The message passing version of ECMWF's weather forecast model

2005 ◽  
pp. 299-304 ◽  
Author(s):  
Saulo R. M. Barros ◽  
David Dent ◽  
Lars Isaksen ◽  
Guy Robinson ◽  
Fritz G. Wollenweber
Keyword(s):  
Message Passing ◽  
Weather Forecast ◽  
Forecast Model ◽  
Weather Forecast Model

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 On the high resolution regional weather forecast model (HRM) and forecasting tropical cyclone motion over the south china sea

2005 ◽  
Vol 27 (4) ◽  
pp. 193-203
Author(s):  
Le Duc ◽  
Le Cong Thang ◽  
Kieu Thi Xin
Keyword(s):  
High Resolution ◽  
South China Sea ◽  
South China ◽  
Weather Forecast ◽  
Forecast Model ◽  
The South China Sea ◽  
The South ◽  
China Sea ◽  
Weather Forecast Model ◽  
Slow Moving

Chan (1995) [2] has found that, only 70% in 60 cases of the tropical cyclone (TC) movement test (TMT-90) developed from steering flows. The 30% remain of cases have to be explained by nonbarotropic processes. We are of the opinion that all weak, slow-moving and unexpected changing TCs over the South China Sea are in this 30% set. The nonlinear interaction between barotropic and nonbarotropic processes has affected on motion and structure of such TCs. In this paper, we use the high resolution weather forecast model (HRM), which is able to simulate meso-scale phenomena in limited regions, to predict motion of TCs in the South China Sea in 2002-2004, including two typical weak, slow-moving and unexpected changing TCs Mekhala and Nepartak. We have chosen two forecast domains with different areas and resolutions. The results show that with the smaller domain, appropriate buffer and higher resolution HRM can predict better motion of TCs operating in the South China Sea.

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.

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