scholarly journals PREVISÃO DE UM CASO DE CICLOGÊNESE BOMBA NO ATLÂNTICO SUL, ATRAVÉS DO MODELO WRF COM ASSIMILAÇÃO VARIACIONAL E INICIALIZAÇÃO POR FILTRO DIGITAL

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Lucas D'Avila Marten ◽  
Francieli Jorge ◽  
Karin Marques ◽  
Fabricio Pereira Harter
Keyword(s):  
Research Data ◽  
Data Archive ◽  
Global Forecast System ◽  
Forecast System ◽  
Weather Research

O presente trabalho tem como objetivo avaliar o impacto da assimilação de dados pelo método variacional, na simulação do Weather Research and Forescast (WRF), em um caso de ciclogênese explosivo, ocorrida no sul do Brasil em 30 de junho de 2020. Avalia-se também a capacidade do Filtro Digital (FD), com a janela de Dolph-Chebyshev (FDDC), em filtrar ondas de gravidade espúrias nas soluções do modelo. A condição inicial para integração do WRF é gerada pelo Global Forecast System (GFS) das 12 UTC, fornecida pelo National Center for Enviroment Prediction (NCEP). Os dados de assimilação foram obtidos através das redes de compartilhamento mundial de dados do Research data Archive (RDA), além dos dados das estações da região sul do Brasil acessadas pelo Banco de Dados Meteorológicos do Instituto Nacional de Meteorologia (INMET). Foram definidos 4 experimentos, EXP1 - previsões do WRF, EXP2 - WRF com o Filtro Digital (WRFDF), EXP3 - WRF com assimilação variacional tridimensional (WRFDA), EXP4 - WRF com assimilação variacional tridimensional e Filtro Digital (WRFDADF). O Filtro Digital mostrou-se uma importante metodologia para eliminação do ruído, gerado pelo desequilíbrio entre os campos de massa e vento, no começo da integração do modelo. Destaca-se a diminuição do erro nos experimentos com assimilação de dados, em especial no vento em superfície.

scholarly journals Comparación entre la Componente Atmosférica del Sistema HWRF y el Modelo WRF-HWRF Utilizando Diferentes Resoluciones Horizontales en la Simulación del Huracán Irma (2017). Parte I

2021 ◽  
Author(s):  
Albenis Pérez-Alarcón ◽  
Oscar Díaz-Rodríguez ◽  
José Carlos Fernández-Alvarez ◽  
Ramón Pérez-Suárez ◽  
Patricia Coll-Hidalgo
Keyword(s):  
Mesoscale Model ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Global Forecast System ◽  
Forecast System ◽  
Estudio De Caso ◽  
Weather Research

Resumen Se realizó un estudio de caso de varias configuraciones de modelos de pronóstico numérico para evaluar la habilidad de los mismos en el pronóstico de la intensidad y trayectoria de los ciclones tropicales. Para ello se seleccionaron 4 configuraciones del dominio de cómputo con 27-9 y 18-6 km de resolución para el HWRF (Hurricane Weather Research and Forecasting Model) y 4 configuraciones para el WRF (Weather Research and Forecasting Model), empleando el núcleo dinámico NMM (Non-hydrostatic Mesoscale Model) con la opción de seguimiento de vórtice. Se realizaron las simulaciones correspondientes al huracán Irma desde el 1ro al 12 de septiembre del 2017 inicializadas con salidas de pronóstico del GFS (Global Forecast System). En la evaluación realizada no se observaron diferencias notables entre las 8 configuraciones, aunque fue deficiente el pronóstico de la intensidad del huracán Irma, con un error en el pronóstico de la velocidad máxima del viento superior a los 50 km/h. La comparación de las salidas de cada configuración con los registros de las boyas y estaciones meteorológicas de superficie evidenció que el comportamiento de las variables viento y presión atmosférica tiene una tendencia similar a los valores registrados en las estaciones, con errores inferiores a los 3.8 m/s para la velocidad del viento y 3 hPa para la presión atmosférica. La configuración que mostró mejor habilidad para el pronóstico de ciclones tropicales fue HWRF_18-6-m (referida al modelo y la resolución horizontal empleada), aunque es la que más capacidad de cómputo requiere.

scholarly journals PENGARUH ASIMILASI DATA PENGINDERAAN JAUH (RADAR DAN SATELIT) PADA PREDIKSI CUACA NUMERIK UNTUK ESTIMASI CURAH HUJAN (IMPACT OF REMOTE SENSING DATA ASSIMILATION (RADAR AND SATELLITE) ON NUMERICAL WEATHER PREDICTION FOR RAINFALL ESTIMATION)

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
Jaka A. I. Paski
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Initial Conditions ◽  
Weather Research And Forecasting ◽  
Observation Data ◽  
Initial Condition ◽  
Global Forecast System ◽  
Forecast System ◽  
Numerical Weather ◽  
Weather Research

One of the main problems in numerical weather modeling was the inaccuracy of initial condition data (initial conditions). This study reinforced the influence of assimilation of remote sensing observation data on initial conditions for predictive numerical rainfall in BMKG radar area Tangerang (Province of Banten and DKI Jakarta) on January 24, 2016. The procedure applied to rainfall forecast was the Weather Research and Forecasting model (WRF) with a down-to-down multi-nesting technique from Global Forecast System (GFS) output, the model was assimilated to radar and satellite image observation data using WRF Data Assimilation (WRFDA) 3DVAR system. Data was used as preliminary data from surface observation data, EEC C-Band radar data, AMSU-A satellite sensor data and MHS sensors. The analysis was done qualitatively by looking at the measurement scale. Observation data was used to know rainfall data. The results of the study showed that producing rainfall predictions with different assimilation of data produced different predictions. In general, there were improvements in the rainfall predictions with assimilation of satellite data was showing the best results. Abstrak Salah satu masalah utama pada pemodelan cuaca numerik adalah ketidak-akuratan data kondisi awal (initial condition). Penelitian ini menguji pengaruh asimilasi data observasi penginderaan jauh pada kondisi awal untuk prediksi numerik curah hujan di wilayah cakupan radar cuaca BMKG Tangerang (Provinsi Banten dan DKI Jakarta) pada 24 Januari 2016. Prosedur yang diterapkan pada prakiraan curah hujan adalah model Weather Research and Forecasting (WRF) dengan teknik multi-nesting yang di-downscale dari keluaran Global Forecast System (GFS), model ini diasimilasikan dengan data hasil observasi citra radar dan satelit menggunakan WRF Data Assimilation (WRFDA) sistem 3DVAR. Data yang digunakan sebagai kondisi awal berasal dari data observasi permukaan, data C-Band radar EEC, data satelit sensor AMSU-A dan sensor MHS. Analisis dilakukan secara kualitatif dengan melihat nilai prediksi spasial distribusi hujan terhadap data observasi GSMaP serta metode bias curah hujan antara model dan observasi digunakan untuk mengevaluasi pengaruh data asimilasi untuk prediksi curah hujan. Hasil penelitian yang diperoleh menunjukkan prediksi curah hujan dengan asimilasi data yang berbeda menghasilkan prediksi yang juga berbeda. Secara umum, asimilasi data penginderaan jauh memberikan perbaikan hasil prediksi estimasi curah hujan di mana asimilasi menggunakan data satelit menunjukan hasil yang paling baik.

In defense of decentralized research data management

Neuroforum ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Michael Hanke ◽  
Franco Pestilli ◽  
Adina S. Wagner ◽  
Christopher J. Markiewicz ◽  
Jean-Baptiste Poline ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Management ◽  
Research Data ◽  
Data Archive ◽  
Individual Scientist ◽  
Computing Platform ◽  
Research Data Management ◽  
Cloud Computing Platform ◽  
Research Objects ◽  
Central Services

Abstract Decentralized research data management (dRDM) systems handle digital research objects across participating nodes without critically relying on central services. We present four perspectives in defense of dRDM, illustrating that, in contrast to centralized or federated research data management solutions, a dRDM system based on heterogeneous but interoperable components can offer a sustainable, resilient, inclusive, and adaptive infrastructure for scientific stakeholders: An individual scientist or laboratory, a research institute, a domain data archive or cloud computing platform, and a collaborative multisite consortium. All perspectives share the use of a common, self-contained, portable data structure as an abstraction from current technology and service choices. In conjunction, the four perspectives review how varying requirements of independent scientific stakeholders can be addressed by a scalable, uniform dRDM solution and present a working system as an exemplary implementation.

Evaluation of Precipitation Forecast from Global Forecast System Over Transboundary Rivers in Africa

2021 ◽  
Author(s):  
Haowen Yue ◽  
Mekonnen Gebremichael
Keyword(s):  
Correlation Coefficients ◽  
Precipitation Forecast ◽  
Nile River ◽  
Lead Times ◽  
General Tendency ◽  
Global Forecast System ◽  
Forecast System ◽  
Transboundary River Basins ◽  
Unbiased Estimates ◽  
Good Agreement

<p>This study evaluates the short-to-medium range precipitation forecasts from Global Forecast System for 14 major transboundary river basins in Africa against GPM IMERG “Early”, IMERG “Final”, and CHIRPSv2 products. Daily precipitation forecasts with lead times of 1-day, 5-day, 10-day, and 15-day and accumulated precipitation forecasts with periods of 1-day, 5-day, 10-day, and 15-day are investigated. The 14 selected basins are (1) Senegal; (2) Volta; (3) Niger; (4) Chad; (5) Nile; (6) Awash; (7) Congo; (8) Omo Gibe; (9) Tana; (10) Pangani; (11) Zambezi; (12) Okavango; (13) Limpopo and (14) Orange. For each basin, several sub-basins are defined by the major dams in the basin. Our preliminary results in the Nile river basin show that in terms of temporal variability, there was a good agreement between the forecasted and observed accumulated precipitation on a 15-day basis. When compared to IMERG “Final”, the correlation coefficients of accumulated GFS forecasts scored as high as 0.75. Thus, GFS products provide relatively reliable accumulated precipitation forecasts. However, the precipitation forecasts were mostly biased: they tend to overpredict rainfall for the eastern part of the Nile river, underestimate rainfall for the northern part of the Nile river and produce almost unbiased estimates for the southern part of the river. Additionally, GFS forecasts have a general tendency to underpredict the area of precipitation across the Nile basin. Although the performance of GFS varies at different locations, the GFS precipitation forecasts can be a good reference to dam operators in Africa. </p>

Evaluation of Global Forecast System (GFS) Medium-Range Precipitation Forecasts in the Nile River Basin

2021 ◽  
Author(s):  
Haowen Yue ◽  
Mekonnen Gebremichael ◽  
Vahid Nourani
Keyword(s):  
Temporal Dynamics ◽  
Disease Outbreaks ◽  
Rain Gauge ◽  
Global Forecast System ◽  
Forecast System ◽  
Forecast Performance ◽  
Weather Forecasts ◽  
Rain Gauges ◽  
Basin Scale ◽  
Medium Range

Abstract Reliable weather forecasts are valuable in a number of applications, such as, agriculture, hydropower, and weather-related disease outbreaks. Global weather forecasts are widely used, but detailed evaluation over specific regions is paramount for users and operational centers to enhance the usability of forecasts and improve their accuracy. This study presents evaluation of the Global Forecast System (GFS) medium-range (1 day – 15 day) precipitation forecasts in the nine sub-basins of the Nile basin using NASA’s Integrated Multi-satellitE Retrievals (IMERG) “Final Run” satellite-gauge merged rainfall observations. The GFS products are available at a temporal resolution of 3-6 hours, spatial resolution of 0.25°, and its version-15 products are available since 12 June 2019. GFS forecasts are evaluated at a temporal scale of 1-15 days, spatial scale of 0.25° to all the way to the sub-basin scale, and for a period of one year (15 June 2019 – 15 June 2020). The results show that performance of the 1-day lead daily basin-averaged GFS forecast performance, as measured through the modified Kling-Gupta Efficiency (KGE), is poor (0 < KGE < 0.5) for most of the sub-basins. The factors contributing to the low performance are: (1) large overestimation bias in watersheds located in wet climate regimes in the northern hemispheres (Millennium watershed, Upper Atbara & Setit watershed, and Khashm El Gibra watershed), and (2) lower ability in capturing the temporal dynamics of watershed-averaged rainfall that have smaller watershed areas (Roseires at 14,110 sq. km and Sennar at 13,895 sq. km). GFS has better bias for watersheds located in the dry parts of the northern hemisphere or wet parts of the southern hemisphere, and better ability in capturing the temporal dynamics of watershed-average rainfall for large watershed areas. IMERG Early has better bias than GFS forecast for the Millennium watershed but still comparable and worse bias for the Upper Atbara & Setit, and Khashm El Gibra watersheds. The variation in the performance of the IMERG Early could be partly explained by the number of rain gauges used in the reference IMERG Final product, as 16 rain gauges were used for the Millennium watershed but only one rain gauge over each Upper Atbara & Setit, and Khashm El Gibra watershed. A simple climatological bias-correction of IMERG Early reduces in the bias in IMERG Early over most watersheds, but not all watersheds. We recommend exploring methods to increase the performance of GFS forecasts, including post-processing techniques through the use of both near-real-time and research-version satellite rainfall products.

scholarly journals Intensidade do vento de dois ciclones tropicais obtida por diferentes conjuntos de dados

2020 ◽  
Vol 13 (1) ◽  
pp. 067
Author(s):  
Christie Andre Souza ◽  
Michelle Simões Reboita
Keyword(s):  
Tropical Cyclones ◽  
The Other ◽  
Data Sets ◽  
Global Forecast System ◽  
Forecast System ◽  
Wind Intensity

Os ciclones tropicais quando atingem ventos com intensidade igual ou superior a 119 km/h desenvolvem uma estrutura conhecida como olho em seu centro. Já os ventos mais intensos do sistema são encontrados imediatamente após o olho. Num estudo recente para os ciclones Haiyan e Haima foi levantada a questão da qualidade dos dados do Global Forecast System (GFS) em representar os ventos uma vez que os ventos máximos apareceram no olho do sistema. Diante disso, esse estudo tem como objetivo avaliar como diferentes conjuntos de dados (GFS, ERA5, ERA-Interim e CCMP) representam os ventos nesses dois ciclones tropicais. A ERA5 e o GFS mostram ventos mais intensos nos ciclones do que os outros dois conjuntos de dados. Todos, exceto o GFS, mostram claramente ventos mais fracos no olho dos ciclones.  Wind intensity of two tropical cyclones obtained by different data sets A B S T R A C TWhen the tropical cyclones reach winds with intensity equal or higher than 119 km/h, they develop a structure known as the eye at its center. The strongest winds in the system are found immediately after the eye. In a recent study for Haiyan and Haima cyclones, the question of the quality of the Global Forecast System (GFS) data in representing the winds once the maximum winds appeared in the system eye was raised. Therefore, this study aims to evaluate how different data sets (GFS, ERA5, ERA-Interim and CCMP) represent the winds in these two tropical cyclones. ERA5 and GFS show cyclones with more intense winds than the other datasets. Except the GFS, the other data clearly show weaker winds in the cyclone eye.Keywords: analyzes; cyclones; meteorology; reanalysis

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