The future of Earth observation in hydrology

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smart-phones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems

Optimalisasi Saluran Komunikasi Berbasis Gelombang Mikro Sebagai Alternatif Sistem Pemantauan Curah Hujan

As a vast archipelagic country with diverse topographic conditions and has an annual average rainfall of more than 2000 mm, Indonesia is prone to hydrometeorological disasters. Based on Indonesia's disaster data, throughout 2021 there were 3,658 incidents of floods and landslides distributed throughout Indonesia. This makes real-time rainfall monitoring with high density indispensable. Indonesia currently has a rainfall monitoring system about 1000 automatic rain gauges, so an increase in the spatial resolution of network is necessary. The increasing density of monitoring equipment using rain gauges and weather radar poses the problem of high procurement and operational costs. Therefore, several alternative rainfall monitoring systems are needed. In this article, we review several studies that focus on the utilization of terrestrial and satellite communication link operating in high frequency bands as an alternative for measuring rainfall. Optimization of the satellite communication system network is more suitable than terrestrial networks to be applied in Indonesia with archipelagic areas because it has a large number of point distributions with wider coverage. The use of artificial intelligence with deep learning techniques such as one dimensional convolutional neural network (1D-CNN) is also very promising to estimate rainfall intensity because it has a high accuracy of 93%.

Rainfall Map from Attenuation Data Fusion of Satellite Broadcast and Commercial Microwave Links

The demand for accurate rainfall rate maps is growing ever more. This paper proposes a novel algorithm to estimate the rainfall rate map from the attenuation measurements coming from both broadcast satellite links (BSLs) and commercial microwave links (CMLs). The approach we pursue is based on an iterative procedure which extends the well-known GMZ algorithm to fuse the attenuation data coming from different links in a three-dimensional scenario, while also accounting for the virga phenomenon as a rain vertical attenuation model. We experimentally prove the convergence of the procedures, showing how the estimation error decreases for every iteration. The numerical results show that adding the BSL links to a pre-existent CML network boosts the accuracy performance of the estimated rainfall map, improving up to 50% the correlation metrics. Moreover, our algorithm is shown to be robust to errors concerning the virga parametrization, proving the possibility of obtaining good estimation performance without the need for precise and real-time estimation of the virga parameters

Novel Approach to Rainfall Rate Estimation based on Fusing Measurements from Terrestrial Microwave and Satellite Links

Reliable, cheap and accurate measurements of rainfall rate are growing to be more and more important in many sectors as: meteorology, agriculture, flood warning, and weather forecasting. Recently, indeed, the development of novel competitive techniques has been pushed in order to improve accuracy and reliability performance, such as commercial microwave links and broadcast satellite links. The aim of the current paper is to extend previous works of the literature based on land wireless links only. The basic idea consists in synergically employing both land and satellite based approaches together, by collecting and properly fusing the corresponding measurements. To this end, an iterative optimization procedure has been developed. As shown by numerical results, the proposed procedure gives the estimated rainfall map with a considerable accuracy and improved performance respect to the conventional algorithm based on terrestrial link only

Concepts for 18/30 GHz satellite communication system, volume 1

Concepts for 18/30 GHz satellite communication systems are presented. Major terminal trunking as well as direct-to-user configurations were evaluated. Critical technologies in support of millimeter wave satellite communications were determined

Near-realtime quantitative precipitation estimation and prediction (RealPEP)

Flash floods in small- to medium-sized catchments and intense precipitation over cities caused by severe local storms pose increasing threats to our society. For the timely prediction of such events, the value of high-resolution and high-quality QPE and corresponding forecasts cannot be overrated. Seamless predictions harmonizing nowcasting and numerical weather prediction (NWP) across forecast lead times from minutes to days would greatly help to improve the value and efficiency of warnings. Organized by the Research Unit on Near-Realtime Precipitation Estimation and Prediction (RealPEP, www2.meteo.uni-bonn.de/realpep) and supported by the Project on Seamless Integrated Forecasting System (SINFONY, www.dwd .de/DE/forschung/forschungsprogramme/sinfony_iafe/sinfony_node.html) of the German Meteorological Service (DWD), an international 3-day online conference was held from 5 to 7 October 2020, dedicated to Precipitation and Flash-Flood Predictions from Minutes to Days (https://indico .scc.kit.edu/event/883/). Most speakers agreed to have their presentations recorded, which we uploaded to YouTube for further distribution (see, e.g., on the conference homepage, https:// indico.scc.kit.edu/event/883/page/588-recorded-talks). The speakers were both invited experts in the respective research fields and researchers from the RealPEP and SINFONY projects. Talks and discussions could be followed on video stream. Interaction between the about 250 participants was enabled by entering written questions and comments via a dedicated tool, which allowed for voting and thus also ranking questions. Registered participants could enter chat rooms from where they could be moved to the speaker room for posing the questions directly to the speakers and the auditorium. On the last day of the conference podium discussions with selected speakers summarized talks and discussions and elaborated on overarching problems, ideas, and developments in the fields of quantitative precipitation estimation (QPE), quantitative precipitation nowcasting (QPN), quantitative precipitation forecasting (QPF), flash-flood prediction (FFP), and their organization into seamless prediction systems, which also constituted the topics of the five sessions during the conference. We report here in particular on the outcomes of the panel discussions

Rainfall attenuation prediction model for dynamic rain fade mitigation technique considering millimeter wave communication link.

Doctoral Degree. University of KwaZulu-Natal, Durban.To deliver modern day broadband services to both fixed and mobile devices, ultra-high speed wireless networks are required. Innovative services such as the Internet-of-Things (IoT) can be facilitated by the deployment of next generation telecommunication networks such as 5G technologies. The deployment of 5G technologies is envisioned as a catalyst in the alleviation of spectrum congestion experienced by current technologies. With their improved network speed, capacity and reduced communication latency, 5G technologies are expected to enhance telecommunication networks for next generation services. These technologies, in addition to using current Long Term Evolution (LTE) frequency range (600 MHz to 6 GHz), will also utilize millimetre wave bands in the range 24-86 GHz. However, these high frequencies are susceptible to signal loss under rain storms. At such high frequencies, the size of the rain drop is comparable to the wavelength of the operating signal frequency, resulting in energy loss in the form of absorption and scattering by water droplets. This study investigates the effect of intense rain storms on link performance to accurately determine and apply dynamic rain fade mitigation techniques such as the use of a combination of modulation schemes to maintain link connectivity during a rain event. The backpropagation neural network (BPNN) model is employed in this study to predict the state of the link for decision making in employment of dynamic rain fade mitigation. This prediction model was tested on all rainfall regimes including intense rain storms and initial results are encouraging. Further on, the prediction model has been tested on a rainfall event rainfall data collected over Butare (2.6078° S, 29.7368° E), Rwanda, and the results demonstrate the portability of the proposed prediction model to other regions. The evolution of R0.01 (rain rate exceeded for 0.01% of the time in an average year) parameter due to intense rain storms over the region of study is examined and detailed analysis shows that this parameter is double the proposed ITU-R value of 60 mm/h. Moreover, an investigation on the largest rain drop size present in each rain storm is carried out for different storm magnitudes. The study goes further to examine the frequency of occurrence of rain storms using the Markov chain approach. Results of this approach show that rain spikes with maximum rain rates from 150 mm/h and above (intense storms) are experienced in the region of study with probability of occurrence of 11.42%. Additionally, rain spike service times for various rain storm magnitudes are analyzed using the queueing theory technique. From this approach, a model is developed for estimation of rain cell diameter that can be useful for site diversity as a dynamic rain fade mitigation strategy. Finally, the study further investigates second-order rain fade statistics at different attenuation thresholds

Realizing future intelligent networks via spatial and multi-temporal data acquisition in disdrometer networks

Abstract: Data acquisition and qualitative precipitation estimation (QPE) via disdrometers play an important role in estimating rain-induced attenuation in wireless networks. However, existing disdrometer observations do not provide sufficient information for modelling intelligent wireless networks. The design of intelligent wireless networks requires that QPE parameters for a location be known at different epochs. This requires that disdrometers with spatial variability should be capable of multi-temporal QPE observations. A disdrometer architecture that addresses this challenge is presented in this paper. The proposed multi–temporal disdrometer incorporates a computing payload for storing QPE related data at multiple epochs. Performance evaluation shows that the use of the proposed multi–temporal disdrometer in QPE related data acquisition increases data suitable for QPE related modelling by up to 52.2% and 49.4% in the short term and long term respectively