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Open AccessArticle

Evaluating Forecast Skills of Moisture from Convective-Permitting WRF-ARW Model during 2017 North American Monsoon Season

1
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ 85721, USA
2
56th Operations Support Squadron Luke Air Force Base, Glendale, AZ 85309-1215, USA
3
Centro de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México, 04510 Mexico City, Mexico
4
Departamento de Fisica de la Universidad de Sonora, Hermosillo, Sonora 83000, Mexico
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(11), 694; https://doi.org/10.3390/atmos10110694
Received: 2 October 2019 / Revised: 28 October 2019 / Accepted: 5 November 2019 / Published: 11 November 2019
(This article belongs to the Special Issue Weather Forecasting and Modeling in Drylands)
This paper examines the ability of the Weather Research and Forecasting model forecast to simulate moisture and precipitation during the North American Monsoon GPS Hydrometeorological Network field campaign that took place in 2017. A convective-permitting model configuration performs daily weather forecast simulations for northwestern Mexico and southwestern United States. Model precipitable water vapor (PWV) exhibits wet biases greater than 0.5 mm at the initial forecast hour, and its diurnal cycle is out of phase with time, compared to observations. As a result, the model initiates and terminates precipitation earlier than the satellite and rain gauge measurements, underestimates the westward propagation of the convective systems, and exhibits relatively low forecast skills on the days where strong synoptic-scale forcing features are absent. Sensitivity analysis shows that model PWV in the domain is sensitive to changes in initial PWV at coastal sites, whereas the model precipitation and moisture flux convergence (QCONV) are sensitive to changes in initial PWV at the mountainous sites. Improving the initial physical states, such as PWV, potentially increases the forecast skills. View Full-Text
Keywords: weather research and forecasting model; convective-permitting parameterizations; global forecast system model; North American Mesoscale model; North American Monsoon precipitation; precipitable water vapor; moisture flux convergence; Global Positioning System; forecast skills of moisture; sensitivity analysis weather research and forecasting model; convective-permitting parameterizations; global forecast system model; North American Mesoscale model; North American Monsoon precipitation; precipitable water vapor; moisture flux convergence; Global Positioning System; forecast skills of moisture; sensitivity analysis
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Risanto, C.B.; Castro, C.L.; Moker, J.M., Jr.; Arellano, A.F., Jr.; Adams, D.K.; Fierro, L.M.; Minjarez Sosa, C.M. Evaluating Forecast Skills of Moisture from Convective-Permitting WRF-ARW Model during 2017 North American Monsoon Season. Atmosphere 2019, 10, 694.

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