Combining space and ground measurements for more accurate rainfall estimates



Jakob, Christian - Monash University
Collis, Scott Matthew - Argonne National Laboratory

Area of research:

Cloud Distributions/Characterizations

Journal Reference:

Louf V, A Protat, R Warren, S Collis, D Wolff, S Raunyiar, C Jakob, and W Petersen. 2018. "An integrated approach to weather radar calibration and monitoring using ground clutter and satellite comparisons." Journal of Atmospheric and Oceanic Technology, , 10.1175/JTECH-D-18-0007.1. ONLINE.


A new integrated radar calibration technique using a combination of radar returns from fixed objects on the ground and satellite measurements yields a long-term radar reflectivity calibration with a much higher accuracy than previously possible.


The new calibration technique, called SCAR (Satellite Clutter Absolute Radar calibration), has been applied to produce a one-of-a-kind, 17-year radar data set at Darwin, Australia, (now available in the ARM Data Center). SCAR is also used at the Australian Bureau of Meteorology to monitor the calibration of the operational weather radar network. The careful quality control and calibration applied to the decade-spanning CPOL data set makes it possible to study seasonal and intra-seasonal precipitation characteristics, rendering this technique especially useful for testing the representation of precipitating cloud systems in earth system models.


Long-term radar observations are increasingly becoming a key tool for our understanding of rainfall systems across the globe. The high time resolution of radar observations, combined with a spatial coverage of several 100 km for a single radar and whole states or countries with radar networks, allows for detailed studies of the processes that create and affect the highly variable rain-bearing systems on Earth. However, radars, like all instruments, require maintenance and upgrades. Those changes tend to shift the measurements in an absolute sense, leading to jumps in long time series and/or inconsistencies within a radar network. The process to counteract those jumps is called calibration.

Any calibration requires a fixed standard to which the changed/upgraded radar can be compared. The SCAR calibration framework makes use of two such fixed standards. First, it uses what most radar techniques try to eliminate, signals that result from the reflection of radar beams off fixed objects on the ground, such as buildings, trees or orography. While those are a nuisance in trying to measure rainfall, they do provide a (relatively) constant environment. If, on the day of a radar upgrade, the return from a building changes, it is unlikely that the building changed. SCAR makes use of this to provide what is called a Relative Calibration Adjustment (RCA) that accurately tracks calibration changes using ground clutter. It then makes use of satellite measurements of radar reflectivity and applies a new version of a technique that matches space measurements to those from the ground. Applying this technique for each period where the RCA indicates that the calibration is stable, this allows for an absolute calibration. We demonstrate that, by using this integrated approach, absolute calibration can be achieved to within 1 dBZ of reflectivity, and monitored to an accuracy better than 0.5 dBZ.

We have applied the new calibration technique to derive a long-term (17 years) calibrated radar data set for the polarimetric research radar at Darwin, Australia (CPOL). The careful work in adjusting the calibration of the CPOL radar greatly enhances the value of this data set for studying climate-relevant cloud processes, such as the four-dimensional distribution of convective and stratiform cloud systems as well as their microphysical properties at a tropical location. The data set is available through the ARM Data Center and currently being used to evaluate convective processes in the Department of Energy’s Energy Exascale Earth System Model (E3SM) as well as in the development and assessment of innovative approaches to representation in climate models.