Abstract

This paper describes the results of analysis of over 825 000 profiles of millimeter-wave radar (MWR) reflectivities primarily collected by zenith-pointing surface radars observing tropical convection associated with various phases of activity of the large-scale tropical circulation. The data principally analyzed in this paper come from surface observations obtained at the Atmospheric Radiation Measurement Manus site during active and break episodes of the Madden–Julian oscillation (MJO) and from observations collected from a shipborne radar during an active phase of the monsoon over the Indian Ocean during the Joint Air–Sea Monsoon Interaction Experiment. It was shown, for example, in a histogram regime analysis that the MWR data produce statistics on convection regimes similar in most respects to the analogous regime analysis of the Tropical Rainfall Measuring Mission radar–radiometer observations. Attenuation of the surface MWRs by heavy precipitation, however, incorrectly shifts a small fraction of the deeper precipitation modes into the shallow modes of precipitation. The principal findings are the following. (i) The cloud and precipitation structures of the different convective regimes are largely identical regardless of the mode of synoptic forcing, that is, regardless of whether the convection occurred during an active phase of the MJO, a transition phase of the MJO, or in an active monsoon period. What changes between these synoptically forced modes of convection are the relative frequencies of occurrences of the different storm regimes. (ii) The cloud structures associated with the majority of cases of observed precipitation (ranging in occurrence from 45% to 53% of all precipitation profiles) were multilayered structures regardless of the mode of synoptic forcing. The predominant multilayered cloud mode was of higher-level cirrus of varying thickness overlying cumulus congestus–like convection. (iii) The majority of water accumulated (i.e., 53%–63%) over each of the periods assigned to the active monsoon (5 days of data), the active MJO (38 days of data), and the transition MJO (53 days of data) fell from these multiple-layered cloud systems. (iv) Solar transmittances reveal that significantly less sunlight (reductions of about 30%–50%) reaches the surface in the precipitating regimes than reaches the surface under drizzle and cloud-only conditions, suggesting that the optical thicknesses of precipitation-bearing clouds significantly exceeds those of nonprecipitating clouds.

Abstract

Many Global Energy and Water Cycle Experiment (GEWEX) Cloud System Study (GCSS) intercomparisons of boundary layer clouds have used a convenient but idealized longwave radiation formula for clouds in their large-eddy simulations (LESs). Under what conditions is this formula justified? Can it be extended to midlevel layer clouds? This note first derives the GCSS formula using an alternative method to effective emissivity. A key simplifying assumption is that the cloud is isothermal in the vertical (and horizontal). However, this assumption does not turn out to be overly restrictive in practice. Then the GCSS formula is compared with a detailed numerical code, BugsRad. Sensitivity studies are performed in which cloud properties, cloud altitude, and thermodynamic profiles are modified. Here, the focus is primarily on midlevel, altostratocumulus layers. The results here show that the GCSS formula can be successfully extended to liquid (ice free), midlevel clouds. The GCSS formula produces remarkably accurate radiative profiles if the parameters are adjusted on a case-by-case basis. However, the formula needs to be calibrated using a more general radiative transfer code.

Abstract

Different approaches for parameterizing the effects of vertical variability of cloudiness on radiative transfer are assessed using a database constructed from observations derived from lidar and millimeter cloud radar data collected from three different locations. Five different methods for dealing with the vertical overlap of clouds were incorporated into a single radiation model that was applied to the lidar/radar data averaged in time. The calculated fluxes and heating rates derived with this model are compared to broadband fluxes and heating rates calculated with the independent column approximation using the time-resolved cloud data. These comparisons provide a way of evaluating the effects of different overlap assumptions on the calculation of domain-mean fluxes. It was demonstrated how two of the most commonly used overlap schemes, the random and maximum-random methods, suffer a severe problem in that the total cloud amount defined by these methods depends on the vertical resolution of the host model thus creating a vertical-resolution-dependent bias in model total cloudiness and radiative fluxes. A new method is introduced to overcome this problem by preserving the total column cloud amount.

Despite these problems, the comparisons presented show that most methods introduce a relatively small bias with respect to the single-column data. This is largely a consequence of the nature of the cloud cover statistics associated with the lidar/radar observations used in this study and might not apply in general. Among the three best-performing methods (random, overcast random, and maximum random), the more commonly used maximum-random method does not perform significantly better than the other two methods with regard to both bias and rms error despite its relative high computational cost. The comparisons also reveal the nature and magnitude of the random errors that are introduced by the subgrid-scale parameterizations. These random errors are large and an inevitable consequence of the parameterization process that treats cloud structure statistically. These errors may be thought of as a source of noise to the general circulation model in which the parameterization is embedded.

Abstract

The impacts of enhanced aerosol concentrations such as those associated with dust intrusions on the trimodal distribution of tropical convection have been investigated through the use of large-domain (10 000 grid points), fine-resolution (1 km), long-duration (100 days), two-dimensional idealized cloud-resolving model simulations conducted under conditions of radiative–convective equilibrium (RCE). The focus of this research is on those aerosols that serve primarily as cloud condensation nuclei (CCN). The results demonstrate that the large-scale organization of convection, the domain-averaged precipitation, and the total cloud fraction show only show a weak response to enhanced aerosol concentrations. However, while the domainwide responses to enhanced aerosol concentrations are weak, aerosol indirect effects on the three tropical cloud modes are found to be quite significant and often opposite in sign, a fact that appears to contribute to the weaker domain response. The results suggest that aerosol indirect effects associated with shallow clouds may offset or compensate for the aerosol indirect effects associated with congestus and deep convection systems and vice versa, thus producing a more moderate domainwide response to aerosol indirect forcing. Finally, when assessing the impacts of aerosol indirect forcing associated with CCN on the characteristics of tropical convection, several aspects need to be considered, including which cloud mode or type is being investigated, the field of interest, and whether localized or systemwide responses are being examined.

Abstract

The role of horizontal inhomogeneity in radiative transfer through cloud fields is investigated within the context of the two-stream approximation. Spatial correlations between cloud optical properties and the radiance field are introduced in the three-dimensional radiative transfer equation and lead to a two-stream model in which the correlations are represented by parameterizations. The behavior of the model is examined using simple single-layer single-column atmospheres. Positive correlations between extinction or scattering and the radiance field are shown to decrease transmission, increase reflection, and increase absorption within inhomogeneous media. The parameterization is used to evaluate the characteristics of inhomogeneous cloud fields observed by radar and lidar over a number of different locations and seasons, revealing that shortwave transfer is generally characterized by negative correlations between extinction and radiance, while longwave transfer is characterized by positive correlations. The results from this characterization are applied to the integration of an atmospheric general circulation model. Model surface temperatures are significantly affected, largely in response to changes in downwelling radiative fluxes at the surface induced by changes in cloud cover and water vapor distributions.

Abstract

Microphysical data and radar reflectivities (Z _e , −15 < Z _e < 10 dB) measured from flights during the NASA Tropical Clouds, Convection, Chemistry and Climate field program are used to relate Z _e at X and W band to measured ice water content (IWC). Because nearly collocated Z _e and IWC were each directly measured, Z _e –IWC relationships could be developed directly. Using the particle size distributions and ice particle masses evaluated based on the direct IWC measurements, reflectivity–snowfall rate (Z _e –S) relationships were also derived. For −15 < Z _e < 10 dB, the relationships herein yield larger IWC and S than given by the retrievals and earlier relationships. The sensitivity of radar reflectivity to particle size distribution and size-dependent mass, shape, and orientation introduces significant uncertainties in retrieved quantities since these factors vary substantially globally. To partially circumvent these uncertainties, a W-band Z _e –S relationship is developed by relating four years of global CloudSat reflectivity observations measured immediately above the melting layer to retrieved rain rates at the base of the melting layer. The supporting assumptions are that the water mass flux is constant through the melting layer, that the air temperature is nearly 0°C, and that the retrieved rain rates are well constrained. Where Z _e > 10 dB, this Z _e –S relationship conforms well to earlier relationships, but for Z _e < 10 dB it yields higher IWC and S. Because not all retrieval algorithms estimate either or both IWC and S, the authors use a large aircraft-derived dataset to relate IWC and S. The IWC can then be estimated from S and vice versa.

Abstract

A Bayesian optimal estimation retrieval is used to determine probability density functions of snow microphysical parameters from ground-based observations taken during four snowfall events in southern Ontario, Canada. The retrieved variables include the parameters of power laws describing particle mass and horizontally projected area. The results reveal nontrivial correlations between mass and area parameters that were not apparent in prior studies. The observations provide information mainly about the mass coefficient , somewhat less information about the mass exponent and the projected area coefficient , and minimal information about the projected area exponent . The expected values for retrieved mass power-law parameters = 0.003 28 and = 2.25 are consistent with those from several prior studies that looked at the mass of aggregate-like particles and precipitating ice aloft as functions of maximum particle dimension. Differences from other studies appear related to differences in the dimensions used to define particle size. The retrieval allows the analysis of relatively large volumes of continuous observations, greatly enhancing sampling relative to single-particle analyses. The retrieved properties are used to constrain 94-GHz (W band) radar scattering properties for a variety of snow particle shapes. Synthetic reflectivities calculated using these scattering properties and observed particle size distributions show that a branched, spatial aggregate-like particle produces good agreement with coincident observed W-band reflectivities. Uncertainties in the synthetic reflectivities, estimated by applying a simple error-propagation model, are substantial and are dominated by the uncertainties in and .

Abstract

Two spaceborne radars currently in orbit enable the sampling of snowfall near the surface and throughout the atmospheric column, namely, CloudSat’s Cloud Profiling Radar (CPR) and the Global Precipitation Measurement mission’s Dual-Frequency Precipitation Radar (GPM-DPR). In this paper, a direct comparison of the CPR’s 2C-SNOW-PROFILE (2CSP), the operational GPM-DPR algorithm (2ADPR) and a neural network (NN) retrieval applied to the GPM-DPR data is performed using coincident observations between both radars. Examination of over 3500 profiles within moderate to strong precipitation (Ka band ≥ 18 dBZ) show that the NN retrieval provides the closest retrieval of liquid equivalent precipitation rate R immediately above the melting level to the R retrieved just below the melting layer, agreeing within 5%. Meanwhile, 2CSP retrieves a maximum value of R at −15°C, decreases by 35% just above the melting layer, and is about 50% smaller than the GPM-DPR retrieved R below the melting layer. CPR-measured reflectivity shows median reduction of 2–3 dB from −15° to −2.5°C, likely the reason for the 2CSP retrieval reduction of R. Two case studies from NASA field campaigns [i.e., Olympic Mountains Experiment (OLYMPEX) and Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS)] provide analogs to the type of precipitating systems found in the comparison between retrieval products. For the snowfall events that GPM-DPR can observe, this work suggests that the 2CSP retrieval is likely underestimating the unattenuated reflectivity, resulting in a potential negative, or low, bias in R. Future work should investigate how frequently the underestimated reflectivity profiles occur within the CPR record and quantify its potential effects on global snowfall accumulation estimation.

Abstract

Cloud ice microphysical properties measured or estimated from in situ aircraft observations are compared with global climate models and satellite active remote sensor retrievals. Two large datasets, with direct measurements of the ice water content (IWC) and encompassing data from polar to tropical regions, are combined to yield a large database of in situ measurements. The intention of this study is to identify strengths and weaknesses of the various methods used to derive ice cloud microphysical properties. The in situ data are measured with total water hygrometers, condensed water probes, and particle spectrometers. Data from polar, midlatitude, and tropical locations are included. The satellite data are retrieved from CloudSat/CALIPSO [the CloudSat Ice Cloud Property Product (2C-ICE) and 2C-SNOW-PROFILE] and Global Precipitation Measurement (GPM) Level2A. Although the 2C-ICE retrieval is for IWC, a method to use the IWC to get snowfall rates S is developed. The GPM retrievals are for snowfall rate only. Model results are derived using the Community Atmosphere Model (CAM5) and the Met Office Unified Model [Global Atmosphere 7 (GA7)]. The retrievals and model results are related to the in situ observations using temperature and are partitioned by geographical region. Specific variables compared between the in situ observations, models, and retrievals are the IWC and S. Satellite-retrieved IWCs are reasonably close in value to the in situ observations, whereas the models’ values are relatively low by comparison. Differences between the in situ IWCs and those from the other methods are compounded when S is considered, leading to model snowfall rates that are considerably lower than those derived from the in situ data. Anomalous trends with temperature are noted in some instances.

Abstract

Retrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth’s atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global Precipitation Measurement (GPM) Core Observatory satellite and CloudSat’s Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow–rain classification methods, orbits, resolutions, sampling, instrument specifications, and algorithm assumptions. After equalizing snow–rain classification methodologies and limiting latitudinal extent, CPR observes nearly 10 (3) times the occurrence (accumulation) of falling snow as GPM’s Dual-Frequency Precipitation Radar (DPR). The occurrence disparity is substantially reduced if CloudSat pixels are averaged to simulate DPR radar pixels and CPR observations are truncated below the 8-dBZ reflectivity threshold. However, even though the truncated CPR- and DPR-based data have similar falling snow occurrences, average snowfall rate from the truncated CPR record remains significantly higher (43%) than the DPR, indicating that retrieval assumptions (microphysics and snow scattering properties) are quite different. Diagnostic reflectivity (Z)–snow rate (S) relationships were therefore developed at Ku and W band using the same snow scattering properties and particle size distributions in a final effort to minimize algorithm differences. CPR–DPR snowfall amount differences were reduced to ~16% after adopting this diagnostic Z–S approach.