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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. [ABSTRACT FROM AUTHOR]

Seventeen years of 3‐hr, 0.25° resolution, precipitation data from the Tropical Rainfall Measurement Mission (TRMM) multi‐satellite precipitation analysis (TMPA) product are used to develop a global climatology of precipitation in tropical cyclones (TCs). Due to very large SDs for rainfall in each stratification by intensity class or cyclone basin, our methodology concentrates on frequency distributions and percentage representation in different rainfall rate categories. The stratifications reveal that the TC rainfall climatologies are dependent on three inter‐related factors: TC intensity, TC size and TC basin. The interdependence of the three is examined. The distributions of TC intensity classes in the different basins are not a significant contributor to the fact that certain basins (Northwest Pacific, North Atlantic) have higher TC rainfall rates than other basins (Northeast Pacific, South Indian). In contrast to this, the distributions of TC size classes between basins are a significant contributor to why some basins are wetter than others. A climatology is also presented of lifetime accumulated rainfall (LAR) in TCs. The record LAR belongs to hurricane Ivan in 2004, with 300 km3 of rain over the 0–350 km radius, and 432 km3 over 0–500 km. The largest LAR values occur almost exclusively in two cyclone basins: The Northwest Pacific and the North Atlantic. Not unexpectedly, LAR is determined primarily by TC duration, which accounts for around 70% of the variance. Examination of the full 17‐year dataset reveals a decreasing trend in both median and extreme TC rainfall rates in all basins except the Northeast Pacific. However, mechanisms responsible for this decrease are yet to be identified and may be primarily due to the sample size or data inhomogeneities. The difference between the trends and those expected from physical principles is a concern which we hope will be taken up by other investigators. [ABSTRACT FROM AUTHOR]

The characteristics of 24-h official forecast errors (OFEs) of tropical cyclone (TC) intensity are analyzed over the North Atlantic, east Pacific, and western North Pacific. The OFE is demonstrated to be strongly anticorrelated with TC intensity change with correlation coefficients of 20.77, 20.77, and 20.68 for the three basins, respectively. The 24-h intensity change in the official forecast closely follows a Gaussian distribution with a standard deviation only 2/3 of that in nature, suggesting the current official forecasts estimate fewer cases of large intensity change. The intensifying systems tend to produce negative errors (underforecast), while weakening systems have consistent positive errors (overforecast). This asymmetrical bias is larger for extreme intensity change, including rapid intensification (RI) and rapid weakening (RW). To understand this behavior, the errors are analyzed in a simple objective model, the trend-persistence model (TPM). The TPM exhibits the same error-intensity change correlation. In the TPM, the error can be understood as it is exactly inversely proportional to the finite difference form of the concavity or second derivative of the intensity-time curve. The occurrence of large negative (positive) errors indicates the intensity-time curve is concave upward (downward) in nature during the TC's rapid intensification (weakening) process. Thus, the fundamental feature of the OFE distribution is related to the shape of the intensity-time curve, governed by TC dynamics. All forecast systems have difficulty forecasting an accelerating rate of change, or a large second derivative of the intensity-time curve. TPM may also be useful as a baseline in evaluating the skill of official forecasts. According to this baseline, official forecasts are more skillful in RW than in RI. [ABSTRACT FROM AUTHOR]

The article examines the reasons behind the record dry spell in Singapore which lasted for 62 days from January to March 2014. Topics covered include southward contraction of the intertropical convergence zone, lack of evidence on the influence of climate change, physical mechanisms associated with the dry spell and the possibility that the dry spell was part of natural climate variability.

The response of sea surface temperature (SST) to tropical cyclones is studied using gridded SST data and global cyclone tracks from the period 1981-2008. A compositing approach is used whereby temperature time series before and after cyclone occurrence at individual cyclone track positions are averaged together. Results reveal a variability of several days in the time of maximum cooling with respect to cyclone passage, with the most common occurrence 1 day after cyclone passage. When compositing is carried out relative to the day of maximum cooling, the global average response to cyclone passage is a local minimum SST anomaly of −0.9°C. The recovery of the ocean to cyclone passage is generally quite rapid with 44%% of the data points recovering to climatological SST within 5 days, and 88%% of the data points recovering within 30 days. Although differences exist between the mean results from the separate tropical cyclone basins, they are in broad agreement with the global mean results. Storm intensity and translation speed affect both the size of the SST response and the recovery time. Cyclones occurring in the first half of the cyclone season disrupt the seasonal warming trend, which is not resumed until 20-30 days after cyclone passage. Conversely, cyclone occurrences in the later half of the season bring about a 0.5°C temperature drop from which the ocean does not recover due to the seasonal cooling cycle. [ABSTRACT FROM AUTHOR]

The analyzed value of sea surface temperature (SST) is examined for all global cases of tropical cyclone formation within 35° latitude of the equator over the period 1981-2008. It is found that 98.3%% of formations occur at SST values exceeding 25.5°C. This practical threshold is relatively insensitive to the exact value of maximum wind speed used to define formation. The threshold is sensitive, however, to short-term variations in SST during development. By expanding the time period such that thresholds are calculated based on the maximum SST during the 48-h period leading to genesis, it is found that 99.5%% of cyclone formations occur above 25.5°C. It is also found that tropical cyclones form over a narrow temperature range with 90.4%% forming over SSTs between 27.5° and 30.5°C when the 48-h period is considered. Without consideration of the 48-h period, an SST threshold of 25.5°C is representative with less than 2%% of formations occurring below this value. When the 48-h period is considered, 26.5°C is the equivalent threshold. The response to warming of the global oceans is also examined. Dividing the 27-yr dataset into an earlier versus a later 13.5-yr period, positive but small changes (++0.2°C) occur in the mean formation temperature. There is no detectable shift of the threshold temperature toward a higher value. [ABSTRACT FROM AUTHOR]

Various earlier studies have demonstrated that rainfall in the Maritime Continent–Indonesia region is strongly related to the El Niño–Southern Oscillation (ENSO) during the dry half of the year but has a very weak association with ENSO during the summer–wet season months. This relationship is investigated over a wider domain through the use of outgoing longwave radiation (OLR) data as a proxy for rainfall. Consistent with the hypothesis of Haylock and McBride, it is found that the large-scale structure of the low-order empirical orthogonal functions (EOFs) of OLR have a strong resemblance to the patterns of correlation between OLR and the Southern Oscillation index (SOI). This supports the hypothesis that the predictable component of rainfall is determined by the component that is spatially coherent, as quantified through EOF analysis. As was found earlier with rainfall, the region of largest correlation between interannual OLR anomalies and the SOI lies in the winter hemisphere. The predictable component of OLR (or rainfall) remains in the region of the Maritime Continent throughout the year and thus does not accompany the minimum OLR (maximum rainfall) during its annual interhemispheric progression as the major monsoon heat source. The sign of the OLR–SOI relationship is such that the Maritime Continent has increased rainfall during a La Niña or cold event. Patterns of correlation between sea surface temperature and the SOI show the existence of a region to the east of the Maritime Continent whereby sea surface temperature anomalies are positive during these (La Niña) conditions. This is in the sense of a direct relationship, that is, positive sea surface temperature anomalies corresponding to increased rainfall. The annual cycle of the sea surface temperature structure of ENSO is represented by the first EOF of the interannual sea surface temperature series for each separate calendar month. The region of the sea surface temperature anomaly giving the direct relationship with Maritime Continent rainfall is part of the “boomerang-shaped” pattern that lies between and has the opposite sign from the anomalies in the eastern-central Pacific and the Indian Oceans. Besides being a fundamental component of the large-scale sea surface temperature structure of ENSO, the boomerang pattern goes through an annual cycle such that it has maximum amplitude in the winter hemisphere. This suggests that interannual variations of Maritime Continent rainfall are in direct response to upstream sea surface temperature anomalies in the ENSO boomerang pattern. [ABSTRACT FROM AUTHOR]

Real-time gridded 24-h quantitative precipitation forecasts from seven operational NWP models are verified over the Australian continent. All forecasts have been mapped to a 1° latitude-longitude grid and have been verified against an operational daily rainfall analysis, mapped to the same grid. The verification focuses on two large subregions: the northern tropical monsoon regime and the southeastern subtropical regime. Statistics are presented of the bias score, probability of detection, and false alarm ratio for a range of rainfall threshold values. The basic measure of skill used in this study, however, is the Hanssen and Kuipers (HK) score and its two components: accuracy for events and accuracy for nonevents. For both regimes the operational models tend to overestimate rainfall in summer and to underestimate it in winter. In the southeastern region the models have HK scores ranging from 0.5 to 0.7, and easily outperform a forecast of persistence. Thus for the current operational NWP models, the 24-h rain forecasts can be considered quite skillful in the subtropics. On the other hand, model skill is quite low in the northern regime with HK values of only 0.2-0.6. During the summer wet season the low skill is associated with an inability to simulate the behavior of tropical convective rain systems. During the winter dry season, it is associated with a low probability of detection for the occasional rainfall event. Thus it could be said that models have no real skill at rainfall forecasts in this monsoonal wet season regime. Model skill falls dramatically for occurrence thresholds greater than 10 mm day&sup-1;. This implies that the models are much better at predicting the occurrence of rain than they are at predicting the magnitude and location of the peak values. [ABSTRACT FROM AUTHOR]

Analyzes the rawinsonde data from the Australian Monsoon Experiment to determine the manner in which the atmospheric stratification of density and moisture respond to large amounts of convective latent heat release. Goals of the study; Data and methodology of the study; Thermodynamic variables examined in the study; Discussion of the results.

Presents a simple dynamical theory for the planetary monsoon. Methodology used to develop the theory; Details on the experiments conducted with SST forcing; What is the theory presented here based on.

Provides information on a study of the empirical orthogonal function (EOF) analysis. Information on the data and methodology used to conduct the study; Results of the study; Discussion on the results of the study.

Proposes that large-scale convective instability represents a viable mechanism for the initial growth of either cloud clusters or tropical cyclones in nature. Governing equations; Convective instabilities; Alternative solutions.

The Dynamical–Statistical–Analog Ensemble Forecast for Landfalling Typhoon Daily Precipitation (DSAEF_LTP_D) model is introduced in this paper. To improve the DSAEF_LTP_D model's forecasting ability, tropical cyclone (TC) translation speed was introduced. Taking Supertyphoon Lekima (2019), which produced widespread heavy rainfall from 9 to 11 August 2019 as the target TC, two simulation experiments associated with the prediction of daily precipitation were conducted: the first involving the DSAEF_LTP_D model containing only the TC track (the actual trajectory of the TC center), named DSAEF_LTP_D-1; and the second containing both TC track and translation speed, named DSAEF_LTP_D-2. The results show the following: 1) With TC translation speed added into the model, the forecasting performance for heavy rainfall (24-h accumulated precipitation exceeding 50 and 100 mm) on 9 and 10 August improves, being able to successfully capture the center of heavy rainfall, but the forecasting performance is the same as DSAEF_LTP_D-1 on 11 August. 2) Compared with four numerical weather prediction (NWP) models (i.e., ECMWF, GFS, GRAPES, and SMS-WARMS), the TS100 + TS50 (the sum of TS values for predicting 24-h accumulated precipitation of ≥100 and ≥50 mm) of DSAEF_LTP_D-2 is comparable to the best performer of the NWP models (ECMWF) on 9 and 10 August, while the performance of DSAEF_LTP_D model for predicting heavy rainfall on 11 August is poor. 3) The newly added similarity regions make up for the deficiency that the similarity regions are narrower when the TC track is northward, which leads to DSAEF_LTP_D-2 having a better forecasting performance for heavy rainfall on 11 August, with the TS100 + TS50 increasing from 0.3021 to 0.4286, an increase of 41.87%. [ABSTRACT FROM AUTHOR]

The Dynamical-Statistical-Analog Ensemble Forecast model for landfalling tropical cyclones (TCs) precipitation (DSAEF_LTP) utilises an operational numerical weather prediction (NWP) model for the forecast track, while the precipitation forecast is obtained by finding analog cyclones, and making a precipitation forecast from an ensemble of the analogs. This study addresses TCs that occurred from 2004 to 2019 in Southeast China with 47 TCs as training samples and 18 TCs for independent forecast experiments. Experiments use four model versions. The control experiment DSAEF_LTP_1 includes three factors including TC track, landfall season, and TC intensity to determine analogs. Versions DSAEF_LTP_2, DSAEF_LTP_3, and DSAEF_LTP_4 respectively integrate improved similarity region, improved ensemble method, and improvements in both parameters. Results show that the DSAEF_LTP model with new values of similarity region and ensemble method (DSAEF_LTP_4) performs best in the simulation experiment, while the DSAEF_LTP model with new values only of ensemble method (DSAEF_LTP_3) performs best in the forecast experiment. The reason for the difference between simulation (training sample) and forecast (independent sample) may be that the proportion of TC with typical tracks (southeast to northwest movement or landfall over Southeast China) has changed significantly between samples. Forecast performance is compared with that of three global dynamical models (ECMWF, GRAPES, and GFS) and a regional dynamical model (SMS-WARMS). The DSAEF_LTP model performs better than the dynamical models and tends to produce more false alarms in accumulated forecast precipitation above 250 mm and 100 mm. Compared with TCs without heavy precipitation or typical tracks, TCs with these characteristics are better forecasted by the DSAEF_LTP model. [ABSTRACT FROM AUTHOR]

The number of tropical cyclones (TCs) in the Australian region exhibits a large variation between different ENSO regimes. While the difference in TC numbers and spatial distribution of genesis locations between the canonical El Niño and La Niña regimes is well known, the authors demonstrate that a statistically significant difference in TC numbers also exists between the recently identified negative-neutral and positive-neutral regimes. Compared to the negative-neutral and La Niña regimes, significantly fewer TCs form in the Australian region during the positive-neutral regime, particularly in the eastern subregion. This difference is attributed to concomitant changes in various large-scale environmental conditions such as sea level pressure, relative vorticity, vertical motion, and sea surface temperature. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) rainfall over the Australian continent is studied using observations from 41 TC seasons 1969/1970 to 2009/2010. A total of 318 storms, whose centers either crossed the coastline or were located within 500 km of the coast, are considered in this study. Mean seasonal (November to April) contributions by TCs to the total rainfall are largest along the northern coastline from 120°-150°E. However, the percentage contributions by TCs are greatest west of 125°E, with mean coastal values of 20-40% and inland values of approximately 20%. Further east, percentages near the coast are only around 10%, and even lower inland. Inland penetration by TC rainfall is generally greatest over western portions of the continent, associated with greater inland penetration of TC tracks. During the peak of the TC season (January to March), TCs contribute around 40% to the rainfall total of coastal regions west of 120°E, while during December, TCs contribute approximately 60-70% to the total rainfall west of 115°E. Rain from TCs varies sharply between TC seasons, with some longitude bands receiving no TC rain during some seasons. For the 110°-115°E longitude band the TC rain contribution is not consistent, varying interannually from 0 to 86%. This has an impact on water supplies, with storage dams falling to low levels during some years, while filling to capacity during TC-related flood events in other years. This underlines why it is important to understand TC rainfall characteristics over the Australian continent. [ABSTRACT FROM AUTHOR]

Abstract: This paper reviews the current understanding of the effect of climate change on extreme sea levels in the South Pacific region. This region contains many locations that are vulnerable to extreme sea levels in the current climate, and projections indicate that this vulnerability will increase in the future. The recent publication of authoritative statements on the relationship between global warming and global sea level rise, tropical cyclones and the El Niño-Southern Oscillation phenomenon has motivated this review. Confident predictions of global mean sea level rise are modified by regional differences in the steric (density-related) component of sea level rise and changing gravitational interactions between the ocean and the ice sheets which affect the regional distribution of the eustatic (mass-related) contribution to sea level rise. The most extreme sea levels in this region are generated by tropical cyclones. The intensity of the strongest tropical cyclones is likely to increase, but many climate models project a substantial decrease in tropical cyclone numbers in this region, which may lead to an overall decrease in the total number of intense tropical cyclones. This projection, however, needs to be better quantified using improved high-resolution climate model simulations of tropical cyclones. Future changes in ENSO may lead to large regional variations in tropical cyclone incidence and sea level rise, but these impacts are also not well constrained. While storm surges from tropical cyclones give the largest sea level extremes in the parts of this region where they occur, other more frequent high sea level events can arise from swell generated by distant storms. Changes in wave climate are projected for the tropical Pacific due to anthropogenically-forced changes in atmospheric circulation. Future changes in sea level extremes will be caused by a combination of changes in mean sea level, regional sea level trends, tropical cyclone incidence and wave climate. Recommendations are given for research to increase understanding of the response of these factors to climate change. Implications of the results for adaptation research are also discussed. [Copyright &y& Elsevier]

With the increasing incidence of heavy rainfall events, particularly over the monsoon regions, the highly dense populations are more vulnerable [1]. Despite the considerable efforts, NWP models still have very limited ability to forecast TC rainfall, especially for the extremely heavy rainfall and rainfall produced around TC landfalling. Built upon the important progresses in science and prediction of monsoon heavy rainfall, including heavy rainfall associated with tropical cyclones in the monsoon regions, and high-priority research topics proposed by the international monsoon science community, the following directions are recommended for future monsoon-heavy-rainfall research. More detailed studies are needed on how extreme monsoon rainfall will change as the climate warms, how the global warming, large-scale circulation [17], and local urban effects impact the heavy rainfall changes over the densely populated vulnerable urban areas, how anthropogenic and natural aerosols influence monsoon heavy rainfall under various synoptic conditions, what are the relationships of circulations and associated heavy rainfall among different monsoon regions (e.g., South Asia, East Asia, Australia), what are the roles played by land/sea-air interaction in modulating the monsoon circulation and rainfall. [Extracted from the article]

The article presents a brief career profile of William M. Gray, Who was emeritus professor of atmospheric science at Colorado State University (CSU) and the head of the Tropical Meteorology Project at CSU's Department of Atmospheric Sciences. Gray made extensive contributions to the study of tropical meteorology and tropical cyclones. Several key areas where Gray was a fundamental contributor are mentioned in this article.