scholarly journals Rainfall estimation of landfalling tropical cyclones over Indian coasts through satellite imagery

MAUSAM ◽  
2021 ◽  
Vol 63 (2) ◽  
pp. 193-202
Author(s):  
CHARAN SINGH ◽  
SUNIT DAS ◽  
R.B. VERMA ◽  
B. L. VERMA ◽  
B.K. BANDYOPADHYAY
Keyword(s):  
Tropical Cyclones ◽  
Heavy Rain ◽  
Maximum Rainfall ◽  
Cyclonic Storm ◽  
Convective Clouds ◽  
Main Emphasis ◽  
Slow Moving ◽  
Strong Convection ◽  
Landfalling Tropical Cyclones ◽  
The Right

One of the most significant impacts of landfalling tropical cyclones is caused by the copiousrainfall associated with it. The main emphasis of present study is to provide some guidance to the operational forecastersfor indicating the possible rainfall over the areas likely to be affected by the cyclones after landfall. Study of 14 pastlandfalling cyclones reveals that the maximum rainfall occurred in the first forward quadrant of tropical cyclonemovement, followed by the second quadrant and the areas near the track of the cyclones. Isohyetal analysis of 24 hoursrainfall for each cyclone reveals that occurrence of heavy rainfall is generally confined up to 150 kms radius from thestorm centre and rainfall is found to generally extend up to 300 kms with gradual decrease in amount. The rainfallreceiving areas are mostly covered with convective clouds with cloud top temperatures of -80 to -60 ºC, prior to and afterthe landfall of the systems. In 93% of tropical cyclones out of the 14 cases studied, 70 % convection lay to the right of thetrack. To examine the rainfall asymmetry due to asymmetry in distribution of convection, cloud top temperatures derivedfrom satellite infrared imagery data have been taken as the proxy of strong convection. It is also revealed in the study thatthe slow moving tropical cyclones cause heavy rain rather than fast moving tropical cyclones. The Bay of Bengalcyclones which crossed coast as cyclonic storm and very severe cyclonic storm caused 71.4% rainfall within the range 0-10 cm, 22.8% rainfall in the range 11-20 cm and 4.3% rainfall within the range 21-30 cm in the area of radius of 300 kmsfrom the centre of the cyclonic storms. For the Arabian Sea tropical cyclones, in general, about 70% rainfall occurredwithin the range 16-25 cm in 24 hours.

Investigation of PR and TMI Version 6 and Version 7 Rainfall Algorithms in Landfalling Tropical Cyclones Relative to the NEXRAD Stage-IV Multisensor Precipitation Estimate Dataset

2013 ◽  
Vol 52 (12) ◽  
pp. 2809-2827 ◽  
Author(s):  
Joseph P. Zagrodnik ◽  
Haiyan Jiang
Keyword(s):  
Tropical Cyclones ◽  
Stage Iv ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Light Rain ◽  
Hourly Rainfall ◽  
Wide Range ◽  
Landfalling Tropical Cyclones ◽  
Rain Rates ◽  
Rainfall Estimates

AbstractRainfall estimates from versions 6 (V6) and 7 (V7) of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) 2A25 and Microwave Imager (TMI) 2A12 algorithms are compared relative to the Next Generation Weather Radar (NEXRAD) Multisensor Precipitation Estimate stage-IV hourly rainfall product. The dataset consists of 252 TRMM overpasses of tropical cyclones from 2002 to 2010 within a 230-km range of southeastern U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) sites. All rainfall estimates are averaged to a uniform 1/7° square grid. The grid boxes are also divided by their TMI surface designation (land, ocean, or coast). A detailed statistical analysis is undertaken to determine how changes to the TRMM rainfall algorithms in the latest version (V7) are influencing the rainfall retrievals relative to ground reference data. Version 7 of the PR 2A25 is the best-performing algorithm over all three surface types. Over ocean, TMI 2A12 V7 is improved relative to V6 at high rain rates. At low rain rates, the new ocean TMI V7 probability-of-rain parameter creates ambiguity in differentiating light rain (≤0.5 mm h−1) and nonraining areas. Over land, TMI V7 underestimates stage IV more than V6 does at a wide range of rain rates, resulting in an increased negative bias. Both versions of the TMI coastal algorithm are also negatively biased at both moderate and heavy rain rates. Some of the TMI biases can be explained by uncertain relationships between rain rate and 85-GHz ice scattering.

Validation Schemes for Tropical Cyclone Quantitative Precipitation Forecasts: Evaluation of Operational Models for U.S. Landfalling Cases

2007 ◽  
Vol 22 (4) ◽  
pp. 726-746 ◽  
Author(s):  
Timothy Marchok ◽  
Robert Rogers ◽  
Robert Tuleya
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Forecast Error ◽  
Heavy Rain ◽  
Track Forecast ◽  
The Core ◽  
Quantitative Precipitation Forecasts ◽  
Operational Models ◽  
Landfalling Tropical Cyclones ◽  
The Impact

Abstract A scheme for validating quantitative precipitation forecasts (QPFs) for landfalling tropical cyclones is developed and presented here. This scheme takes advantage of the unique characteristics of tropical cyclone rainfall by evaluating the skill of rainfall forecasts in three attributes: the ability to match observed rainfall patterns, the ability to match the mean value and volume of observed rainfall, and the ability to produce the extreme amounts often observed in tropical cyclones. For some of these characteristics, track-relative analyses are employed that help to reduce the impact of model track forecast error on QPF skill. These characteristics are evaluated for storm-total rainfall forecasts of all U.S. landfalling tropical cyclones from 1998 to 2004 by the NCEP operational models, that is, the Global Forecast System (GFS), the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model, and the North American Mesoscale (NAM) model, as well as the benchmark Rainfall Climatology and Persistence (R-CLIPER) model. Compared to R-CLIPER, all of the numerical models showed comparable or greater skill for all of the attributes. The GFS performed the best of all of the models for each of the categories. The GFDL had a bias of predicting too much heavy rain, especially in the core of the tropical cyclones, while the NAM predicted too little of the heavy rain. The R-CLIPER performed well near the track of the core, but it predicted much too little rain at large distances from the track. Whereas a primary determinant of tropical cyclone QPF errors is track forecast error, possible physical causes of track-relative differences lie with the physical parameterizations and initialization schemes for each of the models. This validation scheme can be used to identify model limitations and biases and guide future efforts toward model development and improvement.

scholarly journals Probability Distribution and Characterization of Daily Precipitation Related to Tropical Cyclones over the Korean Peninsula

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1214
Author(s):  
Angelika L. Alcantara ◽  
Kuk-Hyun Ahn
Keyword(s):  
Tropical Cyclones ◽  
Probability Distributions ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
Gamma Distributions ◽  
Pearson Type ◽  
Landfalling Tropical Cyclones ◽  
Annual Maximum Rainfall ◽  
Pearson Type Iii Distribution

Rainfall events are known to be driven by various synoptic disturbances or dominant processes in the atmosphere. In spite of the diverse atmospheric contributions, the assumption of homogeneity is commonly adopted when a hydrological frequency analysis is conducted. This study examines how the dominant processes, particularly the landfalling tropical cyclones (TCs) and non-TC events, have various effects to the characteristics of rainfall in South Korea. With rainfall data from the fifty-nine weather stations spread across the country, the multiple contributions of the TC and non-TC rainfall to the relative amount of rainfall, duration, intensity and maximum rainfall, on a seasonal and monthly scale, are first explored in this study. For the second objective, suitable probability distributions for the TC and non-TC time series are identified potentially for a synthetic analysis. Our results indicate that TCs cause a heterogeneous spatial distribution in the rainfall characteristics over the gauge networks particularly in the southern and eastern coastal areas. Some gauges in these areas attribute a significant portion of their amount and annual maximum rainfall to landfalling TCs. The results also show that the Pearson Type III distribution best represents the non-TC wet-day series, while the TC wet-day series can be represented by various distributions including the Weibull and Gamma distributions. From the analysis, we present how the characteristics of TCs differ from non-TCs with the emphasis on the need to consider their individual effects when conducting synthetic analyses.

scholarly journals Sea Surface Salinity Response to Tropical Cyclones Based on Satellite Observations

2021 ◽  
Vol 13 (3) ◽  
pp. 420
Author(s):  
Jingru Sun ◽  
Gabriel Vecchi ◽  
Brian Soden
Keyword(s):  
Tropical Cyclones ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Translation Speed ◽  
Salinity Stratification ◽  
Left Hand ◽  
In Situ Data ◽  
Salinity Response ◽  
The Right

Multi-year records of satellite remote sensing of sea surface salinity (SSS) provide an opportunity to investigate the climatological characteristics of the SSS response to tropical cyclones (TCs). In this study, the influence of TC winds, rainfall and preexisting ocean stratification on SSS evolution is examined with multiple satellite-based and in-situ data. Global storm-centered composites indicate that TCs act to initially freshen the ocean surface (due to precipitation), and subsequently salinify the surface, largely through vertical ocean processes (mixing and upwelling), although regional hydrography can lead to local departure from this behavior. On average, on the day a TC passes, a strong SSS decrease is observed. The fresh anomaly is subsequently replaced by a net surface salinification, which persists for weeks. This salinification is larger on the right (left)-hand side of the storm motion in the Northern (Southern) Hemisphere, consistent with the location of stronger turbulent mixing. The influence of TC intensity and translation speed on the ocean response is also examined. Despite having greater precipitation, stronger TCs tend to produce longer-lasting, stronger and deeper salinification especially on the right-hand side of the storm motion. Faster moving TCs are found to have slightly weaker freshening with larger area coverage during the passage, but comparable salinification after the passage. The ocean haline response in four basins with different climatological salinity stratification reveals a significant impact of vertical stratification on the salinity response during and after the passage of TCs.

scholarly journals A Potential Risk Index Dataset for Landfalling Tropical Cyclones over the Chinese Mainland (PRITC dataset V1.0)

2021 ◽  
Vol 38 (10) ◽  
pp. 1791-1802
Author(s):  
Peiyan Chen ◽  
Hui Yu ◽  
Kevin K. W. Cheung ◽  
Jiajie Xin ◽  
Yi Lu
Keyword(s):  
Tropical Cyclones ◽  
Potential Risk ◽  
Risk Index ◽  
Chinese Mainland ◽  
Current Version ◽  
Term Trend ◽  
The Chinese Mainland ◽  
Landfalling Tropical Cyclones ◽  
Long Term Trend

AbstractA dataset entitled “A potential risk index dataset for landfalling tropical cyclones over the Chinese mainland” (PRITC dataset V1.0) is described in this paper, as are some basic statistical analyses. Estimating the severity of the impacts of tropical cyclones (TCs) that make landfall on the Chinese mainland based on observations from 1401 meteorological stations was proposed in a previous study, including an index combining TC-induced precipitation and wind (IPWT) and further information, such as the corresponding category level (CAT_IPWT), an index of TC-induced wind (IWT), and an index of TC-induced precipitation (IPT). The current version of the dataset includes TCs that made landfall from 1949–2018; the dataset will be extended each year. Long-term trend analyses demonstrate that the severity of the TC impacts on the Chinese mainland have increased, as embodied by the annual mean IPWT values, and increases in TCinduced precipitation are the main contributor to this increase. TC Winnie (1997) and TC Bilis (2006) were the two TCs with the highest IPWT and IPT values, respectively. The PRITC V1.0 dataset was developed based on the China Meteorological Administration’s tropical cyclone database and can serve as a bridge between TC hazards and their social and economic impacts.

Compound Events of Tropical Cyclone and Flooding in India

2021 ◽  
Author(s):  
Akshay Rajeev ◽  
Vimal Mishra
Keyword(s):  
Soil Moisture ◽  
Tropical Cyclones ◽  
Heavy Rain ◽  
Disaster Mitigation ◽  
Infiltration Capacity ◽  
Monsoon Period ◽  
Antecedent Soil Moisture ◽  
Vic Model ◽  
Compound Events ◽  
Strong Winds

<p>India is severely affected by tropical cyclones (TC) each year, which generates intense rainfall and strong winds leading to flooding. Most of the TC induced floods have been attributed to heavy rain associated with them. Here we show that both rainfall and elevated antecedent soil moisture due to temporally compounding tropical cyclones cause floods in the major Indian basins. We assess each basin's response to observed TC events from 1980 to 2019 using the Variable Infiltration Capacity (VIC) model. The VIC model was calibrated (R2 > 0.5) and evaluated against observed hourly streamflow for major river basins in India. We find that rainfall due to TC does not result in floods in the basin, even for rainfall intensities similar to the monsoon period. However, TCs produce floods in the basins, when antecedent soil moisture was high. Our findings have implications for the understanding of TC induced floods, which is crucial for disaster mitigation and management.</p>

scholarly journals On the Relationship between Intensity and Rainfall Distribution in Tropical Cyclones Making Landfall over China

2017 ◽  
Vol 56 (10) ◽  
pp. 2883-2901 ◽  
Author(s):  
Zifeng Yu ◽  
Yuqing Wang ◽  
Haiming Xu ◽  
Noel Davidson ◽  
Yandie Chen ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
Rain Rate ◽  
Vertical Wind Shear ◽  
Intensity Change ◽  
Total Rainfall ◽  
Rainfall Distribution ◽  
Rain Area ◽  
Maximum Rainfall ◽  
Rain Rates ◽  
The Relationship

AbstractTRMM satellite 3B42 rainfall estimates for 133 landfalling tropical cyclones (TCs) over China during 2001–15 are used to examine the relationship between TC intensity and rainfall distribution. The rain rate of each TC is decomposed into axisymmetric and asymmetric components. The results reveal that, on average, axisymmetric rainfall is closely related to TC intensity. Stronger TCs have higher averaged peak axisymmetric rain rates, more averaged total rain, larger averaged rain areas, higher averaged rain rates, higher averaged amplitudes of the axisymmetric rainfall, and lower amplitudes of wavenumbers 1–4 relative to the total rainfall. Among different TC intensity change categories, rapidly decaying TCs show the most rapid decrease in both the total rainfall and the axisymmetric rainfall relative to the total rain. However, the maximum total rain, maximum rain area, and maximum rain rate are not absolutely dependent on TC intensity, suggesting that stronger TCs do not have systematically higher maximum rain rates than weaker storms. Results also show that the translational speed of TCs has little effect on the asymmetric rainfall distribution in landfalling TCs. The maximum rainfall of both the weaker and stronger TCs is generally located downshear to downshear left. However, when environmental vertical wind shear (VWS) is less than 5 m s−1, the asymmetric rainfall maxima are more frequently located upshear and onshore, suggesting that in weak VWS environments the coastline could have a significant effect on the rainfall asymmetry in landfalling TCs.

scholarly journals Quantifying the Radiative Impact of Clouds on Tropopause Layer Cooling in Tropical Cyclones

2020 ◽  
Vol 33 (15) ◽  
pp. 5527-5542
Author(s):  
Louis Rivoire ◽  
Thomas Birner ◽  
John A. Knaff ◽  
Natalie Tourville
Keyword(s):  
Tropical Cyclones ◽  
Spatial Scales ◽  
Radiative Heating ◽  
Orthogonal Polarization ◽  
Heating Rates ◽  
Tropical Tropopause Layer ◽  
Convective Clouds ◽  
Tropical Tropopause ◽  
Convective Region ◽  
Order Of Magnitude

AbstractA ubiquitous cold signal near the tropopause, here called “tropopause layer cooling” (TLC), has been documented in deep convective regions such as tropical cyclones (TCs). Temperature retrievals from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) reveal cooling of order 0.1–1 K day−1 on spatial scales of order 1000 km above TCs. Data from the Cloud Profiling Radar (onboard CloudSat) and from the Cloud–Aerosol Lidar with Orthogonal Polarization [onboard the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)] are used to analyze cloud distributions associated with TCs. Evidence is found that convective clouds within TCs reach the upper part of the tropical tropopause layer (TTL) more frequently than do convective clouds outside TCs, raising the possibility that convective clouds within TCs and associated cirrus clouds modulate TLC. The contribution of clouds to radiative heating rates is then quantified using the CloudSat and CALIPSO datasets: in the lower TTL (below the tropopause), clouds produce longwave cooling of order 0.1–1 K day−1 inside the TC main convective region, and longwave warming of order 0.01–0.1 K day−1 outside; in the upper TTL (near and above the tropopause), clouds produce longwave cooling of the same order as TLC inside the TC main convective region, and up to one order of magnitude smaller outside. Considering that clouds also produce shortwave warming, it is suggested that cloud radiative effects inside and outside TCs only explain modest amounts of TLC while other processes must provide the remaining cooling.

Sign in / Sign up

Export Citation Format

Share Document