Characterizing the uncertainty of CMORPH products for estimating orographic precipitation over Northern California

Satellite-based precipitation products (SPPs), such as the NOAA Climate Prediction Center (CPC) Morphing technique (CMORPH), have greatly extended our ability to monitor global precipitation. However, their performance over complex terrain remains highly uncertain. To improve accuracy, CPC has recently upgraded its operational SPP to the second generation CMORPH–CMORPH2. In addition to such efforts, the reliability of SPPs can be further enhanced by developing robust uncertainty estimates. This study employs a censored, shifted gamma distribution (CSGD)-based error modeling framework to develop error models for both CMORPH2 and its predecessor, CMORPH1, at a 6-hourly scale over Northern California—a typical complex terrain region that challenges most SPPs. Using the Stage IV reference precipitation, the relative improvements of CMORPH2 over CMORPH1 are quantified by comparing their CSGD-derived error statistics. The comparison shows that CMORPH2 outperforms CMORPH1 by reducing the overall bias and detection errors, and by better capturing the orographic gradients of precipitation over the Coast Ranges and Sierra Nevada. Validation results show that the trained error models can appropriately represent the bias and random errors; however, the models for both CMORPH2 and CMORPH1 show reduced skills in the high-altitude Sierra Nevada. With high-resolution regional climate simulations, the uncertainty estimates are further improved by incorporating them as covariates. The simulated precipitation shows substantial improvement of the uncertainty estimates over most areas, including the challenging high-altitude Sierra Nevada. Following precipitation, the simulated integrated water vapor transport (IVT) and convective available potential energy (CAPE) also offer modest improvements, mainly along the coast. Independent verification further demonstrates the robustness of the uncertainty estimates during heavy precipitation events. As NOAA is currently reprocessing CMORPH2 to produce an updated global precipitation record from 1991 onward, this error modeling framework holds potential for broader applications in quantifying the uncertainty of CMORPH2 for estimating orographic precipitation over extended periods and locations. • We developed parametric models for CMORPH uncertainty estimates in complex terrain. • The second-generation CMORPH exhibits superior performance over its predecessor. • The uncertainty estimates of CMORPH are reliable even in heavy precipitation events. • WRF simulations improve the uncertainty estimates for orographic precipitation.