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      New approach for local C-band weather radar precipitation calibration

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          Abstract

          ABSTRACT Weather radar calibration is a topic of great current interest because it is useful for various hydrological applications. Several methods have been developed for adjusting the relation between reflectivity data Z and rainfall intensity R (Z/R) because droplet size distributions in different storm events are unknown and highly variable in time and space. The present study developed and tested a new space and time window-based procedure for optimal local calibration of weather radar using Z/R relations and applying it to convective and stratiform storms in the lower Grijalva river basin in Mexico. Improving rain estimates from the Sabancuy, Campeche radar is essential because it monitors this basin, which is prone to floods. The resulting estimates of the optimal power-law (Z = ARb) window-based procedure (OP) are compared with those of the default Marshall and Palmer (MP) relation using the observed rain gauge records. The appropriate window was selected using a criterion that considers factors affecting the free fall of raindrops. For most of the storms tested, metrics for the OP models showed better values than those calculated for the MP ones. The best MP performance is when using smooth calibration data, achieving similar metric results to that of the OP. The proposed observed calibration method could be useful to improve the default MP model estimates at any weather radar with similar characteristics to the ones analyzed in this work. The resulting Z/R relations could improve precipitation radar estimates for hydrologic model inputs.

          Translated abstract

          RESUMEN La calibración de radares meteorológicos es un tema de gran interés actual, ya que es útil para diversas aplicaciones hidrológicas. No obstante, se han desarrollado varios métodos para ajustar la relación entre los datos de reflectividad Z y la intensidad de lluvia R (Z/R). Esto se debe a que la distribución del tamaño de las gotas para diferentes tormentas es desconocida y muy variable en el tiempo y el espacio. El presente estudio desarrolló y probó un nuevo procedimiento basado en ventanas de tiempo y espacio para la calibración local óptima del radar meteorológico utilizando relaciones Z/R, y lo aplicó a tormentas convectivas y estratiformes en la cuenca baja del río Grijalva en México. Mejorar las estimaciones de lluvia del radar de Sabancuy, Campeche, es fundamental porque esta cuenca es propensa a inundaciones. Las estimaciones resultantes del procedimiento se basan en la optimización de ventanas (OPV) usando la ley de potencia (Z = ARb). Dichas estimaciones se comparan con las resultantes del uso de la relación predeterminada de Marshall y Palmer (MP) utilizando para ello los registros de pluviómetro observados. La ventana apropiada se seleccionó utilizando un criterio que considera los factores que afectan la caída libre de las gotas de lluvia. Para la mayoría de las tormentas probadas, los estadísticos de los modelos OPV mostraron mejores valores que los calculados para los modelos MP. El mejor rendimiento de MP ocurrió cuando se utilizaron datos de calibración suavizados, pero sólo se alcanzaron resultados similares a los obtenidos con OP. El método de calibración propuesto podría ser útil para mejorar las estimaciones del modelo MP por defecto en cualquier radar meteorológico con características similares a las analizadas en este trabajo. Las relaciones Z/R resultantes podrían mejorar las estimaciones del radar de precipitación para la captura de datos en modelos hidrológicos.

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          Most cited references35

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          Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations

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            Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature

            Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by Willmott and Matsuura (2005) and Willmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MAE over RMSE to present their model evaluation statistics when presenting or adding the RMSE measures could be more beneficial. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric, whereas Willmott et al. (2009) indicated that the sums-of-squares-based statistics do not satisfy this rule. In the end, we discussed some circumstances where using the RMSE will be more beneficial. However, we do not contend that the RMSE is superior over the MAE. Instead, a combination of metrics, including but certainly not limited to RMSEs and MAEs, are often required to assess model performance.
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              Power laws, Pareto distributions and Zipf's law

              MEJ Newman (2005)
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                Author and article information

                Journal
                atm
                Atmósfera
                Atmósfera
                Universidad Nacional Autónoma de México, Centro de Ciencias de la Atmósfera (Ciudad de México, Ciudad de México, Mexico )
                0187-6236
                2021
                : 34
                : 2
                : 171-187
                Affiliations
                [2] Zacatecas orgnameUniversidad Autónoma de Zacatecas orgdiv1Doctorado en Ciencias de la Ingeniería Mexico
                [1] Ciudad de México orgnameUniversidad Nacional Autónoma de México orgdiv1Instituto de Geofísica orgdiv2Departamento de Recursos Naturales Mexico
                [3] Villahermosa orgnameUniversidad Juárez Autónoma de Tabasco orgdiv1División Académica de Ciencias Biológicas Mexico
                Article
                S0187-62362021000200171 S0187-6236(21)03400200171
                10.20937/atm.52763
                9ead4333-1799-4492-8dff-b6da77d4e679

                This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

                History
                : 03 March 2020
                : 30 August 2019
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 35, Pages: 17
                Product

                SciELO Mexico

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                Articles

                optimization,space-time windows,Grijalva river,power-law,flood

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