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Jingjing Tian, Xiquan Dong, Baike Xi, and Zhe Feng

Abstract

In this study, the mesoscale convective systems (MCSs) are tracked using high-resolution radar and satellite observations over the U.S. Great Plains during April–August from 2010 to 2012. The spatiotemporal variability of MCS precipitation is then characterized using the Stage IV product. We found that the spatial variability and nocturnal peaks of MCS precipitation are primarily driven by the MCS occurrence rather than the precipitation intensity. The tracked MCSs are further classified into convective core (CC), stratiform rain (SR), and anvil clouds regions. The spatial variability and diurnal cycle of precipitation in the SR regions of MCSs are not as significant as those of MCS precipitation. In the SR regions, the high-resolution, long-term ice cloud microphysical properties [ice water content (IWC) and ice water paths (IWPs)] are provided. The IWCs generally decrease with height. Spatially, the IWC, IWP, and precipitation are all higher over the southern Great Plains than over the northern Great Plains. Seasonally, those ice and precipitation properties are all higher in summer than in spring. Comparing the peak timings of MCS precipitation and IWPs from the diurnal cycles and their composite evolutions, it is found that when using the peak timing of IWPSR as a reference, the heaviest precipitation in the MCS convective core occurs earlier, while the strongest SR precipitation occurs later. The shift of peak timings could be explained by the stratiform precipitation formation process. The IWP and precipitation relationships are different at MCS genesis, mature, and decay stages. The relationships and the transition processes from ice particles to precipitation also depend on the low-level humidity.

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Wenjun Cui, Xiquan Dong, Baike Xi, Zhe Feng, and Jiwen Fan

Abstract

Mesoscale convective systems (MCSs) play an important role in water and energy cycles as they produce heavy rainfall and modify the radiative profile in the tropics and midlatitudes. An accurate representation of MCSs’ rainfall is therefore crucial in understanding their impact on the climate system. The V06B Integrated Multisatellite Retrievals from Global Precipitation Measurement (IMERG) half-hourly precipitation final product is a useful tool to study the precipitation characteristics of MCSs because of its global coverage and fine spatiotemporal resolutions. However, errors and uncertainties in IMERG should be quantified before applying it to hydrology and climate applications. This study evaluates IMERG performance on capturing and detecting MCSs’ precipitation in the central and eastern United States during a 3-yr study period against the radar-based Stage IV product. The tracked MCSs are divided into four seasons and are analyzed separately for both datasets. IMERG shows a wet bias in total precipitation but a dry bias in hourly mean precipitation during all seasons due to the false classification of nonprecipitating pixels as precipitating. These false alarm events are possibly caused by evaporation under the cloud base or the misrepresentation of MCS cold anvil regions as precipitating clouds by the algorithm. IMERG agrees reasonably well with Stage IV in terms of the seasonal spatial distribution and diurnal cycle of MCSs precipitation. A relative humidity (RH)-based correction has been applied to the IMERG precipitation product, which helps reduce the number of false alarm pixels and improves the overall performance of IMERG with respect to Stage IV.

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Wenjun Cui, Xiquan Dong, Baike Xi, and Aaron Kennedy

Abstract

Atmospheric reanalyses have been used in many studies to investigate the variabilities and trends of precipitation because of their global coverage and long record; however, their results must be properly analyzed and their uncertainties must be understood. In this study, precipitation estimates from five global reanalyses [ERA-Interim; MERRA, version 2 (MERRA2); JRA-55; CFSR; and 20CR, version 2c (20CRv2c)] and one regional reanalysis (NARR) are compared against the CPC Unified Gauge-Based Analysis (CPCUGA) and GPCP over the contiguous United States (CONUS) during the period 1980–2013. Reanalyses capture the variability of the precipitation distribution over the CONUS as observed in CPCUGA and GPCP, but large regional and seasonal differences exist. Compared with CPCUGA, global reanalyses generally overestimate the precipitation over the western part of the country throughout the year and over the northeastern CONUS during the fall and winter seasons. These issues may be associated with the difficulties models have in accurately simulating precipitation over complex terrain and during snowfall events. Furthermore, systematic errors found in five global reanalyses suggest that their physical processes in modeling precipitation need to be improved. Even though negative biases exist in NARR, its spatial variability is similar to both CPCUGA and GPCP; this is anticipated because it assimilates observed precipitation, unlike the global reanalyses. Based on CPCUGA, there is an average decreasing trend of −1.38 mm yr−1 over the CONUS, which varies depending on the region with only the north-central to northeastern parts of the country having positive trends. Although all reanalyses exhibit similar interannual variation as observed in CPCUGA, their estimated precipitation trends, both linear and spatial trends, are distinct from CPCUGA.

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Ronald Stenz, Xiquan Dong, Baike Xi, and Robert J. Kuligowski

Abstract

Although satellite precipitation estimates provide valuable information for weather and flood forecasts, infrared (IR) brightness temperature (BT)-based algorithms often produce large errors for precipitation detection and estimation during deep convective systems (DCSs). As DCSs produce greatly varying precipitation rates below similar IR BT retrievals, using IR BTs alone to estimate precipitation in DCSs is problematic. Classifying a DCS into convective-core (CC), stratiform (SR), and anvil cloud (AC) regions allows an evaluation of estimated precipitation distributions among DCS components to supplement typical quantitative precipitation estimate (QPE) evaluations and to diagnose these IR-based algorithm biases. This paper assesses the performance of the National Mosaic and Multi-Sensor Next Generation Quantitative Precipitation Estimation System (NMQ Q2), and a simplified version of the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm, over the state of Oklahoma using Oklahoma Mesonet observations. While average annual Q2 precipitation estimates were about 35% higher than Mesonet observations, strong correlations exist between these two datasets for multiple temporal and spatial scales. Additionally, the Q2-estimated precipitation distribution among DCS components strongly resembled the Mesonet-observed distribution, indicating Q2 can accurately capture the precipitation characteristics of DCSs despite its wet bias. SCaMPR retrievals were typically 3–4 times higher than Mesonet observations, with relatively weak correlations during 2012. Overestimates from SCaMPR retrievals were primarily caused by precipitation retrievals from the anvil regions of DCSs when collocated Mesonet stations recorded no precipitation. A modified SCaMPR retrieval algorithm, employing both cloud optical depth and IR temperature, has the potential to make significant improvements to reduce the wet bias of SCaMPR retrievals over anvil regions of a DCS.

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Wenjun Cui, Xiquan Dong, Baike Xi, and Ronald Stenz

Abstract

This study compares the Global Precipitation Climatology Project (GPCP) 1 Degree Daily (1DD) precipitation estimates over the continental United States (CONUS) with National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (NMQ) Next Generation (Q2) estimates. Spatial averages of monthly and yearly accumulated precipitation were computed based on daily estimates from six selected regions during the period 2010–12. Both Q2 and GPCP daily precipitation estimates show that precipitation amounts over southern regions (<40°N) are generally larger than northern regions (≥40°N). Correlation coefficients for daily estimates over selected regions range from 0.355 to 0.516 with mean differences (GPCP − Q2) varying from −0.86 to 0.99 mm. Better agreements are found in monthly estimates with the correlations varying from 0.635 to 0.787. For spatially averaged precipitation values averaged from grid boxes within selected regions, GPCP and Q2 estimates are well correlated, especially for monthly accumulated precipitation, with strong correlations ranging from 0.903 to 0.954. The comparisons between two datasets are also conducted for warm (April–September) and cold (October–March) seasons. During the warm season, GPCP estimates are 9.7% less than Q2 estimates, while during the cold season GPCP estimates exceed Q2 estimates by 6.9%. For precipitation over the CONUS, although annual means are close (978.54 for Q2 vs 941.79 mm for GPCP), Q2 estimates are much larger than GPCP over the central and southern United States and less than GPCP estimates in the northeastern United States. These results suggest that Q2 may have difficulties accurately estimating heavy rain and snow events, while GPCP may have an inability to capture some intense precipitation events, which warrants further investigation.

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Ronald Stenz, Xiquan Dong, Baike Xi, Zhe Feng, and Robert J. Kuligowski

Abstract

To address gaps in ground-based radar coverage and rain gauge networks in the United States, geostationary satellite quantitative precipitation estimation (QPE) such as the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) can be used to fill in both spatial and temporal gaps of ground-based measurements. Additionally, with the launch of Geostationary Operational Environmental Satellite R series (GOES-R), the temporal resolution of satellite QPEs may be comparable to Weather Surveillance Radar-1988 Doppler (WSR-88D) volume scans as GOES images will be available every 5 min. However, while satellite QPEs have strengths in spatial coverage and temporal resolution, they face limitations, particularly during convective events. Deep convective systems (DCSs) have large cloud shields with similar brightness temperatures (BTs) over nearly the entire system, but widely varying precipitation rates beneath these clouds. Geostationary satellite QPEs relying on the indirect relationship between BTs and precipitation rates often suffer from large errors because anvil regions (little or no precipitation) cannot be distinguished from rain cores (heavy precipitation) using only BTs. However, a combination of BTs and optical depth τ has been found to reduce overestimates of precipitation in anvil regions. A new rain mask algorithm incorporating both τ and BTs has been developed, and its application to the existing SCaMPR algorithm was evaluated. The performance of the modified SCaMPR was evaluated using traditional skill scores and a more detailed analysis of performance in individual DCS components by utilizing the Feng et al. classification algorithm. SCaMPR estimates with the new rain mask benefited from significantly reduced overestimates of precipitation in anvil regions and overall improvements in skill scores.

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