Calibration of Kain–Fritsch cumulus scheme in Weather Research and Forecasting (WRF) model over Western Luzon, Philippines

2021 ◽  
Author(s):  
Jerome T. Tolentino ◽  
Gerry Bagtasa
Keyword(s):  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Cumulus Scheme ◽  
Weather Research

scholarly journals High-resolution mesoscale wind-resource assessment of Fiji using the Weather Research and Forecasting (WRF) model

Energy ◽  
2021 ◽  
pp. 121047
Author(s):  
Kunal K. Dayal ◽  
Gilles Bellon ◽  
John E. Cater ◽  
Michael J. Kingan ◽  
Rajnish N. Sharma
Keyword(s):  
High Resolution ◽  
Wrf Model ◽  
Resource Assessment ◽  
Weather Research And Forecasting ◽  
Wind Resource Assessment ◽  
Wind Resource ◽  
Weather Research

Predicting lightning activity in Greece with the Weather Research and Forecasting (WRF) model

2015 ◽  
Vol 156 ◽  
pp. 1-13 ◽  
Author(s):  
Theodore M. Giannaros ◽  
Vassiliki Kotroni ◽  
Konstantinos Lagouvardos
Keyword(s):  
Wrf Model ◽  
Lightning Activity ◽  
Weather Research And Forecasting ◽  
Weather Research

scholarly journals CORDEX-WRF v1.3: development of a module for the Weather Research and Forecasting (WRF) model to support the CORDEX community

2019 ◽  
Vol 12 (3) ◽  
pp. 1029-1066 ◽  
Author(s):  
Lluís Fita ◽  
Jan Polcher ◽  
Theodore M. Giannaros ◽  
Torge Lorenz ◽  
Josipa Milovac ◽  
...  
Keyword(s):  
Regional Climate ◽  
Regional Scale ◽  
Wrf Model ◽  
Data Access ◽  
Quality Data ◽  
Weather Research And Forecasting ◽  
Time Step ◽  
Mitigation And Adaptation ◽  
Data Requirements ◽  
Weather Research

Abstract. The Coordinated Regional Climate Downscaling Experiment (CORDEX) is a scientific effort of the World Climate Research Program (WRCP) for the coordination of regional climate initiatives. In order to accept an experiment, CORDEX provides experiment guidelines, specifications of regional domains, and data access and archiving. CORDEX experiments are important to study climate at the regional scale, and at the same time, they also have a very prominent role in providing regional climate data of high quality. Data requirements are intended to cover all the possible needs of stakeholders and scientists working on climate change mitigation and adaptation policies in various scientific communities. The required data and diagnostics are grouped into different levels of frequency and priority, and some of them even have to be provided as statistics (minimum, maximum, mean) over different time periods. Most commonly, scientists need to post-process the raw output of regional climate models, since the latter was not originally designed to meet the specific CORDEX data requirements. This post-processing procedure includes the computation of diagnostics, statistics, and final homogenization of the data, which is often computationally costly and time-consuming. Therefore, the development of specialized software and/or code is required. The current paper presents the development of a specialized module (version 1.3) for the Weather Research and Forecasting (WRF) model capable of outputting the required CORDEX variables. Additional diagnostic variables not required by CORDEX, but of potential interest to the regional climate modeling community, are also included in the module. “Generic” definitions of variables are adopted in order to overcome the model and/or physics parameterization dependence of certain diagnostics and variables, thus facilitating a robust comparison among simulations. The module is computationally optimized, and the output is divided into different priority levels following CORDEX specifications (Core, Tier 1, and additional) by selecting pre-compilation flags. This implementation of the module does not add a significant extra cost when running the model; for example, the addition of the Core variables slows the model time step by less than a 5 %. The use of the module reduces the requirements of disk storage by about a 50 %. The module performs neither additional statistics over different periods of time nor homogenization of the output data.

scholarly journals High-Resolution Solar Climate Atlas for Greece under Climate Change Using the Weather Research and Forecasting (WRF) Model

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 761 ◽  
Author(s):  
Theodoros Katopodis ◽  
Iason Markantonis ◽  
Nadia Politi ◽  
Diamando Vlachogiannis ◽  
Athanasios Sfetsos
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Energy Demand ◽  
General Circulation ◽  
Wrf Model ◽  
General Circulation Models ◽  
Weather Research And Forecasting ◽  
Clear Sky ◽  
The Future ◽  
Weather Research

In the context of climate change and growing energy demand, solar technologies are considered promising solutions to mitigate Greenhouse Gas (GHG) emissions and support sustainable adaptation. In Greece, solar power is the second major renewable energy, constituting an increasingly important component of the future low-carbon energy portfolio. In this work, we propose the use of a high-resolution regional climate model (Weather Research and Forecasting model, WRF) to generate a solar climate atlas for the near-term climatological future under the Representative Concentration Pathway (RCPs) 4.5 and 8.5 scenarios. The model is set up with a 5 × 5 km2 spatial resolution, forced by the ERA-INTERIM for the historic (1980–2004) period and by the EC-EARTH General Circulation Models (GCM) for the future (2020–2044). Results reaffirm the high quality of solar energy potential in Greece and highlight the ability of the WRF model to produce a highly reliable future climate solar atlas. Projected changes between the annual historic and future RCPs scenarios indicate changes of the annual Global Horizontal Irradiance (GHI) in the range of ±5.0%. Seasonal analysis of the GHI values indicates percentage changes in the range of ±12% for both scenarios, with winter exhibiting the highest seasonal increases in the order of 10%, and autumn the largest decreases. Clear-sky fraction fclear projects increases in the range of ±4.0% in eastern and north continental Greece in the future, while most of the Greek marine areas might expect above 220 clear-sky days per year.

scholarly journals Approximating Input Data to a Snowmelt Model Using Weather Research and Forecasting Model Outputs in Lieu of Meteorological Measurements

2019 ◽  
Vol 20 (5) ◽  
pp. 847-862 ◽  
Author(s):  
Scott Havens ◽  
Danny Marks ◽  
Katelyn FitzGerald ◽  
Matt Masarik ◽  
Alejandro N. Flores ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Wrf Model ◽  
Temporal Distribution ◽  
Meteorological Variables ◽  
Weather Research And Forecasting ◽  
Atmospheric Flow ◽  
Temperature And Precipitation ◽  
Runoff Forecasting ◽  
Input Variables ◽  
Weather Research

Abstract Forecasting the timing and magnitude of snowmelt and runoff is critical to managing mountain water resources. Warming temperatures are increasing the rain–snow transition elevation and are limiting the forecasting skill of statistical models relating historical snow water equivalent to streamflow. While physically based methods are available, they require accurate estimations of the spatial and temporal distribution of meteorological variables in complex terrain. Across many mountainous areas, measurements of precipitation and other meteorological variables are limited to a few reference stations and are not adequate to resolve the complex interactions between topography and atmospheric flow. In this paper, we evaluate the ability of the Weather Research and Forecasting (WRF) Model to approximate the inputs required for a physics-based snow model, iSnobal, instead of using meteorological measurements, for the Boise River Basin (BRB) in Idaho, United States. An iSnobal simulation using station data from 40 locations in and around the BRB resulted in an average root-mean-square error (RMSE) of 4.5 mm compared with 12 SNOTEL measurements. Applying WRF forcings alone was associated with an RMSE of 10.5 mm, while including a simple bias correction to the WRF outputs of temperature and precipitation reduced the RMSE to 6.5 mm. The results highlight the utility of using WRF outputs as input to snowmelt models, as all required input variables are spatiotemporally complete. This will have important benefits in areas with sparse measurement networks and will aid snowmelt and runoff forecasting in mountainous basins.

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