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The influence of the Unified Noah and Noah‐MP land surface models (LSMs) on the evolution of cumulus clouds reaching convective initiation (CI) is assessed using infrared brightness temperatures (BT) from GOES‐16. Cloud properties from individual cloud objects are examined using output from high‐resolution (500 m horizontal grid spacing) model simulations. Cloud objects are tracked over time and related to observed clouds reaching CI to examine differences in cloud extent, longevity, and growth rate. The results demonstrate that differences in assumed surface properties can lead to large discrepancies in the net surface radiative budget, particularly in the sensible and latent heating components where differences exceed 40 W m−2. These differences lead to changes in the local mesoscale circulation patterns that are more pronounced near the edges of forested and grassland boundaries where lower‐level convergence is stronger. Higher sensible heating from the Noah‐MP LSM produced growth of CI clouds earlier and with increased longevity, which was closer to the timing and growth observed from GOES‐16. The increased cloud growth in the Noah‐MP experiment results from stronger and deeper updrafts, which lofts more cloud water into the upper levels of the troposphere. The weaker updrafts from the Noah LSM experiment results in shallower convection after CI is detected due to slower growth rates. The differences in cloud properties and growth are directly related to the land surfaces they develop above and point to the importance of accurately representing land properties and radiative characteristics when simulating convection in numerical weather prediction models. Plain Language Summary: Weather prediction models consist of many different parameters and assumptions. In this study, we compared how assumptions of the land surface impact the growth of cumulus clouds and thunderstorms across the southeastern United States. It was found that differences in the land surface schemes can directly impact the local circulations where cumulus clouds and convective storms develop; this leads to differences in how large the clouds grow and sustain over time. By tracking clouds using an object‐based methodology, we were able to compare growth characteristics to those observed by geostationary satellites. The model and satellite comparisons helped demonstrate that the cloud growth is quite sensitive to the model interpretation of surface energy balances, particularly over heterogeneous landscapes containing forests and grasslands. The differences in the amount of energy transferred from the surface to the atmosphere lead to downstream differences in cloud updraft strength. These differences in cumulus cloud characteristics influence the formation of ice in the upper levels of clouds, which is essential in the convective storm initiation process. The comparison with satellite data provided the ability to validate cloud growth and further understand the processes leading to longer‐lived thunderstorms. Key Points: The Noah‐MP LSM leads to a more accurate size distribution and growth rate of convection compared to the Noah LSMThe latent and sensible heating components of the surface radiation balance drive differences in local circulation patternsSurface energy imbalances impact updraft characteristics, leading to downstream influences of cloud growth and sustained convection [ABSTRACT FROM AUTHOR]

In this study, a polarimetric radar forward model operator was developed for the Weather Research and Forecasting (WRF) model that was based on a scattering algorithm using the T-matrix methodology. Three microphysics schemes—Thompson, Morrison 2-moment, and Milbrandt-Yau 2-moment—were supported in the operator. This radar forward operator used the microphysics, thermodynamic, and wind fields from WRF model forecasts to compute horizontal reflectivity, radial velocity, and polarimetric variables including differential reflectivity (ZDR) and specific differential phase (KDP) for S-band radar. A case study with severe convective storms was used to examine the accuracy of the radar operator. Output from the radar operator was compared to real radar observations from the Weather Surveillance Radar–1988 Doppler (WSR-88D) radar. The results showed that the radar forward operator generated realistic polarimetric signatures. The distribution of polarimetric variables agreed well with the hydrometer properties produced by different microphysics schemes. Similar to the observed polarimetric signatures, radar operator output showed ZDR and KDP columns from low-to-mid troposphere, reflecting the large amount of rain within strong updrafts. The Thompson scheme produced a better simulation for the hail storm with a ZDR hole to indicate the existence of graupel in the low troposphere. [ABSTRACT FROM AUTHOR]

The evolution of model-based cloud-top brightness temperatures (BT) associated with convective initiation (CI) is assessed for three bulk cloud microphysics schemes in the Weather Research and Forecasting Model. Using a composite-based analysis, cloud objects derived from high-resolution (500 m) model simulations are compared to 5-min GOES-16 imagery for a case study day located near the Alabama–Mississippi border. Observed and simulated cloud characteristics for clouds reaching CI are examined by utilizing infrared BTs commonly used in satellite-based CI nowcasting methods. The results demonstrate the ability of object-based verification methods with satellite observations to evaluate the evolution of model cloud characteristics, and the BT comparison provides insight into a known issue of model simulations producing too many convective cells reaching CI. The timing of CI from the different microphysical schemes is dependent on the production of ice in the upper levels of the cloud, which typically occurs near the time of maximum cloud growth. In particular, large differences in precipitation formation drive differences in the amount of cloud water able to reach upper layers of the cloud, which impacts cloud-top glaciation. Larger cloud mixing ratios are found in clouds with sustained growth leading to more cloud water lofted to the upper levels of the cloud and the formation of ice. Clouds unable to sustain growth lack the necessary cloud water needed to form ice and grow into cumulonimbus. Clouds with slower growth rates display similar BT trends as clouds exhibiting growth, which suggests that forecasting CI using geostationary satellites might require additional information beyond those derived at cloud top. SIGNIFICANCE STATEMENT: Several studies have used weather satellites to examine storm properties; however, they do not provide information about processes occurring within clouds. To address this limitation, we used numerical weather prediction model simulations and an object-based analysis method to learn more about in-cloud processes that influence the evolution of thunderstorms in the southeastern United States. The model and satellite comparison helped demonstrate that differences in the timing of rainfall formation can impact the amount of ice reaching the upper portion of the cloud. When ice forms, the cloud begins to grow rapidly and is more likely to become a long-lived thunderstorm. The results highlight the importance of using satellite data sensitive to clouds to evaluate the conditions under which cumulus clouds transition into severe storms. [ABSTRACT FROM AUTHOR]