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Yixin Wen, Qing Cao, Pierre-Emmanuel Kirstetter, Yang Hong, Jonathan J. Gourley, Jian Zhang, Guifu Zhang, and Bin Yong

Abstract

This study proposes an approach that identifies and corrects for the vertical profile of reflectivity (VPR) by using Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) measurements in the region of Arizona and southern California, where the ground-based Next Generation Weather Radar (NEXRAD) finds difficulties in making reliable estimations of surface precipitation amounts because of complex terrain and limited radar coverage. A VPR identification and enhancement (VPR-IE) method based on the modeling of the vertical variations of the equivalent reflectivity factor using a physically based parameterization is employed to obtain a representative VPR at S band from the TRMM PR measurement at Ku band. Then the representative VPR is convolved with ground radar beam sampling properties to compute apparent VPRs for enhancing NEXRAD quantitative precipitation estimation (QPE). The VPR-IE methodology is evaluated with several stratiform precipitation events during the cold season and is compared to two other statistically based correction methods, that is, the TRMM PR–based rainfall calibration and a range ring–based adjustment scheme. The results show that the VPR-IE has the best overall performance and provides much more accurate surface rainfall estimates than the original ground-based radar QPE. The potential of the VPR-IE method could be further exploited and better utilized when the Global Precipitation Measurement Mission's dual-frequency PR is launched in 2014, with anticipated accuracy improvements and expanded latitude coverage.

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Sheng Chen, Jonathan J. Gourley, Yang Hong, Qing Cao, Nicholas Carr, Pierre-Emmanuel Kirstetter, Jian Zhang, and Zac Flamig

Abstract

In meteorological investigations, the reference variable or “ground truth” typically comes from an instrument. This study uses human observations of surface precipitation types to evaluate the same variables that are estimated from an automated algorithm. The NOAA/National Severe Storms Laboratory’s Multi-Radar Multi-Sensor (MRMS) system relies primarily on observations from the Next Generation Radar (NEXRAD) network and model analyses from the Earth System Research Laboratory’s Rapid Refresh (RAP) system. Each hour, MRMS yields quantitative precipitation estimates and surface precipitation types as rain or snow. To date, the surface precipitation type product has received little attention beyond case studies. This study uses precipitation type reports collected by citizen scientists who have contributed observations to the meteorological Phenomena Identification Near the Ground (mPING) project. Citizen scientist reports of rain and snow during the winter season from 19 December 2012 to 30 April 2013 across the United States are compared to the MRMS precipitation type products. Results show that while the mPING reports have a limited spatial distribution (they are concentrated in urban areas), they yield similar critical success indexes of MRMS precipitation types in different cities. The remaining disagreement is attributed to an MRMS algorithmic deficiency of yielding too much rain, as opposed to biases in the mPING reports. The study also shows reduced detectability of snow compared to rain, which is attributed to lack of sensitivity at S band and the shallow nature of winter storms. Some suggestions are provided for improving the MRMS precipitation type algorithm based on these findings.

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Yixin Wen, Yang Hong, Guifu Zhang, Terry J. Schuur, Jonathan J. Gourley, Zac Flamig, K. Robert Morris, and Qing Cao

Abstract

Ground-based polarimetric weather radar is arguably the most powerful validation tool that provides physical insight into the development and interpretation of spaceborne weather radar algorithms and observations. This study aims to compare and resolve discrepancies in hydrometeor retrievals and reflectivity observations between the NOAA/National Severe Storm Laboratory “proof of concept” KOUN polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) and the spaceborne precipitation radar (PR) on board NASA’s Tropical Rainfall Measuring Mission (TRMM) platform. An intercomparison of PR and KOUN melting-layer heights retrieved from 2 to 5 km MSL shows a high correlation coefficient of 0.88 with relative bias of 5.9%. A resolution volume–matching technique is used to compare simultaneous TRMM PR and KOUN reflectivity observations. The comparisons reveal an overall bias of <0.2% between PR and KOUN. The bias is hypothesized to be from non-Rayleigh scattering effects and/or errors in attenuation correction procedures applied to Ku-band PR measurements. By comparing reflectivity with respect to different hydrometeor types (as determined by KOUN’s hydrometeor classification algorithm), it is found that the bias is from echoes that are classified as rain–hail mixture, wet snow, graupel, and heavy rain. These results agree with expectations from backscattering calculations at Ku and S bands, but with the notable exception of dry snow. Comparison of vertical reflectivity profiles shows that PR suffers significant attenuation at lower altitudes, especially in convective rain and in the melting layer. The attenuation correction performs very well for both stratiform and convective rain, however. In light of the imminent upgrade of the U.S. national weather radar network to include polarimetric capabilities, the findings in this study will potentially serve as the basis for nationwide validation of space-based precipitation products and also invite synergistic development of coordinated space–ground multisensor precipitation products.

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Yixin Wen, Pierre Kirstetter, Yang Hong, Jonathan J. Gourley, Qing Cao, Jian Zhang, Zac Flamig, and Xianwu Xue

Abstract

Over mountainous terrain, ground weather radars face limitations in monitoring surface precipitation as they are affected by radar beam blockages along with the range-dependent biases due to beam broadening and increase in altitude with range. These issues are compounded by precipitation structures that are relatively shallow and experience growth at low levels due to orographic enhancement. To improve surface precipitation estimation, researchers at the University of Oklahoma have demonstrated the benefits of integrating the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) products into the ground-based NEXRAD rainfall estimation system using a vertical profile of reflectivity (VPR) identification and enhancement (VPR-IE) approach. However, the temporal resolution of TRMM limits the application of VPR-IE method operationally. To implement the VPR-IE concept into the National Mosaic and Multi-Sensor QPE (NMQ) system in real time, climatological VPRs from 11 years of TRMM PR observations have been characterized for different stratiform/convective rain types, seasons, and surface rain intensities. Then, these representative profiles are used to adjust ground radar–based precipitation estimates in the NMQ system based on different precipitation structures. This study conducts a comprehensive evaluation of the newly developed climatological VPR-IE (CVPR-IE) method on winter events (January, February, and December) in 2011. The statistical analysis reveals that the CVPR-IE method provides a clear improvement over the original radar QPE in the NMQ system for the study region. Compared to physically based VPRs from real-time PR measurements, climatological VPRs have limitations in representing precipitation structure for individual events. A hybrid correction scheme incorporating both climatological and real-time VPR information is desired for better skill in the future.

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Robert G. Fovell, Yizhe Peggy Bu, Kristen L. Corbosiero, Wen-wen Tung, Yang Cao, Hung-Chi Kuo, Li-huan Hsu, and Hui Su

Abstract

The authors survey a series of modeling studies that have examined the influences that cloud microphysical processes can have on tropical cyclone (TC) motion, the strength and breadth of the wind field, inner-core diabatic heating asymmetries, outer-core convective activity, and the characteristics of the TC anvil cloud. These characteristics are sensitive to the microphysical parameterization (MP) in large part owing to the cloud-radiative forcing (CRF), the interaction of hydrometeors with radiation. The most influential component of CRF is that due to absorption and emission of longwave radiation in the anvil, which via gentle lifting directly encourages the more extensive convective activity that then leads to a radial expansion of the TC wind field. On a curved Earth, the magnitude of the outer winds helps determine the speed and direction of TC motion via the beta drift. CRF also influences TC motion by determining how convective asymmetries develop in the TC inner core. Further improvements in TC forecasting may require improved understanding and representation of cloud-radiative processes in operational models, and more comprehensive comparisons with observations are clearly needed.

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Xuhui Lee, Shoudong Liu, Wei Xiao, Wei Wang, Zhiqiu Gao, Chang Cao, Cheng Hu, Zhenghua Hu, Shuanghe Shen, Yongwei Wang, Xuefa Wen, Qitao Xiao, Jiaping Xu, Jinbiao Yang, and Mi Zhang

Lakes are an important component of the climate system. They provide moisture for precipitation, buffer temperature variations, and contribute to regional atmospheric carbon budgets. This article describes an eddy covariance (EC) mesonet on Lake Taihu, a large (area 2400 km2) and shallow (depth 2 m) lake situated in the heavily populated Yangtze River Delta, China. The mesonet consists of five lake sites, representing different biological attributes and wind–wave patterns, and a land site near the lake shore. Common to all the sites are standard EC instruments for measurement of the momentum, sensible heat, water vapor, and CO2 flux. One site is also equipped with laser-based analyzers for precise measurement of the CO2, CH4, and H2O mixing ratios and their isotopic compositions. To the authors' best knowledge, this is the first lake eddy flux mesonet. Early results reveal evidence of biological and pollution controls on the surface–air fluxes of energy, momentum, and greenhouse gases across the lake. The data will be used to address five science questions: 1) Are lake–air parameterizations established for deep lakes applicable to shallow lakes? 2) Why are lake–land breeze circulations less prevalent in the Taihu lake basin than in lake basins in northern latitudes? 3) How do algal blooms alter the lake–atmosphere interactions? 4) Is this eutrophic lake a source or sink of atmospheric CO2? 5) Does the decay of algal and macrophyte biomass contribute significant amounts of CH4 to the atmosphere?

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Benzhi Zhou, Lianhong Gu, Yihui Ding, Lan Shao, Zhongmin Wu, Xiaosheng Yang, Changzhu Li, Zhengcai Li, Xiaoming Wang, Yonghui Cao, Bingshan Zeng, Mukui Yu, Mingyu Wang, Shengkun Wang, Honggang Sun, Aiguo Duan, Yanfei An, Xu Wang, and Weijian Kong

Abstract

Extreme events often expose vulnerabilities of socioeconomic infrastructures and point to directions of much-needed policy change. Integrated impact assessment of such events can lead to finding of sustainability principles. Southern and central China has for decades been undergoing a breakneck pace of socioeconomic development. In early 2008, a massive ice storm struck this region, immobilizing millions of people. The storm was a consequence of sustained convergence between tropical maritime and continental polar air masses, caused by an anomalously stable atmospheric general circulation pattern in both low and high latitudes. Successive waves of freezing rain occurred during a month period, coating southern and central China with a layer of ice 50–160 mm in thickness. We conducted an integrated impact assessment of this event to determine whether and how the context of socioeconomic and human-disturbed natural systems may affect the transition of natural events into human disasters. We found that 1) without contingency plans, advanced technologies dependent on interrelated energy supplies can create worse problems during extreme events, 2) the weakest link in disaster response lies between science and decision making, 3) biodiversity is a form of long-term insurance for sustainable forestry against extreme events, 4) sustainable extraction of nontimber goods and services is essential to risk planning for extreme events in forest resources use, 5) extreme events can cause food shortage directly by destroying crops and indirectly by disrupting food distribution channels, 6) concentrated economic development increases societal vulnerability to extreme events, and 7) formalized institutional mechanisms are needed to ensure that unexpected opportunities to learn lessons from weather disasters are not lost in distracting circumstances.

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Maria Rugenstein, Jonah Bloch-Johnson, Ayako Abe-Ouchi, Timothy Andrews, Urs Beyerle, Long Cao, Tarun Chadha, Gokhan Danabasoglu, Jean-Louis Dufresne, Lei Duan, Marie-Alice Foujols, Thomas Frölicher, Olivier Geoffroy, Jonathan Gregory, Reto Knutti, Chao Li, Alice Marzocchi, Thorsten Mauritsen, Matthew Menary, Elisabeth Moyer, Larissa Nazarenko, David Paynter, David Saint-Martin, Gavin A. Schmidt, Akitomo Yamamoto, and Shuting Yang

Abstract

We present a model intercomparison project, LongRunMIP, the first collection of millennial-length (1,000+ years) simulations of complex coupled climate models with a representation of ocean, atmosphere, sea ice, and land surface, and their interactions. Standard model simulations are generally only a few hundred years long. However, modeling the long-term equilibration in response to radiative forcing perturbation is important for understanding many climate phenomena, such as the evolution of ocean circulation, time- and temperature-dependent feedbacks, and the differentiation of forced signal and internal variability. The aim of LongRunMIP is to facilitate research into these questions by serving as an archive for simulations that capture as much of this equilibration as possible. The only requirement to participate in LongRunMIP is to contribute a simulation with elevated, constant CO2 forcing that lasts at least 1,000 years. LongRunMIP is an MIP of opportunity in that the simulations were mostly performed prior to the conception of the archive without an agreed-upon set of experiments. For most models, the archive contains a preindustrial control simulation and simulations with an idealized (typically abrupt) CO2 forcing. We collect 2D surface and top-of-atmosphere fields and 3D ocean temperature and salinity fields. Here, we document the collection of simulations and discuss initial results, including the evolution of surface and deep ocean temperature and cloud radiative effects. As of October 2019, the collection includes 50 simulations of 15 models by 10 modeling centers. The data of LongRunMIP are publicly available. We encourage submissions of more simulations in the future.

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