Time series of nearly all products can be visualized for diagnosis of algorithm and gauge performance. For example, a time series can show how precipitation estimates are derived from basic variables. A time series can be created at a gauge location or a lat-lon point.
Following are examples how to diagnose PPS and DP performance at a gauge location. The first plot shows hourly accumulations and the second shows PPS and DP rates that resulted in the accumulations. For more information of the different rainrates and relationships to HHC see: Rainfall Rate Relationships. More info on the different products and tabs is provided below.
TIP: If you enter numbers for a specific latitude and longitude, do not move your mouse into the image below or your selected area will change.
HADS gauge MRIT2 is selected along with hourly accumulations for PPS and DP. Data scales can be changed with the "Plot" tab.
PPS and DP rates are shown and can be realted to the Hybrid Hydrometeor Classification classes. DP variables for the four lowest tilts can be plotted.
Digital Hybrid Scan Reflectivity form the NCDC Level-3 database.
Hydrometeor classification algorithm derived from different tilts and used to determine which rain rate algorithm is used at each azimuth/range bin.
The specific differential phase is a comparison of the returned phase difference between the horizontal and vertical pulses.
A statistical correlation between the reflected horizontal and vertical power returns. It is a good indicator of regions where there is a mixture of precipitation types, such as rain and snow.
The differential reflectivity is a ratio of the reflected horizontal and vertical power returns. Among other things, it is a good indicator of drop shape. In turn the shape is a good estimate of average drop size.
Instantaneous rates are displayed.
One hour accumulation is displayed.
Instantaneous rates are displayed.
One hour accumulation is displayed.
Radar-based precipitation rate and accumulations over various time periods (1- to 72-hour). The rate is derived from the hybrid scan reflectivity using convective, stratiform and tropical Z-R relations identified by the precip_flags (that are in turn identified by VPRs). Note the additional optional Z-Rs used by the NWS.
Radar-based precipitation rate and accumulations for 1 hour. The rate is derived from the hybrid scan reflectivity using convective, stratiform and tropical Z-R relations identified by the precip_flags (that are in turn identified by VPRs). Note the additional optional Z-Rs used by the NWS.
An objective analysis of all gauges from NCEP with a 3 hr delay.
Q2 with a local gauge adjustment that includes a gauge-radar bias QC application
Local gauge corrected radar QPE fields. The local gauge correction is applied onto the 1-h Q2RAD_HSR precipitation field. It runs hourly and uses hourly rain gauge observations from the HADS (Hydrometeorological Automated Data System) data sets at NCEP. In the local gauge correction scheme, radar-gauge biases are calculated at each gauge site and then interpolated onto the MRMS grid using an inverse distance weighted (IDW) mean scheme. The two parameters in the IDW scheme, exponent and radius of influence, are determined through a cross-validation procedure. The interpolated radar-gauge bias field is applied back to the Q2RAD_HSR 1-h precipitation field and a local gauge bias corrected 1-h precipitation field is obtained. Longer-term accumulations are computed by aggregating the 1-h local gauge corrected precipitation fields.
This cref has been edited with a neural network QC algorithm (QCNN; Lakshmanan 2007). The unit of this field is dBZ.
Lakshmanan, V., A. Fritz, T. Smith, K. Hondl, G. J. Stumpf, 2007: An automated technique to quality control radar reflectivity data. J. Appl. Meteor., 46, 288-305.
The composite reflectivity is the maximum reflectivity in each grid column in the 3-D mosaic. This cref has not been quality control edited to remove non-precipitating echoes. The unit of this field is dBZ.
Maximum Composite Reflectivity over the previous hour.
Composite reflectivity with Canadian radars. Note the Canadian radars are at C-Band and they may not have thorough quality control.
The hybrid scan reflectivity is a 2-D mosaic of single radar hybrid scan reflectivity fields. The single radar hybrid scan reflectivity is obtained by searching each grid column in the single radar 3D reflectivity Cartesian grid, from bottom to top, until the first non-missing reflectivity value is identified. The reflectivity value is then recorded as the single radar hybrid scan reflectivity (in dBZ) at the given grid cell, and the height of the grid cell is recorded as the single radar hybrid scan height.
The Seamless HSR at any range/azimuth bin with partial blockages of 10 to 50% is a weighted mean of reflectivities from the PPS-type hybrid scan and from the next tilt above. See here.
The VPR correction uses an average VPR and adjusts the radar data form aloft to the surface. See Zhang, J., Y. Qi, 2010: A Real-Time Algorithm for the Correction of Brightband Effects in Radar-Derived QPE. J. Hydrometeor, 11, 1157–1171.
This variable is the height of the grid level where the column maximum reflectivity (composite reflectivity) is found. The unit is km above MSL.
This field is the height of the lowest non-missing single radar hybrid scan reflectivities at each MRMS grid cell. The unit of HSRH is meters AGL (above ground level).
This is the height above ground for each azimuth and range bin above ground level.
This is the height above ground for each azimuth and range bin above mean sea level.
The Radar Quality Index is an indicator of radar quality with respect to beam height and the 0C height. Data closest to the radar (ground) and below the 0C height have the highest quality, i.e., values close to 1. Data at far ranges and above 0C have the lowest values.
Zhang, J., Y. Qi, C. Langston, B. Kaney, 2011: Radar Quality Index (RQI) - a combined measure for beam blockage and VRR effects in a national network. Weather Radar and Hydrology
The echo top height is obtained by searching each grid column in the 3D reflectivity mosaic grid, from top to bottom, until the first reflectivity value greater than 18 dBZ is found. The height of the grid level where the reflectivity value was found is recorded as the ETP18 value for the given grid cell. The unit of the echo top is km above MSL.
The VIL product is derived based on the procedures described in Greene and Clarke (1972).
Greene, D. R. and R. A. Clark, 1972: Vertically Integrated Liquid Water-A New Analysis Tool. Mon. Wea. Rev., 100, 548-552.
The VILD product is derived based on the procedures described in Amburn and Wolf (1997).
Amburn, S. A. and P. L.Wolf, 1997: VIL Density as a Hail Indicator. Wea. Forecasting, 12, 473-478..
The precipitation flag indicates the surface precipitation type and the radar observation representativeness at each grid point. The representativeness of radar observations with respect to surface precipitation estimation is highly correlated to the height of the radar observation and its proximity to the bright band layer (BBL) and to the surface. The top and bottom heights of the BBL are identified from the vertical profile of reflectivity (if it exists) at each radar site, and then objectively analyzed onto the MRMS grid using the RUC 0C height as the background.
Currently, each grid column in the 3-D mosaic grid is analyzed and classified.
A grid column is classified as being convective if one of the following criteria is satisfied:
Using a VPR to determine if a grid column is tropical (reference Xu et al., 2007, .doc, 1.2 MB):
Temperature soundings are obtained from the RAP 20 km model analysis.
The 2-D surface precipitation phase indicates the precipitation state (frozen or liquid) at each grid cell.
This indicates if a grid point is convective or stratiform.
Objective analysis of the Tropical Identification (TRID) precipitation type. Values range from zero to one. THis field was used to test reduce potential overestimation by using the tropical Z-R far away from a radar identified as tropical. PWR is currently not used.
Indicates severity of hail according to Witt, A., M. D. Eilts, G. J. Stumpf, J. T. Johnson, E. D. Mitchell, K. W. Thomas, 1998: An Enhanced Hail Detection Algorithm for the WSR-88D. Wea. Forecasting, 13, 286–303.).
Probability of severe hail according to Witt, A., M. D. Eilts, G. J. Stumpf, J. T. Johnson, E. D. Mitchell, K. W. Thomas, 1998: An Enhanced Hail Detection Algorithm for the WSR-88D. Wea. Forecasting, 13, 286–303.).
Maximum expected hail size according to Witt, A., M. D. Eilts, G. J. Stumpf, J. T. Johnson, E. D. Mitchell, K. W. Thomas, 1998: An Enhanced Hail Detection Algorithm for the WSR-88D. Wea. Forecasting, 13, 286–303.).
In Time Series, there is no available method for zooming in on just a portion of the map. You will see an image similar to Figure 1. If you would like to select a specific area under the Point tab, you must do the following.
The Plot tab allows you to adjust your scale so that your Time Series will plot within the provided graph. You also have the option of outputing the data to a text file.