Ultra low range sidelobe level pulse compression waveform design for spaceborne meteorological radars.

Meteorological measurements from spaceborne radars present several advantages over current passive techniques, due to the radar capability to discriminate backscattered energy in range. However, the system configuration imposes stringent design requirements in order to guarantee cloud and rain detectability, in particular on the radar waveform. Since power is severely restricted on board a satellite, it is necessary to achieve an efficient range resolution with low transmitted power requirements. Pulse compression theory solves the previous conflicting demand, but the transmitted signal needs to be carefully designed in order to allow the significantly large dynamic range (between 60 and 80 dB depending on the type of meteorological target) needed to carry out the measurements. Several pulse compression range sidelobe reduction techniques of differing natures have been investigated and reported in the literature during the past 50 years. A detailed survey of the most relevant range sidelobe supression procedures has been carried out in order to identify the most suitable frequency modulation candidates which are potentially capable of meeting the stringent specifications of spaceborne radar meteorology. Novel pulse compression waveform design techniques have also been developed, employing linear FM predistortion functions and asymmetric frequency modulation laws, which provide excellent performance in terms of range sidelobe level (below -60 dB) and Doppler tolerance. Different options for the provision of a rain mode for the RA-2 Radar Altimeter (due to fly on European Space Agency ENVISAT satellite) are described, based on altimetry linear FM full-deramp technique concepts. Finally, amplitude modulated pulse compression waveform design alternatives are analysed for the MACSIM radar (Millimetre wave Active Cloud Structure Imaging Mission, European Space Agency Pre Phase A Study), which allow to measure different type of clouds within the Mission required radiometric resolution accuracy

Radar and satellite observations of precipitation: space time variability, cross-validation, and fusion

2017 Fall.Includes bibliographical references.Rainfall estimation based on satellite measurements has proven to be very useful for various applications. A number of precipitation products at multiple time and space scales have been developed based on satellite observations. For example, the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center has developed a morphing technique (i.e., CMORPH) to produce global precipitation products by combining existing space-based observations and retrievals. The CMORPH products are derived using infrared (IR) brightness temperature information observed by geostationary satellites and passive microwave-(PMW) based precipitation retrievals from low earth orbit satellites. Although space-based precipitation products provide an excellent tool for regional, local, and global hydrologic and climate studies as well as improved situational awareness for operational forecasts, their accuracy is limited due to restrictions of spatial and temporal sampling and the applied parametric retrieval algorithms, particularly for light precipitation or extreme events such as heavy rain. In contrast, ground-based radar is an excellent tool for quantitative precipitation estimation (QPE) at finer space-time scales compared to satellites. This is especially true after the implementation of dual-polarization upgrades and further enhancement by urban scale X-band radar networks. As a result, ground radars are often critical for local scale rainfall estimation and for enabling forecasters to issue severe weather watches and warnings. Ground-based radars are also used for validation of various space measurements and products. In this study, a new S-band dual-polarization radar rainfall algorithm (DROPS2.0) is developed that can be applied to the National Weather Service (NWS) operational Weather Surveillance Radar-1988 Doppler (WSR-88DP) network. In addition, a real-time high-resolution QPE system is developed for the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) Dallas-Fort Worth (DFW) dense radar network, which is deployed for urban hydrometeorological applications via high-resolution observations of the lower atmosphere. The CASA/DFW QPE system is based on the combination of a standard WSR-88DP (i.e., KFWS radar) and a high-resolution dual-polarization X-band radar network. The specific radar rainfall methodologies at Sand X-band frequencies, as well as the fusion methodology merging radar observations at different temporal resolutions are investigated. Comparisons between rainfall products from the DFW radar network and rainfall measurements from rain gauges are conducted for a large number of precipitation events over several years of operation, demonstrating the excellent performance of this urban QPE system. The real-time DFW QPE products are extensively used for flood warning operations and hydrological modelling. The high-resolution DFW QPE products also serve as a reliable dataset for validation of Global Precipitation Measurement (GPM) satellite precipitation products. This study also introduces a machine learning-based data fusion system termed deep multi-layer perceptron (DMLP) to improve satellite-based precipitation estimation through incorporating ground radar-derived rainfall products. In particular, the CMORPH technique is applied first to derive combined PMW-based rainfall retrievals and IR data from multiple satellites. The combined PMW and IR data then serve as input to the proposed DMLP model. The high-quality rainfall products from ground radars are used as targets to train the DMLP model. In this dissertation, the prototype architecture of the DMLP model is detailed. The urban scale application over the DFW metroplex is presented. The DMLP-based rainfall products are evaluated using currently operational CMORPH products and surface rainfall measurements from gauge networks

Advanced Remote Sensing Precipitation Input for Improved Runoff Simulation : Local to regional scale modelling

Accurate precipitation data are crucial for hydrological modelling and rainwater runoff management. Precipitation variability exists through a wide range of spatial and temporal scales and cannot be captured well using sparse rain gauge networks. This limitation is further emphasised for urban and mountainous catchments, especially under global warming, causing an increased frequency of extreme events. Recent advances in remote sensing (RS) techniques make monitoring precipitation possible over larger areas at more regular resolutions than conventional rain gauge networks. The RS data can be biased mainly due to the indirect estimations prone to multiple error sources and temporally discrete observations. The wealth of spatiotemporal precipitation data by RS, however, calls for developing data-driven solutions for both the bias correction and hydrological modelling that, in turn, requires new procedures to assure generalization of the existing methods. The present dissertation comprises a comprehensive summary followed by five appended papers, attempting to evaluate quantitative precipitation estimations (QPE) by state-of-the-art instruments/products for local and regional hydrological applications. Accordingly, two recently installed dual polarimetric doppler X-band weather radars (X-WRs) in southern Sweden and multiple Global Precipitation Mission (GPM) products in Iran were studied at the relevant scales for urban hydrology (1–5-min and sub-km) and large water supply river–reservoir system operation (daily-monthly and 0.1°), respectively. The validation against rain gauge observations (Paper I and II) showed a significant dependency of the X-WR and GPM precipitation errors on the radial distance and regional precipitation pattern, respectively. Taking observations from local tipping bucket rain gauges at the 1–30-km ranges as a reference, the apparent problems with a single X-WR is related to the attenuation during heavy rains and overshooting (at higher elevation angle scans). An internationally bias-corrected GPM product called GPM-IMERG-Final shows a generally good correlation to synoptic observations of over 300 rain gauges in Iran except for extreme observations that are much better predicted by the GPM-IMERG Late product during spring, summer, and autumn seasons. To leverage the wealth of spatiotemporally complete and validated precipitation data for hydrological modelling, two novel data-driven procedures using artificial neural networks (ANNs) were developed. As in Paper III, the formulation of the new ANN input variables, namely, ECOVs and CCOVs, representing the event- and catchment-specific areal precipitation coverage ratios, improve monthly runoff estimations in all the studied sub-catchments of the Karkheh River basin (KRB) in the mountainous semi-arid climate of western Iran. Merging the doppler and dual-polarization data in the overlapping coverage of the two XWRs (Paper IV) via an ANN-based QPE improves rainfall detection and accuracy. ANN-assisted estimation of rainfall quantiles, compared to the merging with an empirically based regression model, also shows better results especially related to the extreme 5-min data. Finally, Paper V describes the impact of human activities such as agricultural developments that can equally affect the runoff variation. This fact is considered in Paper III by including MODIS Terra products as additional inputs

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

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

WAVEFORM AND TRANSCEIVER OPTIMIZATION FOR MULTI-FUNCTIONAL AIRBORNE RADAR THROUGH ADAPTIVE PROCESSING

Pulse compression techniques have been widely used for target detection and remote sensing. The primary concern for pulse compression is the sidelobe interference. Waveform design is an important method to improve the sidelobe performance. As a multi-functional aircraft platform in aviation safety domain, ADS-B system performs functions involving detection, localization and alerting of external traffic. In this work, a binary phase modulation is introduced to convert the original 1090 MHz ADS-B signal waveform into a radar signal. Both the statistical and deterministic models of new waveform are developed and analyzed. The waveform characterization, optimization and its application are studied in details. An alternative way to achieve low sidelobe levels without trading o range resolution and SNR is the adaptive pulse compression - RMMSE (Reiterative Minimum Mean-Square error). Theoretically, RMMSE is able to suppress the sidelobe level down to the receiver noise floor. However, the application of RMMSE to actual radars and the related implementation issues have not been investigated before. In this work, implementation aspects of RMMSE such as waveform sensitivity, noise immunity and computational complexity are addressed. Results generated by applying RMMSE to both simulated and measured radar data are presented and analyzed. Furthermore, a two-dimensional RMMSE algorithm is derived to mitigate the sidelobe effects from both pulse compression processing and antenna radiation pattern. In addition, to achieve even better control of the sidelobe level, a joint transmit and receive optimization scheme (JTRO) is proposed, which reduces the impacts of HPA nonlinearity and receiver distortion. Experiment results obtained with a Ku-band spaceborne radar transceiver testbed are presented

Procedures for improved weather radar data quality control

[eng] Weather radar data and its downstream products are essential elements in weather surveillance and key parameters in the initialisation and validation of hydrological and meteorological models, among other downstream applications. Following the quality standards established by the European and global weather radar networking referents, the present thesis aims for the improvement of the base data quality control in the regional weather radar network operated by the Meteorological Service of Catalonia, the XRAD. This objective is accomplished through the analysis, development and implementation of new or existing procedures and algorithms for radar data quality assessment and improvement. Attending to the current radar technology and to the already implemented quality control procedures for the XRAD, the work is focused on the continuous evaluation of the radar system calibration status and on the correction of Doppler velocity data. The quality control algorithms and recommendations presented are easily translatable to any other operative weather radar networking environment. A Sun-based, fully automatic procedure for online monitoring the antenna alignment and the receiver chain calibration is adapted and operationally implemented for the XRAD. This Sun-monitoring technique was developed at the Royal Netherlands and Finnish Meteorological Institutes and is included in the quality control flow of numerous weather radar networks around the world. The method is modified for a robust detection and characterisation of solar interferences in raw data at all scan elevations, even when only data at relatively short ranges is available. The modified detection algorithm is also suitable for detecting interferences from wireless devices, which are stored for monitoring their incidence in the XRAD. The solar interferences detected, in turn, are input observations for the inversion of a two-dimensional Gaussian model that yields estimates of the calibration parameters of interest. A complete theoretical derivation of the model establishes its validity limits and provides analytical estimates of the effective solar widths directly from radar parameters. Results of application of this Sun-monitoring methodology to XRAD data reveal its ability to determine the accuracy of the antenna pointing and to detect changes in receiver calibration and radar system operation status. In order to facilitate the usage of the Sun-monitoring technique and the interpretation of its estimates, the methodology is reproduced under controlled conditions based on the distributions of solar observations collected by two of the XRAD radars. The analysis shows that the accuracy of the estimated calibration parameters is conditioned by the precision, number and distribution of the solar observations which constitute key variables that need to be controlled to ensure reliable estimates. In addition, the Sun-monitoring technique is compared under actual operative conditions with two other common techniques for quantifying the antenna azimuth and elevation pointing offsets. Pointing bias estimates gathered in a dedicated short-term campaign are studied in a direct inter- comparison of the methods that reflects the advantages and limitations in each case. The analysis of the bias estimates reported by the methods in the course of a one-year period reveals that the performance of the techniques depends on the antenna position at the time of the measurement. After this study, a reanalysis of the Sun-monitoring method results is proposed, which allows to additionally quantify the antenna pedestal levelling error. Finally, a post-processing, spatial image filtering algorithm for identification and correction of unfolding errors in dual-PRF Doppler velocity data is proposed. The correction of these errors benefits the usage of radar velocity data in downstream applications such as wind- shear and mesocyclone detection algorithms or assimilation in numerical weather prediction models. The main strengths of the proposed algorithm, in comparison with existing correction techniques, are its robustness to the presence of clustered unfolding errors and that it can be employed independently of post-processing dealiasing algorithms. By means of simulated dual-PRF velocity fields, the correction ability of the algorithm is quantitatively analysed and discussed with particular emphasis on the correction of clustered errors. The quality improvement in real dual-PRF data brought out by the new algorithm is illustrated through application to three selected severe weather events registered by the XRAD.[cat] Seguint els estàndards de qualitat establerts per a les xarxes de radars meteorològics de referència a nivell europeu i global, la present tesi té com a objectiu la millora del control de qualitat de les dades de la xarxa regional de radars meteorològics operada pel Servei Meteorològic de Catalunya (la XRAD). Atenent als procediments de control de qualitat ja implementats per a la XRAD, el treball es centra en l'avaluació contínua de l'estat del calibratge del sistema radar i en la correcció de les dades de velocitat Doppler. Es presenta l'adaptació i aplicació d’un procediment totalment automàtic basat en el Sol, que permet la quantificació remota dels errors d'alineació de l'antena i de calibratge en recepció del radar a la XRAD. El mètode ha estat modificat per a la detecció i caracterització robusta d'interferències solars a les dades primàries de radar. Les interferències solars són utilitzades per a la inversió d'un model físic que proporciona estimacions dels paràmetres de calibratge d'interès. L'algoritme de detecció modificat també és adequat per a la identificació d'interferències procedents de dispositius electrònics externs. Aquestes interferències són emmagatzemades per al seguiment de la seva incidència a la XRAD. La metodologia solar esmentada es modelitza en condicions controlades a partir de la distribució de les observacions solars recollides per dos dels radars de la XRAD. L'anàlisi mostra que la precisió, el nombre i la distribució de les observacions solars constitueixen variables clau que necessiten ser controlades per garantir estimacions fiables dels paràmetres de calibrage. A més, la tècnica solar es compara, sota condicions operatives reals, amb altres dues tècniques habitualment emprades per a la quantificació de l'error d'apuntament de l'antena. A partir d'aquest estudi, es proposa un nou mètode d'anàlisi de les interferències solars, el cual permet quantificar l'error d'anivellament del pedestal de l'antena. Finalment, es desenvolupa i valida un algoritme de filtrat d'imatges per a la identificació i correcció dels errors característics que es donen lloc a les dades dual-PRF de velocitat Doppler. Els punts forts de l'algoritme proposat, en comparació amb les tècniques de correcció existents, són la seva robustesa en la correció d'errors agrupats i que pot emprar- se amb independència dels algoritmes de dealiasing. La millora de la qualitat de les dades reals de velocitat s'il·lustra mitjançant l'aplicació de l’algoritme a tres episodis de temps sever enregistrats per la XRAD

Bibliography of global change, 1992

This bibliography lists 585 reports, articles, and other documents introduced in the NASA Scientific and Technical Information Database in 1992. The areas covered include global change, decision making, earth observation (from space), forecasting, global warming, policies, and trends