2. Application Driven Requirements on Precipitation Data within WaterSENSE
3. Data Verification Setup in the Namoi Catchment
3.1. Verification Strategy
3.2. Namoi Catchment
3.3. Namoi Weather Radar
- Prior to October 2020 (polar data with a radial resolution of 500 m and a temporal resolution of 10 min)
- After October 2020 (polar data with a radial resolution of 250 m and a temporal resolution of 5 min)
3.4. Radar Data Processing
- Clutter map: A radar-specific clutter map has been semi-automatically created based on the historical period (17 April 2020–11 October 2020). Pixels included in the clutter map are permanently deleted and replaced by an interpolation of the neighbouring pixels. Typical applications include the removal of clutter from mountains and towers.
- Speckle filter: This filter removes very small echoes (e.g., ships and air planes), defined by a configurable upper threshold for the number of non-zero pixels, which are surrounded by zero pixels. The filter can either operate on the whole image or on predefined pixels only.
- Reverse speckle filter: This filter deletes groups of zero pixels which are surrounded by non-zero pixels. The threshold for the maximum number of pixels can be configured. The filter can either operate on the whole image or on predefined pixels only.
- Gabella filter: A texture-based filter  is used to smooth extreme peaks in the image, e.g., due to ground clutter within a rain field. The filter can either operate on the whole image or on predefined pixels only.
- Beam blockage filter: A measurement data-based method to correct for beam blockage in polar radar data, which does not require the existence of a digital elevation model or precise knowledge about the radar parameters as other methods do . Instead, a careful visual and statistical analysis of the radar data leads to the determination of beam-specific correction factors.
- Advection correction: This correction overcomes the fact that radar measurements are instantaneous measurements and do not provide rainfall volumes in time. Precipitation cells are detected and identified in consecutive images. A field of motion vectors is calculated, and the precipitation is distributed along the track of the cells based on the motion vector field. This gives a more realistic spatial distribution of the precipitation .
4. EO Data Sources
4.3. First Verification Results
5. Discussion or the Way Ahead
5.2. Method Extensions: Use of Soil Moisture Data
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Prior October 2020||After October 2020|
|Elevations||0.5°, 0.9°, 1.3° and higher elevations not considered here||0.5°, 0.8°, 1.4° and higher elevations not considered here|
|Radial resolution||500 m||250 m|
|Nominal radius||300 km||300 km|
|Reflectivity resolution||159 dBZ-classes||159 dBZ-classes|
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