Janice L. Bytheway

A Physically Based Screen for Precipitation Over Complex Surfaces Using Passive Microwave Observations

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 NASAs 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.

Toward an object‐based assessment of high‐resolution forecasts of long‐lived convective precipitation in the central U.S.

Forecast models have seen vast improvements in recent years, via both increased resolutions and the ability to assimilate observational data, particularly that which has been affected by clouds and precipitation. The High-Resolution Rapid Refresh (HRRR) model is an hourly updated, 3 km model designed for forecasting convective precipitation recently deployed for operational use over the U.S. that initializes latent heating profiles as a function of assimilated radar reflectivity. An object-oriented verification process was developed to validate experimental HRRR convective precipitation forecasts during the 2013 warm season using the NCEP Stage IV multisensor precipitation product. A database of 467 convective precipitation features that were observed during the forecast assimilation period and their corresponding HRRR forecast precipitation features was created. This database was used to evaluate model performance over the entire forecast period, and to relate that performance to model processes, especially those related to precipitation production. Generally, HRRR precipitation is located within 30 km of the observed throughout the forecast period. Validation statistics are best at forecast hour 3, with median biases in mean, maximum, and total rainfall and raining area near 0%. Earlier in the forecast, median biases in the mean and maximum rain rate exceed 30%, with bias values often exceeding 150%. The median bias in areal extent at the beginning of the forecast is near −40%. This low areal bias and POD values <0.6 appear to be related to the models ability to produce deep convection relative to atmospheric moisture content and concentration of rainfall in convective cores.

A Features-Based Assessment of the Evolution of Warm Season Precipitation Forecasts from the HRRR Model over Three Years of Development

AbstractThe High Resolution Rapid Refresh (HRRR) model has been the National Weather Service’s (NWS) operational rapid update model since 2014. The HRRR has undergone continual development, including updates to the Weather Research and Forecasting (WRF) Model core, the data assimilation system, and the various physics packages in order to better represent atmospheric processes, with updated operational versions of the model being implemented approximately every spring. Given the model’s intent for use in convective precipitation forecasting, it is of interest to examine how forecasts of warm season precipitation have changed as a result of the continued model upgrades. A features-based assessment is performed on the first 6 h of HRRR quantitative precipitation forecasts (QPFs) from the 2013, 2014, and 2015 versions of the model over the U.S. central plains in an effort to understand how specific aspects of QPF performance have evolved as a result of continued model development. Significant bias changes we...