Ensemble-Based Gravity Wave Parameter Retrieval for Numerical Weather Prediction

Gravity wave (GW) momentum and energy deposition are large components of the momentum and heat budgets of the stratosphere and mesosphere, affecting predictability across scales. Since weather and climate models cannot resolve the entire GW spectrum, GW parameterizations are required. Tuning these parameterizations is time-consuming and must be repeated whenever model configurations are changed. We introduce a self-tuning approach, called GW parameter retrieval (GWPR), applied when the model is coupled to a data assimilation (DA) system. A key component of GWPR is a linearized model of the sensitivity of model wind and temperature to the GW parameters, which is calculated using an ensemble of nonlinear forecasts with perturbed parameters. GWPR calculates optimal parameters using an adaptive grid search that reduces DA analysis increments via a cost-function minimization. We test GWPR within the Navy Global Environmental Model (NAVGEM) using three latitude-dependent GW parameters: peak momentum flux, phase-speed width of the Gaussian source spectrum, and phase-speed weighting relative to the source-level wind. Compared to a baseline experiment with fixed parameters, GWPR reduces analysis increments and improves 5-day mesospheric forecasts. Relative to the baseline, retrieved parameters reveal enhanced source-level fluxes and westward shift of the wave spectrum in the winter extratropics, which we relate to seasonal variations in frontogenesis. The GWPR reduces stratospheric increments near 60°S during austral winter, compensating for excessive baseline non-orographic GW drag. Tropical sensitivity is weaker due to significant absorption of GW in the stratosphere, resulting in less confidence in tropical GWPR values.

Statistically-informed deep learning for gravitational wave parameter estimation

We introduce deep learning models to estimate the masses of the binary components of black hole mergers, (m1, m2), and three astrophysical properties of the post-merger compact remnant, namely, the final spin, af, and the frequency and damping time of the ringdown oscillations of the fundamental (l=m=2) bar mode, (ωR, ωI). Our neural networks combine a modified WaveNet architecture with contrastive learning and normalizing flow. We validate these models against a Gaussian conjugate prior family whose posterior distribution is described by a closed analytical expression. Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters (m1, m2, af, ωR, ωI) of five binary black holes: GW150914, GW170104, GW170814, GW190521 and GW190630. We use PyCBC Inference to directly compare traditional Bayesian methodologies for parameter estimation with our deep learning based posterior distributions. Our results show that our neural network models predict posterior distributions that encode physical correlations, and that our data-driven median results and 90\% confidence intervals are similar to those produced with gravitational wave Bayesian analyses. This methodology requires a single V100 NVIDIA GPU to produce median values and posterior distributions within two milliseconds for each event. This neural network, and a tutorial for its use, are available at the Data and Learning Hub for Science.

Clustering of Climate Change Impacts on Ocean Waves in the Northwest Atlantic

The provision of reliable results from numerical wave models implemented over vast ocean areas can be considered as a time-consuming process. In this regard, the estimation of areas with maximum similarity in wave climate spatial areas and the determination of associated representative point locations for these areas can play an important role in climate research and in engineering applications. In order to deal with this issue, we apply a state-of-the-art clustering methodology, Geo-SOM, to determine geographical areas with similar wave regimes, in terms of mean wave direction (MWD), mean wave period (T0), as well as significant wave height (Hs). Although this method has many strengths, a weakness is related to detection and accounting of the most extreme and rare events. To resolve this deficiency, an initial pre-processing method (called PG-Geo-SOM) is applied. To evaluate the performance of this method, extreme wave parameters, including Hs and T0, are calculated. We simulate the present climate, represented as 1979 to 2017, compared to the future climate, 2060-2098, following the Intergovernmental Panel on Climate Change (IPCC) future scenario RCP (Representative Concentration Pathway) 8.5 in the Northwest Atlantic. In this approach, the wave parameter data are divided into distinct groups, or clusters, motivated by their geographical positions. For each cluster, the centroid spatial point and the time series of data are extracted, for Hs, MWD, and T0. Extreme values are estimated for 5, 10, 25, 50, and 100-year return periods, using Gumbel, exponential, and Weibull stochastic models, for both present and future climates. Results show that for parameter T0, the impact of climate change for the study area is a decreasing trend, while for Hs, in coastal and shelf areas up to about 1000 km from the coastline, increasing trends are estimated, and in open ocean areas, far from the coast, decreasing trends are obtained.

KARAKTERISTIK GELOMBANG DI BEBERAPA KAWASAN PERAIRAN SEKITAR SEMENANJUNG MINAHASA

The waters surrounding the Minahasa Peninsula are important areas of various activities by use of coastal and marine space. This research aims, firstly to describe direction, period and height of waves at several water areas around the Minahasa Peninsula in various season. Secondly to analyze the characteristic of daily and seasonal waves. The wave parameter, which reanalysis by ECMWF for 5 years (September 2014 to august 2019) were used in this research. By the analysis spasial-temporal, the results are presented by the rose grapich waves direction and stock chart. The wave propagation in the north season and transition season I, is dominated by the northeasterly wave. While the wave propagation in the southern season and the transitional season II, is more diverse directions. The characteristics of the waves formed in the southeast Minahasa waters in the north season and transitional season I, show that the wave period is greater than in the waters of Manado Bay and Bitung waters. Furthermore, the wave characteristics formed in the waters of the Minahasa Peninsula show a significant wave height in Bitung waters which is higher than the waters of Manado Bay and Southeast Minahasa waters.Keywords: Waves, ECMWF, Minahasa Peninsula Waters Perairan sekitar Semenanjung Minahasa merupakan kawasan penting dalam berbagai aktivitas pemanfaatan ruang pantai dan laut. Penelitian ini bertujuan, pertama untuk mendeskripsikan arah, periode, dan tinggi gelombang di beberapa kawasan perairan sekitar Semenanjung Minahasa dalam berbagai musim yang berlangsung. Kedua adalah menganalisis karakteristik gelombang harian dan musiman pada perairan sekitar Semenanjung Minahasa. Data yang digunakan dalam penelitian adalah data reanalysis ECMWF (European Centre for Medium-Range Weather Forecasts) parameter gelombang selama 5 tahun mulai bulan September 2014 sampai Agustus 2019. Dengan metode analisis spasial-temporal hasilnya disajikan dalam bentuk grafik mawar arah datang gelombang dan grafik kotak (stock chart). Rambatan gelombang pada musim Utara dan musim Peralihan I, arah datangnya didominasi dari arah Timur Laut. Sedangkan rambatan gelombang pada musim Selatan dan musim Peralihan II, arah datangnya lebih beragam. Karakteristik gelombang yang terbentuk di perairan Minahasa Tenggara pada musim Utara dan musim Peralihan I memperlihatkan periode gelombangnya lebih besar dari pada perairan Teluk Manado dan Perairan Bitung. Selanjutnya karakteristik gelombang yang terbentuk di perairan Semenanjung Minahasa memperlihatkan tinggi gelombang signifikan di perairan Bitung lebih tinggi dari pada perairan Teluk Manado dan perairan Minahasa Tenggara.Kata kunci: Gelombang, ECMWF, Perairan Semenanjung Minahasa

REFLECTED WAVE PARAMETER ESTIMATION AND AUGMENTATION INDEX CALCULATION FROM PHOTOPLETHYSMOGRAPHY MODEL FOR DIFFERENT AGE GROUPS

The reflected wave of Photoplethysmography (PPG) is generated due to impedance mismatch, when the pulse wave propagates along the arterial branches. The objective of the study is to estimate the reflected wave amplitude and duration from PPG and to determine whether it changes with age. Reflected wave of PPG was modeled using the lognormal model fitting method. The duration and amplitude of reflected wave and augmentation index from modeled PPG was compared with the Slope Sum Function (SSF) and second derivative of PPG. PPG was collected from 120 subjects with age [Formula: see text]40 and [Formula: see text]40 years old for carrying out this experiment. Reflected wave duration from modeled PPG and SSF method shows a mean square error value of [Formula: see text] and [Formula: see text] for subjects [Formula: see text]40 and [Formula: see text]40 years age, respectively. The amplitude of reflected wave from PPG model and Second Derivative of Photolethysmography (SDPPG) results in a mean square error of [Formula: see text] and [Formula: see text] for subjects [Formula: see text]40 and [Formula: see text]40 years age, respectively. Augmentation index calculated from PPG model and SDPPG was similar with good level of agreement in the scatter plot. The modeled PPG helps to identify the reflected wave parameter noninvasively and the results show a considerable variation in these parameters with age.