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Pengfei Shi, Jiangyuan Zeng, Kun-Shan Chen, Hongliang Ma, Haiyun Bi, and Chenyang Cui

Abstract

The Tibetan Plateau (TP), known as the “Third Pole,” is a climate-sensitive and ecology-fragile region. In this study, the spatiotemporal trends of soil moisture (SM) and vegetation were analyzed using satellite-based ESA CCI SM and MODIS LAI data, respectively, in the growing season during the last 20 years (2000–19) over the TP covering diverse climate zones. The climatic drivers (precipitation and air temperature) of SM and LAI variations were fully investigated by using both ERA5 reanalysis and observation-based gridded data. The results reveal the TP is generally wetting and significantly greening in the last 20 years. The SM with significant increasing trend accounts for 21.80% (fraction of grid cells) of the TP, and is about twice of the SM with significant decreasing trend (10.19%), while more than half of the TP (58.21%) exhibits significant increasing trend of LAI. Though the responses of SM and LAI to climatic factors are spatially heterogeneous, precipitation is the dominant driver of SM variation with 48.36% (ERA5) and 32.51% (observation-based) precipitation data showing the strongest significant positive partial correlation with SM. Temperature rise largely explains the vegetation greening, though precipitation also plays an important role in vegetation growth in arid and semiarid zones. The combined trend of SM and LAI indicates the TP is mainly composed of wetting and greening areas, followed by drying and greening regions. The change rate of SM is negative at low altitudes and becomes positive as altitude increases, while the LAI value and its change rate decrease as altitude increases.

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Xiaoyang Li, Ryuichi Kawamura, Atsuko Sugimoto, and Kei Yoshimura

Abstract

Moisture sources and their corresponding temperature and humidity are important for explosive extratropical cyclones’ development regarding latent heating. To clarify the water origins and moisture transport processes within an explosive cyclone, we simulated an explosive cyclone migrating poleward across the Sea of Japan on 30 November 2014, by using an isotopic regional spectral model. In the cyclone’s center area, a replacement of water origins occurred during the cyclone’s development. During the early stage, the warm conveyor belt (WCB) transported large amounts of moisture from the East China Sea and Kuroshio into the cyclone’s inner region. While in the deepening stage, the cold conveyor belt (CCB) and dry intrusion (DI) conveyed more moisture from the northwest Pacific Ocean and the Sea of Japan, respectively. Compared with the contribution of local moisture, that of remote moisture was dominant in the cyclone’s center area. Regarding the water origins of condensation within the frontal system in the deepening stage, the northwest Pacific Ocean vapor, principally transported by the CCB, contributed 35.5% of the condensation in the western warm front. The East China Sea and Kuroshio moisture, conveyed by the WCB, accounted for 32.4% of the condensation in the cold and eastern warm fronts. In addition, condensation from the Sea of Japan, which was mainly triggered by the DI and induced by the topography, occurred on the west coast of the mainland of Japan and near the cyclone center. The spatial distribution of the isotopic composition in condensation and water vapor also supports the water-origin results.

Open access
Yagmur Derin, Pierre-Emmanuel Kirstetter, and Jonathan J. Gourley

Abstract

As a fundamental water flux, quantitative understanding of precipitation is important to understand and manage water systems under a changing climate, especially in transition regions such as the coastal interface between land and ocean. This work aims to assess the uncertainty in precipitation detection over the land–coast–ocean continuum in the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) V06B product. It is examined over three coastal regions of the United States—the West Coast, the Gulf of Mexico, and the East Coast, all of which are characterized by different topographies and precipitation climatologies. Detection capabilities are contrasted over different surfaces (land, coast, and ocean). A novel and integrated approach traces the IMERG detection performance back to its components (passive microwave, infrared, and morphing-based estimates). The analysis is performed by using high-resolution, high-quality Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) rainfall estimates as ground reference. The best detection performances are reported with PMW estimates (hit rates in the range [25%–39%]), followed by morphing ([20%–34%]), morphing+IR ([17%–27%]) and IR ([11%–16%]) estimates. Precipitation formation mechanisms play an important role, especially in the West Coast where orographic processes challenge detection. Further, precipitation typology is shown to be a strong driver of IMERG detection. Over the ocean, IMERG detection is generally better but suffers from false alarms ([10%–53%]). Overall, IMERG displays nonhomogeneous precipitation detection capabilities tracing back to its components. Results point toward a similar behavior across various land–coast–ocean continuum regions of the CONUS, which suggests that results can be potentially transferred to other coastal regions of the world.

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Clement Guilloteau, Efi Foufoula-Georgiou, Pierre Kirstetter, Jackson Tan, and George J. Huffman

Abstract

As more global satellite-derived precipitation products become available, it is imperative to evaluate them more carefully for providing guidance as to how well precipitation space–time features are captured for use in hydrologic modeling, climate studies, and other applications. Here we propose a space–time Fourier spectral analysis and define a suite of metrics that evaluate the spatial organization of storm systems, the propagation speed and direction of precipitation features, and the space–time scales at which a satellite product reproduces the variability of a reference “ground-truth” product (“effective resolution”). We demonstrate how the methodology relates to our physical intuition using the case study of a storm system with rich space–time structure. We then evaluate five high-resolution multisatellite products (CMORPH, GSMaP, IMERG-Early, IMERG-Final, and PERSIANN-CCS) over a period of 2 years over the southeastern United States. All five satellite products show generally consistent space–time power spectral density when compared to a reference ground gauge–radar dataset (GV-MRMS), revealing agreement in terms of average morphology and dynamics of precipitation systems. However, a deficit of spectral power at wavelengths shorter than 200 km and periods shorter than 4 h reveals that all satellite products are excessively “smooth.” The products also show low levels of spectral coherence with the gauge–radar reference at these fine scales, revealing discrepancies in capturing the location and timing of precipitation features. From the space–time spectral coherence, the IMERG-Final product shows superior ability in resolving the space–time dynamics of precipitation down to 200-km and 4-h scales compared to the other products.

Open access
Ping Song and Guosheng Liu

Abstract

Whether precipitation falls in the form of rain or snow is of great importance to glacier accumulation and ablation. Assessments of phase-aware precipitation have been lacking over the vast area of the Tibetan Plateau (TP) due to the scarcity of surface measurements and the low quality of satellite estimates in this region. In this study, we attempt a satellite radar-based method for this precipitation partition, in which the CloudSat radar is used for snowfall while the Global Precipitation Measurement Mission radar is used for rainfall estimation. Assuming that 11-yr snowfall and 5-yr rainfall estimates represent the mean states of precipitation at each phase, the phase partition characteristics, including its annual mean, spatial pattern, seasonal dependence, and variation with elevations, are then discussed. Averaged over the highland area (over 1 km above mean sea level) in TP, the annual total precipitation is estimated to be around 400 mm, of which about 40% falls as snow. The snowfall mass fraction is about 45% in the northern and 30% in the southern part of TP, and about 80% in the cold and 30% in the warm half of the year. Surface elevation is found to be a high-impact factor on total precipitation and its phase partition, generally with total precipitation decreasing but snowfall fraction increasing with the increase of elevation. While there are some shortcomings, the current approach in combining snowfall and rainfall estimates from two satellite radars presents a useful pathway to assessing phase-aware precipitation over the TP region.

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Peter E. Goble, Rebecca A. Bolinger, and Russ S. Schumacher

Abstract

Agricultural droughts afflicting the contiguous United States (CONUS) are serious and costly natural hazards. Widespread damage to a single cash crop may be crippling to rural communities that produce it. While drought is insidious in nature, drought indices derived from meteorological data and drought impact reports both provide essential guidance to decision makers about the location and intensity of developing and ongoing droughts. However, response to dry meteorological conditions is not consistent from one crop type to the next, making crop-specific drought appraisal difficult using weather data alone. Additionally, drought impact reports are often subjective, latent, or both. To rectify this, we developed drought indices using meteorological data, and phenological information for the row crops most commonly grown over CONUS: corn, soybeans, and winter wheat. These are referred to as crop-specific standardized precipitation-evapotranspiration indices (CSPEIs). CSPEIs correlate more closely with end-of-season yields than traditional meteorological indicators for the eastern two thirds of CONUS for corn, and offer an advantage in predicting winter wheat yields for the High Plains. CSPEIs do not always explain a higher fraction of variance than traditional meteorological indicators. In such cases, results provide insight on which meteorological indicators to use to most effectively supplement impacts information.

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Andrew Hoell, Trent W. Ford, Molly Woloszyn, Jason A. Otkin, and Jon Eischeid

Abstract

Characteristics and predictability of drought in the Midwestern United States, spanning the Great Plains to the Ohio Valley, at local and regional scales are examined during 1916-2015. Given vast differences in hydroclimatic variability across the Midwest, drought is evaluated in four regions identified using a hierarchical clustering algorithm applied to an integrated drought index based on soil moisture, snow water equivalent, and three-month runoff from land surface models forced by observed analyses. Highlighting the regions containing the Ohio Valley (OV) and Northern Great Plains (NGP), the OV demonstrates a preference for sub-annual droughts, the timing of which can lead to prevalent dry epochs, while the NGP demonstrates a preference for annual-to-multi-annual droughts. Regional drought variations are closely related to precipitation, resulting in a higher likelihood of drought onset or demise during wet seasons: March-November in the NGP and all year in the OV, with a preference for March-May and September-November. Due to the distinct dry season in the NGP, there is a higher likelihood of longer drought persistence, as the NGP is four times more likely to experience drought lasting at least one year compared to the OV. While drought variability in all regions and seasons are related to atmospheric wave trains spanning the Pacific-North American sector, longer-lead predictability is limited to the OV in December-February because it is the only region/season related to slow-varying sea surface temperatures consistent with El Niño-Southern Oscillation. The wave trains in all other regions appear to be generated in the atmosphere, highlighting the importance of internal atmospheric variability in shaping Midwestern drought.

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Sharon E. Nicholson, Adam T. Hartman, and Douglas A. Klotter

Abstract

This article examines the diurnal cycle of lake-effect rains over Lake Victoria and of rainfall in the surrounding catchment. The analysis focuses on four months, which represent the two wet seasons (April and November) and the two dry seasons (February and July). Lake-effect rains are strongest in April, weakest in July. In all cases there is a nocturnal rainfall maximum over the lake and a daytime maximum over the catchment, with the transition between rainfall over the lake and over the catchment occurring between 1200 and 1500 LST. During the night the surrounding catchment is mostly dry. Conversely, little to no rain falls over the lake during the afternoon and early evening. In most cases the maximum over the lake occurs at either 0600 or 0900 LST and the maximum over the catchment occurs around 1500 to 1800 LST. The diurnal cycle of Mesoscale Convective Systems (MCSs) parallels that of over-lake rainfall. MCS initiation generally begins over the catchment around 1500 LST and increases at 1800 LST. MCS initiation over the lake begins around 0300 LST and continues until 1200 LST. While some MCSs originate over the highlands to the east of the lake, most originate in situ over the lake. Maximum MCS activity over the lake occurs at 0600 LST and is associated with the systems that initiate in situ.

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Sharon E. Nicholson, Adam T. Hartman, and Douglas A. Klotter

Abstract

The purpose of this article is to determine the meteorological factors controlling the lake-effect rains over Lake Victoria. Winds, divergence, vertical motion, specific humidity, Convective Available Potential Energy (CAPE), and Convective Inhibition (CIN) were examined. The local wind regime and associated divergence/convergence are the major factors determining the diurnal cycle of rainfall over the lake and catchment. The major contrast between over-lake rainfall in the wet- and dry-season months is the vertical profile of omega. This appears to be a result of seasonal contrasts in CAPE, CIN, and specific humidity, parameters that play a critical role in vertical motion and convective development.

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Yuhang Zhang and Aizhong Ye

Abstract

Obtaining high-quality quantitative precipitation forecasts is a key precondition for hydrological forecast systems. Due to multisource uncertainties (e.g., initial conditions, model structures and parameters), raw forecasts are subject to systematic biases; hence, statistical post-processing is often required to reduce these errors before the forecasts can proceed to hydrological applications. Machine learning (ML) algorithms are canonical statistical models, and they are diverse in type and variation. It is important to verify and compare their performance in the same scenario (e.g., precipitation post-processing). In this paper, we conduct a large-scale comparison study for the major ML models with diverse model structures and regularization strategies as post-processors for improving the quality of precipitation forecasts. Specifically, we compare the efficiency and effectiveness of 21 ML algorithms on solving this task. Daily reforecast precipitation with lead times up to 8 days from the Global Ensemble Forecast System and corresponding observations are employed to determine the usability of different models in the Yalong River basin in China. The performance of each model is validated by a group of carefully designed experiments and statistical metrics. The results reveal that improvements in model structures are more effective than regularization strategies. Among these algorithms, the optimized extra-trees regressor exhibit the best performance, effectively reduce overestimation and achieve the best skill in forecasting precipitation. Eleven ensemble members and a 2-day time window can be used as predictors to obtain the best model performance. The systematic experiments and findings also offer useful guidelines for other related studies.

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