Experience in developing a method for long-term forecasting of the ice breakup time for the Vyatka River basin

Hydrometeorological research and forecasting ◽

10.37162/2618-9631-2021-4-99-111 ◽

2021 ◽

pp. 99-111

Author(s):

Y.A Pavroz . ◽

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Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

River Basin ◽

Geopotential Height ◽

Sea Surface ◽

River Ice ◽

Northwest Pacific ◽

Ice Breakup ◽

The Impact ◽

Breakup Time

An attempt is made to develop a method for long-term forecasting of the ice breakup time for the Vyatka River basin, to identify the impact of the distribution of sea surface temperature and geopotential height in the informative regions at the levels H100 and H500 over the Northern Hemisphere on the river ice breakup. The location and boundaries of the informative regions in the fields of H100 and H500 were revealed by the discriminant analysis, the EOF expansion coefficients of the fields of anomalies of monthly mean values of H100 and H500 for January and February and the anomalies of monthly mean sea surface temperature in the North Atlantic and Northwest Pacific were used as potential predictors. The stepwise regression analysis allowed deriving good and satisfactory (S/σ = 0.45–0.73) complex prognostic equations for forecasting the ice breakup time for the Vyatka River basin. The essential influence of H100 and H500 geopotential height fields and the spatial distribution of sea surface temperature anomalies in the North Atlantic and Northwest Pacific in January and February on the river ice breakup time is revealed. It is proposed to improve the method by considering the impact of air temperature, maximum ice thickness per winter, and other indirect characteristics on the processes of river ice breakup in the Vyatka River basin. Keywords: ice regime, long-range forecast, river ice breakup, expansion coefficients, geopotential height fields, spring ice phenomena, energy-active zones of the oceans, complex prognostic equation

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Accurate Evaluation of Sea Surface Temperature Cooling Induced by Typhoons Based on Satellite Remote Sensing Observations

Water ◽

10.3390/w12051413 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1413

Author(s):

Jiagen Li ◽

Liang Sun ◽

Yuanjian Yang ◽

Hao Cheng

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Satellite Remote Sensing ◽

Mixed Layer Depth ◽

Heat Content ◽

Sea Surface ◽

Northwest Pacific ◽

Sst Cooling ◽

The Impact

We introduce a novel method to accurately evaluate the satellite-observed sea surface temperature (SST) cooling induced by typhoons with complex tracks, which is widely used but only roughly calculated in previous studies. This method first records the typhoon forcing period and the SST response grid by grid, then evaluates the SST cooling in each grid by choosing the maximum decrease in SST within this time period. This grid-based flexible forcing date method can accurately evaluate typhoon-induced SST cooling and its corresponding date in each grid, as indicated by applying the method to the irregular track of Typhoon Lupit (2009) and three sequential typhoons in 2016 (Malakas, Megi, and Chaba). The method was used to accurately calculate the impact of Typhoon Megi by removing the influence of the other two typhoons. The SST cooling events induced by all typhoons in the northwest Pacific from 2004 to 2018 were extracted well using this method. Our findings provide new insights for accurately calculating the response of the ocean using multi-satellite remote sensing and simulation data, including the sea surface salinity, sea surface height, mixed layer depth, and the heat content of the upper levels of the ocean.

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The impact of north tropical Atlantic sea surface temperature anomalies in the ensuing spring of El Niño on the tropical Indian Ocean and Northwest Pacific

International Journal of Climatology ◽

10.1002/joc.6500 ◽

2020 ◽

Vol 40 (11) ◽

pp. 4978-4991

Author(s):

Jing Ma ◽

Weibang He ◽

Zhehan Chen ◽

Yihang Fu ◽

Jiayue Yin

Keyword(s):

Indian Ocean ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Tropical Indian Ocean ◽

Sea Surface ◽

Northwest Pacific ◽

Tropical Atlantic ◽

Sea Surface Temperature Anomalies ◽

The Impact ◽

North Tropical Atlantic

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Interannual Variability and Trends in Sea Surface Temperature, Lower and Middle Atmosphere Temperature at Different Latitudes for 1980–2019

Atmosphere ◽

10.3390/atmos12040454 ◽

2021 ◽

Vol 12 (4) ◽

pp. 454

Author(s):

Andrew R. Jakovlev ◽

Sergei P. Smyshlyaev ◽

Vener Y. Galin

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Lower Stratosphere ◽

Middle Atmosphere ◽

Southern Oscillation ◽

Lower Troposphere ◽

Reanalysis Data ◽

Sea Surface ◽

The Arctic ◽

The Impact

The influence of sea-surface temperature (SST) on the lower troposphere and lower stratosphere temperature in the tropical, middle, and polar latitudes is studied for 1980–2019 based on the MERRA2, ERA5, and Met Office reanalysis data, and numerical modeling with a chemistry-climate model (CCM) of the lower and middle atmosphere. The variability of SST is analyzed according to Met Office and ERA5 data, while the variability of atmospheric temperature is investigated according to MERRA2 and ERA5 data. Analysis of sea surface temperature trends based on reanalysis data revealed that a significant positive SST trend of about 0.1 degrees per decade is observed over the globe. In the middle latitudes of the Northern Hemisphere, the trend (about 0.2 degrees per decade) is 2 times higher than the global average, and 5 times higher than in the Southern Hemisphere (about 0.04 degrees per decade). At polar latitudes, opposite SST trends are observed in the Arctic (positive) and Antarctic (negative). The impact of the El Niño Southern Oscillation phenomenon on the temperature of the lower and middle atmosphere in the middle and polar latitudes of the Northern and Southern Hemispheres is discussed. To assess the relative influence of SST, CO2, and other greenhouse gases’ variability on the temperature of the lower troposphere and lower stratosphere, numerical calculations with a CCM were performed for several scenarios of accounting for the SST and carbon dioxide variability. The results of numerical experiments with a CCM demonstrated that the influence of SST prevails in the troposphere, while for the stratosphere, an increase in the CO2 content plays the most important role.

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

Climate Dynamics ◽

10.1007/s00382-018-4577-5 ◽

2018 ◽

Vol 53 (1-2) ◽

pp. 173-192 ◽

Cited By ~ 5

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

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STUDY ON CYCLONE INDUCED PHYTOPLANKTON BLOOM IN THE ARABIAN SEA THROUGH GAP-FREE RECONSTRUCTED CHLOROPHYLL-A DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-1391-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 1391-1396

Author(s):

R. Shunmugapandi ◽

S. Gedam ◽

A. B. Inamdar

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Chlorophyll A ◽

Arabian Sea ◽

Phytoplankton Bloom ◽

Suitable Condition ◽

Sea Surface ◽

Cyclone Track ◽

Chl A ◽

The Impact

Abstract. Ocean surface phytoplankton responses to the tropical cyclone (TC)/storms have been extensively studied using satellite observations by aggregating the data into a weekly or bi-weekly composite. The reason behind is the significant limitations found in the satellite-based observation is the missing of valid data due to cloud cover, especially at the time of cyclone track passage. The data loss during the cyclone is found to be a significant barrier to efficiently investigate the response of chl-a and SST during cyclone track passage. Therefore it is necessary to rectify the above limitation to effectively study the impact of TC on the chlorophyll-a concentration (chl-a) and the sea surface temperature (SST) to achieve a complete understanding of their response to the TC prevailed in the Arabian Sea. Intending to resolve the limitation mentioned above, this study aims to reconstruct the MODIS-Aqua chl-a, and SST data using Data Interpolating Empirical Orthogonal Function (DINEOF) for all the 31 cyclonic events occurred in the Arabian Sea during 2003-2018 (16 years). Reconstructed satellite retrieved data covering all the cyclonic events were further used to investigate the chl-a and SST dynamics during TC. From the results, the exciting fact has been identified that only two TC over the eastern-AS were able to induce phytoplankton bloom. On investigating this scenario using sea surface temperature, it was disclosed that the availability of nutrients decides the suitable condition for the phytoplankton to proliferate in the surface ocean. Relevant to the precedent criterion, the results witnessed that the 2 TC (Phyan and Ockhi cyclone) prevailed in the eastern AS invoked a suitable condition for phytoplankton bloom. Other TC found to be less provocative either due to less intensity, origination region or the unsuitable condition. Thereby, gap-free reconstructed daily satellite-derived data efficiently investigates the response of bio-geophysical parameters during cyclonic events. Moreover, this study sensitised that though several TC strikes the AS, only two could impact phytoplankton productivity and SST found to highly consistent with the chl-a variability during the cyclone passage.

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Wavelet Analysis of Seasonal Rainfall Variability of the Upper Blue Nile Basin, Its Teleconnection to Global Sea Surface Temperature, and Its Forecasting by an Artificial Neural Network

Monthly Weather Review ◽

10.1175/mwr-d-13-00085.1 ◽

2014 ◽

Vol 142 (5) ◽

pp. 1771-1791 ◽

Cited By ~ 18

Author(s):

Mohamed Helmy Elsanabary ◽

Thian Yew Gan

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

River Basin ◽

Principal Component ◽

Sea Surface ◽

Seasonal Rainfall ◽

Blue Nile ◽

Artificial Neural ◽

Mean Square Errors ◽

Global Sea Surface Temperature

Abstract Rainfall is the primary driver of basin hydrologic processes. This article examines a recently developed rainfall predictive tool that combines wavelet principal component analysis (WPCA), an artificial neural networks-genetic algorithm (ANN-GA), and statistical disaggregation into an integrated framework useful for the management of water resources around the upper Blue Nile River basin (UBNB) in Ethiopia. From the correlation field between scale-average wavelet powers (SAWPs) of the February–May (FMAM) global sea surface temperature (SST) and the first wavelet principal component (WPC1) of June–September (JJAS) seasonal rainfall over the UBNB, sectors of the Indian, Atlantic, and Pacific Oceans where SSTs show a strong teleconnection with JJAS rainfall in the UBNB (r ≥ 0.4) were identified. An ANN-GA model was developed to forecast the UBNB seasonal rainfall using the selected SST sectors. Results show that ANN-GA forecasted seasonal rainfall amounts that agree well with the observed data for the UBNB [root-mean-square errors (RMSEs) between 0.72 and 0.82, correlation between 0.68 and 0.77, and Hanssen–Kuipers (HK) scores between 0.5 and 0.77], but the results in the foothills region of the Great Rift Valley (GRV) were poor, which is expected since the variability of WPC1 mainly comes from the highlands of Ethiopia. The Valencia and Schaake model was used to disaggregate the forecasted seasonal rainfall to weekly rainfall, which was found to reasonably capture the characteristics of the observed weekly rainfall over the UBNB. The ability to forecast the UBNB rainfall at a season-long lead time will be useful for an optimal allocation of water usage among various competing users in the river basin.

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The Role of Sea Surface Temperature Forcing in the Life-Cycle of Mediterranean Cyclones

Remote Sensing ◽

10.3390/rs12050825 ◽

2020 ◽

Vol 12 (5) ◽

pp. 825 ◽

Cited By ~ 1

Author(s):

Christos Stathopoulos ◽

Platon Patlakas ◽

Christos Tsalis ◽

George Kallos

Keyword(s):

Life Cycle ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Early Warning Systems ◽

Life Cycles ◽

Heat Fluxes ◽

Sea Surface ◽

Mediterranean Cyclones ◽

The Mediterranean ◽

The Impact

Air–sea interface processes are highly associated with the evolution and intensity of marine-developed storms. Specifically, in the Mediterranean Sea, the air–ocean temperature deviations have a profound role during the several stages of Mediterranean cyclonic events. Subsequently, this enhances the need for better knowledge and representation of the sea surface temperature (SST). In this work, an analysis of the impact and uncertainty of the SST from different well-known datasets on the life-cycle of Mediterranean cyclones is attempted. Daily SST from the Real Time Global SST (RTG_SST) and hourly SST fields from the Operational SST and Sea Ice Ocean Analysis (OSTIA) and the NEMO ocean circulation model are implemented in the RAMS/ICLAMS-WAM coupled modeling system. For the needs of the study, the Mediterranean cyclones Trixi, Numa, and Zorbas were selected. Numerical experiments covered all stages of their life-cycles (five to seven days). Model results have been analyzed in terms of storm tracks and intensities, cyclonic structural characteristics, and derived heat fluxes. Remote sensing data from the Integrated Multi-satellitE Retrievals (IMERG) for Global Precipitation Measurements (GPM), Blended Sea Winds, and JASON altimetry missions were employed for a qualitative and quantitative comparison of modeled results in precipitation, maximum surface wind speed, and wave height. Spatiotemporal deviations in the SST forcing rather than significant differences in the maximum/minimum SST values, seem to mainly contribute to the differences between the model results. Considerable deviations emerged in the resulting heat fluxes, while the most important differences were found in precipitation exhibiting spatial and intensity variations reaching 100 mm. The employment of widely used products is shown to result in different outcomes and this point should be taken into consideration in forecasting and early warning systems.

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