scholarly journals Seasonal Tropical Cyclone Rain Volumes over Australia

2013 ◽  
Vol 26 (16) ◽  
pp. 5958-5964 ◽  
Author(s):  
Richard A. Dare
Keyword(s):  
Tropical Cyclone ◽  
Interannual Variability ◽  
Tropical Cyclones ◽  
Correlation Coefficients ◽  
Rainfall Data ◽  
Valuable Resource ◽  
Land Area ◽  
The Mean ◽  
Two Parameters

Abstract For a continent as dry as Australia, where water is a valuable resource, it is important to understand the sources of rainfall. The volume of water contributed by tropical cyclones (TCs) during the November–April season is investigated using 42 seasons of TC and rainfall data. The seasonal total TC rain volume (SRV) ranges from a minimum of 34.2 km3 in 1987/88 to a maximum of 564.4 km3 in 2000/01, with a long-term mean of 198.6 ± 107.4 km3. In terms of mean percentage, TCs contribute 7.6% to the seasonal total rain volume over Australia. The number of landfalling TCs and the number of TCs that individually produce more than the mean individual TC rain volume (25.7 km3) during a season are significant influences on the SRV. The TCs passing near the coast without landfalling have little impact on the SRV. The two parameters that correlate best with SRV are the total time spent over land by TCs during a season and the total land area covered by TCs during a season (correlation coefficients of 0.79 and 0.84, respectively). Although the highest SRVs occur almost exclusively during La Niña and neutral seasons, there is a mixture of ENSO seasons corresponding to the lowest SRVs. In general, the large interannual variability, even within a particular ENSO class, indicates that caution should be used when attempting to relate SRV to the phase of ENSO.

scholarly journals Validity and reliability of an FFQ for use with adolescents in Ho Chi Minh City, Vietnam

2009 ◽  
Vol 13 (3) ◽  
pp. 368-375 ◽  
Author(s):  
Tang K Hong ◽  
Michael J Dibley ◽  
David Sibbritt
Keyword(s):  
Nutrient Intake ◽  
Reliability And Validity ◽  
Correlation Coefficients ◽  
Weighted Kappa ◽  
Ho Chi Minh City ◽  
Kappa Statistics ◽  
Validity And Reliability ◽  
Nutrient Intakes ◽  
The Mean

AbstractObjectiveThe present study evaluates the reliability and validity of an FFQ designed for use with adolescents in urban Vietnam.DesignA cohort study was conducted between December 2003 and June 2004. The FFQ was administered three times over a 6-month period (FFQ 1–3) and nutrient intakes were compared to those obtained from four 24 h recalls collected over the same period (24 h recalls 1–4) using crude, energy-adjusted and de-attenuated correlation coefficients. The level of agreement between the two measurements was also evaluated with Bland–Altman analysis. The percentage of nutrient intakes classified within one quintile, as well as quadratic-weighted kappa statistics, were calculated.SettingHo Chi Minh City, Vietnam.SubjectsA total of 180 students were recruited in three junior high schools.ResultsCoefficients ranged from 0·22 for retinol to 0·78 for fibre for short-term reliability, and from 0·30 for retinol to 0·81 for zinc for long-term reliability. Coefficients for nutrient intakes between the mean of the three FFQ and mean of four 24 h recalls were mostly around 0·40, but higher for energy-adjusted nutrients. After allowing for within-person variation, the mean coefficient was 0·52 for macronutrients and 0·46 for micronutrients. There were a relatively high proportion of nutrient intakes classified within one quintile and a small number grossly misclassified. Kappa values shows ‘fair’ to ‘good’ agreement for all food/nutrient categories, while the Bland–Altman plots indicated that the FFQ is accurate in assessing nutrient intake at a group level.ConclusionsThis newly developed FFQ is a valid tool for measuring nutrient intake in adolescents in urban Vietnam.

Characteristics of Landfalling Tropical Cyclones in the United States and Mexico: Climatology and Interannual Variability

2005 ◽  
Vol 18 (8) ◽  
pp. 1247-1262 ◽  
Author(s):  
Joshua Larson ◽  
Yaping Zhou ◽  
R. Wayne Higgins
Keyword(s):  
United States ◽  
Tropical Cyclone ◽  
Climate Variability ◽  
Interannual Variability ◽  
Atmospheric Circulation ◽  
Tropical Cyclones ◽  
El Niño ◽  
El Nino ◽  
The Arctic ◽  
Landfalling Tropical Cyclones

Abstract The climatology and interannual variability of landfalling tropical cyclones and their impacts on precipitation in the continental United States and Mexico are examined. The analysis is based on National Hurricane Center 6-hourly tropical cyclone track data for the Atlantic and eastern Pacific basins and gridded daily U.S. precipitation data for the period August–October 1950–98. Geographic maps of total tropical cyclone strike days, and the mean and maximum percentage of precipitation due to tropical cyclones, are examined by month. To make the procedures objective, it is assumed that precipitation is symmetric about the storm’s center. While this introduces some uncertainty in the analysis, sensitivity tests show that this assumption is reasonable for precipitation within 5° of the circulation center. The relationship between landfalling tropical cyclones and two leading patterns of interannual climate variability—El Niño–Southern Oscillation (ENSO) and the Arctic Oscillation (AO)—are then examined. Relationships between tropical cyclone frequency and intensity and composites of 200-hPa geopotential height and wind shear anomalies are also examined as a function of ENSO phase and AO phase using classifications devised at the Climate Prediction Center. The data show that tropical cyclone activity in the Atlantic basin is modulated on both seasonal and intraseasonal time scales by the AO and ENSO and that impact of the two modes of climate variability is greater together than apart. This suggests that, during La Niña conditions, atmospheric circulation is more conducive to activity in the main development region during AO-positive conditions than during AO-negative ones and that, during El Niño conditions, atmospheric circulation appears even less conducive to tropical cyclone development during the negative phase of the AO than during the positive phase.

scholarly journals Climatology and Forest Phenology during 1984-2013 around Western Lake Superior, USA.

2020 ◽  
Author(s):  
Matthew Garcia
Keyword(s):  
Remote Sensing ◽  
Interannual Variability ◽  
Land Surface ◽  
Vegetation Index ◽  
Lake Superior ◽  
Growing Season ◽  
Western Lake ◽  
Phenological Cycle ◽  
The Mean

Landsat has a history of use in the diagnosis of land surface phenology, vegetation disturbance, and their impacts on numerous forest biological processes. Studies have connected remote sensing-based phenology to surface climatological patterns, often using average temperatures and derived growing degree day accumulations. I present a detailed examination of remotely sensed forest phenology in the region of western Lake Superior, USA, based on a comprehensive climatological assessment and 1984-2013 Landsat imagery. I use this climatology to explain both the mean annual land surface phenological cycle and its interannual variability in temperate mixed forests. I assess long-term climatological means, trends, and interannual variability for the study period using available weather station data, focusing on numerous basic and derived climate indicators: seasonal and annual temperature and precipitation, the traditionally defined frost-free growing season, and a newly defined metric of the climatological growing season: the warm-season plateau in accumulated chilling days. Results indicate +0.56°C regional warming during the 30-year study period, with cooler springs (–1.26°C) and significant autumn warming (+1.54°C). The duration of the climatological growing season has increased +0.27 days/y, extending primarily into autumn. Summer precipitation in my study area declined by an average –0.34 cm/y, potentially leading to moisture stress that can impair vegetation carbon uptake rates and can render the forest more vulnerable to disturbance. Many changes in temperature, precipitation, and climatological growing season are most prominent in locations where Lake Superior exerts a strong hydroclimatological influence, especially the Minnesota shoreline and in forest areas downwind (southeast) of the lake. I then develop and demonstrate a novel phenoclimatological modeling method, applied over five Landsat footprints across my study area, that combines (1) diagnosis of the mean phenological cycle from remote sensing observations with (2) a partial-least-squares regression (PLSR) approach to modeling vegetation index residuals using these numerous meteorological and climatological observations. While the mean phenology often used to inform land surface models in meteorological and climate modeling systems may explain 50-70% of the observed phenological variability, this mixed modeling approach can explain more than 90% of the variability in phenological observations based on long-term Landsat records for this region.

Passage of Tropical Storm Allison (2001) over southeast Texas recorded in δ18O values of Ostracoda

2008 ◽  
Vol 70 (2) ◽  
pp. 339-342 ◽  
Author(s):  
James R. Lawrence ◽  
Kiseong Hyeong ◽  
Rosalie F. Maddocks ◽  
Kwang-Sik Lee
Keyword(s):  
Tropical Cyclone ◽  
Oxygen Isotope ◽  
Tropical Cyclones ◽  
Tropical Cyclone Activity ◽  
Tropical Storm ◽  
Low Oxygen ◽  
Cyclone Activity ◽  
Southeast Texas

AbstractFreshwater Ostracoda collected in ephemeral pond-waters derived from Tropical Storm Allison (2001, Texas) recorded the unusually low oxygen-isotope values of that storm. Therefore, the potential clearly exists, in locations where tropical cyclones make landfall, to obtain a long-term record of tropical cyclone activity from fossil ostracode calcite.

scholarly journals The Interannual Variability of Tropical Cyclones

2007 ◽  
Vol 135 (10) ◽  
pp. 3587-3598 ◽  
Author(s):  
William M. Frank ◽  
George S. Young
Keyword(s):  
Tropical Cyclone ◽  
Interannual Variability ◽  
Tropical Cyclones ◽  
Large Scale ◽  
Storm Activity ◽  
Modes Of Variability ◽  
The North ◽  
Temporal And Spatial ◽  
Using Data ◽  
The North Atlantic Oscillation

Abstract This paper examines the interannual variability of tropical cyclones in each of the earth’s cyclone basins using data from 1985 to 2003. The data are first analyzed using a Monte Carlo technique to investigate the long-standing myth that the global number of tropical cyclones is less variable than would be expected from examination of the variability in each basin. This belief is found to be false. Variations in the global number of all tropical cyclones are indistinguishable from those that would be expected if each basin was examined independently of the others. Furthermore, the global number of the most intense storms (Saffir–Simpson categories 4–5) is actually more variable than would be expected because of an observed tendency for storm activity to be correlated between basins, and this raises important questions as to how and why these correlations arise. Interbasin correlations and factor analysis of patterns of tropical cyclone activity reveal that there are several significant modes of variability. The largest three factors together explain about 70% of the variance, and each of these factors shows significant correlation with ENSO, the North Atlantic Oscillation (NAO), or both, with ENSO producing the largest effects. The results suggest that patterns of tropical cyclone variability are strongly affected by large-scale modes of interannual variability. The temporal and spatial variations in storm activity are quite different for weaker tropical cyclones (tropical storm through category 2 strength) than for stronger storms (categories 3–5). The stronger storms tend to show stronger interbasin correlations and stronger relationships to ENSO and the NAO than do the weaker storms. This suggests that the factors that control tropical cyclone formation differ in important ways from those that ultimately determine storm intensity.

Exploring Inter-Basin Correlations of Tropical Cyclones and Tropical Cyclone Losses

2020 ◽  
Author(s):  
John Hillier ◽  
James Done ◽  
Hamish Steptoe
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Natural Hazards ◽  
Poisson Regression ◽  
Correlation Coefficients ◽  
Financial Loss ◽  
Financial Model ◽  
Marginal Distributions ◽  
Anthropogenic Processes ◽  
Dependency Structures

<p>Tropical cyclones (TCs) are one of the most costly natural hazards on Earth, and there is a desire to mitigate this risk. It is securely established that TC activity relates to ENSO in all oceanic basins (e.g. N. Atlantic). However, when a recent multi-basin review of correlation coefficients to ENSO was applied to a financial model of losses related to TCs, there appeared to be no significant inter-relationship between the losses between regions (e.g. US, China). It is therefore of interest to examine the chain of environmental and anthropogenic processes from TC genesis to financial loss to examine how correlations degrade. A number of hypotheses are statistically investigated, primarily using Spearman's coefficient and ranks to decouple dependency structures from the marginal distributions, but also Poisson regression.</p>

scholarly journals A Technique to Determine the Radius of Maximum Wind of a Tropical Cyclone

2008 ◽  
Vol 23 (5) ◽  
pp. 1007-1015 ◽  
Author(s):  
France Lajoie ◽  
Kevin Walsh
Keyword(s):  
High Resolution ◽  
Tropical Cyclone ◽  
Simple Technique ◽  
Cloud Data ◽  
Maximum Wind ◽  
Wind Structure ◽  
The Mean ◽  
The Difference ◽  
Two Parameters ◽  
National Hurricane Center

Abstract A simple technique is developed that enables the radius of maximum wind of a tropical cyclone to be estimated from satellite cloud data. It is based on the characteristic cloud and wind structure of the eyewall of a tropical cyclone, after the method developed by Jorgensen more than two decades ago. The radius of maximum wind is shown to be partly dependent on the radius of the eye and partly on the distance from the center to the top of the most developed cumulonimbus nearest to the cyclone center. The technique proposed here involves the analysis of high-resolution IR and microwave satellite imagery to determine these two parameters. To test the technique, the derived radius of maximum wind was compared with high-resolution wind analyses compiled by the U.S. National Hurricane Center and the Atlantic Oceanographic and Meteorological Laboratory. The mean difference between the calculated radius of maximum wind and that determined from observations is 2.8 km. Of the 45 cases considered, the difference in 50% of the cases was ≤2 km, for 33% it was between 3 and 4 km, and for 17% it was ≥5 km, with only two large differences of 8.7 and 10 km.

scholarly journals ANALISIS PENGARUH SIKLON TROPIS GILLIAN TERHADAP CURAH HUJAN DI WILAYAH RIAU DAN SEKITARNYA

2014 ◽  
Vol 15 (2) ◽  
pp. 75
Author(s):  
Rini Mariana Sibarani
Keyword(s):  
Tropical Cyclone ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Tropical Cyclones ◽  
Low Pressure ◽  
Rainfall Data ◽  
Sea Surface ◽  
Pressure Center ◽  
Tropical Cyclone Formation ◽  
Sumatra Island

IntisariSiklon Tropis merupakan gangguan meteorologi yang disebabkan karena adanya pusat tekanan rendah di lautan. Syarat terbentuknya siklon tropis di daerah perairan adalah suhu muka laut (sst) cukup panas (T > 260C). Salah satu Siklon Tropis yang terjadi di perairan Indinesia adalah Siklon Tropis Gillian. Siklon Tropis ini terjadi di Selatan Perairan Indonesia, yang berlangsung selama 5 hari dari tanggal 21 Maret – 25 Maret 2014. Siklon Tropis Gillian ini mempengaruhi kondisi curah hujan di wilayah Indonesia bagian Utara, tepatnya di Pulau Sumatera Bagian Utara. Selama terjadinya Siklon Tropis Gillian mengakibatkan pengurangan Curah hujan di wilayah tersebut, terutama di daerah Provinsi Riau. Dari data yang diperoleh baik dari data Penakar POS METEOROLOGI maupun dari data Satelit TRMM Jaxa mulai tanggal 23 Maret – 27 Maret 2014, curah hujan yang tercatat di wilayah Riau dan sekitarnya mendekati 0 mm. Hal ini membuktikan bahwa Siklon Tropis Gillian di selatan Perairan Jawa mempengaruhi curah hujan di Pulau Sumatera Bagian Utara (Riau).  AbstrackTropical Cyclone is the meteorological disturbance due to the low pressure center in the ocean. Terms of tropical cyclone formation in the waters is the sea surface temperature (sst) is quite warm (T> 260C). Tropical Cyclone Gillian is one of Tropical Cyclone that occurred in the waters of Indinesia. This tropical cyclones occur in the Southern waters of Indonesian, which lasted for 5 days from March 21 to March 25, 2014. Tropical Cyclone Gillian affects rainfall in the northern part of Indonesia, precisely in Northern Sumatra Island. During the Tropical Cyclone Gillian lead to a reduction in rainfall in the region, especially in the province of Riau. Rainfall data from the POS METEOROLOGY and TRMM Satellite Jaxa began on March 23 to March 27, 2014, was recorded in Riau area close to 0 mm. This proves that the Tropical Cyclone Gillian in southern waters of Java affecting rainfall in Northern of Sumatera Island (Riau).

scholarly journals Seven-Day Intensity and Intensity Spread Predictions for Atlantic Tropical Cyclones

2017 ◽  
Vol 32 (1) ◽  
pp. 141-147 ◽  
Author(s):  
Hsiao-Chung Tsai ◽  
Russell L. Elsberry
Keyword(s):  
Tropical Cyclones ◽  
Independent Set ◽  
Correlation Coefficients ◽  
Training Set ◽  
Prediction Technique ◽  
The Mean ◽  
Forecast Interval ◽  
Intensity Spread

Abstract The extension of the Weighted Analog Intensity Atlantic (WAIA) prediction technique for Atlantic tropical cyclones (TCs) from 5 to 7 days revealed a need for two modifications. The first modification for the 7-day WAIA was to randomly select 70% of the TCs in the entire 2000–15 sample to be the training set and use the remaining 30% as the independent set. The second modification was to ensure that appropriate analogs were selected for ending storm situations such as landfall, postrecurvature, and nondevelopment or delayed intensification within the 7-day forecast interval. By simply constraining the analog selection such that the intensity at the last matching point with the target TC track does not exceed 50 kt (where 1 kt = 0.51 m s−1), an increasing overforecast bias with forecast interval was almost eliminated in both the training set and the independent set. With these two analog selection modifications, the mean absolute errors, and the correlation coefficients of the 7-day WAIA intensities with the verifying intensities, are essentially constant from 5 to 7 days, which establishes this WAIA as a viable technique for 7-day intensity forecasts of Atlantic TCs.

HYDROLOGICAL REGIONS IN SCANDINAVIA AND FINLAND

1970 ◽  
Vol 1 (1) ◽  
pp. 5-37 ◽  
Author(s):  
RAGNAR MELIN
Keyword(s):  
Regional Distribution ◽  
Correlation Coefficients ◽  
The Arctic ◽  
High Mountain ◽  
The North ◽  
Arctic Sea ◽  
Regime Types ◽  
The Mean ◽  
One Year

The rivers of Scandinavia and Finland are studied with regard to regime, regional distribution, and long-term variations in discharge. The regime studies are based on monthly mean coefficients, i.e. the relation between the monthly means and the average annual discharge for a 30-year period. Within the two main areas of nival highland and lowland regime and Atlantic snow and rain regime (Fig. 1), different regime types have been studied (Figs. 2 and 3, tables 2-4). The regional distribution has been estimated with correlation coefficients and relations between means for one year expressed as a percentage of the mean discharge of 1931-60. Some of the correlation coefficients are shown in Table 5. The variations of 30- and 10-year mean discharge in percentage of the mean discharge 1911-60. have been studied by the method of moving averages from the year 1901 (Figs. 4-9). As to the 30-year records there are four regions with different trends, the North Calotte with two parts, the Arctic Sea part and the inland, the Scandinavian high mountain district, Vestland in Norway, and the eastern lowland (Fig. 10). The relations between discharge, precipitation, and temperature are discussed. Based on the results of the studies, mainly those of regional distribution together with other elements such as the situation of drainage areas and runoff values, hydrological regions have been delineated, Fig. 11.

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