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Yue Ying

Abstract

The serial ensemble square root filter (EnSRF) typically assumes observation errors to be uncorrelated when assimilating the observations one at a time. This assumption causes the filter solution to be suboptimal when the observation errors are spatially correlated. Using the Lorenz-96 model, this study evaluates the suboptimality due to mischaracterization of observation error spatial correlations. Neglecting spatial correlations in observation errors results in mismatches between the specified and true observation error variances in spectral space, which cannot be resolved by inflating the overall observation error variance. As a remedy, a multiscale observation (MSO) method is proposed to decompose the observations into multiple scale components and assimilate each component with separately adjusted spectral error variance. Experimental results using the Lorenz-96 model show that the serial EnSRF, with the help from the MSO method, can produce solutions that approach the solution from the EnSRF with correctly specified observation error correlations as the number of scale components increases. The MSO method is further tested in a two-layer quasigeostrophic (QG) model framework. In this case, the MSO method is combined with the multiscale localization (MSL) method to allow the use of different localization radii when updating the model state at different scales. The combined method (MSOL) improves the serial EnSRF performance when assimilating observations with spatially correlated errors. With adjusted observation error spectral variances and localization radii, the combined MSOL method provides the best solution in terms of analysis accuracy and filter consistency. Prospects and challenges are also discussed for the implementation of the MSO method for more complex models and observing networks.

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Yue Ying

Abstract

High-resolution models nowadays simulate phenomena across many scales and pose challenges to the design of efficient data assimilation methods that reduce errors at all scales. Smaller-scale features experience rapid nonlinear error growth that gives rise to displacement errors, which cause suboptimal ensemble filter performance. Previous studies have started exploring methods that can reduce displacement errors. In this study, a multiscale alignment (MSA) method is proposed for ensemble filtering. The MSA method iteratively processes the model state from the largest to the smallest scales. At each scale, an ensemble filter is applied to update the state with observations, and the analysis increments are utilized to derive displacement vectors for each member that align the ensemble at smaller scales before the next iteration. The nonlinearity in smaller-scale priors is reduced by removing larger-scale displacement errors. Because the displacement vectors are derived from analysis increments in the state space rather than the nonlinear observation-space cost function formulated in previous studies, this method provides a less costly and more robust way to solve for the displacement vectors. Observing system simulation experiments using a two-layer quasigeostrophic model were conducted to provide a proof of concept of the MSA method. Results show that the MSA method significantly improves the accuracy of posteriors compared to the existing ensemble filter methods with or without multiscale localization. Advantage of the MSA method are more evident when the ensemble size is relatively small and the cycling period is comparable to the average eddy turnover time of the dynamical system.

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Yue Ying and Fuqing Zhang

Abstract

As a follow-up of our recent paper on the practical and intrinsic predictability of multiscale tropical weather and equatorial waves, this study explores the potentials in improving the analysis and prediction of these weather systems through assimilating simulated satellite-based observations with a regional ensemble Kalman filter (EnKF). The observing networks investigated include the retrieved temperature and humidity profiles from the Advanced TIROS Operational Vertical Sounder (ATOVS) and global positioning system radio occultation (GPSRO), the atmospheric motion vectors (AMVs), infrared brightness temperature from Meteosat-7 (Met7-Tb), and retrieved surface wind speed from the Cyclone Global Navigation Satellite System (CYGNSS). It is found that assimilating simulated ATOVS thermodynamic profiles and AMV winds improves the accuracy of wind, temperature, humidity, and hydrometeors for scales larger than 200 km. The skillful forecast lead times can be extended by as much as 4 days for scales larger than 1000 km. Assimilation of Met7-Tb further improves the analysis of cloud hydrometeors even at scales smaller than 200 km. Assimilating CYGNSS surface winds further improves the low-level wind and temperature. Meanwhile, the impact from assimilating the current-generation GPSRO data with better vertical resolution and accuracy is comparable to assimilating half of the current ATOVS profiles, while a hypothetical 25-times increase in the number of GPSRO profiles can potentially exceed the impact from assimilating the current network of retrieved ATOVS profiles. Our study not only shows great promises in further improving practical predictability of multiscale equatorial systems but also provides guidance in the evaluation and design of current and future spaceborne observations for tropical weather.

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Yue Ying and Fuqing Zhang

Abstract

Through a series of convection-permitting regional-scale ensembles based on the Weather Research and Forecasting (WRF) Model, this study investigates the predictability of multiscale weather and convectively coupled equatorial waves during the active phase of a Madden–Julian oscillation (MJO) event over the Indian Ocean from 12 October to 12 November 2011. It is found that the practical predictability limit, estimated by the spread of the ensemble perturbed with realistic initial and boundary uncertainties, is as much as 8 days for horizontal winds, temperature, and humidity for scales larger than 2000 km that include equatorial Rossby, Kelvin, inertia–gravity, and mixed Rossby–gravity waves. The practical predictability limit decreases rapidly as scale decreases, resulting in a predictable time scale less than 1 day for scales smaller than 200 km. Through further experiments using minute initial and boundary perturbations an order of magnitude smaller than the current realistic uncertainties, the intrinsic predictability limit for tropical weather at larger scales (>2000 km) is estimated to be achievable beyond 2 weeks, but the limit is likely still less than 3 days for the small scales (<200 km).

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Baoguo Xie, Qinghong Zhang, and Yue Ying

Abstract

Annual and seasonal trends of precipitable water (PW) and relative humidity (RH) at 850, 700, and 500 hPa are studied using the data from 106 radiosonde stations over China during the period 1979–2005. Analysis shows evidence of an increase in PW associated with the slight warming observed in the lower to midtroposphere over China. The northern part of China shows a significant upward trend of PW in summer, and drying of the atmosphere in winter is found in most regions over China. Annual and seasonal trends in RH at the 850-, 700-, and 500-hPa levels show no significant trends in most regions in China except for Xinjiang, which shows an upward trend, and central China, where there was a downward trend in RH at 500 hPa. It is found that changes in PW are coincident with the warming of the surface and the lower to midtroposphere. The RH in the lower to midtroposphere in most regions over China has remained steady during the most recent 30 years, as might be expected given the increasing of PW and the warming above the surface. The long-term trend of precipitation over China may be linked to the trends of PW and RH at the lower level and midlevel.

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Yue Ying, Fuqing Zhang, and Jeffrey L. Anderson

Abstract

Covariance localization remedies sampling errors due to limited ensemble size in ensemble data assimilation. Previous studies suggest that the optimal localization radius depends on ensemble size, observation density and accuracy, as well as the correlation length scale determined by model dynamics. A comprehensive localization theory for multiscale dynamical systems with varying observation density remains an active area of research. Using a two-layer quasigeostrophic (QG) model, this study systematically evaluates the sensitivity of the best Gaspari–Cohn localization radius to changes in model resolution, ensemble size, and observing networks. Numerical experiment results show that the best localization radius is smaller for smaller-scale components of a QG flow, indicating its scale dependency. The best localization radius is rather insensitive to changes in model resolution, as long as the key dynamical processes are reasonably well represented by the low-resolution model with inflation methods that account for representation errors. As ensemble size decreases, the best localization radius shifts to smaller values. However, for nonlocal correlations between an observation and state variables that peak at a certain distance, decreasing localization radii further within this distance does not reduce analysis errors. Increasing the density of an observing network has two effects that both reduce the best localization radius. First, the reduced observation error spectral variance further constrains prior ensembles at large scales. Less large-scale contribution results in a shorter overall correlation length, which favors a smaller localization radius. Second, a denser network provides more independent pieces of information, thus a smaller localization radius still allows the same number of observations to constrain each state variable.

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Huiqin Hu, Qinghong Zhang, Baoguo Xie, Yue Ying, Jiping Zhang, and Xin Wang

Abstract

The predictability of a dense advection fog event on 21 February 2007 over north China (NC) is investigated with ensemble simulations using the Weather Research and Forecasting Model (WRF). Members with the best and worst simulation are selected from the ensemble, and their initial condition (IC) differences are explored. To test the sensitivity of fog simulation to those differences, the model is initialized with ICs that change linearly from the worst member to the best member, and the changes in simulated results are examined. The improvement in simulations due to the linear improvement of ICs is found to be monotonic. The IC differences at lower levels are of more influence to the simulation than IC differences at higher levels. By removing the IC differences of each meteorological variable individually, it is found that improvements in potential temperature and horizontal wind are more important than that of water vapor mixing ratio in this case. Additionally, the linear improvement in each meteorological variable also contributes monotonically to the simulated results. The budget analyses of the tendency of potential temperature and water vapor mixing ratio show that turbulence mixing and advection are the major factors contributing to the formation of fog. The correct initial temperature field ensures the formation and maintenance of an inversion, and the correct initial wind field ensures the correct transport of temperature and moisture in this case. Further discussion examines the reasons for the monotonic behavior in the simulation improvement.

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Shuguang Wang, Adam H. Sobel, Fuqing Zhang, Y. Qiang Sun, Ying Yue, and Lei Zhou

Abstract

This study investigates the October and November MJO events observed during the Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY)/Dynamics of the MJO (DYNAMO) field campaign through cloud-permitting numerical simulations. The simulations are compared to multiple observational datasets. The control simulation at 9-km horizontal grid spacing captures the slow eastward progression of both the October and November MJO events in surface precipitation, outgoing longwave radiation, zonal wind, humidity, and large-scale vertical motion. The vertical motion shows weak ascent in the leading edge of the MJO envelope, followed by deep ascent during the peak precipitation stage and trailed by a broad second baroclinic mode structure with ascent in the upper troposphere and descent in the lower troposphere. Both the simulation and the observations also show slow northward propagation components and tropical cyclone–like vortices after the passage of the MJO active phase. Comparison with synthesized observations from the northern sounding array shows that the model simulates the passage of the two MJO events over the sounding array region well. Sensitivity experiments to SST indicate that daily SST plays an important role for the November MJO event, but much less so for the October event.

Analysis of the moist static energy (MSE) budget shows that both advection and diabatic processes (i.e., surface fluxes and radiation) contribute to the development of the positive MSE anomaly in the active phase, but their contributions differ by how much they lead the precipitation peak. In comparison to the observational datasets used here, the model simulation may have a stronger surface flux feedback and a weaker radiative feedback. The normalized gross moist stability in the simulations shows an increase from near-zero values to ~0.8 during the active phase, similar to what is found in the observational datasets.

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Jianshun Wang, Qiang Zhang, Liang Zhang, Ying Wang, Ping Yue, Yanbin Hu, and Peilong Ye

Abstract

As impacted by climate change and further global warming, drought turns out to be the most frequent meteorological extreme event worldwide, which severely affects agriculture, ecosystem, water management and even human survival. In this study, the global pattern and development trends & directions on drought monitoring were presented based on Web of Science database by conducting a bibliometric analysis from 1983 to 2020. The following conclusions were drawn. (1) The USA and China were found as the most productive and influential nations, accounting for 24.63% and 14.30% in publication outputs and taking up 5023 and 2040 in local citations, respectively. (2) Chinese Academy of Science was reported as the core institution with 5.73% publication outputs and 829 local citations. (3) Remote Sensing of Environment and Remote Sensing were found as the most influential journals and the most productive journals with 1045 local citations and 210 publication outputs, respectively. (4) Agricultural drought profoundly affecting food security was found as the most concerned drought type in the world. The drought monitoring research mainly focus on the research and development of drought index, the response of terrestrial ecosystems to drought, and the trends and dynamics of drought in context of climate change. This study explored key findings, contradictions, and limitations of drought monitoring studies were summarized and explored. In addition, the development trend and research direction of drought monitoring researches in the future were also explored.

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Shu-peng Ho, Xinan Yue, Zhen Zeng, Chi O. Ao, Ching-Yuang Huang, Emil R. Kursinski, and Ying-Hwa Kuo

The sixth FORMOSAT-3/COSMIC Data Users' Workshop was held on 30 October–1 November 2012 in Boulder, Colorado. The purpose of this workshop is to highlight accomplishments in the areas of global positioning system (GPS) radio occultation (RO) operations and algorithm development, meteorology, climate, and ionospheric applications using COSMIC data accessed from the COSMIC Data Analysis and Archive Center (CDAAC). A summary of both the workshop presentations and recommendations is provided with an update of the outstanding issues and potentially new applications that can be explored using COSMIC-2 data.

THE SIXTH FORMOSAT-3/COSMIC DATA USERS' WORKSHOP

What: More than 130 people

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