A parallel Fortran framework for neural networks and deep learning

This paper describes neural-fortran, a parallel Fortran framework for neural networks and deep learning. It features a simple interface to construct feed-forward neural networks of arbitrary structure and size, several activation functions, and stochastic gradient descent as the default optimization algorithm. Neural-fortran also leverages the Fortran 2018 standard collective subroutines to achieve data-based parallelism on shared- or distributed-memory machines. First, I describe the implementation of neural networks with Fortran derived types, whole-array arithmetic, and collective sum and broadcast operations to achieve parallelism. Second, I demonstrate the use of neural-fortran in an example of recognizing hand-written digits from images. Finally, I evaluate the computational performance in both serial and parallel modes. Ease of use and computational performance are similar to an existing popular machine learning framework, making neural-fortran a viable candidate for further development and use in production.Comment: Submitted to ACM SIGPLAN Fortran Forum. Reviewed by Arjen Markus and Izaak Beekma

Revised Estimates of Ocean Surface Drag in Strong Winds

Air-sea drag governs the momentum transfer between the atmosphere and the ocean, and remains largely unknown in hurricane winds. We revisit the momentum budget and eddy-covariance methods to estimate the surface drag coefficient in the laboratory. Our drag estimates agree with field measurements in low-to-moderate winds, and previous laboratory measurements in hurricane-force winds. The drag coefficient saturates at $2.6 \times 10^{-3}$ and $U_{10} \approx 25\ m\ s^{-1}$, in agreement with previous laboratory results by Takagaki et al. (2012). During our analysis, we discovered an error in the original source code used by Donelan et al. (2004). We present the corrected data and describe the correction procedure. Although the correction to the data does not change the key finding of drag saturation in strong winds, its magnitude and wind speed threshold are significantly changed. Our findings emphasize the need for an updated and unified drag parameterization based on field and laboratory data.Comment: 13 pages, 5 figure

Description of surface transport in the region of the Belizean Barrier Reef based on observations and alternative high-resolution models

Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here for personal use, not for redistribution. The definitive version was published in Ocean Modelling 106 (2016): 74–89, doi:10.1016/j.ocemod.2016.09.010.The gains from implementing high-resolution versus less costly low-resolution models to describe coastal circulation are not always clear, often lacking statistical evaluation. Here we construct a hierarchy of ocean-atmosphere models operating at multiple scales within a 1×1° domain of the Belizean Barrier Reef (BBR). The various components of the atmosphere-ocean models are evaluated with in situ observations of surface drifters, wind and sea surface temperature. First, we compare the dispersion and velocity of 55 surface drifters released in the field in summer 2013 to the dispersion and velocity of simulated drifters under alternative model configurations. Increasing the resolution of the ocean model (from 1/12° to 1/100°, from 1 day to 1 h) and atmosphere model forcing (from 1/2° to 1/100°, from 6 h to 1 h), and incorporating tidal forcing incrementally reduces discrepancy between simulated and observed velocities and dispersion. Next, in trying to understand why the high-resolution models improve prediction, we find that resolving both the diurnal sea-breeze and semi-diurnal tides is key to improving the Lagrangian statistics and transport predictions along the BBR. Notably, the model with the highest ocean-atmosphere resolution and with tidal forcing generates a higher number of looping trajectories and sub-mesoscale coherent structures that are otherwise unresolved. Finally, simulations conducted with this model from June to August of 2013 show an intensification of the velocity fields throughout the summer and reveal a mesoscale anticyclonic circulation around Glovers Reef, and sub-mesoscale cyclonic eddies formed in the vicinity of Columbus Island. This study provides a general framework to assess the best surface transport prediction from alternative ocean-atmosphere models using metrics derived from high frequency drifters’ data and meteorological stations.This research is supported by the National Science Foundation award NSF-OCE 1260424

A Fortran-Keras Deep Learning Bridge for Scientific Computing

Implementing artificial neural networks is commonly achieved via high-level programming languages such as Python and easy-to-use deep learning libraries such as Keras. These software libraries come preloaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way bridge connects environments where deep learning resources are plentiful with those where they are scarce. The paper describes several unique features offered by FKB, such as customizable layers, loss functions, and network ensembles. The paper concludes with a case study that applies FKB to address open questions about the robustness of an experimental approach to global climate simulation, in which subgrid physics are outsourced to deep neural network emulators. In this context, FKB enables a hyperparameter search of one hundred plus candidate models of subgrid cloud and radiation physics, initially implemented in Keras, to be transferred and used in Fortran. Such a process allows the model’s emergent behavior to be assessed, i.e., when fit imperfections are coupled to explicit planetary-scale fluid dynamics. The results reveal a previously unrecognized strong relationship between offline validation error and online performance, in which the choice of the optimizer proves unexpectedly critical. This in turn reveals many new neural network architectures that produce considerable improvements in climate model stability including some with reduced error, for an especially challenging training dataset

A Fortran-Keras Deep Learning Bridge for Scientific Computing

Implementing artificial neural networks is commonly achieved via high-level programming languages like Python and easy-to-use deep learning libraries like Keras. These software libraries come pre-loaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way bridge connects environments where deep learning resources are plentiful, with those where they are scarce. The paper describes several unique features offered by FKB, such as customizable layers, loss functions, and network ensembles. The paper concludes with a case study that applies FKB to address open questions about the robustness of an experimental approach to global climate simulation, in which subgrid physics are outsourced to deep neural network emulators. In this context, FKB enables a hyperparameter search of one hundred plus candidate models of subgrid cloud and radiation physics, initially implemented in Keras, to be transferred and used in Fortran. Such a process allows the model's emergent behavior to be assessed, i.e. when fit imperfections are coupled to explicit planetary-scale fluid dynamics. The results reveal a previously unrecognized strong relationship between offline validation error and online performance, in which the choice of optimizer proves unexpectedly critical. This reveals many neural network architectures that produce considerable improvements in stability including some with reduced error, for an especially challenging training dataset

Implementation of the University of Miami Wave Model (UMWM) into the NASA/GMAO Goddard Earth Observing System Model (GEOS)

Wind generated waves are integral element in air-sea interactions and affect exchange of momentum, heat, water, gases and production of marine aerosol. Motivated by the need to resolve the air-sea interface we have implemented the University of Miami Wave model (UMWM) into the NASA/GMAO Goddard Earth Observing System Model (GEOS). The implementation of the wave model in GEOS aimed to facilitate coupling with the atmosphere and ocean model components with minimal changes to the existing system, while at the same time ensure correctness of the predicted wave energy spectrum and wave diagnostics. Here we describe the implementation of the GEOS/UMWM system and show results from model experiments and verifications. This work is a step toward development of a coupled atmosphere-wave-ocean GEOS system

Fuzzy Multi-criteria Model for Selecting the Best Location for a Regional Landfill

The expected deficit in power supply all over the world demands all types of institutions such as scientific, vocational and governmental to focus an increased attention on the problems of increasing energy efficiency, using renewable energy sources as biomass (biodegradable parts of products, waste and remains in agriculture, forestry and related industries, as well as biodegradable parts of industrial waste and urban litter, according to European Union Directive (EU) 2001/77/EC). Although biomass is often referred to as carbon neutral fuel, it can still contribute to global warming. Energy can be obtained from biomass in different ways, for example by: (1) direct burning (wood, vegetative remains, wood waste) in order to get thermal energy, (2) digestionprocessing animal waste (manure) into biogass, (3) processing biomass into alcohol (ethanol) or producing vegetable oils. It should also be noted that during its life cycle, biomass absorbs CO 2 which is released back into the atmosphere when biomass is used for obtaining energy. The EU puts a lot of effort into stimulating the use of biomass as a fuel. 4% of the overall energy demand in the EU is satisfied by biomass production, which is equal to 69m tons of petroleum. Data found in literature (International Energy Agency (IEA), Head of Communication and Information Office, Paris, France: World Energy Outlook, 2008) suggest that using biomass as a fuel may lead to diversity in energy supply, considerable decrease in emission of gases which cause the greenhouse effect, increasing the employment rate and potential cuts in prices of petroleum as a result of declining demand. One of subissues of using biomass as a fuel is the selection of a biomass burying location. The considered problem is important bearing in mind three different groups of aspects: -economic and social group of aspects (this creates the possibility to improve the competitive position of regional economy, makes new production programs as growing biomass plantation, using biomass for energy, solving the problem of waste disposal and increasing the employment rate); -socially-energetic and ecological group of aspects (preservation and/or improvement in environment protection and natural resources use, improving life quality and a contribution to the use of renewable energy sources); -the aspect of energetics development strategy realisation (improving the reliability of energy supply in both industry and households, decreasing the use of other energy forms and giving contribution to meeting local energetic needs). The problem of selecting location for biomass burying and processing has been discussed in a certain number of papers so far. I

Sea State Based Estimation of White Cap Fraction: Implications for Primary Marine Aerosol Fluxes

Oceanic whitecaps (hereafter, W) or the characteristic whiteness of the sea foam is an important feature for predicting exchange of gases, sea spray aerosols (SSAs), heat and momentum transfer between the ocean and the atmosphere at the air-sea interface. Due to its increased surface emission and brightness temperature, whitecaps are critical for satellite retrievals of ocean albedo, ocean color, ocean surface wind vectors from satellite borne radiometer and microwave instruments. Most of the existing models predict W using wind speed and sea surface temperature (SST). However, numerous publications have pointed out that there are large uncertainties in the predicted W and using parameterizations based on wind-wave state can improve the precision of the predicted W. Here, we integrate the University of Miami Wave Model - 2.0 (UMWM) in Goddard Earth Observing System (GEOS) and use wave diagnostics to predict W. We choose the year 2006 for our global UMWM/GEOS runs because of the availability of W dataset from satellite observations. We run UMWM/GEOS at 0.5o x 0.5o by replaying to MERRA2 meteorology and evaluate the wave diagnostics using measurements from fixed buoys and satellite altimeters. We use three different parameterizations for W based on: 1) Reynolds number, 2) wave dissipation energy, and 3) volume of air entrained by breaking waves. We compare our results of W with previous studies and also with the satellite based observational dataset. Predicting W is important for understanding the processes at the air-sea interface. Therefore, this work is a step further in improving the uncertainties in the aerosol and atmospheric chemistry modules of the global models

The state of Fortran

A community of developers has formed to modernize the Fortran ecosystem. In this article, we describe the high-level features of Fortran that continue to make it a good choice for scientists and engineers in the 21st century. Ongoing efforts include the development of a Fortran standard library and package manager, the fostering of a friendly and welcoming online community, improved compiler support, and language feature development. The lessons learned are common across contemporary programming languages and help reduce the learning curve and increase adoption of Fortran

Surface Ocean Dispersion Observations From the Ship-Tethered Aerostat Remote Sensing System

Oil slicks and sheens reside at the air-sea interface, a region of the ocean that is notoriously difficult to measure. Little is known about the velocity field at the sea surface in general, making predictions of oil dispersal difficult. The Ship-Tethered Aerostat Remote Sensing System (STARSS) was developed to measure Lagrangian velocities at the air-sea interface by tracking the transport and dispersion of bamboo dinner plates in the field of view of a high-resolution aerial imaging system. The camera had a field of view of approximately 300 × 200 m and images were obtained every 15 s over periods of up to 3 h. A series of experiments were conducted in the northern Gulf of Mexico in January-February 2016. STARSS was equipped with a GPS and inertial navigation system (INS) that was used to directly georectify the aerial images. A relative rectification technique was developed that translates and rotates the plates to minimize their total movement from one frame to the next. Rectified plate positions were used to quantify scale-dependent dispersion by computing relative dispersion, relative diffusivity, and velocity structure functions. STARSS was part of a nested observational framework, which included deployments of large numbers of GPS-tracked surface drifters from two ships, in situ ocean measurements, X-band radar observations of surface currents, and synoptic maps of sea surface temperature from a manned aircraft. Here we describe the STARSS system and image analysis techniques, and present results from an experiment that was conducted on a density front that was approximately 130 km offshore. These observations are the first of their kind and the methodology presented here can be adopted into existing and planned oceanographic campaigns to improve our understanding of small-scale and high-frequency variability at the air-sea interface and to provide much-needed benchmarks for numerical simulations