On requirements for a satellite mission to measure tropical rainfall

Tropical rainfall data are crucial in determining the role of tropical latent heating in driving the circulation of the global atmosphere. Also, the data are particularly important for testing the realism of climate models, and their ability to simulate and predict climate accurately on the seasonal time scale. Other scientific issues such as the effects of El Nino on climate could be addressed with a reliable, extended time series of tropical rainfall observations. A passive microwave sensor is planned to provide information on the integrated column precipitation content, its areal distribution, and its intensity. An active microwave sensor (radar) will define the layer depth of the precipitation and provide information about the intensity of rain reaching the surface, the key to determining the latent heat input to the atmosphere. A visible/infrared sensor will provide very high resolution information on cloud coverage, type, and top temperatures and also serve as the link between these data and the long and virtually continuous coverage by the geosynchronous meteorological satellites. The unique combination of sensor wavelengths, coverages, and resolving capabilities together with the low-altitude, non-Sun synchronous orbit provide a sampling capability that should yield monthly precipitation amounts to a reasonable accuracy over a 500- by 500-km grid

Annual report for the CSU-CHILL radar facility

Submitted to the National Science Foundation, Division of Atmospheric Sciences.1 February 1994.Cooperative agreement no. ATM-8919080

Comparisons of precipitation measurements by the Advanced Microwave Precipitation Radiometer and multiparameter radar

Includes bibliographical references.Multiparameter microwave radar measurements are based on dual-polarization and dual-frequency techniques and are well suited for microphysical inferences of complex precipitating clouds, since they depend upon the size, shape, composition, and orientation of a collection of discrete random scatterers. Passive microwave radiometer observations represent path integrated scattering and absorption phenomena of the same scatterers. The response of the upwelling brightness temperatures TB to the precipitation structure depends on the vertical distribution of the various hydrometeors and gases, and the surface features. As a result, combinations of both active and passive techniques contain great potential to markedly improve the longstanding issue of precipitation measurement from space. The NASA airborne Advanced Microwave Precipitation Radiometer (AMPR) and the National Center for Atmospheric Research (NCAR) CP-2 multiparameter radar were jointly operated during the 1991 Convection and Precipitation/Electrification experiment (CaPE) in central Florida. The AMPR is a four channel, high resolution, across-track scanning total power radiometer system using the identical multifrequency feedhorn as the widely utilized Special Sensor Microwave/Imager (SSM/I) satellite system. Surface and precipitation features are separable based on the TB behavior as a function of the AMPR channels. The radar observations are presented in a remapped format suitable for comparison with the multifrequency AMPR imagery. Striking resemblances are noted between the AMPR imagery and the radar reflectivity at successive heights, while vertical profiles of the CP-2 products along the nadir trace suggest a storm structure consistent with the viewed AMPR TB. Directly over the storm cores, the difference between the 37 and 85 GHz TB was noted to approach (and in some cases fall below) zero. Microwave radiative transfer computations show that this is theoretically possible for hail regions suspended aloft in the core of strong convective storms.This work was supported by the NASA Earth Science and Applications Division under Grant NAG8-890. The National Center for Atmospheric Research is sponsored by the National Science Foundation

Final report for the CSU-CHILL radar facility

Submitted to the National Science Foundation, Division of Atmospheric Sciences.6 May 1996.Cooperative agreement no. ATM-8919080

Application of the variational method for correction of wet ice attenuation for X-band dual-polarized radar

2011 Fall.Includes bibliographical references.In recent years there has been a huge interest in the development and use of dual-polarized radar systems operating at X-band (~10 GHz) region of the electromagnetic spectrum. This is due to the fact that these systems are smaller and cheaper allowing for a network to be built, for example, for short range (typically < 30-40 km) hydrological applications. Such networks allow for higher cross-beam spatial resolutions while cheaper pedestals supporting a smaller antenna also allows for higher temporal resolution as compared with large S-band (long range) systems used by the National Weather Service. Dual-polarization radar techniques allow for correction of the strong attenuation of the electromagnetic radar signal due to rain at X-band and higher frequencies. However, practical attempts to develop reliable correction algorithms have been cumbered by the need to deal with the rather large statistical fluctuations or "noise" in the measured polarization parameters. Recently, the variational method was proposed, which overcomes this problem by using the forward model for polarization variables, and uses iterative approach to minimize the difference between modeled and observed values, in a least squares sense. This approach also allows for detection of hail and determination of the fraction of reflectivity due to the hail when the precipitation shaft is composed of a mixture of rain and hail. It was shown that this approach works well with S-band radar data. The purpose of this research is to extend the application of the variational method to the X-band dual-polarization radar data. The main objective is to correct for attenuation caused by rain mixed with wet ice hydrometeors (e.g., hail) in deep convection. The standard dual-polarization method of attenuation-correction using the differential propagation phase between H and V polarized waves cannot account for wet ice hydrometeors along the propagation path. The ultimate goal is to develop a feasible and robust variational-based algorithm for rain and hail attenuation correction for the Collaborate Adaptive Sensing of the Atmosphere (CASA) project

Rationale for the evaluation of renal functional reserve in allogeneic stem cell transplantation candidates: a pilot study

Background. The main purpose of our study was to evaluate the ability of renal functional reserve (RFR) to stratify the risk of acute kidney injury (AKI) occurrence within 100 days of hematopoietic stem cell transplantation (HSCT) and to predict any functional recovery or the onset of chronic kidney disease. A secondary aim was to identify the clinical/laboratory risk factors for the occurrence of AKI. Methods. The study design is prospective observational. We enrolled 48 patients with normal basal glomerular filtration rate (bGFR) who underwent allogenic HSCT. A multiparameter assessment and the Renal Functional Reserve Test (RFR-T) using an oral protein load stress test were performed 15 days before the HSCT. Results. Different RFRs corresponded to the same bGFR values. Of 48 patients, 29 (60%) developed AKI. Comparing the AKI group with the group that did not develop AKI, no statistically significant difference emerged in any characteristic related to demographic, clinical or multiparameter assessment variables except for the estimated GFR (eGFR). eGFR ≤100 mL/min/1.73 m2 was significantly related to the risk of developing AKI (Fisher’s exact test, P = .001). Moreover, RFR-T was lower in AKI+ patients vs AKI– patients, but did not allow statistical significance (28% vs 40%). In AKI patients, RFR &gt;20% was associated with complete functional recovery (one-sided Fisher’s exact test, P = .041). The risk of failure to recover increases significantly when RFR ≤20% (odds ratio = 5.50, 95% confidence interval = 1.06–28.4). Conclusion. RFR identifies subclinical functional deterioration conditions essential for post-AKI recovery. In our cohort of patients with no kidney disease (NKD), the degree of pre-HSCT eGFR is associated with AKI risk, and a reduction in pre-HSCT RFR above a threshold of 20% is related to complete renal functional recovery post-AKI. Identifying eGFR first and RFR second could help select patients who might benefit from changes in transplant management or early nephrological assessment. © The Author(s) 2022

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

Machine learning-based fusion studies of rainfall estimation from spaceborne and ground-based radars

2019 Spring.Includes bibliographical references.Precipitation measurement by satellite radar plays a significant role in researching the water circle and forecasting extreme weather event. Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) has capability of providing a high-resolution vertical profile of precipitation over the tropics regions. Its successor, Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR), can provide detailed information on the microphysical properties of precipitation particles, quantify particle size distribution and quantitatively measure light rain and falling snow. This thesis presents a novel Machine Learning system for ground-based and space borne radar rainfall estimation. The system first trains ground radar data for rainfall estimation using rainfall measurements from gauges and subsequently uses the ground radar based rainfall estimates to train spaceborne radar data in order to get space based rainfall product. Therein, data alignment between spaceborne and ground radar is conducted using the methodology proposed by Bolen and Chandrasekar (2013), which can minimize the effects of potential geometric distortion of spaceborne radar observations. For demonstration purposes, rainfall measurements from three rain gauge networks near Melbourne, Florida, are used for training and validation purposes. These three gauge networks, which are located in Kennedy Space Center (KSC), South Florida Water Management District (SFL), and St. Johns Water Management District (STJ), include 33, 46, and 99 rain gauge stations, respectively. Collocated ground radar observations from the National Weather Service (NWS) Weather Surveillance Radar – 1988 Doppler (WSR-88D) in Melbourne (i.e., KMLB radar) are trained with the gauge measurements. The trained model is then used to derive KMLB radar based rainfall product, which is used to train both TRMM PR and GPM DPR data collected from coincident overpasses events. The machine learning based rainfall product is compared against the standard satellite products, which shows great potential of the machine learning concept in satellite radar rainfall estimation. Also, the local rain maps generated by machine learning system at KMLB area are demonstrate the application potential

Nowcasting for a high-resolution weather radar network

2010 Fall.Includes bibliographical references.Short-term prediction (nowcasting) of high-impact weather events can lead to significant improvement in warnings and advisories and is of great practical importance. Nowcasting using weather radar reflectivity data has been shown to be particularly useful. The Collaborative Adaptive Sensing of the Atmosphere (CASA) radar network provides high-resolution reflectivity data amenable to producing valuable nowcasts. The high-resolution nature of CASA data requires the use of an efficient nowcasting approach, which necessitated the development of the Dynamic Adaptive Radar Tracking of Storms (DARTS) and sinc kernel-based advection nowcasting methodology. This methodology was implemented operationally in the CASA Distributed Collaborative Adaptive Sensing (DCAS) system in a robust and efficient manner necessitated by the high-resolution nature of CASA data and distributed nature of the environment in which the nowcasting system operates. Nowcasts up to 10 min to support emergency manager decision-making and 1-5 min to steer the CASA radar nodes to better observe the advecting storm patterns for forecasters and researchers are currently provided by this system. Results of nowcasting performance during the 2009 CASA IP experiment are presented. Additionally, currently state-of-the-art scale-based filtering methods were adapted and evaluated for use in the CASA DCAS to provide a scale-based analysis of nowcasting. DARTS was also incorporated in the Weather Support to Deicing Decision Making system to provide more accurate and efficient snow water equivalent nowcasts for aircraft deicing decision support relative to the radar-based nowcasting method currently used in the operational system. Results of an evaluation using data collected from 2007-2008 by the Weather Service Radar-1988 Doppler (WSR-88D) located near Denver, Colorado, and the National Center for Atmospheric Research Marshall Test Site near Boulder, Colorado, are presented. DARTS was also used to study the short-term predictability of precipitation patterns depicted by high-resolution reflectivity data observed at microalpha (0.2-2 km) to mesobeta (20-200 km) scales by the CASA radar network. Additionally, DARTS was used to investigate the performance of nowcasting rainfall fields derived from specific differential phase estimates, which have been shown to provide more accurate and robust rainfall estimates compared to those made from radar reflectivity data

Photonic Biosensors: Detection, Analysis and Medical Diagnostics

The role of nanotechnologies in personalized medicine is rising remarkably in the last decade because of the ability of these new sensing systems to diagnose diseases from early stages and the availability of continuous screenings to characterize the efficiency of drugs and therapies for each single patient. Recent technological advancements are allowing the development of biosensors in low-cost and user-friendly platforms, thereby overcoming the last obstacle for these systems, represented by limiting costs and low yield, until now. In this context, photonic biosensors represent one of the main emerging sensing modalities because of their ability to combine high sensitivity and selectivity together with real-time operation, integrability, and compatibility with microfluidics and electric circuitry for the readout, which is fundamental for the realization of lab-on-chip systems. This book, “Photonic Biosensors: Detection, Analysis and Medical Diagnostics”, has been published thanks to the contributions of the authors and collects research articles, the content of which is expected to assume an important role in the outbreak of biosensors in the biomedical field, considering the variety of the topics that it covers, from the improvement of sensors’ performance to new, emerging applications and strategies for on-chip integrability, aiming at providing a general overview for readers on the current advancements in the biosensing field