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

2019 ◽  
Vol 11 (14) ◽  
pp. 1632 ◽  
Author(s):  
Johanna Orellana-Alvear ◽  
Rolando Célleri ◽  
Rütger Rollenbeck ◽  
Jörg Bendix
Keyword(s):  
Random Forest ◽  
Correlation Coefficient ◽  
Radar Data ◽  
Rain Gauge ◽  
High Mountain ◽  
Radar Rainfall ◽  
Site Specific ◽  
High Attenuation ◽  
X Band ◽  
Precipitation Estimation

Despite many efforts of the radar community, quantitative precipitation estimation (QPE) from weather radar data remains a challenging topic. The high resolution of X-band radar imagery in space and time comes with an intricate correction process of reflectivity. The steep and high mountain topography of the Andes enhances its complexity. This study aims to optimize the rainfall derivation of the highest X-band radar in the world (4450 m a.s.l.) by using a random forest (RF) model and single Plan Position Indicator (PPI) scans. The performance of the RF model was evaluated in comparison with the traditional step-wise approach by using both, the Marshall-Palmer and a site-specific Z–R relationship. Since rain gauge networks are frequently unevenly distributed and hardly available at real time in mountain regions, bias adjustment was neglected. Results showed an improvement in the step-wise approach by using the site-specific (instead of the Marshall-Palmer) Z–R relationship. However, both models highly underestimate the rainfall rate (correlation coefficient < 0.69; slope up to 12). Contrary, the RF model greatly outperformed the step-wise approach in all testing locations and on different rainfall events (correlation coefficient up to 0.83; slope = 1.04). The results are promising and unveil a different approach to overcome the high attenuation issues inherent to X-band radars.

scholarly journals The New French Operational Polarimetric Radar Rainfall Rate Product

2013 ◽  
Vol 52 (8) ◽  
pp. 1817-1835 ◽  
Author(s):  
Jordi Figueras i Ventura ◽  
Pierre Tabary
Keyword(s):  
Radar Data ◽  
Rain Gauge ◽  
Rainfall Rate ◽  
Superior Performance ◽  
Radar Rainfall ◽  
Rain Gauge Data ◽  
Frequency Bands ◽  
Radar Network ◽  
Significant Events ◽  
X Band

AbstractIn 2012 the Météo France metropolitan operational radar network consists of 24 radars operating at C and S bands. In addition, a network of four X-band gap-filler radars is being deployed in the French Alps. The network combines polarimetric and nonpolarimetric radars. Consequently, the operational radar rainfall algorithm has been adapted to process both polarimetric and nonpolarimetric data. The polarimetric processing chain is available in two versions. In the first version, now operational, polarimetry is only used to correct for attenuation and filter out clear-air echoes. In the second version there is a more extensive use of polarimetry. In particular, the specific differential phase Kdp is used to estimate rainfall rate in intense rain. The performance of the three versions of radar rainfall algorithms (conventional, polarimetric V1, and polarimetric V2) at different frequency bands (S, C, and X) is evaluated by processing radar data of significant events offline and comparing hourly radar rainfall accumulations with hourly rain gauge data. The results clearly show a superior performance of the polarimetric products with respect to the nonpolarimetric ones at all frequency bands, but particularly at higher frequency. The second version of the polarimetric product, which makes a broader use of polarimetry, provides the best overall results.

scholarly journals Correction of Radar Reflectivity and Differential Reflectivity for Rain Attenuation at X Band. Part II: Evaluation and Application

2005 ◽  
Vol 22 (11) ◽  
pp. 1633-1655 ◽  
Author(s):  
S-G. Park ◽  
M. Maki ◽  
K. Iwanami ◽  
V. N. Bringi ◽  
V. Chandrasekar
Keyword(s):  
Drop Size ◽  
Earth Science ◽  
Radar Data ◽  
Rain Gauge ◽  
Radar Rainfall ◽  
X Band ◽  
Rain Events ◽  
Differential Reflectivity ◽  
Rainfall Estimates ◽  
Good Agreement

Abstract In this paper, the attenuation-correction methodology presented in Part I is applied to radar measurements observed by the multiparameter radar at the X-band wavelength (MP-X) of the National Research Institute for Earth Science and Disaster Prevention (NIED), and is evaluated by comparison with scattering simulations using ground-based disdrometer data. Further, effects of attenuation on the estimation of rainfall amounts and drop size distribution parameters are also investigated. The joint variability of the corrected reflectivity and differential reflectivity show good agreement with scattering simulations. In addition, specific attenuation and differential attenuation, which are derived in the correction procedure, show good agreement with scattering simulations. In addition, a composite rainfall-rate algorithm is proposed and evaluated by comparison with eight gauges. The radar-rainfall estimates from the uncorrected (or observed) ZH produce severe underestimation, even at short ranges from the radar and for stratiform rain events. On the contrary, the reflectivity-based rainfall estimates from the attenuation-corrected ZH does not show such severe underestimation and does show better agreement with rain gauge measurements. More accurate rainfall amounts can be obtained from a simple composite algorithm based on specific differential phase KDP, with the R(ZH_cor) estimates being used for low rainfall rates (KDP ≤ 0.3° km−1 or ZH_cor ≤ 35 dBZ). This improvement in accuracy of rainfall estimation based on KDP is a result of the insensitivity of the rainfall algorithm to natural variations of drop size distributions (DSDs). The ZH, ZDR, and KDP data are also used to infer the parameters (median volume diameter D0 and normalized intercept parameter Nw) of a normalized gamma DSD. The retrieval of D0 and Nw from the corrected radar data show good agreement with those from disdrometer data in terms of the respective relative frequency histograms. The results of this study demonstrate that high-quality hydrometeorological information on rain events such as rainfall amounts and DSDs can be derived from X-band polarimetric radars.

scholarly journals Toward a Polarimetric Radar Classification Scheme for Coalescence-Dominant Precipitation: Application to Complex Terrain

2017 ◽  
Vol 18 (12) ◽  
pp. 3199-3215 ◽  
Author(s):  
Leonardo Porcacchia ◽  
P. E. Kirstetter ◽  
J. J. Gourley ◽  
V. Maggioni ◽  
B. L. Cheong ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Classification Scheme ◽  
Flash Flood ◽  
Radar Data ◽  
High Elevation ◽  
Rain Gauge ◽  
Polarimetric Radar ◽  
Quantitative Precipitation Estimation ◽  
X Band ◽  
Precipitation Estimation

Abstract Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to natural hazards. It is generally difficult to obtain reliable precipitation information over complex areas because of the scarce coverage of ground observations, the limited coverage from operational radar networks, and the high elevation of the study sites. Warm-rain processes have been observed in several flash flood events in complex terrain regions. While they lead to high rainfall rates from precipitation growth due to collision–coalescence of droplets in the cloud liquid layer, their characteristics are often difficult to identify. X-band mobile dual-polarization radars located in complex terrain areas provide fundamental information at high-resolution and at low atmospheric levels. This study analyzes a dataset collected in North Carolina during the 2014 Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign over a mountainous basin where the NOAA/National Severe Storm Laboratory’s X-band polarimetric radar (NOXP) was deployed. Polarimetric variables are used to isolate collision–coalescence microphysical processes. This work lays the basis for classification algorithms able to identify coalescence-dominant precipitation by merging the information coming from polarimetric radar measurements. The sensitivity of the proposed classification scheme is tested with different rainfall-rate retrieval algorithms and compared to rain gauge observations. Results show the inadequacy of rainfall estimates when coalescence identification is not taken into account. This work highlights the necessity of a correct classification of collision–coalescence processes, which can lead to improvements in quantitative precipitation estimation. Future studies will aim at generalizing this scheme by making use of spaceborne radar data.

scholarly journals The New French Operational Radar Rainfall Product. Part II: Validation

2007 ◽  
Vol 22 (3) ◽  
pp. 409-427 ◽  
Author(s):  
P. Tabary ◽  
J. Desplats ◽  
K. Do Khac ◽  
F. Eideliman ◽  
C. Gueguen ◽  
...  
Keyword(s):  
Radar Data ◽  
Rain Gauge ◽  
Radiosonde Data ◽  
Radar Rainfall ◽  
Freezing Level ◽  
Ground Clutter ◽  
Precipitation Estimation ◽  
Upper Level ◽  
Quality Indexes ◽  
Precipitation Events

Abstract A new operational radar-based rainfall product has been developed at Météo-France and is currently being deployed within the French operational network. The new quantitative precipitation estimation (QPE) product is based entirely on radar data and includes a series of modules aimed at correcting for ground clutter, partial beam blocking, and vertical profile of reflectivity (VPR) effects, as well as the nonsimultaneity of radar measurements. The surface rainfall estimation is computed as a weighted mean of the corrected tilts. In addition to the final QPE, a map of quality indexes is systematically generated. This paper is devoted to the validation of the new radar QPE. The VPR identification module has been specifically validated by analyzing 489 precipitation events observed over 1 yr by a representative eight-radar subset of the network. The conceptual model of VPR used in the QPE processing chain is shown to be relevant. A climatology of the three shape parameters of the conceptual VPR (brightband peak, brightband thickness, and upper-level decreasing rate) is established and the radar-derived freezing-level heights are shown to be in good agreement with radiosonde data. A total of 27 precipitation events observed by three S-band radars of the network during the winter of 2005 and the autumns of 2002 and 2003 are used to compare the new radar QPE to the old one. Results are stratified according to the distance to the radar and according to the height of the freezing level. The Nash criterion is increased from 0.23 to 0.62 at close range (below 50 km) and from 0.35 to 0.42 at long range (between 100 and 150 km). The relevance of the proposed quality indexes is assessed by examining their statistical relationship with long-term radar–rain gauge statistics. Mosaics of QPE and quality indexes are also illustrated.

scholarly journals Assessment of Native Radar Reflectivity and Radar Rainfall Estimates for Discharge Forecasting in Mountain Catchments with a Random Forest Model

2020 ◽  
Vol 12 (12) ◽  
pp. 1986 ◽  
Author(s):  
Johanna Orellana-Alvear ◽  
Rolando Célleri ◽  
Rütger Rollenbeck ◽  
Paul Muñoz ◽  
Pablo Contreras ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Random Forest ◽  
Model Performance ◽  
Radar Data ◽  
Rain Gauge ◽  
Random Forest Model ◽  
Mountain Regions ◽  
Radar Rainfall ◽  
Forest Model ◽  
Rainfall Estimates

Discharge forecasting is a key component for early warning systems and extremely useful for decision makers. Forecasting models require accurate rainfall estimations of high spatial resolution and other geomorphological characteristics of the catchment, which are rarely available in remote mountain regions such as the Andean highlands. While radar data is available in some mountain areas, the absence of a well distributed rain gauge network makes it hard to obtain accurate rainfall maps. Thus, this study explored a Random Forest model and its ability to leverage native radar data (i.e., reflectivity) by providing a simplified but efficient discharge forecasting model for a representative mountain catchment in the southern Andes of Ecuador. This model was compared with another that used as input derived radar rainfall (i.e., rainfall depth), obtained after the transformation from reflectivity to rainfall rate by using a local Z-R relation and a rain gauge-based bias adjustment. In addition, the influence of a soil moisture proxy was evaluated. Radar and runoff data from April 2015 to June 2017 were used. Results showed that (i) model performance was similar by using either native or derived radar data as inputs (0.66 < NSE < 0.75; 0.72 < KGE < 0.78). Thus, exhaustive pre-processing for obtaining radar rainfall estimates can be avoided for discharge forecasting. (ii) Soil moisture representation as input of the model did not significantly improve model performance (i.e., NSE increased from 0.66 to 0.68). Finally, this native radar data-based model constitutes a promising alternative for discharge forecasting in remote mountain regions where ground monitoring is scarce and hardly available.

scholarly journals Advancing Precipitation Estimation and Streamflow Simulations in Complex Terrain with X-Band Dual-Polarization Radar Observations

2018 ◽  
Vol 10 (8) ◽  
pp. 1258 ◽  
Author(s):  
Marios Anagnostou ◽  
Efthymios Nikolopoulos ◽  
John Kalogiros ◽  
Emmanouil Anagnostou ◽  
Francesco Marra ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Rain Gauge ◽  
Storm Events ◽  
Error Statistics ◽  
Radar Rainfall ◽  
Radar Observations ◽  
X Band ◽  
Precipitation Estimation ◽  
Rainfall Estimates

In mountain basins, the use of long-range operational weather radars is often associated with poor quantitative precipitation estimation due to a number of challenges posed by the complexity of terrain. As a result, the applicability of radar-based precipitation estimates for hydrological studies is often limited over areas that are in close proximity to the radar. This study evaluates the advantages of using X-band polarimetric (XPOL) radar as a means to fill the coverage gaps and improve complex terrain precipitation estimation and associated hydrological applications based on a field experiment conducted in an area of Northeast Italian Alps characterized by large elevation differences. The corresponding rainfall estimates from two operational C-band weather radar observations are compared to the XPOL rainfall estimates for a near-range (10–35 km) mountainous basin (64 km2). In situ rainfall observations from a dense rain gauge network and two disdrometers (a 2D-video and a Parsivel) are used for ground validation of the radar-rainfall estimates. Ten storm events over a period of two years are used to explore the differences between the locally deployed XPOL vs. longer-range operational radar-rainfall error statistics. Hourly aggregate rainfall estimates by XPOL, corrected for rain-path attenuation and vertical reflectivity profile, exhibited correlations between 0.70 and 0.99 against reference rainfall data and 21% mean relative error for rainfall rates above 0.2 mm h−1. The corresponding metrics from the operational radar-network rainfall products gave a strong underestimation (50–70%) and lower correlations (0.48–0.81). For the two highest flow-peak events, a hydrological model (Kinematic Local Excess Model) was forced with the different radar-rainfall estimations and in situ rain gauge precipitation data at hourly resolution, exhibiting close agreement between the XPOL and gauge-based driven runoff simulations, while the simulations obtained by the operational radar rainfall products resulted in a greatly underestimated runoff response.

scholarly journals X-band dual-polarized radar quantitative precipitation estimate analyses in the Midwestern United States

2017 ◽  
Author(s):  
Micheal J. Simpson ◽  
Neil I. Fox
Keyword(s):  
Ground Truth ◽  
Cost Effective ◽  
Radar Data ◽  
Radar Rainfall ◽  
Differential Phase Shift ◽  
Weather Radars ◽  
X Band ◽  
Precipitation Estimation ◽  
Estimate Quality ◽  
Radar Rainfall Estimate

Abstract. Over the past decade, polarized weather radars have been at the forefront of the search for a replacement of estimating precipitation over the spatially, and temporally inferior tipping buckets. However, many radar-coverage gaps exist within the Continental US (CONUS), proposing a dilemma in that radar rainfall estimate quality degrades with range. One possible solution is that of X-band weather radars. However, the literature as to their long-term performance is lacking. Therefore, the overarching objective of the current study was to analyze two year’s worth of radar data from the X-band dualpolarimetric MZZU radar in central Missouri at four separate ranges from the radar, utilizing tippingbuckets as ground-truth precipitation data. The conventional R(Z)-Convective equation, in addition to several other polarized algorithms, consisting of some combinations of reflectivity (Z), differential reflectivity (ZDR), and the specific differential phase shift (KDP) were used to estimate rainfall. Results indicated that the performance of the algorithms containing ZDR were superior in terms of the normalized standard error (NSE), missed and false precipitation amounts, and the overall precipitation errors. Furthermore, the R(Z,ZDR) and R(ZDR,KDP) algorithms were the only ones which reported NSE values below 100 %, whereas R(Z) and R(KDP) equations resulted in false precipitation amounts equal to or greater than 65 % of the total gauge recorded rainfall amounts. The results show promise in the utilization of the smaller, more cost-effective X-band radars in terms of quantitative precipitation estimation at ranges from 30 to 80 km from the radar.

scholarly journals Automated convective and stratiform precipitation estimation in a small mountainous catchment using X-band radar data in Central Spain

2016 ◽  
Vol 19 (2) ◽  
pp. 315-330 ◽  
Author(s):  
Carolina Guardiola-Albert ◽  
Carlos Rivero-Honegger ◽  
Robert Monjo ◽  
Andrés Díez-Herrero ◽  
Carlos Yagüe ◽  
...  
Keyword(s):  
Rainfall Variability ◽  
Radar Data ◽  
Rain Gauge ◽  
Spatial And Temporal Variability ◽  
Central Spain ◽  
Small Catchment ◽  
Mountainous Catchment ◽  
X Band ◽  
Precipitation Estimation ◽  
Spatio Temporal

For the purposes of weather nowcasting, flood risk monitoring and water resources assessment, it is often difficult to achieve a reliable spatio-temporal representation of rainfall due to a low rain gauge network density. However, quantitative precipitation estimation (QPE) has acquired new prospects with the introduction of weather radars, thanks to their higher spatio-temporal resolution. Although a wide number of QPE algorithms are available for using C-band radar data, only a few studies have employed X-band radar. In this study the microscale rainfall variability in a small catchment is automatically measured using short-range X-band radar variograms and classifying precipitation into convective and stratiform types with a recently published index. The aim is to apply a straightforward geostatistical algorithm, named ordinary kriging of radar errors (OKRE), to integrate X-band radar and rain gauge measurements in a mountainous catchment (15 km2) in central Spain. As expected, convective events presented higher estimation errors due to their complex spatial and temporal variability. Despite this fact, errors are sufficiently small and results are reliable rainfall estimations. The two main contributions of this work are the adaptation of the OKRE method to small spatial scales and its application automatically differentiating between convective and stratiform events.

scholarly journals Radar-Based Quantitative Precipitation Estimation for the Identification of Debris-Flow Occurrence over Earthquake affected Region in Sichuan, China

2017 ◽  
Author(s):  
Zhao Shi ◽  
Fangqiang Wei ◽  
Chandrasekar Venkatachalam
Keyword(s):  
Debris Flow ◽  
Radar Data ◽  
Rain Gauge ◽  
Small Scale ◽  
Mountainous Area ◽  
Radar Rainfall ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
New Generation ◽  
Frequentist Method

Abstract. Both of Ms8.0 Wenchuan earthquake on May 12, 2008 and Ms7.0 Lushan earth quake on April 20, 2013 occurred in Sichuan Province of China. In the earthquake affected mountainous area, a large amount of loose material caused a high occurrence of debris flow during the rainy season. In order to evaluate the rainfall Intensity–Duration (I-D) threshold of the debris flow in the earthquake-affected area, and for filling up the observational gaps caused by the relatively scarce and low altitude deployment of rain gauges in this area, raw data from two S-band China New Generation Doppler weather radar (CINRAD) were captured for six rainfall events which triggered 519 debris flows between 2012 and 2014. Due to the challenges of radar quantitative precipitation estimation (QPE) over mountainous area, a series of improving measures are considered including the hybrid scan mode, the vertical reflectivity profile (VPR) correction, the mosaic of reflectivity, a merged rainfall-reflectivity(R-Z) relationship for convective and stratiform rainfall and rainfall bias adjustment with Kalman filter (KF). For validating rainfall accumulation over complex terrains, the study areas are divided into two kinds of regions by the height threshold of 1.5 km from the ground. Three kinds of radar rainfall estimates are compared with rain gauge measurements. It is observed that the normalized mean bias (NMB) is decreased by 39 % and the fitted linear ratio between radar and rain gauge observation reaches at 0.98. Furthermore, the radar-based I-D threshold derived by the Frequentist method is I = 10.1D−0.52, and it's also found that the I-D threshold is underestimated by uncorrected raw radar data. In order to verify the impacts on observations due to spatial variation, I-D thresholds are identified from the nearest rain gauge observations and radar observations at the rain gauge locations. It is found that both kinds of observations have similar I-D threshold and likewise underestimate I-D thresholds owing to under shooting at the core of convective rainfall. It is indicated that improvement of spatial resolution and measuring accuracy of radar observation will lead to the improvement of identifying debris flow occurrence, especially for events triggered by the small-scale strong rainfall process in the study area.

scholarly journals Radar-based quantitative precipitation estimation for the identification of debris flow occurrence over earthquake-affected regions in Sichuan, China

2018 ◽  
Vol 18 (3) ◽  
pp. 765-780 ◽  
Author(s):  
Zhao Shi ◽  
Fangqiang Wei ◽  
Venkatachalam Chandrasekar
Keyword(s):  
Debris Flow ◽  
Radar Data ◽  
Rain Gauge ◽  
Small Scale ◽  
Mountainous Area ◽  
Radar Rainfall ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Improvement Measures ◽  
Frequentist Method

Abstract. Both Ms 8.0 Wenchuan earthquake on 12 May 2008 and Ms 7.0 Lushan earthquake on 20 April 2013 occurred in the province of Sichuan, China. In the earthquake-affected mountainous area, a large amount of loose material caused a high occurrence of debris flow during the rainy season. In order to evaluate the rainfall intensity–duration (I–D) threshold of the debris flow in the earthquake-affected area, and to fill up the observational gaps caused by the relatively scarce and low-altitude deployment of rain gauges in this area, raw data from two S-band China New Generation Doppler Weather Radar (CINRAD) were captured for six rainfall events that triggered 519 debris flows between 2012 and 2014. Due to the challenges of radar quantitative precipitation estimation (QPE) over mountainous areas, a series of improvement measures are considered: a hybrid scan mode, a vertical reflectivity profile (VPR) correction, a mosaic of reflectivity, a merged rainfall–reflectivity (R − Z) relationship for convective and stratiform rainfall, and rainfall bias adjustment with Kalman filter (KF). For validating rainfall accumulation over complex terrains, the study areas are divided into two kinds of regions by the height threshold of 1.5 km from the ground. Three kinds of radar rainfall estimates are compared with rain gauge measurements. It is observed that the normalized mean bias (NMB) is decreased by 39 % and the fitted linear ratio between radar and rain gauge observation reaches at 0.98. Furthermore, the radar-based I–D threshold derived by the frequentist method is I = 10.1D−0.52 and is underestimated by uncorrected raw radar data. In order to verify the impacts on observations due to spatial variation, I–D thresholds are identified from the nearest rain gauge observations and radar observations at the rain gauge locations. It is found that both kinds of observations have similar I–D thresholds and likewise underestimate I–D thresholds due to undershooting at the core of convective rainfall. It is indicated that improvement of spatial resolution and measuring accuracy of radar observation will lead to the improvement of identifying debris flow occurrence, especially for events triggered by the strong small-scale rainfall process in the study area.

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