Lorenz energy cycle simulated with the model SPEEDY and the impact of ENSO

U ovom radu korišten je rezultat 35-članog ansambla simulacija modelom SPEEDY koji je forsiran donjim rubnim uvjetom u obliku površinske temperature mora kako bi se izvrijednili članovi energije u Lorenzovom ciklusu. Rezultantni članovi raspoložive poetncijalne energije i srednje zonalne kinetičke energije, te istoimeni članovi makroporemećaja prikazani su u pregledu za razdoblje od 1855. do 2010. godine u obliku srednjih godišnjih vrijednosti i srednjih sezonskih vrijednosti za sezone DJF i JJA, ali i u vertikalnom te u longitudionalno-vertikalnom presjeku. Izračunat je i NINO 3.4 indeks za određivanje faze El Niño - Južne oscilacije (ENSO), a na temelju indeksa određeni su El Niño i La Niña događaji, te su članovi Lorenzovog ciklusa promotreni dodatno u obliku kompozita za sve El Niño i La Niña događaje od 1855. do 2010. godine. Utvrđeno je da model SPEEDY zadovoljavajuće reproducira energiju zonalnog strujanja dok za članove makroporemećaja nije jednako uspješan. Utjecaj ENSO faza na članove Lorenzovog ciklusa je uočen i pokazao se najjačim za zimske hemisfere.This thesis uses the products of a 35-member ensemble simulated with the model SPEEDY with forcing applied to the model’s lower boundary conditions in the form of sea surface temperatures to calculate energy reservoirs in the Lorenz energy cycle. Available potential energy and mean kinetic energy, as well as their eddy counterparts, are evaluated for the time period from 1855 until 2010 in the form of yearly averages as well as seasonal averages for the DJF and JJA seasons with their vertical profiles and longitude-altitude cross sections also discussed. NINO 3.4 index is calculated for the aforementioned time interval, allowing for the identification of El Niño - Southern Oscillation phases, El Niño and La Niña, and the discussion of Lorenz energy cycle in the context of those events. Model SPEEDY adequately reproduces zonal-mean energy while the same is not true for eddy energy. ENSO is shown to have an impact on Lorenz energy cycle components with the greatest impact in the winter hemisphere

Lorenz energy cycle simulated with the model SPEEDY and the impact of ENSO

U ovom radu korišten je rezultat 35-članog ansambla simulacija modelom SPEEDY koji je forsiran donjim rubnim uvjetom u obliku površinske temperature mora kako bi se izvrijednili članovi energije u Lorenzovom ciklusu. Rezultantni članovi raspoložive poetncijalne energije i srednje zonalne kinetičke energije, te istoimeni članovi makroporemećaja prikazani su u pregledu za razdoblje od 1855. do 2010. godine u obliku srednjih godišnjih vrijednosti i srednjih sezonskih vrijednosti za sezone DJF i JJA, ali i u vertikalnom te u longitudionalno-vertikalnom presjeku. Izračunat je i NINO 3.4 indeks za određivanje faze El Niño - Južne oscilacije (ENSO), a na temelju indeksa određeni su El Niño i La Niña događaji, te su članovi Lorenzovog ciklusa promotreni dodatno u obliku kompozita za sve El Niño i La Niña događaje od 1855. do 2010. godine. Utvrđeno je da model SPEEDY zadovoljavajuće reproducira energiju zonalnog strujanja dok za članove makroporemećaja nije jednako uspješan. Utjecaj ENSO faza na članove Lorenzovog ciklusa je uočen i pokazao se najjačim za zimske hemisfere.This thesis uses the products of a 35-member ensemble simulated with the model SPEEDY with forcing applied to the model’s lower boundary conditions in the form of sea surface temperatures to calculate energy reservoirs in the Lorenz energy cycle. Available potential energy and mean kinetic energy, as well as their eddy counterparts, are evaluated for the time period from 1855 until 2010 in the form of yearly averages as well as seasonal averages for the DJF and JJA seasons with their vertical profiles and longitude-altitude cross sections also discussed. NINO 3.4 index is calculated for the aforementioned time interval, allowing for the identification of El Niño - Southern Oscillation phases, El Niño and La Niña, and the discussion of Lorenz energy cycle in the context of those events. Model SPEEDY adequately reproduces zonal-mean energy while the same is not true for eddy energy. ENSO is shown to have an impact on Lorenz energy cycle components with the greatest impact in the winter hemisphere

Hindcast of Significant Wave Heights in Sheltered Basins Using Machine Learning and the Copernicus Database

Long-term time series of wave parameters play a critical role in coastal structure design and maritime activities. At sites with limited buoy measurements, methods are used to extend the available time series data. To date, wave hindcasting research using machine learning methods has mainly focused on filling in missing buoy measurements or finding a mapping function between two nearshore buoy locations. This work aims to implement machine learning methods for hindcasting wave parameters using only publicly available Copernicus data. Ensemble regression and artificial neural networks were used as machine learning methods and the optimal hyperparameters were determined by the Bayesian optimization algorithm. As inputs, data from the MEDSEA reanalysis wave model were used for the wave parameters and data from the ERA5 atmospheric reanalysis model were used for the wind parameters. The results of this study show that the normalized RMSE of the test data improved by 29% for Rijeka and 12% for Split compared to the original MEDSEA wave hindcast at buoy locations. The proposed method was extremely efficient in removing bias in the original MEDSEA hindcasts (e.g., NBIAS = -0.35 for Rijeka) to negligible values for both Split and Rijeka (NBIAS < 0.03)

Analysis of beach nourishment and construction in Croatia

Plaže istočne obale Jadrana čine temelj turističke ponude Hrvatske, ali su pod iznimnim pritiskom turizma i klimatskih promjena. Za upravljanje obalom potrebni su podatci, a zasad izostaje i nacionalna strategija. Podatci o plažama prikupljeni su iz regionalnih programa svake županije, potom su prikupljeni podatci o postupku dohranjivanja obale putem ankete jedinica lokalne samouprave, a posebno su i analizirane snimke iz zraka za podatke o nasipavanju (tj. izgradnji) obale. Hrvatska ima 1904 pretežito male šljunčane plaže, provodi dohranu svake druge godine u malim količinama, a ujedno je i nasipala 27 % nove površine, sve pretežito za potrebe plaža, luka i turizma.Beaches on the eastern Adriatic coast are the basis of Croatia’s tourism offering while also being under pressure from climate change. Data is necessary to manage beaches effectively, but data is lacking as well as the long awaited national strategy for coastal management. This paper collected data about beaches from regional documents, nourishment data was obtained by survey from local municipalities while beach construction data was obtained from aerial photogrammetry. Croatia has more than 1904 small gravel beaches and nourishment is performed in small increments beinnaly, while more than 27 % of existing beach area has been constructed all primarily for the needs of beaches, marines and tourism

Filling Missing and Extending Significant Wave Height Measurements Using Neural Networks and an Integrated Surface Database

Wave data play a critical role in offshore structure design and coastal vulnerability studies. For various reasons, such as equipment malfunctions, wave data are often incomplete. Despite the interest in completing the data, few studies have considered constructing a machine learning model with publicly available wind measurements as input, while wind data from reanalysis models are commonly used. In this work, ANNs are constructed and tested to fill in missing wave data and extend the original wave measurements in a basin with limited fetch where wind waves dominate. Input features for the ANN are obtained from the publicly available Integrated Surface Database (ISD) maintained by NOAA. The accuracy of the ANNs is also compared to a state-of-the-art reanalysis wave model, MEDSEA, maintained at Copernicus Marine Service. The results of this study show that ANNs can accurately fill in missing wave data and also extend beyond the measurement period, using the wind velocity magnitude and wind direction from nearby weather stations. The MEDSEA reanalysis data showed greater scatter compared to the reconstructed significant wave heights from ANN. Specifically, MEDSEA showed a 22% higher HH index for expanding wave data and a 33% higher HH index for filling in missing wave data points