Scale‐recursive estimation for multisensor Quantitative Precipitation Forecast verification: A preliminary assessment

Abstract

[1] Precipitation is a highly heterogeneous process with considerable natural variability at scales ranging from a few meters to several hundreds of kilometers. This process is monitored with a variety of sensors (e.g., rain gauges, radars, and satellites) which provide direct or indirect measurements of precipitation at different scales. At the same time, physically based models at the storm, regional, continental, and global scales are used to predict precipitation and rely on the observed data for model verification, model initialization, and data assimilation. Because of the tremendous scale-dependent variability of precipitation fields, merging or comparing observations at different scales, or comparing model outputs at one scale with observations at one or more different scales, is not straightforward. This study explores the use of a recently developed scale-recursive estimation (SRE) framework for the problem of Quantitative Precipitation Forecast (QPF) verification using observations from multiple sensors. SRE can explicitly account for the multiscale variability of precipitation and the scale-dependent uncertainty associated with the model output and the multisensor observations. Special emphasis is placed on the specification of the multiscale structure of precipitation under sparse or noisy data. The results demonstrate the potential of SRE as a powerful tool for assessment of QPFs and also for multisensor data fusion and network design studies.

Impact of satellite rainfall assimilation on Weather Research and Forecasting model predictions over the Indian region

“…Rainfall is a highly variable parameter that varies in scales from few meters to several kilometers [Tustison et al, 2003]. The importance of accurate rainfall observation and forecast are widely recognized.…”

An Analysis of Rainfall Measurements over Different Spatio-Temporal Scales and Potential Implications for Uncertainty in Satellite Data Validation

“…Precipitation is known to vary in all possible spatial scales ranging from a few meters to several kilometers (McCollum and Krajewski 1998;Tustison et al 2003) posing a difficulty not only in the validation of the precipitation but also in its retrieval using satellite observations. Several researchers (e.g., Wilheit 1986;Chiu et al 1990;Varma et al 2004;Varma and Liu 2006) described and also suggested a possible stochastic solution to the beam filling problem that leads to severe underestimation of the precipitation at microwave frequencies.…”

“…A number of researchers have examined the precipitation variability on a small scale and reported its spatial distribution as a lognormal distribution (e.g., Lopez 1976;Kedem and Slud 1994). Tustison et al (2001Tustison et al ( , 2003 examined the scale dependency of the precipitation and the errors associated with them when interpolated from one scale to another. Gupta and Waymire (1993) examined the precipitation mesoscale variability and modeled the variability in short time intervals based on the concept of random cascades.…”

On variational downscaling, fusion, and assimilation of hydrometeorological states: A unified framework via regularization

“…Furthermore, a few studies have addressed methodologies to optimally combine the products of the TRMM precipitation radar (PR) with the TRMM microwave imager (TMI) using Bayesian inversion and weighted least squares (WLS) approaches [e.g., Masunaga and Kummerow, 2005;Kummerow et al, 2010]. From another direction, Gaussian filtering methods on Markovian tree-like structures, the socalled scale recursive estimation (SRE), have been proposed to merge spaceborne and ground-based rainfall observations at multiple scales [e.g., Gorenburg et al, 2001;Tustison et al, 2003;Bocchiola, 2007;Van de Vyver and Roulin, 2009;Wang et al, 2011], see also Kumar [1999] for soil moisture applications. Recently, using the Gaussian-scale mixture probability model and an adaptive filtering approach, Ebtehaj and FoufoulaGeorgiou [2011a] proposed a fusion methodology in the wavelet domain to merge TRMM-PR and ground-based NEXRAD measurements, aiming to preserve the nonGaussian structure and local extremes of precipitation fields.…”