Tropical Cyclone Precipitation Research Articles

Exposure to precipitation from tropical cyclones has increased over the continental United States from 1948 to 2019

AbstractExtreme precipitation from tropical cyclones can generate large-scale inland flooding and cause substantial damage. Here, we quantify spatiotemporal changes in population risk and exposure to tropical cyclone precipitation in the continental eastern United States over the period 1948–2019 using high-resolution in-situ precipitation observations. We find significant increases in the magnitude and likelihood of these extreme events due to increased rainfall rates and reduced translation speeds of tropical cyclones over land. We then develop a social exposure index to quantify exposure and risk of tropical cyclone precipitation as a function of both physical risk and socio-economic activities. Increased social exposure is primarily due to the increased risk of tropical cyclone precipitation, but there are regional differences. We identify exposure hotspots in the south-eastern United States, where rapid population growth and economic development amplify societal exposure to tropical cyclone hazards. Our multi-scale evaluation framework can help identify locations that should be targeted for mitigation and adaptation activities to increase their climate resilience.

Baldcypress false ring formation linked to summer hydroclimatic extremes in the southeastern United States

We describe the utility of false rings in Taxodium distichum (i.e. baldcypress) as a proxy for hydroclimatic extreme events in three different river basins (Pascagoula, Mobile, and Choctawhatchee) that discharge into the northern Gulf of Mexico. False rings occur as a result of a change in the environmental limiting resource for tree stem growth, and in T. distichum, false ring production is usually a result of increases in mid-growing season water availability. Our results show that false ring occurrence (from 1931 to 2018) is similar across sites but occur in different years, suggesting that false ring production is indicative of tree response to its local environment. False ring production in T. distichum has previously been correlated with summer streamflow, the season when tropical cyclone precipitation (TCP) is highest. To assess a stand-wide response, we define high false ring (HFR) years as all years when 20% of trees produced a false ring. We show total TCP in July is the best predictor for HFR years in T. distichum, and false ring production in smaller river basins captures local TCP better than larger river basins. Additionally, HFR years coincide with summers of anomalously high precipitation, anomalously low temperatures, and a positive phase of the North Atlantic Oscillation. 77% of HFR years occur in seasons when there is heavy tropical cyclone activity near sample sites, building a foundation to use false ring records as robust TCP proxies with hydroclimate reconstruction potential.

Does ERA5 Mark a New Era for Resolving the Tropical Cyclone Environment?

Abstract The synoptic environment around tropical cyclones plays a significant role in vortex evolution. To capture the environment, the operational and research communities calculate diagnostic quantities. To aid with applications and research, the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED) combines disparate data sources. A key part of TC PRIMED is the environmental context. Often, environmental diagnostics come from multiple sources. However, TC PRIMED uses the European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis (ERA5) product to provide a more complete representation of the storm environment from a single source. Reanalysis products usually poorly resolve tropical cyclones and their surrounding environment. To understand the uncertainty of large-scale diagnostics, ERA5 is compared to the Statistical Hurricane Intensity Prediction Scheme developmental dataset and the National Oceanic and Atmospheric Administration Gulfstream IV-SP dropwindsondes. This analysis highlights biases in the ERA5 environmental diagnostic quantities. Thermodynamic fields show the largest biases. The boundary layer exhibits a cold temperature bias that limits the amount of convective instability; also, the upper troposphere contains temperature biases and shows a high relative humidity bias. However, the upper-troposphere large-scale kinematic fields and derived metrics are low biased. In the lower troposphere, the temperature gradient and advection calculated from the thermal wind suggest that the low-level wind field is not representative of the observed distribution. These diagnostics comparisons provide uncertainty so that users of TC PRIMED can assess the implications for specific research and operational applications.

Stochastic tropical cyclone precipitation field generation

AbstractTropical cyclones are important drivers of coastal flooding which have severe negative public safety and economic consequences. Due to the rare occurrence of such events, high spatial and temporal resolution historical storm precipitation data are limited in availability. This article introduces a statistical tropical cyclone space‐time precipitation generator given limited information from storm track datasets. Given a handful of predictor variables that are common in either historical or simulated storm track ensembles such as pressure deficit at the storm's center, radius of maximal winds, storm center and direction, and distance to coast, the proposed stochastic model generates space‐time fields of quantitative precipitation over the study domain. Statistically novel aspects include that the model is developed in Lagrangian coordinates with respect to the dynamic storm center that uses ideas from low‐rank representations along with circular process models. The model is trained on a set of tropical cyclone data from an advanced weather forecasting model over the Gulf of Mexico and southern United States, and is validated by cross‐validation. Results show the model appropriately captures spatial asymmetry of cyclone precipitation patterns, total precipitation as well as the local distribution of precipitation at a set of case study locations along the coast. We additionally compare our model against a widely‐used statistical forecast, and illustrate that our approach better captures uncertainty, as well as storm characteristics such as asymmetry.

Spatial and temporal heterogeneity of tropical cyclone precipitation over China from 1959 to 2018

Tropical cyclone precipitation (TCP) can cause serious floods and urban waterlogs as well as cause various secondary disasters, such as landslides and debris flows, which negatively affect human lives and the sustainable development of the economy. This study applied the prewhitening Mann-Kendall test, empirical orthogonal function, and continuous wavelet transform to investigate the long-term trend, spatiotemporal pattern, and periodicity of TCP at monthly, interannual, and interdecadal timescales over China. The recurrence risks of extreme TCP were analyzed using the return period estimation model. The results showed that 1) TCP displayed a significant increasing trend, especially in eastern China, inland areas, and Guangxi Province. The TCP periodicities were 2.5 and 4.9 years across all of China. However, TCP cycles had large discrepancies in the time and frequency domains in different subregions. 2) Monthly TCP demonstrated a decreasing trend in May and an increasing trend from June to October in all of China. The TCP in northeastern China and southern China tended to decrease in July and August, respectively. 3) TCP demonstrated a decreasing tendency from the 1960s–1980s followed by a rebounding trend in the 1990s–2010s. In addition, TCP showed a dipole mode in the 1970s and 2000s. 4) There was an increasing recurrence risk of extreme TCP in the Yangtze River Delta, Hainan Province, southeastern Guangxi Province, and southwestern Guangdong Province. It is therefore necessary to improve forecasting of extreme TCP events to improve risk management and prevention capacity of natural disasters, especially in regions with high population and economy exposure.

Examinations on global changes in the total and spatial extent of tropical cyclone precipitation relating to rapid intensification

Moderate tropical cyclone precipitation (TCP) is of great significance to regional water resource supply, while extreme TCP could bring significant adverse impacts to ecosystems and society, especially when tropical cyclones intensify rapidly, leaving no time to take prevention actions. Whether rapid intensification (RI) of tropical cyclones (TCs) affect TCP in both land and ocean remains unknown. Here we classified TCs which have undergone increases in the maximum sustained wind speed (MSW) by at least 30 knots within 24-h into RI category. We analyzed TCP totals provided by daily precipitation from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and spatial extent from 1983 to 2019 in the four categories based on regions (land and ocean) and RI-experiencing characteristics (with- and without-RI). TCP totals and spatial extent was identified by the restricted moving neighborhood method and semi-variogram framework. The results show that TCP totals on the ocean are larger than those on the land, since RI-experiencing TCP are higher than TCP without RI-experiencing, although RI processes tend to increase TCP totals in the extremely high percentiles more significantly on land than ocean. The effects of RI processes on global TCP spatial extent are not statistically significant, and there are no definite relations between MSW and TCP spatial extent. The four regions of the Northeast Pacific Ocean (EP), South Pacific Ocean (SP), Northwest Pacific Ocean (WP), and North Atlantic Ocean (NA) show increases in regional mean and extreme TCP totals. The highest increase in the extreme TCP totals (0.37 mm day−1 year−1) over the NA region occurs in the RI_ocean category, which is 2.6 times the average positive enhancement trend across all basins. The increasing rate of the extreme TCP totals over the WP region is higher in track points with RI-experiencing than without RI-experiencing. The category of RI_land over the regions of NA, EP and SP shows a significant increase in the regional mean TCP spatial extent.

Parameterized Tropical Cyclone Precipitation Model for Catastrophe Risk Assessment in China

Abstract Inland flooding and mudslides from tropical cyclone (TC) rainstorms are among the most destructive natural hazards in China, resulting in considerable direct economic losses and large numbers of fatalities. In this paper, a TC precipitation model (TCPM) is improved by incorporating the effects of complex terrain through a set of new parameters (e.g., slope, roughness, and attenuation distance) for a more accurate assessment of TC rainfall hazards in China. Moreover, by introducing parameterized spiral rainbands, the model could more accurately capture the intensity of extreme precipitation. The model comprehensively considers dynamic and thermodynamic precipitation factors and is adept at capturing the climate characteristics of TC precipitation and the probability distribution of extreme TC precipitation in China. The model is verified by providing two comparisons. One is analysis including detailed results of three typical TC cases, and the other uses empirical cumulative distribution functions for extreme observations and simulations of historical landfalling TCs in China during the period 1960–2018. The comparisons reveal that the TCPM shows impressive performance for strong TCs with heavy precipitation within 200–300 km of the TC center. Moreover, both the modeled extreme hourly and total TC precipitation probability distributions are consistent with the observations. However, the model needs to be further improved for TCs with dispersive or long-distance precipitation. Significance Statement In this paper, an optimized and physics-based model for the simulation of tropical cyclone precipitation is described and used to estimate the risk of TC rainfall hazards in China. The work is innovative in that it considers the effect of complex terrain from three perspectives, including slope, roughness, and attenuation distance. The simulations demonstrated that the model is adept at capturing the main climate characteristics of TC precipitation and the probability distribution of extreme TC precipitation in China, which is simple to run several hundred thousand times, with bright application prospects in catastrophe risk assessment.

Climatology of Different Classifications of Tropical Cyclones Landfalling in Guangdong Province of China during 1951–2020

The climatology of different classifications (based on the intensity at the landfall time) of tropical cyclones (TCs) making landfall in Guangdong Province of China during 1951–2020 (70 years) is investigated using the best track data from the China Meteorological Administration and ERA5 reanalysis data. There were 234 TCs making landfall in Guangdong Province, with more severe tropical storms (STSs, 30.8%) and typhoons (TYs, 27.3%), and less tropical depressions (TDs, 19.7%) and tropical storms (TSs, 22.2%) during the past 70 years. The frequency of the landfall TCs had a significant interannual oscillation of 2–5 years. Landfall TCs generated over the western North Pacific (WNP) were usually more and stronger than those generated over the South China Sea (SCS). The TCs generated over the WNP had longer lifetime duration and shorter on-land duration than those generated over the SCS. TCs making landfall in western Guangdong were the most, followed by central Guangdong and eastern Guangdong. The composite analysis using TC-relative coordinates indicated that the precipitation of different classifications of TCs making landfall in Guangdong Province was asymmetric, which was stronger in the south of the TC center. The position of the maximum precipitation showed a cyclonic rotation around the TC center with increasing TC intensity. Generally, the vertical velocity, moisture flux, warm core, and vertical wind shear enhanced with the increasing landfall TC intensity. The vertical velocity and moisture flux of different classifications of TCs also showed an asymmetric structure related to the distribution of TC precipitation. TSs, STSs, and TYs had a double warm-core configuration. The precipitation of the TDs and TSs usually occurred over the down-shear of average vertical wind shear, those of the STSs and TYs over the left-of-shear.

Unveiling four decades of intensifying precipitation from tropical cyclones using satellite measurements

Increases in precipitation rates and volumes from tropical cyclones (TCs) caused by anthropogenic warming are predicted by climate modeling studies and have been identified in several high intensity storms occurring over the last half decade. However, it has been difficult to detect historical trends in TC precipitation at time scales long enough to overcome natural climate variability because of limitations in existing precipitation observations. We introduce an experimental global high-resolution climate data record of precipitation produced using infrared satellite imagery and corrected at the monthly scale by a gauge-derived product that shows generally good performance during two hurricane case studies but estimates higher mean precipitation rates in the tropics than the evaluation datasets. General increases in mean and extreme rainfall rates during the study period of 1980–2019 are identified, culminating in a 12–18%/40-year increase in global rainfall rates. Overall, all basins have experienced intensification in precipitation rates. Increases in rainfall rates have boosted the mean precipitation volume of global TCs by 7–15%/year, with the starkest rises seen in the North Atlantic, South Indian, and South Pacific basins (maximum 59–64% over 40 years). In terms of inland rainfall totals, year-by-year trends are generally positive due to increasing TC frequency, slower decay over land, and more intense rainfall, with an alarming increase of 81–85% seen from the strongest global TCs. As the global trend in precipitation rates follows expectations from warming sea surface temperatures (11.1%/°C), we hypothesize that the observed trends could be a result of anthropogenic warming creating greater concentrations of water vapor in the atmosphere, though retrospective studies of TC dynamics over the period are needed to confirm.

US Gulf Coast tropical cyclone precipitation influenced by volcanism and the North Atlantic subtropical high

Understanding the response of tropical cyclone precipitation to ongoing climate change is essential to determine associated flood risk. However, instrumental records are short-term and fail to capture the full range of variability in seasonal totals of precipitation from tropical cyclones. Here we present a 473-year-long tree-ring proxy record comprised of longleaf pine from excavated coffins, a historical house, remnant stumps, and living trees in southern Mississippi, USA. We use cross-dating dendrochronological analyses calibrated with instrumental records to reconstruct tropical cyclone precipitation stretching back to 1540 CE. We compare this record to potential climatic controls of interannual and multidecadal tropical cyclone precipitation variability along the Gulf Coast. We find that tropical cyclone precipitation declined significantly in the two years following large Northern Hemisphere volcanic eruptions and is influenced by the behavior of the North Atlantic subtropical high-pressure system. Additionally, we suggest that tropical cyclone precipitation variability is significantly, albeit weakly, related to Atlantic multidecadal variability. Finally, we suggest that we need to establish a network for reconstructing precipitation from tropical cyclones in the Southeast USA if we want to capture regional tropical cyclone behavior and associated flood risks.

Prediction of tropical cyclone precipitation over China by the DSAEF_LTP model

AbstractThe Dynamical–Statistical–Analog Ensemble Forecast for Landfalling Typhoon Precipitation (DSAEF_LTP) model was used to forecast the accumulated precipitation of 92 tropical cyclone cases from 2004 to 2018 in China. Through the basic experiment of 76 training samples from 2004 to 2016, the best forecasting scheme of the DSAEF_LTP model in China was selected. The forecasting performance of the DSAEF_LTP model in 16 independent samples (2017–2018) with this scheme was found to be comparable to four numerical weather prediction (NWP) models (European Centre for Medium‐Range Weather Forecasts, Global Forecast System, Global/Regional Assimilation and Prediction Enhanced System, and Shanghai Meteorological Service WRF ADAS Real‐Time Modeling System). Specifically, the prediction ability of the DSAEF_LTP model ranked third among the five models. Meanwhile, the prediction performance of the DSAEF_LTP model in different areas demonstrated clear regional characteristics with the single best forecasting scheme for the whole of China, providing a skillful forecast in the coastal areas of southeast and south China. To improve the performance of the DSAEF_LTP model, China was divided into three subregions (south, southeast, and northern China), and subregion experiments were conducted. After regionalization, the forecasting performance of the DSAEF_LTP model improved to some extent. In general, the TSsum (sum of the threat score with precipitation over 250 mm and 100 mm) of the DSAEF_LTP model in the subregion experiments was 0.236, which represented an increase of 19.8% from the value of 0.197 in the basic experiment. Compared with the four NWP models, the TSsum of the DSAEF_LTP model was higher than that of the Global Forecast System (0.225), ranking it second only to the Shanghai Meteorological Service WRF ADAS Real‐Time Modeling System (0.248). In addition, the bias scores of different models showed that the DSAEF_LTP model had a tendency of false alarms, whereas the NWP models tended to misses.

A spatio-temporal graph-guided convolutional LSTM for tropical cyclones precipitation nowcasting

Real-time and accurate Tropical Cyclone (TC) precipitation nowcasting plays an important role in disaster prevention and mitigation. The strong ability of deep learning has been proved in many precipitation nowcasting studies. However, few deep learning solutions focus on the precipitation caused by TC, which has complex patterns and is potential for disasters. In this paper, we develop a novel framework for TC precipitation nowcasting. To effectively utilize the TC information, we design a multi-source information fusion mechanism between precipitation and TC. More importantly, we propose a Graph-guided Spatio-Temporal Module (GSTM), which designs a construction scheme of dynamic reasoning graphs to capture the spatio-temporal relationship between intra-frame and inter-frame. It is also the first attempt to introduce the graph concept into precipitation nowcasting. We utilize the TC and precipitation data from 2017 to 2019 in the Northwest Pacific Ocean and divide the data into training (3524 sequences), validation (352 sequences), and test set (705 sequences) at a ratio of 10:1:2. Extensive experiments demonstrate that compared with the Convolution LSTM model, our model obtains a significant improvement by 2.74%, 4.24%, 6.29%, 5.78% in F1-score when the precipitation intensity is 5, 10, 20, 30 mm/h, respectively. Meanwhile, we select three typical TC precipitation cases for visual analysis, which proves that our model has excellent perception ability in the details of TC spatial distribution and can deal with different TC scenarios effectively. The effectiveness of our model indicates that the method based on deep learning has strong applicability and development prospects in the TC precipitation nowcasting, providing infinite possibilities and potential for this field.

Contribution of Winter SSTA in the Tropical Eastern Pacific to Changes of Tropical Cyclone Precipitation over Southeast China

Contribution of Winter SSTA in the Tropical Eastern Pacific to Changes of Tropical Cyclone Precipitation over Southeast China

Isotopic variability in tropical cyclone precipitation is controlled by Rayleigh distillation and cloud microphysics

Tropical cyclones produce rainfall with extremely negative isotope values (δ18O and δ2H), but the controls on isotopic fractionation during tropical cyclones are poorly understood. Here we studied the isotopic composition of rainfall at sites across central Texas during Hurricane Harvey (2017) to better understand these processes. Rainfall δ18O trend towards more negative values as a result of Rayleigh distillation of precipitation-generating airmasses as they travel towards the center of the storm. Superimposed on these gradual changes are abrupt isotopic shifts with exceptionally low deuterium excess values. These appear to be controlled by microphysical processes associated with the passage of spiral rainbands over the sampling locations. Isotope-enabled climate modeling suggests that it may be possible to identify the signature of tropical cyclones from annually resolved isotopic proxy records, but will depend on the size of the storm and the proximity of the site to the core of the storm system.

Characteristics and Causes of Extremely Persistent Heavy Rainfall of Tropical Cyclone In-Fa (2021)

The characteristics and causes of the persistent precipitation of an extreme-rainfall tropical cyclone (TC), In-Fa, in 2021 are studied. It is shown that the extremity of In-Fa’s precipitation was mainly due to two aspects: massively accumulated quantity and an extremely long impact time. The heavy precipitation in Zhejiang resulted from the accumulation of very long but moderate precipitation, while that in Jiangsu resulted from the coaction of both long duration and strong intensity. The weak steering flow brought about by the large scale environment and the long continuation of the TC’s circulation were the two most important background conditions for the extremely long duration of heavy rainfall in Zhejiang and Jiangsu. Continuous energy input through the transportation of warmer and wetter air resulted in the persistence of In-Fa’s circulation. The terrain effect under the continuous northeasterly/northerly airflow caused by the slow movement of the TC was the major influencing factor for the extreme precipitation in Zhejiang. The convergence brought about by the TC’s vortex during landfall played another role in Zhejiang’s heavy rainfall. On the one hand, the terrain led to the development of low-level vertical circulation, resulting in convergence and updraft in the windward side of Siming Mountain; on the other hand, the terrain also provided for the vertical transport of water vapor. The main factors for the extreme precipitation in Jiangsu were the long continuation of low-level jets that caused persistent low-level convergence and the development of a mesoscale rainband. The convergence zone was located in the western front and evolved with the changing of the jets. The high-energy/high-humidity conditions and their consistency with the location of the convergence provided favorable conditions for the triggering and development of convection.

Changes in Typhoon Regional Heavy Precipitation Events over China from 1960 to 2018

In earlier studies, objective techniques have been used to determine the contribution of tropical cyclones to precipitation (TCP) in a region, where the Tropical cyclone Precipitation Event (TPE) and the Regional Heavy Precipitation Events (RHPEs) are defined and investigated. In this study, TPE and RHPEs are combined to determine the Typhoon Regional Heavy Precipitation Events (TRHPEs), which is employed to evaluate the contribution of tropical cyclones to regional extreme precipitation events. Based on the Objective Identification Technique for Regional Extreme Events (OITREE) and the Objective Synoptic Analysis Technique (OSAT) to define TPE, temporal and spatial overlap indices are developed to identify the combined events as TRHPE. With daily precipitation data and TC best-track data over the western North Pacific from 1960 to 2018, 86 TRHPEs have been identified. TRHPEs contribute as much as 20% of the RHPEs, but 100% of events with extreme individual precipitation intensities. The major TRHPEs continued for approximately a week after tropical cyclone landfall, indicating a role of post landfall precipitation. The frequency and extreme intensity of TRHPEs display increasing trends, consistent with an observed positive trend in the mean intensity of TPEs as measured by the number of daily station precipitation observations exceeding 100 mm and 250 mm. More frequent landfalling Southeast and South China TCs induced more serious impacts in coastal areas in the Southeast and the South during 1990–2018 than 1960–89. The roles of cyclone translation speed and “shifts” in cyclone tracks are examined as possible explanations for the temporal trends.

Where does the moisture for North Atlantic tropical cyclones come from?

Abstract In this study, we identified the origin of the moisture associated with the tropical cyclones (TCs) precipitation in the North Atlantic Ocean basin during their three well-differentiated life stages between 1980 and 2018. The HURDAT2 database was used to detect the location of 598 TCs during their genesis, maximum intensification peak, and dissipation phases. The global outputs of the Lagrangian FLEXPART model were then used to determine the moisture sources. Using a K-means cluster analysis technique, seven different regions were identified as the most common locations for the genesis and maximum intensity of the TC phases, while six regions were found for the dissipation points. Our results showed that the origin of moisture precipitating was not entirely local over the areas of TC occurrence. The North Atlantic Ocean to the north of the Inter Tropical Convergence Zone at 10°N (NATL) -especially from tropical latitudes, the Caribbean Sea and the Gulf of Mexico- provides most of the moisture for TCs (∼87%). The Atlantic Ocean basin southward the ITCZ (SATL) played a non-negligible role (∼11%), with its contribution being most pronounced during the TC genesis phase, while the eastern tropical Pacific Ocean made the smallest contribution (∼2%). The moisture supported by TCs varied depending on their category, being higher for hurricanes than for major hurricanes or tropical storms. Additionally, the approach permitted to estimate the mean residence time of the water vapour uptake that produce the precipitation during TC activity, which ranged between 2.6 and 2.9 days.

Global Tropical Cyclone Precipitation Estimation via a Multitask Convolutional Neural Network Based on HURSAT-B1 Data

Fast and accurate global tropical cyclone (TC) precipitation estimation from satellite observations is still a challenging issue. In this article, we propose an effective model based on a multitask convolutional neural network (CNN) to estimate near-real-time global TC precipitation from HURSAT-B1 data. Our network mainly consists of three modules: the feature extraction module, the wind grade classification module, and the precipitation estimation module. The first module aims at extracting the spatial features of satellite imageries, the second module focuses on classifying the wind grades of the satellite imageries into six categories that are used to assist in estimating TC precipitation, and the third module is to estimate TC precipitation. To evaluate the effectiveness of our proposed model, we compare it with multiple linear regression (MLR) and random forest (RF) models based on integrated multisatellite retrievals for the global precipitation measurement (GPM) mission (IMERG). Besides, four typical TC events are selected to specifically analyze the temporal and spatial distribution of TC precipitation estimation. Experimental results show that the probability of detection and accuracy achieved by our proposed model are 0.68 and 0.81, while the correlation coefficient (CC) and MSE are 0.61 and 7.80, respectively. In terms of the four TC events, our proposed model obtains a more consistent and continuous spatial distribution of precipitation than MLR and RF. More importantly, our proposed model can achieve high spatiotemporal results, which has the potential to serve as an operational algorithm for global TC precipitation estimation.

Simulating Tropical Cyclone Passive Microwave Rainfall Imagery Using Infrared Imagery via Generative Adversarial Networks

Tropical cyclones (TCs) generally carry large amounts of water vapor and can cause large-scale extreme rainfall. Passive microwave (PMW) rainfall (PMR) estimation of TC with high spatial and temporal resolution is crucial for disaster warning of TC, but remains a challenging problem due to low temporal resolution of microwave sensors. This study attempts to solve this problem by directly predicting PMW rainfall images (PMRIs) from satellite infrared (IR) images of TC. We develop a generative adversarial network (GAN) to simulate PMRI using IR images and establish the mapping relationship between TC cloud-top brightness temperature and PMR, and the algorithm is named tropical cyclone rainfall (TCR)-GAN. Meanwhile, a new dataset that is available as a benchmark, Dataset of TC IR-to-Rainfall Prediction (TCIRRP), was established, which is expected to advance the development of artificial intelligence in this direction. The experimental results show that the algorithm can effectively extract key features from IR. The end-to-end deep learning approach shows potential as a technique that can be applied globally and provides a new perspective TC precipitation prediction via satellite, which is expected to provide important insights for real-time visualization of TC rainfall globally in operations.

Tropical Cyclone Precipitation Response to Surface Warming in Aquaplanet Simulations With Uniform Thermal Forcing

AbstractWhile many modeling studies have attempted to estimate how tropical cyclone (TC) precipitation is impacted by climate change, the multitude of analysis techniques and methodologies have resulted in varying conclusions. Simplified models may be able to help overcome this problem. Radiative‐convective equilibrium (RCE) model simulations have been used in various configurations to study fundamental aspects of Earth's climate. While many RCE modeling studies have focused on TC genesis, intensification, and size, limited work has been done using RCE to study TC precipitation. In this study, the response of TC precipitation to sea surface temperature (SST) change is analyzed in global Community Atmosphere Model (CAM) aquaplanet simulations run with Radiative‐Convective Equilibrium Model Intercomparison Project protocols, with the addition of planetary rotation. We expect that the insight gained about how TC precipitation responds to SST warming will help predict how TCs in the real world respond to climate change. In the CAM RCE simulations, the warmer SST simulations have less TCs on average, but the TCs tend to be larger in outer size and more intense. As simulation SST increases, more extreme precipitation rates occur within TCs, and more of the TC precipitation comes from these extreme rates. For extreme (99th percentile) TC precipitation, SST, and TC intensity increases dominate the 8.6% per K increase, while TC outer size changes have little impact. For accumulated TC precipitation, SST, and TC intensity contributions are still the majority, but TC outer size changes also contribute to the 6.6% per K increase.