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Important aspects of the adjustments to aerosol-cloud interactions can be examined using the relationship between cloud droplet number concentration (Nd) and liquid water path (LWP). Specifically, this relation can constrain the role of aerosols in leading to thicker or thinner clouds in response to adjustment mechanisms. This study investigates the satellite retrieved relationship between Nd and LWP for a selected case of mid-latitude continental clouds using high-resolution Large-eddy simulations (LES) over a large domain in weather prediction mode. Since the satellite retrieval uses the adiabatic assumption to derive the Nd, we have also considered adiabatic Nd (NAd) from the LES model for comparison. The joint histogram analysis shows that the NAd-LWP relationship in the LES model and the satellite is in approximate agreement. In both cases, the peak conditional probability (CP) is confined to lower NAd and LWP; the corresponding mean LWP (LWP) shows a weak relation with NAd. The CP shows a larger spread at higher NAd (>50 cm–3), and the LWP increases non-monotonically with increasing NAd in both cases. Nevertheless, both lack the negative NAd-LWP relationship at higher NAd, the entrainment effect on cloud droplets. In contrast, the model simulated Nd-LWP clearly illustrates a much more nonlinear (an increase in LWP with increasing Nd and a decrease in LWP at higher Nd) relationship, which clearly depicts the cloud lifetime and the entrainment effect. Additionally, our analysis demonstrates a regime dependency (marine and continental) in the NAd-LWP relation from the satellite retrievals. Comparing local vs large-scale statistics from satellite data shows that continental clouds exhibit only a weak nonlinear NAd-LWP relationship. Hence a regime-based Nd-LWP analysis is even more relevant when it comes to warm continental clouds and their comparison to satellite retrievals.

Important aspects of the adjustments to aerosol-cloud interactions can be examined using the relationship between cloud droplet number concentration (Nd) and liquid water path (LWP). Specifically, this relation can constrain the role of aerosols in leading to thicker or thinner clouds in response to adjustment mechanisms. This study investigates the satellite retrieved relationship between Nd and LWP for a selected case of mid-latitude continental clouds using high-resolution Large-eddy simulations (LES) over a large domain in weather prediction mode. Since the satellite retrieval uses the adiabatic assumption to derive the Nd, we have also considered adiabatic Nd (NAd) from the LES model for comparison. The joint histogram analysis shows that the NAd-LWP relationship in the LES model and the satellite is in approximate agreement. In both cases, the peak conditional probability (CP) is confined to lower NAd and LWP; the corresponding mean LWP (LWP) shows a weak relation with NAd. The CP shows a larger spread at higher NAd (>50 cm–3), and the LWP increases non-monotonically with increasing NAd in both cases. Nevertheless, both lack the negative NAd-LWP relationship at higher NAd, the entrainment effect on cloud droplets. In contrast, the model simulated Nd-LWP clearly illustrates a much more nonlinear (an increase in LWP with increasing Nd and a decrease in LWP at higher Nd) relationship, which clearly depicts the cloud lifetime and the entrainment effect. Additionally, our analysis demonstrates a regime dependency (marine and continental) in the NAd-LWP relation from the satellite retrievals. Comparing local vs large-scale statistics from satellite data shows that continental clouds exhibit only a weak nonlinear NAd-LWP relationship. Hence a regime-based Nd-LWP analysis is even more relevant when it comes to warm continental clouds and their comparison to satellite retrievals.

Important aspects of the adjustments to aerosol-cloud interactions can be examined using the relationship between cloud droplet number concentration (Nd) and liquid water path (LWP). Specifically, this relation can constrain the role of aerosols in leading to thicker or thinner clouds in response to adjustment mechanisms. This study investigates the satellite retrieved relationship between Nd and LWP for a selected case of mid-latitude continental clouds using high-resolution Large-eddy simulations (LES) over a large domain in weather prediction mode. Since the satellite retrieval uses the adiabatic assumption to derive the Nd, we have also considered adiabatic Nd (NAd) from the LES model for comparison. The joint histogram analysis shows that the NAd-LWP relationship in the LES model and the satellite is in approximate agreement. In both cases, the peak conditional probability (CP) is confined to lower NAd and LWP; the corresponding mean LWP (LWP) shows a weak relation with NAd. The CP shows a larger spread at higher NAd (>50 cm–3), and the LWP increases non-monotonically with increasing NAd in both cases. Nevertheless, both lack the negative NAd-LWP relationship at higher NAd, the entrainment effect on cloud droplets. In contrast, the model simulated Nd-LWP clearly illustrates a much more nonlinear (an increase in LWP with increasing Nd and a decrease in LWP at higher Nd) relationship, which clearly depicts the cloud lifetime and the entrainment effect. Additionally, our analysis demonstrates a regime dependency (marine and continental) in the NAd-LWP relation from the satellite retrievals. Comparing local vs large-scale statistics from satellite data shows that continental clouds exhibit only a weak nonlinear NAd-LWP relationship. Hence a regime-based Nd-LWP analysis is even more relevant when it comes to warm continental clouds and their comparison to satellite retrievals.

A methodology is presented to evaluate the accuracy of cloud cover fraction (CCf) forecasts generated by numerical weather prediction (NWP) and climate models. It is demonstrated with a case study consisting of simulations from theWeather Research and Forecasting (WRF) model. In this study, since the WRF CCf forecasts were initialized with reanalysis fields from the North American Mesoscale (NAM) Forecast System, the characteristics of the NAM CCf products were also evaluated. The procedures relied extensively upon manually-generated, binary cloud masks created from VIIRS (Visible Infrared Imager Radiometry Suite) imagery, which were subsequently converted into CCf truth at the resolution of the NAM and WRF gridded data. The initial results from the case study revealed biases toward under-clouding in the NAM CCf analyses and biases toward over-clouding in the WRF CCf products. These biases were evident in images created from the gridded NWP products when compared to VIIRS imagery and CCf truth data. Thus, additional simulations were completed to help assess the internal procedures used in the WRF model to translate moisture forecast fields into layered CCf products. Two additional sets of WRF CCf 24 h forecasts were generated for the region of interest using WRF restart files. One restart file was updated with CCf truth data and another was not changed. Over-clouded areas in the updated WRF restart file that were reduced with an update of the CCf truth data became over-clouded again in the WRF 24 h forecast, and were nearly identical to those from the unchanged restart file. It was concluded that the conversion of WRF forecast fields into layers of CCf products deserves closer examination in a future study.

A methodology is presented to evaluate the accuracy of cloud cover fraction (CCf) forecasts generated by numerical weather prediction (NWP) and climate models. It is demonstrated with a case study consisting of simulations from theWeather Research and Forecasting (WRF) model. In this study, since the WRF CCf forecasts were initialized with reanalysis fields from the North American Mesoscale (NAM) Forecast System, the characteristics of the NAM CCf products were also evaluated. The procedures relied extensively upon manually-generated, binary cloud masks created from VIIRS (Visible Infrared Imager Radiometry Suite) imagery, which were subsequently converted into CCf truth at the resolution of the NAM and WRF gridded data. The initial results from the case study revealed biases toward under-clouding in the NAM CCf analyses and biases toward over-clouding in the WRF CCf products. These biases were evident in images created from the gridded NWP products when compared to VIIRS imagery and CCf truth data. Thus, additional simulations were completed to help assess the internal procedures used in the WRF model to translate moisture forecast fields into layered CCf products. Two additional sets of WRF CCf 24 h forecasts were generated for the region of interest using WRF restart files. One restart file was updated with CCf truth data and another was not changed. Over-clouded areas in the updated WRF restart file that were reduced with an update of the CCf truth data became over-clouded again in the WRF 24 h forecast, and were nearly identical to those from the unchanged restart file. It was concluded that the conversion of WRF forecast fields into layers of CCf products deserves closer examination in a future study.