ECPC’s Global to Regional Fire Weather Forecast System

2003 ◽  
pp. 609-613
Author(s):  
John Roads ◽  
Shyn-Chin Chen ◽  
Jack Ritchie ◽  
F. Fujioka ◽  
H. Juang ◽  
...  
Keyword(s):  
Weather Forecast ◽  
Fire Weather ◽  
Forecast System

Design of the Efficient Archival Warehouse for Weather Forecast System

2011 ◽  
pp. 749-762 ◽  
Author(s):  
Slawomir Walkowiak ◽  
Lukasz Ligowski ◽  
Konrad Wawruch ◽  
Witold R. Rudnicki
Keyword(s):  
Weather Forecast ◽  
Forecast System

scholarly journals Classification of Active Fires and Weather Conditions in the Lower Amur River Basin

2020 ◽  
Vol 12 (19) ◽  
pp. 3204
Author(s):  
Hiroshi Hayasaka ◽  
Galina V. Sokolova ◽  
Andrey Ostroukhov ◽  
Daisuke Naito
Keyword(s):  
River Basin ◽  
Boreal Forests ◽  
Weather Condition ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Amur River Basin ◽  
Fire Weather ◽  
Strong Wind ◽  
Amur River ◽  
Active Fire

Most wildland fires in boreal forests occur during summer, but major fires in the lower Amur River Basin of the southern Khabarovsk Krai (SKK) mainly occur in spring. To reduce active fires in the SKK, we carried out daily analysis of MODIS (Moderate Resolution Imaging Spectroradiometer) hotspot (HS) data and various weather charts. HS data of 17 years from 2003 were used to identify the average seasonal fire occurrence. Active fire-periods were extracted by considering the number of daily HSs and their continuity. Weather charts, temperature maps, and wind maps during the top 12 active fire-periods were examined to clarify each fire weather condition. Analysis results showed that there were four active fire-periods that occurred in April, May, July, and October. Weather charts during the top active fire-periods showed active fires in April and October occurred under strong wind conditions (these wind velocities were over 30 km h−1) related to low-pressure systems. The very active summer fire at the end of June 2012 occurred related to warm air mass advection promoted by large westerly meandering. We showed clear fire weather conditions in the SKK from March to October. If a proper fire weather forecast is developed based on our results, more efficient and timely firefighting can be carried out.

scholarly journals Development of Verification Methodology for Extreme Weather Forecasts

2017 ◽  
Vol 32 (2) ◽  
pp. 479-491 ◽  
Author(s):  
Hong Guan ◽  
Yuejian Zhu
Keyword(s):  
Extreme Precipitation ◽  
Weather Forecast ◽  
Extreme Weather ◽  
Ensemble Forecast ◽  
Precipitation Forecast ◽  
Forecast System ◽  
Extreme Precipitation Events ◽  
Cold Events ◽  
Extreme Cold ◽  
Verification Methodology

Abstract In 2006, the statistical postprocessing of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) and North American Ensemble Forecast System (NAEFS) was implemented to enhance probabilistic guidance. Anomaly forecasting (ANF) is one of the NAEFS products, generated from bias-corrected ensemble forecasts and reanalysis climatology. The extreme forecast index (EFI), based on a raw ensemble forecast and model-based climatology, is another way to build an extreme weather forecast. In this work, the ANF and EFI algorithms are applied to extreme cold temperature and extreme precipitation forecasts during the winter of 2013/14. A highly correlated relationship between the ANF and EFI allows the determination of two sets of thresholds to identify extreme cold and extreme precipitation events for the two algorithms. An EFI of −0.78 (0.687) is approximately equivalent to a −2σ (0.95) ANF for the extreme cold event (extreme precipitation) forecast. The performances of the two algorithms in forecasting extreme cold events are verified against analysis for different model versions, reference climatology, and forecasts. The verification results during the winter of 2013/14 indicate that ANF forecasts more extreme cold events with a slightly higher skill than EFI. The bias-corrected forecast performs much better than the raw forecast. The current upgrade of the GEFS has a beneficial effect on the extreme cold weather forecast. Using the NCEP Climate Forecast System Reanalysis and Reforecast (CFSRR) as a climate reference gives a slightly better score than the 40-yr reanalysis. The verification methodology is also extended to an extreme precipitation case, showing a broad potential use in the future.

scholarly journals Fire weather index: the skill provided by ECMWF ensemble prediction system

2020 ◽  
Author(s):  
Francesca Di Giuseppe ◽  
Claudia Vitolo ◽  
Blazej Krzeminski ◽  
Jesús San-Miguel
Keyword(s):  
Weather Forecast ◽  
Research Centre ◽  
Ensemble Prediction ◽  
Fire Weather ◽  
Prediction System ◽  
Weather Index ◽  
Fire Weather Index ◽  
Ensemble Prediction System ◽  
Medium Range Weather Forecast ◽  
Medium Range

Abstract. In the framework of the EU Copernicus program, the European Centre for Medium-range Weather Forecast (ECMWF) on behalf of the Joint Research Centre (JRC) is forecasting daily fire weather indices using its medium range ensemble prediction system. The use of weather forecast in place of local observations can extend early warnings up to 1–2 weeks allowing for greater proactive coordination of resource-sharing and mobilization within and across countries. Using one year of pre-operational service in 2017 and the fire weather index (FWI) here we assess the capability of the system globally and analyze in detail three major events in Chile, Portugal and California. The analysis shows that the skill provided by the ensemble forecast system extends to more than 10 days when compared to the use of mean climate making a case of extending the forecast range to the sub-seasonal to seasonal time scale. However accurate FWI prediction does not translate into accuracy in the forecast of fire activity globally. Indeed when all 2017 detected fires are considered, including agricultural and human induced burning, high FWI values only occurs in 50 % of the cases and only in Boreal regions. Nevertheless for very important events mostly driven by weather condition, FWI forecast provides advance warning that could be instrumental in setting up management strategies.

The Performance of Geoinformation Software for Fire Weather Forecast

2019 ◽  
Author(s):  
Vladimir Glagolev ◽  
Ruslan Bazhenov ◽  
Dmitry Luchaninov ◽  
Natalya Parkhomenko ◽  
Olga Ivanova
Keyword(s):  
Weather Forecast ◽  
Fire Weather

Mobile Decision Support for Time-Critical Decision Making

2011 ◽  
pp. 638-644 ◽  
Author(s):  
Frada Burstein ◽  
J. Cowie
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Weather Forecasting ◽  
Weather Forecast ◽  
Expert Advice ◽  
Fire Weather ◽  
Wide Availability ◽  
Information And Communication ◽  
Time Critical ◽  
Available Information

The wide availability of advanced information and communication technology has made it possible for users to expect a much wider access to decision support. Since the context of decision making is not necessarily restricted to the office desktop, decision support facilities have to be provided through access to technology anywhere, anytime, and through a variety of mediums. The spread of e-services and wireless devices has increased accessibility to data, and in turn, influenced the way in which users make decisions while on the move, especially in time-critical situations. For example, on site decision support for fire weather forecasting during bushfires can include real-time evaluation of quality of local fire weather forecast in terms of accuracy and reliability. Such decision support can include simulated scenarios indicating the probability of fire spreading over nearby areas that rely on data collected locally at the scene and broader data from the regional and national offices. Decision Support Systems (DSS) available on mobile devices, which triage nurses can rely on for immediate, expert advice based on available information, can minimise delay in actions and errors in triage at emergency departments (Cowie & Godley, 2006).

Impact of the Different Components of 4DVAR on the Global Forecast System of the Meteorological Service of Canada

2007 ◽  
Vol 135 (6) ◽  
pp. 2355-2364 ◽  
Author(s):  
Stéphane Laroche ◽  
Pierre Gauthier ◽  
Monique Tanguay ◽  
Simon Pellerin ◽  
Josée Morneau
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Forecast ◽  
Forecast Model ◽  
Variational Data Assimilation ◽  
Global Forecast System ◽  
Forecast System ◽  
Medium Range Weather Forecast ◽  
Medium Range ◽  
The Impact

Abstract A four-dimensional variational data assimilation (4DVAR) scheme has recently been implemented in the medium-range weather forecast system of the Meteorological Service of Canada (MSC). The new scheme is now composed of several additional and improved features as compared with the three-dimensional variational data assimilation (3DVAR): the first guess at the appropriate time from the full-resolution model trajectory is used to calculate the misfit to the observations; the tangent linear of the forecast model and its adjoint are employed to propagate the analysis increment and the gradient of the cost function over the 6-h assimilation window; a comprehensive set of simplified physical parameterizations is used during the final minimization process; and the number of frequently reported data, in particular satellite data, has substantially increased. The impact of these 4DVAR components on the forecast skill is reported in this article. This is achieved by comparing data assimilation configurations that range in complexity from the former 3DVAR with the implemented 4DVAR over a 1-month period. It is shown that the implementation of the tangent-linear model and its adjoint as well as the increased number of observations are the two features of the new 4DVAR that contribute the most to the forecast improvement. All the other components provide marginal though positive impact. 4DVAR does not improve the medium-range forecast of tropical storms in general and tends to amplify the existing, too early extratropical transition often observed in the MSC global forecast system with 3DVAR. It is shown that this recurrent problem is, however, more sensitive to the forecast model than the data assimilation scheme employed in this system. Finally, the impact of using a shorter cutoff time for the reception of observations, as the one used in the operational context for the 0000 and 1200 UTC forecasts, is more detrimental with 4DVAR. This result indicates that 4DVAR is more sensitive to observations at the end of the assimilation window than 3DVAR.

scholarly journals Perbandingan Model Kopel ECMWF System 4 Dan CFSV2 Untuk Prediksi Musim Di Indonesia

Megasains ◽  
2020 ◽  
Vol 11 (01) ◽  
pp. 1-11
Author(s):  
Robi Muharsyah
Keyword(s):  
Weather Forecast ◽  
Global Precipitation Climatology Project ◽  
Southeast Asian ◽  
Climate Forecast ◽  
Post Processing ◽  
Forecast System ◽  
Climate Forecast System ◽  
Precipitation Climatology ◽  
Climate Assessment ◽  
Global Precipitation

Kajian ini bertujuan untuk membandingkan curah hujan harian keluaran langsung (raw) dari dua model kopel: European Center Medium Weather Forecast System 4 (S4) dan Climate Forecast System Version 2 (CFSv2) sebagai model prediksi musim operasional pada periode Juni, Juli dan Agustus (JJA) dan Desember, Januari dan Februari (DJF). Kemampuan kedua model diukur berdasarkan ketersediaan prediksi reforecast yang diverifikasi terhadap data observasi curah hujan Global Precipitation Climatology Project (GPCP) dan Southeast Asian Observation - Southeast Asian Climate Assessment and Dataset (SA-OBS SACAD) untuk wilayah Bumi Maritim Indonesia (BMI). Ukuran verifikasi yang dipakai berupa bias aktual, bias relatif, spread anggota ensemble dalam bentuk boxplot dan akumulasi curah hujan per musim, serta korelasi spasial. Hasilnya, untuk DJF, kemampuan kedua model cenderung overestimate untuk wilayah perairan di sekitar tipe-C. Sebaliknya, untuk prediksi curah hujan di daratan keduanya underestimate. Sementara itu, untuk JJA, bias kedua model saling berkebalikan khususnya di pulau Kalimantan. Kajian ini juga menggunakan metode post-processing statistik koreksi bias untuk mengetahui pengaruhnya terhadap semua anggota ensemble pada kedua model

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