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Yu Nie, Hong-Li Ren, and Yang Zhang

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Considerable progress has been made in understanding the internal eddy–mean flow feedback in the subseasonal variability of the North Atlantic Oscillation (NAO) during winter. Using daily atmospheric and oceanic reanalysis data, this study highlights the role of extratropical air–sea interaction in the NAO variability during autumn when the daily sea surface temperature (SST) variability is more active and eddy–mean flow interactions are still relevant. Our analysis shows that a horseshoe-like SST tripolar pattern in the North Atlantic Ocean, marked by a cold anomaly in the Gulf Stream and two warm anomalies to the south of the Gulf Stream and off the western coast of northern Europe, can induce a quasi-barotropic NAO-like atmospheric response through eddy-mediated processes. An initial southwest–northeast tripolar geopotential anomaly in the North Atlantic forces this horseshoe-like SST anomaly tripole. Then the SST anomalies, through surface heat flux exchange, alter the spatial patterns of the lower-tropospheric temperature and thus baroclinicity anomalies, which are manifested as the midlatitude baroclinicity shifted poleward and reduced baroclinicity poleward of 70°N. In response to such changes of the lower-level baroclinicity, anomalous synoptic eddy generation, eddy kinetic energy, and eddy momentum forcing in the midlatitudes all shift poleward. Meanwhile, the 10–30-day low-frequency anticyclonic wave activities in the high latitudes decrease significantly. We illustrate that both the latitudinal displacement of midlatitude synoptic eddy activities and intensity variation of high-latitude low-frequency wave activities contribute to inducing the NAO-like anomalies.

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Fengge Su, Yang Hong, and Dennis P. Lettenmaier

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Satellite-based precipitation estimates with high spatial and temporal resolution and large areal coverage provide a potential alternative source of forcing data for hydrological models in regions where conventional in situ precipitation measurements are not readily available. The La Plata basin in South America provides a good example of a case where the use of satellite-derived precipitation could be beneficial. This study evaluates basinwide precipitation estimates from 9 yr (1998–2006) of Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA; 3B42 V.6) through comparison with available gauged data and the Variable Infiltration Capacity (VIC) semidistributed hydrology model applied to the La Plata basin. In general, the TMPA estimates agreed well with the gridded gauge data at monthly time scales, most likely because of the monthly adjustment to gauges performed in TMPA. The agreement between TMPA and gauge precipitation estimates was reduced at daily time scales, particularly for high rain rates. The TMPA-driven hydrologic model simulations were able to capture the daily flooding events and to represent low flows, although peak flows tended to be biased upward. There was a good agreement between TMPA-driven simulated flows in terms of their reproduction of seasonal and interannual streamflow variability. This analysis shows that TMPA has potential for hydrologic forecasting in data-sparse regions.

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Zhe Li, Dawen Yang, Yang Hong, Jian Zhang, and Youcun Qi

Abstract

Understanding spatiotemporal rainfall patterns in mountainous areas is of great importance for prevention of natural disasters such as flash floods and landslides. There is little knowledge about rainfall variability over historically underobserved complex terrains, however, and especially about the variations of hourly rainfall. In this study, the spatiotemporal variations of hourly rainfall in the Three Gorges region (TGR) of China are investigated with gauge and newly available radar data. The spatial pattern of hourly rainfall has been examined by a number of statistics, and they all show that the rainfall variations are time-scale and location dependent. In general, the northern TGR receives more-intense and longer-duration rainfall than do other parts of the TGR, and short-duration storms could occur in most of the TGR. For temporal variations, the summer diurnal cycle shifts from a morning peak in the west to a late-afternoon peak in the east while a mixed pattern of two peaks exists in the middle. In statistical terms, empirical model–based estimation indicates that the correlation scale of hourly rainfall is about 40 km. Further investigation shows that the correlation distance varies with season, from 30 km in the warm season to 60 km in the cold season. In addition, summer rainstorms extracted from radar rainfall data are characterized by short duration (6–8 h) and highly localized patterns (5–17 and 13–36 km in the minor and major directions, respectively). Overall, this research provides quantitative information about the rainfall regime in the TGR and shows that the combination of gauge and radar data is useful for characterizing the spatiotemporal pattern of storm rainfall over complex terrain.

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Zhe Li, Dawen Yang, Bing Gao, Yang Jiao, Yang Hong, and Tao Xu

Abstract

The present study aims to evaluate three global satellite precipitation products [TMPA 3B42, version 7 (3B42 V7); TMPA 3B42 real time (3B42 RT); and Climate Prediction Center morphing technique (CMORPH)] during 2003–12 for multiscale hydrologic applications—including annual water budgeting, monthly and daily streamflow simulation, and extreme flood modeling—via a distributed hydrological model in the Yangtze River basin. The comparison shows that the 3B42 V7 data generally have a better performance in annual water budgeting and monthly streamflow simulation, but this superiority is not guaranteed for daily simulation, especially for flood monitoring. It is also found that, for annual water budgeting, the positive (negative) bias of the 3B42 RT (CMORPH) estimate is mainly propagated into the simulated runoff, and simulated evapotranspiration tends to be more sensitive to negative bias. Regarding streamflow simulation, both near-real-time products show a region-dependent bias: 3B42 RT tends to overestimate streamflow in the upper Yangtze River, and, in contrast, CMORPH shows serious underestimation in those downstream subbasins while it is able to effectively monitor streamflow into the Three Gorges Reservoir. Using 394 selected flood events, the results indicate that 3B42 RT and CMORPH have competitive performances for near-real-time flood monitoring in the upper Yangtze, but for those downstream subbasins, 3B42 RT seems to perform better than CMORPH. Furthermore, the inability of all satellite products to capture some key features of the July 2012 extreme floods reveals the deficiencies associated with them, which will limit their hydrologic utility in local flood monitoring.

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Yang Hong, Kuo-Lin Hsu, Soroosh Sorooshian, and Xiaogang Gao

Abstract

A satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described. This algorithm extracts local and regional cloud features from infrared (10.7 μm) geostationary satellite imagery in estimating finescale (0.04° × 0.04° every 30 min) rainfall distribution. This algorithm processes satellite cloud images into pixel rain rates by 1) separating cloud images into distinctive cloud patches; 2) extracting cloud features, including coldness, geometry, and texture; 3) clustering cloud patches into well-organized subgroups; and 4) calibrating cloud-top temperature and rainfall (T bR) relationships for the classified cloud groups using gauge-corrected radar hourly rainfall data. Several cloud-patch categories with unique cloud-patch features and T bR curves were identified and explained. Radar and gauge rainfall measurements were both used to evaluate the PERSIANN CCS rainfall estimates at a range of temporal (hourly and daily) and spatial (0.04°, 0.12°, and 0.25°) scales. Hourly evaluation shows that the correlation coefficient (CC) is 0.45 (0.59) at a 0.04° (0.25°) grid scale. The averaged CC of daily rainfall is 0.57 (0.63) for the winter (summer) season.

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

Abstract

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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Benjamin A. Toms, Jeffrey B. Basara, and Yang Hong

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A road ice prediction model was developed on the basis of existing data networks with an objective of providing a computationally efficient method of road ice forecasting. Icing risk was separated into three distinct road ice formation mechanisms: hoarfrost, freezing fog, and frozen precipitation. Hoarfrost parameterizations were mostly gathered as presented in previous literature, with modifications incorporated to account for diffusional ice crystal growth-rate complexity. Freezing-fog parameterizations were based on previous fog typological analyses under the assumption that fog formation mechanisms are similar in above- and subfreezing temperatures. Frozen-precipitation parameterizations were primarily unique to the developed model but were also partially based on previous research. Diagnostic analyses use a synthesis of Automated Surface Observing System (ASOS), Automated Weather Observing System (AWOS), and Oklahoma Mesonet data. Prognostic analyses utilize the National Digital Forecast Database (NDFD), a 2.5-km gridded database of forecast meteorological variables output from National Weather Service Weather Forecast Offices. A frequency analysis was performed using the diagnostic parameterizations to determine general road icing risk across the state of Oklahoma. The frequency analyses aligned well with expected temporal maxima and confirmed the viability of the developed parameterizations. Further, a fog typological analysis showed the implemented freezing-fog-formation parameterizations to capture 89% of fog events. These results suggest that the developed model, identified as the Road-Ice Model (RIM), may be implemented as a robust option for analyzing the potential for road ice development based on the background meteorological environment.

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Hong-Bo Liu, Jing Yang, Da-Lin Zhang, and Bin Wang

Abstract

During the mei-yu season of the summer of 2003, the Yangtze and Huai River basin (YHRB) encountered anomalously heavy rainfall, and the northern YHRB (nYHRB) suffered a severe flood because of five continuous extreme rainfall events. A spectral analysis of daily rainfall data over YHRB reveals two dominant frequency modes: one peak on day 14 and the other on day 4 (i.e., the quasi-biweekly and synoptic-scale mode, respectively). Results indicate that the two scales of disturbances contributed southwesterly and northeasterly anomalies, respectively, to the mei-yu frontal convergence over the southern YHRB (sYHRB) at the peak wet phase. An analysis of bandpass-filtered circulations shows that the lower and upper regions of the troposphere were fully coupled at the quasi-biweekly scale, and a lower-level cyclonic anomaly over sYHRB was phase locked with an anticyclonic anomaly over the Philippines. At the synoptic scale, the strong northeasterly components of an anticyclonic anomaly with a deep cold and dry layer helped generate the heavy rainfall over sYHRB. Results also indicate the passages of five synoptic-scale disturbances during the nYHRB rainfall. Like the sYHRB rainfall, these disturbances originated from the periodical generations of cyclonic and anticyclonic anomalies at the downstream of the Tibetan Plateau. The nYHRB rainfalls were generated as these disturbances moved northeastward under the influence of monsoonal flows and higher-latitude eastward-propagating Rossby wave trains. It is concluded that the sYHRB heavy rainfall resulted from the superposition of quasi-biweekly and synoptic-scale disturbances, whereas the intermittent passages of five synoptic-scale disturbances led to the flooding rainfall over nYHRB.

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Lingzhi Zhong, Rongfang Yang, Lin Chen, Yixin Wen, Ruiyi Li, Guoqiang Tang, and Yang Hong

Abstract

This study presents a statistical analysis of the variability of the vertical structure of precipitation in the eastern downstream region of the Tibetan Plateau as measured by the Precipitation Radar (PR) on the National Aeronautics and Space Administration Tropical Rainfall Measuring Mission (TRMM) satellite. Data were analyzed over an 11-yr time span (January 2004–December 2014). The results show the seasonal and spatial variability of the storm height, freezing level, and bright band for different types of precipitation as well as the characteristics of intensity-related and type-related vertical profiles of reflectivity (VPR). Major findings were as follows: About 90% of the brightband peak reflectivity of stratiform precipitation was less than 32 dBZ, and 40% of the maximum reflectivity of convective precipitation exceeded 35 dBZ. The intensity of surface rainfall rates also depended on the shapes of VPRs. For stratiform precipitation, ice–snow aggregation was faster during moderate and heavy rainfall than it was in light rainfall. Since both the moisture and temperature are lower in winter, the transformation efficiency of hydrometeors becomes slower. Typical Ku-band representative climatological VPRs (CPRs) for stratiform precipitation have been created on the basis of the integration of normalized VPR shape for the given area and the rainfall intensity. All of the findings indicate that the developed CPRs can be used to improve surface precipitation estimates in regions with complex terrain where the ground-based radar net has limited visibility at low levels.

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Yang Yang, James C. McWilliams, X. San Liang, Hong Zhang, Robert H. Weisberg, Yonggang Liu, and Dimitris Menemenlis

Abstract

The submesoscale energetics of the eastern Gulf of Mexico (GoM) are diagnosed using outputs from a 1/48° MITgcm simulation. Employed is a recently developed, localized multiscale energetics formalism with three temporal-scale ranges (or scale windows), namely, a background flow window, a mesoscale window, and a submesoscale window. It is found that the energy cascades are highly inhomogeneous in space. Over the eastern continental slope of the Campeche Bank, the submesoscale eddies are generated via barotropic instability, with forward cascades of kinetic energy (KE) following a weak seasonal variation. In the deep basin of the eastern GoM, the submesoscale KE exhibits a seasonal cycle, peaking in winter, maintained via baroclinic instability, with forward available potential energy (APE) cascades in the mixed layer, followed by a strong buoyancy conversion. A spatially coherent pool of inverse KE cascade is found to extract energy from the submesoscale KE reservoir in this region to replenish the background flow. The northern GoM features the strongest submesoscale signals with a similar seasonality as seen in the deep basin. The dominant source for the submesoscale KE during winter is from buoyancy conversion and also from the forward KE cascades from mesoscale processes. To maintain the balance, the excess submesoscale KE must be dissipated by smaller-scale processes via a forward cascade, implying a direct route to finescale dissipation. Our results highlight that the role of submesoscale turbulence in the ocean energy cycle is region and time dependent.

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