Improving Multisensor Precipitation Estimation via Adaptive Conditional Bias–Penalized Merging of Rain Gauge Data and Remotely Sensed Quantitative Precipitation Estimates

Abstract

We describe and evaluate adaptive conditional bias–penalized cokriging (CBPCK) for improved multisensor precipitation estimation using rain gauge data and remotely sensed quantitative precipitation estimates (QPE). The remotely sensed QPEs used are radar-only and radar–satellite-fused estimates. For comparative evaluation, true validation is carried out over the continental United States (CONUS) for 13–30 September 2015 and 7–9 October 2016. The hourly gauge data, radar-only QPE, and satellite QPE used are from the Hydrometeorological Automated Data System, Multi-Radar Multi-Sensor System, and Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR), respectively. For radar–satellite fusion, conditional bias–penalized Fisher estimation is used. The reference merging technique compared is ordinary cokriging (OCK) used in the National Weather Service Multisensor Precipitation Estimator. It is shown that, beyond the reduction due to mean field bias (MFB) correction, both OCK and adaptive CBPCK additionally reduce the unconditional root-mean-square error (RMSE) of radar-only QPE by 9%–16% over the CONUS for the two periods, and that adaptive CBPCK is superior to OCK for estimation of hourly amounts exceeding 1 mm. When fused with the MFB-corrected radar QPE, the MFB-corrected SCaMPR QPE for September 2015 reduces the unconditional RMSE of the MFB-corrected radar by 4% and 6% over the entire and western half of the CONUS, respectively, but is inferior to the MFB-corrected radar for estimation of hourly amounts exceeding 7 mm. Adaptive CBPCK should hence be favored over OCK for estimation of significant amounts of precipitation despite larger computational cost, and the SCaMPR QPE should be used selectively in multisensor QPE.

High-resolution typhoon precipitation integrations using satellite infrared observations and multi-source data

“…In contrast, microwave radars can survey larger areas and can better capture the spatial variability of rainfall fields; however, the accuracy of radar-based measurements is highly influenced by electromagnetic attenuation and the uncertainty in the relationship between the radar reflectivity factor and precipitation, particularly under extreme rainfall conditions [Marra et al, 2015;Bárdossy et al, 2017]. Satellite-based quantitative precipitation estimation (QPE) can be implemented on a large scale with a high spatial-temporal resolution, offering large scale capability with high spatial-temporal resolutions Wang et al, 2018;Jozaghi et al, 2019], but quantitatively inferring the amount of surface precipitation from space is still a serious challenge, especially during typhoon periods. Nevertheless, with the continuous improvement of meteorological satellites, satellite-based QPE technologies have undergone considerable development [Boushaki et al, 2009;Nguyen et al, 2018].…”

High-resolution modeling and prediction of urban floods using WRF-Hydro and data assimilation

“…We note here that the last two terms in Eq. ( 12) represent a sample statistic for the objective function used in CBpenalized optimal linear estimation for improved estimation of extremes (Brown and Seo, 2013;Seo, 2012;Seo et al, 2014;Kim et al, 2016, Seo et al, 2018aShen et al, 2019, Jozaghi et al 2019.…”

Ensemble Streamflow Data Assimilation using WRF-Hydro and DART: Hurricane Florence Flooding

“…In addition, a conditional bias-penalized Kalman filter was developed for improved estimation and prediction of hydrologic extremes. The filter operates by minimizing a weighted sum of error variances and Type-II squared errors, different from the conventional Kalman filter which is based on least square minimization (Seo et al, 2018;Lee et al, 2019;Jozaghi et al, 2019). In a recent study, Emery et al (2020a) proposed updating the boundary fluxes based on the 2 https://doi.org/10.5194/hess-2020-642 Preprint.…”