scholarly journals Trade-Offs between Measurement Accuracy and Resolutions in Configuring Phased-Array Radar Velocity Scans for Ensemble-Based Storm-Scale Data Assimilation

2009 ◽  
Vol 48 (6) ◽  
pp. 1230-1244 ◽  
Author(s):  
Huijuan Lu ◽  
Qin Xu
Keyword(s):  
Measurement Error ◽  
Measurement Accuracy ◽  
Phased Array ◽  
Error Variance ◽  
Observation Error ◽  
Phased Array Radar ◽  
Trade Offs ◽  
Wind Analysis ◽  
Model Predictions ◽  
Radar Velocity

Abstract Assimilation experiments are carried out with simulated radar radial-velocity observations to examine the impacts of observation accuracy and resolutions on storm-scale wind assimilation with an ensemble square root filter (EnSRF) on a storm-resolving grid (Δx = 2 km). In this EnSRF, the background covariance is estimated from an ensemble of 40 imperfect-model predictions. The observation error includes both measurement error and representativeness error, and the error variance is estimated from the simulated observations against the simulated “truth.” The results show that the analysis is not significantly improved when the measurement error is overly reduced (from 4 to 1 m s−1) and becomes smaller than the representativeness error. The analysis can be improved by properly coarsening the observation resolution (to 2 km in the radial direction) with an increase in measurement accuracy and further improved by properly enhancing the temporal resolution of radar volume scans (from every 5 to 2 or 1 min) with a decrease in measurement accuracy. There can be an optimal balance or trade-off between measurement accuracy and resolutions (in space and time) for configuring radar scans, especially phased-array radar scans, to improve storm-scale radar wind analysis and assimilation.

High-Temporal-Resolution Capabilities of the National Weather Radar Testbed Phased-Array Radar

2011 ◽  
Vol 50 (3) ◽  
pp. 579-593 ◽  
Author(s):  
Pamela L. Heinselman ◽  
Sebastián M. Torres
Keyword(s):  
Temporal Resolution ◽  
Phased Array ◽  
Weather Radar ◽  
High Temporal Resolution ◽  
Data Archiving ◽  
Phased Array Radar ◽  
Control Interface ◽  
Trade Offs ◽  
Address Data ◽  
Processing Techniques

Abstract Since 2007 the advancement of the National Weather Radar Testbed Phased-Array Radar (NWRT PAR) hardware and software capabilities has been supporting the implementation of high-temporal-resolution (∼1 min) sampling. To achieve the increase in computational power and data archiving needs required for high-temporal-resolution sampling, the signal processor was upgraded to a scalable, Linux-based cluster with a distributed computing architecture. The development of electronic adaptive scanning, which can reduce update times by focusing data collection on significant weather, became possible through functionality added to the radar control interface and real-time controller. Signal processing techniques were implemented to address data quality issues, such as artifact removal and range-and-velocity ambiguity mitigation, absent from the NWRT PAR at its installation. The hardware and software advancements described above have made possible the development of conventional and electronic scanning capabilities that achieve high-temporal-resolution sampling. Those scanning capabilities are sector- and elevation-prioritized scanning, beam multiplexing, and electronic adaptive scanning. Each of these capabilities and related sampling trade-offs are explained and demonstrated through short case studies.

Overview of the Airborne Phased Array Radar Observing Simulator

2021 ◽  
Author(s):  
Wen-Chau Lee ◽  
Jothiram Vivekanandan ◽  
Scott Ellis ◽  
Kevin Manning ◽  
George Bryan ◽  
...  
Keyword(s):  
Signal Processing ◽  
Phased Array ◽  
Software Infrastructure ◽  
High Data ◽  
Front End ◽  
Phased Array Radar ◽  
Trade Offs ◽  
Processing Software ◽  
Airborne Phased Array Radar ◽  
Specific Section

<p>The proposed airborne phased array radar (APAR) system consists of four removable, dual-polarized, C-band AESAs (Active Electronic Scanning Array) strategically located on the fuselage of the NSF/NCAR C-130. Conceptually, the radar system is divided into the front-end, the backend, and aircraft-specific section with the front-end primarily consisting of AESAs and the signal processor is in the backend. The aircraft specific section includes a power system and a GPS antenna.</p><p>As part of the risk reduction of the APAR development, the APAR Observing Simulator (AOS) system has been developed to provide simulated APAR data collection sampled from a C-130 flying by/through realistic numerical weather simulations of high-impact weather events. Given that APAR is designed to extend beyond capabilities of the existing airborne tail Doppler radars (e.g., NOAA TDRs and the retired NSF/NCAR ELDORA), a verification of signal processing software and algorithms is needed before the radar is physically built to ensure that the signal processing software infrastructure can handle high data rates and complicated, multiplex scanning that will be part of normal APAR operations.  Furthermore, several algorithms that will need to ingest large amounts of APAR data at very high rates are under development, including dual-Doppler wind synthesis, radar reflectivity attenuation correction, rain rate estimation, and hydrometeor classification. These algorithms need to be tested and verified before the implementation. </p><p>The AOS will also serve as a planning tool for future Principal Investigators (PIs) who will use it to design and test different flight and scanning strategies based on simulated storms to yield the best scientific outcomes before their field deployment takes place. This will enable better understanding of trade-offs among various sampling regimes/strategies during the planning and enhance future field programs' efficiency and effectiveness.</p>

On the concept of model structural error

2005 ◽  
Vol 52 (6) ◽  
pp. 167-175 ◽  
Author(s):  
K. Beven
Keyword(s):  
Measurement Error ◽  
Input Data ◽  
Total Error ◽  
Observation Error ◽  
Data Errors ◽  
Structural Error ◽  
Alternative Approach ◽  
Perfect Model ◽  
Model Predictions ◽  
Additive Term

A consideration of model structural error leads to some particularly interesting tensions in the model calibration/conditioning process. In applying models we can usually only assess the total error on some output variable for which we have observations. This total error may arise due to input and boundary condition errors, model structural errors and error on the output observation itself (not only measurement error but also as a result of differences in meaning between what is modelled and what is measured). Statistical approaches to model uncertainty generally assume that the errors can be treated as an additive term on the (possibly transformed) model output. This allows for compensation of all the sources of error, as if the model predictions are correct and the total error can be treated as “measurement error.” Model structural error is not easily evaluated within this framework. An alternative approach to put more emphasis on model evaluation and rejection is suggested. It is recognised that model success or failure within this framework will depend heavily on an assessment of both input data errors (the “perfect” model will not produce acceptable results if driven with poor input data) and effective observation error (including a consideration of the meaning of observed variables relative to those predicted by a model).

scholarly journals Global 3D Features of Error Variances of GPS Radio Occultation and Radiosonde Observations

2020 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Xu Xu ◽  
Xiaolei Zou
Keyword(s):  
Radio Occultation ◽  
Weather Prediction ◽  
Lower Troposphere ◽  
Error Variance ◽  
Specific Humidity ◽  
Temperature Error ◽  
Observation Error ◽  
Bending Angle ◽  
Gps Radio Occultation ◽  
Low Latitudes

Global Positioning System (GPS) radio occultation (RO) and radiosonde (RS) observations are two major types of observations assimilated in numerical weather prediction (NWP) systems. Observation error variances are required input that determines the weightings given to observations in data assimilation. This study estimates the error variances of global GPS RO refractivity and bending angle and RS temperature and humidity observations at 521 selected RS stations using the three-cornered hat method with additional ERA-Interim reanalysis and Global Forecast System forecast data available from 1 January 2016 to 31 August 2019. The global distributions, of both RO and RS observation error variances, are analyzed in terms of vertical and latitudinal variations. Error variances of RO refractivity and bending angle and RS specific humidity in the lower troposphere, such as at 850 hPa (3.5 km impact height for the bending angle), all increase with decreasing latitude. The error variances of RO refractivity and bending angle and RS specific humidity can reach about 30 N-unit2, 3 × 10−6 rad2, and 2 (g kg−1)2, respectively. There is also a good symmetry of the error variances of both RO refractivity and bending angle with respect to the equator between the Northern and Southern Hemispheres at all vertical levels. In this study, we provide the mean error variances of refractivity and bending angle in every 5°-latitude band between the equator and 60°N, as well as every interval of 10 hPa pressure or 0.2 km impact height. The RS temperature error variance distribution differs from those of refractivity, bending angle, and humidity, which, at low latitudes, are smaller (less than 1 K2) than those in the midlatitudes (more than 3 K2). In the midlatitudes, the RS temperature error variances in North America are larger than those in East Asia and Europe, which may arise from different radiosonde types among the above three regions.

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