Description and evaluation of the CASA dual-Doppler system

2011 Spring.Includes bibliographical references.Long range weather surveillance radars are designed for observing weather events for hundreds of kilometers from the radar and operate over a large coverage domain independently of weather conditions. As a result a loss in spatial resolution and limited temporal sampling of the weather phenomenon occurs. Due to the curvature of the Earth, long-range weather radars tend to make the majority of their precipitation and wind observations in the middle to upper troposphere, resulting in missed features associates with severe weather occurring in the lowest three kilometers of the troposphere. The spacing of long-range weather radars in the United States limits the feasibility of using dual-Doppler wind retrievals that would provide valuable information on the kinematics of weather events to end-users and researchers. The National Science Foundation Center for Collaborative Adapting Sensing of the Atmosphere (CASA) aims to change the current weather sensing model by increasing coverage of the lowest three kilometers of the troposphere by using densely spaced networked short-range weather radars. CASA has deployed a network of these radars in south-western Oklahoma, known as Integrated Project 1 (IP1). The individual radars are adaptively steered by an automated system known as the Meteorological Command and Control (MCC). The geometry of the IP1 network is such that the coverage domains of the individual radars are overlapping. A dual-Doppler system has been developed for the IP1 network which takes advantage of the overlapping coverage domains. The system is comprised of two subsystems, scan optimization and wind field retrieval. The scan strategy subsystem uses the DCAS model and the number of dual-Doppler pairs in the IP1 network to minimizes the normalized standard deviation in the wind field retrieval. The scan strategy subsystem also minimizes the synchronization error between two radars. The retrieval itself is comprised of two steps, data resampling and the retrieval process. The resampling step map data collected in radar coordinates to a common Cartesian grid. The retrieval process uses the radial velocity measurements to estimate the northward, eastward, and vertical component of the wind. The error in the retrieval is related to the beam crossing angle. The best retrievals occur at beam crossing angles greater than 30 degrees. During operations statistics on the scan strategy and wind field retrievals are collected in real-time. For the scan strategy subsystem statistics on the beam crossing angels, maximum elevation angle, number of elevation angles, maximum observable height, and synchronization time between radars in a pair are collected by the MCC. These statistics are used to evaluate the performance of the scan strategy subsystem. Observations of a strong wind event occurring on April 2, 2010 are used to evaluate the decision process associated with the scan strategy optimization. For the retrieval subsystem, the normalized standard deviation for the wind field retrieval is used to evaluate the quality of the retrieval. Wind fields from an EF2 tornado observed on May 14, 2009 are used to evaluate the quality of the wind field retrievals in hazardous wind events. Two techniques for visualizing vector fields are available, streamlines and arrows. Each visualization technique is evaluated based on the task of visualizing small and large scale phenomenon. Applications of the wind field retrievals include the computation of the vorticity and divergence fields. Vorticity and divergence for an EF2 tornado observed on May 14, 2009 are evaluated against vorticity and divergence for other observed tornadoes

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

An inverse method to retrieve 3D radar reflectivity composites

Dense radar networks offer the possibility of getting better Quantitative Precipitation Estimates (QPE) than those obtained with individual radars, as they allow increasing the coverage and improving quality of rainfall estimates in overlapping areas. Well-known sources of error such as attenuation by intense rainfall or errors associated with range can be mitigated through radar composites. Many compositing techniques are devoted to operational uses and do not exploit all the information that the network is providing. In this work an inverse method to obtain high-resolution radar reflectivity composites is presented. The method uses a model of radar sampling of the atmosphere that accounts for path attenuation and radar measurement geometry. Two significantly different rainfall situations are used to show detailed results of the proposed inverse method in comparison to other existing methodologies. A quantitative evaluation is carried out in a 12 h-event using two independent sources of information: a radar not involved in the composition process and a raingauge network. The proposed inverse method shows better performance in retrieving high reflectivity values and reproducing variability at convective scales than existing methods.Peer ReviewedPostprint (author's final draft

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

The Status of the ACRF Millimeter Wave Cloud Radars (MMCRs), the Path Forward for Future MMCR Upgrades, the Concept of 3D Volume Imaging Radar and the UAV Radar

The United States (U.S.) Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) operates millimeter wavelength cloud radars (MMCRs) in several climatological regimes. The MMCRs, are the primary observing tool for quantifying the properties of nearly all radiatively important clouds over the ACRF sites. The first MMCR was installed at the ACRF Southern Great Plains (SGP) site nine years ago and its original design can be traced to the early 90s. Since then, several MMCRs have been deployed at the ACRF sites, while no significant hardware upgrades have been performed. Recently, a two-stage upgrade (first C-40 Digital Signal Processors [DSP]-based, and later the PC-Integrated Radar AcQuisition System [PIRAQ-III] digital receiver) of the MMCR signal-processing units was completed. Our future MMCR related goals are: 1) to have a cloud radar system that continues to have high reliability and uptime and 2) to suggest potential improvements that will address increased sensitivity needs, superior sampling and low cost maintenance of the MMCRs. The Traveling Wave Tube (TWT) technology, the frequency (35-GHz), the radio frequency (RF) layout, antenna, the calibration and radar control procedure and the environmental enclosure of the MMCR remain assets for our ability to detect the profile of hydrometeors at all heights in the troposphere at the ACRF sites

USING A LOW-ORDER MODEL TO DETECT AND CHARACTERIZE INTENSE VORTICES IN MULTIPLE-DOPPLER RADAR DATA

A new multiple-Doppler radar analysis technique is presented for the objective detection and characterization of intense vortices. The technique consists of fitting radial wind data from two or more radars to a simple analytical model of a vortex and its near-environment. The model combines a uniform flow, linear shear flow, linear divergence flow (all of which comprise a broadscale flow), and modified combined Rankine vortex. The vortex and its environment are allowed to translate. A cost-function accounting for the discrepancy between the model and observed radial winds is evaluated over space and time so that observations can be used at the actual times and locations they were acquired. The parameters in the low-order model are determined by minimizing this cost function.The development of the method is initially guided by emulated radial velocity observations of analytical vortices. A high-resolution Advanced Regional Prediction System (ARPS) simulation of a supercellular tornado is then used to generate more realistic pseudo-observations. Finally, the technique is tested using real dual-Doppler tornado and mesocyclone observations from a variety of radar platforms including Weather Surveillance Radar - 1988 Doppler (WSR-88D), Terminal Doppler Weather Radar (TDWR), Shared Mobile Atmospheric Research and Teaching Radar (SMART-R), and Doppler on Wheels (DOW). The technique shows skill in detecting intense vortices and, when the vortex is well-resolved, in retrieving key model parameters including vortex location, translational velocity, radius and maximum tangential wind speed. In cases where the vortex is not well-resolved, additional vortex characteristics computed from the retrieved model parameters and verified against radial velocity observations can still provide useful information about vortex size and strength

ASSIMILATION OF ATTENUATED DATA FROM X-BAND NETWORK RADARS USING ENSEMBLE KALMAN FILTER

To use reflectivity data from X-band radars for quantitative precipitation estimation and storm-scale data assimilation, the effect of attenuation must be properly accounted for. Traditional approaches try to make correction to the attenuated reflectivity first before using the data. An alternative, theoretically more attractive approach builds the attenuation effect into the reflectivity observation operator of a data assimilation system, such as an ensemble Kalman filter (EnKF), allowing direct assimilation of the attenuated reflectivity and taking advantage of microphysical state estimation using EnKF methods for a potentially more accurate solution.This study first tests the approach for the CASA (Center for Collaborative Adaptive Sensing of the Atmosphere) X-band radar network configuration through observing system simulation experiments (OSSE) for a quasi-linear convective system (QLCS) that has more significant attenuation than isolated storms. To avoid the problem of potentially giving too much weight to fully attenuated reflectivity, an analytical, echo-intensity-dependent model for the observation error (AEM) is developed and is found to improve the performance of the filter. By building the attenuation into the forward observation operator and combining it with the application of AEM, the assimilation of attenuated CASA observations is able to produce a reasonably accurate analysis of the QLCS inside CASA radar network coverage. Compared with foregoing assimilation of radar data with weak radar reflectivity or assimilating only radial velocity data, our method can suppress the growth of spurious echoes while obtaining a more accurate analysis in the terms of root-mean-square (RMS) error. Sensitivity experiments are designed to examine the effectiveness of AEM by introducing multiple sources of observation errors into the simulated observations. The performance of such an approach in the presence of resolution-induced model error is also evaluated and good results are obtained.The same EnKF framework with attenuation correction is used to test different possible configurations of 2 hypothetical radars added to the existing network of 4 CASA radars through OSSEs. Though plans to expand the CASA radar network did not materialize, such experiments can provide guidance in the site selection of future X-band or other short-wavelength radar networks, as well as examining the benefit of X-band radar networks that consist of a much larger number of radars. Two QLCSs with different propagation speeds are generated and serve as the truth for our OSSEs. Assimilation and forecast results are compared among the OSSEs, assimilating only X-band or short-wavelength radar data. Overall, radar networks with larger downstream spatial coverage tend to provide overall the best analyses and 1-hour forecasts. The best analyses and forecasts of convective scale structure, however, are obtained when Dual- or Multi-Doppler coverage is preferred, even at the expense of minor loss in spatial coverage.Built-in attenuation correction is then applied, for the first time, to a real case (the 24 May 2011 tornadic storm near Chickasha, Oklahoma), using data from the X-band CASA radars. The attenuation correction procedure is found to be very effective--the analyses obtained using attenuated data are better than those obtained using pre-corrected data when all the values of reflectivity observations are assimilated. The effectiveness of the procedure is further examined by comparing the deterministic and ensemble forecasts started from the analysis of each experiment. The deterministic forecast experiment results indicate that assimilating un-corrected observations directly actually retains some information that might be lost in the pre-corrected CASA observations by forecasting a longer-lasting trailing line, similar to that observed in WSR-88D data. In the ensemble forecasts, assimilating un-corrected observations directly, using our attenuation-correcting EnKF, results in a forecast with a more intense tornado track than the experiment that assimilates all values of pre-corrected CASA data.This work is the first to assimilate attenuated observations from a radar network in OSSEs, as well as the first attempt to directly assimilate real, uncorrected CASA data into a numerical weather prediction (NWP) model using EnKF

On Small Satellites for Oceanography: A Survey

The recent explosive growth of small satellite operations driven primarily from an academic or pedagogical need, has demonstrated the viability of commercial-off-the-shelf technologies in space. They have also leveraged and shown the need for development of compatible sensors primarily aimed for Earth observation tasks including monitoring terrestrial domains, communications and engineering tests. However, one domain that these platforms have not yet made substantial inroads into, is in the ocean sciences. Remote sensing has long been within the repertoire of tools for oceanographers to study dynamic large scale physical phenomena, such as gyres and fronts, bio-geochemical process transport, primary productivity and process studies in the coastal ocean. We argue that the time has come for micro and nano satellites (with mass smaller than 100 kg and 2 to 3 year development times) designed, built, tested and flown by academic departments, for coordinated observations with robotic assets in situ. We do so primarily by surveying SmallSat missions oriented towards ocean observations in the recent past, and in doing so, we update the current knowledge about what is feasible in the rapidly evolving field of platforms and sensors for this domain. We conclude by proposing a set of candidate ocean observing missions with an emphasis on radar-based observations, with a focus on Synthetic Aperture Radar.Comment: 63 pages, 4 figures, 8 table