Search

Accurate quantitative precipitation estimates (QPEs) at high spatial and temporal resolution are difficult to obtain in regions of complex terrain due to the large spatial heterogeneity of orographically enhanced precipitation, sparsity of gauges, precipitation phase variations, and terrain effects that impact the quality of remotely sensed estimates. The large uncertainty of QPE in these regions also makes the evaluation of high-resolution quantitative precipitation forecasts (QPFs) challenging, as it can be difficult to choose a reference QPE that is reliable at both high and low elevations. In this paper we demonstrate a methodology to combine information from multiple high-resolution hourly QPE products to evaluate QPFs from NOAA's High-Resolution Rapid Refresh (HRRR) model in a region of Northern California. The methodology uses the quantiles of monthly QPE distributions to determine a range of hourly precipitation that correspond to "good," "possible," "underestimated," or "overestimated" QPFs. In this manuscript, we illustrate the use of the methodology to evaluate QPFs for seven atmospheric river events that occurred during the 2016–17 wet season in Northern California. Because the presence of frozen precipitation is often not captured by traditional QPE products, we evaluate QPFs both for all precipitation, and with likely frozen precipitation excluded. The methodology is shown to provide useful information to evaluate model performance while taking into account the uncertainty of available QPE at various temporal and spatial scales. The potential of the technique to evaluate changes between model versions is also shown. [ABSTRACT FROM AUTHOR]

Physically based passive microwave precipitation retrievals are difficult to develop over land because high nonuniform land emissivity values are difficult to distinguish from those of clouds. This paper uses an empirical approach to determine the covariance of emissivity at different microwave window channels and relies on this covariance to estimate the portion of the observed brightness temperatures that may be attributable to rainfall. One year (2006) of global cloud-free surface emissivity values were retrieved using data sets from multiple instruments on NASA's Aqua satellite. Correlations between the emissivities at different channels were developed for use in an empirical model within an optimal estimation retrieval. The optimal estimation retrieves surface temperature, total column water vapor, cloud water, and the emissivity at the 10.7-GHz horizontally polarized channel. From this retrieval and the covariance of emissivities, the 89.0-GHz brightness temperature at both polarizations can be estimated. Significant differences between the observed and retrieved high-resolution brightness temperatures are used to screen for precipitation, and results are compared to ground-based radar data for several study regions representing a variety of land surface types in the U.S. The Heidke Skill Score is used to determine the robustness of this methodology and, in all cases, demonstrates at least some increase in skill relative to random chance. [ABSTRACT FROM AUTHOR]