scholarly journals Monthly daily-mean rainfall forecast over Indonesia using machine learning and artificial intelligence ensemble

2021 ◽  
Vol 893 (1) ◽  
pp. 012030
Author(s):  
H Harsa ◽  
M N Habibie ◽  
A S Praja ◽  
S P Rahayu ◽  
T D Hutapea ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Southern Oscillation ◽  
Longwave Radiation ◽  
Reanalysis Data ◽  
Physical Models ◽  
Climate Data ◽  
Input Dataset ◽  
Forecast Models ◽  
Spatial Grid

Abstract A daily mean rainfall in a month forecast method is presented in this paper. The method provides spatial forecast over Indonesia and employs ensemble of Machine Learning and Artificial Intelligence algorithms as its forecast models. Each spatial grid in the forecast output is processed as an individual dataset. Therefore, each location in the forecast output has different stacked ensemble models as well as their model parameter settings. Furthermore, the best ensemble model is chosen for each spatial grid. The input dataset of the model consists of eight climate data (i.e., East and West Dipole Mode Index, Outgoing Longwave Radiation, Southern Oscillation Index, and Nino 1.2, 3, 4, 3.4) and monthly rainfall reanalysis data, ranging from January 1982 until December 2019. There are four assessment procedures performed on the models: daily mean rainfall establishment as a response function of climate patterns, and one-up to three-month lead forecast. The results show that, based on their performance, these non-Physical models are considerable to complement the existing forecast models.

scholarly journals Spectral Empirical Orthogonal Function Analysis of Weather and Climate Data

2019 ◽  
Vol 147 (8) ◽  
pp. 2979-2995 ◽  
Author(s):  
Oliver T. Schmidt ◽  
Gianmarco Mengaldo ◽  
Gianpaolo Balsamo ◽  
Nils P. Wedi
Keyword(s):  
Frequency Domain ◽  
Empirical Orthogonal Function ◽  
Southern Oscillation ◽  
Function Analysis ◽  
Reanalysis Data ◽  
Climate Data ◽  
Quasi Biennial Oscillation ◽  
Linear Dynamical Systems ◽  
Orthogonal Function ◽  
Spectral Density Matrix

Abstract We apply spectral empirical orthogonal function (SEOF) analysis to educe climate patterns as dominant spatiotemporal modes of variability from reanalysis data. SEOF is a frequency-domain variant of standard empirical orthogonal function (EOF) analysis, and computes modes that represent the statistically most relevant and persistent patterns from an eigendecomposition of the estimated cross-spectral density matrix (CSD). The spectral estimation step distinguishes the approach from other frequency-domain EOF methods based on a single realization of the Fourier transform, and results in a number of desirable mathematical properties: at each frequency, SEOF yields a set of orthogonal modes that are optimally ranked in terms of variance in the L2 sense, and that are coherent in both space and time by construction. We discuss the differences between SEOF and other competing approaches, as well as its relation to dynamical modes of stochastically forced, nonnormal linear dynamical systems. The method is applied to ERA-Interim and ERA-20C reanalysis data, demonstrating its ability to identify a number of well-known spatiotemporal coherent meteorological patterns and teleconnections, including the Madden–Julian oscillation (MJO), the quasi-biennial oscillation (QBO), and the El Niño–Southern Oscillation (ENSO) (i.e., a range of phenomena reoccurring with average periods ranging from months to many years). In addition to two-dimensional univariate analyses of surface data, we give examples of multivariate and three-dimensional meteorological patterns that illustrate how this technique can systematically identify coherent structures from different sets of data. The MATLAB code used to compute the results presented in this study, including the download scripts for the reanalysis data, is freely available online.

scholarly journals Comparison of a coupled snow thermodynamic and radiative transfer model with in-situ active microwave signatures of snow-covered smooth first-year sea ice

2015 ◽  
Vol 9 (3) ◽  
pp. 3293-3329
Author(s):  
M. C. Fuller ◽  
T. Geldsetzer ◽  
J. Yackel ◽  
J. P. S. Gill
Keyword(s):  
Sea Ice ◽  
Longwave Radiation ◽  
Reanalysis Data ◽  
Physical Models ◽  
Radiative Transfer Model ◽  
First Year ◽  
Transfer Model ◽  
Data Inversion ◽  
Microwave Backscatter

Abstract. Within the context of developing data inversion and assimilation techniques for C-band backscatter over sea ice, snow physical models may be used to drive backscatter models for comparison and optimization with satellite observations. Such modeling has potential to enhance understanding of snow on sea ice properties required for unambiguous interpretation of active microwave imagery. An end-to-end modeling suite is introduced, incorporating regional reanalysis data (NARR), a snow model (SNTHERM), and a multi-layer snow and ice active microwave backscatter model (MSIB). This modeling suite is assessed against measured snow on sea ice geophysical properties, and against measured active microwave backscatter. NARR data was input to the SNTHERM snow thermodynamic model, in order to drive the MISB model for comparison to detailed geophysical measurements and surface-based observations of C-band backscatter of snow on first-year sea ice. The NARR data was well correlated to available in-situ measurements, with the exception of long wave incoming radiation and relative humidity, which impacted SNTHERM simulations of snow temperature. SNTHERM reasonably represented snow grain size and density when compared to observations. The application of in-situ salinity profiles to one SNTHERM snow profile resulted in simulated backscatter close to that driven by in-situ snow properties. In other test cases, the simulated backscatter remained 4 to 6 dB below observed for higher incidence angles, and when compared to an average simulated backscatter of in-situ end-member snowcovers. Development of C-band inversion and assimilation schemes employing SNTHERM89.rev4 should consider sensitivity of the model to bias in incoming longwave radiation, the effects of brine, and the inability of SNTHERM89.Rev4 to simulate water accumulation and refreezing at the bottom and mid-layers of the snowpack with regard to thermodynamic response, brine wicking and volume processes, snow dielectrics, and microwave backscatter from snow on first-year sea-ice.

scholarly journals HIRS Outgoing Longwave Radiation—Daily Climate Data Record: Application toward Identifying Tropical Subseasonal Variability

2018 ◽  
Vol 10 (9) ◽  
pp. 1325 ◽  
Author(s):  
Carl Schreck ◽  
Hai-Tien Lee ◽  
Kenneth Knapp
Keyword(s):  
High Resolution ◽  
Outgoing Longwave Radiation ◽  
Large Scale ◽  
Southern Oscillation ◽  
Longwave Radiation ◽  
Climate Data ◽  
Convective Systems ◽  
Subseasonal Variability ◽  
Data Record ◽  
Climate Data Record

This study describes the development of a new globally gridded climate data record (CDR) for daily outgoing longwave radiation (OLR) using the High-Resolution Infrared Radiation Sounder (HIRS) sensor. The new product, hereafter referred to as HIRS OLR, has several differences and advantages over the widely-used daily OLR dataset derived from the Advanced Very High-Resolution Radiometer (AVHRR) sensor on the same NOAA Polar Operational Environmental Satellites (POES), hereafter AVHRR OLR. As a CDR, HIRS OLR has been intersatellite-calibrated to provide the most homogeneous record possible. AVHRR OLR only used the daytime and nighttime overpasses from a single satellite at a time, which creates some challenges for resolving the large diurnal cycle of OLR. HIRS OLR leverages all available overpasses and then calibrates geostationary estimates of OLR to represent that cycle more faithfully. HIRS also has more spectral channels, including those for measuring water vapor, which provides a more accurate measure of OLR. This difference is particularly relevant for large-scale convective systems such as the El Niño–Southern Oscillation and the Madden–Julian Oscillation, whereby the HIRS OLR can better identify the subtropical variability between the tropical convection and the extratropical teleconnections.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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