Search

Marine low clouds are critical to the climate system because of their extensive coverage and associated controls on boundary layer dynamics and radiative energy balance. The primary foci for this study are marine low cloud observations over a heavily instrumented site on the Azores archipelago in the Eastern North Atlantic and their associated raindrop size distribution (DSD) properties, relative low cloud contributions to the precipitation, and additional sampling (instrument, environmental) considerations. The contribution from low clouds (e.g., cloud top < 4 km) to the overall precipitation over midlatitude oceans is poorly understood, in part because of the lack of coupled, high‐quality measurements of precipitation and low cloud properties. Cloud regime and precipitation breakdowns performed for a multiyear (2014–2017) record emphasize diurnal precipitation and raindrop size distribution characteristics for both low and deeper clouds, as well as differences between the two disdrometer types used. Results demonstrate that marine low clouds over this Eastern North Atlantic location account for a significant (45%) contribution to the total rainfall and exhibit a diurnal cycle in cloud (thickness, top, and base) and precipitation characteristics similar to satellite records. Additional controls on observed surface rainfall characteristics of low clouds allowed by the extended ground‐based facility data sets are also explored. From those analyses, it is suggested that the synoptic state exerts a significant control on low cloud and surface precipitation properties. Key Points: Low clouds over ENA account for a significant (45%) contribution to the total rainfallLow cloud diurnal cycle suggests a rainfall peak during the overnight hours and a secondary peak after local noonSynoptic state (sfc pressure and 850‐mb winds) exerts a significant control on low cloud properties including rainfall rate and drop size [ABSTRACT FROM AUTHOR]

In this second part of a cloud microphysics scheme intercomparison study, we focus on biases and variabilities of stratiform precipitation properties for a midlatitude squall line event simulated with a cloud‐resolving model implemented with eight cloud microphysics schemes. Most of the microphysics schemes underestimate total stratiform precipitation, mainly due to underestimation of stratiform precipitation area. All schemes underestimate the frequency of moderate stratiform rain rates (2–6 mm/hr), which may result from low‐biased ice number and mass concentrations for 0.2–2‐mm diameter particles in the stratiform ice region. Most simulations overestimate ice water content (IWC) at altitudes above 7 km for temperatures colder than −20 °C but produce a decrease of IWC approaching the melting level, which is opposite to the trend shown by in situ observations. This leads to general underestimations of stratiform IWC below 5‐km altitude and rainwater content above 1‐km altitude for a given rain rate. Stratiform precipitation area positively correlates with the convective condensate detrainment flux but is modulated by hydrometeor type, size, and fall speed. Stratiform precipitation area also changes by up to 17%–25% through alterations of the lateral boundary condition forcing frequency. Stratiform precipitation, rain rate, and area across the simulations vary by a factor of 1.5. This large variability is primarily a result of variability in the stratiform downward ice mass flux, which is highly correlated with convective condensate horizontal detrainment strength. The variability of simulated local microphysical processes in the stratiform region plays a secondary role in explaining variability in simulated stratiform rainfall properties. Plain Language Summary: This is a unique model intercomparison study about different microphysics parametrizations commonly used, with the purposes of examining model biases and variability as well as identifying major factors/processes leading to bias and variability. The study simulated a well‐observed squall line MCS from MC3E field campaign, and focused on the stratiform precipitation, following on our part 1 study focusing on convective part. We employed a more constrained approach compared with past intercomparison studies to better identify processes contributing to the differences. Another unique part is our comprehensive model evaluation, that is, we identify stratiform columns and evaluate vertical evolution of cloud properties including size distribution. We find that most of the microphysics schemes underestimate total stratiform precipitation, mainly due to underestimation of stratiform precipitation area. Moderate stratiform rain rates are underestimated, mainly due to incorrect vertical evolution of ice particles. Stratiform precipitation properties across the simulations vary by a factor of 1.5, primarily a result of variability in detrained condensate amount. In addition, we find that stratiform precipitation area correlates well with detrainment amount and is modulated by the detrained hydrometeor properties. So convective microphysics plays a key role in determining stratiform properties. Key Points: Underestimation of total stratiform precipitation in most simulations is mainly due to underestimation of stratiform precipitation areaLow‐biased rainwater content is caused by low‐biased stratiform ice water content below 5‐km altitude for a given rain rateSimulated stratiform precipitation variability is correlated with variability in the convective condensate being detrained [ABSTRACT FROM AUTHOR]

This study summarizes the precipitation properties collected during the GoAmazon2014/5 campaign near Manaus in central Amazonia, Brazil. Precipitation breakdowns, summary radar rainfall relationships and self-consistency concepts from a coupled disdrometer and radar wind pro- filer measurements are presented. The properties of Amazon cumulus and associated stratiform precipitation are discussed, including segregations according to seasonal (wet or dry regime) variability, cloud echo-top height and possible aerosol influences on the apparent oceanic characteristics of the precipitation drop size distributions. Overall, we observe that the Amazon precipitation straddles behaviors found during previous U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program tropical deployments, with distributions favoring higher concentrations of smaller drops than ARM continental examples. Oceanic-type precipitation characteristics are predominantly observed during the Amazon wet seasons. An exploration of the controls on wet season precipitation properties reveals that wind direction, compared with other standard radiosonde thermodynamic parameters or aerosol count/regime classifications performed at the ARM site, provides a good indicator for those wet season Amazon events having an oceanic character for their precipitation drop size distributions. [ABSTRACT FROM AUTHOR]

A substantial fraction of precipitation is associated with mesoscale convective systems (MCSs), which are currently poorly represented in climate models. Convective parameterizations are highly sensitive to the assumptions of an entraining plume model, in which high equivalent potential temperature air from the boundary layer is modified via turbulent entrainment. Here we show, using multiinstrument evidence from the Green Ocean Amazon field campaign (2014-2015; GoAmazon2014/5), that an empirically constrained weighting for inflow of environmental air based on radar wind profiler estimates of vertical velocity and mass flux yields a strong relationship between resulting buoyancy measures and precipitation statistics. This deep-inflow weighting has no free parameter for entrainment in the conventional sense, but to a leading approximation is simply a statement of the geometry of the inflow. The structure further suggests the weighting could consistently apply even for coherent inflow structures noted in field campaign studies for MCSs over tropical oceans. For radar precipitation retrievals averaged over climate model grid scales at the GoAmazon2014/5 site, the use of deep-inflow mixing yields a sharp increase in the probability and magnitude of precipitation with increasing buoyancy. Furthermore, this applies for both mesoscale and smaller-scale convection. Results from reanalysis and satellite data show that this holds more generally: Deepinflow mixing yields a strong precipitation-buoyancy relation across the tropics. Deep-inflow mixing may thus circumvent inadequacies of current parameterizations while helping to bridge the gap toward representing mesoscale convection in climate models. [ABSTRACT FROM AUTHOR]

This study provides an overview of precipitation processes and their sensitivities to environmental conditions in the Central Amazon Basin near Manaus during the GoAmazon2014/5 and ACRIDICON-CHUVA experiments. This study takes advantage of the numerous measurement platforms and instrument systems operating during both campaigns to sample cloud structure and environmental conditions during 2014 and 2015; the rainfall variability among seasons, aerosol loading, land surface type, and topography has been carefully characterized using these data. Differences between the wet and dry seasons were examined from a variety of perspectives. The rainfall rates distribution, total amount of rainfall, and raindrop size distribution (the mass-weighted mean diameter) were quantified over both seasons. The dry season generally exhibited higher rainfall rates than the wet season and included more intense rainfall periods. However, the cumulative rainfall during the wet season was 4 times greater than that during the total dry season rainfall, as shown in the total rainfall accumulation data. The typical size and life cycle of Amazon cloud clusters (observed by satellite) and rain cells (observed by radar) were examined, as were differences in these systems between the seasons. Moreover, monthly mean thermodynamic and dynamic variables were analysed using radiosondes to elucidate the differences in rainfall characteristics during the wet and dry seasons. The sensitivity of rainfall to atmospheric aerosol loading was discussed with regard to mass-weighted mean diameter and rain rate. This topic was evaluated only during the wet season due to the insignificant statistics of rainfall events for different aerosol loading ranges and the low frequency of precipitation events during the dry season. The impacts of aerosols on cloud droplet diameter varied based on droplet size. For the wet season, we observed no dependence between land surface type and rain rate. However, during the dry season, urban areas exhibited the largest rainfall rate tail distribution, and deforested regions exhibited the lowest mean rainfall rate. Airplane measurements were taken to characterize and contrast cloud microphysical properties and processes over forested and deforested regions. Vertical motion was not correlated with cloud droplet sizes, but cloud droplet concentration correlated linearly with vertical motion. Clouds over forested areas contained larger droplets than clouds over pastures at all altitudes. Finally, the connections between topography and rain rate were evaluated, with higher rainfall rates identified at higher elevations during the dry season. [ABSTRACT FROM AUTHOR]

Routine cloud, precipitation and thermodynamic observations collected by the Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) and Aerial Facility (AAF) during the 2-year US Department of Energy (DOE) ARM Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) campaign are summarized. These observations quantify the diurnal to large-scale thermodynamic regime controls on the clouds and precipitation over the undersampled, climatically important Amazon basin region. The extended ground deployment of cloud-profiling instrumentation enabled a unique look at multiple cloud regimes at high temporal and vertical resolution. This longer-term ground deployment, coupled with two short-term aircraft intensive observing periods, allowed new opportunities to better characterize cloud and thermodynamic observational constraints as well as cloud radiative impacts for modeling efforts within typical Amazon "wet" and "dry" seasons. [ABSTRACT FROM AUTHOR]

Tracking clouds using scanning cloud radars can help to document the temporal evolution of cloud properties well before large-drop formation (weather radar 'first echo'). These measurements also complement cloud and precipitation tracking using geostationary satellites and weather radars. Here, two-dimensional (2D) along-wind range-height indicator observations of a population of shallow cumuli (with and without precipitation) from the 35-GHz scanning Atmospheric Radiation Measurement Program (ARM) cloud radar (SACR) at the U.S. Department of Energy (DOE)-ARM Southern Great Plains (SGP) site are presented. Observations from the ARM SGP network of scanning precipitation radars are used to provide the larger-scale context of the cloud field and to highlight the advantages of the SACR to detect the numerous small nonprecipitating cloud elements. A new cloud identification and tracking algorithm (CITA) is developed to track cloud elements. In CITA, a cloud element is identified as a region having a contiguous set of pixels exceeding a preset reflectivity and size threshold. The high temporal resolution of the SACR 2D observations (30 s) allows for an area superposition criteria algorithm to match cloud elements at consecutive times. Following CITA, the temporal evolution of cloud-element properties (number, size, and maximum reflectivity) is presented. The vast majority of the designated elements during this cumulus event were short-lived nonprecipitating clouds having an apparent life cycle shorter than 15 min. The advantages and disadvantages of cloud tracking using an SACR are discussed. [ABSTRACT FROM AUTHOR]

This study presents radar-based precipitation estimates collected during the 2-month U.S. Department of Energy Atmospheric Radiation Measurement Program (ARM)-NASA Midlatitude Continental Convective Clouds Experiment (MC3E). Emphasis is on the usefulness of radar observations from the C-band and X-band scanning ARM precipitation radars (CSAPR and XSAPR, respectively) for rainfall estimation products to distances within 100 km of the Lamont, Oklahoma, ARM facility. The study utilizes a dense collection of collocated ARM, NASA Global Precipitation Measurement, and nearby surface Oklahoma Mesonet gauge records to evaluate radar-based hourly rainfall products and campaign-optimized methods over individual gauges and for areal rainfall characterizations. Rainfall products are also evaluated against the performance of a regional NWS Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band dual-polarization radar product. Results indicate that the CSAPR system may achieve similar point- and areal-gauge bias and root-mean-square (RMS) error performance to a WSR-88D reference for the variety of MC3E deep convective events sampled. The best campaign rainfall performance was achieved when using radar relations capitalizing on estimates of the specific attenuation from the CSAPR system. The XSAPRs demonstrate limited capabilities, having modest success in comparison with the WSR-88D reference for hourly rainfall accumulations that are under 10 mm. All rainfall estimation methods exhibit a reduction by a factor of 1.5-2.5 in RMS errors for areal accumulations over a 15-km2 NASA dense gauge network, with the smallest errors typically associated with dual-polarization radar methods. [ABSTRACT FROM AUTHOR]

The variability of rainfall and drop size distributions (DSDs) as a function of large-scale atmospheric conditions and storm characteristics is investigated using measurements from the Atmospheric Radiation Measurement Program (ARM) facility at Darwin, Australia. Observations are obtained from an impact disdrometer with a near continuous record of operation over five consecutive wet seasons (2006-11). Bulk rainfall characteristics are partitioned according to diurnal accumulation, convective and stratiform precipitation classifications, objective monsoonal regime, and MJO phase. Findings support previous Darwin studies suggesting a significant diurnal and DSD parameter signal associated with both convective-stratiform and wet season monsoonal regime classification. Negligible MJO phase influence is determined for cumulative disdrometric statistics over the Darwin location. [ABSTRACT FROM AUTHOR]

As part of the evolution and future enhancement of the Next Generation Weather Radars (NEXRAD), the National Severe Storms Laboratory recently upgraded the KOUN Weather Surveillance Radar-1988 Doppler (WSR-88D) to include a polarimetric capability. The proof of concept was tested in central Oklahoma during a 1-yr demonstration project referred to as the Joint Polarization Experiment (JPOLE). This paper presents an overview of polarimetric algorithms for rainfall estimation and hydrometeor classification and their performance during JPOLE. The quality of rainfall measurements is validated on a large dataset from the Oklahoma Mesonet and Agricultural Research Service Micronet rain gauge networks. The comparison demonstrates that polarimetric rainfall estimates are often dramatically superior to those provided by conventional rainfall algorithms. Using a synthetic R(Z,KDP, ZDR) polarimetric rainfall relation, rms errors are reduced by a factor of 1.7 for point measurements and 3.7 for areal estimates [when compared to results from a conventional R(Z) relation]. Radar data quality improvement, hail identification, rain/snow discrimination, and polarimetric tornado detection are also illustrated for selected events. [ABSTRACT FROM AUTHOR]

As part of the Joint Polarization Experiment (JPOLE), the National Severe Storms Laboratory conducted an operational demonstration of the polarimetric utility of the Norman, Oklahoma (KOUN), Weather Surveillance Radar-1988 Doppler (WSR-88D). The capability of the KOUN radar to estimate rainfall is tested on a large dataset representing different seasons and different types of rain. A dense gauge network—the Agricultural Research Service (ARS) Micronet—is used to validate different polarimetric algorithms for rainfall estimation. One-hour rain totals are estimated from the KOUN radar using conventional and polarimetric algorithms and are compared with hourly accumulations measured by the gauges. Both point and areal rain estimates are examined. A new “synthetic” rainfall algorithm has been developed for rainfall estimation. The use of the synthetic polarimetric algorithm results in significant reduction in the rms errors of hourly rain estimates when compared with the conventional nonpolarimetric relation: 1.7 times for point measurements and 3.7 times for areal rainfall measurements. [ABSTRACT FROM AUTHOR]

Aerosol-cloud interactions remain the largest uncertainty in climate projections. Ultrafine aerosol particles smaller than 50 nanometers (UAP<50) can be abundant in the troposphere but are conventionally considered too small to affect cloud formation. Observational evidence and numerical simulations of deep convective clouds (DCCs) over the Amazon show that DCCs forming in a low-aerosol environment can develop very large vapor supersaturation because fast droplet coalescence reduces integrated droplet surface area and subsequent condensation. UAP<50 from pollution plumes that are ingested into such clouds can be activated to form additional cloud droplets on which excess supersaturation condenses and forms additional cloud water and latent heating, thus intensifying convective strength. This mechanism suggests a strong anthropogenic invigoration of DCCs in previously pristine regions of the world. [ABSTRACT FROM AUTHOR]