3D Reflectivity Mosaic
Virtual cross section of reflectivity from the 3D Reflectivity Mosaic for a brightband event.
A related project to Project QPESUMS involves the ingest of level II radar data, resampling the data on a common 3D grid, and mosaicking the reflectivity from multiple radars to produce seamless reflectivity products. The Mosaic is the first high-resolution mosaic of WSR-88D radar data that is produced in 3D and in real-time.
Hybrid Scans Derived from 30-m DEM
The WISH team was tasked with deriving all watershed boundaries at 2 mi2 resolution for the entire US. The National Elevation Database, available from the US Geological Survey, was used for the National Basin Delineation Project and now is being used to derive high-resolution, terrain-based hybrid scans for all WSR-88D radars. These files serve as the lower boundary in the 3D Mosaic and for QPESUMS. The link below shows many examples of hybrid scans that are currently being derived:
http://cimms.ou.edu/~langston/hybridscan/ 
Time-series of bright band top and bottom heights from the KIWA radar overlaid on reflectivity observations from an independent vertically-pointing radar.
Brightband Identification
Enhanced reflectivity from the melting of hydrometeors can cause severe overestimation of surface rainfall. QPESUMS employs a brightband identification (BBID) algorithm to search for the brightband using a full volume of radar data and report its height. This information is then used in QPESUMS to ensure that contaminated reflectivity from this layer is removed. Moreover, the brightband height reflects melting and thus indicates the altitude of the rain-snow line. During the winters of 2000-2001, a vertically-pointing radar was deployed in Camp Verde, Arizona. High-resolution observations of reflectivity from this radar enable the comparison to results from the BBID operating on Phoenix, AZ WSR-88D radar data.
Convective-Stratiform Segregation
Composite reflectivity and associated convective echoes.
QPESUMS separates convective from stratiform echo in order to 1) supply appropriate reflectivity to rainfall (Z-R) relationships, and 2) adapt the estimation scheme for stratiform echo sampled at high heights relative to the melting layer. Thermodynamic information is utilized from numerical model analyses to supplement radar data in identifying high reflectivity displaced vertically at cold temperatures. After each grid point has been deemed as being either convective or stratiform, differential Z-R equations are applied as appropriate on a grid cell-by-grid cell basis. Stratiform echo sampled at far range during the cool season must be identified and separated from convective echo so that multisensor scheme can apply calibrated satellite precipitation rates to those grid cells.
Calibration of Satellite by Radar
It is well recognized that radar-derived precipitation accumulations have range-dependent biases, especially during the cool season and over complex terrain. Shallow profiles of reflectivity combined with poor low-level radar coverage exacerbate the underestimation at far ranges. QPESUMS uses a unique technique to identify stratiform grid cells that have been measured below the bright band, match the radar-based rain rates with collocated satellite cloud-top temperatures, and then use a regression technique to relate the 2 variables. Assuming the regression yields an acceptable correlation between satellite and radar data, the parameters from the fitted curve are then used to supply satellite-based precipitation estimates to the stratiform grid cells in need of adjustment. In essence, the satellite field dictates the spatial variability of the precipitation, while the magnitude is a function of the regression parameters. The regression utilizes radar and satellite data that are less than 1 hour old.
Verification Software
An example of the Gauge/QPE map for a rainfall event in Taiwan.
It is vital to the development of QPESUMS to monitor the progress of algorithm improvements. For this purpose, a web page has been designed to display all QPESUMS precipitation products and a full suite of intermediate output. A unique gauge comparison tool provides the user the ability to evaluate the performance of precipitation products statistically and qualitatively with a graphical representation. A flexible, web-based interface allows the user to choose the verification region as a function of geography (e.g., a specific basin), elevation, and range from the nearest radar. The user can also choose their units to be metric or English. The web page is updated hourly with the latest QPESUMS graphics and rain gauge verification data. Time series plots of rain gauge estimates overlaid with QPESUMS accumulations are available as well as scattergrams and gauge bias histograms for a given time interval. A complete evaluation is accomplished for individual hours up to a seasonal timeframe.
Radar Calibration Diagnostics
From evaluation of long-term QPESUMS precipitation products, it was discovered that substantial differences often exist in equidistant zones between adjacent radars. Software was thus designed to quantify the reflectivity differences between radars for every deployment region. The web-based interface allows users to view the time-averaged reflectivity differences in a graphical product as well as time series plots for specific radar pairs for a user-defined time period. Initial observations show that radar reflectivity differences can be consistently large (e.g., > 2 dB), suggestive of radar calibration problems. This software is used by the National Weather Service's Radar Operations Center to diagnose radars that have drifted out of calibration. The relative reflectivity differences quantified by this software can complement absolute calibration methods when attempting to calibrate an entire radar network.
