Comparison of radar-based precipitation mosaics (original) (raw)
Related papers
Atmospheric Research, 2003
The National Severe Storms Laboratory (NSSL) has played the primary role in the development and evaluation of U.S. National Weather Service (NWS) severe weather applications for the Weather Surveillance Radar-1988 Doppler (WSR-88D). NSSL developed many of the primary detection algorithms for the radar, and is currently developing improvements to these algorithms. The traditional WSR-88D severe weather algorithms have been designed for use with a single-radar data source. Although the algorithm guidance has led to an improvement of the NWS severe weather warning statistics, it is understood that effective warning decisions can only be made via the integration of information from many sources, including input from multiple remote sensors (multiple radars, mesoscale models, satellite, lightning, etc.). Therefore, these traditional single-radar severe weather algorithms have been updated to take advantage of additional data sources in order to reduce the uncertainty of the measurements and increase the accuracy of the diagnoses of severe weather.
Journal of Atmospheric and Oceanic Technology, 1998
One advantage of dual-polarization radars is the ability to differentiate between water and ice phases in storms. The application of difference reflectivity (Z DP) in the analysis of mixed-phase precipitation is presented. Here, Z DP analysis is used to obtain the fraction of water and ice in mixed-phase precipitation. The techniques developed are applied to data collected on 9 August 1991 during the Convection and Precipitation Electrification experiment. Time series of storm total liquid and ice water contents are computed. The liquid and ice water contents are used in a water budget equation to obtain the net latent heating of the convective storm. It is shown that the latent heating profile shows good correlation with the updraft and electric field increases in the time evolution of the storm.
Journal of Hydrometeorology, 2012
This study evaluated 24-, 6-, and 1-h radar precipitation estimated from the National Mosaic and Multisensor Quantitative Precipitation Estimation System (NMQ) and the Weather Surveillance Radar-1988 Doppler (WSR-88D) Precipitation Processing System (PPS) over the conterminous United States (CONUS) for the warm season Precipitation gauge observations from the Automated Surface Observing System (ASOS) were used as the ground truth. Gridded StageIV multisensor precipitation estimates were applied for supplementary verification. The comparison of the two systems consisted of a series of analyses including the linear correlation coefficient (CC) and the root-mean-square error (RMSE) between the radar precipitation estimates and the gauge observations, large precipitation amount detection categorical scores, and the reliability of precipitation amount distribution. Data stratified for the 12 CONUS River Forecast Centers (RFCs) and for the cold rains events with bright-band effects were analyzed additionally. Major results are 1) the linear CC of NMQ versus ASOS are generally higher than that of PPS versus ASOS over CONUS, while the spatial variations stratified by the RFCs may switch with seasons; 2) compared to the precipitation distribution of ASOS, NMQ shows less deviation than PPS; 3) for the cold rains verified against ASOS, NMQ has higher CC and PPS has lower RMSE for 6-h and higher RMSE for 1-h cold rains; and 4) for the precipitation detection categorical scores, either NMQ or PPS can be superior, depending on the time interval and season. The verification against StageIV gridded precipitation estimates showed that NMQ consistently had higher correlations and lower biases than did PPS.
2021
A 2009 National Research Council study recommended that new mesoscale observing networks be integrated with existing networks to form a nationwide “network of networks”. The report also recommended that research testbeds be established, such as the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) DFW Testbed, to ascertain the potential benefit of proposed observing systems. In this work, we use various conventional and non-conventional observing systems from the DFW Testbed in a series of observing system experiments (OSEs). Of special interest are radar data from Terminal Doppler Weather Radars and CASA X-band radars, as well as novel surface observations. The Advanced Regional Prediction System (ARPS) model is used to perform OSEs that are designed to assess the impact of these observing systems. A three-dimensional variational analysis system and companion complex cloud analysis are used to produce analysis increments, which are assimilated in ARPS using Increme...
A Comparison of Radar Reflectivity Estimates of Rainfall from Collocated Radars
Journal of Atmospheric and Oceanic Technology, 1999
Radar reflectivity-based rainfall estimates from collocated radars are examined. The usual large storm-tostorm variations in radar bias and high correlation between radar estimates and rain gauge observations are found. For three storms in Colorado, the radar bias factor (the ratio between gauge observations and radar estimates) with the National Center for Atmospheric Research's S-band, dual-polarization radar (S-Pol) varied from 0.78 (an overestimate with radar) to 1.88. The correlation coefficient between gauge and radar amounts varied from 0.78 to 0.90. For a collocated Weather Surveillance Radar-1988 Doppler (WSR-88D), the bias factor varied from 0.56 to 1.49, and the correlation between gauge and radar amounts ranged from 0.77 to 0.87. In Kansas, bias factors varied from 0.86 to 1.41 for S-Pol (10 storms) and 0.82 to 1.71 for a paired WSR-88D (9 storms). The spread in correlation coefficients was 0.82-0.95 for S-Pol and 0.87-0.95 for the WSR-88D.