MSC Introductory Weather Radar Course

A measure of the dispersion of velocities within the pulse volume. Standard deviation of the velocity spectrum. Spectral width depends among others from the turbulence within the pulse volume.

Description

Visualisation of areas with high velocity and high spectrum width (potential risk For air traffic). No height information given. V and W must be scanned together to make this product.

Parameters

Range
Image size
Height (top and bottom)

Specular reflections may be caused by radiowaves reflected by water surface or windows/buildings. Direction of the reflected radiowaves is determined by the incidence angle. Specular reflections may cause problems for both reflectivity and velocity measurements as the direction of the radar beam is changed.

Example Image

Shows local radial velocity values relative to a moving storm. Can be aplied on polar volume data of radial velocity.

There are several different principles of choosing the data (compositing algorithm). Problems related to cartesian products cause information loss or bias, thus quality indicator fields are necessary when calculating the optimal values of data from two or more radars. There are principles of data selection based on

• choosing the nearest radar
• choosing the radar closest to earth
• choosing maximum and average values
  

All of them have some problems. Most problmetic is the approach related to averaging. Information on elevation angles and radar heights should be available to use any of these principles. One possible solution is based on generating a so called seek matrix, which is in essence a pointer table. The advantage of this method is the fast pace of getting information, but is suitable for supercomputers only due to the considerable amounts of memory required by the matrix and data.

Bending of the radar beam in the vertical which is less than under standard refractive conditions. This causes the beam to be higher than indicated, and leads to underestimation of cloud heights.

Problems caused to radar products

Radar receives 5 cm microwaves from the sun, which are seen mostly when the sun is near the horizon. Thus the problem is mostly encountered in higher latitudes than elsewhere.

Example Image

A relatively long-lived storm possessing all of the following characteristics: rotation, with a high correlation between horizontal vorticity and updraft strength; existence of a BWER and/or hook echo; and a continuous mode of propagation (appears to be a steady state system). This storm type is typically associated with the most intense tornadoes and largest hailstones.

Bending of the radar beam in the vertical which is greater than under standard refractive conditions. This causes the beam to be lower than indicated, and often results in extensive ground return as well as an overestimation of cloud top heights.

Description

2D-map with the precipitation estimates at surface level accumulated during a predefined period of time.

Parameters

Product data quantity: precipitation amount
Image size: number of pixels per row (# of columns) and per column (# of rows)
Pixel size: horizontal and vertical extension of the pixel in km
Level slicing method: list of values in 0.1 mm or formula parameters (see section 7 in WD21_99)
Adjustment method: None, Radar-to-gage factors (RT or CL), Gage-tuned Z-R relationship (RT or CL), Radar-to-gage regression (LL or NL or RT or CL), Vertical profile (SY or RE)
Accumulation period: in minutes
Accumulation method: Sampling-and-hold or Interpolation

Notes

• RT: real-time,
• CL: climatological,
• LL: linear,
• NL: non-linear,
• SY: synthetic,

Example Image