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Long Yang, James Smith, Mary Lynn Baeck, and Efrat Morin

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Flash flooding in the arid/semiarid southwestern United States is frequently associated with convective rainfall during the North American monsoon. In this study, we examine flood-producing storms in central Arizona based on analyses of dense rain gauge observations and stream gauging records as well as North American Regional Reanalysis fields. Our storm catalog consists of 102 storm events during the period of 1988–2014. Synoptic conditions for flood-producing storms are characterized based on principal component analyses. Four dominant synoptic modes are identified, with the first two modes explaining approximately 50% of the variance of the 500-hPa geopotential height. The transitional synoptic pattern from the North American monsoon regime to midlatitude systems is a critical large-scale feature for extreme rainfall and flooding in central Arizona. Contrasting spatial rainfall organizations and storm environment under the four synoptic modes highlights the role of interactions among synoptic conditions, mesoscale processes, and complex terrains in determining space–time variability of convective activities and flash flood hazards in central Arizona. We characterize structure and evolution properties of flood-producing storms based on storm tracking algorithms and 3D radar reflectivity. Fast-moving storm elements can be important ingredients for flash floods in the arid/semiarid southwestern United States. Contrasting storm properties for cloudburst storms highlight the wide spectrum of convective intensities for extreme rain rates in the arid/semiarid southwestern United States and exhibit comparable vertical structures to their counterparts in the eastern United States.

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Long Yang, Maofeng Liu, James A. Smith, and Fuqiang Tian

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The August 1975 flood in central China was one of the most destructive floods in history. Catastrophic flooding was the product of extreme rainfall from Typhoon Nina over a 3-day period from 5 to 7 August 1975. Despite the prominence of the August 1975 flood, relatively little is known about the evolution of rainfall responsible for the flood. Details of extreme rainfall and flooding for the August 1975 event in central China are examined based on empirical analyses of rainfall and streamflow measurements and based on downscaling simulations using the Weather Research and Forecasting (WRF) Model, driven by Twentieth Century Reanalysis (20CR) fields. Key hydrometeorological features of the flood event are placed in a climatological context through hydroclimatological analyses of 20CR fields. Results point to the complex evolution of rainfall over the 3-day period with distinctive periods of storm structure controlling rainfall distribution in the flood region. Blocking plays a central role in controlling anomalous storm motion of Typhoon Nina and extreme duration of heavy rainfall. Interaction of Typhoon Nina with a second tropical depression played a central role in creating a zone of anomalously large water vapor transport, a central feature of heavy rainfall during the critical storm period on 7 August. Analyses based on the quasigeostrophic omega equation identified the predominant role of warm air advection for synoptic-scale vertical motion. Back-trajectory analyses using a Lagrangian parcel tracking algorithm are used to assess and quantify water vapor transport for the flood. The analytical framework developed in this study is designed to improve hydrometeorological approaches for flood-control design.

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Long Zhao, Zong-Liang Yang, and Timothy J. Hoar

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Very few frameworks exist that estimate global-scale soil moisture through microwave land data assimilation (DA). Toward this goal, such a framework has been developed by linking the Community Land Model, version 4 (CLM4), and a microwave radiative transfer model (RTM) with the Data Assimilation Research Testbed (DART). The deterministic ensemble adjustment Kalman filter (EAKF) within DART is utilized to estimate global multilayer soil moisture by assimilating brightness temperature observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). A 40-member ensemble of Community Atmosphere Model, version 4.0 (CAM4.0), reanalysis is adopted to drive CLM4 simulations. Space-specific, time-invariant microwave parameters are precalibrated to minimize uncertainties in RTM. Besides, various methods are designed to upscale AMSR-E observations for computational efficiency and time shift CAM4.0 forcing to facilitate global daily assimilations. A series of experiments are conducted to quantify the DA sensitivity to microwave parameters, choice of assimilated observations, and different CLM4 updating schemes. Evaluation results indicate that the newly established CLM4–RTM–DART framework improves the open-loop CLM4-simulated soil moisture. Precalibrated microwave parameters, rather than their default values, can ensure a more robust global-scale performance. In addition, updating near-surface soil moisture is capable of improving soil moisture in deeper layers (0–30 cm), while simultaneously updating multilayer soil moisture fails to obtain intended improvements. Future work is needed to address the systematic bias in CLM4 that cannot be fully covered through the ensemble spread in CAM4.0 reanalysis.

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Guoqiang Tang, Ali Behrangi, Ziqiang Ma, Di Long, and Yang Hong

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Precipitation phase has an important influence on hydrological processes. The Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) uses temperature data from reanalysis products to implement rain–snow classification. However, the coarse resolution of reanalysis data may not reveal the spatiotemporal variabilities of temperature, necessitating appropriate downscaling methods. This study compares the performance of eight air temperature T a downscaling methods in the contiguous United States and six mountain ranges using temperature from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) as the benchmark. ERA-Interim T a is downscaled from the original 0.75° to 0.1°. The results suggest that the two purely statistical downscaling methods [nearest neighbor (NN) and bilinear interpolation (BI)] show similar performance with each other. The five downscaling methods based on the free-air temperature lapse rate (TLR), which is calculated using temperature and geopotential heights at different pressure levels, notably improves the accuracy of T a. The improvement is particularly obvious in mountainous regions. We further calculated wet-bulb temperature T w, for rain–snow classification, using T a and dewpoint temperature from ERA-Interim and PRISM. TLR-based downscaling methods result in more accurate T w compared to NN and BI in the western United States, whereas the improvement is limited in the eastern United States. Rain–snow partitioning is conducted using a critical threshold of T w with Snow Data Assimilation System (SNODAS) snowfall data serving as the benchmark. ERA-Interim-based T w using TLR downscaling methods is better than that using NN/BI and IMERG precipitation phase. In conclusion, TLR-based downscaling methods show promising prospects in acquiring high-quality T a and T w with high resolution and improving rain–snow partitioning, particularly in mountainous regions.

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Fuqiang Tian, Shiyu Hou, Long Yang, Hongchang Hu, and Aizhong Hou

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This study investigates the dependency of the evaluation of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) rainfall product on the gauge density of a ground-based rain gauge network as well as rainfall intensity over five subregions in mainland China. High-density rain gauges (1.5 gauges per 100 km2) provide exceptional resources for ground validation of satellite rainfall estimates over this region. Eight different gauge networks were derived with contrasting gauge densities ranging from 0.04 to 4 gauges per 100 km2. The evaluation focuses on two warm seasons (April–October) during 2014 and 2015. The results show a strong dependency of the evaluation metrics for the IMERG rainfall product on gauge density and rainfall intensity. A dense rain gauge network tends to provide better evaluation metrics, which implies that previous evaluations of the IMERG rainfall product based on a relatively low-density gauge network might have underestimated its performance. The decreasing trends of probability of detection with gauge density indicate a limited ability to capture light rainfall events in the IMERG rainfall product. However, IMERG tends to overestimate (underestimate) light (heavy) rainfall events, which is a consistent feature that does not show strong dependency on gauge densities. The results provide valuable insights for the improvement of a rainfall retrieval algorithm adopted in the IMERG rainfall product.

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Gang Chen, Kun Zhao, Guifu Zhang, Hao Huang, Su Liu, Long Wen, Zhonglin Yang, Zhengwei Yang, Lili Xu, and Wenjian Zhu

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In this study, the capability of using a C-band polarimetric Doppler radar and a two-dimensional video disdrometer (2DVD) to estimate monsoon-influenced summer rainfall during the Observation, Prediction and Analysis of Severe Convection of China (OPACC) field campaign in 2014 and 2015 in eastern China is investigated. Three different rainfall R estimators, for reflectivity at horizontal polarization [R(Z h)], for reflectivity at horizontal polarization and differential reflectivity factor [R(Z h, Z dr)], and for specific differential phase [R(K DP)], are derived from 2-yr 2DVD observations of summer precipitation systems. The radar-estimated rainfall is compared to gauge observations from eight rainfall episodes. Results show that the two polarimetric estimators, R(Z h, Z dr) and R(K DP), perform better than the traditional Z hR relation [i.e., R(Z h)]. The K DP-based estimator [i.e., R(K DP)] produces the best rainfall accumulations. The radar rainfall estimators perform differently across the three organized convective systems (mei-yu rainband, typhoon rainband, and squall line). Estimator R(Z h) overestimates rainfall in the mei-yu rainband and squall line, and R(Z h, Z dr) mitigates the overestimation in the mei-yu rainband but has a large bias in the squall line. QPE from R(K DP) is the most accurate among the three estimators, but it possesses a relatively large bias for the squall line compared to the mei-yu case. The high variability of drop size distribution (DSD) related to the precipitation microphysics in different types of rain is largely responsible for the case-dependent QPE performance using any single radar rainfall estimator. The squall line has a distinct ice-phase process with a large mean size of raindrops, while the mei-yu rainband and typhoon rainband are composed of smaller raindrops. Based on the statistical QPE error in the Z HZ DR space, a new composite rainfall estimator is constructed by combining R(Z h), R(Z h, Z dr), and R(K DP) and is proven to outperform any single rainfall estimator.

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Kingtse C. Mo, Lindsey N. Long, Youlong Xia, S. K. Yang, Jae E. Schemm, and Michael Ek

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Drought indices derived from the Climate Forecast System Reanalysis (CFSR) are compared with indices derived from the ensemble North American Land Data Assimilation System (NLDAS) and the North American Regional Reanalysis (NARR) over the United States. Uncertainties in soil moisture, runoff, and evapotranspiration (E) from three systems are assessed by comparing them with limited observations, including E from the AmeriFlux data, soil moisture from the Oklahoma Mesonet and the Illinois State Water Survey, and streamflow data from the U.S. Geological Survey (USGS). The CFSR has positive precipitation (P) biases over the western mountains, the Pacific Northwest, and the Ohio River valley in winter and spring. In summer, it has positive biases over the Southeast and large negative biases over the Great Plains. These errors limit the ability to use the standardized precipitation indices (SPIs) derived from the CFSR to measure the severity of meteorological droughts. To compare with the P analyses, the Heidke score for the 6-month SPI derived from the CFSR is on average about 0.5 for the three-category classification of drought, floods, and neutral months. The CFSR has positive E biases in spring because of positive biases in downward solar radiation and high potential evaporation. The negative E biases over the Great Plains in summer are due to less P and soil moisture in the root zone. The correlations of soil moisture percentile between the CFSR and the ensemble NLDAS are regionally dependent. The correlations are higher over the area east of 100°W and the West Coast. There is less agreement between them over the western interior region.

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Yonghwan Kwon, Zong-Liang Yang, Long Zhao, Timothy J. Hoar, Ally M. Toure, and Matthew Rodell

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This paper addresses continental-scale snow estimates in North America using a recently developed snow radiance assimilation (RA) system. A series of RA experiments with the ensemble adjustment Kalman filter are conducted by assimilating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature T B at 18.7- and 36.5-GHz vertical polarization channels. The overall RA performance in estimating snow depth for North America is improved by simultaneously updating the Community Land Model, version 4 (CLM4), snow/soil states and radiative transfer model (RTM) parameters involved in predicting T B based on their correlations with the prior T B (i.e., rule-based RA), although degradations are also observed. The RA system exhibits a more mixed performance for snow cover fraction estimates. Compared to the open-loop run (0.171 m RMSE), the overall snow depth estimates are improved by 1.6% (0.168 m RMSE) in the rule-based RA whereas the default RA (without a rule) results in a degradation of 3.6% (0.177 m RMSE). Significant improvement of the snow depth estimates in the rule-based RA is observed for tundra snow class (11.5%, p < 0.05) and bare soil land-cover type (13.5%, p < 0.05). However, the overall improvement is not significant (p = 0.135) because snow estimates are degraded or marginally improved for other snow classes and land covers, especially the taiga snow class and forest land cover (7.1% and 7.3% degradations, respectively). The current RA system needs to be further refined to enhance snow estimates for various snow types and forested regions.

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Hao Huang, Guifu Zhang, Kun Zhao, Su Liu, Long Wen, Gang Chen, and Zhengwei Yang

Abstract

Drop size distribution (DSD) is a fundamental parameter in rain microphysics. Retrieving DSDs from polarimetric radar measurements extends the capabilities of rain microphysics research and quantitative precipitation estimation. In this study, issues in rain DSD retrieval were studied with simulated and measured data. It was found that a three-parameter gamma distribution model was not suitable for directly retrieving DSD from polarimetric radar measurements. A statistical constraint, such as the shape–slope relation used in the constrained-gamma (C-G) distribution model, helped to reduce the uncertainties and errors in the retrieval. The inclusion of specific differential phase (K DP) measurements resulted in more accurate DSD retrieval and rain physical parameter estimation if the measurement errors were properly characterized in the error minimization analysis (EMA), which was verified using two real precipitation events. The study demonstrated the potential of using full polarimetric radar measurements to improve rain DSD retrieval.

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Zhihua He, Long Yang, Fuqiang Tian, Guangheng Ni, Aizhong Hou, and Hui Lu

Abstract

The aim of this study is to evaluate the accuracy of daily rainfall estimates based on the GPM level-3 final product derived from the IMERG algorithm (abbreviated as IMERG) and TRMM 3B42, version 7 (abbreviated as 3B42), in the upper Mekong River basin, a mountainous region in southwestern China. High-density rain gauges provide exceptional resources for ground validation of satellite rainfall estimates over this region. The performance of the two satellite rainfall products is evaluated during two rainy seasons (May–October) over the period 2014–15, as well as their applications in hydrological simulations. Results indicate that 1) IMERG systematically reduces the bias value in rainfall estimates at the gridbox scale and presents a greater ability to capture rainfall variability at the local domain scale compared with 3B42; 2) IMERG improves the ability to capture rain events with moderate intensities and presents higher capability in detecting occurrences of extreme rain events, but significantly overestimates the amounts of these extreme events; and 3) IMERG generally produces comparable daily streamflow simulations to 3B42 and tends to outperform 3B42 in driving hydrological simulations when calibrating model parameters using each rainfall input. This study provides an early evaluation of the IMERG rainfall product over a mountainous region. The findings indicate the potential of the IMERG product in overestimating extreme rain events, which could serve as the basis for further improvement of IMERG rainfall retrieval algorithms. The hydrological evaluations described here could shed light on the emerging application of retrospectively generated IMERG products back to the TRMM era.

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