Uncertainty of Tropical Cyclone Wind Radii on Sea Surface Temperature Cooling

2021 ◽  
Author(s):  
Iam-Fei Pun ◽  
John Knaff ◽  
Charles Sampson
Keyword(s):  
Tropical Cyclone ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Prediction Models ◽  
Weather Prediction ◽  
Sea Surface ◽  
Weather Research And Forecasting ◽  
Translation Speed ◽  
Ocean Stratification ◽  
Sst Cooling

<p>The sea surface temperature (SST) beneath a tropical cyclone (TC) is of great importance to its dynamics; therefore, understanding and accurately estimating the magnitude of SST cooling is of vital importance.  Existing studies have explored important influences on SST such as TC translation speed, maximum surface winds, ocean thermal condition and ocean stratification.  But the influence of the TC wind radii (or collectively called the TC size) on SST has been largely overlooked.  In this study we assess the influence of wind radii uncertainty on SST cooling by a total of 15,983 numerical simulations for the western North Pacific during the 2014-2018 seasons.  Results show a 6-20% SST cooling error induced using wind radii from the Joint Typhoon Warning Center official forecast and a 35-40% SST cooling error using wind radii from the operational runs of the Hurricane Weather Research and Forecasting (HWRF) model.  Our results indicate that SST cooling is most sensitive to the radius of 64 kt winds.  The correlation between SST cooling induced by the TC and its size is 0.49, which is highest among all the parameters tested.  This suggests that it is extremely important to get TC size correct in order to predict the SST cooling response, which then impacts TC evolution in numerical weather prediction models.</p>

scholarly journals The impact of climate model sea surface temperature biases on tropical cyclone simulations

2018 ◽  
Vol 53 (1-2) ◽  
pp. 173-192 ◽  
Author(s):  
Wei-Ching Hsu ◽  
Christina M. Patricola ◽  
Ping Chang
Keyword(s):  
Tropical Cyclone ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Model ◽  
Sea Surface ◽  
The Impact

scholarly journals ANALISIS KORELASI CURAH HUJAN DAN SUHU PERMUKAAN LAUT WILAYAH INDONESIA, SERTA IMPLIKASINYA UNTUK PRAKIRAAN CURAH HUJAN (STUDI KASUS KABUPATEN CILACAP) (CORRELATION ANALYSIS OF RAINFALL AND INDONESIA SEA SURFACE TEMPERATURE, AND ITS ...

Agromet ◽  
2007 ◽  
Vol 21 (2) ◽  
pp. 46 ◽  
Author(s):  
W. Estiningtyas ◽  
F. Ramadhani ◽  
E. Aldrian
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Negative Correlation ◽  
Prediction Models ◽  
Time Series Data ◽  
Sea Surface ◽  
Rainfall Pattern ◽  
Series Data ◽  
Rainfall Prediction

<p>Significant decrease in rainfall caused extreme climate has significant impact on agriculture sector, especialy food crops production. It is one of reason and push developing of rainfall prediction models as anticipate from extreme climate events. Rainfall prediction models develop base on time series data, and then it has been included anomaly aspect, like rainfall prediction model with Kalman filtering method. One of global parameter that has been used as climate anomaly indicator is sea surface temperature. Some of research indicate, there are relationship between sea surface temperature and rainfall. Relationship between Indonesian rainfall and global sea surface temperature has been known, but its relationship with Indonesian’s sea surface temperature not know yet, especialy for rainfall in smaller area like district. So, therefore the research about relationship between rainfall in distric area and Indonesian’s sea surface temperature and it application for rainfall prediction is needed. Based on Indonesian’s sea surface temperature time series data Januari 1982 until Mei 2006 show there are zona of Indonesian’s sea surface temperature (with temperature more than 27,6 0C) dominan in Januari-Mei and moved with specific pattern. Highest value of spasial correlation beetwen Cilacap’s rainfall and Indonesian’s sea surface temperature is 0,30 until 0,50 with different zona of Indonesian’s sea surface temperature. Highest positive correlation happened in March and July. Negative correlation is -0,30 until -0,70 with highest negative correlation in May and June. Model validation resulted correlation coeffcient 85,73%, fits model 20,74%, r2 73,49%, RMSE 20,5% and standart deviation 37,96. Rainfall prediction Januari-Desember 2007 period indicated rainfall pattern is near same with average rainfall pattern, rainfall less than 100/month. The result of this research indicate Indonesian’s sea surface temperature can be used as indicator rainfall condition in distric area, that means rainfall in district area can be predicted based on Indonesian’s sea surface temperature in zona with highest correlation in every month.</p><p>------------------------------------------------------------------</p><p>Penurunan curah hujan yang cukup signifikan akibat iklim ekstrim telah membawa dampak yang cukup signifikan pula pada sektor pertanian, terutama produksi tanaman pangan. Hal ini menjadi salah satu alasan yang mendorong semakin berkembangnya model-model prakiraan hujan sebagai upaya antipasi terhadap kejadian iklim ekstrim. Model prakiraan hujan yang pada awalnya hanya berbasis pada data time series, kini telah berkembang dengan memperhitungkan aspek anomali iklim, seperti model prakiraan hujan dengan metode filter Kalman. Salah satu indikator global yang dapat digunakan sebagai indikator anomali iklim adalah suhu permukaan laut. Dari berbagai hasil penelitian diketahui bahwa suhu permukaan laut ini memiliki keterkaitan dengan kejadian curah hujan. Hubungan curah hujan Indonesia dengan suhu permukaan laut global sudah banyak diketahui, tetapi keterkaitannya dengan suhu permukaan laut wilayah Indonesia belum banyak mendapat perhatian, terutama untuk curah hujan pada cakupan yang lebih sempit seperti kabupaten. Oleh karena itu perlu dilakukan penelitian yang mengkaji hubungan kedua parameter tersebut serta mengaplikasikannya untuk prakiraan curah hujan pada wilayah Kabupaten. Hasil penelitian berdasarkan data suhu permukaan laut wilayah Indonesia rata-rata Januari 1982 hingga Mei 2006 menunjukkan zona dengan suhu lebih dari 27,6 0C yang dominan pada bulan Januari-Mei dan bergerak dengan pola yang cukup jelas. Korelasi spasial antara curah hujan kabupaten Cilacap dengan SPL wilayah Indonesia rata-rata bulan Januari-Desember menunjukkan korelasi positip tertinggi antara 0,30 hingga 0,50 dengan zona SPL yang beragam. Korelasi tertinggi terjadi pada bulan Maret dan Juli. Sedangkan korelasi negatip berkisar antara -0,30 hingga -0,70 dengan korelasi negatip tertinggi pada bulan Mei dan Juni. Validasi model prakiraan hujan menghasilkan nilai koefisien korelasi 85,73%, fits model 20,74%, r2 sebesar 73,49%, RMSE 20,5% dan standar deviasi 37,96. Hasil prakiraan hujan bulanan periode Januari-Desember 2007 mengindikasikan pola curah hujan yang tidak jauh berbeda dengan rata-rata selama 19 tahun (1988-2006) dengan jeluk hujan kurang dari 100 mm/bulan. Hasil penelitian mengindikasikan bahwa SPL wilayah Indonesia dapat digunakan sebagai indikator untuk menunjukkan kondisi curah hujan di suatu wilayah (kabupaten), artinya curah hujan dapat diprediksi berdasarkan perubahan SPL pada zona-zona dengan korelasi yang tertinggi pada setiap bulannya.</p>

scholarly journals Environmental Factors Affecting Tropical Cyclone Power Dissipation

2007 ◽  
Vol 20 (22) ◽  
pp. 5497-5509 ◽  
Author(s):  
Kerry Emanuel
Keyword(s):  
Tropical Cyclone ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Scales ◽  
North Pacific ◽  
Wind Shear ◽  
Western North Pacific ◽  
Vertical Wind Shear ◽  
Sea Surface ◽  
Strongly Correlated

Abstract Revised estimates of kinetic energy production by tropical cyclones in the Atlantic and western North Pacific are presented. These show considerable variability on interannual-to-multidecadal time scales. In the Atlantic, variability on time scales of a few years and more is strongly correlated with tropical Atlantic sea surface temperature, while in the western North Pacific, this correlation, while still present, is considerably weaker. Using a combination of basic theory and empirical statistical analysis, it is shown that much of the variability in both ocean basins can be explained by variations in potential intensity, low-level vorticity, and vertical wind shear. Potential intensity variations are in turn factored into components related to variations in net surface radiation, thermodynamic efficiency, and average surface wind speed. In the Atlantic, potential intensity, low-level vorticity, and vertical wind shear strongly covary and are also highly correlated with sea surface temperature, at least during the period in which reanalysis products are considered reliable. In the Pacific, the three factors are not strongly correlated. The relative contributions of the three factors are quantified, and implications for future trends and variability of tropical cyclone activity are discussed.

scholarly journals Change in the Recent Warming Trend of Sea Surface Temperature in the East Sea (Sea of Japan) over Decades (1982–2018)

2019 ◽  
Vol 11 (22) ◽  
pp. 2613 ◽  
Author(s):  
Eun-Young Lee ◽  
Kyung-Ae Park
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Of Japan ◽  
Warming Trend ◽  
Frequent Occurrence ◽  
Sea Surface ◽  
East Sea ◽  
The Arctic ◽  
Arctic Oscillation Index ◽  
Sst Cooling

Long-term trends of sea surface temperature (SST) of the East Sea (Sea of Japan, EJS) were estimated by using 37-year-long satellite data, for the observation period from 1982 to 2018. Overall, the SST tended to increase with time, for all analyzed regions. However, the warming trend was steeper in the earlier decades since the 1980s and slowed down during the recent two decades. Based on the analysis of the occurrence of events with extreme SST (high in the summertime and low in the wintertime), a shift toward the more frequent occurrence of events with extremely high SST and the less frequent occurrence of events with extremely low SST has been observed. This supports the observations of the consistent warming of the EJS. However, seasonal trends revealed continuous SST warming in the summertime, but frequent extreme SST cooling in the wintertime, in recent decades. The observed reduction in the warming rates occurred more frequently in specific regions of the EJS, where the occurrence frequency of events with extremely low SST was unusually high in the recent decade. The recent tendency toward the SST cooling was distinctively connected with variations in the Arctic Oscillation index. This suggests that changes in the Arctic Ocean environment likely affect the recently observed SST changes in the EJS, as one of the marginal seas in the mid-latitude region far from the polar region.

scholarly journals Statistical Characterization of the Observed Cold Wake Induced by North Atlantic Hurricanes

2019 ◽  
Vol 11 (20) ◽  
pp. 2368
Author(s):  
Koen Haakman ◽  
Juan-Manuel Sayol ◽  
Carine G. van der Boog ◽  
Caroline A. Katsman
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Atlantic ◽  
Barrier Layer ◽  
Sea Surface ◽  
Translation Speed ◽  
Layer Potential ◽  
Statistical Characterization ◽  
Barrier Layer Thickness ◽  
Atlantic Hurricanes

This work quantifies the magnitude, spatial structure, and temporal evolution of the cold wake left by North Atlantic hurricanes. To this end we composited the sea surface temperature anomalies (SSTA) induced by hurricane observations from 2002 to 2018 derived from the international best track archive for climate stewardship (IBTrACS). Cold wake characteristics were distinguished by a set of hurricane and oceanic properties: Hurricane translation speed and intensity, and the characteristics of the upper ocean stratification represented by two barrier layer metrics: Barrier layer thickness (BLT) and barrier layer potential energy (BLPE). The contribution of the above properties to the amplitude of the cold wake was analyzed individually and in combination. The mean magnitude of the hurricane-induced cooling was of 1.7 °C when all hurricanes without any distinction were considered, and the largest cooling was found for slow-moving, strong hurricanes passing over thinner barrier layers, with a cooling above 3.5 °C with respect to pre-storm sea surface temperature (SST) conditions. On average the cold wake needed about 60 days to disappear and experienced a strong decay in the first 20 days, when the magnitude of the cold wake had decreased by 80%. Differences between the cold wakes yielded by mostly infrared and merged infrared and microwave remote sensed SST data were also evaluated, with an overall relative underestimation of the hurricane-induced cooling of about 0.4 °C for infrared-mostly data.

scholarly journals Spatial and Temporal Characterization of Sea Surface Temperature Response to Tropical Cyclones*

2013 ◽  
Vol 26 (11) ◽  
pp. 3745-3765 ◽  
Author(s):  
Wei Mei ◽  
Claudia Pasquero
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Tropical Cyclones ◽  
Mixed Layer ◽  
Temperature Response ◽  
Maximum Amplitude ◽  
Sea Surface ◽  
Translation Speed ◽  
Linear Behavior ◽  
Sst Anomaly

Abstract The spatial structure and temporal evolution of the sea surface temperature (SST) anomaly (SSTA) associated with the passage of tropical cyclones (TCs), as well as their sensitivity to TC characteristics (including TC intensity and translation speed) and oceanic climatological conditions (represented here by latitude), are thoroughly examined by means of composite analysis using satellite-derived SST data. The magnitude of the TC-generated SSTA is larger for more intense, slower-moving, and higher-latitude TCs, and it occurs earlier in time for faster-moving and higher-latitude storms. The location of maximum SSTA is farther off the TC track for faster-moving storms, and it moves toward the track with time after the TC passage. The spatial extension of the cold wake is greater for more intense and for slower-moving storms, but its shape is quite independent of TC characteristics. Consistent with previous studies, the calculations show that the mean SSTA over a TC-centered box nearly linearly correlates with the wind speed for TCs below category 3 intensity while for stronger TCs the SSTA levels off, both for tropical and subtropical regions. While the linear behavior is expected on the basis of the more vigorous mixing induced by stronger winds and is derived from a simple mixed-layer model, the level-off for intense TCs is discussed in terms of the dependence of the maximum amplitude of the area-mean SSTA on TC translation speed and depth of the prestorm mixed layer. Finally, the decay time scale of the TC-induced SSTA is shown to be dominated by environmental conditions and has no clear dependence on its initial magnitude and on TC characteristics.

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