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Christian D. Kummerow, David L. Randel, Mark Kulie, Nai-Yu Wang, Ralph Ferraro, S. Joseph Munchak, and Veljko Petkovic

Abstract

The Goddard profiling algorithm has evolved from a pseudoparametric algorithm used in the current TRMM operational product (GPROF 2010) to a fully parametric approach used operationally in the GPM era (GPROF 2014). The fully parametric approach uses a Bayesian inversion for all surface types. The algorithm thus abandons rainfall screening procedures and instead uses the full brightness temperature vector to obtain the most likely precipitation state. This paper offers a complete description of the GPROF 2010 and GPROF 2014 algorithms and assesses the sensitivity of the algorithm to assumptions related to channel uncertainty as well as ancillary data. Uncertainties in precipitation are generally less than 1%–2% for realistic assumptions in channel uncertainties. Consistency among different radiometers is extremely good over oceans. Consistency over land is also good if the diurnal cycle is accounted for by sampling GMI product only at the time of day that different sensors operate. While accounting for only a modest amount of the total precipitation, snow-covered surfaces exhibit differences of up to 25% between sensors traceable to the availability of high-frequency (166 and 183 GHz) channels. In general, comparisons against early versions of GPM’s Ku-band radar precipitation estimates are fairly consistent but absolute differences will be more carefully evaluated once GPROF 2014 is upgraded to use the full GPM-combined radar–radiometer product for its a priori database. The combined algorithm represents a physically constructed database that is consistent with both the GPM radars and the GMI observations, and thus it is the ideal basis for a Bayesian approach that can be extended to an arbitrary passive microwave sensor.

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Yunheng Wang, Jidong Gao, Patrick S. Skinner, Kent Knopfmeier, Thomas Jones, Gerry Creager, Pamela L. Heiselman, and Louis J. Wicker

Abstract

A real-time, weather adaptive, dual-resolution, hybrid Warn-on-Forecast (WoF) analysis and forecast system using the WRF-ARW forecast model has been developed and implemented. The system includes two components, an ensemble analysis and forecast component, and a deterministic hybrid three-dimensional ensemble–variational (3DEnVAR) analysis and forecast component. The goal of the system is to provide on-demand, ensemble-based, and physically consistent gridded analysis and forecast products to forecasters for making warning decisions. Both components, the WRF-DART system with 36 ensemble members and the hybrid 3DEnVAR system, assimilate radar data, satellite-retrieved cloud water path, and surface observations at 15-min intervals with dual-resolution capability. In the current hybrid configuration, one-way coupling of the two analysis systems is performed: ensemble covariances derived from the WRF-DART system are incorporated into the hybrid 3DEnVAR system with each data assimilation (DA) cycle. This study examines deterministic, 3-h forecasts launched from the hybrid 3DEnVAR analyses every 30 min for three severe weather events in 2017. The performance of the deterministic component is evaluated for four configurations: dual-resolution coupling, single-resolution coupling, forecasts initialized using a cloud analysis for reflectivity assimilation, and forecasts initialized from the WRF-DART ensemble mean. Quantitative and subjective evaluation of composite reflectivity and updraft helicity (UH) swath forecasts for the three events indicate that the dual-resolution strategy without the cloud analysis performs best among the four configurations and provides the most realistic prediction of reflectivity patterns and UH tracks.

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Zexia Duan, C. S. B. Grimmond, Chloe Y. Gao, Ting Sun, Changwei Liu, Linlin Wang, Yubin Li, and Zhiqiu Gao

Abstract

Quantitative knowledge of the water and energy exchanges in agroecosystems is vital for irrigation management and modeling crop production. In this study, the seasonal and annual variabilities of evapotranspiration (ET) and energy exchanges were investigated under two different crop environments—flooded and aerobic soil conditions—using three years (June 2014–May 2017) of eddy covariance observations over a rice–wheat rotation in eastern China. Across the whole rice–wheat rotation, the average daily ET rates in the rice paddies and wheat fields were 3.6 and 2.4 mm day−1, respectively. The respective average seasonal ET rates were 473 and 387 mm for rice and wheat fields, indicating a higher water consumption for rice than for wheat. Averaging for the three cropping seasons, rice paddies had 52% more latent heat flux than wheat fields, whereas wheat had 73% more sensible heat flux than rice paddies. This resulted in a lower Bowen ratio in the rice paddies (0.14) than in the wheat fields (0.4). Because eddy covariance observations of turbulent heat fluxes are typically less than the available energy (R n − G; i.e., net radiation minus soil heat flux), energy balance closure (EBC) therefore does not occur. For rice, EBC was greatest at the vegetative growth stages (mean: 0.90) after considering the water heat storage, whereas wheat had its best EBC at the ripening stages (mean: 0.86).

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H. J. S. Fernando, I. Gultepe, C. Dorman, E. Pardyjak, Q. Wang, S. W Hoch, D. Richter, E. Creegan, S. Gaberšek, T. Bullock, C. Hocut, R. Chang, D. Alappattu, R. Dimitrova, D. Flagg, A. Grachev, R. Krishnamurthy, D. K. Singh, I. Lozovatsky, B. Nagare, A. Sharma, S. Wagh, C. Wainwright, M. Wroblewski, R. Yamaguchi, S. Bardoel, R. S. Coppersmith, N. Chisholm, E. Gonzalez, N. Gunawardena, O. Hyde, T. Morrison, A. Olson, A. Perelet, W. Perrie, S. Wang, and B. Wauer

Abstract

C-FOG is a comprehensive bi-national project dealing with the formation, persistence, and dissipation (life cycle) of fog in coastal areas (coastal fog) controlled by land, marine, and atmospheric processes. Given its inherent complexity, coastal-fog literature has mainly focused on case studies, and there is a continuing need for research that integrates across processes (e.g., air–sea–land interactions, environmental flow, aerosol transport, and chemistry), dynamics (two-phase flow and turbulence), microphysics (nucleation, droplet characterization), and thermodynamics (heat transfer and phase changes) through field observations and modeling. Central to C-FOG was a field campaign in eastern Canada from 1 September to 8 October 2018, covering four land sites in Newfoundland and Nova Scotia and an adjacent coastal strip transected by the Research Vessel Hugh R. Sharp. An array of in situ, path-integrating, and remote sensing instruments gathered data across a swath of space–time scales relevant to fog life cycle. Satellite and reanalysis products, routine meteorological observations, numerical weather prediction model (WRF and COAMPS) outputs, large-eddy simulations, and phenomenological modeling underpin the interpretation of field observations in a multiscale and multiplatform framework that helps identify and remedy numerical model deficiencies. An overview of the C-FOG field campaign and some preliminary analysis/findings are presented in this paper.

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A. Korolev, G. McFarquhar, P. R. Field, C. Franklin, P. Lawson, Z. Wang, E. Williams, S. J. Abel, D. Axisa, S. Borrmann, J. Crosier, J. Fugal, M. Krämer, U. Lohmann, O. Schlenczek, M. Schnaiter, and M. Wendisch

Abstract

Mixed-phase clouds represent a three-phase colloidal system consisting of water vapor, ice particles, and coexisting supercooled liquid droplets. Mixed-phase clouds are ubiquitous in the troposphere, occurring at all latitudes from the polar regions to the tropics. Because of their widespread nature, mixed-phase processes play critical roles in the life cycle of clouds, precipitation formation, cloud electrification, and the radiative energy balance on both regional and global scales. Yet, in spite of many decades of observations and theoretical studies, our knowledge and understanding of mixed-phase cloud processes remains incomplete. Mixed-phase clouds are notoriously difficult to represent in numerical weather prediction and climate models, and their description in theoretical cloud physics still presents complicated challenges. In this chapter, the current status of our knowledge on mixed-phase clouds, obtained from theoretical studies and observations, is reviewed. Recent progress, along with a discussion of problems and gaps in understanding the mixed-phase environment is summarized. Specific steps to improve our knowledge of mixed-phase clouds and their role in the climate and weather system are proposed.

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Corey K. Potvin, Jacob R. Carley, Adam J. Clark, Louis J. Wicker, Patrick S. Skinner, Anthony E. Reinhart, Burkely T. Gallo, John S. Kain, Glen S. Romine, Eric A. Aligo, Keith A. Brewster, David C. Dowell, Lucas M. Harris, Israel L. Jirak, Fanyou Kong, Timothy A. Supinie, Kevin W. Thomas, Xuguang Wang, Yongming Wang, and Ming Xue

Abstract

The 2016–18 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments (SFE) featured the Community Leveraged Unified Ensemble (CLUE), a coordinated convection-allowing model (CAM) ensemble framework designed to provide empirical guidance for development of operational CAM systems. The 2017 CLUE included 81 members that all used 3-km horizontal grid spacing over the CONUS, enabling direct comparison of forecasts generated using different dynamical cores, physics schemes, and initialization procedures. This study uses forecasts from several of the 2017 CLUE members and one operational model to evaluate and compare CAM representation and next-day prediction of thunderstorms. The analysis utilizes existing techniques and novel, object-based techniques that distill important information about modeled and observed storms from many cases. The National Severe Storms Laboratory Multi-Radar Multi-Sensor product suite is used to verify model forecasts and climatologies of observed variables. Unobserved model fields are also examined to further illuminate important intermodel differences in storms and near-storm environments. No single model performed better than the others in all respects. However, there were many systematic intermodel and intercore differences in specific forecast metrics and model fields. Some of these differences can be confidently attributed to particular differences in model design. Model intercomparison studies similar to the one presented here are important to better understand the impacts of model and ensemble configurations on storm forecasts and to help optimize future operational CAM systems.

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D. N. Whiteman, B. Demoz, G. Schwemmer, B. Gentry, P. Di Girolamo, D. Sabatino, J. Comer, I. Veselovskii, K. Evans, R-F. Lin, Z. Wang, A. Behrendt, V. Wulfmeyer, E. Browell, R. Ferrare, S. Ismail, and J. Wang

Abstract

The NASA GSFC Scanning Raman Lidar (SRL) participated in the International H2O Project (IHOP) that occurred in May and June 2002 in the midwestern part of the United States. The SRL system configuration and methods of data analysis were described in Part I of this paper. In this second part, comparisons of SRL water vapor measurements and those of Lidar Atmospheric Sensing Experiment (LASE) airborne water vapor lidar and chilled-mirror radiosonde are performed. Two case studies are then presented: one for daytime and one for nighttime. The daytime case study is of a convectively driven boundary layer event and is used to characterize the daytime SRL water vapor random error characteristics. The nighttime case study is of a thunderstorm-generated cirrus cloud case that is studied in its meteorological context. Upper-tropospheric humidification due to precipitation from the cirrus cloud is quantified as is the cirrus cloud optical depth, extinction-to-backscatter ratio, ice water content, cirrus particle size, and both particle and volume depolarization ratios. A stability and back-trajectory analysis is performed to study the origin of wave activity in one of the cloud layers. These unprecedented cirrus cloud measurements are being used in a cirrus cloud modeling study.

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Adam J. Clark, John S. Kain, David J. Stensrud, Ming Xue, Fanyou Kong, Michael C. Coniglio, Kevin W. Thomas, Yunheng Wang, Keith Brewster, Jidong Gao, Xuguang Wang, Steven J. Weiss, and Jun Du

Abstract

Probabilistic quantitative precipitation forecasts (PQPFs) from the storm-scale ensemble forecast system run by the Center for Analysis and Prediction of Storms during the spring of 2009 are evaluated using area under the relative operating characteristic curve (ROC area). ROC area, which measures discriminating ability, is examined for ensemble size n from 1 to 17 members and for spatial scales ranging from 4 to 200 km.

Expectedly, incremental gains in skill decrease with increasing n. Significance tests comparing ROC areas for each n to those of the full 17-member ensemble revealed that more members are required to reach statistically indistinguishable PQPF skill relative to the full ensemble as forecast lead time increases and spatial scale decreases. These results appear to reflect the broadening of the forecast probability distribution function (PDF) of future atmospheric states associated with decreasing spatial scale and increasing forecast lead time. They also illustrate that efficient allocation of computing resources for convection-allowing ensembles requires careful consideration of spatial scale and forecast length desired.

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Yetang Wang, Minghu Ding, J. M. van Wessem, E. Schlosser, S. Altnau, Michiel R. van den Broeke, Jan T. M. Lenaerts, Elizabeth R. Thomas, Elisabeth Isaksson, Jianhui Wang, and Weijun Sun

Abstract

In this study, 3265 multiyear averaged in situ observations and 29 observational records at annual time scale are used to examine the performance of recent reanalysis and regional atmospheric climate model products [ERA-Interim, JRA-55, MERRA, the Polar version of MM5 (PMM5), RACMO2.1, and RACMO2.3] for their spatial and interannual variability of Antarctic surface mass balance (SMB), respectively. Simulated precipitation seasonality is also evaluated using three in situ observations and model intercomparison. All products qualitatively capture the macroscale spatial variability of observed SMB, but it is not possible to rank their relative performance because of the sparse observations at coastal regions with an elevation range from 200 to 1000 m. In terms of the absolute amount of observed snow accumulation in interior Antarctica, RACMO2.3 fits best, while the other models either underestimate (JRA-55, MERRA, ERA-Interim, and RACMO2.1) or overestimate (PMM5) the accumulation. Despite underestimated precipitation by the three reanalyses and RACMO2.1, this feature is clearly improved in JRA-55. However, because of changes in the observing system, especially the dramatically increased satellite observations for data assimilation, JRA-55 presents a marked jump in snow accumulation around 1979 and a large increase after the late 1990s. Although precipitation seasonality over the whole ice sheet is common for all products, ERA-Interim provides an unrealistic estimate of precipitation seasonality on the East Antarctic plateau, with high precipitation strongly peaking in summer. ERA-Interim shows a significant correlation with interannual variability of observed snow accumulation measurements at 28 of 29 locations, whereas fewer than 20 site observations significantly correlate with simulations by the other models. This suggests that ERA-Interim exhibits the highest performance of interannual variability in the observed precipitation.

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X. Y. Zhang, Y. Q. Wang, W. L. Lin, Y. M. Zhang, X. C. Zhang, S. Gong, P. Zhao, Y. Q. Yang, J. Z. Wang, Q. Hou, X. L. Zhang, H. Z. Che, J. P. Guo, and Y. Li

Before and during the 2008 Beijing Olympics from June to September, ground-based and satellite monitoring were carried out over Beijing and its vicinity (BIV) in a campaign to quantify the outcomes of various emission control measures. These include hourly surface PM10 and PM2.5 and their fraction of black carbon (BC), organics, nitrate, sulfate, ammonium, and daily aerosol optical depth (AOD), together with hourly reactive gases, surface ozone, and daily columnar NO2 from satellite. The analyses, excluding the estimates from weather contributions, demonstrate that after the control measures, including banning ~300,000 “yellow-tag” vehicles from roads, the even–odd turn of motor vehicles on the roads, and emission reduction aiming at coal combustion, were implemented, air quality in Beijing improved substantially. The levels of NO, NO2, NOx, CO, SO2, BC, organics, and nitrate dropped by about 30%–60% and the ozone moderately increased by ~40% while the sulfate and ammonium exhibited different patterns during various control stages. Weather conditions have a great impact on the summertime secondary aerosol (~80% of total PM) and O3 formations over BIV. During the Olympic Game period, various atmospheric components decreased dramatically at Beijing compared to the same period in the previous years. This decrease was related not only to the implementation of rigorous control measures, but also to the favorable weather processes. The subtropical high was located to the south so that Beijing's weather was dominated by the interaction between a frequently eastward shifting trough in the westerlies and a cold continental high with clear to cloudy days or showery weather.

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