Advancements in mesoscale ensemble prediction strategies: Application to Mediterranean high-impact weather

[cat] La predictibilitat d'esdeveniments d'alt impacte a la regi o Mediterr ania ha millorat substancialment al llarg de les darreres d ecades. No obstant aix o, una representaci o precisa d'aspectes dels sistemes convectius rellevants per la societat, tals com el moment en qu e es produeixen, i la seva localitzaci o i intensitat encara suposen un repte. Aquestes febleses de la predicci o a escala convectiva provenen d'imprecisions a l'estimaci o de l'estat atmosf eric inicial, la formulaci o de processos f sics rellevants i la natura ca otica dels sistema associada a la seva no linealitat. En el marc probabil stic imposat per les incerteses intr nseques implicades en la predicci o num erica del temps, l'entitat matem atica que quanti ca la incertesa en l'estat atmosf eric es la funci o densitat de probabilitat. Malgrat aix o, el c alcul de la seva evoluci o temporal es inviable per situacions realistes amb els recursos computacionals disponibles actualment. La modesta aproximaci o habitual per estimar aquesta evoluci o es l' us d'un discret i petit nombre de mostres de l'estat del sistema, que es coneix com a predicci o per conjunts (ensemble forecasting). L'objectiu general d'aquesta Tesi es entendre millor els l mits de la predictibilitat i contribuir a una millora de la predicci o de temps sever a la regi o Mediterr ania. En primer lloc, s'avalua l'evoluci o temporal de les funcions densitat de probabilitat per sistemes de baixa complexitat amb un cert grau de realisme adoptant el formalisme de Liouville. En segon lloc, es dissenya una estrat egia de mostreig per crear pertorbacions a les condicions inicials per abastos de predicci o curts (24-36 h). La t ecnica es basa en el m etode de breeding, que utilitza la din amica completa no lineal per identi car modes de creixement r apid. La modi caci o proposada est a dirigida a ajustar l'escala de les pertorbacions per tal de cobrir l'ample rang d'escales rellevants per la predicci o de curt abast. En tercer lloc, s'investiga el potencial de varis m etodes per tenir en compte la incertesa en el model per a un episodi recent de precipitacions intenses i inundacions que va oc orrer al llarg de la costa Mediterr ania espanyola (12-13 setembre de 2019). S'avaluen m ultiples estrat egies estoc astiques en front l'aproximaci o ordin aria de multif sica en termes de diversitat i habilitat de l'ensemble. Les t ecniques considerades inclouen pertorbacions estoc astiques a les tend encies f siques i pertorbacions a par ametres in uents de l'esquema de microf sica. Finalment, aquestes estrat egies de generaci o d'ensembles s'utilitzen com a for cament meteorol ogic per a un model hidrol ogic per tal d'investigar la predictibilitat 21 22 CONTENTS hidrometeorol ogica de l'episodi del 12-13 setembre de 2019. Les t ecniques desenvolupades, juntament amb l'assimilaci o de dades mitjan cant Ensemble Kalman Filter es comparen amb altres estrat egies populars, tals com el downscaling d'un model global i l'aproximaci o de multif sica. Els resultats d'aquesta Tesi s on rellevants des d'una perspectiva te orica, ja que la soluci o de l'equaci o de Liouville revela estructures complexes per la funci o densitat de probabilitat que podrien comprometre les hip otesis de compacitat i suavitat assumides per la majoria d'eines d'interpretaci o i post proc es d'ensembles. Per altra banda, les estrat egies de generaci o d'ensembles desenvolupades mostren potencial per millorar la predicci o d'esdeveniments d'alt impacte, que es demostra per una major diversitat i habilitat dels ensembles comparades amb les estrat egies de refer encia. Aquests resultats prometedors posen les bases per un sistema avan cat d'alertes a la regi o Mediterr ania per encarar els esdeveniments de temps sever.[spa] La predictibilidad de eventos de alto impacto en la regi on Mediterr anea ha mejorado sustancialmente a lo largo de las ultimas d ecadas. No obstante, una representaci on precisa de aspectos relevantes de los sistemas convectivos relevantes para la sociedad, como el momento en el que se producen, su localizaci on e intensidad a un suponen un reto. Estas debilidades de la predicci on a escala convectiva provienen de imprecisiones en la estimaci on del estado atmosf erico inicial, la formulaci on de los procesos f sicos relevantes y la naturaleza ca otica del sistema asociada a su no linealidad. En el marco probabilista impuesto por las incertidumbres intr nsecas implicadas en la predicci on num erica del tiempo, la entidad matem atica que cuanti ca la incertidumbre en el estado atmosf erico inicial es la funci on densidad de probabilidad. Sin embargo, el c alculo de su evoluci on temporal es inviable para situaciones realistas con los recursos computacionales disponibles actualmente. La modesta aproximaci on habitual para estimar esta evoluci on en el uso de un discreto y peque~no n umero de muestras del estado del sistema, lo que se conoce como predicci on por conjuntos (ensemble forecasting). El objetivo general de esta Tesis es entender mejor los l mites de la predictibilidad y contribuir a una mejora de la predicci on del tiempo severo en la regi on Mediterr anea. En primer lugar, se eval ua la evoluci on temporal de las funciones densidad de probabilidad para sistemas de baja complejidad con un cierto grado de realismo adoptando el formalismo te orico de Liouville. En segundo lugar, se dise~na una estrategia de muestreo para crear perturbaciones en les condiciones iniciales para alcances de predicci on cortos (24-36 h). La t ecnica se basa en el m etodo de breeding, que utiliza la din amica completa no lineal para identi car modos de crecimiento r apido. La modi caci on propuesta est a dirigida a ajustar la escala de las perturbaciones para cubrir el amplio rango de escalas relevantes para la predicci on de corto alcance. En tercer lugar, se investiga el potencial de varios m etodos para tener en cuenta la incertidumbre en el modelo para un episodio reciente de precipitaciones intensas e inundaciones que ocurri o a lo largo de la costa Mediterr anea espa~nola (12-13 de septiembre de 2019). Se eval uan m ultiples estrategias estoc asticas frente a la aproximaci on ordinaria de multif sica en t erminos de diversidad y habilidad del ensemble. Las t ecnicas consideradas incluyen perturbaciones estoc asticas en las tendencias f sicas y perturbaciones en par ametros in uyentes del esquema de microf sica. Finalmente, estas estrategias de generaci on de ensembles se usan como forzamiento meteorol ogico para un modelo hidrol ogico con el n de investigar la predictibilidad hidrometeorol ogica del episodio del 12-13 de septiembre de 2019. Las t ecnicas desarrolladas, junto a la asimilaci on de datos mediante Ensemble Kalman Filter se comparan con otras estrategias populares, como el dowscaling de un modelo global y la aproximaci on de multif sica. Los resultados de esta Tesis son relevantes desde una perspectiva te orica, ya que la soluci on de la ecuaci on de Liouville revela estructuras complejas para la funci on densidad de probabilidad que podr an comprometer las hip otesis de compacidad y suavidad asumidas por la mayor a de herramientas de interpretaci on y pos proceso de ensembles. Por otro lado, las estrategias de generaci on de ensembles desarrolladas muestran potencial para mejorar la predicci on de eventos de alto impacto, que se demuestra por una mayor diversidad y habilidad de los ensembles comparadas con las estrategias de referencia. Estos resultados prometedores sientan las bases para un sistema avanzado de alertas en la regi on Mediterr anea para afrontar los eventos de tiempo severo.[eng] The predictability of meteorological high-impact events in the Mediterranean region has substantially improved over the last decades. Nevertheless, a precise representation of socially relevant aspects of convective systems, such as their timing, location, and intensity is still challenging. These weaknesses of convective-scale forecasting stem from inaccuracies in the estimation of the atmospheric initial state, formulation of relevant physical processes, and the chaotic nature of the system associated with its nonlinearity. In the probabilistic framework imposed by the intrinsic uncertainties involved in numerical weather prediction, the mathematical entity that quanti es the uncertainty in the atmospheric state is the probability density function. However, the computation of its time evolution is unfeasible for realistic situations with the current available computational resources. The usual modest approach to estimate this evolution is the use of a discrete and small number of samples of the state of the system, which is known as ensemble forecasting. The general aim of this Thesis is to better understand the predictability limits and contribute towards the improvement of severe weather forecasting in the Mediterranean region. Firstly, the time evolution of probability density functions for low complexity systems with a certain degree of realism is evaluated by adopting the Liouville formalism. Secondly, a sampling strategy to create initial condition perturbations for the short-range (24-36 h) is designed. The technique is based on the breeding method, which uses the full nonlinear dynamics to identify fast-growing modes. The proposed modi cation is aimed at tailoring the scale of the perturbations in order to cover the wide range of scales relevant for short-range forecasting. Thirdly, the potential of several methods to account for model uncertainty is investigated for a recent heavy precipitation and ash ood episode occurred along the Spanish Mediterranean coast (12-13 September 2019). Multiple stochastic strategies are evaluated against the ordinary multiphysics approach in terms of ensemble diversity and skill. The considered techniques include stochastically perturbed physics tendencies and perturbations to in uential parameters within the microphysics scheme. Finally, these ensemble generation strategies are used as the meteorological forcing for a hydrological model in order to investigate the hydrometeorological predictability of the 12-13 September 2019 episode. The developed techniques, along with data assimilation by means of Ensemble Kalman Filter are compared to other popular strategies, such as the downscaling from a global model and the multiphysics approach. The results of this Thesis are relevant from a theoretical perspective, as the solution of the Liouville equation reveals complex structures for the probability density function that could compromise the hypothesis of compactness and smoothness assumed by most current ensemble interpretation and postprocessing tools. Conversely, the ensemble generation strategies developed show potential to improve the forecasting of high-impact events, proven by higher ensemble diversity and skill compared to the benchmark strategies. These encouraging results lay the foundations for an advanced warning system in the Mediterranean region to deal with severe weather events

Improving the rainfall nowcast for fine temporal and spatial scales suitable for urban hydrology

Accurate Quantitative Precipitation Forecasts (QPF) at high spatial and temporal resolution are crucial for urban flood prediction. Typically, Lagrangian persistence based on radar data is used to nowcast rainfall intensities with up to 3 hours lead time, but nevertheless is not able to deliver reliable QPFs past 20 min lead time (known as well as the predictability limit). Especially, for extreme events causing pluvial floods, accurate QPFs cannot be achieved past 5 min lead time. Furthermore when compared to gauge recordings, the QPFs are not useful at all. There is an essential need to provide better QPFs by improving the rainfall field supplied to the nowcast and by employing non-linear processes for the extrapolation of rainfall into the future. This study is focused on these two main problems, and it investigates different geostatistical and data-driven methods for the improvement of the QPFs at fine scales. The study was conducted within the Hannover radar range where observations between 2000 to 2018 were available. The skill of the nowcast models was assessed on the point (1 km2 and 5 min) and storm scale, based on continuous criteria comparing both radar and gauge observations. A total of 100 gauge measurements inside the study area were as well employed for the assessment. From the period 2000-2012, 93 events of different properties were distinguished and used as a basis for the method development and assessment. Two state-of-the-art nowcast models (HyRaTrac and Lucas-Kanade) were chosen as reference and used as benchmarks for improvement. To improve the rainfall field, a real time merging between radar and gauge data was investigated. Among different merging techniques (mean field bias, quantile bias correction and kriging interpolation), conditional merging (CM) yielded the best rainfall field. When fed to the reference nowcast models, it led to improvements of up to 1 hour of the predictability limit and of the agreement between radar based QPFs and gauge data. To improve the QPF accuracy even further, two different data driven techniques were developed in order to learn non-linear behaviours from past observed rainfall. First, a nearest neighbour approach (k-NN) was developed and employed instead of Lagrangian Persistence on the HyRaTrac nowcast model. The k-NN method accounts for the non-linearity of the storm evolution by consulting k-similar past storms. A deterministic nowcast issued by averaging the behaviours from the 3 most similar storms yielded the best results, extending the predictability limit at the storm scale to 2-3 hours. Second, an ensemble nowcast accounting for the 10 closest neighbours was generated in order to estimate the uncertainty of the QPF. Third, a deep convolution neural network (CNN) was trained on past merged data, in order to learn the non-linearity of the rainfall process. The network based on the last 15 min of observed radar images proved to successfully capture death and decay and partly birth processes, and extended the rainfall predictability limit at the point scale to 3 hours. Lastly, the methods were tested on 17 convective extreme events, extracted from the period 2013-2018, to compare the tested methods for an urban flood nowcast application. The CNN based on merged data outperformed both reference methods as well as the k-NN based nowcast, with the predictability limit reaching 30 – 40 min. The k-NN, although better than the Lagrangian persistence, suffered greatly from the shortcomings of the storm tracking algorithm present under fast moving and extreme storms. To conclude, even though clear improvements were achieved, there is a clear limit to the data-driven methods that cannot be overcome, unless coupled with the convection initialization from Numerical Weather Prediction (NWP) models. Nevertheless, complex relationships learned from past observed data, together with a better rainfall field as input, were proven to be useful in increasing the QPF accuracy and predictability limits for urban hydrology application.Quantitative Niederschlagsvorhersagen (QPF) in hoher räumlicher und zeitlicher Auflösung sind entscheidend für die Prognose urbaner Sturzfluten. Der auf Radardaten basierende Lagrange Ansatz wird typischerweise für Regenintensitätsvorhersagen mit einem Horizont von 3 Stunden verwendet. Zuverlässig ist dieser allerdings nur bis 20 Minuten (bekanntes Prognoselimit). Bei extremen Niederschlagsereignissen, die urbane Sturzfluten verursachen, ist das Limit sogar bereits bei 5 Minuten erreicht. Außerdem kommt es zu deutlichen Abweichungen zwischen der QPF und den Messdaten an Niederschlagsstationen. Eine Verbesserung der QPF ist demnach zwingend erforderlich. Eine solche Verbesserung kann durch die Anpassung des Eingabe-Niederschlagsfeldes und durch die Anwendung nichtlinearer Prozesse für die Extrapolation des Niederschlags erreicht werden. Die vorliegende Studie konzentriert sich auf diese beiden Hauptprobleme und untersucht verschiedene geostatistische und Data-Mining Methoden zur Verbesserung der QPF auf solchen Skalen. Die Studie wurde im Radarbereich von Hannover durchgeführt, wo Beobachtungsdaten von 2000 bis 2018 verfügbar sind. Die Güte der Nowcast-Modelle wurde auf der Punkteskala (1 km2 und 5 min.) anhand kontinuierlicher Kriterien evaluiert und in Relation zu Radar- und Stationsbeobachtungen gesetzt. Hierfür wurden insgesamt 100 Stationsmessungen innerhalb des Untersuchungsgebietes verwendet. Aus dem Zeitraum 2000 bis 2012 wurden 93 Ereignisse mit unterschiedlichen Eigenschaften als Grundlage für die Methodenentwicklung und -beurteilung ausgewertet. Zwei gängige Nowcast-Modelle (HyRaTrac und Lucas-Kanade) wurden als Referenzmodelle ausgewählt und als Maßstab für Verbesserungen eingesetzt. Um das Niederschlagsfeld zu verbessern, wurden Radar- und Stationsdaten in Echtzeit zusammengeführt. Unter den verschiedenen Methoden (Mean Field Bias, Quantile Mapping Bias, Kriging-Interpolation) ergab das Conditional Merging (CM) das optimalste Niederschlagsfeld. Als Input für die beiden Referenzmodelle verwendet, führte das CM zu einer Verlängerung des Prognoselimits auf bis zu eine Stunde. Auch die Übereinstimmung der radargestützten QPF mit den Stationsdaten verbesserte sich. Um das Prognoselimit noch weiter auszudehnen, wurden zwei verschiedene Data-Mining Techniken entwickelt, um die nichtlinearen Verhaltensweisen aus vergangenen Regenfällen zu erlernen: Zunächst wurde ein Nächster-Nachbar-Ansatz (k-NN) entwickelt und anstelle der Lagrange Persistenz im HyRaTrac-Nowcast-Modell eingesetzt. Die k-NN-Methode berücksichtigt die Nichtlinearität der Regensturmentwicklung, indem k-ähnliche vergangene Stürme herangezogen werden. Ein deterministischer Nowcast, der durch Mittelwertbildung der Verhaltensweisen der drei ähnlichsten Stürme erstellt wurde, lieferte die besten Ergebnisse und verlängerte das Prognoselimit auf bis zu zwei-drei Stunden. Ein Ensemble-Nowcast, bei dem die zehn nächsten Nachbarn berücksichtigt wurden, wurde ebenfalls erstellt, um die Unsicherheit des QPF abzuschätzen. Zudem wurde ein künstliches neuronales Netz (CNN) basierend auf vergangenen Daten entwickelt, um die Nichtlinearität des Niederschlagsprozesses zu berücksichtigen. Das neuronale Netz, das mit den beobachteten Radarbildern der letzten 15 Minuten gefüttert wurde, erwies sich als erfolgreich in der Erfassung von Todes-, Zerfalls- und Geburtsprozessen von Stürmen und konnte das Prognoselimit auf bis zu drei Stunden erweitern. Um die Wirksamkeit der entwickelten Methoden für die Vorhersage urbaner Sturzfluten zu untersuchen, wurden sie auf 17 konvektive Extremereignisse aus dem Zeitraum 2013 bis 2018 angewendet. Der k-NN Ansatz war zwar besser als die Lagrange Persistenz, litt aber stark unter den Fehlern des Sturmverfolgungs-Algorithmus bei schnellen und extremen Stürmen. Das CNN übertraf sowohl die Referenzmethoden als auch den k-NN-basierten Nowcast. Das Prognoselimit konnte so von 5 auf 30 bis 40 Minuten erweitert werden. Für eine weitere Verbesserung zeichnete sich letztlich eine klare Grenze ab, die nur mit der Konvektionsinitialisierung aus Numerischen Wettervorhersagemodellen (NWP-Modellen) überwunden werden kann. Im Vergleich mit den ausgewählten Referenzmodellen, können, durch die hier entwickelten Methoden, die Genauigkeit und das Prognoselimit der QPF in der städtischen Hydrologie erheblich verbessert werden

Development of an early warning system to predict sewer overflow

Flash flooding in our city is still a fairly common phenomenon.Unfortunately, the development of a flash flood forecasting system in urban areas is not a simple and unambiguous procedure.While attending the PhD course in Civil and Environmental Engineering, research activity has been given to realize an urban overflowing prediction system that was best as possible suited to the drainage network of the city of Palermo. With the support of radar data and hybrid hydraulic model for drainage network has been possible to demonstrate the effectiveness of this instrument, while the reduction of residual flood risk has been supported by modern resilience measures

Continental and global scale flood forecasting systems

Floods are the most frequent of natural disasters, affecting millions of people across the globe every year. The anticipation and forecasting of floods at the global scale is crucial to preparing for severe events and providing early awareness where local flood models and warning services may not exist. As numerical weather prediction models continue to improve, operational centres are increasingly using the meteorological output from these to drive hydrological models, creating hydrometeorological systems capable of forecasting river flow and flood events at much longer lead times than has previously been possible. Furthermore, developments in, for example, modelling capabilities, data and resources in recent years have made it possible to produce global scale flood forecasting systems. In this paper, the current state of operational large scale flood forecasting is discussed, including probabilistic forecasting of floods using ensemble prediction systems. Six state-of-the-art operational large scale flood forecasting systems are reviewed, describing similarities and differences in their approaches to forecasting floods at the global and continental scale. Currently, operational systems have the capability to produce coarse-scale discharge forecasts in the medium-range and disseminate forecasts and, in some cases, early warning products, in real time across the globe, in support of national forecasting capabilities. With improvements in seasonal weather forecasting, future advances may include more seamless hydrological forecasting at the global scale, alongside a move towards multi-model forecasts and grand ensemble techniques, responding to the requirement of developing multi-hazard early warning systems for disaster risk reduction

The WWRP Polar Prediction Project (PPP)

Mission statement: “Promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on time scales from hours to seasonal”. Increased economic, transportation and research activities in polar regions are leading to more demands for sustained and improved availability of predictive weather and climate information to support decision-making. However, partly as a result of a strong emphasis of previous international efforts on lower and middle latitudes, many gaps in weather, sub-seasonal and seasonal forecasting in polar regions hamper reliable decision making in the Arctic, Antarctic and possibly the middle latitudes as well. In order to advance polar prediction capabilities, the WWRP Polar Prediction Project (PPP) has been established as one of three THORPEX (THe Observing System Research and Predictability EXperiment) legacy activities. The aim of PPP, a ten year endeavour (2013-2022), is to promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on hourly to seasonal time scales. In order to achieve its goals, PPP will enhance international and interdisciplinary collaboration through the development of strong linkages with related initiatives; strengthen linkages between academia, research institutions and operational forecasting centres; promote interactions and communication between research and stakeholders; and foster education and outreach. Flagship research activities of PPP include sea ice prediction, polar-lower latitude linkages and the Year of Polar Prediction (YOPP) - an intensive observational, coupled modelling, service-oriented research and educational effort in the period mid-2017 to mid-2019

Exploitation of X-band weather radar data in the Andes high mountains and its application in hydrology: a machine learning approach

Rainfall in the tropical Andes high mountains is paramount for understanding complex hydrological and ecological phenomena that take place in this distinctive area of the world. Here, rainfall drives imminent hazards such as severe floods, rainfall-induced landslides, different types of erosion, among others. Nonetheless, sparse and uneven distributed rain gauge networks as well as low- resolution satellite imagery are not sufficient to capture its high variability and complex dynamics in the irregular topography of high mountains at appropriate temporal and spatial scales. This results in both, a lack of knowledge about rainfall patterns, as well as a poor understanding of rainfall microphysics, which to date are largely underexplored in the tropical Andes. Therefore, this investigation focuses on the deployment and exploitation of single-polarization (SP) X-band weather radars in the Andean high mountain regions of southern Ecuador, applicable to quantitative precipitation estimation (QPE) and discharge forecasting. This work leverages radar rainfall data by exploring a machine learning (ML) approach. The main aims of the thesis were: (i) The deployment of a first X-band weather radar network in tropical high mountains, (ii) the physically-based QPE of X-band radar retrievals, (iii) the optimization of radar QPE by using a ML-based model and (iv) a discharge forecasting application using a ML-based model and SP X-band radar data. As a starting point, deployment of the first weather radar network in tropical high mountains was carried out. A complete framework for data transmission was set for communication among the network. The highest radar in the network (4450 m a.s.l.) was selected in this study for exploiting the potential of SP X-band radar data in the Andes. First and foremost, physically-based QPE was performed through the derivation of Z-R relationships. For this, data from three disdrometers at different geographic locations and elevation were used. Several rainfall events were selected in order to perform a classification of rainfall types based on the mean volume diameter (Dm [mm]). Derived Z-R relations confirmed the high variability in their parameters due to different rainfall types in the study area. Afterwards, the optimization of radar QPE was pursued by using a ML approach as an alternative to the common physically-based QPE method by means of the Z-R relation. For this, radar QPE was tackled by using two different approaches. The first one was conducted by implementing a step-wise approach where reflectivity correction is performed in a step-by-step basis (i.e., clutter removal, attenuation correction). Finally a locally derived Z-R relationship was applied for obtaining radar QPE. Rain gauge-bias adjustment was neglected because the availability of rain gauge data at near-real time is limited and infrequent in the study area. The second one was conducted by an implementation of a radar QPE model that used the Random Forest (RF) algorithm and reflectivity derived features as inputs for the model. Finally, the performances of both models were compared against rain gauge data. The results showed that the ML-based model outperformed the step-wise approach, making it possible to obtain radar QPE without the need of rain gauge data after the model was implemented. It also allowed to extend the useful range of the radar image (i.e., up to 50 km). Radar QPE can be generally used as input for discharge forecasting models if available. However, one could expect from ML-based models as RF, the ability to map radar data to the target variable (discharge) without any intermediate step (e.g., transformation from reflectivity to rainfall rate). Thus, a comparison for discharge forecasting was performed between RF models that used different input data type. Input data for the relevant models were obtained either from native reflectivity records (i.e., reflectivity corrected from unrealistic measurements) or derived radar-rainfall data (i.e., radar QPE). Results showed that both models performed alike. This proved the suitability of using native radar data (reflectivity) for discharge forecasting in mountain regions. This could be extrapolated in the advantages of deploying radar networks and use their information directly to fed early-warning systems regardless of the availability of rain gauges at ground. In summary, this investigation (i) participated on the deployment of the first weather radar network in tropical high mountains, (ii) significantly contributed to a deeper understanding of rainfall microphysics and its variability in the high tropical Andes by using disdrometer data and (iii) exploited, for the very first time, the native X-band radar reflectivity as a suitable input for ML-based models for both, optimized radar QPE and discharge forecasting. The latter highlighted the benefits and potentials of using a ML approach in radar hydrology. The research generally accounted for ground monitoring limitations commonly found in mountain regions and provided a promising alternative with leveraging the cost-effective X-band technology in the steep terrain of the Andean Cordillera

CIRA annual report FY 2016/2017

Reporting period April 1, 2016-March 31, 2017