scholarly journals Statistical downscaling of MM5 model output to better assess avalanche threats

2010 ◽  
Vol 51 (54) ◽  
pp. 14-18 ◽  
Author(s):  
K. Srinivasan ◽  
Ajay Kumar ◽  
Jyoti Verma ◽  
Ashwagosha Ganju
Keyword(s):  
Statistical Downscaling ◽  
Winter Season ◽  
Threat Assessment ◽  
Weather Forecast ◽  
Western Himalaya ◽  
Forecast Model ◽  
Weather Data ◽  
Model Output ◽  
Mm5 Model ◽  
Weather Forecast Model

AbstractIn this study, we use MM5 weather-forecast model output and observed surface weather data from 11 stations in the western Himalaya to develop a statistical downscaling model (SDM) to better predict precipitation, 10 m wind speed and 2 m temperature. The analysis covers three consecutive winters: 2004/05, 2005/06 and 2006/07. The performance of the SDM was assessed using an independent dataset from the 2007/08 winter season. This assessment shows that the SDM technique substantially improves the forecast over specific station locations, which is important for avalanche-threat assessment.

Real-Time Weather Analytics

2020 ◽  
Vol 17 (4) ◽  
pp. 15-31
Author(s):  
Lavanya K. ◽  
Sathyan Venkatanarayanan ◽  
Anay Anand Bhoraskar
Keyword(s):  
Data Storage ◽  
Weather Forecasting ◽  
Modern Science ◽  
Weather Forecast ◽  
Forecast Model ◽  
Data Availability ◽  
Third Party ◽  
Weather Data ◽  
Storage Devices ◽  
Weather Forecast Model

Weather forecasting is one of the biggest challenges that modern science is still contending with. The advent of high-power computing, technical advancement of data storage devices, and incumbent reduction in the storage cost have accelerated data collection to turmoil. In this background, many artificial intelligence techniques have been developed and opened interesting window of opportunity in hitherto difficult areas. India is on the cusp of a major technology overhaul with millions of people's data availability who were earlier unconnected with the internet. The country needs to fast forward the innovative use of available data. The proposed model endeavors to forecast temperature, precipitation, and other vital information for usability in the agrarian sector. This project intends to develop a robust weather forecast model that learns automatically from the daily feed of weather data that is input through a third-party API source. The weather feed is sourced from openweathermap, an online service that provides weather data, and is streamed into the forecast model through Kafka components. The LSTM neural network used by the forecast model is designed to continuously learn from predictions and perform actual analysis. The model can be architected to be implemented across very large applications having the capability to process large volumes of streamed or stored data.

High-resolution forest fire weather index computations using satellite remote sensing

2003 ◽  
Vol 33 (6) ◽  
pp. 1134-1143 ◽  
Author(s):  
Kyung-Soo Han ◽  
Alain A Viau ◽  
François Anctil
Keyword(s):  
Remote Sensing ◽  
Relative Humidity ◽  
Weather Forecast ◽  
Forecast Model ◽  
Meteorological Station ◽  
Fire Weather ◽  
Model Output ◽  
Weather Index ◽  
Fire Weather Index ◽  
Weather Forecast Model

Wildfires are important in regions dominated by forest, such as found in large parts of Canada. The principal objective of this study was to provide homogeneously distributed indices for the Canadian Fire Weather Index (FWI) System. The FWI was calculated using four sets of input variables: meteorological station measurements (OBS); weather forecast model output (SIM); meteorological station measurements and remote sensing estimations combined (SAT1); and weather forecast model output and remote sensing estimations combined (SAT2). Remote sensing parameterization of air temperature and relative humidity was performed. The air temperature and relative humidity reproduced showed good agreement with ground-based measurements (R2 = 0.77 and SE = 1.48°C; R2 = 0.73 and SE = 5%, respectively). For the FWI regionalized using this requirement, category SAT1 showed the best fit. Category SAT2 produced more precise results (0.09 to 2.19% of the normalized root mean square error) versus SIM.

scholarly journals Some Experimental Lessons on Digital Filtering in the ALADIN-France 3DVAR Based on Near-Ground Examination

2011 ◽  
Vol 139 (3) ◽  
pp. 774-785 ◽  
Author(s):  
Claude Fischer ◽  
Ludovic Auger
Keyword(s):  
Data Assimilation ◽  
Digital Filter ◽  
Three Dimensional ◽  
Weather Forecast ◽  
Forecast Model ◽  
Horizontal Wind ◽  
Screen Level ◽  
Incremental Formulation ◽  
Weather Forecast Model ◽  
Observation Impact

Abstract This paper deals with the characteristics and effects of digital filter initialization, as implemented in the operational three-dimensional variational data assimilation (3DVAR) system of the Aire Limitée Adaptation Dynamique Développement International (ALADIN)-France regional weather forecast model. First, a series of findings on the properties of the initialization of the model are discussed. Examples of initial spinup linked with inertia–gravity wave occurrence are shown, and the major sources for their generation are listed. These experimental results are compared with past and present experiences concerning the use and need for digital filter initialization. Furthermore, the impacts of switching to an incremental formulation of the filter in data assimilation mode are demonstrated. Second, the effects of the filter formulation on the results of an observation impact study are illustrated. The latter consists of implementing screen-level, 10-m horizontal wind information into the ALADIN 3DVAR analysis. There can, indeed, be some delicate interference between observation impact evaluation and the effects of filtering, at least on short-term forecasts. The paper is concluded with some general considerations on the experimental evaluation of spinup and the link between the assimilation system design and model state filtering.

scholarly journals A Multi-purpose Weather Forecast Model for the Mondsee Catchment

GI_Forum ◽  
2015 ◽  
Vol 1 ◽  
pp. 600-609 ◽  
Author(s):  
Hermann Klug ◽  
Liviu Oana
Keyword(s):  
Weather Forecast ◽  
Forecast Model ◽  
Weather Forecast Model

scholarly journals Simulation of seasonal dynamics of the global electric circuit diurnal variation

2019 ◽  
Vol 55 (5) ◽  
pp. 76-84
Author(s):  
N. V. Ilin ◽  
M. V. Shatalina ◽  
N. N. Slyunyaev
Keyword(s):  
Diurnal Variation ◽  
Northern Hemisphere ◽  
Seasonal Dynamics ◽  
Annual Variation ◽  
Winter Season ◽  
Electric Circuit ◽  
Weather Forecast ◽  
Forecast Model ◽  
Global Electric Circuit ◽  
Average Value

Based on the ionospheric potential (IP) parameterization, the seasonal dynamics of the diurnal variation of IP for 20162017 were modeled for the first time using the numerical weather forecast model WRF-ARW. The diurnal variation of the IP, averaged over the annual simulation periods, shows good agreement with the classical Carnegie curve. The proposed parametrization correctly reproduces the basic characteristics of the stationary global electric circuit generators. The annual variation does not show a precise repeatability from year to year, but in the winter season of the Northern Hemisphere a lower IP value was obtained, and in the summer - an increased one. The model diurnal variation demonstrates stable seasonal trends, and in the northern hemisphere, the variation is characterized by only one strongly distinguished maximum IP in the 16-18 UTC area of ~120% of the average value, while in the summer season the daily variation curve has two maxima with smaller value (~ 107% of average): morning at 89 UTC and evening at 1820 UTC. The model annual variation of the diurnal variation agrees with the experimental data of the surface field measuring in Antarctica in the period 20062011. The proposed parametrization and modeling technique made possible the accurate reproduction of the IP variation maximums times, their seasonal variability, and decreasing of the amplitude of variation in the summer period of the Northern Hemisphere.

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