Estimation of sea surface temperature using passive microwave satellite imagery

Mesoscale eddies are oceanic vortices with radii of tens of kilometers, which live on for several months or even years. They carry large amounts of heat, salt, nutrients, and pollutants from their regions of formation to remote areas, making it important to detect and track them. Using satellite altimetric maps, mesoscale eddies have been detected via remote sensing with advancing performance over the last years [1]. However, the spatio-temporal interpolation between satellite track measurements, needed to produce these maps, induces a limit to the spatial resolution (1/12&#176; in the Med Sea) and large amounts of uncertainty in non-measured areas.Nevertheless, mesoscale oceanic eddies also have a visible signature on other satellite imagery such as Sea Surface Temperature (SST), portraying diverse patterns of coherent vortices, temperature gradients, and swirling filaments. Learning the regularities of such signatures defines a challenging pattern recognition task, due to their complex structure but also to the cloud coverage which can corrupt a large fraction of the image.We introduce a novel Deep Learning approach to classify sea temperature eddy signatures [2]. We create a large dataset of SST patches from satellite imagery in the Mediterranean Sea, containing Anticyclonic, Cyclonic, or No Eddy signatures, based on altimetric eddy detections of the DYNED-Atlas [3]. Our trained Convolutional Neural Network (CNN) can differentiate between these signatures with an accuracy of more than 90%, robust to a high level of cloud coverage.We furtherly evaluate the efficiency of our classifier on SST patches extracted from oceanographic numerical model outputs in the Mediterranean Sea. Our promising results suggest that the CNN could complement the detection, tracking, and prediction of the path of mesoscale oceanic eddies.[1] Chelton, D. B., Schlax, M. G. and Samelson, R. M. (2011). Global observations of nonlinear mesoscale eddies. Progress in oceanography, 91(2),167-216.[2] E. Moschos, A. Stegner, O. Schwander and P. Gallinari, "Classification of Eddy Sea Surface Temperature Signatures Under Cloud Coverage," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3437-3447, 2020, doi: 10.1109/JSTARS.2020.3001830.[3] https://www.lmd.polytechnique.fr/dyned/

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ANALISIS HUJAN ES DI KOTA LUBUKLINGGAU DENGAN MEMANFAATKAN DATA CITRA SATELIT HIMAWARI-8 DAN RADIOSONDE

Prosiding SNFA (Seminar Nasional Fisika dan Aplikasinya) ◽

10.20961/prosidingsnfa.v3i0.28526 ◽

2019 ◽

Vol 3 ◽

pp. 130

Author(s):

Heriyanto Wicaksono ◽

Fazrul Rafsanjani Sadarang ◽

Ahmad Fadlan

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Satellite Imagery ◽

Air Temperature ◽

Air Pressure ◽

Peak Temperature ◽

Sea Surface ◽

Atmospheric Conditions ◽

Air Humidity ◽

Sumatra Island

Abstract: The phenomenon of hail again hit Indonesia. The hail occurred in Lubuklinggau City, South Sumatra on October 15 2018, at around 16.20 WIB. Based on AWS Tugu Mulyo observation data, the rainfall on 15 October 2018 was recorded at 26.8 mm which included the medium rainfall category according to BMKG. This research aims to analyze the state of the atmosphere, satellite imagery, sea surface temperature anomalies, and air lability during the hailstorm in Lubuklinggau. Analysis of atmospheric conditions using air temperature data (T), air humidity (RH), and air pressure (P) results of observations of the surface before, during, and after the event. The Himawari satellite image with a resolution of 0.02º x 0.02º is processed with the SATAID application and is used to view the cloud growth phase. Air lability was analyzed by processing radiosonde data from Weather Wyoming Web using the RAOB application 5.7. The results of the analysis show that in the event of hail, the surface air temperature has decreased significantly, the surface air humidity has a significant increase, and the lowest surface air pressure is lower than the day before the hail. The air lability index shows that before the occurrence of hail, atmospheric conditions are unstable causing massive growth of convective clouds. The anomaly of sea surface temperature around Sumatra Island is quite warm, which is 0.5ºC. 1,8ºC which results in the possibility of cloud formation around Sumatra Island getting bigger. Based on satellite imagery, the peak temperature of the cloud at 16.00 WIB is -10.3ºC and at 16.10 WIB the cloud peak temperature reaches -67.8ºC. The significant decrease in cloud peak temperature in the 10-minute period indicates the presence of cloud growth due to a very strong updraft so that the peak temperature of the cloud becomes very cold. The temperature of the cloud peak reaching -67.8ºC shows that there is a convective cloud that is strong enough when there is hail in Lubuklinggau.Abstrak: Fenomena hujan es kembali melanda Indonesia. Hujan es tersebut terjadi di Kota Lubuklinggau, Sumatra Selatan pada tanggal 15 Oktober 2018 sekitar pukul 16.20 WIB. Berdasarkan data pengamatan AWS Tugu Mulyo, curah hujan pada tanggal 15 Oktober 2018 tercatat sebesar 26,8 mm yang termasuk kategori hujan sedang menurut BMKG. Penelitian kali ini bertujuan untuk menganalisis keadaan atmosfer, citra satelit, anomali suhu permukaan laut, dan labilitas udara pada saat terjadi hujan es di Lubuklinggau. Analisis keadaan atmosfer menggunakan data suhu udara (T), kelembapan udara (RH), dan tekanan udara (P) hasil pengamatan permukaan sebelum, saat, dan sesudah kejadian. Citra satelit Himawari dengan resolusi 0.02º x 0.02º diolah dengan aplikasi SATAID dan digunakan untuk melihat fase pertumbuhan awan. Labilitas udara dianalisis dengan mengolah data radiosonde dari Weather Wyoming Web menggunakan aplikasi RAOB 5.7. Hasil analisis menunjukkan bahwa pada saat terjadi hujan es, suhu udara permukaan mengalami penurunan yang signifikan, kelembapan udara permukaan mengalami kenaikan yang signifikan, serta tekanan udara permukaan terendah lebih rendah daripada hari sebelum terjadinya hujan es. Indeks labilitas udara menunjukkan bahwa sebelum terjadinya hujan es, kondisi atmosfer dalam keadaan labil sehingga menyebabkan pertumbuhan awan konvektif yang masif. Anomali suhu permukaan laut di sekitar Pulau Sumatera cukup hangat, yaitu 0,5ºC s.d. 1,8ºC yang mengakibatkan peluang terbentuknya awan di sekitar Pulau Sumatera semakin besar. Berdasarkan citra satelit, suhu puncak awan pada jam 16.00 WIB sebesar -10,3ºC dan pada jam 16.10 WIB suhu puncak awan mencapai -67,8ºC. Penurunan suhu puncak awan yang signifikan dalam kurun waktu 10 menit tersebut mengindikasikan adanya pertumbuhan awan akibat updraft yang sangat kuat sehingga suhu puncak awan menjadi sangat dingin. Suhu puncak awan yang mencapai -67,8ºC menunjukkan bahwa terdapat awan konvektif yang cukup kuat saat teradi hujan es di Lubuklinggau.

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Construction of a climate data record of sea surface temperature from passive microwave measurements

Remote Sensing of Environment ◽

10.1016/j.rse.2019.111485 ◽

2020 ◽

Vol 236 ◽

pp. 111485 ◽

Cited By ~ 2

Author(s):

Emy Alerskans ◽

Jacob L. Høyer ◽

Chelle L. Gentemann ◽

Leif Toudal Pedersen ◽

Pia Nielsen-Englyst ◽

...

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Passive Microwave ◽

Sea Surface ◽

Microwave Measurements ◽

Climate Data ◽

Data Record ◽

Climate Data Record

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PERBANDINGAN ANTARA INFORMASI SUHU PERMUKAAN LAUT DARI DATA SATELIT DENGAN HASIL PEMODELAN DI WPP NRI-716

Bumi Lestari Journal of Environment ◽

10.24843/blje.2016.v16.i01.p05 ◽

1970 ◽

Vol 16 (1) ◽

Author(s):

Komang Iwan Suniada

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Satellite Imagery ◽

Cloud Cover ◽

Sea Surface ◽

Surface Information ◽

Sst Model ◽

Cloud Modeling ◽

Modeling Data ◽

Satellite Sensor

Sea Surface Temperature (SST) data and information recently become a valuableinformation since its association with the climate, oceanography condition and fisherieshave been discovered. Unfortunately, SST information using satellite imagery frequentlyconstrained by atmospheric cloud cover since satellite sensor disability to gather any landor ocean surface information through the cloud. Modeling data is very required to fill theblank data resulted from satellite imagery under cloudy condition. This study conducted atSulawesi Sea to North Halmahera which is included to Fisheries Managing Area (FMA)716, to find out the strength and direction relationship between SST model and SST satellite.Result indicates there is a strong and same direction relationship between SST model andSST satellite (r=0.704, n=1516) with 0.2C diferrence so that SST model can be used to fillor substitute the blank of SST satellite.

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