Evaluation of Cloud Microphysical Properties Derived from MODIS and Himawari-8 Using In-Situ Aircraft Measurements over the Southern Ocean

Cloud microphysical properties from aircraft measurements during the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study are used to evaluate the cloud products from the geostationary satellite Himawari‐8 (H‐8) and the polar‐orbiting satellite the Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to the in situ aircraft observations when aircraft flew horizontally near cloud tops, the cloud droplet effective radius (r_e) and number concentration (N_d) from H‐8 (MODIS) are 33% (26%–31%) and 2% (9–13%) larger. Both the H‐8 and MODIS retrievals behave similarly for liquid‐only and mixed‐phase low‐level clouds, indicating the weak sensitivity of the satellite cloud retrieval performance to cloud phase. The r_e and N_d of the cloud profiles from aircraft measurements were also used to compare with the satellite product. It shows that H‐8 r_e and N_d agree better with aircraft measurements when considering only the in situ data acquired in the upper portions (highest 20%) of the clouds. Roughly, the r_e overestimation by H‐8 decreases from 18% to 3% when considering the upper portions of clouds compared to all cloud layer averages, except for one case with drizzles appeared. In addition, the performance of MODIS r_e and N_d is highly dependent on the wavelengths the retrieval method uses. The droplet r_e retrievals using wavelength of 1.6 μm have much larger biases than that using the other two channels. The potential effects of the cloud vertical variation and the photon penetration depth, the cloud heterogeneity, the cloud droplet size spectra, and the drizzle on satellite retrievals have also been discussed

Evaluation of Cloud Microphysical Properties Derived from MODIS and Himawari-8 Using In-Situ Aircraft Measurements over the Southern Ocean

Cloud microphysical properties from aircraft measurements during the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study are used to evaluate the cloud products from the geostationary satellite Himawari‐8 (H‐8) and the polar‐orbiting satellite the Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to the in situ aircraft observations when aircraft flew horizontally near cloud tops, the cloud droplet effective radius (r_e) and number concentration (N_d) from H‐8 (MODIS) are 33% (26%–31%) and 2% (9–13%) larger. Both the H‐8 and MODIS retrievals behave similarly for liquid‐only and mixed‐phase low‐level clouds, indicating the weak sensitivity of the satellite cloud retrieval performance to cloud phase. The r_e and N_d of the cloud profiles from aircraft measurements were also used to compare with the satellite product. It shows that H‐8 r_e and N_d agree better with aircraft measurements when considering only the in situ data acquired in the upper portions (highest 20%) of the clouds. Roughly, the r_e overestimation by H‐8 decreases from 18% to 3% when considering the upper portions of clouds compared to all cloud layer averages, except for one case with drizzles appeared. In addition, the performance of MODIS r_e and N_d is highly dependent on the wavelengths the retrieval method uses. The droplet r_e retrievals using wavelength of 1.6 μm have much larger biases than that using the other two channels. The potential effects of the cloud vertical variation and the photon penetration depth, the cloud heterogeneity, the cloud droplet size spectra, and the drizzle on satellite retrievals have also been discussed

Application of Support Vector Machines to a Small-Sample Prediction

The support vector machines (SVMs) is one kind of novel small-sample machine learning methods based on solid theoretical background. Highly nonlinear regression and classification are their two applications. Different from conventional statistics methods, the SVMs employs the structural risk minimizing principle, which leads to high predication precision. For this method is not essentially related to probability measure and Law of Large Numbers, the final decision function is only determined by a small fraction of sample, called support vectors. Consequently, the complexity of computation only depends on the number of support vectors rather than the dimensions of the original sample space. In most occasions of oil and gas development, only small samples are available to predict the results of one measure. Introduction of SVMs into these applications can significantly improve prediction precision

Spatiotemporal Variations of Precipitation in China Using Surface Gauge Observations from 1961 to 2016

Long-term precipitation trend is a good indicator of climate and hydrological change. The data from 635 ground stations are used to quantify the temporal trends of precipitation with different intensity in China from 1961 to 2016. These sites are roughly uniformly distributed in the east or west regions of China, while fewer sites exist in the western region. The result shows that precipitation with a rate of 70%. With a 95% confidence level, there is no significant temporal change of annually averaged precipitation in the whole of China. Seasonally, there are no significant temporal changes except for a robust decreasing trend in autumn. Spatially, significant differences in the temporal trends of precipitation are found among various regions. The increasing trend is the largest in Northwest China, and the decreasing trend is the largest in North China. The annually averaged number of precipitation days shows a decreasing trend in all regions except for Northwest China. Regarding precipitation type, the number of light precipitation days shows a robust decreasing trend for almost all regions, while other types show no significant change. Considering the high frequency, the temporal trends of light precipitation could highly explain the temporal variation of the total precipitation amount in China

Spatiotemporal Variations of Precipitation in China Using Surface Gauge Observations from 1961 to 2016

Long-term precipitation trend is a good indicator of climate and hydrological change. The data from 635 ground stations are used to quantify the temporal trends of precipitation with different intensity in China from 1961 to 2016. These sites are roughly uniformly distributed in the east or west regions of China, while fewer sites exist in the western region. The result shows that precipitation with a rate of 70%. With a 95% confidence level, there is no significant temporal change of annually averaged precipitation in the whole of China. Seasonally, there are no significant temporal changes except for a robust decreasing trend in autumn. Spatially, significant differences in the temporal trends of precipitation are found among various regions. The increasing trend is the largest in Northwest China, and the decreasing trend is the largest in North China. The annually averaged number of precipitation days shows a decreasing trend in all regions except for Northwest China. Regarding precipitation type, the number of light precipitation days shows a robust decreasing trend for almost all regions, while other types show no significant change. Considering the high frequency, the temporal trends of light precipitation could highly explain the temporal variation of the total precipitation amount in China

Spatial Representativeness of PM_(2.5) Concentrations Obtained Using Reduced Number of Network Stations

Haze has been a focused air pollution phenomenon in China, and its characterization is highly desired. Aerosol properties obtained from a single station are frequently used to represent the haze condition over a large domain, such as tens of kilometers, which could result in high uncertainties due to their spatial variation. Using a high resolution network observation over an urban city in North China from November 2015 to February 2016, this study examines the spatial representativeness of ground station observations of particulate matter with diameters less than 2.5 μm (PM_(2.5)). We developed a new method to determine the representative area of PM_(2.5) measurements from limited stations. The key idea is to determine the PM_(2.5) spatial representative area using its spatial variability and temporal correlation. We also determine stations with large representative area using two grid networks with different resolutions. Based on the high spatial resolution measurements, the representative area of PM_(2.5) at one station can be determined from the grids with high correlations and small differences of PM_(2.5). The representative area for a single station in the study period ranges from 0.25 to 16.25 km^2, but is less than 3 km^2 for more than half of the stations. The representative area varies with locations, and observation at 10 optimal stations would have a good representativeness of those obtained from 169 stations for the four-month time scale studied. Both evaluations with an empirical orthogonal function (EOF) analysis and with independent dataset corroborate the validity of the results found in this study