Abstract

Comparisons of precipitation between general circulation models (GCMs) and observations are often confounded by a mismatch between model output and instrument measurements, including variable type and temporal and spatial resolutions. To mitigate these differences, the radar-simulator Quickbeam within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) simulates reflectivity from model variables at the subgrid scale. This work adapts Quickbeam to the dual-frequency precipitation radar (DPR) on board the Global Precipitation Measurement (GPM) satellite. The longer wavelength of the DPR is used to evaluate moderate to heavy precipitation in GCMs, which is missed when Quickbeam is used as a cloud radar simulator. Latitudinal and land–ocean comparisons are made between COSP output from the Community Atmospheric Model version 5 (CAM5) and DPR data. Additionally, this work improves the COSP subgrid algorithm by applying a more realistic, nondeterministic approach to assigning GCM gridbox convective cloud cover when convective cloud is not provided as a model output. Instead of assuming a static 5% convective cloud coverage, DPR convective precipitation coverage is used as a proxy for convective cloud coverage. For example, DPR observations show that convective rain typically only covers about 1% of a 2° grid box, but that the median convective rain area increases to over 10% in heavy rain cases. In our CAM5 tests, the updated subgrid algorithm improved the comparison between reflectivity distributions when the convective cloud cover is provided versus the default 5% convective cloud-cover assumption.

Abstract

The November 2011 Madden–Julian oscillation (MJO) event during the Dynamics of the MJO (DYNAMO) field campaign is simulated with the Regional Atmospheric Modeling System (RAMS) cloud-resolving model to examine the relationship between precipitation and surface latent heat flux (LHFLX) for deep convective clusters within the MJO and to discern the importance of surface LHFLX for organizing MJO convection. First, a simulation similar in size to the DYNAMO northern sounding array was run with interactive surface fluxes. Composites for precipitation, surface LHFLX, wind speed, wind vectors, and near-surface specific humidity are described for various-sized convective clusters during different MJO regimes. The precipitation–LHFLX relationship generally evolves as follows for an individual cluster. About 2 h before cluster identification, the maximum LHFLX occurs upwind of maximum precipitation. As cluster identification time is approached, LHFLX and precipitation maxima become coincident. At and after the cluster is identified, maximum LHFLXs move downwind of the precipitation maximum with a local minimum in LHFLXs behind the precipitation maximum.

Sensitivity simulations with spatially homogenized LHFLXs were then run to determine the impacts of local LHFLX feedbacks on convective organization. Using area-averaged convective versus stratiform precipitation fraction and a simple convective aggregation index to quantify organization, no systematic difference in convective organization was detected between the control and sensitivity simulations, suggesting that local LHFLX variability is not important to convective organization in this model. Implications of these results are discussed.

Abstract

Cloud-resolving simulations are used to evaluate the importance of topography to the diurnal cycle (DC) of precipitation (DCP) over Luzon, Philippines, and surrounding ocean during the July–August 2016 boreal summer intraseasonal oscillation (BSISO) event. Composites of surface precipitation for each 30-min time increment during the day are made to determine the mean DCP. The mean DCP is computed separately for suppressed and active BSISO conditions and compared across three simulations with varying topography—flat, true, and doubled topographic height. The magnitude of the topographic height helps to dictate the timing, intensity, and location of diurnal precipitation over and near Luzon. For example, the mean DCP in the true topography run peaks 1.5 h later, is broader by 1 h, and has a 9% larger amplitude during active conditions relative to suppressed conditions. By contrast, the flat run mean DCP is earlier and narrower by 0.5 h with a 5% smaller amplitude during active conditions versus suppressed conditions. Within the suppressed or active BSISO conditions, the mean DCP peak and amplitude increase as the topographic height increases. The presence of elevated topography focuses precipitation over the coastal mountains during suppressed conditions, while dictating which side of the domain (i.e., east Luzon and the Philippine Sea vs west Luzon and the South China Sea) more precipitation occurs in during active conditions. These topographic-induced changes are discussed in terms of mechanical and thermodynamic forcing differences between the two large-scale BSISO regimes for the three runs.

Abstract

While the boreal summer Madden–Julian oscillation (MJO) is commonly defined as a planetary-scale disturbance, the convective elements that constitute its cloud dipole exhibit pronounced variability in their morphology. We therefore investigate the relationship between the intraseasonal cloud anomaly of the MJO and the convective elements that populate its interior by simulating a boreal summer MJO event over the Maritime Continent using a cloud-resolving model. A progressive relationship between convective cell morphology and the MJO within the convectively enhanced region of the MJO was identified and characterized as follows: anomalously long-lasting cells in the initial phases, followed by an increased number of cells in the intermediate phases, progressing into more expansive cells in the terminal phases. A progressive relationship does not seem to exist within the convectively suppressed region of the MJO within the simulated domain, however. Within the convectively enhanced region of the MJO, the progressive relationship is partially explained by the evolution of bulk atmospheric characteristics, such as instability and wind shear. Positive midlevel moisture anomalies coincide with anomalously long-lasting convective cells, which is hypothesized to further cascade into an increase in convective cell volume, although variability in the number of convective cells seems to be related to an unidentified variable. This intraseasonal relationship between convective cell morphology and the boreal summer MJO within the Maritime Continent may have broader implications for the large-scale structure and evolution of the MJO, related to both convective moistening and cloud-radiative feedbacks.

Abstract

The intensity of deep convective storms is driven in part by the strength of their updrafts and cold pools. In spite of the importance of these storm features, they can be poorly represented within numerical models. This has been attributed to model parameterizations, grid resolution, and the lack of appropriate observations with which to evaluate such simulations. The overarching goal of the Colorado State University Convective CLoud Outflows and UpDrafts Experiment (C³LOUD-Ex) was to enhance our understanding of deep convective storm processes and their representation within numerical models. To address this goal, a field campaign was conducted during July 2016 and May–June 2017 over northeastern Colorado, southeastern Wyoming, and southwestern Nebraska. Pivotal to the experiment was a novel “Flying Curtain” strategy designed around simultaneously employing a fleet of uncrewed aerial systems (UAS; or drones), high-frequency radiosonde launches, and surface observations to obtain detailed measurements of the spatial and temporal heterogeneities of cold pools. Updraft velocities were observed using targeted radiosondes and radars. Extensive datasets were successfully collected for 16 cold pool–focused and seven updraft-focused case studies. The updraft characteristics for all seven supercell updraft cases are compared and provide a useful database for model evaluation. An overview of the 16 cold pools’ characteristics is presented, and an in-depth analysis of one of the cold pool cases suggests that spatial variations in cold pool properties occur on spatial scales from O(100) m through to O(1) km. Processes responsible for the cold pool observations are explored and support recent high-resolution modeling results.

Abstract

Forecasts by mid-2015 for a strong El Niño during winter 2015/16 presented an exceptional scientific opportunity to accelerate advances in understanding and predictions of an extreme climate event and its impacts while the event was ongoing. Seizing this opportunity, the National Oceanic and Atmospheric Administration (NOAA) initiated an El Niño Rapid Response (ENRR), conducting the first field campaign to obtain intensive atmospheric observations over the tropical Pacific during El Niño.

The overarching ENRR goal was to determine the atmospheric response to El Niño and the implications for predicting extratropical storms and U.S. West Coast rainfall. The field campaign observations extended from the central tropical Pacific to the West Coast, with a primary focus on the initial tropical atmospheric response that links El Niño to its global impacts. NOAA deployed its Gulfstream-IV (G-IV) aircraft to obtain observations around organized tropical convection and poleward convective outflow near the heart of El Niño. Additional tropical Pacific observations were obtained by radiosondes launched from Kiritimati , Kiribati, and the NOAA ship Ronald H. Brown, and in the eastern North Pacific by the National Aeronautics and Space Administration (NASA) Global Hawk unmanned aerial system. These observations were all transmitted in real time for use in operational prediction models. An X-band radar installed in Santa Clara, California, helped characterize precipitation distributions. This suite supported an end-to-end capability extending from tropical Pacific processes to West Coast impacts. The ENRR observations were used during the event in operational predictions. They now provide an unprecedented dataset for further research to improve understanding and predictions of El Niño and its impacts.