Seasonal and Diurnal Variability of Rain Heights at An Equatorial Station

Seasonal and diurnal rain heights variation at Universiti Teknologi Malaysia, Johor was studied. Slant path rain attenuation prediction and modeling is crucial to satellite equipment design; a major input is the rain height. One year meteorological ground-based, S-band, 3D RAPIC precipitation radar data at 500m resolution sourced from the Malaysian Meteorological Department was complemented with two-year TRMM PR data sourced from JAXA Earth Observation Research Center. After filtering, sorting, extraction and decoding of the data, vertical reflectivity profiles were constructed; from which rain height parameters were extracted. TRMM PR processed monthly (3A25) and daily (2A23) rainfall precipitation data were similarly used to obtain rain height parameters to investigate the seasonal and diurnal variations. Results from this work suggested that rain height parameters are influenced by both seasonal and diurnal variations. Higher seasonal variability was observed during south-west and pre-southwest monsoons. Rain heights were also observed to be higher in the night than in the day time

A study of the structure of radar rainfall and its errors

Els objectius principals d’aquesta tesi són dos: d’una banda estudiar l’estructura de la variabilitat de la precipitació a diferents escales espacials i temporals, i de l’altra, estudiar l’estructura dels errors en les estimacions quantitatives de precipitació a través de radar. Pel que fa a l’estudi de l’estructura de la precipitació es proposa un marc de comparació per a mètodes de downscaling basat en valorar el grau amb què cada mètode és capaç de reproduir la variabilitat observada a les diferents escales de la pluja i la seva estructura multifractal. Finalment es proposa un mètode de downscaling tridimensional per a generar camps de precipitació d’alta resolució. Partint de dades mesurades amb radar, és capaç de reproduir la variabilitat a totes les escales de la pluja, i a la vegada, conservar l’estructura vertical de la precipitació observada pel radar. En aquesta tesi s’estudia també l’estructura dels errors associats a les mesures de radar, tant terrestre com embarcat en satèl·lit, que queden després de la cadena de correcció. Es realitza un estudi mitjançant simulació física de les observacions del radar, sobre un camp de precipitació d’alta resulució, per caracteritzar l’error relacionat amb la distància d’observació. També es caracteritza l’error total en les estimacions quantitatives de pluja dels radars terrestres mitjançant comparació contra un producte de referència basat en la combinació de radar i pluviòmetres. L’estructura de l’error trobada ha estat usada per generar un ensemble d’estimacions de pluja, que representa la incertesa en les estimacions, i pot ser emprat per aplicacions probabilístiques. Pel que fa a l’estudi de l’estructura de l’error associat a les estimacions de radar embarcat en satel·lit, s’han realitzat comparacions del radar embarcat en el satèl·lit TRMM contra equipament terrestre, per tal de caracteritzar, sota diverses condicions, les diferències en les mesures de precipitació.The principal objectives of this thesis are two: on one hand study the structure of the precipitation’s variability at different spatial and temporal scales, and on the other hand study the structure of the errors in the quantitative precipitation estimates by radar. In relation to the precipitation structure, a comparison framework for downscaling methods is proposed. Within this framework, the capability of each method reproducing the variability and multifractal behaviour observed in rainfall can be tested. A three-dimensional downscaling method to generate high-resolution precipitation fields from radar observations is proposed. The method is capable to reproduce the variability of rainfall at all scales and, at the same time, preserve the vertical structure of precipitation observed by the radar. In this thesis the structure of the errors that remain after the correction chain in radar measurements (both ground- and space-borne) is also studied. Simulation of the radar physical measurement process over high-resolution precipitation fields is performed to characterize the error related with range. The overall error in quantitative precipitation estimates by radar is characterized through comparison of radar estimates with a reference product based on a radar-raingauges merging. The error structure is used to generate a radar ensemble of precipitation estimates that represents the uncertainty in the measurements and can be used in probabilistic applications. Regarding the study of the errors associated to spaceborne radar measurements, comparisons of TRMM Precipitation Radar with ground equipment are performed to characterize the discrepancies between the precipitation estimates under different conditions

Development of a new global rain model for radio regulation

Signal attenuation due to rain scatter is the dominant fade mechanism on the majority of high-capacity microwave telecommunications links, both terrestrial and Earth-space. These links carry a large proportion of the information that underpins the way modern life functions and is a vital component of national infrastructure. Many studies have established the virtuous cycle that exists between the development of telecommunications infrastructure and economic growth. Therefore, it is important that rain fade models exist for the design and optimisation of telecommunications networks, globally, but especially in developing countries.A set of internationally recognised and agreed radio propagation models is maintained by the International Telecommunications Union - Radiocommunication Sector (ITU-R) in the form of Recommendations. A fundamental input parameter to many of these models is the point one-minute rain rate exceeded for 0.01% (about 50 minutes) of an average year. Historically, the collection of one-minute rain rates has been rare and so very few regions of the world have measured this important parameter. Where local data are not available, the full distribution of one-minute rain rates, including the 0.01% exceeded rate, can be obtained from Rec. ITU-R P.837-7. The input parameters to this Recommendation are the average monthly temperatures and rain accumulations.The network of meteorological stations is very sparse in equatorial developing countries. This limits the reliability of monthly rain accumulation statistics. ITU-R models are validated against DBSG3: the database of link and meteorological measurements maintained by ITU-R Study Group 3. However, there is very little data from the Tropics in DBSG3. Therefore, there are legitimate concerns that the ITU-R P.837-7 model may not work accurately in the Tropics.This thesis uses rain rates derived from the satellite Earth observation Tropical Rain Measuring Mission, TRMM, to estimate point one-minute rain rate distributions in the Tropics. Two distinct uses of these data have been tested. Initially, the measured distributions of TRMM rain rates were used to estimate rain distributions in the Tropics. A method was developed to transform TRMM rain rate distributions to those needed for radio systems, based on UK rain radar data. In many cases, this method performed better than Rec. ITU-R P.837-7, particularly with databases of rain rates not included in DBSG3. To extend the work to global application, TRMM data were used to estimate the monthly rain rate distributions conditional upon monthly temperature and accumulation, as used in Rec. ITU-R P.837-7. These were then used to replace the analytic distributions in the Recommendation. The method worked well on several databases of measurements, but appeared to be biased in temperate regions. The measured TRMM conditional distributions were replaced by curve-fit approximations and a hybrid method was developed that combined the standard Rec. ITU-R P.837-7 prediction with the curve-fit TRMM prediction. This algorithm performed as well as or better than Rec. ITU-R P.837-7 for most test databases and at most time percentages.The direct use of satellite Earth observation data to produce distributions of point one-minute rain rates is a radical departure from methods used before. This thesis has shown the potential of satellite-based measurements to replace the current methods based on downscaling numerical weather prediction output. In the future when more satellite data are available, spanning the globe, this suggests that direct use of satellite data will become standard

Global Precipitation Measurement

This chapter begins with a brief history and background of microwave precipitation sensors, with a discussion of the sensitivity of both passive and active instruments, to trace the evolution of satellite-based rainfall techniques from an era of inference to an era of physical measurement. Next, the highly successful Tropical Rainfall Measuring Mission will be described, followed by the goals and plans for the Global Precipitation Measurement (GPM) Mission and the status of precipitation retrieval algorithm development. The chapter concludes with a summary of the need for space-based precipitation measurement, current technological capabilities, near-term algorithm advancements and anticipated new sciences and societal benefits in the GPM era

TOWARDS IMPROVED QPE BY CAPITALIZING GROUND- AND SPACE- BASED PRECIPITATION MEASUREMENTS

There are primarily two types of weather radar systems offering precipitation measurements covering relatively large areas: (1) Ground-based Radar (GR) networks such as the NEXRAD and (2) Spaceborne radars onboard meteorological satellites. Ground-based polarimetric weather radar is arguably the most powerful validation tool that provides physical insight into the development and interpretation of space-borne weather radar algorithms and observations. To achieve the synergy between ground- and space-borne weather radar, this study first aims to compare and resolve discrepancies in hydrometeor retrievals and reflectivity observations between the NOAA/National Severe Storm Laboratory (NSSL) “proof of concept” polarimetric WSR-88D radar (KOUN) and the space-borne precipitation radar (PR) onboard NASA’s Tropical Rainfall Measuring Mission (TRMM) platform. The comparisons reveal an overall bias <0.2% between PR and KOUN. The bias is hypothesized to be from non-Raleigh scattering effects and/or errors in attenuation correction procedures applied to Ku-band PR measurements. Provided the upgrade of the U.S. national weather radar network to include polarimetric capabilities, the findings in this study will potentially serve as the basis for nation-wide validation of space precipitation products and also invite synergistic development of coordinated space/ground multisensor precipitation products. On the other hand, due to inadequate radar coverage from intervening terrain blockages, ground QPE needs enhancement aided by spaceborne radars. In the second part of the talk, I will introduce an approach that identifies and corrects for vertical profile of reflectivity (VPR) by using TRMM PR measurements in the region of Arizona and southern California, where ground-based NEXRAD radars are difficult to obtain reliable ground precipitation estimation due to complex terrain and limited radar coverage. A VPR Identification and Enhancement (VPR-IE) method based on the modeling of the vertical variations of the equivalent reflectivity factor using a physically-based parameterization and climatological information is employed to obtain VPRs at S-band from the TRMM PR measurement at Ku-band. The VPR-IE methodology is comprehensively evaluated with all stratiform precipitation events in cold season in the year of 2011. The results show that the VPR-IE has overall good performance and provides much more accurate surface rainfall estimates than original radar QPE in NMQ system. With the recent availability of GPM Dual-frequency PR, the VPR-IE approach is anticipated to be more robust and more useful by extending to higher latitude mountainous regions

Precipitation observations from high frequency spaceborne polarimetric synthetic aperture radar and ground-based radar: theory and model validation

2010 Fall.Includes bibliographical references.Global weather monitoring is a very useful tool to better understand the Earth's hydrological cycle and provide critical information for emergency and warning systems in severe cases. Developed countries have installed numerous ground-based radars for this purpose, but they obviously are not global in extent. To address this issue, the Tropical Rainfall Measurement Mission (TRMM) was launched in 1997 and has been quite successful. The follow-on Global Precipitation Measurement (GPM) mission will replace TRMM once it is launched. However, a single precipitation radar satellite is still limited, so it would be beneficial if additional existing satellite platforms can be used for meteorological purposes. Within the past few years, several X-band Synthetic Aperture Radar (SAR) satellites have been launched and more are planned. While the primary SAR application is surface monitoring, and they are heralded as "all weather'' systems, strong precipitation induces propagation and backscatter effects in the data. Thus, there exists a potential for weather monitoring using this technology. The process of extracting meteorological parameters from radar measurements is essentially an inversion problem that has been extensively studied for radars designed to estimate these parameters. Before attempting to solve the inverse problem for SAR data, however, the forward problem must be addressed to gain knowledge on exactly how precipitation impacts SAR imagery. This is accomplished by simulating storms in SAR data starting from real measurements of a storm by ground-based polarimetric radar. In addition, real storm observations by current SAR platforms are also quantitatively analyzed by comparison to theoretical results using simultaneous acquisitions by ground radars even in single polarization. For storm simulation, a novel approach is presented here using neural networks to accommodate the oscillations present when the particle scattering requires the Mie solution, i.e., particle diameter is close to the radar wavelength. The process of transforming the real ground measurements to spaceborne SAR is also described, and results are presented in detail. These results are then compared to real observations of storms acquired by the German TerraSAR-X satellite and by one of the Italian COSMO-SkyMed satellites both operating in co-polar mode (i.e., HH and VV). In the TerraSAR-X case, two horizontal polarization ground radars provided simultaneous observations, from which theoretical attenuation is derived assuming all rain hydrometeors. A C-band fully polarimetric ground radar simultaneously observed the storm captured by the COSMO-SkyMed SAR, providing a case to begin validating the simulation model. While previous research has identified the backscatter and attenuation effects of precipitation on X-band SAR imagery, and some have noted an impact on polarimetric observations, the research presented here is the first to quantify it in a holistic sense and demonstrate it using a detailed model of actual storms observed by ground radars. In addition to volumetric effects from precipitation, the land backscatter is altered when water is on or near the surface. This is explored using TRMM, Canada's RADARSAT-1 C-band SAR and Level 3 NEXRAD ground radar data. A weak correlation is determined, and further investigation is warranted. Options for future research are then proposed

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

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of 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 a 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

Spatial and temporal properties of precipitation uncertainty structures over tropical oceans, The

2015 Spring.Includes bibliographical references.The global distribution of precipitation has been measured from space using a series of passive microwave radiometers for over 40 years. However, our knowledge of precipitation uncertainty is still limited. While previous studies have shown that the uncertainty associated with the surface rain rate tends to vary with geographic location and season, most likely as a consequence of inappropriate and inaccurate microphysical assumptions in the forward model, the internal uncertainty structure remains largely unknown. Hence, a classification scheme is introduced, in which the overall precipitation uncertainty consists of random noise, constant biases, and region-dependent cyclic patterns. It is hypothesized that those cyclic patterns are the result of an imperfect forward model simulation of precipitation variation associated with regional atmospheric cycles. To investigate the hypothesis, differences from ten years of collocated surface rain rate measurements from TRMM Microwave Imager and Precipitation Radar are used as a proxy to characterize the precipitation uncertainty structure. The results show that the recurring uncertainty patterns over tropical ocean basins are clearly impacted by a hierarchy of regionally prominent atmospheric cycles with multiple time scales, from the diurnal cycle to multi-annual oscillation. Spectral analyses of the uncertainty time series have also confirmed the same argument. Moreover, the relative importance of major uncertainty sources varies drastically not only from one basin to another, but also with different choices of sampling resolutions. Following the classification scheme and hypothesis proposed in this study, the magnitudes of un-explained precipitation uncertainty can be reduced up to 68% and 63% over the equatorial central Pacific and eastern Atlantic, respectively