Visualization and Quantitative Assessment to Verify Precipitation Forecasts Associated with Mid-Latitude Cyclones across the Eastern United States

Analysis was conducted to verify forecast against observation precipitation associated with mid-latitude cyclones over the Eastern US in winter and spring 2013 using Geographic Information Systems (GIS). The forecast data are day two 24-hour Quantitative Precipitation Forecasts (QPF) produced by the Global Forecast System (GFS) model. The analysis methods produced categorical geographic error maps of hits, misses and false alarms in spatial relation to the mid-latitude cyclones and traditional verification scores for each day. A hypothesis test was also performed to determine if the GFS mean forecast precipitation over the study area is significantly different from the mean observed precipitation during mid-latitude cyclones. The spatial verification maps, as an analytical and visualization tool, provided evidences on geographical relationship between correct predictions (hits and correct negatives) and incorrect predictions (misses and false alarms). Working together with quantitative scores and hypothesis test, spatial verification maps reveal that the GFS model has a tendency to over forecast precipitation coverage associated with mid-latitude cyclones over the Eastern US and often moves the mid-latitude cyclones too fast.

Genetic Fuzzy Based Scalable System of Distributed Robots for a Collaborative Task

This paper introduces a new genetic fuzzy based paradigm for developing scalable set of decentralized homogenous robots for a collaborative task. In this work, the number of robots in the team can be changed without any additional training. The dynamic problem considered in this work involves multiple stationary robots that are assigned with the goal of bringing a common effector, which is physically connected to each of these robots through cables, to any arbitrary target position within the workspace of the robots. The robots do not communicate with each other. This means that each robot has no explicit knowledge of the actions of the other robots in the team. At any instant, the robots only have information related to the common effector and the target. Genetic Fuzzy System (GFS) framework is used to train controllers for the robots to achieve the common goal. The same GFS model is shared among all robots. This way, we take advantage of the homogeneity of the robots to reduce the training parameters. This also provides the capability to scale to any team size without any additional training. This paper shows the effectiveness of this methodology by testing the system on an extensive set of cases involving teams with different number of robots. Although the robots are stationary, the GFS framework presented in this paper does not put any restriction on the placement of the robots. This paper describes the scalable GFS framework and its applicability across a wide set of cases involving a variety of team sizes and robot locations. We also show results in the case of moving targets.

Tropical and Extratropical Cyclone Detection Using Deep Learning

Extracting valuable information from large sets of diverse meteorological data is a time-intensive process. Machine-learning methods can help to improve both speed and accuracy of this process. Specifically, deep-learning image-segmentation models using the U-Net structure perform faster and can identify areas that are missed by more restrictive approaches, such as expert hand-labeling and a priori heuristic methods. This paper discusses four different state-of-the-art U-Net models designed for detection of tropical and extratropical cyclone regions of interest (ROI) from two separate input sources: total precipitable water output from the Global Forecast System (GFS) model and water vapor radiance images from the Geostationary Operational Environmental Satellite (GOES). These models are referred to as International Best Track Archive for Climate Stewardship (IBTrACS)-GFS, Heuristic-GFS, IBTrACS-GOES, and Heuristic-GOES. All four U-Nets are fast information extraction tools and perform with an ROI detection accuracy ranging from 80% to 99%. These are additionally evaluated with the Dice and Tversky intersection-over-union (IoU) metrics, having Dice coefficient scores ranging from 0.51 to 0.76 and Tversky coefficients ranging from 0.56 to 0.74. The extratropical cyclone U-Net model performed 3 times as fast as the comparable heuristic model used to detect the same ROI. The U-Nets were specifically selected for their capabilities in detecting cyclone ROI beyond the scope of the training labels. These machine-learning models identified more ambiguous and active ROI missed by the heuristic model and hand-labeling methods that are commonly used in generating real-time weather alerts, having a potentially direct impact on public safety.

Validação das previsões de três ondas de frio pelo modelo GFS no Centro-Norte do Estado de São Paulo e Triângulo Mineiro

As ondas de frio são períodos de declínio acentuado da temperatura do ar que podem causar prejuízos econômicos e problemas de saúde pública. Por isso, a previsão desses sistemas atmosféricos se faz importante. Nesse sentido, o objetivo do presente estudo é validar a previsão do modelo GFS em três casos de ondas de frio (OF) sobre Estado de São Paulo e Triângulo Mineiro. Para isso, foi feita a análise sinótica de três OF a partir das condições iniciais do GFS, e de parâmetros observados em 22 estações meteorológicas automáticas (EMA) do INMET, que foram agrupadas em três categorias dependendo do tipo de relevo (topo, encosta e baixada). Foram validadas as previsões de 72, 48 e 24 horas dos campos sinóticos em relação às análises do modelo e aos dados medidos em EMA. As análises estatísticas indicaram que nos três casos houve um viés predominantemente negativo na previsão de T2M, sendo mais acentuado no horário das 0900 UTC, próximo do horário da temperatura mínima; as EMA de baixada apresentaram um erro médio maior. Nos campos sinóticos, foi constatado nos três casos que o viés das variáveis atmosféricas tende a diminuir com a aproximação do dia pico da OF, com maiores erros na previsão de 72 horas. Validation of the forecasting of three cold waves by GFS model over Center-North of São Paulo State and Triângulo Mineiro A B S T R A C TCold waves are periods of sharp decline in air temperature that can cause economic damage and public health problems. Therefore, the forecast of these atmospheric systems is important. Then, the objective of the present study is to validate the prediction of the Global Forecast System (GFS) model in three cases of cold waves (OF) over São Paulo State and Triângulo Mineiro. For this purpose, synoptic analysis of the three OF was made through the initial conditions of the GFS and parameters observed in 22 automatic meteorological stations (EMA) from INMET, which were grouped into three categories depending on the type of topography (top, slope and valley). The 72, 48 and 24-hour predictions of the synoptic fields were validated in relation to the analysis of the model and the data measured at EMA. Statistical analysis showed a negative bias in the T2M forecast in the three cases of OF bias, being more pronounced at 0900 UTC, close to the minimum temperature time; EMA located in valley had a higher average error. In the synoptic fields, the bias of atmospheric variables tends to decrease with the approach of the peak day of OF, with greater errors in the 72-hour forecast.Keywords: cold waves, southeastern Brazil, weather forecast, GFS model

High resolution modelling of severe wind patterns over the north-west Black Sea Region

The research studies the performance of the convective-permitting Harmonie model in reproducing mesoscale features of the wind regime over the north-western part of the Black Sea. It allowed establishing the optimum configuration, projection and geometry of the model's high spatial resolution area over Ukrainian part of the Black Sea, preparing a digital format for a coastline based on the resolution and conducting numerical experiments to verify informativeness and stability of computations. It also presents a detailed description of the forecasting procedure which includes a data flow from the Meteorological Archiving and Retrieving System (MARS) of the European Center, creating boundary conditions, forecast computations and a model output composition for the particular region and domain resolution. The results have shown that the Harmonie model with the 2.5 km spatial resolution and the 60 second time step is able to reproduce detailed spatial variability of a near-surface wind field and its evolution to the corresponding scales. In particular, the model is able to simulate mesoscale circulation features of approximately ten km over a homogeneous sea surface and to track their evolution; to monitor the position of convergence zones; to highlight the spatial characteristics of a lee-side wind attenuation band along the coast line when wind blows from the shore; to specify mesoscale wind patterns in bays and along the coastline with complex orography; to reproduce the weakening of a wind velocity over an urban area due to increased surface roughness. Two operational forecasting systems, GFS-WRF and ARPEGE/IFS-Harmonie are compared by the following components: numerical solvers, sub-grid parameterizations, efficiency of computer resources and intellectual potential. The GFS model output with the 25 km spatial resolution is able to correctly reproduce over the region only large-scale atmospheric patterns. However, for rapid changes in the atmospheric circulation accompanied by changes in the wind direction to the opposite and wind increase, the model simulations are delayed in terms of wind field evolution. Additionally, because of crude spatial resolution, the GFS model is unable to describe mesoscale atmospheric features due to differences in surface types, orography, thermal contrasts, etc. Comparison of the both model outputs versus observations from Odesa, Chornomorsk and Yuzhnyi port during severe wind conditions has shown that the discrepancy between the models and observations within the allowable error value (5 m/s) occurred only for Odesa port with regard to the Harmonie model for weak wind velocity. The difference partially increases for moderate wind from the shore, while for strong wind from east and south directions indicates disagreement between the model results and observations and achieves critical values of 20-25 m/s. Such values are mainly determined by the discrepancy in wind direction (up to 180°). The comparison results clearly indicate the doubtful representativeness of wind observations at Chornomorsk and Yuzhnyi stations in general, and at Odesa station in particular.