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Jie Yang, Qingquan Liu, Feng Ding, and Renhui Ding

Abstract

The observation accuracy of the surface air temperature less than 0.1 K is a requirement, stated by the meteorological and climatological community. However, the accuracy of a temperature sensor inside a shield is affected by a number of factors including solar radiation, wind speed, upwelling longwave radiation, air density, sun elevation angle, sun azimuth angle, underlying surface, precipitation, moisture, structure, and coating of the radiation shield. Due to these factors, the temperature error of the temperature sensor may be much larger than 1 K under adverse conditions. To improve the observation accuracy, this paper proposed a spherical temperature sensor array. A series of analytical calculations based on a computational fluid dynamics (CFD) method is performed to verify the design principle of this sensor array. The calculation results show that the temperature error ratio can be assumed as a constant. To verify the accuracy of this sensor array, simulations and observation experiments are conducted. The simulation results show that the mean difference between the temperature provided by this sensor array and the reference air temperature is 0.072 K. The field experiment results show that a root-mean-square error (RMSE) and a mean absolute error (MAE) between the temperature provided by this sensor array and the reference air temperature are 0.173 and 0.153 K, respectively.

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Jie Feng, Jianping Li, Ruiqiang Ding, and Zoltan Toth

Abstract

Instabilities play a critical role in understanding atmospheric predictability and improving weather forecasting. The bred vectors (BVs) are dynamically evolved and flow-dependent nonlinear perturbations, indicating the most unstable modes of the underlying flow. Especially over smaller areas, however, BVs with different initial seeds may to some extent be constrained to a small subspace, missing potential forecast error growth along other unstable perturbation directions.

In this paper, the authors study the nonlinear local Lyapunov vectors (NLLVs) that are designed to capture an orthogonal basis spanning the most unstable perturbation subspace, thus potentially ameliorating the limitation of BVs. The NLLVs are theoretically related to the linear Lyapunov vectors (LVs), which also form an orthogonal basis. Like BVs, NLLVs are generated by dynamically evolving perturbations with a full nonlinear model. In simulated forecast experiments, a set of mutually orthogonal NLLVs show an advantage in predicting the structure of forecast error growth when compared to using a set of BVs that are not fully independent. NLLVs are also found to have a higher local dimension, enabling them to better capture localized instabilities, leading to increased ensemble spread.

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Jie Feng, Ruiqiang Ding, Deqiang Liu, and Jianping Li

Abstract

Nonlinear local Lyapunov vectors (NLLVs) are developed to indicate orthogonal directions in phase space with different perturbation growth rates. In particular, the first few NLLVs are considered to be an appropriate orthogonal basis for the fast-growing subspace. In this paper, the NLLV method is used to generate initial perturbations and implement ensemble forecasts in simple nonlinear models (the Lorenz63 and Lorenz96 models) to explore the validity of the NLLV method.

The performance of the NLLV method is compared comprehensively and systematically with other methods such as the bred vector (BV) and the random perturbation (Monte Carlo) methods. In experiments using the Lorenz63 model, the leading NLLV (LNLLV) captured a more precise direction, and with a faster growth rate, than any individual bred vector. It may be the larger projection on fastest-growing analysis errors that causes the improved performance of the new method. Regarding the Lorenz96 model, two practical measures—namely the spread–skill relationship and the Brier score—were used to assess the reliability and resolution of these ensemble schemes. Overall, the ensemble spread of NLLVs is more consistent with the errors of the ensemble mean, which indicates the better performance of NLLVs in simulating the evolution of analysis errors. In addition, the NLLVs perform significantly better than the BVs in terms of reliability and the random perturbations in resolution.

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Shuoyi Ding, Wen Chen, Juan Feng, and Hans-F. Graf

Abstract

Combined impacts of the Pacific decadal oscillation (PDO) and two types of La Niña on climate anomalies in Europe are studied. Particularly, the conjunction of the negative PDO phase and two different types of La Niña events favors strong and significant North Atlantic Oscillation (NAO) pattern anomalies with opposite polarity. For the central Pacific (CP) La Niña, a clear positive NAO signal can be detected, which is accompanied by positive surface air temperature (SAT) anomaly and a dipolar structure of precipitation anomalies in Europe. In addition, a typical negative Pacific–North America (PNA) teleconnection pattern forms, including a high pressure anomaly over the southeastern United States, which may contribute to the development and maintenance of the NAO anomaly by strengthening the baroclinicity and the local eddy–mean flow interaction. However, for the eastern Pacific (EP) La Niña, a zonal wave train in the high latitudes can be observed, which is quite different from the typical PNA structure. Here, an anomalous anticyclone over southern Greenland supports a negative NAO pattern through the local eddy–mean flow interaction and the associated vorticity advection. Hence, reversed SAT and precipitation anomalies occur over Europe. Further analyses indicate that the wave trains emanating from the North Pacific and the synoptic eddy–mean flow interaction play essential roles in forming the anomalous NAO phases. The different wave trains for the CP and EP La Niña events may be attributed to the differences in the location and intensity of anomalous convection induced by different types of SST anomaly as well as by the corresponding background westerly wind anomalies in the upper troposphere.

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Feng Ding, Andrey Savtchenko, Thomas Hearty, Jennifer Wei, Michael Theobald, Bruce Vollmer, Baijun Tian, and Eric Fetzer

Abstract

The Atmospheric Infrared Sounder (AIRS) on board NASA’s Aqua satellite provides more than 16 years of data. Its monthly gridded (Level 3) product has been widely used for climate research and applications. Since counts of successful soundings in a grid cell are used to derive monthly averages, this averaged by observations (ABO) approach effectively gives equal importance to all participating soundings within a month. It is conceivable then that days with more observations due to day-to-day orbit shift and regimes with better retrieval skills will contribute disproportionately to the monthly average within a cell. Alternatively, the AIRS Level 3 monthly product can be produced through an averaged by days (ABD) approach, where the monthly mean in a grid cell is a simple average of the daily means. The effects of these averaging methods on the AIRS version 6 monthly product are assessed quantitatively using temperature and water vapor at the surface and 500 hPa. The ABO method results in a warmer (slightly colder) global mean temperature at the surface (500 hPa) and a drier global mean water vapor than ABD method. The AIRS multiyear monthly mean temperature and water vapor from both methods are also compared with the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) product and evaluated with a simulation experiment, indicating the ABD method has lower error and is more closely correlated with MERRA-2. In summary, the ABD method is recommended for future versions of the AIRS Level 3 monthly product and more data services supporting Level 3 aggregation are needed.

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Ding Chenchen, Fumin Ren, Yanan Liu, John L. McBride, and Tian Feng

Abstract

The intensity of the tropical cyclone has been introduced into the Dynamical-Statistical-Analog Ensemble Forecast (DSAEF) for Landfalling Typhoon (or tropical cyclone) Precipitation (DSAEF_LTP) model. Moreover, the accumulated precipitation prediction experiments have been conducted on 21 target tropical cyclones with daily precipitation ≥ 100 mm in South China from 2012 to 2016. The best forecasting scheme for the DSAEF_LTP model is identified, and the performance of the prediction is compared with three numerical weather prediction models (the European Centre for Medium-Range Weather Forecasts, the Global Forecast System, and T639). The forecasting ability of the DSAEF_LTP model for heavy rainfall (accumulated precipitation ≥ 250 and ≥100 mm) improves when the intensity of the tropical cyclone is introduced, giving some advantages over the three numerical weather prediction models. The selection of analog tropical cyclones with a maximum intensity (during precipitation over land) equaling to or higher than the initial intensity of the target tropical cyclone gives better forecasts. The prediction accuracy for accumulated precipitation is higher for tropical cyclones with higher intensity and higher observed precipitation, with in both cases positive linear correlations with the threat score.

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Jian Zhang, Kenneth Howard, Carrie Langston, Steve Vasiloff, Brian Kaney, Ami Arthur, Suzanne Van Cooten, Kevin Kelleher, David Kitzmiller, Feng Ding, Dong-Jun Seo, Ernie Wells, and Chuck Dempsey

The National Mosaic and Multi-sensor QPE (Quantitative Precipitation Estimation), or “NMQ”, system was initially developed from a joint initiative between the National Oceanic and Atmospheric Administration's National Severe Storms Laboratory, the Federal Aviation Administration's Aviation Weather Research Program, and the Salt River Project. Further development has continued with additional support from the National Weather Service (NWS) Office of Hydrologic Development, the NWS Office of Climate, Water, and Weather Services, and the Central Weather Bureau of Taiwan. The objectives of NMQ research and development (R&D) are 1) to develop a hydrometeorological platform for assimilating different observational networks toward creating high spatial and temporal resolution multisensor QPEs for f lood warnings and water resource management and 2) to develop a seamless high-resolution national 3D grid of radar reflectivity for severe weather detection, data assimilation, numerical weather prediction model verification, and aviation product development.

Through about ten years of R&D, a real-time NMQ system has been implemented (http://nmq.ou.edu). Since June 2006, the system has been generating high-resolution 3D reflectivity mosaic grids (31 vertical levels) and a suite of severe weather and QPE products in real-time for the conterminous United States at a 1-km horizontal resolution and 2.5 minute update cycle. The experimental products are provided in real-time to end users ranging from government agencies, universities, research institutes, and the private sector and have been utilized in various meteorological, aviation, and hydrological applications. Further, a number of operational QPE products generated from different sensors (radar, gauge, satellite) and by human experts are ingested in the NMQ system and the experimental products are evaluated against the operational products as well as independent gauge observations in real time.

The NMQ is a fully automated system. It facilitates systematic evaluations and advances of hydrometeorological sciences and technologies in a real-time environment and serves as a test bed for rapid science-to-operation infusions. This paper describes scientific components of the NMQ system and presents initial evaluation results and future development plans of the system.

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David Kitzmiller, Suzanne Van Cooten, Feng Ding, Kenneth Howard, Carrie Langston, Jian Zhang, Heather Moser, Yu Zhang, Jonathan J. Gourley, Dongsoo Kim, and David Riley

Abstract

This study investigates evolving methodologies for radar and merged gauge–radar quantitative precipitation estimation (QPE) to determine their influence on the flow predictions of a distributed hydrologic model. These methods include the National Mosaic and QPE algorithm package (NMQ), under development at the National Severe Storms Laboratory (NSSL), and the Multisensor Precipitation Estimator (MPE) and High-Resolution Precipitation Estimator (HPE) suites currently operational at National Weather Service (NWS) field offices. The goal of the study is to determine which combination of algorithm features offers the greatest benefit toward operational hydrologic forecasting. These features include automated radar quality control, automated ZR selection, brightband identification, bias correction, multiple radar data compositing, and gauge–radar merging, which all differ between NMQ and MPE–HPE. To examine the spatial and temporal characteristics of the precipitation fields produced by each of the QPE methodologies, high-resolution (4 km and hourly) gridded precipitation estimates were derived by each algorithm suite for three major precipitation events between 2003 and 2006 over subcatchments within the Tar–Pamlico River basin of North Carolina. The results indicate that the NMQ radar-only algorithm suite consistently yielded closer agreement with reference rain gauge reports than the corresponding HPE radar-only estimates did. Similarly, the NMQ radar-only QPE input generally yielded hydrologic simulations that were closer to observations at multiple stream gauging points. These findings indicate that the combination of ZR selection and freezing-level identification algorithms within NMQ, but not incorporated within MPE and HPE, would have an appreciable positive impact on hydrologic simulations. There were relatively small differences between NMQ and HPE gauge–radar estimates in terms of accuracy and impacts on hydrologic simulations, most likely due to the large influence of the input rain gauge information.

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Xiaofang Feng, Qinghua Ding, Liguang Wu, Charles Jones, Ian Baxter, Robert Tardif, Samantha Stevenson, Julien Emile-Geay, Jonathan Mitchell, Leila M. V. Carvalho, Huijun Wang, and Eric J. Steig

Abstract

In the past 40 years, the global annual mean surface temperature has experienced a nonuniform warming, differing from the spatially uniform warming simulated by the forced responses of large multimodel ensembles to anthropogenic forcing. Rather, it exhibits significant asymmetry between the Arctic and Antarctic, with intermittent and spatially varying warming trends along the Northern Hemisphere (NH) midlatitudes and a slight cooling in the tropical eastern Pacific. In particular, this “wavy” pattern of temperature changes over the NH midlatitudes features strong cooling over Eurasia in boreal winter. Here, we show that these nonuniform features of surface temperature changes are likely tied together by tropical eastern Pacific sea surface temperatures (SSTs), via a global atmospheric teleconnection. Using six reanalyses, we find that this teleconnection can be consistently obtained as a leading circulation mode in the past century. This tropically driven teleconnection is associated with a Pacific SST pattern resembling the interdecadal Pacific oscillation (IPO), and hereafter referred to as the IPO-related bipolar teleconnection (IPO-BT). Further, two paleo-reanalysis reconstruction datasets show that the IPO-BT is a robust recurrent mode over the past 400 and 2000 years. The IPO-BT mode may thus serve as an important internal mode that regulates high-latitude climate variability on multidecadal time scales, favoring a warming (cooling) episode in the Arctic accompanied by cooling (warming) over Eurasia and the Southern Ocean (SO). Thus, the spatial nonuniformity of recent surface temperature trends may be partially explained by the enhanced appearance of the IPO-BT mode by a transition of the IPO toward a cooling phase in the eastern Pacific in the past decades.

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