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Christopher M. Hartman, Xingchao Chen, Eugene E. Clothiaux, and Man-Yau Chan

Abstract

Recent studies have shown that the assimilation of all-sky infrared (IR) observations can be beneficial for tropical cyclone analyses and predictions. The assimilation of Tail Doppler Radar (TDR) radial velocity observations has also been shown to improve tropical cyclone analyses and predictions; however, there is a paucity of literature on the impacts of simultaneously assimilating them with all-sky infrared IR brightness temperatures (BTs). This study examines the impacts of assimilating combinations of GOES-16 all-sky IR brightness temperatures, NOAA P-3 TDR radial velocities, and conventional observations from the Global Telecommunications System (GTS) on the analyses and forecasts of Hurricane Dorian (2019). It is shown that including IR and/or TDR observations on top of conventional GTS observations significantly reduces both track and intensity forecast errors. Track errors are reduced the most (25% at lead times greater than 48 h) when TDR and GTS observations are assimilated. In terms of intensity, errors are always lower at lead times greater than 48 h when IR BTs are assimilated. Simultaneously assimilating TDR and IR observations has the potential to further improve the intensity forecast by as much as 37% at a lead time of 48 h to 72 h. The improved intensity forecasts produced by the experiments assimilating all three observation sources are shown to be a result of the competing effects of IR assimilation producing an overly broad area of strong cyclonic circulation and TDR assimilation constraining that circulation to a more realistic size and intensity. Interestingly, the order in which observations are assimilated has non-negligible impacts on the analyses and forecasts of Dorian.

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William R Cotton and Robert Walko

Abstract

We examine the potential role of aerosol pollution on the rainfall and intensity of hurricane Harvey. For this study, we use the global model, OLAM, with aerosol estimates from the global atmospheric chemistry model GEOS-Chem. Two sets of simulations of hurricane Harvey were performed. Simulations in the first set cover the intensification phase of Harvey until initial landfall in Texas and focus on the sensitivity of storm track and intensity, while simulations in the second set examine the sensitivity of storm track and precipitation during the period after initial landfall when record flooding occurred near Houston. During each period, simulations were performed with no anthropogenic sources of aerosol, with both natural and anthropogenic aerosol sources, and with both sources enhanced ten times.

During the rapid intensification phase, the results indicate that aerosol amounts had very little impact on storm motion. Moreover, very little difference was found on the intensity of the simulated storm to aerosol amounts for the no-anthropogenic vs the GEOS-Chem estimated amounts with anthropogenic sources. However, when both natural and anthropogenic aerosol amounts were enhanced ten times, the simulated storm intensity was enhanced appreciably in terms of minimum sea-level pressure.

During the second period of the simulation, through which Harvey remained a tropical storm, the main result was that very little sensitivity was found in precipitation or any other TC characteristic to aerosol concentrations. We cannot definitively state why the individual convective cells did not respond to high aerosol concentrations during this phase of the storm. However, the abundant precipitation in all three simulations scavenged the vast majority of aerosol as it flowed radially inward, and we speculate that this modulated the potential impact of aerosols on the inner TC and eyewall

Overall, the simulated response of hurricane Harvey to aerosols was far less spectacular than what has been simulated in the past. We conclude that this is because hurricane Harvey was a strongly dynamically-driven storm system that as a result was relatively impervious to the effects of aerosols.

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Victoria A. Johnson, Kimberly E. Klockow-McClain, Randy A. Peppler, and Angela M. Person

Abstract

Residents of the Oklahoma City metropolitan area are frequently threatened by tornadoes. Previous research indicates that perceptions of tornado threat affect behavioral choices when severe weather threatens, and as such are important to study. In this paper, we examine the potential influence of tornado climatology on risk perception. Residents across central Oklahoma were surveyed about their perceptions of tornado proneness for their home location, and this was compared to the local tornado climatology. Mapping and programming tools were then used to identify relationships between respondents’ perceptions and actual tornado events. Research found that some dimensions of the climatology, such as tornado frequency, nearness, and intensity have complex effects on risk perception. In particular, tornadoes that were intense, close, and recent had the strongest positive influence on risk perception, but weaker tornadoes appeared to produce an “inoculating” effect. Additional factors were influential, including sharp spatial discontinuities between neighboring places that were not tied to any obvious physical feature or the tornado climatology. Respondents holding lower perceptions of risk also reported lower rates of intention to prepare during tornado watches. By studying place-based perceptions, this research aims to provide a scientific basis for improved communication efforts before and during tornado events, and for identifying vulnerable populations.

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Piyush Srivastava and Maithili Sharan

Abstract

In this study, an attempt has been made to analyze the possible uncertainties in the parameterization of surface fluxes associated with the form of non-dimensional wind and temperature profile functions used in weather and climate models under convective conditions within the framework of Monin-Obukhov similarity theory (MOST). For this purpose, these functions, which are commonly known as similarity functions, are classified into four categories based on the resemblance in their functional behaviour. The bulk flux algorithm is used for the estimation of transfer coefficients of momentum and heat using four different classes of similarity functions. Uncertainty in the estimated values of fluxes is presented in the form of deviation in the predicted values of momentum and heat transfer coefficients and their variation with the Monin-Obukhov stability parameter. The analysis suggests that a large deviation in the values of estimated fluxes might occur if different forms of similarity functions are utilized for the estimation of surface fluxes. Recommendations are made for the form of similarity function for momentum based on the analysis of one year-long turbulence observations over an Indian region. The study suggests that there is a distinct need to carry out a careful analysis of turbulence data in free convective conditions for determining a consistent functional form of the similarity functions to be utilized in the atmospheric models universally.

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Jingqiu Yang, Haishan Chen, Yidi Song, Siguang Zhu, Botao Zhou, and Jie Zhang

Abstract

Eurasian continent experienced significant warming during the past decades. West Asia locates in arid/semi-arid zone and its warming amplification has drawn lots of attention. However, the climatic effect of such a warming is not clear yet. In this study, we explored the possible impacts of recent land surface warming over West Asia on the atmospheric general circulation and climate. Results show that abnormal spring land surface warming over West Asia tends to increase precipitation over North China but decrease (increase) precipitation (air temperature) over Northeast China in early summer (June). It is noted that the precipitation anomalies are much stronger over the eastern region of North/Northeast China. Further analysis suggests abnormal spring land surface warming can trigger eastward-propagating disturbance via diabatic heating, which benefits intensified the atmospheric circumglobal teleconnection (CGT) pattern, causing anomalous circulation and climate in early summer over northern China. Sensitivity experiments demonstrate that abnormal spring land surface warming can increase the atmospheric baroclinic instability and trigger Rossby waves that propagate along the westerly jet stream (WJS), resulting in the formation of CGT. Due to persistent land surface thermal forcing and the interaction between the basic flow (especially WJS) and CGT, the CGT tends to be intensified. The anomalous wave center over East Asia in early summer is responsible for the precipitation increases (decreases) over North (Northeast) China and the evident warming in Northeast China. Our results suggest that the spring land surface thermal anomalies over West Asia can be a potential signal for short-term prediction of early summer climate over northern China.

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Joseph Coz, Frank Alsheimer, and Bernhard Lee Lindner

Abstract

Coastal nuisance flooding has increased by an order of magnitude over the past half century, but the National Weather Service has a limited suite of statistical tools to forecast them. Such a tool was developed using coastal flood events from 1996—2014 in Charleston, South Carolina, which were identified and classified by prevailing synoptic conditions based on composite mean sea level pressure anomalies. The synoptic climatology indicated low level northeasterly winds dominated the forcing in anticyclonic and cyclonic events, while a southeasterly surge was the main forcing component for frontal events. Tidal anomalies between flood events and previous low tides were used to create linear regression models for each composite classification studied for forecasting levels of coastal flood magnitude. Beta tests using data from 2018—2019 confirmed the effectiveness of the models with RMSE values less than 0.3 ft and MAE values less than 0.25 ft for each event type. The veracity of the methods was further verified by a multiple day case study from November 2018, where the model was tested against both statistically predicted heights and heights based on ETSS Model (v2.2). The RMSE and MAE for the statical model were 0.18 and 0.15 respectively, while the same values for the ETSS model were 0.28 and 0.23 respectively.

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Yue Li, James T. Randerson, Natalie M. Mahowald, and Peter J. Lawrence

Abstract

Phosphorus contained in atmospheric mineral dust aerosol originating from Africa fertilizes tropical forests in Amazonia. However, the mechanisms influencing this nutrient transport pathway remain poorly understood. Here we use the Community Earth System Model to investigate how large-scale deforestation affects mineral dust aerosol transport and deposition in the tropics. We find that the surface biophysical changes that accompany deforestation produce a warmer, drier and windier surface environment that perturbs atmospheric circulation and enhances long-range dust transport from North Africa to the Amazon. Tropics-wide deforestation weakens the Hadley circulation, which in turn, leads to a northward expansion of the Hadley cell and increases surface air pressure over the Sahara Desert. The high pressure anomaly over the Sahara, in turn, increases northeasterly winds across North Africa and the tropical North Atlantic Ocean, which subsequently increases dust transport to the South America continent. We estimate that the annual atmospheric phosphorus deposition from dust significantly increases by 27% (P < 0.01) in the Amazon under a scenario of complete deforestation. These interactions exemplify how land surface changes can modify tropical nutrient cycling, which in turn, may have consequences for long-term changes in tropical ecosystem productivity and biodiversity.

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Will Boyles and Matthias Katzfuss

Abstract

The ensemble Kalman filter (EnKF) is a popular technique for data assimilation in high-dimensional nonlinear state-space models. The EnKF represents distributions of interest by an ensemble, which is a form of dimension reduction that enables straightforward forecasting even for complicated and expensive evolution operators. However, the EnKF update step involves estimation of the forecast covariance matrix based on the (often small) ensemble, which requires regularization. Many existing regularization techniques rely on spatial localization, which may ignore long-range dependence. Instead, our proposed approach assumes a sparse Cholesky factor of the inverse covariance matrix, and the nonzero Cholesky entries are further regularized. The resulting method is highly flexible and computationally scalable. In our numerical experiments, our approach was more accurate and less sensitive to misspecification of tuning parameters than tapering-based localization.

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Xia Pengfei, Ye Shirong, Xu Caijun, and Jiang Weiping

Abstract

Tropospheric hydrostatic delay is one of the major source of errors in Global Navigation Satellite System (GNSS) navigation and positioning, and an important parameter in GNSS meteorology. This work first proposes a new method of computing zenith hydrostatic delay (ZHD) based on precipitable water vapor (PWV), using radiosonde data. Next, using these calculations as a reference, the performance of three empirical ZHD models and three ZHD integral models in China is assessed using benchmark values obtained from 8 years (2010-2017) of radiosonde data recorded at 75 stations across China. Finally, we provide a new revised ZHD model that can be applied to China and validate its performance using radiosonde data collected in China in 2018. The statistical results indicate that the ZHD can be estimated by this new model with an accuracy of several millimeters. Due to its performance and simplicity, this new model is shown to be the optimal ZHD model for use in China.

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Amit Bhardwaj, Vasubandhu Misra, Ben Kirtman, Tirusew Asefa, Carolina Maran, Kevin Morris, Ed Carter, Christopher Martinez, and Daniel Roberts

Abstract

We present here the analysis of 20 years of high-resolution experimental winter seasonal CLImate reForecasts for Florida (CLIFF). These winter seasonal reforecasts were dynamically downscaled by a regional atmospheric model at 10km grid spacing from a global model run at T62 spectral resolution (~210km grid spacing at the equator) forced with sea surface temperatures (SST) obtained from one of the global models in the North American Multimodel Ensemble (NMME). CLIFF was designed in consultation with water managers (in utilities and public water supply) in Florida targeting its five water management districts, including two smaller watersheds of two specific stakeholders in central Florida that manage public water supply. This enterprise was undertaken in an attempt to meet the climate forecast needs of water management in Florida.

CLIFF has 30 ensemble members per season generated by changes to the physics and the lateral boundary conditions of the regional atmospheric model. Both deterministic and probabilistic skill measures of the seasonal precipitation at the zero-month lead for November-December-January (NDJ) and one-month lead for December-January-February (DJF) show that CLIFF has higher seasonal prediction skill than persistence. The results of the seasonal prediction skill of land surface temperature are more sobering than precipitation, although, in many instances, it is still better than the persistence skill.

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