The Search for Atmospheric Laminar Channels: Experimental Results and Method Dissemination

In this paper, a practical application of theoretical developments found in our previous works is explored in relation to atmospheric lidar data. Multifractal structures, previously named “laminar channels”, have been identified in atmospheric profiles—these exhibit cellular and self-structuring properties, and are spatially ordered across the atmospheric profile. Furthermore, these structures have been connected to the spontaneous emergence of turbulent behavior in the calm atmospheric flow. Calculating the location and occurrence of these channels can help identify features of atmospheric evolution, such as the development of the planetary boundary layer (PBL). Employing this theoretical background to atmospheric lidar data, attempts are made to confirm this suggestion and extract information about atmospheric structure and evolution by analyzing turbulent vortex scale dynamics and scale-corresponding Lyapunov exponents that form the basis of identifying the laminar channels in atmospheric lidar profiles. A parameter named “scale laminarity index” is then introduced, which quantifies the relation between vortex scale and chaoticity throughout the profile. Finally, the algorithmic methods employed in this study are described and distributed for future use.

Evaluating the Consistency and Continuity of Pixel-Scale Cloud Property Data Records From Aqua and SNPP

The Aqua, SNPP, and JPSS satellites carry a combination of hyperspectral infrared sounders (AIRS, CrIS) and high-spatial-resolution narrowband imagers (MODIS, VIIRS). They provide an opportunity to acquire high-quality long-term cloud data records and are a key component of the existing Program of Record of cloud observations. By matching observations from sounders and imagers across different platforms at pixel scale, this study evaluates the self-consistency and continuity of cloud retrievals from Aqua and SNPP by multiple algorithms, including the AIRS Version-7 retrieval algorithm and the Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) Version-2 for sounders, and the Standard Aqua-MODIS Collection-6.1 and the NASA MODIS-VIIRS continuity cloud products for imagers. Metrics describing detailed statistical distributions at sounder field of view (FOV) and the joint histograms of cloud properties are evaluated. These products are found highly consistent despite their retrieval from different sensors using different algorithms. Differences between the two sounder cloud products are mainly due to cloud clearing and treatment of clouds in scenes with unsuccessful atmospheric profile retrievals. The sounder subpixel cloud heterogeneity evaluated using the standard deviation of imager retrievals at sounder FOV shows good agreement between the standard and continuity products from different satellites. However, impact of algorithm and instrument differences between MODIS and VIIRS is revealed in cloud top pressure retrievals and in the imager cloud distribution skewness. Our study presents a unique aspect to examine NASA’s progress toward building a continuous cloud data record with sufficient quality to investigate clouds’ role in global environmental change.

Validation and Utility of Satellite Retrievals of Atmospheric Profiles in Detecting and Monitoring Significant Weather Events

Infrared and microwave sounder measurements from polar-orbiting satellites are used to retrieve profiles of temperature, water vapor, and trace gases utilizing a suite of algorithms called the National Oceanic and Atmospheric Administration (NOAA) Unique Combined Atmospheric Processing System (NUCAPS). Meteorologists operationally use the retrievals similar to radiosonde measurements to assess atmospheric stability and aid them in issuing forecasts and severe weather warnings. Measurements of trace gases by NUCAPS enable detection, tracking, and monitoring of greenhouse gases and emissions from fires that impact air quality. During the polar winters, when ultraviolet measurements of ozone are not possible, absorption features in the infrared spectrum of the sounders enable the assessment of ozone concentration in the stratosphere. These retrievals are used as inputs to monitor the ozone hole over Antarctica. This article illustrates the utility of NUCAPS atmospheric profile retrievals in assessing meteorological events using several examples of severe thunderstorms, tropical cyclones, fires, and ozone maps.

Soil moisture control of precipitation re-evaporation over a heterogeneous land surface

Soil moisture heterogeneity can induce mesoscale circulations due to differential heating between dry and wet surfaces, which can, in turn, trigger precipitation. In this work, we conduct cloud-permitting simulations over a 100 km × 25 km idealized land surface, with the domain split equally between a wet and dry region, each with homogeneous soil moisture. In contrast to previous studies that prescribed initial atmospheric profiles, each simulation is run with fixed soil moisture for 100 days to allow the atmosphere to equilibrate to the given land surface rather than prescribing the initial atmospheric profile. It is then run for one additional day, allowing the soil moisture to freely vary. Soil moisture controls the resulting precipitation over the dry region through three different mechanisms: as the dry domain gets drier, (1) the mesoscale circulation strengthens, increasing water vapor convergence over the dry domain, (2) surface evaporation declines over the dry domain, decreasing water vapor convergence over the dry domain and (3) precipitation efficiency declines due to increased re-evaporation, meaning proportionally less water vapor over the dry domain becomes surface precipitation. We find that the third mechanism dominates when soil moisture is small in the dry domain: drier soils ultimately lead to less precipitation in the dry domain due to its impact on precipitation efficiency. This work highlights an important new mechanism by which soil moisture controls precipitation, through its impact on precipitation re-evaporation and efficiency.

Neural Network-Based Models for Estimating Weighted Mean Temperature in China and Adjacent Areas

The weighted mean temperature (Tm) is a key parameter when converting the zenith wet delay (ZWD) to precipitation water vapor (PWV) in ground-based Global Navigation Satellite System (GNSS) meteorology. Tm can be calculated via numerical integration with the atmospheric profile data measured along the zenith direction, but this method is not practical in most cases because it is not easy for general users to get real-time atmospheric profile data. An alternative method to obtain an accurate Tm value is to establish regional or global models on the basis of its relations with surface meteorological elements as well as the spatiotemporal variation characteristics of Tm. In this study, the complex relations between Tm and some of its essentially associated factors including the geographic position and terrain, surface temperature and surface water vapor pressure were considered to develop Tm models, and then a non-meteorological-factor Tm model (NMFTm), a single-meteorological-factor Tm model (SMFTm) and a multi-meteorological-factor Tm model (MMFTm) applicable to China and adjacent areas were established by adopting the artificial neural network technique. The generalization performance of new models was strengthened with the help of an ensemble learning method, and the model accuracies were compared with several representative published Tm models from different perspectives. The results show that the new models all exhibit consistently better performance than the competing models under the same application conditions tested by the data within the study area. The NMFTm model is superior to the latest non-meteorological model and has the advantages of simplicity and utility. Both the SMFTm model and MMFTm model show higher accuracy than all the published Tm models listed in this study; in particular, the MMFTm model is about 14.5% superior to the first-generation neural network-based Tm (NN-I) model, with the best accuracy so far in terms of the root-mean-square error.