다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Glacial melting in the Tibetan Plateau affects the water resources of millions of people. This study finds that—partly owing to changes in atmospheric circulations and precipitation patterns—the most intensive glacier shrinkage is in the Himalayan region, whereas glacial retreat in the Pamir Plateau region is less apparent.

Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings

Climate strongly influences global wildfire activity, and recent wildfire surges may signal fire weather-induced pyrogeographic shifts. Here we use three daily global climate data sets and three fire danger indices to develop a simple annual metric of fire weather season length, and map spatio-temporal trends from 1979 to 2013. We show that fire weather seasons have lengthened across 29.6 million km2 (25.3%) of the Earth's vegetated surface, resulting in an 18.7% increase in global mean fire weather season length. We also show a doubling (108.1% increase) of global burnable area affected by long fire weather seasons (>1.0 σ above the historical mean) and an increased global frequency of long fire weather seasons across 62.4 million km2 (53.4%) during the second half of the study period. If these fire weather changes are coupled with ignition sources and available fuel, they could markedly impact global ecosystems, societies, economies and climate.

Climate-induced variations in global wildfire danger from 1979-2013

Deep learning in environmental remote sensing: Achievements and challenges

Energetics of the Mesosphere and Thermosphere

The quantitative precipitation estimation system for Dallas–Fort Worth (DFW) urban remote sensing network

Compared to traditional single-polarization radar, dual-polarization radar has a number of advantages for quantitative precipitation estimation because more information about the drop size distribution and hydrometeor type can be gleaned. In this paper, an improved dual-polarization rainfall methodology is proposed, which is driven by a region-based hydrometeor classification mechanism. The objective of this study is to incorporate the spatial coherence and self-aggregation of dual-polarization observables in hydrometeor classification and to produce robust rainfall estimates for operational applications. The S-band dual-polarization data collected from the NASA Polarimetric (NPOL) radar during the GPM Iowa Flood Studies (IFloodS) ground validation field campaign are used to demonstrate and evaluate the proposed rainfall algorithm. Results show that the improved rainfall method provides better performance than a few single- and dual-polarization algorithms in previous studies. This paper also inve...

An Improved Dual-Polarization Radar Rainfall Algorithm (DROPS2.0): Application in NASA IFloodS Field Campaign

The Colorado State Na temperature lidar has been in regular nighttime operation since 1991 By the end of January 1993, 94 nights of quality data with more than 4 hrs each night have been collected Analysis of a total of 22,999 photocount files gives rise to high resolution seasonally and nightly averaged temperature and Na density profiles, from which the thermal structure of a midlatitude mesopause emerges Contrary to the reference atmosphere of CIRA 1986, two prevailing temperature minima are seen at altitudes 863 ± 25 km and 990 ± 29 km with the lower minimum having the dominating effect The trend of the mean nightly temperature of the lower minimum is observed to vary monotonically between a low of 172 K at summer solstice and a high of 212 K at nearly one month following winter solstice As in the polar regions, this is a clear signature of a wave-driven diabatic circulation observed at a midlatitude The temperature of the upper minimum fluctuates around 189 ± 8 K year-round Like the polar mesopause, the mesopause altitude follows a bistable pattern, although the seasonal variation of the mesopause temperature is much more complicated Based on the seasonal variations of these two temperature minima, the observed patterns of mesopause altitude and temperature can be explained Although the mechanisms responsible for the formation of double temperature minima are not clear at present, the lower temperature minimum with a robust 40 K annual temperature change plays the role of the mesopause commonly known

Observed thermal structure of a midlatitude mesopause

Space-based precipitation products are often used for regional and/or global hydrologic modeling and climate studies. A number of precipitation products at multiple space and time scales have been developed based on satellite observations. However, their accuracy is limited due to the restrictions on spatiotemporal sampling of the satellite sensors and the applied parametric retrieval algorithms. Similarly, a ground-based weather radar is widely used for quantitative precipitation estimation (QPE), especially after the implementation of dual-polarization capability and urban scale deployment of high-resolution X-band radar networks. Ground-based radars are often used for the validation of various spaceborne measurements and products. This article introduces a novel machine learning-based data fusion framework to improve the satellite-based precipitation retrievals by incorporating dual-polarization measurements from a ground radar network. The prototype architecture of this fusion system is detailed. In particular, a deep learning multi-layer perceptron (MLP) model is designed to produce the rainfall estimates using the geostationary satellite infrared (IR) data and low earth orbit satellite passive microwave (PMW)-based retrievals as inputs. The high-quality rainfall products from the ground radar network are used as the target labels to train this MLP model. An urban scale demonstration study over the Dallas–Fort Worth (DFW) metroplex is presented. In addition, the Climate Prediction Center morphing technique (i.e., CMORPH) is adopted for preprocessing of the satellite observations. Rainfall products from this deep learning system are evaluated using the standard CMORPH products. The results show that the proposed data fusion framework can be used for generating accurate precipitation estimates and could be considered as an alternative tool for developing future satellite retrieval algorithms.

A Machine Learning System for Precipitation Estimation Using Satellite and Ground Radar Network Observations

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2019GL084771

Haonan Chen

Papers

The quantitative precipitation estimation system for Dallas–Fort Worth (DFW) urban remote sensing network

An Improved Dual-Polarization Radar Rainfall Algorithm (DROPS2.0): Application in NASA IFloodS Field Campaign

Observed thermal structure of a midlatitude mesopause

A Machine Learning System for Precipitation Estimation Using Satellite and Ground Radar Network Observations

Rainfall Estimation From Ground Radar and TRMM Precipitation Radar Using Hybrid Deep Neural Networks