Impact Based Forecast analysis uses multi-model ensemble data and National Digital Forecast data in ArcMap 10.8.1

The number of people seeking public weather service information is growing, making it a challenge for all bureaus of meteorology around the world. Although, in the last decade, routine public weather service information has brought excellent weather forecast information for people and services to people with rapid, accurate, widely available, and easy to grasp information, which they may get in a variety of places, such as a website or an application. However, in this decade and in the future, it will not be enough. People want information such as what the impact should be and how people react to that impact, which should be displayed on a static Geographic Information System (GIS) map in a standard format. We will investigate and create an IBF map based on multi-model ensemble data and National Digital Forecast (NDF) data in this work. Then, using the GIS software ArcMap 10.8.1, we rank and score the geographic disaster data to determine the impact area. To create the effect area, we will employ primary and advanced methods of ArcMap 10.8.1. The information on the IBF map will be immediately understood by stakeholders and users.

An Investigation of Adaptive Radius for the Covariance Localization in Ensemble Data Assimilation

The covariance matrix estimated from the ensemble data assimilation always suffers from filter collapse because of the spurious correlations induced by the finite ensemble size. The localization technique is applied to ameliorate this issue, which has been suggested to be effective. In this paper, an adaptive scheme for Schur product covariance localization is proposed, which is easy and efficient to implement in the ensemble data assimilation frameworks. A Gaussian-shaped taper function is selected as the localization taper function for the Schur product in the adaptive localization scheme, and the localization radius is obtained adaptively through a certain criterion of correlations with the background ensembles. An idealized Lorenz96 model with an ensemble Kalman filter is firstly examined, showing that the adaptive localization scheme helps to significantly reduce the spurious correlations in the small ensemble with low computational cost and provides accurate covariances that are similar to those derived from a much larger ensemble. The investigations of adaptive localization radius reveal that the optimal radius is model-parameter-dependent, vertical-level-dependent and nearly flow-dependent with weather scenarios in a realistic model; for example, the radius of model parameter zonal wind is generally larger than that of temperature. The adaptivity of the localization scheme is also illustrated in the ensemble framework and shows that the adaptive scheme has a positive effect on the assimilated analysis as the well-tuned localization.

Deep Learning for Latent Space Data Assimilation LSDA in Subsurface Flow Systems

We present a deep learning architecture for efficient reduced-order implementation of ensemble data assimilation. Specifically, deep learning is used to improve two important aspects of data assimilation workflows: (i) low-rank representation of complex reservoir property distributions for geologically consistent feature-based model updating, and (ii) efficient prediction of the statistical information that are required for model updating. The proposed method uses deep convolutional autoencoders to nonlinearly map the original complex and high-dimensional parameters onto a low-dimensional parameter latent space that compactly represents the original parameters. In addition, a low-dimensional data latent space is constructed to predict the observable response of each model parameter realization, which can be used to compute the statistical information needed for the data assimilation step. The two mappings are developed as a joint deep learning architecture with two autoencoders that are connected and trained together. The training uses an ensemble of model parameters and their corresponding production response predictions as needed in implementing the standard ensemble-based data assimilation frameworks. Simultaneous training of the two mappings leads to a joint data-parameter manifold that captures the most salient information in the two spaces for a more effective data assimilation, where only relevant data and parameter features are included. Moreover, the parameter-to-data mapping provides a fast forecast model that can be used to increase the ensemble size for a more accurate data assimilation, without a major computational overhead. We implement the developed approach to a series of numerical experiments, including a 3D example based on the Volve field in the North Sea. For data assimilation methods that involve iterative schemes, such as ensemble smoothers with multiple data assimilation or iterative forms of ensemble Kalman filter, the proposed approach offers a computationally competitive alternative. Our results show that a fully low-dimensional implementation of ensemble data assimilation using deep learning architectures offers several advantages compared to standard algorithms, including joint data-parameter reduction that respects the salient features in each space, geologically consistent feature-based updates, increased ensemble sizes to improve the accuracy and computational efficiency of the calculated statistics for the update step.

Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network

The mountainous snow cover is highly variable at all temporal and spatial scales. Snowpack models only imperfectly represent this variability, because of uncertain meteorological inputs, physical parameterisations, and unresolved terrain features. In-situ observations of the height of snow (HS), despite their limited representativeness, could help constrain intermediate and large scale modelling errors by means of data assimilation. In this work, we assimilate HS observations from an in-situ network of 295 stations covering the French Alps, Pyrenees and Andorra, over the period 2009–2019. In view of assimilating such observations into a spatialised snow cover modelling framework, we investigate whether such observations can be used to correct neighbouring snowpack simulations. We use CrocO, an ensemble data assimilation framework of snow cover modelling, based on a Particle Filter suited to the propagation of information from observed to unobserved areas. This ensemble system already benefits from meteorological observations, assimilated within SAFRAN analysis scheme. CrocO also proposes various localisation strategies to assimilate snow observations. These approaches are evaluated in a Leave-One-Out setup against the operational deterministic model and its ensemble open-loop counterpart, both running without HS assimilation. Results show that intermediate localisation radius of 35–50 km yield a slightly lower root mean square error (RMSE), and a better Spread-Skill than the strategy assimilating all the observations from a whole mountain range. Significant continuous ranked probability score (CRPS) improvements of about 13 % are obtained in the areas where the open-loop modelling errors are the largest, e.g. the Haute-Ariège, Andorra and the Extreme Southern Alps. Over these areas, weather station observations are generally sparser, resulting in more uncertain meteorological analyses, and therefore snow simulations. In-situ HS observations thus shows an interesting complementarity with meteorological observations to better constrain snow cover simulations over large areas.

Ensemble Riemannian data assimilation over the Wasserstein space

In this paper, we present an ensemble data assimilation paradigm over a Riemannian manifold equipped with the Wasserstein metric. Unlike the Euclidean distance used in classic data assimilation methodologies, the Wasserstein metric can capture the translation and difference between the shapes of square-integrable probability distributions of the background state and observations. This enables us to formally penalize geophysical biases in state space with non-Gaussian distributions. The new approach is applied to dissipative and chaotic evolutionary dynamics, and its potential advantages and limitations are highlighted compared to the classic ensemble data assimilation approaches under systematic errors.

Prediksi Tweet Netizen Menggunakan Random Forest, Decision Tree, Naïve Bayes, dan Ensemble Algorithm

Gubernur DKI Jakarta saat ini, meski sudah terpilih sejak tahun 2017 selalu menarik untuk dibicarakan atau bahkan dikomentari. Komentar yang muncul berasal dari media secara langsung atau melalui media sosial. Twitter menjadi salah satu media sosial yang sering digunakan sebagai media untuk mengomentari gubernur terpilih bahkan bisa menjadi trending topic di media sosial Twitter. Netizen yang berkomentar pun beragam, ada yang selalu menge-Tweet kritik, ada yang berkomentar Positif, dan ada pula yang hanya me-retweet. Dalam penelitian ini, prediksi apakah Netizen aktif akan cenderung selalu menimbulkan komentar Positif atau Negatif akan dilakukan dalam penelitian ini. Model algoritma yang digunakan adalah Decision Tree, Naïve Bayes, Random Forest, dan juga Ensemble. Data Twitter yang diolah harus melalui preprocessing terlebih dahulu sebelum dilanjutkan menggunakan Rapidminer. Dalam uji coba menggunakan Rapidminer dilakukan dalam empat kali uji coba dengan membagi menjadi dua bagian yaitu data testing dan data latih. Perbandingan yang dilakukan adalah 10% data pengujian: 90% data pelatihan, kemudian 20% data pengujian: 80% data pelatihan, kemudian 30% data pengujian: 70% data pelatihan, dan yang terakhir adalah 35% data pengujian: 65% data pelatihan. Rata-rata Akurasi untuk algoritma Decision Tree adalah 93,15%, sedangkan untuk algoritma Naïve Bayes Akurasinya adalah 91,55%, kemudian untuk algoritma Random Forest adalah 93,41, dan yang terakhir adalah algoritma Ensemble dengan Akurasi sebesar 93,42%. sini. 65% data pelatihan. Rata-rata Akurasi untuk algoritma Decision Tree adalah 93,15%, sedangkan untuk algoritma Naïve Bayes Akurasinya adalah 91,55%, kemudian untuk algoritma Random Forest adalah 93,41, dan yang terakhir adalah algoritma Ensemble dengan Akurasi sebesar 93,42%. sini. 65% data pelatihan. Rata-rata Akurasi untuk algoritma Decision Tree adalah 93,15%, sedangkan untuk algoritma Naïve Bayes Akurasinya adalah 91,55%, kemudian untuk algoritma Random Forest adalah 93,41, dan yang terakhir adalah algoritma Ensemble dengan Akurasi sebesar 93,42%. sini.

Spatiotemporal Variations of Water Vapor Content and Its Relationship with Meteorological Elements in the Third Pole

Water vapor content plays an important role in climate change and the ecosystem in the Tibetan Plateau (TP) through its complicated interaction with the meteorological elements. However, due to the complex topography of the Tibetan Plateau, it is unreliable to attempt to understand the variation pattern of water vapor content using only observational data. Satellite and reanalysis data can be a good substitute for observational data, but their accuracy still needs to be evaluated. Therefore, based on radiosonde stations data, comprehensive assessment of water vapor content on the TP and surrounding areas derived from ERA-5, Second Modern-Era Retrospective analysis for Research and Applications (MERRA2), Atmospheric Infrared Sounder (AIRS)-only, and weighted ensemble data was performed in the context of spatial and temporal distribution at the annual and seasonal scale. Based on precipitation from Gauge V3.0 and Tropical Rainfall Measuring Mission satellite (TRMM) and temperature from ERA-5, the relationship between water vapor content and temperature and precipitation was analyzed. The results show that water vapor content decreases from southeast to northwest, and ERA-5, MERRA2, and AIRS-only can reasonably reproduce the spatial distribution of annual and seasonal water vapor content, with ERA-5 being more reliable in reproducing the spatial distribution. Over the past 50 years, the water vapor content has shown a gradual increasing trend. The variation trends of AIRS-only, MERRA2, ERA-5, and weighted ensemble data are almost consistent with the radiosonde stations data, with MERRA2 being more reliable in capturing water vapor content over time. Weighted ensemble data is more capable of capturing water vapor content characteristics than simple unweighted products. The empirical orthogonal function (EOF) analysis shows that the first spatial mode values of water vapor content and temperature are positive over the TP, while the values of precipitation present a “negative-positive-negative” distribution from south to north over the TP. In the second spatial mode of EOF analysis, the values of water vapor content, air temperature, and precipitation are all negative. The first temporal modes of EOF analysis, water vapor content, air temperature, and precipitation all show an increasing trend. In conclusion, there is a clear relationship of water vapor content with temperature and precipitation.