Knowledge of cloud-base height (CBH) is important to describe cloud radiative feedbacks in numerical models and is of practical relevance to the aviation community. Whereas satellite remote sensing with passive radiometers traditionally has provided a ready means for estimating cloud-top height (CTH) and cloud water path (CWP), assignment of CBH requires heavy assumptions on the distribution of CWP within the cloud profile. An attempt to retrieve CBH has been included as part of the VIIRS environmental data records, produced operationally as part of the Suomi–National Polar-Orbiting Partnership (SNPP) and the forthcoming Joint Polar Satellite System. Through formal validation studies tied to the program, it was found that the operational CBH algorithm failed to meet performance specifications in many cases. This paper presents a new methodology for retrieving CBH of the uppermost cloud layer, developed through statistical analyses relating cloud geometric thickness (CGT) to CTH and CWP. The semiempirical approach, which relates these parameters via piecewise fitting, enlists A-Train satellite data [CloudSat cloud profiling radar (CPR), CALIPSO/CALIOP, and Aqua MODIS]. CBH is provided as the residual difference between CTH and CGT. By eliminating cloud type–dependent assumptions on CWP distribution, artifacts common to the operational algorithm (which contribute to high errors) are reduced. Special accommodations are made for handling optically thin cirrus and deep convection. An application to SNPP VIIRS is demonstrated, and the results are compared against global CloudSat observations. From the VIIRS–CloudSat daytime matchups (September–October 2013 and January–May 2015), the new algorithm outperforms the operational SNPP VIIRS algorithm, particularly when the retrieved CTH is accurate. Best performance is expected for single-layer liquid-phase clouds.

The vertical distribution of liquid and ice water content and their partitioning is studied using 34 cases of in situ measured microphysical properties in midlatitude mixed-phase clouds, with liquid water path ranging from near zero to ~248 g m⁻², total water path ranging from near zero to ~562 g m⁻², and cloud-top temperature ranging from −2° to −38°C. The 34 profiles were further divided into three cloud types depending on their vertical extents and altitudes. It is found that both the vertical distribution of liquid water within a cloud and the liquid water fraction (of total condensed water) as a function of temperature or relative position in a cloud layer are cloud-type dependent. In particular, it is found that the partitioning between liquid and ice water for midlevel shallow clouds is relatively independent on the vertical position within the cloud while it clearly depends on cloud mean temperature. For synoptic snow clouds, however, liquid water fraction increases with the decrease of altitude within the cloud. While the liquid water fraction in synoptic clouds also decreases with lowering temperature, its magnitude is only about 50% near 0°C.

Satellites have provided decades of valuable cloud observations, but the data from conventional passive radiometers are biased toward information from at or near cloud top. Tied with the Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Calibration/Validation research, we developed a statistical Cloud Base Height (CBH) algorithm using the National Aeronautics and Space Administration (NASA) A-Train satellite data. This retrieval, which is currently part of the National Oceanic and Atmospheric Administration (NOAA) Enterprise Cloud Algorithms, provides key information needed to display clouds in a manner that goes beyond the typical top-down plan view. The goal of this study is to provide users with high-quality three-dimensional (3D) cloud structure information which can maximize the benefits and performance of JPSS cloud products. In support of the JPSS Proving Ground Aviation Initiative, we introduced Cloud Vertical Cross-sections (CVCs) along flight routes over Alaska where satellite data are extremely helpful in filling significant observational gaps. Valuable feedback and insights from interactions with aviation users allowed us to explore a new approach to provide satellite-based 3D cloud data. The CVC is obtained from multiple cloud retrieval products with supplementary data such as temperatures, Pilot Reports (PIREPs), and terrain information. We continue to improve the product demonstrations based on user feedback, extending the domain to the contiguous United States with the addition of the Geostationary Operational Environmental Satellite (GOES)-16 Advanced Baseline Imager (ABI). Concurrently, we have refined the underlying science algorithms for improved nighttime and multilayered cloud retrievals by utilizing Day/Night Band (DNB) data and exploring machine learning approaches. The products are evaluated using multiple satellite data sources and surface measurements. This paper presents our accomplishments and continuing efforts in both scientific and user-engagement improvements since the beginning of the VIIRS era.

Inaccurate mixed‐phase cloud parameterizations over the Southern Ocean remain one of the largest sources of disagreement among global models in determining shortwave cloud radiative feedbacks. Suitable global observations supporting model improvements are currently unavailable. The conventional satellite cloud phase retrieval from passive radiometers is strongly biased toward cloud top without further information on the subcloud phase. Mixed‐phase clouds with the liquid‐top mixed‐phase (LTMP) structures are often classified simply as supercooled liquid. This paper presents a daytime multispectral detection algorithm for LTMP clouds, based on differential absorption between liquid and ice in shortwave infrared bands (1.61 and 2.25 μm). The LTMP algorithm, previously developed for polar‐orbiting sensors, is applied to Himawari‐8 Advanced Himawari Imager (the first of the next‐generation geostationary satellites) to probe subcloud phase for mixed‐phase clouds over the Southern Ocean. The results are compared with spaceborne active sensor data from CloudSat and CALIPSO. Ship‐based field experiment measurements are examined for selected cases to provide a more direct assessment of algorithm performance. The results show that applying the LTMP algorithm to geostationary satellites has potential to provide advanced time‐resolved observations for mixed‐phase clouds globally with improved sublayer cloud phase information that can support enhancement and validation of global models.

Surface rain radar data from areas surrounding Japan and in the tropics are used to study the variability of rainfall over an area similar to a satellite microwave radiometric instantaneous field of view (FOV). The results of such variability are applied to radiative transfer simulations to modify the brightness temperature versus rain rate relationship. First, the surface radar data from different geographical regions are used to develop a relationship between fractional rain cover and average rain rate over microwave radiometric FOV‐sized areas. This resultant relationship is also compared with spaceborne Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) observations. It is found that the rainfall type (convective or stratiform) accounts for the most of variations in this relationship. The sub‐FOV rainfall variability is then investigated using the same surface radar data sets. The conditional probability distribution function of precipitation within the FOV‐sized area is parameterized in terms of average rain of the area. The form of the model function does not depend upon the characteristics of the two radars and their geographical locations and can be parameterized by a combination of two lognormal distributions. By analyzing collocated TRMM Microwave Imager (TMI) and PR data, it is shown that the averaged relationship between observed brightness temperatures and rain rates at 19.3 and 37 GHz shows a significant disagreement with that simulated by a plane‐parallel radiative transfer model for convective rain events, although it shows a better agreement for stratiform rain events. Presumably, this disagreement is mainly caused by variability of the rain field within satellite instantaneous FOV. With the application of the fractional rain area and the probability distribution function derived from this study to the radiative transfer simulations, we are able to bring results of the radiative transfer simulations much closer to the observations.