhanced TCs' intensity. These breaking and non‐breaking wave‐coupled processes consistently cool sea surface temperature, resulting in improvement of the modeled SST. Observed improvements in full‐year TC cases ranging from Categories I to IV in this study suggest that an accurate characterization of ocean wave‐coupled processes is crucial for improving TCs' intensity forecasts and advancing our understanding of severe weather events in both, the atmosphere and ocean.

Performance of a hybrid gain ensemble data assimilation system based on the GRAPES_Meso model

In this paper, a hybrid gain data assimilation (HGDA) method was introduced into the mesoscale version of the Global/Regional Assimilation and Prediction System (GRAPES_Meso) model. To further improve the forecasting performance of the GRAPES_Meso model, a scheme combining the HGDA and multiscale incremental analysis update (IAU) methods was first proposed in this work. To evaluate the performance of the HGDA method in the GRAPES_Meso model, different initialization schemes, including the GRAPES three-dimensional variational (3DVAR), HGDA and HGDA multiscale IAU schemes, were compared for a tropical cyclone (TC) case (Lupit, 2021). The results showed that the HGDA scheme outperformed the GRAPES-3DVAR scheme in forecasting the TC position and intensity. Using the optimal relaxation times, the HGDA multiscale IAU scheme attained the best performance in forecasting the TC position, intensity and precipitation. In addition, 3-month quasioperational comparative experiments were conducted with different assimilation schemes. Rainfall validation showed that the HGDA multiscale IAU method yielded a higher threat score (TS) and equitable threat score (ETS) and a lower bias score than did the 3DVAR multiscale IAU method. Especially in the first 18 h of the forecast, the HGDA scheme could increase the TS and ETS values by 11% and 6%, respectively, compared to the control and 3DVAR experiments. The results demonstrated that the HGDA method resulted in better prediction performance of the GRAPES_Meso model than did the GRAPES-3DVAR method.

Urbanization Impacts on Tropical Cyclone Rainfall Extremes‐Inferences From Observations and Convection‐Permitting Model Experiments Over South China

AbstractHow urbanization affects tropical cyclone (TC) rainfall is inferred from station observations over the Great Bay Area (GBA, located on the South China coast) and numerical model experiments. Observations from 41 TCs indicate that surface wind is noticeably weaker in urban compared to rural stations during TC passages, while the urban heat island effect is considerably suppressed. Extreme (99th percentile of) hourly rainfall for these TC events during 2008–2017 is more intense over urban compared to rural stations. For eight selected TC cases, dynamical downscaling was carried out using the convection‐resolving Weather Research and Forecasting model, each with three parallel experiments: “Nourban” in which the urban area was replaced by cropland; “AH0” (“AH300”) in which the diurnal maximum anthropogenic heat (AH) was set to 0 (300 W/m2) in city locations. Both AH0 and AH300 show a significant increase in urban hourly rainfall intensity and probability in all ranges (most obvious for heavy rainfall >40 mm/hr), over the GBA mega‐city. Further diagnosis indicates increased moisture flux convergence in urban areas over a 2‐day period during the landfall, contributes to increased rainfall, likely induced by the surface roughness. The influence of AH, however, is found to be insignificant. The increase in accumulated rainfall due to urbanization is proportional to the storm residence time over the city, implying greater rainfall exacerbation for slower or larger TCs. Overall, urbanization intensifies extreme TC rainfall over the coastal GBA mega‐city mainly due to surface frictional convergence, with stronger intensification for storms residing longer over the city.

A review of recent research progress on the effect of external influences on tropical cyclone intensity change

Over the past four years, significant research has advanced our understanding of how external factors influence tropical cyclone (TC) intensity changes. Research on air-sea interactions shows that increasing the moisture disequilibrium is a very effective way to increase surface heat fluxes and that ocean salinity-stratification plays a non-negligible part in TC intensity change. Vertical wind shear from the environment induces vortex misalignment, which controls the onset of significant TC intensification. Blocking due to upper-level outflow from TCs can reduce the magnitude of vertical wind shear, making for TC intensification. Enhanced TC-trough interactions are vital for rapid intensification in some TC cases because of strengthened warm air advection, but upper-level troughs are found to limit TC intensification in other cases due to dry midlevel air intrusions and increased shear. Aerosol effects on TCs can be divided into direct effects involving aerosol-radiation interactions and indirect effects involving aerosol-cloud interactions. The radiation absorption by the aerosols can change the temperature profile and affect outer rainbands through changes in stability and microphysics. Sea spray and sea salt aerosols are more important in the inner region, where the aerosols increase precipitation and latent heating, promoting more intensification. For landfalling TCs, the intensity decay is initially more sensitive to surface roughness than soil moisture, and the subsequent decay is mainly due to the rapid reduction in surface moisture fluxes. These new insights further sharpen our understanding of the mechanisms by which external factors influence TC intensity changes.

Assessing the benefit of variational quality control for assimilating Aeolus Mie and Rayleigh wind profiles in NOAA's global forecast system during tropical cyclones

AbstractIn this article, we show a “proof‐of‐concept” study to assess the utility of a variational quality control algorithm in increasing the number of assimilated Aeolus Mie‐cloudy and Rayleigh‐clear winds in National Oceanic and Atmospheric Administration (NOAA)'s global data assimilation and forecast system. The National Centers for Environmental Prediction (NCEP) Variational Quality Control (NCEP‐VQC) algorithm was tuned and applied during the minimization process. This type of quality control uses optimal control theory principles to treat outliers in the probability density function (PDF) of observational departure statistics, assuming that the observation errors follow a family of logistic distributions. In the case of Aeolus Mie‐cloudy and Rayleigh‐clear winds, the NCEP‐VQC algorithm permitted the relaxation of the gross error and one of the recommended ESA quality controls (reject Rayleigh‐clear observations below 850 hPa), assigned adaptive observation weights ranging from 0 to 1, and led to an increase in the number of retained Aeolus observations for the calculation of global analyses, which in turn improved the verification statistics on analyzed tropical storms This article discusses the advantage of implementing the NCEP‐VQC algorithm in the Aeolus data assimilation, the benefits of retaining more wind profiles that contribute to the analysis calculation, and shows improvements in the initialization and short‐term forecasts on several tropical cyclone cases.

Development and validation of a parametric tropical cyclone wave height prediction model

A parametric model for computationally efficient wave height estimation within tropical cyclone conditions is developed. An updated wind vortex model is used to force a moving grid version of the WAVEWATCH III spectral model, to generate a large synthetic tropical cyclone wave field database (approximately 400 simulations). This extensive database is used to develop the parametric model which accounts for the key variables defining the magnitude and spatial distribution of waves within tropical cyclones. The model uses JONSWAP-type scaling and accounts for the effects of the moving vortex of the tropical cyclone in defining the wave field. A total of 28 buoy observations together with satellite altimeters measurements during the passage of 17 tropical cyclones (hurricanes) in the North Atlantic Ocean and Gulf of Mexico were used to validate the predicted wave heights. The resulting model is computationally efficient, making it ideal for engineering applications which require simulation of numerous tropical cyclone cases. Despite some limitations, it is a useful tool for the prediction of wave heights under extreme tropical cyclone forcing.

Validasi Data Model Prediksi Curah Hujan Satelit GPM, GSMaP, dan CHIRPS Selama Periode Siklon Tropis Seroja 2021 di Provinsi Nusa Tenggara Timur

Rainfall is also one of the meteorological parameters that are very influential in life. Measurements of this rainfall can also vary both from direct observations and remote sensing results from satellites. But often, data from satellites is not accurate for the conditions that occur in an area. The purpose of this study is to test the accuracy of product data from various GPM, GSMaP, and CHIRPS satellites with observational data using the Seroja tropical cyclone case study that will occur in 2021 in East Nusa Tenggara Province. Based on the results of the analysis, it can be seen that there are variations in various assessments, including RMSE, correlation, and contingency table analysis. In terms of correlation, it shows that the lowest correlation is in the Ruteng area based on data from the GSMaP satellite, and the highest correlation is in the Larantuka area based on data from the GSMaP satellite. Meanwhile, the satellite data that has the smallest error value is from the CHIRPS satellite data in the Maumere area.

Estimating tropical cyclone intensity using dynamic balance convolutional neural network from satellite imagery

Accurate estimation of tropical cyclone (TC) intensity helps to understand the evolution of TCs throughout their life cycle and plays an essential role in mitigating TC impact. Although TC intensity estimation methods based on deep learning have made significant progress, the existing techniques do not apply good methods to overcome the intensity overestimation and underestimation problems caused by the unbalanced distribution of TC data. Therefore, we propose a dynamic balance convolutional neural network to overcome these issues. The model consists of two branches, one branch is the learning of the raw data, and the other is the learning of strong (weak) TCs that account for a few data samples. Finally, the model is dynamically adjusted by adaptive trade-off parameters, gradually from the learning of the raw data to the learning of strong (weak) TCs, thus reducing errors in underestimation (overestimation) of strong (weak) TCs. Furthermore, an attention mechanism is employed to obtain the correlation between channels to improve the accuracy of TC intensity estimation further. We used globally 1285 TC cases from 2003–2016 to train the model and globally 94 TC cases from 2017 as independent test data. The results showed that the root-mean-square error of TC intensity estimation was 8.32 kt, 35% lower than that of the advanced Dvorak technique and 26% lower than that of the deep learning method visual geometry group (VGG). For a subset of 482 samples (from East Pacific and Atlantic) analyzed with reconnaissance observations, a root-mean-square intensity difference of 7.95 kt is achieved. Finally, we explored the model’s feature learning process and the contribution of each component of the satellite image to the TC intensity estimation through model visualization.

Hurricane season complexity: The case of North-Atlantic tropical cyclones

The forecast of tropical cyclone (TC) seasons remains an elusive subject of study. In order to characterize their complexity, the energy of North Atlantic (NA) TC seasons is studied in this research work. The time evolution of the accumulated cyclone energy probability distribution is analyzed using mobile windows to calculate the statistical parameters: mean, standard deviation, kurtosis and skewness. A nonstationary distribution with clear excess of extreme energetic seasons when compared to a Gaussian distribution was found for the most recent years. The data also show a clear correlation between variability and mean, i.e. more energetic periods are more variable. Finally, the dynamics is analyzed using several techniques such as the lag plot, Hurst exponent and fractal dimension. A stochastic but persistent behavior was found. It is finally concluded that the NA TC season energy seems to be increasing both in magnitude and variability. This helps to elucidate the possible effect of global warming into increasing hurricane hazards.