Design of the Efficient Archival Warehouse for Weather Forecast System

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.

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

International Journal of Biometeorology ◽

10.1007/s00484-008-0174-3 ◽

2008 ◽

Vol 52 (8) ◽

pp. 805-814 ◽

Cited By ~ 63

Author(s):

Heike Vogel ◽

Andreas Pauling ◽

Bernhard Vogel

Keyword(s):

Numerical Simulation ◽

Birch Pollen ◽

Weather Forecast ◽

Forecast System ◽

Pollen Dispersion

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

Monthly Weather Review ◽

10.1175/mwr3408.1 ◽

2007 ◽

Vol 135 (6) ◽

pp. 2355-2364 ◽

Cited By ~ 33

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.

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

Megasains ◽

10.46824/megasains.v11i01.3 ◽

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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