Diverse Explorations of Rainfall Nowcasting with TrajGRU: Mitigating Smoothness and Fading Out Challenges for Longer Lead Times

Machine learning models offer promising potential in precipitation nowcasting. However, a common issue faced by many of these models is the tendency to produce blurry precipitation nowcasts, which are unrealistic. Previous research on the deep learning model - TrajGRU (Shi et al., 2017) indicated that data imbalance in radar images and the...

A new Framework for assessing “The Value” of an Amateur Weather Station: Enabled by Web bots

A personal weather station (PWS) is a set of weather measuring instruments that is owned and operated by an individual, club, association, or business. Even tho these stations might be less advanced as the professional stations, the amount of amateur stations that are active is much higher. With these PWS networks in full effect and still...

Evaluation and Improvement of a Stochastic Parametric Tropical Cyclone Rainfall Model - for Flood Impact Assessment

BaCla is a new stochastic parametric precipitation model to estimate rainfall associated with Tropical cyclones (TCs) in a computationally efficient way. It is validated for a number of calibration cases along with the current benchmark IPET deterministic method. The results of these models are compared with the observed StageIV-based rainfall....

Perceptual losses in precipitation nowcasting: Exploring limits and potential

Accurate short term rain predictions are important for flood early warning systems, emergency services, energy management and other services that that make weather dependent decisions. Recently introduced machine learning models suffer from blurry and unrealistic predictions at longer lead times, causing poor performance on the rarer heavy...

Consolidating Gauge, Weather Radar and GPM DPR Level 2A Observations of High-intensity Rainfall Events in the Netherlands

The Dual-Frequency Precipitation Radar on board the Global Precipitation Measurement (GPM) mission core satellite has been providing precipitation products across the globe for over 6 years, thereby even supplying precipitation estimates for areas on Earth where surface-based precipitation measurements are not possible, like in remote regions,...

Applying deep learning vs machine learning models to reproduce dry spells at point scale from satellite information in a data-scarce region: the case of northern Ghana

Large parts of the world rely on rainfed agriculture for their food security. In Africa, 90% of the agricultural yields rely only on precipitation for irrigation purposes and approximately 80% of the population’s livelihood is highly dependent on its food production. Parts of Ghana are prone to droughts and flood events due to increasing...

Parametric Precipitation Model for Tropical Cyclone Radial Rainfall Profiles: Reducing the biases in the Bader model for the North Atlantic

Torrential rain from tropical cyclones can have a devastating impact, causing loss of life and billions in damages. To better understand the risk faced by coastal communities, it is important to estimate how often a tropical cyclone could occur and how much rainfall it will produce. One way to do this is by analyzing past storms and building...

Nowcasting heavy precipitation in the Netherlands: a deep learning approach

Accurate short-term forecasts, also known as nowcasts, of heavy precipitation are desirable for creating early warning systems for extreme weather and its consequences, e.g. urban flooding. In this research, we explore the use of machine learning for short-term prediction of heavy summer rainfall showers in the Netherlands. We explore the use of...

Prediction of rain cell growth and decay using numerical weather prediction models

Radar rainfall nowcasting stands for the prediction of rainfall amounts and intensities over the next 6 hours by means of statistical extrapolation of radar measurements. It is the principal ingredient for modern flood forecasting and early warning systems. Radar forecasts are generated by identifying and tracking rainfall cells in radar images...

Automated quality-control filters for undetected rainfall in citizen rain gauge data

Flooding in cities, known as Urban Pluvial Flooding (UPF), causes disruption of society, damage to cities and inconvenience for people. Cities are expected to become more vulnerable to UPF due to more frequent and more intense extreme rainfall events with high spatial variability as result of climate change. Additionally increasing urbanisation...