The Nowcast Guidance Development Task of the Decision Assistance Branch maintains and refines operational radar mosaic products and is developing new mosaic products at substantially higher spatial and temporal resolution.



Operational Products  

   A 10-km mosaic is generated and disseminated twice per hour, at approximately
00:10 and 00:40.  It is created by compositing data from Radar Coded Messages (RCM's)
transmitted by each WSR-88D site, and executing several automated quality-control (QC)
algorithms to identify and remove nonprecipitation echo features such as birds, insects, 
aircraft and terrain.
   

10-km digital reflectivity mosaic 620X720 pixcels



10-km digital reflectivity mosaic 920X720 pixcels


Coded text messages describe the nature, intensity, coverage, and movement of radar echoes within individual 230-km radar umbrellas. The format matches historical standards originally used when the messages were manually coded by meteorological technicians examining a reflectivity display. The messages are now derived from RCM's at NWS headquarters by an automated process. They are transmitted at approximately 00:40 each hour.

Current Text Radar Observations (ROB'S)

National 10_km mosaic, with time loop display and links to individual sites data




Products related to mosaic generation  

Unedited 10-km digital reflectivity mosaic (920X720 pixcels)


This mosaic is the first step in production of the final quality-controlled product. It features both precipitation and echoes from migrating birds, insects, and aircraft. In a few places, echoes from ground terrain also appear. However, the digital hybrid scan procedure used to generate the RCM reflectivity generally removes surface targets such as terrain and ground clutter; most features in this mosaic are from aerial backscatterers.

During late autumn through late winter, most nonprecipitation echoes are from aircraft, which appear as 'spot' targets in many areas. From late February through early June, and again from mid-August through early November, migrating birds commonly appear near radar sites, especially at night. In some periods of intense migration, these echoes may extend over several south-central states simultaneously, appearing as large areas of level 1 (15-29 dBZ) echoes. In summer, insects may appear near radar sites at any time of day.

Plot of surface precipitation reports

This image shows the location of recent surface reports of precipitation and thunder. Sites reporting no precipitation appear as white dots, those reporting rain as blue dots, and those reporting snow or freezing precipitation as red or green dots.

Enhanced satellite-derived infrared temperature analysis from GOES

An infrared temperature analysis is prepared by merging data from the eastern an d western GOES. The map projection is identical to that of the RCM mosaic itself. In general, colder temperatures indicate a greater likelihood that coincident radar echoes are from precipitation rather than other targets.

Mosaic indicating precipitation type

This edited reflectivity mosaic has been enhanced to indicate areas where precipitation is likely to be snow. Boxes with reflectivity that have a high conditional probability of snow, based on the most recent Model Output Statistics forecasts and current surface observations, are colored a bright cyan distinct from the rest of the reflectivity spectrum. At present, we do not attempt to identify freezing or mixed precipitation.



Under Development:
High-Resolution Reflectivity Mosaics 

Experimental 2-km and 4-km resolution real-time mosaics


 
  The NWS program for radar mosaic development includes plans for
reflectivity mosaics to be issued at 2 km spatial resolution at 5 minute
intervals.  These mosaics will also feature at least 16 reflectivity
levels, rather than the 7 levels included in the Radar Coded Message.


An unedited version of these mosaics will be made available to users. Such mosaics are useful in identifying nonprecipitation features that are still of meteorological significance; outflow boundaries, for example, are often indicated by concentrations of flying insects.

We are presently refining methods of editing nonprecipitation features from these high-resolution mosaics by automated processes. These mosaics feature higher spatial resolution, greater precision, and a larger dynamic range than do those based on RCM's. These characteristics enabled us to attempt to apply feature-recognition methods in addition to the simpler meteorological checks used for the RCM mosaic. However, the base reflectivity products from which the HR mosaics are created contain more nonprecipitation echoes than do the RCM's. Ground clutter appears in most base reflectivity products, and nonprecipitation targets are represented down to 5 dBZ, causing insect and bird returns to cover significantly more area than they do in RCM's.

The radar images contain 16 data levels (or 'gray levels' in some terminology). The categories are < 5 dBZ, 5-9 dBZ, 10-14 dBZ, ..., 70-74 dBZ, > 74 dBZ. Nonprecipitation features are often easily distinguished from precipitation by their location relative to radar sites, the spectrum of echo intensities, and texture characteristics.

Therefore, attempts were made to apply texture measures and feature-recognition principles to the unedited mosaics, as has been done with cloud-typing algorithms applied to satellite data. Several researchers manually identified echo types (birds, ground clutter, anomalous propagation, stratiform and convective precipitation) within a series of mosaics produced between April and July 2001. Each case was characterized by an area of 30x30 km containing no echoes other than the dominant type. A set of 32 texture and spectrum features were then derived from the digital image within the region, as follows:

1-11)= PERCENTAGE AREA COVERAGE BY ECHO CATEGORIES <5,5-9,10-14,...,50+

12-21)= PERCENTAGE AREA COVERAGE BY ECHO CATEGORIES 5-9,10-14,...,50+ WITHIN THE FRACTION OF THE AREA COVERED BY > 5 DBZ

22) = TEXTURE (a measure of mean box-to-box differences in echo level)

23) = MEAN ECHO LEVEL (DBZ) 24) = STANDARD DEVIATION OF ECHOES
25) = PERCENTAGE OF ECHOES GE 5 DBZ THAT ARE IN THE RANGE 5-29 26) = MEAN DIFFERENCE

27) = STANDARD DEVIATION OF GRAY-LEVEL DIFFERENCE VECTOR TEXTURE

28) = LOCAL HOMOGENEITY

29) = CONTRAST

30) = CLUSTER PROMINENCE

31) = GRAY-LEVEL DISTRIBUTION (NOT AN IMPORTANT PREDICTOR)

32) = A REGRESSION FORMULA DIFFERENTIATING PRECIPITATION/NONPRECIPITATION, USING ITEMS 14,24 AND 27 OF THIS LIST

Linear regression equations were derived to see what combination was most effective in determining echo type. A combination of predictors 2,6,14,24 and 27 could correctly identify echo type 95 percent of the time within the dependent data sample.


While this method alone was very effective, it was considered more important to leave all the rain in the image than removing all of the non-rain echoes. Another method using the texture parameters was developed. If either of these methods determined that 30X30 km area was rain, the area would not be removed. A BP network was created that used all the above parameters. While the results were similar to the regression, it did identify some isolated rain areas that the linear regression equations missed.

To use these methods to radar image editing, a 30X30 km floating box region is logically moved across a radar image at 5-km intervals. The various statistical parameters above are calculated for the area within the floating box. If the area is considered to be a nonprecipitation echo by both methods, a circular region of echoes is removed from the center of the 30-km box by resetting the echo level to zero.

This 'radar-only' method is fairly effective and is very simple to apply, being based only on the information within the single radar mosaic itself. However, enough incorrect editing results are generally detected that we believe it adviseabladvisable ancillary information to the editing procedure whenever possible.

A 'precipitation area' mask for the conterminous United States is now routinely derived from information collected during operational editing of the 10-km RCM mosaic. This information includes lightning-strike locations, surface reports of precipitation, and the number of radars detecting 15-dBZ reflectivity over a given place. Echoes detected by three or more network radars (or by two or more radars during late autumn and winter) generally indicate an echo feature with such significant vertical development that it is almost certainly precipitation. Any combination of lightning, surface observations, or multiple-radar detections are considered to verify precipitation within a region of 30-km radius. These areas are ignored during other editing procedures.

Finally, satellite and humidity information are applied to identifying areas that are very unlikely to experience precipitation. This method was first developed for quality control of the 10-km RCM mosaic. Satellite IR temperatures and various other statistical predictors derived from Aviation Model forecasts were correlated with the results of manual editing of radar images. Through screening regression several equations were developed that relate these predictors to the probability that a human analyst would judge an echo area to be precipitation. When this probability is sufficiently low (generally < 20%) echoes appearing in the unedited mosaic are removed.




Unedited mosaic July 27, 1230 UTC.



Manually edited mosaic July 27, 1230 UTC.


July 27, 1230 UTC, using automated edit