Nowcasting of Extreme Rainfall in Dutch Cities

Nowcasting of Extreme Rainfall in Dutch Cities

Lin, Steven (TU Delft Civil Engineering and Geosciences)

Uijlenhoet, R. (mentor)
Imhoff, Ruben (graduation committee)
Schleiss, M.A. (graduation committee)

Delft University of Technology

Civil Engineering | Environmental Engineering

2022-10-20

Extreme rainfall brings substantial threats to lives, infrastructure, and the economy in cities. Radar rainfall nowcasting was proven able to provide forecasts up to 2 to 3 hours in advance on a catchment scale. However, an extensive evaluation of nowcasting skills for urban areas has not been performed yet. In this study, we selected 80 extreme events that occurred in 5 main Dutch cities (Amsterdam, The Hague, Groningen, Maastricht, and Eindhoven) from 2008 to 2021. We evaluated the performance of probabilistic nowcasts with 20 ensemble members applying short-term ensemble prediction system (STEPS) from Pysteps for these cities, focusing on analyzing the dependence on rainfall characteristics and city sizes. Nowcasts in Eindhoven (96 km2) and Maastricht (67 km2) had higher errors because the rainfall intensity of their events was higher. Besides, nowcasts at small areas showed higher error, especially when the size was below 100 km2. We found that forecast errors were higher and the forecast was less reliable for the 1-h event durations than for 24-h durations. Despite these differences, skillful lead times measured by Pearson correlation in all the cities were about 20 to 24 minutes for both the 1-hour and 24-hour events. CARROTS (Climatology-based Adjustments for Radar Rainfall in an Operational Setting) adjusted the bias in real-time QPE and QPF, but QPF still reduced with increasing lead time. Also, CARROTS did not adjust the rainfall spatial distribution much, so the skillful lead time did not change much. The skillful lead time in this study was shorter than the counterparts on the catchment scale because small areas are more sensitive to the displacement of forecast rainfall. Still, such lead time is similar to the findings in other research on short convective rainfall over small areas. Future research could try to apply machine learning, 3-dimensional nowcasting, or blending numerical weather prediction in the nowcasting process to better forecast the growth and decay of rainfall at a longer lead time.

nowcasting
extreme precipitation
urban flooding

http://resolver.tudelft.nl/uuid:b7a61b8b-db93-4c7c-b49c-812910add341

Student theses

master thesis

© 2022 Steven Lin