Numerical simulation of birch pollen dispersion with an operational weather forecast system

2008 ◽  
Vol 52 (8) ◽  
pp. 805-814 ◽  
Author(s):  
Heike Vogel ◽  
Andreas Pauling ◽  
Bernhard Vogel
Keyword(s):  
Numerical Simulation ◽  
Birch Pollen ◽  
Weather Forecast ◽  
Forecast System ◽  
Pollen Dispersion

Numerical modelling of birch pollen dispersion in Canada

2020 ◽  
pp. 110554
Author(s):  
Alain Robichaud ◽  
Paul Comtois
Keyword(s):  
Numerical Modelling ◽  
Birch Pollen ◽  
Pollen Dispersion

scholarly journals Detection and thermal description of medicanes from numerical simulation

2014 ◽  
Vol 14 (5) ◽  
pp. 1059-1070 ◽  
Author(s):  
M. A. Picornell ◽  
J. Campins ◽  
A. Jansà
Keyword(s):  
Numerical Simulation ◽  
Thermal Structure ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Small Scale ◽  
Medium Range Weather Forecast ◽  
Warm Core ◽  
Strong Winds ◽  
Nwp Model ◽  
Ecmwf Model

Abstract. Tropical-like cyclones rarely affect the Mediterranean region but they can produce strong winds and heavy precipitations. These warm-core cyclones, called MEDICANES (MEDIterranean hurriCANES), are small in size, develop over the sea and are infrequent. For these reasons, the detection and forecast of medicanes are a difficult task and many efforts have been devoted to identify them. The goals of this work are to contribute to a proper description of these structures and to develop some criteria to identify medicanes from numerical weather prediction (NWP) model outputs. To do that, existing methodologies for detecting, characterizating and tracking cyclones have been adapted to small-scale intense cyclonic perturbations. First, a mesocyclone detection and tracking algorithm has been modified to select intense cyclones. Next, the parameters that define the Hart's cyclone phase diagram are tuned and calculated to examine their thermal structure. Four well-known medicane events have been described from numerical simulation outputs of the European Centre for Medium-Range Weather Forecast (ECMWF) model. The predicted cyclones and their evolution have been validated against available observational data and numerical analyses from the literature.

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

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

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.

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

scholarly journals MACC regional multi-model ensemble simulations of birch pollen dispersion in Europe

2015 ◽  
Vol 15 (6) ◽  
pp. 8243-8281 ◽  
Author(s):  
M. Sofiev ◽  
U. Berger ◽  
M. Prank ◽  
J. Vira ◽  
J. Arteta ◽  
...  
Keyword(s):  
Source Term ◽  
Birch Pollen ◽  
Model Ensemble ◽  
Robust Performance ◽  
Pollen Dispersion ◽  
Ensemble Simulations ◽  
Pollen Concentrations ◽  
Ensemble Mean ◽  
Modelling Experiment ◽  
The Impact

Abstract. The paper presents the first-ever ensemble modelling experiment for the birch pollen in Europe. The 7-models strong European ensemble of MACC-ENS, tested in trial simulations over the season of 2010, has been run through the season of 2013. The simulations have been compared with observations in 11 countries, members of European Aeroallergen Network, for both individual models and the ensemble mean and median. It is shown that the models successfully reproduced the timing of the very late season of 2013, generally being within a couple of days from the observed season start. End of the season was generally predicted later than observed, for 5 days or more, which is a known feature of the source term used in the study. Absolute pollen concentrations during the season were somewhat under-estimated in the southern part of the birch habitation area. In the northern part of Europe, a record-low pollen season was strongly over-estimated by all models. Median of the multi-model ensemble demonstrated robust performance, successfully eliminating the impact of outliers, which was particularly useful since for most of models this was the first experience of pollen forecasting.

scholarly journals Determining Economical Irrigation Depths in a Sandy Field Using a Combination of Weather Forecast and Numerical Simulation

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3403
Author(s):  
Hassan M. Abd El Baki ◽  
Haruyuki Fujimaki
Keyword(s):  
Numerical Simulation ◽  
Numerical Scheme ◽  
Crop Production ◽  
Irrigation System ◽  
Weather Forecast ◽  
Net Income ◽  
Irrigation Amount ◽  
Green Fodder ◽  
Fodder Yield ◽  
Automated Irrigation System

Advancement of modern technologies has given numerical simulations a crucial role to effectively manage irrigation. A new numerical scheme to determine irrigation depths was incorporated into WASH 2D, which is a numerical simulation model of crop response to irrigation. Based on two predicted points of cumulative transpiration—water price and quantitative weather forecast—the scheme can optimize an irrigation depth in which net income is maximized. A field experiment was carried out at the Arid Land Research Center, Tottori, Japan, in 2019, to evaluate the effectiveness of the scheme on net income and crop production compared to a tensiometer-based automated irrigation system. Sweetcorn (Zea mays L., Amaenbou 86) was grown in three water balance lysimeters per each treatment, filled with sandy soil. The scheme could achieve a 4% higher net income, due to a 7% increase in green fodder yield, and an 11% reduction in irrigation amount, compared with the automated irrigation method. These results indicate that the numerical scheme, in combination with quantitative weather forecasts, can be a useful tool to determine irrigation depths, maximize net incomes which are farmers’ targets, and avoid large investments that are required for the automated irrigation system.

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