Estimating hourly full-coverage PM over China based on TOA reflectance data from the Fengyun-4A satellite.

It is challenging to retrieve hourly ground-level PM on a national scale in China due to the sparse site measurements and the limited coverage of Low Earth Orbit (LEO) satellite observations. The new geostationary meteorological satellite of China, Fengyun-4A (FY-4A), provides a unique opportunity to fill this gap. In this study, the Random Forest (RF) algorithm was applied to retrieve hourly PM of China directly from FY-4A Top-of-Atmosphere (TOA) reflectance data.

Improved PM predictions of WRF-Chem via the integration of Himawari-8 satellite data and ground observations.

The new-generation geostationary satellites feature higher radiometric, spectral, and spatial resolutions, thereby making richer data available for the improvement of PM predictions. Various aerosol optical depth (AOD) data assimilation methods have been developed, but the accurate representation of the AOD-PM relationship remains challenging. Empirical statistical methods are effective in retrieving ground-level PM, but few have been evaluated in terms of whether and to what extent they can help improve PM predictions.

A Physics-Based DNI Model Assessing All-Sky Circumsolar Radiation.

By investigating the long-term observations at Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP), we find that the routinely used Beer-Bouguer-Lambert law and the models that empirically separate direct normal irradiance (DNI) from measurements of global horizontal irradiance (GHI) have dramatic and unexpected bias in computing cloudy-sky DNI. This bias has led to tremendous uncertainty in estimating the electricity generation by solar energy conversion systems. To effectively reduce the bias, this study proposes a physical solution of all-sky DNI that computes solar radiation in the infinite-narrow beam along the sun direction and the scattered radiation falls within the circumsolar region.

Cloud Products from the Earth Polychromatic Imaging Camera (EPIC): Algorithms and Initial Evaluation.

This paper presents the physical basis of the EPIC cloud product algorithms and an initial evaluation of their performance. Since June 2015, EPIC has been providing observations of the sunlit side of the Earth with its 10 spectral channels ranging from the UV to the near-IR. A suite of algorithms has been developed to generate the standard EPIC Level 2 Cloud Products that include cloud mask, cloud effective pressure/height, and cloud optical thickness.

The implications of dust ice nuclei effect on cloud top temperature in a complex mesoscale convective system.

Mineral dust is the most important natural source of atmospheric ice nuclei (IN) which may significantly mediate the properties of ice cloud through heterogeneous nucleation and lead to crucial impacts on hydrological and energy cycle. The potential dust IN effect on cloud top temperature (CTT) in a well-developed mesoscale convective system (MCS) was studied using both satellite observations and cloud resolving model (CRM) simulations. We combined satellite observations from passive spectrometer, active cloud radar, lidar, and wind field simulations from CRM to identify the place where ice cloud mixed with dust particles.

Nonlinear physical segmentation algorithm for determining the layer boundary from lidar signal.

Layer boundary (base and top) detection is a basic problem in lidar data processing, the results of which are used as inputs of optical properties retrieval. However, traditional algorithms not only require manual intervention but also rely heavily on the signal-to-noise ratio. Therefore, we propose a robust and automatic algorithm for layer detection based on a novel algorithm for lidar signal segmentation and representation.

De-noising and retrieving algorithm of Mie lidar data based on the particle filter and the Fernald method.

The signal-to-noise ratio (SNR) of an atmospheric lidar decreases rapidly as range increases, so that maintaining high accuracy when retrieving lidar data at the far end is difficult. To avoid this problem, many de-noising algorithms have been developed; in particular, an effective de-noising algorithm has been proposed to simultaneously retrieve lidar data and obtain a de-noised signal by combining the ensemble Kalman filter (EnKF) and the Fernald method. This algorithm enhances the retrieval accuracy and effective measure range of a lidar based on the Fernald method, but sometimes leads to a shift (bias) in the near range as a result of the over-smoothing caused by the EnKF.

Approach for selecting boundary value to retrieve Mie-scattering lidar data based on segmentation and two-component fitting methods.

Fernald method is regarded as the standard method for retrieving lidar data, but the retrieval can be performed only when a boundary value is given. Generally, we can select clear atmosphere above the tropopause as a reference to determine the boundary value, but we need to use the slope method to fit the boundary value when the detecting range is lower than the tropopause. The slope method involves significant uncertainty because this algorithm is based on two hypotheses: one is that aerosol vertical distribution is homogeneous, and the other is that either molecule or aerosol components exist in the atmosphere.

Impact of vertical stratification of inherent optical properties on radiative transfer in a plane-parallel turbid medium.

The atmosphere is often divided into several homogeneous layers in simulations of radiative transfer in plane-parallel media. This artificial stratification introduces discontinuities in the vertical distribution of the inherent optical properties at boundaries between layers, which result in discontinuous radiances and irradiances at layer interfaces, which lead to errors in the radiative transfer simulations. To investigate the effect of the vertical discontinuity of the atmosphere on radiative transfer simulations, a simple two layer model with only aerosols and molecules and no gas absorption is used.