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

2020 ◽  
Vol 13 (2) ◽  
pp. 443-460
Author(s):  
Sébastien Riette
Keyword(s):  
Climate Model ◽  
Mesoscale Model ◽  
Weather Research And Forecasting ◽  
Small Scale ◽  
Forecasting Model ◽  
Time Step ◽  
Numerical Weather ◽  
Numerical Weather Model ◽  
Weather Model ◽  
Weather Research

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.

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

2019 ◽  
Author(s):  
Sébastien Riette
Keyword(s):  
Climate Model ◽  
Small Scale ◽  
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. The tool provides a framework to plug external parametrizations, run them in an offline mode, save the results and plot diagnostics. With the help of this tool, the origin of the time-step dependency of the microphysical scheme used in the Météo-France small scale operational numerical weather model was identified. The sources of dependency lied in some process formulations and in the algorithm used to allow the competition between the different processes. Some corrections have been introduced and their efficiency was checked with the tool. This usage illustrates how the tool can be used in 0D or 1D mode, with schemes coming from different models and with different time-advance methods to produce different kinds of plots.

scholarly journals PREDIKSI DEBIT SUNGAI BENGAWAN SOLO MENGGUNAKAN NUMERICAL WEATHER MODEL GLOBAL FORECAST SYSTEM DAN INTEGRATED FLOOD ANALYSIS SYSTEM (Prediction of discharge in Bengawan Solo River using Numerical Weather Model Global Forecast System and Integrated Flood Analysis System)

2021 ◽  
Vol 5 (1) ◽  
pp. 41-50
Author(s):  
Deffi Munadiyat Putri ◽  
◽  
Aries Kristianto ◽  
Keyword(s):  
Weather Research And Forecasting ◽  
Global Forecast System ◽  
Forecast System ◽  
Numerical Weather ◽  
Numerical Weather Model ◽  
Weather Model ◽  
Simulation Based ◽  
Discharge Simulation ◽  
Flood Analysis ◽  
Analysis System

Flood is one of the most common hydro-meteorological disasters. Bengawan Solo is one of the watersheds in Indonesia that also hit by this disaster. This study discusses the flood disaster in the Bengawan Solo area in early March 2019. The purpose of this study is to conduct a discharge simulation using numerical weather model Global Forecast System (GFS) data through Integrated Flood Analysis System (IFAS) so it is possible to predict discharge in the future. There are three types of numerical weather model GFS data that have been downscale using weather research and forecasting model which differentiated based on spin-up time. The numerical weather model product is then used as rainfall data input for IFAS simulation. Based on the analysis, the flood discharge simulation using an 84-hour spin-up time has a satisfactory performance in describing the change in discharge with respect to time. This happens because numerical weather models will be better at quantifying processes that occur on a meso scale with spatial scale of 10 to 1000 km. The result of this research shows that it is possible to predict river discharge up to 84 hours before the disaster so this is can support the mitigation process for hydrometeorological disasters.

scholarly journals Evaluation of the Sensitivity of the Weather Research and Forecasting Model to Parameterization Schemes for Regional Climates of Europe over the Period 1990–95

2013 ◽  
Vol 26 (3) ◽  
pp. 1002-1017 ◽  
Author(s):  
P. A. Mooney ◽  
F. J. Mulligan ◽  
R. Fealy
Keyword(s):  
Land Surface ◽  
Climate Model ◽  
Correlation Coefficients ◽  
Longwave Radiation ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Land Surface Models ◽  
Surface Models ◽  
Regional Climates ◽  
Weather Research

Abstract The Weather Research and Forecasting model (WRF) is used to downscale interim ECMWF Re-Analysis (ERA-Interim) data for the climate over Europe for the period 1990–95 with grid spacing of 0.44° for 12 combinations of physical parameterizations. Two longwave radiation schemes, two land surface models (LSMs), two microphysics schemes, and two planetary boundary layer (PBL) schemes have been investigated while the remaining physics schemes were unchanged. WRF simulations are compared with Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) observations gridded dataset (E-OBS) for surface air temperatures (T2), precipitation, and mean sea level pressure (MSLP) in eight subregions within the model domain to assess the performance of the different parameterizations on widely varying regional climates. This work shows that T2 is modeled well by WRF with high correlation coefficients (0.8 < R < 0.95) and biases less than 4°C. T2 shows greatest sensitivity to land surface models, some sensitivity to longwave radiation schemes, and less sensitivity to microphysics and PBL schemes. Precipitation is not well modeled by WRF with low correlation coefficients (0.1 < R < 0.3) and high root-mean-square differences (RMSDs; 8–9 mm day−1). Precipitation shows sensitivity to LSMs in summer. No significant bias has been observed in the MSLP modeled by WRF. Correlation coefficients are typically in the range 0.7 < R < 0.8 while RMSDs are in the range 6–10 hPa. MSLP output is sensitive to longwave radiation scheme in summer but is relatively insensitive to either microphysics or the choice of LSM. The optimum combination of parameterizations for all three state variables examined is strongly dependent on subregion and demonstrates the need to carefully select parameterization combinations when attempting to use WRF as a regional climate model.

scholarly journals A Decade of Antarctic Science Support Through Amps

2012 ◽  
Vol 93 (11) ◽  
pp. 1699-1712 ◽  
Author(s):  
Jordan G. Powers ◽  
Kevin W. Manning ◽  
David H. Bromwich ◽  
John J. Cassano ◽  
Arthur M. Cayette
Keyword(s):  
Real Time ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Prediction System ◽  
Numerical Weather ◽  
The Antarctic ◽  
The U.S ◽  
Weather Research

The Antarctic Mesoscale Prediction System (AMPS) is a real-time numerical weather prediction (NWP) system covering Antarctica that has served a remarkable range of groups and activities for a decade. It employs the Weather Research and Forecasting model (WRF) on varying-resolution grids to generate numerical guidance in a variety of tailored products. While its priority mission has been to support the forecasters of the U.S. Antarctic Program, AMPS has evolved to assist a host of scientific and logistical needs for an international user base. The AMPS effort has advanced polar NWP and Antarctic science and looks to continue this into another decade. To inform those with Antarctic scientific and logistical interests and needs, the history, applications, and capabilities of AMPS are discussed.

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

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 ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Mountainous Terrain ◽  
Weather Research

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.

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

2014 ◽  
Vol 31 (9) ◽  
pp. 2008-2014 ◽  
Author(s):  
Xin Zhang ◽  
Ying-Hwa Kuo ◽  
Shu-Ya Chen ◽  
Xiang-Yu Huang ◽  
Ling-Feng Hsiao
Keyword(s):  
Data Assimilation ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Observation Operator ◽  
Gps Radio Occultation ◽  
Phase Observation ◽  
Excess Phase ◽  
Weather Research

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.

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