A comparison among four different retrieval methods for ice-cloud properties using data from CloudSat, CALIPSO, and MODIS

The A-Train constellation of satellites provides a new capability to measure vertical cloud profiles that leads to more detailed information on ice-cloud microphysical properties than has been possible up to now. A variational radar–lidar ice-cloud retrieval algorithm (VarCloud) takes advantage of the complementary nature of the CloudSat radar and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar to provide a seamless retrieval of ice water content, effective radius, and extinction coefficient from the thinnest cirrus (seen only by the lidar) to the thickest ice cloud (penetrated only by the radar). In this paper, several versions of the VarCloud retrieval are compared with the CloudSat standard ice-only retrieval of ice water content, two empirical formulas that derive ice water content from radar reflectivity and temperature, and retrievals of vertically integrated properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) radiometer. The retrieved variables typically agree to within a factor of 2, on average, and most of the differences can be explained by the different microphysical assumptions. For example, the ice water content comparison illustrates the sensitivity of the retrievals to assumed ice particle shape. If ice particles are modeled as oblate spheroids rather than spheres for radar scattering then the retrieved ice water content is reduced by on average 50% in clouds with a reflectivity factor larger than 0 dBZ. VarCloud retrieves optical depths that are on average a factor-of-2 lower than those from MODIS, which can be explained by the different assumptions on particle mass and area; if VarCloud mimics the MODIS assumptions then better agreement is found in effective radius and optical depth is overestimated. MODIS predicts the mean vertically integrated ice water content to be around a factor-of-3 lower than that from VarCloud for the same retrievals, however, because the MODIS algorithm assumes that its retrieved effective radius (which is mostly representative of cloud top) is constant throughout the depth of the cloud. These comparisons highlight the need to refine microphysical assumptions in all retrieval algorithms and also for future studies to compare not only the mean values but also the full probability density function

Observed relationships between cloud vertical structure and convective aggregation over tropical ocean

Using the satellite-infrared-based Simple Convective Aggregation Index (SCAI) to determine the degree of aggregation, 5 years of CloudSat-CALIPSO cloud profiles are composited at a spatial scale of 10 degrees to study the relationship between cloud vertical structure and aggregation. For a given large-scale vertical motion and domain-averaged precipitation rate, there is a large decrease in anvil cloud (and in cloudiness as a whole) and an increase in clear sky and low cloud as aggregation increases. The changes in thick anvil cloud are proportional to the changes in total areal cover of brightness temperatures below 240 K (cold cloud area, CCA), which is negatively correlated with SCAI. Optically thin anvil cover decreases significantly when aggregation increases, even for a fixed CCA, supporting previous findings of a higher precipitation efficiency for aggregated convection. Cirrus, congestus, and mid-level clouds do not display a consistent relationship with the degree of aggregation. We present the lidar-observed low-level cloud cover (where the lidar is not attenuated) as our best estimate of the true low-level cloud cover and show that it increases as aggregation increases. Qualitatively, the relationships between cloud distribution and SCAI do not change with sea-surface temperature, while cirrus clouds are more abundant and low-level clouds less at higher sea-surface temperatures. For the observed regimes, the vertical cloud profile varies more evidently with SCAI than with mean precipitation rate. These results confirm that convective scenes with similar vertical motion and rainfall can be associated with vastly different cloudiness (both high and low cloud) and humidity depending on the degree of convective aggregation

A multi-satellite climatology of clouds, radiation and precipitation in southern West Africa and comparison to climate models

Southern West Africa (SWA) has a large population that relies on highly variable monsoon rainfall, yet climate models show little consensus over projected precipitation in this region. Understanding of the current and future climate of SWA is further complicated by rapidly increasing anthropogenic emissions and a lack of surface observations. Using multiple satellite observations, the ERA-Interim reanalysis, and four climate models, we document the climatology of cloud, precipitation and radiation over SWA in June-July, highlight discrepancies among satellite products, and identify shortcomings in climate models and ERA-Interim. Large differences exist between monthly mean cloud cover estimates from satellites, which range from 68 to 94 %. In contrast, differences among satellite observations in top of atmosphere outgoing radiation and surface precipitation are smaller, with monthly means of about 230 W m–2 of longwave radiation, 145 W m–2 of shortwave radiation and 5.87 mm day–1 of precipitation. Both ERA-Interim and the climate models show less total cloud cover than observations, mainly due to underestimating low cloud cover. Errors in cloud cover, along with uncertainty in surface albedo, lead to a large spread of outgoing shortwave radiation. Both ERA-Interim and the climate models also show signs of convection developing too early in the diurnal cycle, with associated errors in the diurnal cycles of precipitation and outgoing longwave radiation. Clouds, radiation and precipitation are linked in an analysis of the regional energy budget, which shows that inter-annual variability of precipitation and dry static energy divergence are strongly linked

The potential use of operational radar network data to evaluate the representation of convective storms in NWP models

Operational forecasting centres increasingly rely on convection-permitting NWP simulations to assist in their forecasting of convective events. The evaluation of upgrades in the underlying NWP modeling system normally happens through routine verification using traditional metrics on two-dimensional fields, such as gridded rainfall data. Object- and process-based evaluation can identify specific physical mechanisms for model improvement, but such evaluation procedures normally require targeted and expensive field campaigns. Here, we explore the potential use of the UK operational radar network observations and its derived 3D composite product for evaluating the representation of convective storms in the Met Office Unified Model. A comparison of the 1 km x 1 km x 0.5 km 3D radar composites against observations made with the research-grade radar at Chilbolton in the southern UK indicates that the 3D radar composite data can reliably be used to evaluate the morphology of convective storms. The 3D radar composite data are subsequently used to evaluate the development of convective storms in the Met Office Unified Model. Such analysis was heretofore unavailable due to a lack of high-frequency three-dimensional radar data. The operational nature of the UK radar data makes these 3D composites a valuable resource for future studies of the initiation, growth, development, and organisation of convective storms over the UK

Intensification of single cell storms prior to lightning onset

Single cell storms in the UK can produce lightning, despite apparently only having developed to towering cumulus rather than cumulonimbus. Such marginal thunderstorms still present severe weather hazards but are difficult to identify and predict and therefore provide a warning. Observations from the Met Office radar mosaic and ATDNet (Arrival Time Difference Network) show that these single cell storms demonstrate a characteristic increase in the area of high reflectivity storm core during the 15 minutes prior to the first lightning. By using the Met Office Unified Model to investigate reflectivity development in modelled storms, a microphysical explanation for the observed reflectivity increase is identified. During a rapid reflectivity increase, the updraft area at the melting layer, the peak updraft velocity and the storm graupel mass increase. The three quantities examined are linked to each other and to the generation of charge within the storm. The production of graupel is promoted by the increase in updraft area and charge separation is enhanced by the faster peak updraft velocity. This explains some of the physical differences between single cell storms that produce lightning and apparently similar storm systems which do not. It also provides a new basis with which to predict lightning hazard for marginal storms

Statistics of convective cloud turbulence from a comprehensive turbulence retrieval method for radar observations

Turbulent mixing processes are important in determining the evolution of convective clouds,and the production of convective precipitation. However, the exact nature of these impacts remains uncertain due to limited observations. Model simulations show that assumptions made in parametrizing turbulence can have a marked effect on the characteristics of simulated clouds. This leads to significant uncertainty in forecasts from convection‐permitting numerical weather prediction (NWP) models. This contribution presents a comprehensive method to retrieve turbulence using Doppler weather radar to investigate turbulence in observed clouds. This method involves isolating the turbulent component of the Doppler velocity spectrum width, expressing turbulence intensity as an eddy dissipation rate, ϵ. By applying this method throughout large datasets of observations collected over the southern United Kingdom using the (0.28° beam‐width) Chilbolton Advanced Meteorological Radar (CAMRa), statistics of convective cloud turbulence are presented. Two contrasting case days are examined: a shallow “shower” case, and a “deep convection” case, exhibiting stronger and deeper updraughts. In our observations, ϵ generally ranges from 10−3 to 10−1 m2/s3, with the largest values found within, around and above convective updraughts. Vertical profiles of ϵ suggest that turbulence is much stronger in deep convection; 95th percentile values increase with height from 0.03 to 0.1 m2/s3, compared to approximately constant values of 0.02–0.03 m2/s3 throughout the depth of shower cloud. In updraught regions on both days, the 95th percentile of ϵ has significant (p < 10−3) positive correlations with the updraught velocity, and the horizontal shear in the updraught velocity, with weaker positive correlations with updraught dimensions. The ϵ‐retrieval method presented considers a very broad range of conditions, providing a reliable framework for turbulence retrieval using high‐resolution Doppler weather radar. In applying this method across many observations, the derived turbulence statistics will form the basis for evaluating the parametrization of turbulence in NWP models

Transient aggregation of convection: observed behavior and underlying processes

Convective self-aggregation is among the most striking features emerging from radiative-convective equilibrium simulations, but its relevance to convective disturbances observed in the real atmosphere remains under debate. This work seeks the observational signals of convective aggregation intrinsic to the life cycle of cloud clusters. To this end, composite time series of the Simple Convective Aggregation Index (SCAI), a metric of aggregation, and other variables from satellite measurements are constructed around the temporal maxima of precipitation. All the parameters analyzed are large-scale means over 10o×10o domains. The composite evolution for heavy precipitation regimes shows that cloud clusters are gathered into fewer members during a period of ±12 h has precipitation picks up. The high-cloud cover per cluster expands as the number of clusters drops, suggesting a transient occurrence of convective aggregation. The sign of the transient aggregation is less evident or entirely absent in light precipitation regimes. An energy budget analysis is performed in search of the physical processes underlying the transient aggregation. The column moist static energy (MSE) accumulates before the precipitation peak and dissipates after, accounted for primarily by the horizontal MSE advection. The domain-averaged column radiative cooling is greater in a more aggregated composite than in a less aggregated one, although the role of radiative-convective feedback behind this remains unclear

The representation of the West-African Monsoon vertical cloud structure in the Met Office Unified Model: an evaluation with CloudSat

Weather and climate model simulations of the West African Monsoon (WAM) have generally poor representation of the rainfall distribution and monsoon circulation because key processes, such as clouds and convection, are poorly characterized. The vertical distribution of cloud and precipitation during the WAM are evaluated in Met Office Unified Model simulations against CloudSat observations. Simulations were run at 40-km and 12-km horizontal grid length using a convection parameterization scheme and at 12-km, 4-km, and 1.5-km grid length with the convection scheme effectively switched off, to study the impact of model resolution and convection parameterization scheme on the organisation of tropical convection. Radar reflectivity is forward-modelled from the model cloud fields using the CloudSat simulator to present a like-with-like comparison with the CloudSat radar observations. The representation of cloud and precipitation at 12-km horizontal grid length improves dramatically when the convection parameterization is switched off, primarily because of a reduction in daytime (moist) convection. Further improvement is obtained when reducing model grid length to 4 km or 1.5 km, especially in the representation of thin anvil and mid-level cloud, but three issues remain in all model configurations. Firstly, all simulations underestimate the fraction of anvils with cloud top height above 12 km, which can be attributed to too low ice water contents in the model compared to satellite retrievals. Secondly, the model consistently detrains mid-level cloud too close to the freezing level, compared to higher altitudes in CloudSat observations. Finally, there is too much low-level cloud cover in all simulations and this bias was not improved when adjusting the rainfall parameters in the microphysics scheme. To improve model simulations of the WAM, more detailed and in-situ observations of the dynamics and microphysics targeting these non-precipitating cloud types are required

Modes of coastal precipitation over southwest India and their relationship to intraseasonal variability

The west coast of India, dominated by the Western Ghats mountain range, is among the rainiest places in the tropics. The interaction between the land-sea contrast of the coast, the monsoonal westerlies, and the oblique mountains is subject to complex intraseasonal variability, which has not previously been explored in depth. This study investigates that variability from the perspective of the land-sea contrast, using empirical orthogonal function analysis to discern regimes of onshore and offshore rainfall over southwest India and the eastern Indian Ocean. Locally, it is found that the rainfall is most sensitive to mid-tropospheric humidity: when this is anomalously high, deep convection is encouraged; when this is anomalously low, it is suppressed. A moisture tracking algorithm is employed to determine the primary sources of the anomalously wet and dry mid-tropospheric air. There are important secondary contributions from low-level vorticity and cross-shore moisture flux. The dominant control on intraseasonal variability in coastal precipitation is found to be the BSISO: over 75% of the strongest offshore events occur during phases 3 and 4; and about 40% of the strongest onshore events occur during phases 5 and 6. The location of monsoon low-pressure systems is also shown to be important in determining the magnitude and location of coastal rainfall

Convective initiation and storm life‐cycles in convection‐permitting simulations of the Met Office Unified Model over South Africa

Convective initiation is a challenge for convection‐permitting models due to its sensitivity to sub‐km processes. We evaluate the representation of convective storms and their initiation over South Africa during four summer months in Met Office Unified Model simulations at 1.5‐km horizontal grid length. Storm size distributions from the model compare well against radar observations, but rainfall in the model is predominantly produced by large storms (50 km in diameter or larger) in the evening, whereas radar observations show most rainfall occurs throughout the afternoon, from storms 10‐50 km in diameter. In all months, modelled maximum number of storm initiations occurs at least 2 hours prior to the radar‐observed maximum. However, the diurnal cycle of rainfall compares well between model and observations, suggesting the numerous storm initiations in the simulations do not produce much rainfall. Modelled storms are generally less intense than in the radar observations, especially in early summer. In February, when tropical influences dominate, the simulated storms are of similar intensity to observed storms. Simulated storms tend to reach their peak intensity in the first 15 minutes after initiation, then gradually become less intense as they grow. In radar observations, storms reach their peak intensity 15‐30 minutes into their life cycle, stay intense as they grow larger, then gradually weaken after they have reached their maximum diameter. Two November case studies of severe convection are analysed in detail. Higher resolution grid length initiates convection slightly earlier (300 m cf. 1.5 km) with the same science settings. Two 1.5‐km simulations that apply more sub‐grid mixing have delayed convective initiation. Analysis of soundings indicates little difference in convective indices, suggesting that differences in convection may be attributed to choices in sub‐grid mixing parameters