A full physics algorithm to retrieve nighttime sea surface temperature with IASI: Toward an independent homogeneous long time-series for climate studies

Sea surface salinity (SSS) and precipitation are important climate (paleoclimate) parameters. To obtain long time series data of SSS/precipitation one use coral proxy. In this study, seawater d18O is extracted from d18O content in Bali coral using centering method. The result shows more convincing that d18Obali is influenced by both seawater d18O and sea surface temperature (SST). In the interannual/decadal scale the variation d18Obali clearly shows the variation of seawater d18O, it is supposed that highly variation of precipitation contribute to the seawater d18O variation which mirrored by coral d18Obali. Keywords: coral d18O, seawater d18O, precipitation, sea surface salinity, sea surface temperature Salinitas permukaan laut (SSS) dan curah hujan merupakan parameter penting untuk studi iklim maupun paleoiklim (iklim masa lampau). Untuk mendapatkan data dalam urut-urutan waktu (timeseries) yang panjang dari SSS dan curah hujan diperlukan data proksi geokimia dalam koral. Dalam studi ini kandungan d18O dalam air laut dapat di rekonstruksi dari kandungan d18O dalam koral dengan menggunakan metode centering. Hasilnya menunjukkan bahwa d18O dalam koral dipengaruhi oleh kandungan d18O dalam air laut dan SST. Dalam resolusi tahunan dan puluhan tahunan variasi d18Obali dalam koral menunjukkan dengan jelas variasi d18O dalam air laut, hal ini diduga bahwa dalam resolusi tahunan dan puluhan tahunan variasi curah hujan sangat tinggi yang berkontribusi pada tingginya variasi d18Obali dalam air laut sehingga dapat terekam oleh koral. Kata kunci: d18O koral, d18O air laut, curah hujan, salinitas permukaan laut, suhu permukaan laut.

Download Full-text

EXTREME VALUES OF SEA SURFACE TEMPERATURE ASSOCIATED WITH LONG-PERIOD PHENOMENA OCCURRED DURING 1960-2015 IN THE COLOMBIAN PACIFIC OCEAN

Proceedings of International Conference "Managinag risks to coastal regions and communities in a changinag world" (EMECS'11 - SeaCoasts XXVI) ◽

10.31519/conferencearticle_5b1b943a9e4336.75393991 ◽

2017 ◽

Author(s):

Diaz Juan Navia ◽

Bolaños Nancy Villegas ◽

Igor Malikov ◽

...

Keyword(s):

Time Series ◽

Pacific Ocean ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Extreme Values ◽

Sea Surface ◽

Absolute Maximum ◽

Data Set ◽

Arctic Oscillation Index ◽

Colombian Pacific

Sea Surface Temperature Anomalies (SSTA), in four coastal hydrographic stations of Colombian Pacific Ocean, were analyzed. The selected hydrographic stations were: Tumaco (1°48'N-78°45'W), Gorgona island (2°58'N-78°11'W), Solano Bay (6°13'N-77°24'W) and Malpelo island (4°0'N-81°36'W). SSTA time series for 1960-2015 were calculated from monthly Sea Surface Temperature obtained from International Comprehensive Ocean Atmosphere Data Set (ICOADS). SSTA time series, Oceanic Nino Index (ONI), Pacific Decadal Oscillation index (PDO), Arctic Oscillation index (AO) and sunspots number (associated to solar activity), were compared. It was found that the SSTA absolute minimum has occurred in Tumaco (-3.93°C) in March 2009, in Gorgona (-3.71°C) in October 2007, in Solano Bay (-4.23°C) in April 2014 and Malpelo (-4.21°C) in December 2005. The SSTA absolute maximum was observed in Tumaco (3.45°C) in January 2002, in Gorgona (5.01°C) in July 1978, in Solano Bay (5.27°C) in March 1998 and Malpelo (3.64°C) in July 2015. A high correlation between SST and ONI in large part of study period, followed by a good correlation with PDO, was identified. The AO and SSTA have showed an inverse relationship in some periods. Solar Cycle has showed to be a modulator of behavior of SSTA in the selected stations. It was determined that extreme values of SST are related to the analyzed large scale oscillations.

Download Full-text

A Process-Oriented Approach to Identify Evolutions of Sea Surface Temperature Anomalies with a Time-Series of a Raster Dataset

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10080500 ◽

2021 ◽

Vol 10 (8) ◽

pp. 500

Author(s):

Lianwei Li ◽

Yangfeng Xu ◽

Cunjin Xue ◽

Yuxuan Fu ◽

Yuanyu Zhang

Keyword(s):

Time Series ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Global Climate ◽

Sea Surface ◽

Temperature Anomalies ◽

Global Climate Changes ◽

Space And Time ◽

Novel Approach ◽

Sea Surface Temperature Anomalies

It is important to consider where, when, and how the evolution of sea surface temperature anomalies (SSTA) plays significant roles in regional or global climate changes. In the comparison of where and when, there is a great challenge in clearly describing how SSTA evolves in space and time. In light of the evolution from generation, through development, and to the dissipation of SSTA, this paper proposes a novel approach to identifying an evolution of SSTA in space and time from a time-series of a raster dataset. This method, called PoAIES, includes three key steps. Firstly, a cluster-based method is enhanced to explore spatiotemporal clusters of SSTA, and each cluster of SSTA at a time snapshot is taken as a snapshot object of SSTA. Secondly, the spatiotemporal topologies of snapshot objects of SSTA at successive time snapshots are used to link snapshot objects of SSTA into an evolution object of SSTA, which is called a process object. Here, a linking threshold is automatically determined according to the overlapped areas of the snapshot objects, and only those snapshot objects that meet the specified linking threshold are linked together into a process object. Thirdly, we use a graph-based model to represent a process object of SSTA. A node represents a snapshot object of SSTA, and an edge represents an evolution between two snapshot objects. Using a number of child nodes from an edge’s parent node and a number of parent nodes from the edge’s child node, a type of edge (an evolution relationship) is identified, which shows its development, splitting, merging, or splitting/merging. Finally, an experiment on a simulated dataset is used to demonstrate the effectiveness and the advantages of PoAIES, and a real dataset of satellite-SSTA is used to verify the rationality of PoAIES with the help of ENSO’s relevant knowledge, which may provide new references for global change research.

Download Full-text

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 ◽

10.29244/j.agromet.21.2.46-60 ◽

2007 ◽

Vol 21 (2) ◽

pp. 46 ◽

Cited By ~ 2

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

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.------------------------------------------------------------------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.

Download Full-text

Time Series Analysis of CMIP5 Model and Observed Sea Surface Temperature Anomaly Along Indian Coastal Zones

Journal of Coastal Research ◽

10.2112/si86-035.1 ◽

2019 ◽

Vol 86 (sp1) ◽

pp. 239

Author(s):

Dhanya Joseph ◽

Vazhamattom Benjamin Liya ◽

Girindran Rojith ◽

Pariyappanal Ulahannan Zacharia ◽

George Grinson

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Temperature Anomaly ◽

Sea Surface ◽

Coastal Zones ◽

Cmip5 Model ◽

Series Analysis ◽

Sea Surface Temperature Anomaly

Download Full-text

Week Ahead Time Series Prediction of Sea Surface Temperature Using Nonlinear Autoregressive Network with and Without Exogenous Inputs

Algorithms for Intelligent Systems - Applications of Machine Learning ◽

10.1007/978-981-15-3357-0_16 ◽

2020 ◽

pp. 235-256

Author(s):

Geetali Saha ◽

N. C. Chauhan

Keyword(s):

Time Series ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Time Series Prediction ◽

Sea Surface ◽

Nonlinear Autoregressive

Download Full-text

Improving the Classification Accuracy of Annual Crops Using Time Series of Temperature and Vegetation Indices

Remote Sensing ◽

10.3390/rs12193202 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3202

Author(s):

Xinran Chen ◽

Yulin Zhan ◽

Yan Liu ◽

Xingfa Gu ◽

Tao Yu ◽

...

Keyword(s):

Time Series ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Classification Accuracy ◽

Spectral Feature ◽

Temperature Index ◽

Long Time Series ◽

Ndvi Time Series ◽

Long Time

Accurate cropland classification is important for agricultural monitoring and related decision-making. The commonly used input spectral features for classification cannot be employed to effectively distinguish crops that have similar spectro-temporal features. This study attempted to improve the classification accuracy of crops using both the thermal feature, i.e., the land surface temperature (LST), and the spectral feature, i.e., the normalized difference vegetation index (NDVI), for classification. To amplify the temperature differences between the crops, a temperature index, namely, the modified land surface temperature index (mLSTI) was built using the LST. The mLSTI was calculated by subtracting the average LST of an image from the LST of each pixel. To study the adaptability of the proposed method to different areas, three study areas were selected. A comparison of the classification results obtained using the NDVI time series and NDVI + mLSTI time series showed that for long time series from June to November, the classification accuracy when using the mLSTI and NDVI time series was higher (85.6% for study area 1 in California, 96.3% for area 2 in Kansas, and 91.2% for area 3 in Texas) than that when using the NDVI time series alone (82.0% for area 1, 94.7% for area 2, and 90.9% for area 3); the same was true in most of the cases when using the shorter time series. With the addition of the mLSTI time series, the shorter time series achieved higher classification accuracy, which is beneficial for timely crop identification. The sorghum and soybean crops, which exhibit similar NDVI feature curves in this study, could be better distinguished by adding the mLSTI time series. The results demonstrated that the classification accuracy of crops can be improved by adding mLSTI long time series, particularly for distinguishing crops with similar NDVI characteristics in a given study area.

Download Full-text

A Novel Method for Sea Surface Temperature Prediction Based on Deep Learning

Mathematical Problems in Engineering ◽

10.1155/2020/6387173 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Xuan Yu ◽

Suixiang Shi ◽

Lingyu Xu ◽

Yaya Liu ◽

Qingsheng Miao ◽

...

Keyword(s):

Time Series ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Root Mean Square ◽

Spatial Information ◽

Sea Surface ◽

The Yellow Sea ◽

Percentage Error ◽

Good Prediction ◽

Mean Square

Sea surface temperature (SST) forecasting is the task of predicting future values of a given sequence using historical SST data, which is beneficial for observing and studying hydroclimatic variability. Most previous studies ignore the spatial information in SST prediction and the forecasting models have limitations to process the large-scale SST data. A novel model of SST prediction integrated Deep Gated Recurrent Unit and Convolutional Neural Network (DGCnetwork) is proposed in this paper. The DGCnetwork has a compact structure and focuses on learning deep long-term dependencies in SST time series. Temporal information and spatial information are all included in our procedure. Differential Evolution algorithm is applied in order to configure DGCnetwork’s optimum architecture. Optimum Interpolation Sea Surface Temperature (OISST) data is selected to conduct experiments in this paper, which has good temporal homogeneity and feature resolution. The experiments demonstrate that the DGCnetwork significantly obtains excellent forecasting result, predicting SST by different lengths flexibly and accurately. On the East China Sea dataset and the Yellow Sea dataset, the accuracy of the prediction results is above 98% on the whole and all mean absolute error (MAE) values are lower than 0.33°C. Compared with the other models, root mean square error (RMSE), root mean square percentage error (RMSPE), and mean absolute percentage Error (MAPE) of the proposed approach reduce at least 0.1154, 0.2594, and 0.3938. The experiments of SST time series show that the DGCnetwork model maintains good prediction results, better performance, and stronger stability, which has reached the most advanced level internationally.

Download Full-text