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

On the distinctiveness of oceanic raindrop regimes

Representation of the drop size distribution (DSD) of rainfall is a key element of characterizing precipitation in models and observations, with a functional form necessary to calculate the precipitation flux and the drops' interaction with radiation. With newly available oceanic disdrometer measurements, this study investigates the validity of commonly used DSDs, potentially useful a priori constraints for retrievals, and the impacts of DSD variability on radiative transfer. These data are also compared with leading satellite-based estimates over ocean, with the disdrometers observing a larger number of small drops and significantly more variability in number concentrations. This indicates that previous appraisals of raindrop variability over ocean may have been underestimates. Forward model errors due to DSD variability are shown to be significant for both active and passive sensors. The modified gamma distribution is found to be generally adequate to describe rain DSDs but may cause systematic errors for high-latitude or stratocumulus rain retrievals. Depending on the application, an exponential or generalized gamma function may be preferable for representing oceanic DSDs. An unsupervised classification algorithm finds a variety of DSD shapes that differ from commonly used DSDs but does not find a singular set that best describes the global variability

Remote Sensing of Precipitation: Part II

Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earth’s atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products

Coupling X-band dual-polarized mini-radars and hydro-meteorological forecast models: the HYDRORAD project

Abstract. Hydro-meteorological hazards like convective outbreaks leading to torrential rain and floods are among the most critical environmental issues world-wide. In that context weather radar observations have proven to be very useful in providing information on the spatial distribution of rainfall that can support early warning of floods. However, quantitative precipitation estimation by radar is subjected to many limitations and uncertainties. The use of dual-polarization at high frequency (i.e. X-band) has proven particularly useful for mitigating some of the limitation of operational systems, by exploiting the benefit of easiness to transport and deploy and the high spatial and temporal resolution achievable at small antenna sizes. New developments on X-band dual-polarization technology in recent years have received the interest of scientific and operational communities in these systems. New enterprises are focusing on the advancement of cost-efficient mini-radar network technology, based on high-frequency (mainly X-band) and low-power weather radar systems for weather monitoring and hydro-meteorological forecasting. Within the above context, the main objective of the HYDRORAD project was the development of an innovative \\mbox{integrated} decision support tool for weather monitoring and hydro-meteorological applications. The integrated system tool is based on a polarimetric X-band mini-radar network which is the core of the decision support tool, a novel radar products generator and a hydro-meteorological forecast modelling system that ingests mini-radar rainfall products to forecast precipitation and floods. The radar products generator includes algorithms for attenuation correction, hydrometeor classification, a vertical profile reflectivity correction, a new polarimetric rainfall estimators developed for mini-radar observations, and short-term nowcasting of convective cells. The hydro-meteorological modelling system includes the Mesoscale Model 5 (MM5) and the Army Corps of Engineers Hydrologic Engineering Center hydrologic and hydraulic modelling chain. The characteristics of this tool make it ideal to support flood monitoring and forecasting within urban environment and small-scale basins. Preliminary results, carried out during a field campaign in Moldova, showed that the mini-radar based hydro-meteorological forecasting system can constitute a suitable solution for local flood warning and civil flood protection applications

Real-time rain rate evaluation via satellite downlink signal attenuation measurement

We present the NEFOCAST project (named by the contraction of "Nefeleâ", which is the Italian spelling for the mythological cloud nymph Nephele, and "forecast"), funded by the Tuscany Region, about the feasibility of a system for the detection and monitoring of precipitation fields over the regional territory based on the use of a widespread network of new-generation Eutelsat "SmartLNB" (smart low-noise block converter) domestic terminals. Though primarily intended for interactive satellite services, these devices can also be used as weather sensors, as they have the capability of measuring the rain-induced attenuation incurred by the downlink signal and relaying it on an auxiliary return channel. We illustrate the NEFOCAST system architecture, consisting of the network of ground sensor terminals, the space segment, and the service center, which has the task of processing the information relayed by the terminals for generating rain field maps. We discuss a few methods that allow the conversion of a rain attenuation measurement into an instantaneous rainfall rate. Specifically, we discuss an exponential model relating the specific rain attenuation to the rainfall rate, whose coefficients were obtained from extensive experimental data. The above model permits the inferring of the rainfall rate from the total signal attenuation provided by the SmartLNB and from the link geometry knowledge. Some preliminary results obtained from a SmartLNB installed in Pisa are presented and compared with the output of a conventional tipping bucket rain gauge. It is shown that the NEFOCAST sensor is able to track the fast-varying rainfall rate accurately with no delay, as opposed to a conventional gauge

Raindrop Size Distribution variability from high resolution\ud disdrometer networks

The characteristics of the raindrop size distribution (DSD) have been widely studied since Marshall and Palmer (1948) introduced specific version of exponential distribution for the observed size spectra, based on measurements of raindrops records on dyed filter papers. Across the decades, interest in measuring and studying rain DSD has grown due to applications in cloud physics studies, in calibration of space-borne and ground-based microwave active precipitation sensors and in soil science and agriculture. The study of DSD and of the processes that determine it, are always been challenging from both theoretical and experimental point of view. Moreover, the study of DSD in natural rain is hindered by the difficulties (logistic and economic) in the management of dense disdrometer networks. Based on the unprecedented datasets available, this Thesis aims to contribute in characterizing, from a microphysical point of view, the precipitation structure and the processes that generate it. In particular, the vertical and horizontal DSD variability is analyzed, starting from the study of collisional break-up mechanism in natural rain. The signature of collisional break-up, first evidenced in a particular shape of Doppler power spectrum of a microwave disdrometer, is then searched and characterized in DSD spectrum, assessing its variability with altitude. The horizontal variability of DSD is studied both analyzing the occurrence of equilibrium DSD among the different datasets available and evaluating the correlation of integral and non-integral DSD parameters at small scale. In the first part of the Thesis, an overview on past and recent studies on different aspects of DSD is given. The main mechanisms that govern the rain development are firstly summarized, then the DSD parameterization and the DSD variability in natural rain are discussed. Finally, the description of the characteristics of instruments and of the field campaigns considered in this work are presented. The vertical variability of DSD has been studied thanks to the development of specific algorithms able to detect and characterize both the collisional break-up and the equilibrium DSD. I analyzed the signature of collisional break-up both on the Pludix Doppler power spectrum and on DSD spectrum. The analysis is carried out developing two algorithms that detect the collisional break-up as well as estimate the break-up diameter as function of altitude. The results show a decrease of break-up diameter with altitude, due to the reduction of air density, that plays a critical role in the energetic balance of the collision between two raindrops. The analysis also indicates that, regardless the altitude, the collisional break-up occurs if the kinetic energy of the collision exceeds 12.2 μJ. The results, together with the detailed analysis of some case study at high altitude (over the Tibetan Plateau), show also that the dominance of the break-up process is required to reach the equilibr ium DSD. The study of the DSD variability was deepened focusing the analysis on the 2DVD DSD properties to evaluate the occurrence of equilibrium DSD in natural rain. Another algorithm, based on 2DVD characteristics, is set up to automatically detect the equilibrium DSD by using the great amount of high quality disdrometric data available from the datasets of Ground Validation program of NASAGlobal Precipitation Measurement mission. The results shows a good agreement between the experimental equilibrium DSD and the equilibrium DSD obtained by theoretical models. The analysis shows also that the equilibrium DSD is mainly reached during convective rain and its dependence on season and latitude (no equilibrium DSD is observed at high latitude - 60°N). The occurrence of equilibrium DSD is a rare event in natural rain (maximum 8% of selected minutes), while an increase is observed if transition situations are considered. The results are also analyzed to estimate the goodness of fitting the equilibrium DSD by a three parameter gamma distribution, that is widely used to parameterize the DSD. The low correlation between the experimental DSDs and the gamma distribution evidences that the gamma is not the best parametric form to fit the experimental equilibrium DSD. The behavior of the rain and DSD parameters is studied as function of break-up occurrence and shows that they can be considered an additional indicators to screen out the situations that are not expected to reach the equilibrium DSD. The data collected from two high-resolution disdrometric dataset are used to study the horizonta l DSD spatial variability at small scale. The size of the measuring fields are different but comparable with a ground radar pixel or satellite footprint and this makes the analysis of the particular interest . The rainfall rate and other DSD parameters are analyzed using a three parameter exponential function to estimate their correlation at small scale. The estimated correlation distance shows that the most of the rain and DSD parameters are correlated within a radar pixel or satellite footprint (generally, the integral DSD parameters – rainfall rate, radar reflectivity, liquid water content, etc. – are less correlated than the non integral DSD parameters – maximum diameter, mean mass diameter, etc.). The root mean square error evidences a very good fit of the function used with respect the experimental data, indicating a good reliability of data. The results presented in this Thesis, first, increase the knowledge of break-up phenomenon and its effect on the DSD up to reach the equilibrium DSD, and can be used to improve the parameterizat ion form for break-up and equilibrium DSD occurrence and the modeling of cloud and precipitat ion mechanisms. Secondly, they give reliable indications about the spatial variability of the structure of precipitation within a radar pixel and/or a satellite footprint, with an immediate application to the interpretation of remote sensing measurements to improve precipitation retrieval from radar/satellite measurements, especially after the launch of Dual-frequency Polarization Radar in the frame of Global Precipitation Measurement mission. The results obtained in this Thesis lead to the study of many other aspects that can be investigated to better characterize the precipitation. The time evolution of the precipitation with particular emphasis to the time necessary to the break-up to modify the DSD to reach equilibrium DSD can be investigated by using the algorithms proposed here. A new parameterization of DSD affected by break-up and of equilibrium DSD is necessary to improve the remote sensing of precipitation. Finally, a deeper study of DSD spatial variability is needed to have more information about rain structures at small/medium spatial scales, by different techniques and datasets in different season/location

Raindrop Size Distribution variability from high resolution disdrometer networks

The characteristics of the raindrop size distribution (DSD) have been widely studied since Marshall and Palmer (1948) introduced specific version of exponential distribution for the observed size spectra, based on measurements of raindrops records on dyed filter papers. Across the decades, interest in measuring and studying rain DSD has grown due to applications in cloud physics studies, in calibration of space-borne and ground-based microwave active precipitation sensors and in soil science and agriculture. The study of DSD and of the processes that determine it, are always been challenging from both theoretical and experimental point of view. Moreover, the study of DSD in natural rain is hindered by the difficulties (logistic and economic) in the management of dense disdrometer networks. Based on the unprecedented datasets available, this Thesis aims to contribute in characterizing, from a microphysical point of view, the precipitation structure and the processes that generate it. In particular, the vertical and horizontal DSD variability is analyzed, starting from the study of collisional break-up mechanism in natural rain. The signature of collisional break-up, first evidenced in a particular shape of Doppler power spectrum of a microwave disdrometer, is then searched and characterized in DSD spectrum, assessing its variability with altitude. The horizontal variability of DSD is studied both analyzing the occurrence of equilibrium DSD among the different datasets available and evaluating the correlation of integral and non-integral DSD parameters at small scale. In the first part of the Thesis, an overview on past and recent studies on different aspects of DSD is given. The main mechanisms that govern the rain development are firstly summarized, then the DSD parameterization and the DSD variability in natural rain are discussed. Finally, the description of the characteristics of instruments and of the field campaigns considered in this work are presented. The vertical variability of DSD has been studied thanks to the development of specific algorithms able to detect and characterize both the collisional break-up and the equilibrium DSD. I analyzed the signature of collisional break-up both on the Pludix Doppler power spectrum and on DSD spectrum. The analysis is carried out developing two algorithms that detect the collisional break-up as well as estimate the break-up diameter as function of altitude. The results show a decrease of break-up diameter with altitude, due to the reduction of air density, that plays a critical role in the energetic balance of the collision between two raindrops. The analysis also indicates that, regardless the altitude, the collisional break-up occurs if the kinetic energy of the collision exceeds 12.2 μJ. The results, together with the detailed analysis of some case study at high altitude (over the Tibetan Plateau), show also that the dominance of the break-up process is required to reach the equilibr ium DSD. The study of the DSD variability was deepened focusing the analysis on the 2DVD DSD properties to evaluate the occurrence of equilibrium DSD in natural rain. Another algorithm, based on 2DVD characteristics, is set up to automatically detect the equilibrium DSD by using the great amount of high quality disdrometric data available from the datasets of Ground Validation program of NASAGlobal Precipitation Measurement mission. The results shows a good agreement between the experimental equilibrium DSD and the equilibrium DSD obtained by theoretical models. The analysis shows also that the equilibrium DSD is mainly reached during convective rain and its dependence on season and latitude (no equilibrium DSD is observed at high latitude - 60°N). The occurrence of equilibrium DSD is a rare event in natural rain (maximum 8% of selected minutes), while an increase is observed if transition situations are considered. The results are also analyzed to estimate the goodness of fitting the equilibrium DSD by a three parameter gamma distribution, that is widely used to parameterize the DSD. The low correlation between the experimental DSDs and the gamma distribution evidences that the gamma is not the best parametric form to fit the experimental equilibrium DSD. The behavior of the rain and DSD parameters is studied as function of break-up occurrence and shows that they can be considered an additional indicators to screen out the situations that are not expected to reach the equilibrium DSD. The data collected from two high-resolution disdrometric dataset are used to study the horizonta l DSD spatial variability at small scale. The size of the measuring fields are different but comparable with a ground radar pixel or satellite footprint and this makes the analysis of the particular interest . The rainfall rate and other DSD parameters are analyzed using a three parameter exponential function to estimate their correlation at small scale. The estimated correlation distance shows that the most of the rain and DSD parameters are correlated within a radar pixel or satellite footprint (generally, the integral DSD parameters – rainfall rate, radar reflectivity, liquid water content, etc. – are less correlated than the non integral DSD parameters – maximum diameter, mean mass diameter, etc.). The root mean square error evidences a very good fit of the function used with respect the experimental data, indicating a good reliability of data. The results presented in this Thesis, first, increase the knowledge of break-up phenomenon and its effect on the DSD up to reach the equilibrium DSD, and can be used to improve the parameterizat ion form for break-up and equilibrium DSD occurrence and the modeling of cloud and precipitat ion mechanisms. Secondly, they give reliable indications about the spatial variability of the structure of precipitation within a radar pixel and/or a satellite footprint, with an immediate application to the interpretation of remote sensing measurements to improve precipitation retrieval from radar/satellite measurements, especially after the launch of Dual-frequency Polarization Radar in the frame of Global Precipitation Measurement mission. The results obtained in this Thesis lead to the study of many other aspects that can be investigated to better characterize the precipitation. The time evolution of the precipitation with particular emphasis to the time necessary to the break-up to modify the DSD to reach equilibrium DSD can be investigated by using the algorithms proposed here. A new parameterization of DSD affected by break-up and of equilibrium DSD is necessary to improve the remote sensing of precipitation. Finally, a deeper study of DSD spatial variability is needed to have more information about rain structures at small/medium spatial scales, by different techniques and datasets in different season/location