The Galactic Center Weather Forecast

In accretion-based models for Sgr A* the X-ray, infrared, and millimeter emission arise in a hot, geometrically thick accretion flow close to the black hole. The spectrum and size of the source depend on the black hole mass accretion rate $\dot{M}$. Since Gillessen et al. have recently discovered a cloud moving toward Sgr A* that will arrive in summer 2013, $\dot{M}$ may increase from its present value $\dot{M}_0$. We therefore reconsider the "best-bet" accretion model of Moscibrodzka et al., which is based on a general relativistic MHD flow model and fully relativistic radiative transfer, for a range of $\dot{M}$. We find that for modest increases in $\dot{M}$ the characteristic ring of emission due to the photon orbit becomes brighter, more extended, and easier to detect by the planned Event Horizon Telescope submm VLBI experiment. If $\dot{M} \gtrsim 8 \dot{M}_0$ this "silhouette of the black hole will be hidden beneath the synchrotron photosphere at 230 GHz, and for $\dot{M} \gtrsim 16 \dot{M}_0$ the silhouette is hidden at 345 GHz. We also find that for $\dot{M} > 2 \dot{M}_0$ the near-horizon accretion flow becomes a persistent X-ray and mid-infrared source, and in the near-infrared Sgr A* will acquire a persistent component that is brighter than currently observed flares.Comment: 15 pages, 5 figures, accepted to ApJ Letter

A weather forecast model accuracy analysis and ECMWF enhancement proposal by neural network

This paper presents a neural network approach for weather forecast improvement. Predicted parameters, such as air temperature or precipitation, play a crucial role not only in the transportation sector but they also influence people's everyday activities. Numerical weather models require real measured data for the correct forecast run. This data is obtained from automatic weather stations by intelligent sensors. Sensor data collection and its processing is a necessity for finding the optimal weather conditions estimation. The European Centre for Medium-Range Weather Forecasts (ECMWF) model serves as the main base for medium-range predictions among the European countries. This model is capable of providing forecast up to 10 days with horizontal resolution of 9 km. Although ECMWF is currently the global weather system with the highest horizontal resolution, this resolution is still two times worse than the one offered by limited area (regional) numeric models (e.g., ALADIN that is used in many European and north African countries). They use global forecasting model and sensor-based weather monitoring network as the input parameters (global atmospheric situation at regional model geographic boundaries, description of atmospheric condition in numerical form), and because the analysed area is much smaller (typically one country), computing power allows them to use even higher resolution for key meteorological parameters prediction. However, the forecast data obtained from regional models are available only for a specific country, and end-users cannot find them all in one place. Furthermore, not all members provide open access to these data. Since the ECMWF model is commercial, several web services offer it free of charge. Additionally, because this model delivers forecast prediction for the whole of Europe (and for the whole world, too), this attitude is more user-friendly and attractive for potential customers. Therefore, the proposed novel hybrid method based on machine learning is capable of increasing ECMWF forecast outputs accuracy to the same level as limited area models provide, and it can deliver a more accurate forecast in real-time.Web of Science1923art. no. 514

Weather and Climate Summary and Forecast: January 2020 Report

This report provides a summary of the weather and climate forecast for January 2020. It includes forecast information specific to the Pacific Northwest and the western United States, as well as forecast information for other portions of the United States and abroad

Weather and Climate Summary and Forecast: November 2019 Report

This report provides a summary of the weather and climate forecast for November 2019. It includes forecast information specific to the Pacific Northwest and the western United States, as well as forecast information for other portions of the United States and abroad

Comparison of nonhomogeneous regression models for probabilistic wind speed forecasting

In weather forecasting, nonhomogeneous regression is used to statistically postprocess forecast ensembles in order to obtain calibrated predictive distributions. For wind speed forecasts, the regression model is given by a truncated normal distribution where location and spread are derived from the ensemble. This paper proposes two alternative approaches which utilize the generalized extreme value (GEV) distribution. A direct alternative to the truncated normal regression is to apply a predictive distribution from the GEV family, while a regime switching approach based on the median of the forecast ensemble incorporates both distributions. In a case study on daily maximum wind speed over Germany with the forecast ensemble from the European Centre for Medium-Range Weather Forecasts, all three approaches provide calibrated and sharp predictive distributions with the regime switching approach showing the highest skill in the upper tail

A study for systematic errors of the GLA forecast model in tropical regions

From the sensitivity studies performed with the Goddard Laboratory for Atmospheres (GLA) analysis/forecast system, it was revealed that the forecast errors in the tropics affect the ability to forecast midlatitude weather in some cases. Apparently, the forecast errors occurring in the tropics can propagate to midlatitudes. Therefore, the systematic error analysis of the GLA forecast system becomes a necessary step in improving the model's forecast performance. The major effort of this study is to examine the possible impact of the hydrological-cycle forecast error on dynamical fields in the GLA forecast system