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Donglian Sun
,
Yunyue Yu
,
Li Fang
, and
Yuling Liu

Abstract

For most land surface temperature (LST) regression algorithms, a set of optimized coefficients is determined by manual separation of the different subdivisions of atmospheric and surface conditions. In this study, a machine-learning technique, the regression tree (RT) technique, is introduced with the aim of automatically finding these subranges and the thresholds for the stratification of regression coefficients. The use of RT techniques in LST retrieval has the potential to contribute to the determination of optimal regression relationships under different conditions. Because of the lack of split-window channels for the Geostationary Operational Environmental Satellite (GOES) M–Q series (GOES-12GOES-15, plus GOES-Q), a dual-window LST algorithm was developed by combining the infrared 11-μm channel with the shortwave-infrared (SWIR) 3.9-μm channel, which presents lower atmospheric absorption than does the infrared split-window channels (11 and 12 μm). The RT technique was introduced to derive the regression models under different conditions. The algorithms were used to derive the LST product from GOES observations and were evaluated against the 2004 Surface Radiation budget network. The results indicate that the RT technique outperforms the traditional regression method.

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Richard A. Anthes
,
Ying-Hwa Kuo
,
Stanley G. Benjamin
, and
Yu-Fang Li

Abstract

The development of mesoscale features in numerical model forecasts of the environment of severe local storms is examined for two of the SESAME-1979 cases. The results show that a 10-layer model with a horizontal resolution of about 100 km, simple physics and initialized with essentially synoptic-scale data, is capable of generating and maintaining mesoscale phenomena in the 0–24 h time period. These results indicate that some mesoscale phenomena are predictable for periods of time longer than the lifetime of the mesoscale feature itself. Mesoscale features produced in the forecasts of the 10–11 April and 25–26 April cases include low-level jets, mesoscale convective complexes, upper-level jet streaks, cold and warm frontogenesis, drylines, mountain waves and capping inversions (lids). The development and structure of these phenomena in the model forecast are examined in detail and the interactions among the phenomena are emphasized. The results strongly confirm the conclusions from earlier studies that improved forecasts of mesoscale weather systems are possible through the use of fine-mesh models. Improved results can be expected with the incorporation of better surface and boundary-layer physics and with the use of mesoscale observations in the initial conditions.

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Xu Dao
,
Yu-Chi Lin
,
Fang Cao
,
Shi-Ying Di
,
Yihang Hong
,
Guanhua Xing
,
Jianjun Li
,
Pingqing Fu
, and
Yan-Lin Zhang

Abstract

The North China Plain (NCP) is becoming one of the most polluted areas characterized by a high frequency of haze pollution. However, the spatial and temporal evolutions of aerosol chemical compositions in such a highly polluted region are not well understood due to the lack of a long-term and comprehensive observation-based network. China’s National Aerosol Composition Monitoring Network (NACMON) has conducted comprehensive offline and online measurements of compositions and optical properties of airborne aerosols in order to systematically investigate the formation process, source apportionments of haze, and interactions between haze pollution and climate change. The objective of the observations is to provide information for policy makers to make strategies for the alleviation of haze occurrence. In this paper, we present instrumentations and methodologies as well as the preliminary results of the offline observations in NACMON stations over the NCP region. The implications and future perspectives of the network are also summarized. Benefiting from simultaneous observations from this network, we found that secondary aerosols were the dominant component in haze pollution. High anthropogenic emissions, low wind speed, and high relative humidity (RH) facilitated gas-to-particle transformation and resulted in high PM2.5 formation (PM2.5 is particulate matter that is smaller than 2.5 μm in diameter). Sulfate-dominant or nitrate-dominant aerosols during the haze period were driven by ambient RH. Moreover, the contributions of coal combustion and biomass burning to PM2.5 revealed downward trends, whereas secondary aerosols showed upward trends over the last decade. Thus, we highlighted that strict control of anthropogenic emissions of precursor gases, such as NO x , NH3, and volatile organic compounds (VOCs), will be an important way to decrease PM2.5 pollution in the NCP region.

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Xu Dao
,
Yu-Chi Lin
,
Fang Cao
,
Shi-Ying Di
,
Yihang Hong
,
Guanhua Xing
,
Jianjun Li
,
Pingqing Fu
, and
Yan-Lin Zhang
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Wanchun Zhang
,
Jianping Guo
,
Yucong Miao
,
Huan Liu
,
Yu Song
,
Zhang Fang
,
Jing He
,
Mengyun Lou
,
Yan Yan
,
Yuan Li
, and
Panmao Zhai

Abstract

Strongly influenced by thermodynamic stability, the planetary boundary layer (PBL) is key to the exchange of heat, momentum, and moisture between the ground surface and free troposphere. The PBL with different thermodynamic stability across the whole of China, however, is not yet well understood. In this study, the occurrence frequency and spatial distribution of the convective boundary layer (CBL), neutral boundary layer (NBL), and stable boundary layer (SBL) were systematically investigated, based on intensive summertime soundings launched at 1400 Beijing time (BJT) throughout China’s radiosonde network (CRN) for the period 2012 to 2016. Overall, the occurrences of CBL, NBL, and SBL account for 70%, 26%, and 4%, respectively, suggesting that CBL dominates in summer throughout China. In terms of the spatial pattern of PBL height, a prominent north–south gradient can be found with higher PBL height in northwest China. In addition, the PBL heights of CBL and NBL were found to be positively (negatively) associated with near-surface air temperature (humidity), whereas no apparent relationship was found for SBL. Furthermore, clouds tend to reduce the occurrence frequency, irrespective of PBL type. Roughly 70% of SBL cases occur under overcast conditions, much higher than those for NBL and CBL, indicating that clouds govern to some extent the occurrence of SBL. In contrast, except for the discernible changes in PBL height under overcast conditions relative to those under clear-sky conditions, the changes in PBL height under partly cloudy conditions are no more than 170 m for both NBL and CBL types.

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Yujun He
,
Bin Wang
,
Jiabei Fang
,
Yongqiang Yu
,
Lijuan Li
,
Juanjuan Liu
,
Li Dong
,
Ye Pu
,
Yiyuan Li
,
Shiming Xu
,
Li Liu
,
Yanluan Lin
,
Wenyu Huang
,
Xiaomeng Huang
,
Yong Wang
,
Hongbo Liu
, and
Kun Xia

Abstract

The Pacific decadal oscillation (PDO) is the most dominant decadal climate variability over the North Pacific and has substantial global impacts. However, the interannual and decadal PDO prediction skills are not satisfactory, which may result from the failure of appropriately including the North Pacific midlatitude air–sea interaction (ASI) in the initialization for climate predictions. Here, we present a novel initialization method with a climate model to crack this nutshell and achieve successful PDO index predictions up to 10 years in advance. This approach incorporates oceanic observations under the constraint of ASI, thus obtaining atmospheric initial conditions (ICs) consistent with oceanic ICs. During predictions, positive atmospheric feedback to sea surface temperature changes and time-delayed negative ocean circulation feedback to the atmosphere over the North Pacific play essential roles in the high PDO index prediction skills. Our findings highlight a great potential of ASI constraints during initialization for skillful PDO predictions.

Significance Statement

The Pacific decadal oscillation is a prominent decadal climate variability over the North Pacific. However, accurately predicting the Pacific decadal oscillation remains a challenge. In this study, we use an advanced initialization method where the oceanic observations are incorporated into a climate model constrained by air–sea interactions. We can successfully predict the Pacific decadal oscillation up to 10 years in advance, which is hardly achieved by the state-of-the-art climate prediction systems. Our results suggest that the constraint of air–sea interaction during initialization is important to skillful predictions of the climate variability on decadal time scales.

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