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Haiyan Jiang and Cheng Tao

Abstract

Based on the 12-yr (1998–2009) Tropical Rainfall Measuring Mission (TRMM) precipitation feature (PF) database, both radar and infrared (IR) observations from TRMM are used to quantify the contribution of tropical cyclones (TCs) to very deep convection (VDC) in the tropics and to compare TRMM-derived properties of VDC in TCs and non-TCs. Using a radar-based definition, it is found that the contribution of TCs to total VDC in the tropics is not much higher than the contribution of TCs to total PFs. However, the area-based contribution of TCs to overshooting convection defined by IR is 13.3%, which is much higher than the 3.2% contribution of TCs to total PFs. This helps explain the contradictory results between previous radar-based and IR-based studies and indicates that TCs only contribute disproportionately large amount of overshooting convection containing mainly small ice particles that are barely detected by the TRMM radar. VDC in non-TCs over land has the highest maximum 30- and 40-dBZ height and the strongest ice-scattering signature derived from microwave 85- and 37-GHz observations, while VDC in TCs has the coldest minimum IR brightness temperature and largest overshooting distance and area. This suggests that convection is much more intense in non-TCs over land but is much deeper or colder in TCs. It is found that VDC in TCs usually has smaller environmental shear but larger total precipitable water and convective available potential energy than those in non-TCs. These findings offer evidence that TCs may contribute disproportionately to troposphere-to-stratosphere heat and moisture exchange.

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Cheng Tao and Haiyan Jiang

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Shear-relative distributions of four types of precipitation/convection in tropical cyclones (TCs) are statistically analyzed using 14 years of Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data. The dataset of 1139 TRMM PR overpasses of tropical storms through category-2 hurricanes over global TC-prone basins is divided by future 24-h intensity change. It is found that increased and widespread shallow precipitation (defined as where the 20-dBZ radar echo height <6 km) around the storm center is a first sign of rapid intensification (RI) and could be used as a predictor of the onset of RI. The contribution to total volumetric rain and latent heating from shallow and moderate precipitation (20-dBZ echo height between 6 and 10 km) in the inner core is greater in RI storms than in non-RI storms, while the opposite is true for moderately deep (20-dBZ echo height between 10 and 14 km) and very deep precipitation (20-dBZ echo height ≥14 km). The authors argue that RI is more likely triggered by the increase of shallow–moderate precipitation and the appearance of more moderately to very deep convection in the middle of RI is more likely a response or positive feedback to changes in the vortex. For RI storms, a cyclonic rotation of frequency peaks from shallow (downshear right) to moderate (downshear left) to moderately and very deep precipitation (upshear left) is found and may be an indicator of a rapidly strengthening vortex. A ring of almost 90% occurrence of total precipitation is found for storms in the middle of RI, consistent with the previous finding of the cyan and pink ring on the 37-GHz color product.

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Cheng Tao and Haiyan Jiang

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Global distribution of hot towers in tropical cyclones (TCs) is statistically quantified using an 11-yr Tropical Rainfall Measuring Mission (TRMM) Tropical Cyclone Precipitation Feature (TCPF) database. From 6003 individual TRMM overpasses of 869 TCs, about 1.6% of TC convective systems are found to penetrate 14 km and about 0.1% of them even reach the 380-K potential temperature level. Among six TC-prone basins, the highest population of TC convective systems and those with hot towers are found over the northwest Pacific (NWP) basin. However, the greatest percentage of TCPFs that are hot towers [overshooting TCPFs (OTCPFs)] is found over the North Indian Ocean basin. Larger overshooting distance and ice mass are also found in this basin. The monthly variation of OTCPFs resembles that of TC activities in each basin. The percentage of OTCPFs is much higher in the inner core (IC) region (10%) than that in the inner rainband (IB; 2%) and outer rainband (OB; 1%) regions. OTCPFs in the IC region have much larger overshooting distance, area, volume, and ice mass than those in the IB and OB regions. The percentage of OTCPFs in the IC region increases as both TC intensity and intensification rate increase. About 17% of IC features in rapidly intensifying storms penetrate over 14 km, while the percentage is down to 11% for slowly intensifying, 9% for neutral, and 8% for weakening storms. A very good linear relationship is found between TC intensification rate and the percentage of TCPFs that are hot towers in the IC region.

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Haiyan Jiang, Cheng Tao, and Yongxian Pei

Abstract

A statistical passive microwave intensity estimation (PMW-IE) algorithm for estimating the intensity of tropical cyclones (TCs) in the North Atlantic and northeastern and central Pacific basins is developed and tested. The algorithm is derived from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) 85-GHz brightness temperatures and near-surface rain-rate retrievals to provide objective estimates of current maximum sustained surface winds (Vmax) and 6-h future Vmax of TCs. The full record of TRMM data (1998–2013) including 2326 TMI overpasses of 503 TCs is separated into dependent samples (1998–2010) for model development and independent samples (2011–13) for model verification. The best track intensities are used as dependent variables in a stepwise multiple-regression approach. Separately for each basin, three regression models are derived using selected 1) 85-GHz-only variables, 2) rain-rate-only variables, and 3) combined 85-GHz and rain variables. The algorithms are evaluated using independent samples and those with contemporaneous aircraft-reconnaissance measurements. Rain-only and combined models perform better than the 85-GHz-only model. Lower errors are found for estimating the 6-h future Vmax than estimating the current Vmax using all three models. This suggests that it is optimal to use passive-microwave-retrieved rain variables observed a few hours earlier to estimate TC intensity. The MAE (RMSE) of 6-h future Vmax is 9 (12) kt (1 kt ≈ 0.51 m s−1) when testing the combined models with ATL and EPA independent samples. Aircraft-reconnaissance-based independent samples yields a MAE of 9.6 kt and RMSE of 12.6 kt for estimating 6-h future Vmax.

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Cheng Tao, Haiyan Jiang, and Jonathan Zawislak

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Using 16-yr Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) observations, rainfall properties in the inner-core region of tropical cyclones (TCs) and the relative importance of stratiform and convective precipitation are examined with respect to the evolution of rapid intensification (RI) events. The onset of RI follows a significant increase in the occurrence and azimuthal coverage of stratiform rainfall in all shear-relative quadrants, especially upshear left. The importance of the increased stratiform occurrence in RI storms is further confirmed by the comparison of two groups of slowly intensifying (SI) storms with one group that underwent RI and the other that did not. Statistically, SI storms that do not undergo RI during their life cycle have a much lower percent occurrence of stratiform rain within the inner core. The relatively greater areal coverage of stratiform rain in RI cases appears to be related to the moistening/humidification of the inner core, particularly in the upshear quadrants. In contrast to rainfall frequency, rainfall intensity and total volumetric rain do not increase much until several hours after RI onset, which is more likely a response or positive feedback rather than the trigger of RI. Despite a low frequency of occurrence, the overall contribution to total volumetric rain by convective precipitation is comparable to that of stratiform rain, owing to its intense rain rate.

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Yang Hong, David Gochis, Jiang-tao Cheng, Kuo-lin Hsu, and Soroosh Sorooshian

Abstract

Robust validation of the space–time structure of remotely sensed precipitation estimates is critical to improving their quality and confident application in water cycle–related research. In this work, the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) precipitation product is evaluated against warm season precipitation observations from the North American Monsoon Experiment (NAME) Event Rain Gauge Network (NERN) in the complex terrain region of northwestern Mexico. Analyses of hourly and daily precipitation estimates show that the PERSIANN-CCS captures well active and break periods in the early and mature phases of the monsoon season. While the PERSIANN-CCS generally captures the spatial distribution and timing of diurnal convective rainfall, elevation-dependent biases exist, which are characterized by an underestimate in the occurrence of light precipitation at high elevations and an overestimate in the occurrence of precipitation at low elevations. The elevation-dependent biases contribute to a 1–2-h phase shift of the diurnal cycle of precipitation at various elevation bands. For reasons yet to be determined, the PERSIANN-CCS significantly underestimated a few active periods of precipitation during the late or “senescent” phase of the monsoon. Despite these shortcomings, the continuous domain and relatively high spatial resolution of PERSIANN-CCS quantitative precipitation estimates (QPEs) provide useful characterization of precipitation space–time structures in the North American monsoon region of northwestern Mexico, which should prove useful for hydrological applications.

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Cheng Tao, Yunyan Zhang, Qi Tang, Hsi-Yen Ma, Virendra P. Ghate, Shuaiqi Tang, Shaocheng Xie, and Joseph A. Santanello

Abstract

Using the 9-yr warm-season observations at the Atmospheric Radiation Measurement Southern Great Plains site, we assess the land–atmosphere (LA) coupling in the North American Regional Reanalysis (NARR) and two climate models: hindcasts with the Community Atmosphere Model version 5.1 by Cloud-Associated Parameterizations Testbed (CAM5-CAPT) and nudged runs with the Energy Exascale Earth System Model Atmosphere Model version 1 Regionally Refined Model (EAMv1-RRM). We focus on three local convective regimes and diagnose model behaviors using the local coupling metrics. NARR agrees well with observations except a slightly warmer and drier surface with higher downwelling shortwave radiation and lower evaporative fraction. On clear-sky days, it shows warmer and drier early-morning conditions in both models with significant underestimates in surface evaporation by EAMv1-RRM. On the majority of the ARM-observed shallow cumulus days, there is no or little low-level clouds in either model. When captured in models, the simulated shallow cumulus shows much less cloud fraction and lower cloud bases than observed. On the days with late-afternoon deep convection, models tend to present a stable early-morning lower atmosphere more frequently than the observations, suggesting that the deep convection is triggered more often by elevated instabilities. Generally, CAM5-CAPT can reproduce the local LA coupling processes to some extent due to the constrained early-morning conditions and large-scale winds. EAMv1-RRM exhibits large precipitation deficits and warm and dry biases toward mid-to-late summers, which may be an amplification through a positive LA feedback among initial atmosphere and land states, convection triggering and large-scale circulations.

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Guoxing Chen, Wei-Chyung Wang, Lijun Tao, Huang-Hsiung Hsu, Chia-Ying Tu, and Chao-Tzuen Cheng

Abstract

This study used both observations and global climate model simulations to investigate the characteristics of winter extreme snowfall events along the coast (the Interstate 95 corridor) of the northeast United States where several mega-cities are located. Observational analyses indicate that, during 1980–2015, 110 events occurred when four coastal cities—Boston, New York City, Philadelphia, and Washington, D.C.—had either individually or collectively experienced daily snowfall exceeding the local 95th percentile thresholds. Boston had the most events, with a total of 69, followed by 40, 36, and 30 (moving southward) in the other three cities. The associated circulations at 200 and 850 hPa were categorized via K-means clustering. The resulting three composite circulations are characterized by the strength and location of the jet at 200 hPa and the coupled low pressure system at 850 hPa: a strong jet overlying the cities coupled with an inland trough, a weak and slightly southward shifted jet coupled with a cyclone at the coast, and a weak jet stream situated to the south of the cities coupled with a cyclone over the coastal oceans. Comparative analyses were also conducted using the GFDL High Resolution Atmospheric Model (HiRAM) simulation of the same period. Although the simulated extreme events do not provide one-to-one correspondence with observations, the characteristics nevertheless show consistency notably in total number of occurrences, intraseasonal and multiple-year variations, snow spatial coverage, and the associated circulation patterns. Possible future change in extreme snow events was also explored utilizing the HiRAM RCP8.5 (2075–2100) simulation. The analyses suggest that a warming global climate tends to decrease the extreme snowfall events but increase extreme rainfall events.

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Xiaoyan Zhang, Jianping Huang, Gang Li, Yongwei Wang, Cheng Liu, Kaihui Zhao, Xinyu Tao, Xiao-Ming Hu, and Xuhui Lee

Abstract

The Weather Research and Forecasting (WRF) Model is used in large-eddy simulation (LES) mode to investigate a lake-breeze case occurring on 12 June 2012 over the Lake Taihu region of China. Observational data from 15 locations, wind profiler radar, and the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to evaluate the WRF nested-LES performance in simulating lake breezes. Results indicate that the simulated temporal and spatial variations of the lake breeze by WRF nested LES are consistent with observations. The simulations with high-resolution grid spacing and the LES scheme have a high correlation coefficient and low mean bias when evaluated against 2-m temperature, 10-m wind, and horizontal and vertical lake-breeze circulations. The atmospheric boundary layer (ABL) remains stable over the lake throughout the lake-breeze event, and the stability becomes even stronger as the lake breeze reaches its mature stage. The improved ABL simulation with LES at a grid spacing of 150 m indicates that the non-LES planetary boundary layer parameterization scheme does not adequately represent subgrid-scale turbulent motions. Running WRF fully coupled to a lake model improves lake-surface temperature and consequently the lake-breeze simulations. Allowing for additional model spinup results in a positive impact on lake-surface temperature prediction but is a heavy computational burden. Refinement of a water-property parameter used in the Community Land Model, version 4.5, within WRF and constraining the lake-surface temperature with observational data would further improve lake-breeze representation.

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Tao Zhang, Martin P. Hoerling, Klaus Wolter, Jon Eischeid, Linyin Cheng, Andrew Hoell, Judith Perlwitz, Xiao-Wei Quan, and Joseph Barsugli

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The failed Southern California (SCAL) winter rains during the 2015/16 strong El Niño came as a surprise and a disappointment. Similarities were drawn to very wet winters during several historical strong El Niño events, leading to heightened expectations that SCAL’s multiyear drought would abate in 2016. Ensembles of atmospheric model simulations and coupled model seasonal forecasts are diagnosed to determine both the potential predictability and actual prediction skill of the failed rains, with a focus on understanding the striking contrast of SCAL precipitation between the 2016 and 1998 strong El Niño events. The ensemble mean of simulations indicates that the December–February 2016 winter dryness was not a response to global boundary forcings, which instead generated a wet SCAL signal. Nor was the extreme magnitude of observed 1998 wetness entirely reconcilable with a boundary-forced signal, indicating it was not a particularly precise analog for 2016. Furthermore, model simulations indicate the SCAL 2016 wet signal was 20%–50% less intense than its simulated 1998 counterpart. Such a weaker signal was captured in November 2015 initialized seasonal forecasts, indicating dynamical model skill in predicting a less prolific 2016 rainy season and a capability to forewarn that 2016 would not likely experience the flooding rains of 1998. Analysis of ensemble spread indicates that 2016 dryness was an extreme climate event having less than 5% likelihood in the presence of 2016 global forcings, even though its probability of occurrence was 3–4 times greater in 2016 compared to 1998. Therefore, the failed seasonal rains themselves are argued to be primarily a symptom of subseasonal variability unrelated to boundary forcings whose predictability remains to be explored.

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