1. Algorithm name
Quality-based vertically integrated liquid water (VIL) product generation – Product2D: VIL
2. Basic description
a) Physical basis of the algorithm
The algorithm generates vertically integrated liquid water (VIL) 2-D product from radar reflectivity volume with quality information using product2D_PPI algorithm.
b) Amount of validation performed so far
Not performed yet.
c) References (names and contact information of all developers during the evolutionary history, scientific papers)
IMGW, Department of Ground Based Remote Sensing.
3. ODIM metadata requirements for I/O
Input data: VOL
- General “what”: source(NOD).
- For particular SCANs:
- Dataset-specific “where” for data and QI: nbins, nrays.
- Data-specific “what” for data: gain, offset, nodata, undetect.
- Data-specific “what” for QI: gain, offset, nodata, undetect.
Output data: Cartesian data
- Top-level “where”: lon, lat, xsize, ysize, xscale, yscale.
- Dataset-specific “what”: product.
- Data-specific “what” for data: gain, offset, nodata, undetect.
- Data-specific “what” for QI: gain, offset, nodata, undetect.
4. Input data
a) What kind of radar data (including the list of previous algorithms and quality flags applied)
object=PVOL:
- quantity=DBZH, otherwise TH,
- quantity=QIND (generated e.g. by QI_TOTAL algorithm).
b) Other data (optional and mandatory, applying “universally” agreed formats, geometry)
Defined projection and domain of Cartesian output.
5. Logical steps, using any of: text, flow charts, graphics, equations (or references to equations), conditional branches in “all possible cases”.
The algorithm generates vertically integrated liquid water (VIL) 2-D products in Cartesian coordinates from radar reflectivity volume and based on the data quality information.
Algorithm parameters
Set of the algorithm parameters:
Description | Denotation | Unit | Default value |
Lower height limit for VIL product generation (a.s.l.) | VIL_hMin | km | 1 |
Upper height limit for VIL product generation (a.s.l.) | VIL_hMax | km | 10 |
Coefficient c in Z-M formula | VIL_ZMc | - | 24000 |
Coefficient d in Z-M formula | VIL_ZMd | - | 1.82 |
Algorithm description
Vertically integrated liquid water (VIL) product represents Cartesian image of the water content residing in a user-defined layer in the atmosphere (in dBA) (Fig. 1). Generally, in the first step a vertical profile of liquid water content M (based on Z-M relationship) is determined by interpolation between all pairs of neighbouring measurements. Then the VIL in a preset range of height (between h_{min} and h_{max}) is calculated by integration of the profile. In order to find the vertical profile of M(h), values between two measurements M’ and M” detected at heights h’ and h” respectively, the linear interpolation is applied:
Fig. 1. Scheme of generation of vertically integrated liquid water product (VIL).
Radar reflectivity Z is related to liquid water content M (in cm^{3} m^{-3}) according to so called Z-M relationship:
where constants: c = 24 000; d = 1.82 (as proposed by Selex, 2010).
The VIL (vertically integrated liquid water content) is defined as:
In the VIL algorithm there are considered only measurements (Cartesian pixels on PPIs) between h_{min} and h_{max}, and two closest ones (below h_{min} and above h_{max}).
The following ranges of integration are considered:
- If the highest measurement is higher than h_{max}, then the two measurements above and below h_{max} are interpolated and values of M below h_{max} are taken for integration, else the integration is performed up to height of the highest measurement.
- If the lowest measurement is lower than h_{min}, then the two measurements above and below h_{min} are interpolated and values of M above h_{min} are taken for integration, else the integration is performed with the assumption that the lowest measurement was also at h_{min}.
Data quality characterization
Quality of the VIL depends on the two factors:
- quality of reflectivity data from which VIL was determined, QI_{source},
- how large fraction of investigated heights (between h_{min} and h_{max}) was scanned, QI_{scope},
and the final quality index QI is taken as a product of the both factors:
The value of the first component QI_{source} is taken as an average quality of all measurements defining VIL. If VIL = “nodata” (there is no measurement between h_{min} and h_{max}) then QI_{source} = “nodata”, and if VIL = “undetect” (there is no detected reflectivity between h_{min} and h_{max}) then QI_{source} = 1.
Fig. 2. Quality characterization for vertically integrated liquid water product in terms of scope.
The second component QI_{scope} is determined based on heights of the lowest and highest scans for considered Cartesian pixel (h_{lowest} and h_{highest} respectively) in relation to h_{min} and h_{max} (Fig. 2):
- if h_{highest} < h_{min} then
QI_{ scope} = “nodata” and QI = “nodata”.
- if h_{highest} ≥ h_{min} and h_{lowest} <= h_{max} then QI_{scope} depends on what part of height range between h_{min} and h_{max} was scanned:
- if h_{lowest} > h_{max} then
QI_{ scope} = “nodata” and QI = “nodata”.
6. Output
a) Data type using ODIM notation where possible, e.g. DBZH
Input quantity as IMAGE object (in Cartesian coordinates) with:
- "how": task - "pl.imgw.product2d.vil",
- "how": task_args
- parameters inherited from PPI algorithm (interpolation method and selected quality field name),
- parameters of VIL algorithm,
b) Quality index (QI) field
Quality index field QIND as IMAGE object with:
- "how": task - "pl.imgw.product2d.vil",
- "how": task_args - parameters inherited from PPI algorithm (interpolation method and selected quality field name).
7. Outline of a test concept exemplifying the algorithm, as a suggestion for checking that an implementation has been successful.
TBD
Attachments
- Fig_2_VIL.gif (12.8 KB) - added by jan 5 years ago.
- Fig_1_VIL.gif (7.1 KB) - added by jan 5 years ago.