Assessment of Different WRF Configurations Performance for a Rain Event over Panama

Lorenzo, Maibys Sierra; Bezanilla‐Morlot, Arnoldo; Centella-Artola, Abel; León-Marcos, Anisbel; Borrajero-Montejo, Israel; Ferrer-Hernández, Adrián Luis; Salazar-Gaitán, Jesús Leonel; Lau-Melo, Alcely; Picado-Traña, Freddy; Pérez-Fernández, Joel

doi:10.4236/acs.2020.103016

Cited by 5 publications

(5 citation statements)

References 34 publications

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“…Regarding verification methods, the following were used: (i) the traditional method based on point-to-point computing of different statistical metrics and (ii) the categorical verification method based on grid-to-grid computing of the hits, misses, correct rejection and false alarms parameters (see [2] for more details, and also [6] for an example of usage). The first method was applied with the station meteorological surface and the GPM data, while the second one was used with the FFGS products and SisPI, taking as precipitation reference the estimated by the GMP product.…”

Section: Data Used and Verification Methodsmentioning

confidence: 99%

Precipitation Forecast Verification of the FFGS and SisPI Tools during the Impact of the Tropical Storm Isaias over the Dominican Republic

Lorenzo¹,

Medina

Sille

et al. 2021

The 4th International Electronic Conference on Atmospheric Sciences

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During 2020, the Dominican Republic received the impact of several tropical organisms, among those that generated the greatest losses in the country, Tropical Storm Isaias stands out because of the significant precipitation and flooding it caused. The study analyzes the ability of the products of Flash Flood Guidance System (FFGS) and the Nowcasting and Very Short Range Prediction System (Spanish acronym SisPI) for the quantitative precipitation forecast (QPF) of the rains generated by Isaias on 30 and 31 July 2020 over the Dominican Republic. Various traditional verification methods are used in the study. The results show that both numerical weather-based systems are powerful tools for the QPF, and also to contribute to the prevention and mitigation of disasters caused by the extreme hydro-meteorological event analyzed.

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Section: Data Used and Verification Methodsmentioning

confidence: 99%

Precipitation Forecast Verification of the FFGS and SisPI Tools during the Impact of the Tropical Storm Isaias over the Dominican Republic

Lorenzo¹,

Medina

Sille

et al. 2021

The 4th International Electronic Conference on Atmospheric Sciences

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“…The AGCM does a good job of replicating the current climate in Central America [4] and in most of the world [13]. Forecasting (WRF) model was performed to forecast one-day precipitation with different physical parameterizations [10]. This is the only study on forecasting for Panama, but it is not a study on a climate time-scale.…”

Section: Experiments Designmentioning

confidence: 99%

“…Only a few studies on high-resolution models (e.g., [8][9][10]) have been published in Central America. Climate change projections for Central America and Mexico were initially performed with a regional climate model [8].…”

Section: Introductionmentioning

confidence: 99%

“…Climate change projections for Central America and Mexico were initially performed with a regional climate model [8]. Dynamical downscaling with horizonal resolution of 27, 9, and 3 km over Panama using the Weather Research and Forecasting (WRF) model was performed to forecast one-day precipitation with different physical parameterizations [10]. This is the only study on forecasting for Panama, but it is not a Atmosphere 2021, 12, 1543 2 of 12 study on a climate time-scale.…”

Section: Introductionmentioning

confidence: 99%

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Panama’s Current Climate Replicability in a Non-Hydrostatic Regional Climate Model Nested in an Atmospheric General Circulation Model

et al. 2021

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To simulate the current climate, a 20-year integration of a non-hydrostatic regional climate model (NHRCM) with grid spacing of 5 and 2 km (NHRCM05 and NHRCM02, respectively) was nested within the AGCM. The three models did a similarly good job of simulating surface air temperature, and the spatial horizontal resolution did not affect these statistics. NHRCM02 did a good job of reproducing seasonal variations in surface air temperature. NHRCM05 overestimated annual mean precipitation in the western part of Panama and eastern part of the Pacific Ocean. NHRCM05 is responsible for this overestimation because it is not seen in MRI-AGCM. NHRCM02 simulated annual mean precipitation better than NHRCM05, probably due to a convection-permitting model without a convection scheme, such as the Kain and Fritsch scheme. Therefore, the finer horizontal resolution of NHRCM02 did a better job of replicating the current climatological mean geographical distributions and seasonal changes of surface air temperature and precipitation.

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“…Auzani et al (2022) used Radial Basis Function Neural Network (RBFNN) algorithm to predict rainfall and found that the model works better the more hidden nodes and the optimum learning rate is 0.01 with the RMSE 49% and the percentage accuracy is 57%. Sierra-Lorenzo et al (2020) tested a set of 15 combinations of microphysics and cumulus parameterizations for the WRF numerical model in the forecast of a rain event on January 16 2018 over Panama and found that the scheme of Betts-Miller-Janjic showed the highest correlation factor and performed quite well representing the vertical profiles of relative humidity, temperature and wind.…”

Section: Introductionmentioning

confidence: 99%

Analysis on the Heavy Rain Weather Process in Most Areas of China from July 26 to 30, 2022

Zhang¹

2023

GEP

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To analyze a new heavy rain case over China during the year 2022, by using the data from NCC-CMA and NCEP, a heavy rain weather process in most areas of China from July 26 to 30, 2022 was analyzed. Synoptic methods were used in this research and results show that under the influence of low vortex and wind shear, the abundant water vapor supply brought by the southeast airflow in the lower level and the lifting of the Taihang Mountain, heavy rain weather occurred in the northern part of Henan, China. In the west of Liaoning and Jilin, the rainfall process had the characteristics of frontal rainfall and the stable precipitation resulted in heavy rain weather. To sum up, the rainfall process was mainly affected by the upper-level trough, low-level wind shear and low-level jet.

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Assessment of Different WRF Configurations Performance for a Rain Event over Panama

Cited by 5 publications

References 34 publications

Precipitation Forecast Verification of the FFGS and SisPI Tools during the Impact of the Tropical Storm Isaias over the Dominican Republic

Precipitation Forecast Verification of the FFGS and SisPI Tools during the Impact of the Tropical Storm Isaias over the Dominican Republic

Panama’s Current Climate Replicability in a Non-Hydrostatic Regional Climate Model Nested in an Atmospheric General Circulation Model

Analysis on the Heavy Rain Weather Process in Most Areas of China from July 26 to 30, 2022

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