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

Forecasting Model ◽

Global Forecast System ◽

Forecast System ◽

Estudio De Caso ◽

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.

Performance Comparison of High Resolution Weather Research and Forecasting Model Output with North American Mesoscale Model Initialization Grid Forecasts

10.21236/ada561957 ◽

2012 ◽

Author(s):

John Raby ◽

Jeff Passner ◽

Gail Vaucher ◽

Yasmina Raby

Keyword(s):

High Resolution ◽

North American ◽

Mesoscale Model ◽

Performance Comparison ◽

Forecasting Model ◽

Model Output ◽

Model Initialization ◽

Change in Weather Research and Forecasting (WRF) Model Accuracy with Age of Input Data from the Global Forecast System (GFS)

10.21236/ad1016607 ◽

2016 ◽

Cited By ~ 1

Author(s):

James L. Cogan

Keyword(s):

Input Data ◽

Wrf Model ◽

Global Forecast System ◽

Forecast System ◽

Model Accuracy ◽

Development of “Physical Parametrizations with PYthon” (PPPY, version 1.1) and its usage to reduce the time-step dependency in a microphysical scheme

Geoscientific Model Development ◽

10.5194/gmd-13-443-2020 ◽

2020 ◽

Vol 13 (2) ◽

pp. 443-460

Author(s):

Sébastien Riette

Keyword(s):

Climate Model ◽

Mesoscale Model ◽

Small Scale ◽

Forecasting Model ◽

Time Step ◽

Numerical Weather ◽

Numerical Weather Model ◽

Weather Model ◽

Abstract. To help develop and compare physical parametrizations such as those found in a numerical weather or climate model, a new tool was developed. This tool provides a framework with which to plug external parametrizations, run them in an offline mode (using one of the two time-advance methods available), save the results and plot diagnostics. The software can be used in a 0-D and a 1-D mode with schemes originating from various models. As for now, microphysical schemes from the Meso-NH model, the AROME (Applications of Research to Operations at Mesoscale) model and the Weather Research and Forecasting model have been successfully plugged. As an application, Physical Parametrizations with PYthon (PPPY) is used in this paper to suppress the origin of the time-step dependency of the microphysical scheme used in the Météo-France small-scale operational numerical weather model. The tool helped to identify the origin of the dependency and to check the efficiency of the introduced corrections.

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)

Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital ◽

10.30536/j.pjpdcd.1017.v14.a2642 ◽

2018 ◽

Vol 14 (2) ◽

Cited By ~ 1

Author(s):

Jaka A. I. Paski

Keyword(s):

Remote Sensing ◽

Data Assimilation ◽

Initial Conditions ◽

Observation Data ◽

Initial Condition ◽

Global Forecast System ◽

Forecast System ◽

Numerical Weather ◽

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.

An integrated wind-forecast system based on the weather research and forecasting model, Kalman filter, and data assimilation with nacelle-wind observation

Journal of Renewable and Sustainable Energy ◽

10.1063/1.4966693 ◽

2016 ◽

Vol 8 (5) ◽

pp. 053308 ◽

Cited By ~ 7

Author(s):

Yuzhang Che ◽

Feng Xiao

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Forecasting Model ◽

Forecast System ◽

Wind Observation ◽

Land-Use Improvements in the Weather Research and Forecasting Model over Complex Mountainous Terrain and Comparison of Different Grid Sizes

Boundary-Layer Meteorology ◽

10.1007/s10546-021-00617-1 ◽

2021 ◽

Author(s):

Alessio Golzio ◽

Silvia Ferrarese ◽

Claudio Cassardo ◽

Gugliemina Adele Diolaiuti ◽

Manuela Pelfini

Keyword(s):

Boundary Layer ◽

Land Use ◽

Land Cover ◽

Mixing Layer ◽

Local Area ◽

Heat Fluxes ◽

Forecasting Model ◽

Mountainous Terrain ◽

AbstractWeather forecasts over mountainous terrain are challenging due to the complex topography that is necessarily smoothed by actual local-area models. As complex mountainous territories represent 20% of the Earth’s surface, accurate forecasts and the numerical resolution of the interaction between the surface and the atmospheric boundary layer are crucial. We present an assessment of the Weather Research and Forecasting model with two different grid spacings (1 km and 0.5 km), using two topography datasets (NASA Shuttle Radar Topography Mission and Global Multi-resolution Terrain Elevation Data 2010, digital elevation models) and four land-cover-description datasets (Corine Land Cover, U.S. Geological Survey land-use, MODIS30 and MODIS15, Moderate Resolution Imaging Spectroradiometer land-use). We investigate the Ortles Cevadale region in the Rhaetian Alps (central Italian Alps), focusing on the upper Forni Glacier proglacial area, where a micrometeorological station operated from 28 August to 11 September 2017. The simulation outputs are compared with observations at this micrometeorological station and four other weather stations distributed around the Forni Glacier with respect to the latent heat, sensible heat and ground heat fluxes, mixing-layer height, soil moisture, 2-m air temperature, and 10-m wind speed. The different model runs make it possible to isolate the contributions of land use, topography, grid spacing, and boundary-layer parametrizations. Among the considered factors, land use proves to have the most significant impact on results.

Simulating the meteorology and PM10 concentrations in Arizona dust storms using the Weather Research and Forecasting model with Chemistry (Wrf-Chem)

Journal of the Air & Waste Management Association ◽

10.1080/10962247.2017.1357662 ◽

2018 ◽

Vol 68 (3) ◽

pp. 177-195 ◽

Cited By ~ 3

Author(s):

Peter Hyde ◽

Alex Mahalov ◽

Jialun Li

Keyword(s):

Dust Storms ◽

Forecasting Model ◽

Parallelization Strategies for the GPS Radio Occultation Data Assimilation with a Nonlocal Operator in the Weather Research and Forecasting Model

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-13-00195.1 ◽

2014 ◽

Vol 31 (9) ◽

pp. 2008-2014 ◽

Cited By ~ 2

Author(s):

Xin Zhang ◽

Ying-Hwa Kuo ◽

Shu-Ya Chen ◽

Xiang-Yu Huang ◽

Ling-Feng Hsiao

Keyword(s):

Data Assimilation ◽

Radio Occultation ◽

Weather Prediction ◽

Forecasting Model ◽

Observation Operator ◽

Gps Radio Occultation ◽

Phase Observation ◽

Excess Phase ◽

Abstract The nonlocal excess phase observation operator for assimilating the global positioning system (GPS) radio occultation (RO) sounding data has been proven by some research papers to produce significantly better analyses for numerical weather prediction (NWP) compared to the local refractivity observation operator. However, the high computational cost and the difficulties in parallelization associated with the nonlocal GPS RO operator deter its application in research and operational NWP practices. In this article, two strategies are designed and implemented in the data assimilation system for the Weather Research and Forecasting Model to demonstrate the capability of parallel assimilation of GPS RO profiles with the nonlocal excess phase observation operator. In particular, to solve the parallel load imbalance problem due to the uneven geographic distribution of the GPS RO observations, round-robin scheduling is adopted to distribute GPS RO observations among the processing cores to balance the workload. The wall clock time required to complete a five-iteration minimization on a demonstration Antarctic case with 106 GPS RO observations is reduced from more than 3.5 h with a single processing core to 2.5 min with 106 processing cores. These strategies present the possibility of application of the nonlocal GPS RO excess phase observation operator in operational data assimilation systems with a cutoff time limit.