A Bayesian optimal estimation methodology is applied to retrieve the time-varying ice particle mass–dimensional (M–D) relationships (i.e., M = amDbm) and the associated uncertainties using the in situ data that were collected by the NASA WB-57 during the Midlatitude Airborne Cirrus Properties Experiment (MACPEX) in March and April 2011. The authors utilize the coincident measurements of bulk ice water content and projected cross-sectional area to constrain M–D relationships and estimate the uncertainties. It is demonstrated that the additional information provided by the particle area with respect to size could contribute considerable improvements to the algorithm performance. Extreme variability of M–D properties is found among cases as well as within individual cases, indicating the nondiscrete nature of ice crystal habits within cloud volumes and further suggesting the risk of assuming a constant M–D relationship in different conditions. Relative uncertainties of am are approximately from 50% to 80%, and relative uncertainties of bm range from 6% to 9.5%, which would cause approximately 2.5-dB uncertainty in forward-modeled radar reflectivity or a factor-of-2 uncertainty in ice water content.

Remote sensing retrievals and ice microphysical parameterizations in global climate models typically use assumptions about the distribution of ice mass as a function of particle size using mass‐dimensional (m‐D) relationships. This study investigates the ice crystal m‐D properties of tropical anvil cirrus during the Tropical Composition, Cloud and Climate Coupling Experiment (TC4) to better document the distribution of ice mass with size in this particular class of tropical ice clouds. Two optimal estimation algorithms (XIWC and MZ) are used to estimate the m‐D relationship for each particle size distribution (PSD) collected in situ. The XIWC algorithm minimizes the difference between measured ice water content (IWC) and PSD calculated IWC, while the MZ algorithm minimizes the difference between measured radar reflectivity factors and those calculated from the in situ PSDs. Results from these algorithms are compared to previous studies to establish consistency of the methodologies. The XIWC results show that both parameters in the m‐D relationship increase with temperature. Changes in m‐D with temperature have substantial implications for remote sensing retrievals. With the prefactor varying by a factor of 5 and the exponent varying by some 16% over a typical range of ice cloud temperatures, forward modeling errors in radar reflectivity could be typically in excess of 5 dB, further suggesting that retrievals of IWC and precipitation rates from radar measurements in ice clouds be in error by factors easily exceeding 3.

The NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex) employed the NASA P-3, Stratton Park Engineering Company (SPEC) Learjet 35, and a host of satellites and surface sensors to characterize the coupling of aerosol processes, cloud physics, and atmospheric radiation within the Maritime Continent’s complex southwest monsoonal environment. Conducted in the late summer of 2019 from Luzon Philippines in conjunction with the Office of Naval Research Propagation of Intraseasonal Tropical OscillatioNs (PISTON) experiment with its R/V Sally Ride stationed in the North Western Tropical Pacific, CAMP2Ex documented diverse biomass burning, industrial and natural aerosol populations and their interactions with small to congestus convection. The 2019 season exhibited El Nino and associated drought, high biomass burning emissions, and an early monsoon transition allowing for observation of pristine to massively polluted environments as they advected through intricate diurnal mesoscale and radiative environments into the monsoonal trough. CAMP2Ex’s preliminary results indicate 1) increasing aerosol loadings tend to invigorate congestus convection in height and increase liquid water paths; 2) lidar, polarimetry, and geostationary Advanced Himawari Imager remote sensing sensors have skill in quantifying diverse aerosol and cloud properties and their interaction; and 3) high resolution remote sensing technologies are able to greatly improve our ability to evaluate the radiation budget in complex cloud systems. Through the development of innovative informatics technologies, CAMP2Ex provides a benchmark dataset of an environment of extremes for the study of aerosol, cloud and radiation processes as well as a crucible for the design of future observing systems.

A Bayesian Markov chain Monte Carlo (MCMC) algorithm is utilized to compare the skill of an A-Train-like observing system with a cloud, convection, and precipitation (CCP) observing system like that contemplated for the 2020s by the 2017 National Academy of Sciences Decadal Survey. The main objective is to demonstrate a framework for observational trade space studies. This initial work focuses on weakly precipitating warm shallow cumulus constructed from in situ data. Radiative computations are based on Mie theory with spherical assumptions. Simulated measurements in the CCP configuration consist of W- and Ka-band radar reflectivity and path-integrated attenuation, 31 and 94 GHz brightness temperatures (Tb), and visible and near-infrared reflectances. The collection of measurements in the CloudSat configuration is identical, but includes a single 94 GHz radar frequency, and the uncertainty in the 94 GHz microwave brightness temperature is increased to mimic the CloudSat Tb product. The experiments demonstrate that it remains a challenge to diagnose cloud properties in the presence of light rain because of the tendency of microwave remote sensing to respond to the higher moments of the hydrometeor populations. Rain properties are significantly better constrained than cloud properties, even in the optimal CCP configuration. The addition of Ka-band measurements places substantial constraints on the precipitation rain effective radius and rain rates. The Tb offers important information regarding the column-integrated condensate mass, the measurement accuracy of which appears more likely to affect the retrievals of clouds with low liquid water path. The constraints provided by reflectances are largely restricted to regions near the cloud top, particularly in the raining cases.

Estimates of cloud droplet effective radius (re) and optical thickness (𝝉) can be derived using reflected sunlight in a visible non-absorbing channel combined with reflectances from a near IR channel that is absorbing (e.g., The bi-spectral method or BSM). Discrepancies between BSM-estimated re and collocated in situ measurements are commonly attributed to a violation of the assumptions used in the BSM algorithm such as plane parallel geometry, and a single mode droplet size distribution. This research uses Markov Chain Monte Carlo experiments to examine the impact of precipitation on BSM-retrieved re near optical cloud top by comparing the retrievals and associated uncertainties obtained from two types of experiments assuming a unimodal or bimodal drop size distribution. Where rain is present, BSMretrieved re overestimates the true cloud mode re. Moreover, there is no longer a unique measure of re within the precipitating liquidphased clouds, resulting in a substantial increase in retrieval uncertainties. This leads to a corresponding loss of information on total number concentration and liquid water content near cloud top. It is found that re biases are not strongly correlated with properties exclusively pertaining to rain, such as rain water content or precipitation rates, but tend to be a function of the ratio between rain and cloud water content and the cloud total number concentration. These results highlight the need for additional independent information such as from an active or passive microwave sensor that can identify the presence of precipitation and constrain additional aspects of bimodal droplet distributions. Index terms-MODIS, cloud microphysics, Bayesian methods, error analysis, cloud remote sensing.