Juan Ruiz

Centro de Investigaciones del Mar y la Atmósfera (CIMA/CONICET-UBA) | CIMA / CONICET-UBA

Observations that are assimilated into numerical weather prediction systems are conformed by numerous data sets and their impact should be objectively evaluated. This can be efficiently achieved by the Forecast Sensitivity to Observation Impact (FSOI) methodology. In this study we explore the application of the ensemble formulation of FSOI (EFSOI)...

In this study, the assimilation of tide gauge and altimetry data into a two‐dimensional barotropic numerical model for the southwestern Atlantic continental shelf (SWACS) was developed. To do this, the preoperative 4‐day storm surges ensemble prediction system developed by Dinápoli et al. (2021, Journal of the Royal Meteorological Society 147 : 557...

Quantifying forecast uncertainty is a key aspect of state-of-the-art numerical weather prediction and data assimilation systems. Ensemble-based data assimilation systems incorporate state-dependent uncertainty quantification based on multiple model integrations. However, this approach is demanding in terms of computations and development. In this w...

The improvement of numerical weather forecasts is a key element to predict high-impact weather events, associated with deep moist convection. The observations that are assimilated into numerical weather prediction systems are conformed by numerous data sets and their impact should be objectively evaluated. This can be efficiently estimated by the F...

Atmospheric electrical activity is one of the most damaging meteorological phenomena. Studies suggest that storm electrical discharges are correlated with severe weather events such as hail, strong surface winds, etc. To study these correlations, in addition to real data, numerical simulations can be used. In this work, we investigated the electric...

This paper evaluates the impact of assimilating high-resolution surface networks and satellite observations using the WRF-GSI-LETKF over central and north eastern Argentina where the surface and upper air observing networks are relatively coarse. A case study corresponding to a huge mesoscale convective system (MCS) that developed during November 2...

Data assimilation is a relevant framework to merge a dynamical model with noisy observations. When various models are in competition, the question is to find the model that best matches the observations. This matching can be measured by using the model evidence, defined by the likelihood of the observations given the model. This study explores the...

Ensemble forecasting is, so far, the most successful approach to produce relevant forecasts with an estimation of their uncertainty. The main limitations of ensemble forecasting are the high computational cost and the difficulty to capture and quantify different sources of uncertainty, particularly those associated with model errors. In this work w...

Some of the strongest storms on Earth occur over central-eastern Argentina. These storms are associated with severe weather phenomena such as big hail, heavy rain, intense wind gusts, and, occasionally, tornadoes. The relatively recent expansion of the radar network in this area brings the chance to better characterize severe weather storms. This s...

The errors in numerical weather forecasts resulting from limited ensemble size are explored using 1000~member forecasts of convective weather over Germany at 3 km resolution. A large number of forecast variables at different lead times were examined, and their distributions could be classified into three categories: quasi‐normal (e.g. tropospheric...

To represent the complex individual interactions in the dynamics of disease spread informed by data, the coupling of an epidemiological agent-based model with the ensemble Kalman filter is proposed. The statistical inference of the propagation of a disease by means of ensemble-based data assimilation systems has been studied in previous works. The...

Data assimilation is a relevant framework to merge a dynamical model with noisy observations. When various models are in competition, the question is to find the model that best matches the observations. This matching can be measured by using the model evidence, defined by the likelihood of the observations given the model. This study explores the...

Abstract ID and Title: 912270: Classification models for supercell detection based on machine learning Final Abstract Number: A15Q-05 Presentation Type: Online Poster Session Number and Title: A15Q: Machine Learning for Weather and Climate: Predictions and Applications III Poster Location: Online Only; Session Date and Time: Monday, 13 Decembe...

A fundamental aspect of data assimilation techniques is the quantification of forecast error uncertainty, as this has a major impact on the quality of the analysis produced and, consequently, on the forecasts generated from it. Most of the current operational assimilation systems obtain a state-dependent uncertainty quantification based on ensemble...

Producing an accurate weather forecast and a reliable quantification of its uncertainty is an open scientific challenge. Ensemble forecasting is, so far, the most successful approach to produce relevant forecasts along with an estimation of their uncertainty. The main limitations of ensemble forecasting are the high computational cost and the diffi...

Non-Gaussian forecast error is a challenge for ensemble-based data assimilation (DA), particularly for more nonlinear convective dynamics. In this study, we investigate the degree of the non-Gaussianity of forecast error distributions at 1 km resolution using a 1000-member ensemble Kalman filter, and how it is affected by the DA update frequency an...

Drylines are a frequent feature of the atmospheric circulation in Argentina. Recent work described their climatological characteristics and synoptic-scale processes associated with their formation. However, little is known about the mesoscale processes that are relevant for their life cycle. In this paper, we investigate the influence of these mech...

To represent the complex individual interactions in the dynamics of disease spread informed by data, the coupling of an epidemiological agent-based model with the ensemble Kalman filter is proposed. The statistical inference of the propagation of a disease by means of ensemble-based data assimilation systems has been studied in previous work. The m...

This paper describes the lessons learned from the implementation of a regional ensemble data assimilation and forecast system during the intensive observing period of the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign (central Argentina, November-December...

Observations that are assimilated into numerical weather prediction systems are conformed by numerous data sets and the impact of the observations must be objectively evaluated. The Forecast Sensitivity to Observation (FSO) provides an efficient impact evaluation of each observation on forecasts. This study proposes applying the simpler ensemble fo...

El estudio aquí presentado fue impulsado en el marco de un proyecto para mejorar la estimación de la precipitación en Argentina y se concentra en la estructura espacial de los patrones de precipitación. En esta línea, se aplicó una metodología para estimar las distancias de correlación de la precipitación diaria basada en el cálculo de un coeficien...

Analyzing the evolution of thunderstorms is critical in determining the potential for the development of severe weather events. Existing visualization systems for short‐term weather forecasting (nowcasting) allow for basic analysis and prediction of storm developments. However, they lack advanced visual features for efficient decision‐making. We de...

Quantifying forecast uncertainty is a key aspect of state-of-the-art data assimilation systems which has a large impact on the quality of the analysis and then the following forecast. In recent years, most operational data assimilation systems incorporate state-dependent uncertainty quantification approaches based on 4-dimensional variational appro...

This article provides an overview of the experimental design, execution, education and public outreach, data collection, and initial scientific results from the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign. RELAMPAGO was a major field campaign conducted...

This study investigates the impact of applying different types of initial and boundary perturbations for convective-scale ensemble data assimilation systems. Several OSSEs were performed with a 2-km horizontal resolution, considering a realistic environment, taking model error into account, and combining different perturbations' types with warm/col...

Non-Gaussian forecast error is a challenge for ensemble-based data assimilation (DA), particularly for more nonlinear convective dynamics. In this study, we investigate the degree of non-Gaussianity of forecast error distributions at 1-km resolution using a 1000-member ensemble Kalman filter, and how it is affected by the DA update frequency and ob...

En la presente Nota Técnica se describe el sistema de control de calidad para datos de radares meteorológicos implementado en el Servicio Meteorológico Nacional de Argentina. En particular, se abordan las principales características de los filtros desarrollados para corregir en la variable reflectividad fenómenos asociados a interferencias electrom...

En la presente Nota Técnica se describe el esquema de implementación del sistema de control de calidad de datos de radar meteorológico descrito en Arruti y otros (2021). Se listan los requerimientos relevados de distintos usuarios para realizar una ejecución operativa, a continuación se describe el diseño de arquitectura elegida para responder las...

Uncertainty quantification in numerical weather and climate prediction is usually achieved using a Monte Carlo estimation (i.e., ensemble forecasting) of the forecast probability distribution function of the state of the system. In this work, we present a method for uncertainty quantification based on neural networks and using a likelihood-based lo...

Specification of suitable initial conditions to accurately forecast high-impact weather events associated with intense thunderstorms still poses a significant challenge for convective-scale forecasting. Radar data assimilation has been showing encouraging results to produce an accurate estimate of the state of the atmosphere at the mesoscale, as it...

This study presents the first convective‐scale 1000‐member ensemble simulation over central Europe, which provides a unique data set for various applications. A comparison to the operational regional 40‐member ensemble of Deutscher Wetterdienst shows that the 1000‐member simulation overall exhibits realistic spread properties. Based on this, we dis...

Quantitative volcanic ash cloud forecasts are prone to uncertainties coming from the source term quantification (e.g., the eruption strength or vertical distribution of the emitted particles), with consequent implications for an operational ash impact assessment. We present an ensemble-based data assimilation and forecast system for volcanic ash di...

Central and northern Argentina are one of the most favorable regions for the occurrence of high impact meteorological events. These events can generate intense precipitation, large hail and/or extreme winds, causing enormous damages to the population. Therefore, it is essential to make progress in improving very short-term weather forecasts (0-6 ho...

Drylines have been identified as relevant synoptic-scale phenomena that frequently occur in several regions around the world. Despite previous works and the experience of local forecasters that recognizes the occurrence of drylines in Argentina and suggests its possible association with convection initiation, knowledge about the mechanisms leading...

One of the big challenges in numerical weather prediction is to reduce the uncertainty in the initial conditions. At the National Meteorological Service (SMN) of Argentina, many efforts have been carried out to address this issue. In this work, the regional Local Ensemble Transform Kalman Filter coupled with the Weather Research and Forecasting mod...

Quantitative volcanic ash cloud forecasts are prone to uncertainties coming from the source term quantification (e.g. eruption strength or vertical distribution of the emitted particles), with consequent implications on operational ash impact assessment. We present an ensemble-based data assimilation and forecast system for volcanic ash dispersal a...

Stochastic parametrizations are increasingly used to represent the uncertainty associated with model errors in ensemble forecasting and data assimilation. One of the challenges associated with the use of these parametrizations is the characterization of the statistical properties of the stochastic processes within their formulation. In this work, a...

With the aim of developing a rapid refresh regional data assimilation system over Argentina which could be used and tested during RELAMPAGO field campaign, some experiments are presented using different types of data for an intense precipitation case study. Short range quantitative probabilistic forecasts are skillful when compared against the IMER...

Stochastic parameterizations are increasingly being used to represent the uncertainty associated with model errors in ensemble forecasting and data assimilation. One of the challenges associated with the use of these parameterizations is the optimization of the properties of the stochastic forcings within their formulation. In this work a hierarchi...

Low visibility events are sometimes associated with delays and accidents related with air and land transportation. An accurate forecast of low visibility events can help to reduce the economical and human life losses associated with this phenomenon. This work contributes to the improvement of visibility forecast proposing a dynamic-statistical mode...

Durante el mes de junio de 1967 aire frío de origen polar avanzó desde el continente antártico hacia el centro de Argentina, donde dejó temperaturas mínimas extremas, nevadas y cuantiosos daños a los cultivos. Cincuenta años después, este trabajo revisita dicha situación sinóptica haciendo uso de datos de reanálisis y modelado numérico con el fin d...

Probabilistic weather forecasts are amongst the most popular ways to quantify numerical forecast uncertainties. The analog regression method can quantify uncertainties and express them as probabilities. The method comprises the analysis of errors from a large database of past forecasts generated with a specific numerical model and observational dat...

In recent years, there has been a growing interest in applying data assimilation (DA) methods, originally designed for state estimation, to the model selection problem. Along these lines, Carrassi et al. (2017) introduced the contextual formulation of model evidence (CME) and showed that CME can be efficiently computed using a hierarchy of ensemble...

One of the big challenges in numerical weather prediction is to reduce the uncertainty in the initial conditions. At the National Meteorological Service (SMN) of Argentina, many efforts have been carried out to address this issue. In this work, the regional Local Ensemble Transform Kalman Filter coupled with the Weather Research and Forecasting mod...

One of the big challenges in numerical weather prediction is to reduce the uncertainty in the initial conditions. Another one, is to represent all the dynamical and physical processes that take place on the atmosphere with an adequate resolution. At the National Meteorological Service (SMN) of Argentina many efforts have been carried out to progres...

Los eventos de visibilidad reducida producen complicaciones y accidentes en el transporte aéreo y terrestre. Por tal motivo, su pronóstico ayuda a reducir las pérdidas materiales y humanas asociadas a dichos fenómenos. El presente estudio contribuye a mejorar el pronóstico de visibilidad mediante un modelo dinámico-estadístico que produce pronóstic...

Following the invention of the telegraph, electronic computer, and remote sensing, “big data” is bringing another revolution to weather prediction. As sensor and computer technologies advance, orders of magnitude bigger data are produced by new sensors and high-precision computer simulation or “big simulation.” Data assimilation (DA) is a key to nu...

Oceanic and atmospheric global numerical models represent explicitly the large-scale dynamics while the smaller-scale processes are not resolved so that their effects in the large-scale dynamics are included through subgrid-scale parameterizations. These parameterizations represent small-scale effects as a function of the resolved variables. In thi...

We describe a new approach allowing for systematic causal attribution of weather and climate-related events, in near-real time. The method is purposely designed to facilitate its implementation at meteorological centers by relying on data treatments that are routinely performed when numerically forecasting the weather. Namely, we show that causal a...

Sudden local severe weather is a threat, and we explore what the highest-end supercomputing and sensing technologies can do to address this challenge. Here we show that using the Japanese flagship “K” supercomputer, we can synergistically integrate “big simulations” of 100 parallel simulations of a convective weather system at 100-m grid spacing an...

The phased-array weather radar (PAWR) is a new-generation weather radar that can make a 100-m-resolution three-dimensional (3D) volume scan every 30 s for 100 vertical levels, producing ~100 times more data than the conventional parabolic-antenna radar with a volume scan typically made every 5 min for 15 scan levels. This study takes advantage of o...

Improving the initial conditions of short-range numerical weather prediction (NWP) models is one of the main goals of the meteorological community. Development of data assimilation and ensemble forecast systems is essential in any national weather service (NWS). In this sense, the local ensemble transform Kalman filter (LETKF) is a methodology that...

Very short-term numerical weather forecasts are useful for identifying potential development areas of high impact weather events associated with deep convections (e.g. convective systems, strong winds, tornados, etc.) that can significantly affect people and their activities. One way to generate adequate forecasts is to use high spatial resolution...

During November-December 2012, high-resolution (4 km-38 vertical levels), convection-allowing 48 hours WRF-ARW forecasts were produced at the National Weather Service of Argentina. The aim of this paper is to evaluate hourly quantitative precipitation forecasts to assess the model performance on representing its location, intensity, spatial variabi...

We perform a quantitative comparison between the FALL3D volcanic ash dispersion model and satellite retrievals during the first days of the 2011 Cordón Caulle eruption. The ash dispersion model was initialized using WRF-ARW meteorological fields with 15 km horizontal resolution and eruption column heights estimated using GOES-13 Imager radiances an...

Weather conditions affect multiple aspects of human life such as economy, safety, security, and social activities. For this reason, weather forecast plays a major role in society. Currently weather forecasts are based on Numerical Weather Prediction (NWP) models that generate a representation of the atmospheric flow. Interactive visualization of ge...

When a volcanic eruption occurs the emitted particles are injected into the atmosphere and because of gravitational effect they settle down. The biggest particles (>64 mm) follow ballistic trajectories, while the others are advected by wind. The volcanic plume, that can have a horizontal extension of a few meters to hundred of kilometers, are mainl...

Severe convective storms have devastating consequences for the human environment, exposure and vulnerability of the population to high impact weather events (HIW) is increasing as a result of population growth, urbanization and climate change. Improving our ability to understand the processes capable of triggering storms and mitigate their conseque...

This work presents the features of the high-resolution forecast system implemented experimentally at the National Meteorological Service (NMS). The main aim is to show, with a case study, the operational products and tools produced, their potential and prospects for new developments to forecast high-impact weather events (HIWE) over the center and...

This study develops and tests a quality control (QC) algorithm for reflectivity from the single polarization phased array weather radar (PAWR) in Osaka, with particular focus on clutter detection, in preparation for radar data assimilation into a high resolution numerical model. The QC algorithm employs a Bayesian classification that combines the i...