The launch of CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) in 2006 provided the first opportunity to incorporate information about the vertical distribution of cloud and aerosols directly into global estimates of atmospheric radiative heating. Vertical profiles of radar and lidar backscatter from CloudSat’s Cloud Profiling Radar (CPR) and the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard CALIPSO naturally complement Moderate Resolution Imaging Spectroradiometer (MODIS) radiance measurements, providing a nearly complete depiction of the cloud and aerosol properties that are essential for deriving high-vertical-resolution profiles of longwave (LW) and shortwave (SW) radiative fluxes and heating rates throughout the atmosphere. This study describes a new approach for combining vertical cloud and aerosol information from CloudSat and CALIPSO with MODIS data to assess impacts of clouds and aerosols on top-of-atmosphere (TOA) and surface radiative fluxes. The resulting multisensor cloud–aerosol product is used to document seasonal and annual mean distributions of cloud and aerosol forcing globally from June 2006 through April 2011. Direct comparisons with Clouds and the Earth’s Radiant Energy System (CERES) TOA fluxes exhibit a close correlation, with improved errors relative to CloudSat-only products. Sensitivity studies suggest that remaining uncertainties in SW fluxes are dominated by uncertainties in CloudSat liquid water content estimates and that the largest sources of LW flux uncertainty are prescribed surface temperature and lower-tropospheric humidity. Globally and annually averaged net TOA cloud radiative effect is found to be −18.1 W m⁻². The global, annual mean aerosol direct radiative effect is found to be −1.6 ± 0.5 W m⁻² (−2.5 ± 0.8 W m⁻² if only clear skies over the ocean are considered), which, surprisingly, is more consistent with past modeling studies than with observational estimates that were based on passive sensors.

Four different types of estimates of the surface downwelling longwave radiative flux (DLR) are reviewed. One group of estimates synthesizes global cloud, aerosol, and other information in a radiation model that is used to calculate fluxes. Because these synthesis fluxes have been assessed against observations, the global-mean values of these fluxes are deemed to be the most credible of the four different categories reviewed. The global, annual mean DLR lies between approximately 344 and 350 W m−2 with an error of approximately ±10 W m−2 that arises mostly from the uncertainty in atmospheric state that governs the estimation of the clear-sky emission. The authors conclude that the DLR derived from global climate models are biased low by approximately 10 W m−2 and even larger differences are found with respect to reanalysis climate data. The DLR inferred from a surface energy balance closure is also substantially smaller that the range found from synthesis products suggesting that current depictions of surface energy balance also require revision. The effect of clouds on the DLR, largely facilitated by the new cloud base information from the CloudSat radar, is estimated to lie in the range from 24 to 34 W m−2 for the global cloud radiative effect (all-sky minus clear-sky DLR). This effect is strongly modulated by the underlying water vapor that gives rise to a maximum sensitivity of the DLR to cloud occurring in the colder drier regions of the planet. The bottom of atmosphere (BOA) cloud effect directly contrast the effect of clouds on the top of atmosphere (TOA) fluxes that is maximum in regions of deepest and coldest clouds in the moist tropics.

High vertical resolution CloudSat radar measurements, supplemented with cloud boundaries and aerosol information from the CALIPSO lidar, are used to examine radiative heating features in the atmosphere that have not previously been characterized by passive sensors. The monthly and annual mean radiative heating/cooling structure for a 4 year period between 2006 and 2010 is derived. The mean atmospheric radiative cooling rate from CloudSat/CALIPSO is 0.98 K d⁻¹ (1.34 K d⁻¹ between 150 and 950 hPa) and is largely a reflection of the Earth's mean water vapor distribution, with sharp vertical gradients introduced by clouds. It is found that there is a minimum in cooling in the tropical lower to middle troposphere, a cooling maximum in the upper‐boundary layer of the Southern Hemisphere poleward of −10° latitude, and a minimum in cooling in the lower boundary layer in the middle to high latitudes of both hemispheres. Clouds tops tend to strongly cool the upper‐boundary layer all year in the midlatitudes to high latitudes of the Southern Hemisphere (where peak seasonal mean winter cooling is 3.4 K d⁻¹), but this cooling is largely absent in the corresponding parts of the Northern Hemisphere during boreal winter, resulting in a hemispheric asymmetry in cloud radiative cooling.

The evolution of model-based cloud-top brightness temperatures (BT) associated with convective initiation (CI) is assessed for three bulk cloud microphysics schemes in the Weather Research and Forecasting Model. Using a composite-based analysis, cloud objects derived from high-resolution (500 m) model simulations are compared to 5-min GOES-16 imagery for a case study day located near the Alabama–Mississippi border. Observed and simulated cloud characteristics for clouds reaching CI are examined by utilizing infrared BTs commonly used in satellite-based CI nowcasting methods. The results demonstrate the ability of object-based verification methods with satellite observations to evaluate the evolution of model cloud characteristics, and the BT comparison provides insight into a known issue of model simulations producing too many convective cells reaching CI. The timing of CI from the different microphysical schemes is dependent on the production of ice in the upper levels of the cloud, which typically occurs near the time of maximum cloud growth. In particular, large differences in precipitation formation drive differences in the amount of cloud water able to reach upper layers of the cloud, which impacts cloud-top glaciation. Larger cloud mixing ratios are found in clouds with sustained growth leading to more cloud water lofted to the upper levels of the cloud and the formation of ice. Clouds unable to sustain growth lack the necessary cloud water needed to form ice and grow into cumulonimbus. Clouds with slower growth rates display similar BT trends as clouds exhibiting growth, which suggests that forecasting CI using geostationary satellites might require additional information beyond those derived at cloud top.

Discrepancies between Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) oceanic rainfall retrievals are prevalent between El Niño and La Niña conditions with TMI exhibiting systematic shifts in precipitation. To investigate the causality of this relationship, this paper focuses on the evolution of precipitation organization between El Niño and La Niña and their impacts on TRMM precipitation. The results indicate that discrepancies are related to shifts from isolated deep convection during La Niña toward organized precipitation during El Niño with the largest variability occurring in the Pacific basins. During El Niño, organized systems are more frequent, have increased areal coverage of stratiform rainfall, and penetrate deeper into the troposphere compared to La Niña. The increased stratiform raining fraction leads to larger increases in TMI rain rates than PR rain rate retrievals. Reanalysis and water vapor data from the Atmospheric Infrared Sounder (AIRS) indicate that organized systems are aided by midtropospheric moisture increases accompanied by increased convective frequency. During La Niña, tropical rainfall is dominated by isolated deep convection due to drier midtropospheric conditions and strong mid- and upper-level zonal wind shear. To examine tropical rainfall–sea surface temperature relations, regime-based bias corrections derived using ground validation (GV) measurements are applied to the TRMM rain estimates. The robust connection with GV-derived biases and oceanic precipitation leads to a reduction in TMI-PR regional differences and tropics-wide precipitation anomalies. The improved agreement between PR and TMI estimates yields positive responses of precipitation to tropical SSTs of 10% °C−1 and 17% °C−1, respectively, consistent with 15% °C−1 from the Global Precipitation Climatology Project (GPCP).

Over the tropical oceans, large discrepancies in TRMM passive and active microwave rainfall retrievals become apparent during El Niño–Southern Oscillation (ENSO) events. This manuscript describes the application of defined precipitation regimes to aid the validation of instantaneous rain rates from TRMM using the S-band radar located on the Kwajalein Atoll. Through the evaluation of multiple case studies, biases in rain-rate estimates from the TRMM radar (PR) and radiometer (TMI) are best explained when derived as a function of precipitation organization (e.g., isolated vs organized) and precipitation type (convective vs stratiform). When examining biases at a 1° × 1° scale, large underestimates in both TMI and PR rain rates are associated with predominately convective events in deep isolated regimes, where TMI and PR retrievals are underestimated by 37.8% and 23.4%, respectively. Further, a positive bias of 33.4% is observed in TMI rain rates within organized convective systems containing large stratiform regions. These findings were found to be consistent using additional analysis from the DYNAMO field campaign. When validating at the TMI footprint scale, TMI–PR differences are driven by stratiform rainfall variability in organized regimes; TMI overestimates this stratiform precipitation by 92.3%. Discrepancies between TMI and PR during El Niño events are related to a shift toward more organized convective systems and derived TRMM rain-rate bias estimates are able to explain 70% of TMI–PR differences during El Niño periods. An extension of the results to passive microwave retrievals reveals issues in discriminating convective and stratiform rainfall within the TMI field of view (FOV), and significant reductions in bias are found when convective fraction is constrained within the Bayesian retrieval.