Biases in the Prediction of Convective Storm Characteristics with a Convection Allowing Ensemble

The severity, duration, and spatial extent of thunderstorm impacts is related to convective storm mode. This study assesses the skill of the High Resolution Rapid Refresh Ensemble (HRRR-E) and its deterministic counterpart (HRRRv4) at predicting convective mode and storm macrophysical properties using 35 convective events observed during the 2020 warm season across the eastern U.S. Seven cases were selected from each of five subjectively-determined convective organization modes: tropical cyclones, mesoscale convective systems (MCSs), quasi-linear convective systems, clusters, and cellular convection. These storm events were assessed using an object-based approach to identify convective storms and determine their individual size. Averaged across all 35 cases, both the HRRR-E and HRRRv4 predicted storm areas were generally larger than observed, with this bias being a function of storm lifetime and convective mode. Both modeling systems also under-predicted the rapid increase in storm counts during the initiation period, particularly for the smaller-scale storm modes. Interestingly, performance of the HRRRv4 differed from that of the HRRR-E, with the HRRRv4 generally having a larger bias in total storm area than the HRRR-E due to HRRRv4 predicting up to 66% more storm objects than the HRRR-E. The HRRR-E accurately predicted the convective mode 65% of the time, with complete misses being very rare (<5% of the time overall). However, an evaluation of rank histograms across all 35 cases revealed that the HRRR-E tended to be under-dispersive when predicting storm size for all but the MCS mode.

The sensitivity of cloud radiative forcing with respect to the macrophysical properties and organization of the trade-wind cloud field during EUREC4A

&lt;p&gt;The clouds in the Atlantic trade-wind region are known to have an important role in the global climate system, but the interactions between the microphysical, macrophysical and radiative properties of these clouds are complex. This work seeks to understand how the macrophysical properties and organization of the cloud field impact the large-scale cloud radiative forcing in order to provide the necessary information for the evaluation of the representation of these clouds in models. During the 2020 EUREC&lt;sup&gt;4&lt;/sup&gt;A campaign, the German HALO aircraft was equipped for the first time with two instruments - the BACARDI instrument, a broadband radiometer that encompasses a set of pyrgeometers and pyranometers to measure the upward and downward solar and terrestrial radiation at flight level, and the VELOX Thermal IR imager. Simultaneously, one-minute resolution observations of the flight domain were obtained by the GOES-E satellite, thus providing information about the properties of the clouds on a spatial scale compatible with the large footprint of the BACARDI instrument. Using the products of these three instruments, we observe how the changing cloud field (e.g. cloud fraction, mean liquid water path (LWP), cloud top height, degree of clustering) in the EUREC&lt;sup&gt;4&lt;/sup&gt;A domain impacts the radiation measured at flight level. We see that although cloud fraction plays a significant role as expected, it is not sufficient to parameterize the cloud radiative effects. Furthermore, the results indicate that the general organization of the cloud field as well as other properties describing the cloud population are necessary, but their relative importance varies between different cloud scenes.&lt;/p&gt;

Evaluation of AIRS Cloud Phase Classification over the Arctic Ocean against Combined CloudSat–CALIPSO Observations

Cloud phase retrievals from the Atmospheric Infrared Sounder (AIRS) are evaluated against combined CloudSat–CALIPSO (CCL) observations using four years of data (2007–10) over the Arctic Ocean. AIRS cloud phase is evaluated over sea ice and open ocean separately using collocated CCL and AIRS fields of view (FOVs). In addition, AIRS and CCL cloud phase occurrences are evaluated seasonally, zonally, and with respect to total column water vapor (TCWV) and the temperature difference between 1000 and 300 hPa (ΔT1000−300). Last, collocated MODIS cloud information is implemented in a 1-month case study to assess the relationship between AIRS and CCL phase decisions, cloud cover, and cloud phase throughout the AIRS FOV. Depending on the surface type, AIRS classification skill for single-layer ice and liquid-phase clouds is over the ranges of 85%–95% and 22%–32%, respectively. Most unknown and liquid AIRS phase classifications correspond to mixed-phase clouds. AIRS ice-phase relative occurrence is biased low relative to CCL. However, the liquid-phase relative occurrence is similar between the two instruments. When compared with the CCL climatology, AIRS accurately represents the seasonal cycle of liquid and ice cloud phase across the Arctic as well as the relationship between cloud phase and TCWV and ΔT1000−300 regime in some cases. The more heterogeneous the MODIS cloud macrophysical properties within an AIRS FOV are, the more likely it is that the AIRS FOV is classified as unknown phase.

Cloud macrophysical properties from airborne observations during EUREC4A

&lt;p&gt;&lt;span&gt;Trade wind cumulus clouds are the predominant cloud type over the tropical Atlantic east of the island of Barbados. Parameters describing their macroscopic shape can help characterizing and comparing general features of clouds. This characterizing will indirectly help to constrain estimates of climate sensitivity, because models with different structures of trade wind cumuli feature different response to increased CO2 contents.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Two aircraft campaigns with the HALO (High Altitude LOng range) aircraft took place in the recent past in this region: NARVAL-South (Next-generation Aircraft Remote-Sensing for VALidation studies) in December 2013, during the dry season, and NARVAL2 in August 2016, during the wet season. During these two campaigns, a wide range of cloud regimes from shallow to deep convection were sampled. This past observations are now extended with observations from this year&amp;#8217;s measurement campaign EUREC&lt;sup&gt;4&lt;/sup&gt;A, again during the dry season. EUREC&lt;sup&gt;4&lt;/sup&gt;A is endorsed as WCRP capstone experiment and the synergy of four research aircraft, four research vessels and numerous additional observations will provide comprehensive characterizations of trade wind clouds and their environment.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Part of the NARVAL payload on HALO is a 35 GHz cloud radar, which has been deployed on HALO on several missions since 2013. These cloud radar measurements are used to segment individual clouds entities by applying connected component analysis to the radar cloud mask. From these segmented individual clouds, macrophysical parameters are derived to characterize each individual cloud. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;This presentation will give an overview of the cloud macrophysics observed from HALO during EUREC&lt;sup&gt;4&lt;/sup&gt;A. Typical macrophysical parameters, i.e. cloud depth, cloud length, cloud fraction, are analyzed. We will relate these to observations from past campaigns and assess the representativeness of EUREC&lt;sup&gt;4&lt;/sup&gt;A. As special focus of the EUREC&lt;sup&gt;4&lt;/sup&gt;A campaign, measurements will be performed during different times of the day to detect diurnal cycles. Macrophysical parameters can be used to characterize changes over the day and cloud scenes of similar clouds types can be identified.&lt;/span&gt;&lt;/p&gt;

Model evaluation by a cloud classification based on multi-sensor observations

The detailed understanding of clouds and their macrophysical properties is crucial to reduce uncertainties of cloud feedbacks and related processes in current climate and weather prediction models. Comprehensive evaluation of cloud characteristics using observations is the first step towards any improvement. An advanced observational product was developed by the Cloudnet project. A multi-sensor synergy of active and passive remote-sensing instruments is used to generate a Target Classification providing detailed information about cloud phase and structure. Nevertheless, this valuable product is only available for observations and there is yet no comparable surrogate for models. Therefore, a new cloud classification algorithm is presented to calculate a comparable classification for models by using the temperature, dew point and all hydrometeor profiles. The study explains the algorithm and shows possible evaluation methods making use of the new synthetic cloud classification. For example, the statistics of the vertical cloud distribution as well as e.g. the accuracy of cloud forecasts can be investigated regarding different cloud types. The algorithm and methods are exemplarily tested on two months of operational weather forecast data of the COSMO-DE model and compared to a Cloudnet supersite in Germany. Additionally, the cloud classification is applied to Large Eddy Simulations with a similar resolution as of the observations showing detailed cloud structures.

Statistical analysis of contrail to cirrus evolution during the Contrail and Cirrus Experiment (CONCERT)

Air traffic affects cloudiness, and thus climate, by emitting exhaust gases and particles. The study of the evolution of contrail properties is very challenging due to the complex interplay of vortex dynamics and the atmospheric environment (e.g. temperature, supersaturation). Despite substantial progress in recent years, the optical, microphysical, and macrophysical properties of contrails and ambient cirrus during contrail formation and subsequent ageing are still subject to large uncertainties due to instrumental and observational limitations and the large number of variables influencing the contrail life cycle. In this study, various contrail cases corresponding to different aircraft types and atmospheric conditions are investigated using a statistical method based on the in situ optical measurements performed during the Contrail and Cirrus Experiments (CONCERT) campaigns 2008 and 2011. The two aircraft campaigns encompass more than 17 aircraft contrail cases. A principal component analysis (PCA) of the angular scattering coefficients measured by the polar nephelometer is implemented. The goal is to classify the sampled ice cloud measurements in several clusters representative of different contrail development stages (primary wake, young contrail, aged contrail, and cirrus). Extinction and asymmetry coefficients, nitrogen oxide concentrations, and relative humidity with respect to ice and particle size distributions are analysed for each cluster to characterize the evolution of ice cloud properties during the contrail to cirrus evolution. The PCA demonstrates that contrail optical properties are well suited to identify and discriminate between the different contrail growth stages and to characterize the evolution of contrail properties.