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William R. Burrows and Curtis J. Mooney

Abstract

Blizzard conditions occur regularly in the Canadian Arctic, with high impact on travel and life there. These extreme conditions are challenging to forecast for this vast domain because the observation network is sparse and remote sensing coverage is limited. To establish occurrence statistics we analyzed METeorological Aerodrome Reports (METARs) from Canadian Arctic stations between October and May 2014-2018. Blizzard conditions occur most frequently in open tundra east and north of the boreal forest boundary, with highest frequency found on the northwest side of Hudson Bay and over flat terrain in central Baffin Island. Except in sheltered locations, the reported cause of reduced visibility is blowing snow without precipitating snow in about one-half to two-thirds of METARs made by a human observer, even higher at some stations.

We produce three products that forecast blizzard conditions from post-processed NWP model output. The blizzard potential (BP), generated from expert’s rules, is intended for warning well in advance of areas where blizzard conditions may develop. A second product (BH) stems from regression equations for the probability of visibility ≤ 1 km in blowing snow and/or concurrent snow derived by Baggaley and Hanesiak (2005). A third product (RF), generated with the Random Forest ensemble classification algorithm, makes a consensus YES/NO forecast for blizzard conditions. We describe the products, provide verification, and show forecasts for a significant blizzard event. Receiver Operator Characteristic curves and critical success index scores show RF forecasts have greater accuracy than BP and BH forecasts at all lead times.

Open access
Victor C. Mayta, George N. Kiladis, Juliana Dias, Pedro L. Silva Dias, and Maria Gehne

Abstract

Rainfall over tropical South America is known to be modulated by convectively coupled Kelvin waves (CCKWs). In this work, the origin and dynamical features of South American Kelvin waves are revisited using satellite-observed brightness temperature, radiosonde, and reanalysis datasets. Two main types of CCKWs over the Amazon are considered: Kelvin waves with a Pacific precursor, and Kelvin waves with a precursor originating over South America. Amazonian CCKW’s associated with a preexisting Kelvin convection in the eastern Pacific account for about 35% of the total events. The cases with South American precursors are associated with either pressure surges in the western Amazon from extratropical wave train activity, responsible for 40% of the total events, or “in situ” convection that locally excites CCKWs, accounting for the remaining 25%. The analysis also suggests that CCKWs associated with different precursors are sensitive to Pacific sea surface temperature. Kelvin wave events with a Pacific precursor are more common during ENSO warm events, while Kelvin waves with extratropical South American precursors show stronger activity during La Niña events. This study also explores other triggering mechanisms of CCKWs over the Amazon. These mechanisms are associated with: 1) extratropical Rossby wave trains not necessarily of extratropical South American origin; 2) CCKWs initiated in response to the presence of the southern and/or double Intertropical Convergence Zone (ITCZ) in the Eastern Pacific Ocean; 3) and possible interaction between CCKWs and other equatorial waves in the Amazon.

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Marquette N. Rocque and Steven A. Rutledge

Abstract

This study uses shipborne [R/V Roger Revelle and R/V Mirai] radar, upper-air, ocean, and surface meteorology datasets from the DYNAMO field campaign to investigate the diurnal cycle (DC) of precipitation over the central Indian Ocean related to two distinct Madden-Julian oscillations (MJOs) observed. This study extends earlier studies on the MJO DC by examining the relationship between the DC of convective organization and the local environment and comparing these results on- and off-equator. During the suppressed phase on-equator, the DC of rain rates exhibited two weak maxima at 15 LT and 01 LT, which was largely controlled by the presence of sub-MCS nonlinear precipitation features (PFs). During the active phase on-equator, MCS nonlinear features dominated the rain volume, and the greatest increase in rain rates occurred between 21-01 LT. This maximum coincided with the maxima in convective available potential energy (CAPE) and sensible heat flux, and the column moistened significantly over night. Off-equator, the environment was much drier and there was little large-scale upward motion as a result of limited deep convection. The DC of rain rates during the active phase off-equator was most similar to the DC observed during the suppressed phase on-equator, while rainfall off-equator during the suppressed phase did not vary much throughout the day. The DC of MCS nonlinear PFs closely resembled the DC of rainfall during both phases off-equator, and the DC of environmental parameters, including sea surface temperature, CAPE, and latent heat flux, was typically much weaker off-equator compared to on-equator.

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Basivi Radhakrishna and Thota Narayana Rao

Abstract

The diurnal cycle of rainfall by large-scale systems (LSS) and small-scale systems (SSS) has been studied over a complex terrain region (Gadanki) in southern peninsular India using eight years of data from a network of 36 rain gauges. The diurnal cycle of accumulated rainfall by LSS and SSS shows peaks at 22 LT and 19 LT, respectively, during the southwest monsoon (SWM) and 19 LT and ~17 LT during the northeast monsoon (NEM). Irrespective of the season and system size, the diurnal mode is the dominant mode of variation and explains ~60% (~43%) of variance during the SWM and ~54% (~36%) during the NEM in LSS (SSS) presence. The correlation structure of rainfall is anisotropic with an axis ratio of ~1.5 for LSS and ~1.4 for SSS. Propagating systems are prevalent (80 to 90% of times produce rain) in the presence of LSS during both seasons and play a dominant role in altering the diurnal cycle of rainfall over the Gadanki region. The conducive environment, like the presence of large RH, updrafts in lower- and mid-troposphere, and large lower- and small mid-tropospheric shears, favors convective initiation and propagation of precipitating systems during LSS in SWM and NEM. The atmosphere favors the convective initiation between 18 and 20 LT. The dry mid-troposphere and weak upward motion in the mid-troposphere inhibits mesoscale organization and forms SSS during the SWM. During the NEM, somewhat drier mid-troposphere than in LSS and small L-shear inhibits the convective organization and forms SSS between 15 and 18 LT.

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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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