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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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Juanzhen Sun and Ying Zhang

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This paper presents a case study on the assimilation of observations from multiple Doppler radars of the Next Generation Weather Radar (NEXRAD) network. A squall-line case documented during the International H2O Project (IHOP_2002) is used for the study. Radar radial velocity and reflectivity observations from four NEXRADs are assimilated into a convection-permitting model using a four-dimensional variational data assimilation (4DVAR) scheme. A mesoscale analysis using a supplementary sounding, velocity–azimuth display (VAD) profiles, and surface observations from Meteorological Aerodrome Reports (METAR) are produced and used to provide a background and boundary conditions for the 4DVAR radar data assimilation. Impact of the radar data assimilation is assessed by verifying the skill of the subsequent very short-term (5 h) forecasts.

Assimilation and forecasting experiments are conducted to examine the impact of radar data assimilation on the subsequent precipitation forecasts. It is found that the 4DVAR radar data assimilation significantly reduces the model spinup required in the experiments without radar data assimilation, resulting in significantly improved 5-h forecasts. Additional experiments are conducted to study the sensitivity of the precipitation forecasts with respect to 4DVAR cycling configurations. Results from these experiments suggest that the forecasts with three 4DVAR cycles are improved over those with cold start, but the cycling impact seems to diminish with more cycles. The impact of observations from each of the individual radars is also examined by conducting a set of experiments in which data from each radar are alternately excluded. It is found that the accurate analysis of the environmental wind surrounding the convective cells is important in successfully predicting the squall line.

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Shibo Gao, Juanzhen Sun, Jinzhong Min, Ying Zhang, and Zhuming Ying

Abstract

Radar reflectivity observations contain valuable information on precipitation and have been assimilated into numerical weather prediction models for improved microphysics initialization. However, low-reflectivity (or so-called no rain) echoes have often been ignored or not effectively used in radar data assimilation schemes. In this paper, a scheme to assimilate no-rain radar observations is described within the framework of the Weather Research and Forecasting Model’s three-dimensional variational data assimilation (3DVar) system, and its impact on precipitation forecasts is demonstrated. The key feature of the scheme is a neighborhood-based approach to adjusting water vapor when a grid point is deemed as no rain. The performance of the scheme is first examined using a severe convective case in the Front Range of the Colorado Rocky Mountains and then verified by running the 3DVar system in the same region, with and without the no-rain assimilation scheme for 68 days and 3-hourly rapid update cycles. It is shown that the no-rain data assimilation method reduces the bias and false alarm ratio of precipitation over its counterpart without that assimilation. The no-rain assimilation also improved humidity, temperature, and wind fields, with the largest error reduction in the water vapor field, both near the surface and at upper levels. It is also shown that the advantage of the scheme is in its ability to conserve total water content in cycled radar data assimilation, which cannot be achieved by assimilating only precipitation echoes.

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Ying Zhang, Zhanqing Li, and Andreas Macke

Abstract

This study investigates and accounts for the influence of various ice cloud parameters on the retrieval of the surface solar radiation budget (SSRB) from reflected flux at the top of the atmosphere (TOA). The optical properties of ice clouds depend on ice crystal shape, size distribution, water content, and the vertical profiles of geometric and microphysical structure. As a result, the relationship between the SSRB and TOA-reflected flux for an ice cloud atmosphere is more complex and differs from that for water cloud and cloudless atmospheres. The sensitivities of the relationship between the SSRB and TOA-reflected flux are examined with respect to various ice cloud parameters. Uncertainties in the retrieval of the SSRB due to inadequate knowledge of various ice cloud parameters are evaluated thoroughly. The uncertainty study is concerned with both pure ice clouds and multiphase clouds (ice cloud above water cloud). According to the magnitudes of errors in the SSRB retrieval caused by different input variables, parameterized correction terms were introduced. If the input variables are known accurately, errors in the retrieval of the SSRB under a wide range of ice cloud conditions are expected to diminish substantially, to less than 10 W m−2 for 91% of the simulated ice cloud cases. In comparison, the same accuracy may be attained for only 19% of the retrievals for the same ice cloud cases using the retrieval algorithm designed for non-ice-cloud conditions.

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Baoguo Xie, Qinghong Zhang, and Yue Ying

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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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Yueting Gong, Ying Li, and Da-Lin Zhang

Abstract

Tropical cyclones (TCs) tend to change translation direction and speed when moving across Taiwan’s Central Mountain Range (CMR), which makes forecasting of landfalling points a challenging task. This study examines the statistical characteristics of unusual TC tracks around Taiwan Island during the 66-yr period of 1949–2014. Results show that 1) about 10% more TCs were deflected to the right than to the left as they moved across the CMR, but with more occurrences of the latter on Taiwan’s eastern coast and southern strait; 2) TCs around Taiwan Island moved slower than the average speed over the western North Pacific Ocean but then exhibited anomalous acceleration along Taiwan’s eastern coast and anomalous deceleration over the southern Taiwan Strait; 3) about 33% of TCs passing the island were accompanied by terrain-induced secondary low pressure centers (SCs), more favored in the northwestern, southwestern, and southeastern quadrants, with the TC–SC separation distance varying from 33 to 643 km; 4) about 36% of landfalling TCs experienced discontinuous tracks, with an average separation distance of 141 km at the time when TC centers were replaced by SCs, and smaller Froude numbers than those associated with continuous-tracking TCs; and 5) a total of 12 TCs had looping movements near Taiwan Island, most of which were accompanied by SCs on their southern or western sides. Results also indicate that a stronger SC was likely to take place when a stronger TC approached the CMR with a shorter separation distance and that a weaker SC was likely to take place when a weaker TC approached the CMR with a longer separation distance.

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Nannan Qin, Da-Lin Zhang, and Ying Li

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It is well known that hurricane intensification is often accompanied by continuous contraction of the radius of maximum wind (RMW) and eyewall size. However, a few recent studies have shown rapid and then slow contraction of the RMW/eyewall size prior to the onset and during the early stages of rapid intensification (RI) of hurricanes, respectively, but a steady state in the RMW (S-RMW) and eyewall size during the later stages of RI. In this study, a statistical analysis of S-RMWs associated with rapidly intensifying hurricanes is performed using the extended best-track dataset during 1990–2014 in order to examine how frequently, and at what intensity and size, the S-RMW structure tends to occur. Results show that about 53% of the 139 RI events of 24-h duration associated with 55 rapidly intensifying hurricanes exhibit S-RMWs, and that the percentage of the S-RMW events increases to 69% when RI events are evaluated at 12-h intervals, based on a new RI rate definition of 10 m s−1 (12 h)−1; both results satisfy the Student’s t tests with confidence levels of over 95%. In general, S-RMWs tend to appear more frequently in more intense storms and when their RMWs are contracted to less than 50 km. This work suggests a new fruitful research area in studying the RI of hurricanes with S-RMWs.

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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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Canbo Xiao, Weifeng Zhang, and Ying Chen

Abstract

This study focuses on mechanisms of shelf valley bathymetry affecting the spread of riverine freshwater in the nearshore region. In the context of Changjiang River, a numerical model is used with different no-tide idealized configurations to simulate development of unforced river plumes over a sloping bottom, with and without a shelf valley off the estuary mouth. All simulated freshwater plumes are surface-trapped with continuously growing bulges near the estuary mouth and narrow coastal currents downstream. The simulations indicate that a shelf valley tends to compress the bulge along the direction of the valley long axis and modify the incident angle of the bulge flow impinging toward the coast, which then affects the strength of the coastal current. The bulge compression results from geostrophic adjustment and isobath-following tendency of the depth-averaged flow in the bulge region. Generally, the resulting change in the direction of the bulge impinging flow enhances down-shelf momentum advection and freshwater delivery into the coastal current. Sensitivity simulations with altered river discharges Q, Coriolis parameter, shelf bottom slope, valley geometry, and ambient stratification show that enhancement of down-shelf freshwater transport in the coastal current, ΔQ c, increases with increasing valley depth within the bulge region and decreasing slope Burger number of the ambient shelf. Assuming potential vorticity conservation, a scaling formula of ΔQ c/Q is developed, and it agrees well with results of the sensitivity simulations. Mechanisms of valley influences on unforced river plumes revealed here will help future studies of topographic influence on river plumes under more realistic conditions.

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