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Tao, Cheng

Publications and source records attributed to Tao, Cheng.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Superior Daily and Sub-Daily Precipitation Statistics for Intense and Long-Lived Storms in Global Storm-Resolving Models

Here, daily and sub-daily precipitation statistics are investigated from three global model ensembles: (a) global storm-resolving models (GSRMs) with typical horizontal resolutions of ~4 km, (b) “high”-resolution global models with typical resolutions of ~50 km and (c) “standard”-resolution global models with typical resolutions of ~100 km. Compared to two satellite rainfall datasets, GSRMs convincingly exhibit superior performance for statistics of heavier rain rate events including their diurnal cycle, spatial propagation and the amount contributed by intense precipitation, but not for statistics of weaker or shorter duration precipitation. Both high- and standard-resolution models fail to simulate the correct phase and amplitude of diurnal cycle of precipitation and the propagating convection in the Central US, but high-resolution models show relative improvement in the distribution of precipitation frequency and amount, especially for intense precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Long-term single-column model intercomparison of diurnal cycle of precipitation over midlatitude and tropical land

General Circulation Models (GCMs) have for decades exhibited difficulties in modelling the diurnal cycle of precipitation (DCP). This issue can be related to inappropriate representation of the processes controlling sub-diurnal phenomena like convection. In this study, 11 single-column versions of GCMs are used to investigate the interactions between convection and environmental conditions, processes that control nocturnal convections, and the transition from shallow to deep convection on a diurnal time-scale. Long-term simulations are performed over two continental land sites: the Southern Great Plains (SGP) in the USA for 12 summer months from 2004 to 2015 and the Manacapuru site at the central Amazon (MAO) in Brazil for two full years from 2014 to 2015. The analysis is done on two regimes: afternoon convective regime and nocturnal precipitation regime. Most models produce afternoon precipitation too early, likely due to the missing transition of shallow-to-deep convection in these models. At SGP, the unified convection schemes better simulate the onset time of precipitation. At MAO, models produce the heating peak in a much lower level compared with observation, indicating too shallow convection in the models. For nocturnal precipitation, models that produce most of nocturnal precipitation all allow convection to be triggered above the boundary layer. This indicates the importance of model capability to detect elevated convection for simulating nocturnal precipitation. Sensitivity studies indicate that (a) nudging environmental variables towards observations has a minor impact on DCP, (b) unified treatment of shallow and deep convection and the capability to capture mid-level convection can help models better capture DCP, and (c) the interactions of the atmosphere with other components in the climate system (e.g. land) are also important for DCP simulations in coupled models. Finally, these results provide long-term statistical insights on which physical processes are essential in climate models to simulate DCP.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Land–Atmosphere Coupling at the U.S. Southern Great Plains: A Comparison on Local Convective Regimes between ARM Observations, Reanalysis, and Climate Model Simulations

Using the 9-yr warm-season observations at the Atmospheric Radiation Measurement Southern Great Plains site, in this work 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.

58 GEOSCIENCES↗
DOE OSTI2020

ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation

A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility Infrastructure Team at Lawrence Livermore National Laboratory to facilitate the use of long-term, high-frequency measurements from ARM in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Description of the ARM Large-Scale Forcing Data from the Constrained Variational Analysis (VARANAL) Version 2

This technical report represents an update of the previous technical report written by Zhang et al. (2001a) (available at http://www.arm.gov/publications/tech_reports/arm-tr-005.pdf), which described the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility constrained variational analysis (VARANAL) that is used to develop the large-scale forcing data for driving single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulation models (LES). The VARANAL algorithm was originally developed by Zhang and Lin (1997) and Zhang et al. (2001b) at Stony Brook University and was migrated to the Lawrence Livermore National Laboratory (LLNL) as the ARM operational objective analysis system in May 1999. Since then, the algorithm has been evolved with time along with the availability of new observations and techniques to meet various modeling needs. Major updates include: (1) The method used to develop multi-year continuous forcing data (Xie et al. 2004), (2) The incorporation of eddy correlation flux measurement system (ECOR) turbulent fluxes into the analysis (Tang et al. 2019), and (3) Improvements to the workflow (e.g., implementing part of the code into the ARM Data Integrator [ADI]) to increase efficiency. The ARM large-scale forcing data have been widely used for SCM/CRM/LES to understand and improve physical processes in models. Zhang et al. (2016) has provided a comprehensive review of the SCM concept, early efforts to derive forcing data for SCM studies, efforts of the ARM constrained variational analysis, and previous SCM studies using ARM cases. This technical report focuses on the constrained variational analysis algorithm and the introduction of the ARM VARANAL products.

58 GEOSCIENCES↗
DOE OSTI2019

Regional Moisture Budget and Land-Atmosphere Coupling Over the U.S. Southern Great Plains Inferred From the ARM Long-Term Observations

Ten–year warm–season (May–August) observations at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site are used to examine the large–scale atmospheric moisture budget and the strength of land–atmosphere coupling. Atmospheric moisture budget components, computed from the ARM variational analysis data, are compared between different wet/dry scenarios and convection regimes. Consistent with previous studies, large–scale moisture flux convergence dominates the SGP regional moisture budget on the daily time scale, but surface evaporation does play a more prominent role on late–afternoon deep convection days. Moreover, the relationship between moisture flux convergence and precipitation increases significantly from dry year to wet years. The land–atmosphere coupling strength is quantified for local convective events within the “Local L–A Coupling Process Chain” (Santanello et al., 2018, https://doi.org/10.1175/BAMS–D–17–0001.1). The impact of land surface on the evolution of planetary boundary layer is highly dependent on the vegetation leaf area index. A significantly larger surface sensible heat flux is found over the SGP forest region in midsummer, which is accompanied by a much higher planetary boundary layer development and a large increase in shallow cumulus cloud fraction. Here the significant relationship between afternoon precipitation and surface turbulent flux only exists in the northeast part of the ARM SGP site, which is mainly covered by grassland/pasture.

58 GEOSCIENCES↗
DOE OSTI2019

The QCECOR Value-Added Product: Quality-Controlled Eddy Correlation Flux Measurements

Eddy correlation flux (ECOR) measurement system stations are used by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility to provide surface turbulence flux measurements. In recent years, ARM has significantly enhanced its ECOR systems and made them available at all its primary research observatories and mobile facilities. However, the original ECOR data were not corrected through common eddy correlation corrections and still contain suspicious data due to instrument and measurement uncertainties. Such uncertainties in the ECOR surface turbulence fluxes hampered their uses by the user community. To improve the ECOR data, we develop a value-added product (VAP) —the Quality-Controlled Eddy Correlation Flux (QCECOR) — by applying the following necessary corrections and quality control (QC) procedures: Eddy correlation corrections. Determination of the quality-controlled energy fluxes from the corrected ECOR fluxes. Stringent QC checks. Flagging possible incorrect fluxes due to hydrometeor impacts using the collocated surface energy balance system (SEBS) wetness measurements. The QCECOR VAP currently contains two variables: surface latent heat flux (LH) and sensible heat flux (SH), together with their QC flags. When SEBS are collocated with ECOR, the wetness measurements from SEBS are used to flag the LH that may be incorrect due to hydrometeors such as precipitation, dew, or frost. An indeterminate flag is given to those that fail the wetness test. The QCECOR data are currently available at the Southern Great Plains (SGP) atmospheric observatory for the ARM extended facilities of E1, E3, E5, E6, E10, E14, E16, E21, E24, E31, E33, E37, E38, E39, and E41 updated to 2018. The data are also available at the following AMF sites: PYE (Point Reyes, 2005), NIM (Niamey, Niger, 2006), FKB (Black Forest, Germany, 2007), HFE (Shouxian, China, 2008), GRW (Azores, 2009 to 2010), and MAO (Manacapuru, Brazil, 2014-2015). The algorithm is now in the process of moving to production mode. After that, QCECOR will run automatically for all available ARM sites.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Description of the ARM Large-Scale Forcing Data from the Constrained Variational Analysis (VARANAL) Version 2

This technical report represents an update of the previous technical report written by Zhang et al., which described the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility constrained variational analysis (VARANAL) that is used to develop the large-scale forcing data for driving single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulation models (LES). The VARANAL algorithm was originally developed by Zhang and Lin and Zhang et al. at Stony Brook University and was migrated to the Lawrence Livermore National Laboratory (LLNL) as the ARM operational objective analysis system in May 1999. Since then, the algorithm has been evolved with time along with the availability of new observations and techniques to meet various modeling needs. Major updates include: 1. The method used to develop multi-year continuous forcing data, 2. The incorporation of eddy correlation flux measurement system (ECOR) turbulent fluxes into the analysis, and 3. Improvements to the workflow (e.g., implementing part of the code into the ARM Data Integrator [ADI]) to increase efficiency. The ARM large-scale forcing data have been widely used for SCM/CRM/LES to understand and improve physical processes in models. Zhang et al. has provided a comprehensive review of the SCM concept, early efforts to derive forcing data for SCM studies, efforts of the ARM constrained variational analysis, and previous SCM studies using ARM cases. This technical report focuses on the constrained variational analysis algorithm and the introduction of the ARM VARANAL products. We also extended the VARANAL algorithm into a three-dimensional constrained variational analysis (3DCVA) and designed an ensemble framework to address the forcing uncertainty. The 3D large-scale forcing data are released as another datastream named “varanal3d”. Please refer to the ARM varanal3d technical report for more information.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Estimation of Tropical Cyclone Intensity in the North Atlantic and Northeastern Pacific Basins Using TRMM Satellite Passive Microwave Observations

Here, 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 assessed 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 implies 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.

58 GEOSCIENCES↗
DOE OSTI2018

Classifying Precipitation Types in Tropical Cyclones Using the NRL 37 GHz Color Product

The tropical cyclone (TC) webpage developed by the U.S. Naval Research Lab (NRL) has been widely used by the community for real-time TC monitoring and forecasting. Using 14 years of Tropical Rainfall Measuring Mission Microwave Imager and Precipitation Radar data in TCs, precipitation/convection types are quantitatively classified based on vertical radar profiles corresponding to seven different color regions in the NRL's 37 GHz color composite product. The bright cyan and all pink (including pure pink, bright cyan/pink, and weak cyan/pink, and green/pink) regions are identified as nearly 100% precipitative regions. The bright cyan and pure pink regions are shallow and deep convective precipitation, respectively, while the green/pink, weak cyan/pink, and bright cyan/pink regions are mainly stratiform rain. Eighty-four percent of pixels in the weak cyan region are precipitating, including either shallow convective, stratiform, or anvil precipitation. Seventy-six percent of pixels in the green region are nonprecipitating pixels, and 24% of them are very lightly precipitating pixels, which could be contaminated from clouds with high liquid water content. The ability to perform TC precipitation-type analysis of this detail (convective versus stratiform and deep versus shallow) was previously only possible using radar-based classification algorithms. In conclusion, using the 37 GHz precipitation-type analysis, a significant increase of coverage of stratiform precipitation and shallow convection in the inner core is found between 3 and 21 hr before the onset of TC rapid intensification.

54 ENVIRONMENTAL SCIENCES↗