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A New Global Solar-induced Chlorophyll Fluorescence (SIF) Data Product from TanSat Measurements
Lu YAO, Dongxu YANG, Yi LIU, Jing WANG, Liangyun LIU, Shanshan DU, Zhaonan CAI, Naimeng LU, Daren LYU, Maohua WANG, Zengshan YIN, Yuquan ZHENG
2021, 38(3): 341-345. doi: 10.1007/s00376-020-0204-6
The Chinese Carbon Dioxide Observation Satellite Mission (TanSat) is the third satellite for global CO2 monitoring and is capable of detecting weak solar-induced chlorophyll fluorescence (SIF) signals with its advanced technical characteristics. Based on the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing (IAPCAS) platform, we successfully retrieved the TanSat global SIF product spanning the period of March 2017 to February 2018 with a physically based algorithm. This paper introduces the new TanSat SIF dataset and shows the global seasonal SIF maps. A brief comparison between the IAPCAS TanSat SIF product and the data-driven SVD (singular value decomposition) SIF product is also performed for follow-up algorithm optimization. The comparative results show that there are regional biases between the two SIF datasets and the linear correlations between them are above 0.73 for all seasons. The future SIF data product applications and requirements for SIF space observation are discussed.
Phase Two of the Integrative Monsoon Frontal Rainfall Experiment (IMFRE-II) over the Middle and Lower Reaches of the Yangtze River in 2020
Chunguang CUI, Xiquan DONG, Bin WANG, Hao YANG
2021, 38(3): 346-356. doi: 10.1007/s00376-020-0262-9
Phase Two of the Integrative Monsoon Frontal Rainfall Experiment (IMFRE-II) was conducted over the middle and lower reaches of the Yangtze River during the period 16 June to 19 July 2020. This paper provides a brief overview of the IMFRE-II field campaign, including the multiple ground-based remote sensors, aircraft probes, and their corresponding measurements during the 2020 mei-yu period, as well as how to use these numerous datasets to answer scientific questions. The highlights of IMFRE-II are: (1) to the best of our knowledge, IMFRE-II is the first field campaign in China to use ground-based, airborne, and spaceborne platforms to conduct comprehensive observations over the middle and lower reaches of the Yangtze River; and (2) seven aircraft flights were successfully carried out, and the spectra of ice particles, cloud droplets, and raindrops at different altitudes were obtained. These in-situ measurements will provide a “cloud truth” to validate the ground-based and satellite-retrieved cloud and precipitation properties and quantitatively estimate their retrieval uncertainties. They are also crucial for the development of a warm (and/or cold) rain conceptual model in order to better understand the cloud-to-rain conversion and accretion processes in mei-yu precipitation events. Through an integrative analysis of ground-based, aircraft, and satellite observations and model simulations, we can significantly improve our cloud and precipitation retrieval algorithms, investigate the microphysical properties of cloud and precipitation, understand in-depth the formation and dissipation mechanisms of mei-yu frontal systems, and improve cloud microphysics parameterization schemes and model simulations.
Integrative Monsoon Frontal Rainfall Experiment (IMFRE-I): A Mid-Term Review
Chunguang CUI, Xiquan DONG, Bin WANG, Baike XI, Yi DENG, Yihui DING
2021, 38(3): 357-374. doi: 10.1007/s00376-020-0209-1
The mei-yu season, typically occurring from mid-June to mid-July, on average, contributes to 32% of the annual precipitation over the Yangtze–Huai River Valley (YHRV) and represents one of the three heavy-rainfall periods in China. Here, we briefly review the large-scale background, synoptic pattern, moisture transport, and cloud and precipitation characteristics of the mei-yu frontal systems in the context of the ongoing Integrative Monsoon Frontal Rainfall Experiment (IMFRE) field campaign. Phase one of the campaign, IMFRE-I, was conducted from 10 June to 10 July 2018 in the middle reaches of the YHRV. Led by the Wuhan Institute of Heavy Rain (IHR) with primary support from the National Natural Science Foundation of China, IMFRE-I maximizes the use of our observational capacity enabled by a suite of ground-based and remote sensing instruments, most notably the IHR Mesoscale Heavy Rainfall Observing System (MHROS), including different wavelengths of radars, microwave radiometers, and disdrometers. The KA350 (Shanxi King-Air) aircraft participating in the campaign is equipped with Ka-band cloud radar and different probes. The comprehensive datasets from both the MHROS and aircraft instruments are combined with available satellite observations and model simulations to answer the three scientific questions of IMFRE-I. Some highlights from a previously published special issue are included in this review, and we also briefly introduce the IMFRE-II field campaign, conducted during June–July 2020, where the focus was on the spatiotemporal evolutions of the mei-yu frontal systems over the middle and lower reaches of the YHRV.
Original Paper
Intermodel Diversity of Simulated Long-term Changes in the Austral Winter Southern Annular Mode: Role of the Southern Ocean Dipole
Fei ZHENG, Jianping LI, Shuailei YAO
2021, 38(3): 375-386. doi: 10.1007/s00376-020-0241-1
The Southern Annular Mode (SAM) plays an important role in regulating Southern Hemisphere extratropical circulation. State-of-the-art models exhibit intermodel spread in simulating long-term changes in the SAM. Results from Atmospheric Model Intercomparison Project (AMIP) experiments from 28 models archived in CMIP5 show that the intermodel spread in the linear trend in the austral winter (June−July−August) SAM is significant, with an intermodel standard deviation of 0.28 (10 yr)−1, larger than the multimodel ensemble mean of 0.18 (10 yr)−1. This study explores potential factors underlying the model difference from the aspect of extratropical sea surface temperature (SST). Extratropical SST anomalies related to the SAM exhibit a dipole-like structure between middle and high latitudes, referred to as the Southern Ocean Dipole (SOD). The role of SOD-like SST anomalies in influencing the SAM is found in the AMIP simulations. Model performance in simulating the SAM trend is linked with model skill in reflecting the SOD−SAM relationship. Models with stronger linkage between the SOD and the SAM tend to simulate a stronger SAM trend. The explained variance is about 40% in the AMIP runs. These results suggest improved simulation of the SOD−SAM relationship may help reproduce long-term changes in the SAM.
Determination of Surface Precipitation Type Based on the Data Fusion Approach
2021, 38(3): 387-399. doi: 10.1007/s00376-020-0165-9
Hazardous events related to atmospheric precipitation depend not only on the intensity of surface precipitation, but also on its type. Uncertainty related to determination of the precipitation type (PT) leads to financial losses in many areas of human activity, such as the power industry, agriculture, transportation, and many more. In this study, we use machine learning (ML) algorithms with the data fusion approach to more accurately determine surface PT. Based on surface synoptic observations, ERA5 reanalysis, and radar data, we distinguish between liquid, mixed, and solid precipitation types. The study domain considers the entire area of Poland and a period from 2015 to 2017. The purpose of this work is to address the question: “How can ML techniques applied in observational and NWP data help to improve the recognition of the surface PT?” Despite testing 33 parameters, it was found that a combination of the near-surface air temperature and the depth of the warm layer in the 0–1000 m above ground level (AGL) layer contains most of the signal needed to determine surface PT. The accrued probability of detection for liquid, solid, and mixed PTs according to the developed Random Forest model is 98.0%, 98.8%, and 67.3%, respectively. The application of the ML technique and data fusion approach allows to significantly improve the robustness of PT prediction compared to commonly used baseline models and provides promising results for operational forecasters.
Classification of Northeast China Cold Vortex Activity Paths in Early Summer Based on K-means Clustering and Their Climate Impact
Yihe FANG, Haishan CHEN, Yi LIN, Chunyu ZHAO, Yitong LIN, Fang ZHOU
2021, 38(3): 400-412. doi: 10.1007/s00376-020-0118-3
The classification of the Northeast China Cold Vortex (NCCV) activity paths is an important way to analyze its characteristics in detail. Based on the daily precipitation data of the northeastern China (NEC) region, and the atmospheric circulation field and temperature field data of ERA-Interim for every six hours, the NCCV processes during the early summer (June) seasons from 1979 to 2018 were objectively identified. Then, the NCCV processes were classified using a machine learning method (k-means) according to the characteristic parameters of the activity path information. The rationality of the classification results was verified from two aspects, as follows: (1) the atmospheric circulation configuration of the NCCV on various paths; and (2) its influences on the climate conditions in the NEC. The obtained results showed that the activity paths of the NCCV could be divided into four types according to such characteristics as the generation origin, movement direction, and movement velocity of the NCCV. These included the generation-eastward movement type in the east of the Mongolia Plateau (eastward movement type or type A); generation-southeast long-distance movement type in the upstream of the Lena River (southeast long-distance movement type or type B); generation-eastward less-movement type near Lake Baikal (eastward less-movement type or type C); and the generation-southward less-movement type in eastern Siberia (southward less-movement type or type D). There were obvious differences observed in the atmospheric circulation configuration and the climate impact of the NCCV on the four above-mentioned types of paths, which indicated that the classification results were reasonable.
Sensitivity of Snowfall Characteristics to Meteorological Conditions in the Yeongdong Region of Korea
Yoo-Jun KIM, So-Ra IN, Hae-Min KIM, Jin-Hwa LEE, Kyu Rang KIM, Seungbum KIM, Byung-Gon KIM
2021, 38(3): 413-429. doi: 10.1007/s00376-020-0157-9
This study investigates the characteristics of cold clouds and snowfall in both the Yeongdong coastal and mountainous regions under different meteorological conditions based on the integration of numerical modeling and three-hourly rawinsonde observations with snow crystal photographs for a snowfall event that occurred on 29−30 January 2016. We found that rimed particles predominantly observed turned into dendrite particles in the latter period of the episode when the 850 hPa temperature decreased at the coastal site, whereas the snow crystal habits at the mountainous site were largely needle or rimed needle. Rawinsonde soundings showed a well-defined, two-layered cloud structure along with distinctive wind-directional shear, and an inversion in the equivalent potential temperature above the low-level cloud layer. The first experiment with a decrease in lower-layer temperature showed that the low-level cloud thickness was reduced to less than 1.5 km, and the accumulated precipitation was decreased by 87% compared with the control experiment. The difference in precipitation amount between the single-layered experiment and control experiment (two-layered) was not so significant to attribute it to the effect of the seeder−feeder mechanism. The precipitation in the last experiment by weakening wind-directional shear was increased by 1.4 times greater than the control experiment specifically at the coastal site, with graupel particles accounting for the highest proportion (~62%). The current results would improve snowfall forecasts in complicated geographical environments such as Yeongdong in terms of snow crystal habit as well as snowfall amount in both time and space domains.
Increases in Anthropogenic Heat Release from Energy Consumption Lead to More Frequent Extreme Heat Events in Urban Cities
Bin LIU, Zhenghui XIE, Peihua QIN, Shuang LIU, Ruichao LI, Longhuan WANG, Yan WANG, Binghao JIA, Si CHEN, Jinbo XIE, Chunxiang SHI
2021, 38(3): 430-445. doi: 10.1007/s00376-020-0139-y
With economic development and rapid urbanization, increases in Gross Domestic Product and population in fast-growing cities since the turn of the 21st Century have led to increases in energy consumption. Anthropogenic heat flux released to the near-surface atmosphere has led to changes in urban thermal environments and severe extreme temperature events. To investigate the effects of energy consumption on urban extreme temperature events, including extreme heat and cold events, a dynamic representation scheme of anthropogenic heat release (AHR) was implemented in the Advanced Research version of the Weather Research and Forecasting (WRF) model, and AHR data were developed based on energy consumption and population density in a case study of Beijing, China. Two simulations during 1999−2017 were then conducted using the developed WRF model with 3-km resolution with and without the AHR scheme. It was shown that the mean temperature increased with the increase in AHR, and more frequent extreme heat events were produced, with an annual increase of 0.02−0.19 days, as well as less frequent extreme cold events, with an annual decrease of 0.26−0.56 days, based on seven extreme temperature indices in the city center. AHR increased the sensible heat flux and led to surface energy budget changes, strengthening the dynamic processes in the atmospheric boundary layer that reduce AHR heating efficiency more in summer than in winter. In addition, it was concluded that suitable energy management might help to mitigate the impact of extreme temperature events in different seasons.
Numerical Simulation to Evaluate the Effects of Upward Lightning Discharges on Thunderstorm Electrical Parameters
Tianxue ZHENG, Yongbo TAN, Yiru WANG
2021, 38(3): 446-459. doi: 10.1007/s00376-020-0154-z
A theoretical discussion of the discharge effects of upward lightning simulated with a fine-resolution 2D thunderstorm model is performed in this paper, and the results reveal that the estimates of the total induced charge on the upward lightning discharge channels range from 0.67 to 118.8 C, and the average value is 19.0 C, while the ratio of the induced charge on the leader channels to the total opposite-polarity charge in the discharge region ranges from 5.9% to 47.3%, with an average value of 14.7%. Moreover, the average value of the space electrostatic energy consumed by upward lightning is 1.06×109 J. The above values are lower than those related to intracloud lightning discharges. The density of the deposited opposite-polarity charge is comparable in magnitude to that of the preexisting charge in the discharge area, and the deposition of these opposite-polarity charges rapidly destroys the original space potential well in the discharge area and greatly reduces the space electric field strength. In addition, these opposite-polarity charges are redistributed with the development of thunderstorms. The space charge redistribution caused by lightning discharges partly accounts for the complexity of the charge structures in a thunderstorm, and the complexity gradually decreases with the charge neutralization process.
Future Precipitation Extremes in China under Climate Change and Their Physical Quantification Based on a Regional Climate Model and CMIP5 Model Simulations
Peihua QIN, Zhenghui XIE, Jing ZOU, Shuang LIU, Si CHEN
2021, 38(3): 460-479. doi: 10.1007/s00376-020-0141-4
The atmospheric water holding capacity will increase with temperature according to Clausius–Clapeyron scaling and affects precipitation. The rates of change in future precipitation extremes are quantified with changes in surface air temperature. Precipitation extremes in China are determined for the 21st century in six simulations using a regional climate model, RegCM4, and 17 global climate models that participated in CMIP5. First, we assess the performance of the CMIP5 models and RCM runs in their simulation of extreme precipitation for the current period (RF: 1982–2001). The CMIP5 models and RCM results can capture the spatial variations of precipitation extremes, as well as those based on observations: OBS and XPP. Precipitation extremes over four subregions in China are predicted to increase in the mid-future (MF: 2039–58) and far-future (FF: 2079–98) relative to those for the RF period based on both the CMIP5 ensemble mean and RCM ensemble mean. The secular trends in the extremes of the CMIP5 models are predicted to increase from 2008 to 2058, and the RCM results show higher interannual variability relative to that of the CMIP5 models. Then, we quantify the increasing rates of change in precipitation extremes in the MF and FF periods in the subregions of China with the changes in surface air temperature. Finally, based on the water vapor equation, changes in precipitation extremes in China for the MF and FF periods are found to correlate positively with changes in the atmospheric vertical wind multiplied by changes in surface specific humidity (significant at the p < 0.1 level).
Hybrid Method to Identify Second-trip Echoes Using Phase Modulation and Polarimetric Technology
Shuai ZHANG, Jinzhong MIN, Chian ZHANG, Xingyou HUANG, Jun LIU, Kaihua WEI
2021, 38(3): 480-492. doi: 10.1007/s00376-020-0223-3
For pulse Doppler radars, the widely used method for identifying second-trip echoes (STs) in the signal processing level yields significant misidentification in regions of high turbulence and severe wind shear. In the data processing level, although the novel algorithm for ST identification does not yield significant misidentification in specific regions, its overall identification performance is not ideal. Therefore, this paper proposes a hybrid method for the identification of STs using phase modulation (signal processing) and polarimetric technology (data processing). Through this approach, most of the STs are removed, whereas most of the first-trip echoes (FTs) remain untouched. Compared with the existing method using a signal quality index filter with an optimized threshold, the hybrid method exhibits superior performance (Heidke skill scores of 0.98 versus 0.88) on independent test datasets, especially in high-turbulence and severe-wind-shear regions, for which misidentification is significantly reduced.
Understanding the Soil Temperature Variability at Different Depths: Effects of Surface Air Temperature, Snow Cover, and the Soil Memory
Haoxin ZHANG, Naiming YUAN, Zhuguo MA, Yu HUANG
2021, 38(3): 493-503. doi: 10.1007/s00376-020-0074-y
The soil temperature (ST) is closely related to the surface air temperature (AT), but their coupling may be affected by other factors. In this study, significant effects of the AT on the underlying ST were found, and the time taken to propagate downward to 320 cm can be up to 10 months. Besides the AT, the ST is also affected by memory effects—namely, its prior thermal conditions. At deeper depth (i.e., 320 cm), the effects of the AT from a particular season may be exceeded by the soil memory effects from the last season. At shallower layers (i.e., < 80 cm), the effects of the AT may be blocked by the snow cover, resulting in a poorly synchronous correlation between the AT and the ST. In northeastern China, this snow cover blockage mainly occurs in winter and then vanishes in the subsequent spring. Due to the thermal insulation effect of the snow cover, the winter ST at layers above 80 cm in northeastern China were found to continue to increase even during the recent global warming hiatus period. These findings may be instructive for better understanding ST variations, as well as land−atmosphere interactions.
Skill Assessment of Copernicus Climate Change Service Seasonal Ensemble Precipitation Forecasts over Iran
2021, 38(3): 504-521. doi: 10.1007/s00376-020-0025-7
Medium to long-term precipitation forecasting plays a pivotal role in water resource management and development of warning systems. Recently, the Copernicus Climate Change Service (C3S) database has been releasing monthly forecasts for lead times of up to three months for public use. This study evaluated the ensemble forecasts of three C3S models over the period 1993–2017 in Iran’s eight classified precipitation clusters for one- to three-month lead times. Probabilistic and non-probabilistic criteria were used for evaluation. Furthermore, the skill of selected models was analyzed in dry and wet periods in different precipitation clusters. The results indicated that the models performed best in western precipitation clusters, while in the northern humid cluster the models had negative skill scores. All models were better at forecasting upper-tercile events in dry seasons and lower-tercile events in wet seasons. Moreover, with increasing lead time, the forecast skill of the models worsened. In terms of forecasting in dry and wet years, the forecasts of the models were generally close to observations, albeit they underestimated several severe dry periods and overestimated a few wet periods. Moreover, the multi-model forecasts generated via multivariate regression of the forecasts of the three models yielded better results compared with those of individual models. In general, the ECMWF and UKMO models were found to be appropriate for one-month-ahead precipitation forecasting in most clusters of Iran. For the clusters considered in Iran and for the long-range system versions considered, the Météo France model had lower skill than the other models.