Optimization of X-Band Radar Rainfall Retrieval in the Southern Andes of Ecuador Using a Random Forest Model

Orellana‐Alvear, Johanna; Célleri, Rolando; Rollenbeck, Rütger; Bendix, Jörg

doi:10.3390/rs11141632

Cited by 19 publications

(26 citation statements)

References 47 publications

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“…The effect of a topographic factor on radar rainfall estimation errors in mountainous region has been revealed by (Gabella, M, et al, 2000;Gabella, M, et al, 2001) and (Orellana-alvear, J, et al, 2019). The topographic factor is one of three variables evaluated by Gabella et al (2001) in their research.…”

Section: Radar Rainfall Evaluation Techniquesmentioning

confidence: 99%

“…The design of this measuring network is more difficult in area with complex topography and convective conditions, where information provided through rain gauges measurement is very limited (Ozkaya, A & Akyurek, 2019;Yoon, S.-S. & Bae, D.-H, 2013;Burcea, S, et al, 2012). Contrary, rainfall radar provides better coverage both in time and space (Orellana-alvear et al, 2019). Even though the rainfall radar can be overcome the limitations of the rain gauge, rainfall estimates using radar are not precise enough due to various sources of error (Burcea, S, et al, 2012;Rossa, AM, et al, 2010;Delrieu, G, et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…X-band radar has several advantages compared to S and C band radars, involving higher spatial and temporal resolution, smaller antenna size, lower transmitted power for the same sensitivity, and lower costs (Orellana-alvear et al, 2019;Yu et al, 2018;Park et al, 2005). However, X-band radar is still unable to avoid the attenuation caused by heavy rainfall, where the signal received by radar is lower than the noise level (Burcea, S, et al, 2012;Hirano, K, et al, 2014;Shi, Z, et al, 2017;Yoon, S.-S. & Bae, D.-H, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Correcting Radar Rainfall Estimates Based on Ground Elevation Function

Hambali

Legono

Jayadi

2019

Journal of the Civil Engineering Forum

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X-band radar gives several advantages for quantitative rainfall estimation, involving higher spatial and temporal resolution, also the ability to reduce attenuation effects and hardware calibration errors. However, the estimates error due to attenuation in heavy rainfall condition cannot be avoided. In the mountainous region, the impact of topography is considered to contribute to radar rainfall estimates error. To have more reliable estimated radar rainfall to be used in various applications, a rainfall estimates correction needs to be applied. This paper discusses evaluation and correction techniques for radar rainfall estimates based on ground elevation function. The G/R ratio is used as a primary method in the correction process. The novel approach proposed in this study is the use of correction factor derived from the relationship between Log (G/R) parameter and elevation difference between radar and rain gauge stations. A total of 4590 pairs of rainfall data from X-band MP radar and 15 rain gauge stations in the Mt. Merapi region were used in evaluation and correction process. The results show the correction method based on the elevation function is relatively good in correcting radar rainfall depth with values of Log (G/R) decreased up to 81.1%, particularly for light rainfall (≤ 20 mm/hour) condition. Also, the method is simple to apply in a real-time system.

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Section: Radar Rainfall Evaluation Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Correcting Radar Rainfall Estimates Based on Ground Elevation Function

Hambali

Legono

Jayadi

2019

Journal of the Civil Engineering Forum

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“…Equations (13) and (14) were used for retrieving D 0 and N w from the observed Z H and Z DR . Equation (15) was used for classification: If the retrieved logN w is greater (smaller) than that given by this equation, the pixel is assigned as convective (stratiform) rain (hereafter the DSD-based method).…”

Section: Classifications Of Precipitation Types Using Disdrometer Andmentioning

confidence: 99%

“…Alvear et al [13] derived different rain-type Z-R relations using disdrometer data observed at three different geographic and height based on mean drop volume diameter thresholds in the high Andes of southern Ecuador. Alvear et al [14] proposed random forest model (FM) to get more accurate rainfall amounts using X-band radar installed on the highest mountain in the world. They determined that FM is promising and unveiled a different approach to overcome the high attenuation issues inherent to X-band radars.…”

Section: Introductionmentioning

confidence: 99%

Rainfall Estimates with Respect to Rainfall Types Using S-Band Polarimetric Radar in Korea

2019

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To investigate the impact of rainfall type on rainfall estimation using polarimetric variables, rainfall relations such as those between rain rate (R) and specific differential phase (KDP), between R and KDP/differential reflectivity (ZDR), and between R and reflectivity (Z)/ZDR, were examined with respect to the precipitation type classified using drop size distributions (DSDs) measured by a disdrometer. The classification of rainfall type was assessed using four different methods: temporal rainfall variation; and the relations between intercept parameter (N0) and R; normalized intercept parameter (Nw) and median diameter (D0); and slope parameter (Λ) and R. The logN0–R relation discriminated between convective and stratiform rain with less standard deviation than the other methods as shown by the Z–ZDR scatter with respect to the rainfall types. The transition type from convective to stratiform and vice versa occurred in the stratiform rain region for all methods. To apply the classified rainfall relations to radar rainfall estimation, logNw and D0 were retrieved from polarimetric variables to discriminate the rainfall types in the radar domain. The DSD classification was verified with the vertical profile of reflectivity extracted at two positions corresponding to gage sites. Statistical analysis of four different rainfall events showed that rainfall estimation using the relations with precipitation type were better than those obtained without classification. The R(KDP,ZDR) relation with classification performed best on rainfall estimation for all rainfall events. The greatest improvement in rainfall estimation was obtained from R(Z,ZDR) with classification. We conclude that the classification of rainfall type leads to more accurate rainfall estimation. The different relations R(KDP), R(KDP,ZDR), and R(Z,ZDR) with respect to the rain types using polarimetric radar show improvement compared to estimation without consideration of rainfall type, in Korea.

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Determination of climatic conditions related to precipitation anomalies in the Tropical Andes by means of the random forest algorithm and novel climate indices

Córdova

Orellana‐Alvear

Rollenbeck

et al. 2022

Intl Journal of Climatology

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Understanding precipitation and its relation with atmospheric and oceanic conditions is vital in the face of climate change. This is crucial in the Tropical Andes (TA) because millions of people depend on water originated in the cordillera. Unfortunately, the paucity of meteorological monitoring that exists in mountainous regions is accentuated in the tropics. In this context, climate indices, remotely sensed, and gridded datasets, are useful tools to study climate and precipitation in the TA, and additional climate indices can be calculated from reanalysis datasets. The combination of this information with traditional indices has the potential to improve our understanding of precipitation. Our objective was to use the k‐means algorithm to regionalize precipitation in the TA (different regions have different climate), and then use the random forest algorithm to study the variables related to precipitation in each of these regions in seasonal timescales. Here, we show the suitability of the random forest algorithm to reveal climate processes and the high potential of the novel climate indices to improve the regressions. We found that convective available potential energy was the most important variable for precipitation in the northern TA, except for the Chocó, where v at 850 hPa was the most important one. Meanwhile, vertical integral of divergence of moisture flux was the most important one in the southern TA. Interestingly, in the DJF season when the South American low‐level jet (SALLJ) is more active, u and v at 850 hPa showed their lowest relative importance and the total column of water vapour showed its maximum, this could indicate that precipitation anomalies are controlled by atmospheric moisture availability rather than by the speed of the SALLJ during DJF. These results deepen our understanding of precipitation anomalies in the TA and the related oceanic and atmospheric conditions. The proposed methodology was proven to be suitable and it could be used in the future to test and formulate new hypotheses, and to forecast seasonal precipitation.

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Optimization of X-Band Radar Rainfall Retrieval in the Southern Andes of Ecuador Using a Random Forest Model

Cited by 19 publications

References 47 publications

Correcting Radar Rainfall Estimates Based on Ground Elevation Function

Correcting Radar Rainfall Estimates Based on Ground Elevation Function

Rainfall Estimates with Respect to Rainfall Types Using S-Band Polarimetric Radar in Korea

Determination of climatic conditions related to precipitation anomalies in the Tropical Andes by means of the random forest algorithm and novel climate indices

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