Conditional simulation of surface rainfall fields using modified phase annealing

Abstract

Abstract. The accuracy of quantitative precipitation estimation (QPE) over a given region and period is of vital importance across multiple domains and disciplines. However, due to the intricate temporospatial variability and the intermittent nature of precipitation, it is challenging to obtain QPE with adequate accuracy. This paper aims to simulate rainfall fields while honoring both the local constraints imposed by the point-wise rain gauge observations and the global constraints imposed by the field measurements obtained from weather radar. The conditional simulation method employed in this study is modified phase annealing (PA), which is practically an evolution from the traditional simulated annealing (SA). Yet unlike SA, which implements perturbations in the spatial field, PA implements perturbations in Fourier space, making it superior to SA in many respects. PA is developed in two ways. First, taking advantage of the global characteristic of PA, the method is only used to deal with global constraints, and the local ones are handed over to residual kriging. Second, except for the system temperature, the number of perturbed phases is also annealed during the simulation process, making the influence of the perturbation more global at initial phases and decreasing the global impact of the perturbation gradually as the number of perturbed phases decreases. The proposed method is used to simulate the rainfall field for a 30 min event using different scenarios: with and without integrating the uncertainty of the radar-indicated rainfall pattern and with different objective functions.

SF-CNN: Signal Filtering Convolutional Neural Network for Precipitation Intensity Estimation

“…The methods consider various containers to collect precipitation and measure the precipitation intensities, playing a critical role in and measuring of precipitation intensity [17]. The rain gauges are the direct and accurate approach to obtaining precipitation [18] and can provide the high-precision measurement of the precipitation intensity at a single sampling point [19]. However, it has the problems of the density of sampling, residual water, and water evaporation.…”

Conditional simulation of spatial rainfall fields using random mixing: a study that implements full control over the stochastic process

“…RM, on the other hand, is an excellent tool that performs conditional simulation in Gaussian space, but it is not irreplaceable. Another conditional simulation method could have been used, such as phase annealing (Yan et al, 2020). RM was employed in this study due to (a) its relatively high efficiency, which makes the mass production of realizations possible, and (b) code availability (a Python package for the conditional simulation of random spatial fields using RM is available, with the authors giving practical demonstrations of the application of the method; Hörning and Haese (2021)).…”

“…Precipitation is one of the most important factors in hydrology and meteorology. The accuracy of spatial precipitation estimates with relatively high spatiotemporal resolution is of vital importance in various fields of research and practice, such as the promotion of meteorological and hydrological monitoring, forecasting performed to enhance our ability to cope with natural disasters, the study of climate trends and variability, and the management of water resources (Yilmaz et al, 2005;Michaelides et al, 2009;Jiang et al, 2012;Liu et al, 2017). Yet, unlike many other hydrometeorological variables such as temperature and humidity, precipitation occurs intermittently in space and time, i.e.…”

“…variations in the vertical profile reflectivity (VPR), static/dynamic clutter, signal attenuation, anomalous propagation of the beam, and uncertainty about the Z-R relationship (see Doviak and Zrnić, 1993;Collier, 1999;Fabry, 2015, for details). Despite these various potential error sources, radar-based precipitation estimation is still, however, considered a valid indicator of precipitation patterns (Méndez Antonio et al, 2009;Yan et al, 2020). In summary, radars and rain gauges are two complementary sources of precipitation information: the former are inaccurate in general but are valid indicators of the spatial pattern of the rainfall field; the latter are relatively accurate but lack spatial coverage.…”