A New Spatial Distance Metric for Verification of Precipitation
Source abstract
Abstract
Precipitation is an essential meteorological variable affecting the biosphere and human societies. At the same time, precipitation is notoriously difficult to predict and verify. A new spatial distance metric for verification of precipitation is presented. It is called the Precipitation Smoothing Distance (PSD). The aim was to develop a measure that would provide a good and meaningful approximation of the displacement of precipitation events in the two fields. An estimate of spatial displacement is very appealing for forecast interpretation because it is easy to understand and mimics how humans tend to judge fields by eye. Contrary to most other distance metrics, the new metric does not require thresholding and can thus be used to analyze binary and non-binary fields (e.g., continuous or multi-level). The analysis of idealized situations showed that the new metric provides a meaningful approximation of the displacement. Typically the estimate of displacement provided by PSD was better than the results provided by most other metrics. The measure is also not overly sensitive to noise, its results are directly related to the actual displacements of precipitation events, and the events with a larger magnitude have a bigger influence on the resulting value. The analysis of ECMWF precipitation forecasts over Europe and North Africa confirmed that the new metric provides a meaningful approximation of the displacement even in more complex real-world situations.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Precipitation Measurement and Analysis · Meteorological Phenomena and Simulations · Hydrology and Drought Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Mathematics and Physics, University of Ljubljana, Jadranska Cesta 19, 1000 Ljubljana, Slovenia
Funding
Slovenian Research Agency
References (17)
Show source-provided references
1. Brown, B.G., Gilleland, E., and Ebert, E.E. (2012). Forecasts of Spatial Fields. Forecast Verification, John Wiley and Sons, Ltd.10.1002/9781119960003.ch6 ↗
2. The setup of the MesoVICT project10.1175/BAMS-D-17-0164.1 ↗
3. Estimating the displacement in precipitation forecasts using the Fractions Skill Score10.1002/qj.3212 ↗
4. The Method for Object-Based Diagnostic Evaluation (MODE) Applied to Numerical Forecasts from the 2005 NSSL/SPC Spring Program10.1175/2009WAF2222241.1 ↗
5. Förstner, W., and Ruwiedel, S. (1992). An error metric for binary images. Robust Computer Vision, Wichmann.
6. A new characterization within the spatial verification framework for false alarms, misses, and overall patterns10.1175/WAF-D-16-0134.1 ↗
7. Scale-Selective Verification of Rainfall Accumulations from High-Resolution Forecasts of Convective Events10.1175/2007MWR2123.1 ↗
8. Assessing the spatial and temporal variation in the skill of precipitation forecasts from an NWP model10.1002/met.57 ↗
9. A long-term assessment of precipitation forecast skill using the Fractions Skill Score10.1002/met.296 ↗
10. Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P. (2007). Numerical Recipes 3rd Edition: The Art of Scientific Computing, 3 ed., Cambridge University Press.
11. Smith, S.W. (1999). The Scientist and Engineer’s Guide to Digital Signal Processing, California Technical Publishing.
12. A Novel Set of Geometric Verification Test Fields with Application to Distance Measures10.1175/MWR-D-19-0256.1 ↗
13. Déqué, M. (2011). Deterministic Forecasts of Continuous Variables. Forecast Verification, Wiley.10.1002/9781119960003.ch5 ↗
14. Behaviour of verification measures for deterministic binary forecasts with respect to random changes and thresholding10.1002/qj.3050 ↗
15. Analysis of Fractions Skill Score properties for random precipitation fields and ECMWF forecasts10.1002/qj.2849 ↗
16. Applicability of prewhitening to eliminate the influence of serial correlation on the Mann-Kendall test10.1029/2001WR000861 ↗
17. Weisstein, E.W., and Circular Segment (2022, January 07). From MathWorld—A Wolfram Web Resource. Available online: https://mathworld.wolfram.com/CircularSegment.html.