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Landfalling tropical cyclones (TCs) often decay rapidly due to a decrease in moisture and energy fluxes over land when compared to the ocean surface. Occasionally, however, these cyclones maintain intensity or reintensify over land. Post-landfall maintenance and intensification of TCs over land may be a result of fluxes of moisture and energy derived from anomalously wet soils. These soils act similarly to a warm sea surface, in a phenomenon coined the "brown ocean effect." Tropical Storm (TS) Bill (2015) made landfall over a region previously moistened by anomalously heavy rainfall and displayed periods of reintensification and maintenance over land. This study evaluates the role of the brown ocean effect on the observed maintenance and intensification of TS Bill using a combination of existing and novel approaches, including the evaluation of precursor conditions at varying temporal scales and making use of composite backward trajectories. Comparisons were made to landfalling TCs with similar paths that did not undergo TC maintenance and/or intensification (TCMI) as well as to TS Erin (2007), a known TCMI case. We show that the antecedent environment prior to TS Bill was similar to other known TCMI cases, but drastically different from the non-TCMI cases analyzed in this study. Furthermore, we show that contributions of evapotranspiration to the overall water vapor budget were nonnegligible prior to TCMI cases and that evapotranspiration along storm inflow was significantly (p < 0.05) greater for TCMI cases than non-TCMI cases suggesting a potential upstream contribution from the land surface. [ABSTRACT FROM AUTHOR]

Tropical Storm Bill produced over 400 mm of rainfall in portions of southern Oklahoma from 16 to 20 June 2015, adding to the catastrophic urban and river flooding that occurred throughout the region in the month prior to landfall. The unprecedented excessive precipitation event that occurred across Oklahoma and Texas during May and June 2015 resulted in anomalously high soil moisture and latent heat fluxes over the region, acting to increase the available boundary layer moisture. Tropical Storm Bill progressed inland over the region of anomalous soil moisture and latent heat fluxes, which helped maintain polarimetric radar signatures associated with tropical, warm rain events. Vertical profiles of polarimetric radar variables such as ZH, ZDR, KDP, and ρhv were analyzed in time and space over Texas and Oklahoma. The profiles suggest that Tropical Storm Bill maintained warm rain signatures and collision–coalescence processes as it tracked hundreds of kilometers inland away from the landfall point consistent with tropical cyclone precipitation characteristics. Dual-frequency precipitation radar observations from the NASA GPM DPR were also analyzed post-landfall and showed similar signatures of collision–coalescence while Bill moved over north Texas, southern Oklahoma, eastern Missouri, and western Kentucky. [ABSTRACT FROM AUTHOR]

Hurricane Harvey produced unprecedented widespread rainfall amounts over 1000 mm in portions of southeast Texas, including Houston, from 26 to 31 August 2017. The highly efficient and prolonged warm rain processes associated with Harvey played a key role in the catastrophic flooding that occurred throughout the region. Precipitation efficiency (PE) is widely referred to in the scientific literature when discussing excessive precipitation events that lead to catastrophic flash flooding, but has yet to be explored or quantified in tropical cyclones coincident with polarimetric radar observations. With the introduction of dual-polarization radar to the NEXRAD WSR-88D network, polarimetric radar variables such as ZH, ZDR, and KDP can be used to gain insight into the precipitation processes that contribute to enhanced PE. It was found that 6-h mean values of ZH between 35 and 45 dBZ, ZDR between 1 and 1.5 dB, and KDP greater than 1° km−1 were collocated with the regions of PE greater than 100% between 27 and 29 August. Additionally, supercell thunderstorms embedded in the outer bands of Harvey were identified via 3–6 km Multi-Radar Multi-Senor (MRMS) rotation tracks and were collocated with swaths of enhanced positive ZH, ZDR, and KDP. A polarimetric rainfall relationship estimates that 1-h mean rainfall rates in these supercells were as high as 85 mm h−1 and made a significant contribution to the excessive precipitation event that occurred over the region. [ABSTRACT FROM AUTHOR]

In the U.S. Great Plains (GP), diagnosing precipitation variability is key in developing an understanding of the present and future availability of water in the region. Building on previous work investigating U.S. GP pluvial years, this study uses ERA twentieth century (ERA-20C) reanalysis data to investigate key circulation anomalies driving GP precipitation anomalies during a subset of GP pluvial years (called in this paper Pattern pluvial years). With previous research showing links between tropical Pacific sea surface temperature (SST) anomalies and GP climate variability, this study diagnoses the key circulation anomalies through an analysis of SSTs and their influence on the atmosphere. Results show that during Pattern southern Great Plains (SGP) pluvial years, central tropical Pacific SST anomalies are coincident with key atmospheric anomalies across the Pacific basin and North America. During northern Great Plains (NGP) Pattern pluvial years, no specific pattern of oceanic anomalies emerges that forces the circulation anomaly feature inherent in specific NGP pluvial years. Utilizing the results for SGP pluvial years, a conceptual model is developed detailing the identified pathway for the occurrence of circulation patterns that are favorable for pluvial years over the SGP. Overall, results from this study show the importance of the identified SGP atmospheric anomaly signal and the potential for predictability of such events. [ABSTRACT FROM AUTHOR]

Global "hot spots" for land–atmosphere coupling have been identified through various modeling studies—both local and global in scope. One hot spot that is common to many of these analyses is the U.S. southern Great Plains (SGP). In this study, we perform a mesoscale analysis, enabled by the Oklahoma Mesonet, that bridges the spatial and temporal gaps between preceding local and global analyses of coupling. We focus primarily on east–west variations in seasonal coupling in the context of interannual variability over the period spanning 2000–15. Using North American Regional Reanalysis (NARR)-derived standardized anomalies of convective triggering potential (CTP) and the low-level humidity index (HI), we investigate changes in the covariance of soil moisture and the atmospheric low-level thermodynamic profile during seasonal hydrometeorological extremes. Daily CTP and HI z scores, dependent upon climatology at individual NARR grid points, were computed and compared to in situ soil moisture observations at the nearest mesonet station to provide nearly collocated annual composites over dry and wet soils. Extreme dry and wet year CTP and HI z-score distributions are shown to deviate significantly from climatology and therefore may constitute atmospheric precursors to extreme events. The most extreme rainfall years differ from climatology but also from one another, indicating variability in the strength of land–atmosphere coupling during these years. Overall, the covariance between soil moisture and CTP/HI is much greater during drought years, and coupling appears more consistent. For example, propagation of drought during 2011 occurred under antecedent CTP and HI conditions that were identified by this study as being conducive to positive dry feedbacks demonstrating potential utility of this framework in forecasting regional drought propagation. [ABSTRACT FROM AUTHOR]

With the increasing use of the term "flash drought" within the scientific community, Otkin et al. provide a general definition that identifies flash droughts based on their unusually rapid rate of intensification. This study presents an objective percentile-based methodology that builds upon that work by identifying flash droughts using standardized evaporative stress ratio (SESR) values and changes in SESR over some period of time. Four criteria are specified to identify flash droughts: two that emphasize the vegetative impacts of flash drought and two that focus on the rapid rate of intensification. The methodology was applied to the North American Regional Reanalysis (NARR) to develop a 38-yr flash drought climatology (1979–2016) across the United States. It was found that SESR derived from NARR data compared well with the satellite-based evaporative stress index for four previously identified flash drought events. Furthermore, four additional flash drought cases were compared with the U.S. Drought Monitor (USDM), and SESR rapidly declined 1–2 weeks before a response was evident with the USDM. From the climatological analysis, a hot spot of flash drought occurrence was revealed over the Great Plains, the Corn Belt, and the western Great Lakes region. Relatively few flash drought events occurred over mountainous and arid regions. Flash droughts were categorized based on their rate of intensification, and it was found that the most intense flash droughts occurred over the central Great Plains, Corn Belt, and western Great Lakes region. [ABSTRACT FROM AUTHOR]

Precipitation variability has increased in recent decades across the Great Plains (GP) of the United States. Drought and its associated drivers have been studied in the GP region; however, periods of excessive precipitation (pluvials) at seasonal to interannual scales have received less attention. This study narrows this knowledge gap with the overall goal of understanding GP precipitation variability during pluvial periods. Through composites of relevant atmospheric variables from the ECMWF twentieth-century reanalysis (ERA-20C), key differences between southern Great Plains (SGP) and northern Great Plains (NGP) pluvial periods are highlighted. The SGP pluvial pattern shows an area of negative height anomalies over the southwestern United States with wind anomalies consistent with frequent synoptic wave passages along a southward-shifted North Pacific jet. TheNGPpattern during pluvial periods, by contrast, depicts anomalously low heights in the northwestern United States and an anomalously extended Pacific jet. Analysis of daily heavy precipitation events reveals the key drivers for these pluvial events, namely, an east-west height gradient and associated stronger poleward moisture fluxes. Therefore, the results show that pluvial years over the GP are likely driven by synoptic-scale processes rather than by anomalous seasonal precipitation driven by longer time-scale features. Overall, the results present a possible pathway to predicting the occurrence of pluvial years over the GP and understanding the causes of GP precipitation variability, potentially mitigating the threats of water scarcity and excesses for the public and agricultural sectors. [ABSTRACT FROM AUTHOR]

ABSTRACT: The purpose of this study was to investigate the seasonal to interannual variability of the temporal and spatial distributions of land–atmosphere coupling (LAC) at the mesoscale within the Southern Great Plains (SGP) of the United States. The North American Regional Reanalysis data set from 1979 to 2014 was used to complete this study. To further expand the relationship between soil moisture and precipitation, LAC was examined for the effects of soil moisture variability on latent heat flux (SM‐E) and the impact of latent heat flux variability on precipitation (E‐P). Results revealed that within the SGP there is a temporal and spatial seasonal evolution of the SM‐E relationship and dry boreal summer month (June, July and August, JJA) periods exhibit a stronger E‐P relationship relative to pluvial boreal summer month periods. Further, the variability of coupling was large both within‐season (i.e. JJA) as well as at the interannual scale while the interannual spatial and temporal coherence was such that no specific locations showed consistent coupling within the domain. Thus, the results indicate that while the SGP domain is sensitive to coupling, the location of preferred coupling is likely due to non‐local factors at the mesoscale embedded within synoptic conditions as well as the regional climate. [ABSTRACT FROM AUTHOR]

ABSTRACT Agriculture is a critical industry to the economy of the Great Plains ( GP) region of North America and sensitive to change in weather and climate. Thus, improved knowledge of meteorological and climatological conditions during the growing season and associated variability across spatial and temporal scales is important. A distinct climate feature in the GP is the asynchronicity (AS) between the timing of temperature and precipitation maxima. This study investigated a long-term observational data set to quantify the AS and to address the impacts of climate variability and change. Global Historical Climate Network Daily ( GHCN-Daily) data were utilized for this study; 352 GHCN-Daily stations were identified based on specific criteria and the dates of the precipitation and temperature maxima for each year were identified at daily and weekly intervals. An asynchronous difference index ( ADI) was computed by determining the difference between these dates averaged over each decade. Analysis of daily and weekly ADI revealed two physically distinct regimes of ADI (positive and negative), with comparable shifts in the timing of both the maximum of precipitation and temperature over all six states within the GP examined when comparing the two different regimes. Time series analysis of decadal average ADI yielded moderate shifts (∼5 to 10 days from linear regression analysis) in ADI in several states with increased variability occurring over much of the study region. [ABSTRACT FROM AUTHOR]

Observations from the Oklahoma Mesonet and high resolution Weather Research and Forecasting model simulations were used to evaluate the effect that the dry line and large-scale atmospheric patterns had on drought evolution during 2011. Mesonet observations showed that a “dry” and “wet” pattern developed across Oklahoma due to anomalous atmospheric patterns. The location of the dry line varied due to this “dry” and “wet” pattern, with the average dry line location around 1.5° longitude further to the east than climatology. Model simulations were used to further quantify the impact of variable surface conditions on dry line evolution and convective initiation (CI) during April and May 2011. Specifically, soil moisture conditions were altered to depict “wet” and “dry” conditions across the domain by replacing the soil moisture values by each soil category’s porosity or wilting point value. Overall, the strength of the dry line boundary, its position, and subsequent CI were dependent on the modification of soil moisture. The simulations demonstrated that modifying soil moisture impacted the nature of the dry line and showed that soil moisture conditions during the first half of the warm season modified the dry line pattern and influenced the evolution and perpetuation of drought over Oklahoma. [ABSTRACT FROM AUTHOR]

A road ice prediction model was developed on the basis of existing data networks with an objective of providing a computationally efficient method of road ice forecasting. Icing risk was separated into three distinct road ice formation mechanisms: hoarfrost, freezing fog, and frozen precipitation. Hoarfrost parameterizations were mostly gathered as presented in previous literature, with modifications incorporated to account for diffusional ice crystal growth-rate complexity. Freezing-fog parameterizations were based on previous fog typological analyses under the assumption that fog formation mechanisms are similar in above- and subfreezing temperatures. Frozen-precipitation parameterizations were primarily unique to the developed model but were also partially based on previous research. Diagnostic analyses use a synthesis of Automated Surface Observing System (ASOS), Automated Weather Observing System (AWOS), and Oklahoma Mesonet data. Prognostic analyses utilize the National Digital Forecast Database (NDFD), a 2.5-km gridded database of forecast meteorological variables output from National Weather Service Weather Forecast Offices. A frequency analysis was performed using the diagnostic parameterizations to determine general road icing risk across the state of Oklahoma. The frequency analyses aligned well with expected temporal maxima and confirmed the viability of the developed parameterizations. Further, a fog typological analysis showed the implemented freezing-fog-formation parameterizations to capture 89% of fog events. These results suggest that the developed model, identified as the Road-Ice Model (RIM), may be implemented as a robust option for analyzing the potential for road ice development based on the background meteorological environment. [ABSTRACT FROM AUTHOR]

• Rapid intensification is the main flash drought driver east of Rocky Mountains (RM). • West of the RM, rapid intensification often occurs without reaching drought. • SESR showed strong potential in identifying drought, particularly east of the RM. • SESR is able to represent meteorological and agricultural drought. Flash droughts develop rapidly (∼1 month timescale) and produce significant ecological, agricultural, and socioeconomical impacts. Recent advances in our understanding of flash droughts have resulted in methods to identify and quantify flash drought events. However, few studies have been done to isolate the individual rapid intensification and drought components of flash drought, which could further determine their causes, evolution, and predictability. This study utilized the standardized evaporative stress ratio (SESR) to quantify individual components of flash drought from 1979 – 2019, using evapotranspiration (ET) and potential evapotranspiration (PET) data from the North American Regional Reanalysis (NARR) dataset. The temporal change in SESR was utilized to quantify the rapid intensification component of flash drought. The drought component was also determined using SESR and compared to the United States Drought Monitor. The results showed that SESR was able to represent the spatial coverage of drought well for regions east of the Rocky Mountains. Furthermore, the rapid intensification component agreed well with previous flash drought studies, with the overall climatology of rapid intensification events showing similar hotspots to the flash drought climatology east of the Rocky Mountains. The rapid intensification climatology suggested areas west of the Rocky Mountains experience rapid drying more often than east of the Rocky Mountains. [ABSTRACT FROM AUTHOR]

Flooding is routinely one of the most deadly weather-related hazards in the United States, which highlights the need for more hydrometeorological research related to forecasting these hazardous events. Building upon previous literature, a synergistic study analyzes hydrometeorological aspects of major urban flood events in the United States from 1977 through 2014 caused by locally heavy precipitation. Primary datasets include upper-air soundings and climatological precipitable water (PW) distributions. A major finding of this work is that major urban flood events are associated with extremely anomalous PW values, many of which exceeded the 99th percentile of the associated climatological dataset and all of which were greater than 150% of the climatological mean values. However, of the 40 cases examined in this study, only 15 had PW values that exceeded 50.4 mm (2 in.), illustrating the importance of including the location-specific PW climatology in a PW analysis relevant to the potential for flash floods. Additionally, these events revealed that, despite geographic location and time of year, most had a warm cloud depth of at least 6 km, which is defined here as the layer between the lifting condensation level and the height of the −10°C level. A 'composite' flood sounding was also calculated and revealed a characteristically tropical structure, despite cases related to tropical cyclones being excluded from the study. [ABSTRACT FROM AUTHOR]

The WxChallenge, a project developed at the University of Oklahoma, brings a state-of-the-art, fun, and exciting forecast contest to participants at colleges and universities across North America. The challenge is to forecast the maximum and minimum temperatures, precipitation, and maximum wind speeds for select locations across the United States over a 24-h prediction period. The WxChallenge is open to all undergraduate and graduate students, as well as higher-education faculty, staff, and alumni. Through the use of World Wide Web interfaces accessible by personal computers, tablet computer, and smartphones, the WxChallenge provides a state-of-the-art portal to aid participants in submitting forecasts and alleviate many of the administrative issues (e.g., tracking and scoring) faced by local managers and professors. Since its inception in 2006, 110 universities have participated in the contest and it has been utilized as part of the curricula for 140 classroom courses at various institutions. The inherently challenging nature of the WxChallenge has encouraged its adoption as an educational tool. As its popularity has grown, professors have seen the utility of the Wx-Challenge as a teaching aid and it has become an instructional resource of many meteorological classes at institutions for higher learning. In addition to evidence of educational impacts, the competition has already begun to leave a cultural and social mark on the meteorological learning experience. [ABSTRACT FROM AUTHOR]

On 13 May 2009, 13 surface-based observing stations within central and western Oklahoma recorded maximum wind speeds in excess of 22.5 m s−1, along with gusts exceeding 28 m s−1 in isolated locations, during a localized wind storm that lasted in excess for over an hour. These wind speeds were associated with an enhanced mesoscale pressure gradient that developed during the late evening of 12 May 2009 and early morning hours of 13 May 2009, which then slowly propagated from west to east. Ultimately, the event produced localized wind damage in northern portions of Oklahoma City. Analysis of the synoptic and mesoscale conditions present at each stage of the event revealed that specific parameters associated with heat bursts occurred during the event, but with the inclusion of varying mesoscale and microscale influences. As such, a close examination of the mesoscale pressure field indicated that a dynamically variable mesoscale convective system (MCS) produced a mesohigh/mesolow couplet that ultimately created the strong pressure gradient. The result was a prolonged period (in excess of 1 h) of enhanced wind speed values across a swath approximately 300 km in length and 50 km in width that were not directly associated with the anomalously warmer and drier conditions that occurred due to the heat burst. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The Oklahoma City Micronet (OKCNET) is an operational surface observing network designed to improve atmospheric monitoring across the Oklahoma City, Oklahoma, metropolitan area. The 40 station network consists of 4 Oklahoma Mesonet stations and 36 micronet stations mounted on traffic signals at an average station spacing of approximately 3 km. Using several technical innovations as well as existing infrastructure in Oklahoma City, data are collected and quality assured in near real-time at an interval of 1 min for the traffic signal sites and 5 min for the Mesonet sites. Because OKCNET also spans a land use gradient from rural to urban, the spatial and temporal densities of OKCNET observations have shed new insights on atmospheric processes (e.g. the urban heat island, severe thunderstorm evolution) across the Oklahoma City metropolitan area. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

During late July and early August 2008, an intense heat wave occurred in Oklahoma City. To quantify the impact of the urban heat island (UHI) in Oklahoma City on observed and apparent temperature conditions during the heat wave event, this study used observations from 46 locations in and around Oklahoma City. The methodology utilized composite values of atmospheric conditions for three primary categories defined by population and general land use: rural, suburban, and urban. The results of the analyses demonstrated that a consistent UHI existed during the study period whereby the composite temperature values within the urban core were approximately 0.5°?C warmer during the day than the rural areas and over 2°?C warmer at night. Further, when the warmer temperatures were combined with ambient humidity conditions, the composite values consistently revealed even warmer heat-related variables within the urban environment as compared with the rural zone. [ABSTRACT FROM AUTHOR]

Oklahoma is a region that is well known for its high frequency of severe thunderstorms, which vary in activity, mode, and coverage. In particular, this region experiences a significant number of highly organized supercell thunderstorms that pose hazards such as high winds, large hail, and tornadoes. This demonstration study focuses on the development and analysis of a 10-yr sample of supercell storms resulting from organized severe weather events in Oklahoma. Geographic information systems (GIS) were used as the primary tool to develop and analyze the 10-yr supercell dataset. The use of GIS technologies within the field of meteorology has increased dramatically in recent years and will likely continue as additional atmospheric science data formats become available in popular GIS software packages such as the Environmental Systems Research Institute’s ArcGIS series. For this specific study, GIS served as a critical component for developing individual georeferenced storm features and analyzing the life span and characteristics of 943 supercell thunderstorms. The results of a series of spatial storm frequency, initiation, termination, and direction analyses are presented. Results revealed that for the period spanning 1994–2003 supercell storms resulting from organized severe weather events were most frequent across several regions, including east-central Oklahoma, southwest Oklahoma, and west-central through northeast Oklahoma, with an overall mean track from the southwest to northeast. Supercell tracks were predominantly southwesterly during the first 5 months of the year, northwesterly from June through September, and once again southwesterly from October through the end of the year. A final set of analyses and examples illustrate the utility of storm feature–based climatologies. [ABSTRACT FROM AUTHOR]

Severe thunderstorms are an important and relatively common component of the annual weather across the State of Oklahoma. Such weather brings hazardous features such as, large hail, damaging winds, and tornadoes, while also providing beneficial precipitation vital to the state's agricultural and hydrological needs. In any given year, severe weather activity is dictated by seasonal and monthly changes in atmospheric conditions, which frequently determine the type and severity of subsequent storms. This study focuses on a 10-year period between 1994 and 2003 to quantify the spatial and temporal characteristics of severe squall line storms across Oklahoma. A squall line is a linearly organized set of storms that has a sharp radar reflectivity gradient at its leading edge typically followed by a less intense stratiform region. Squall line storms are one of the most significant storm modes observed in Oklahoma due to their large aerial extent, long durations, high winds, heavy rains, and hail. For this study, geographic information systems (GIS) were used to store geo-referenced storm data and spatially analyse storm events across varying timescales. The analysis revealed that squall lines were most predominant across eastern Oklahoma with a decreasing westward gradient. The annual averaged storm track was from the west to east, while the monthly mean squall line tracks were oriented from southwest to northeast from January through April, from west to east during May, from northwest to southeast from June through September, and from southwest to northeast through the end of the year. In addition, high-resolution analyses of squall line initiation and termination locations revealed important geographical variability in typical storm lifecycle. Copyright © 2007 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Soil moisture is an important component in many hydrologic and land–atmosphere interactions. Understanding the spatial and temporal nature of soil moisture on the mesoscale is vital to determine the influence that land surface processes have on the atmosphere. Recognizing the need for improved in situ soil moisture measurements, the Oklahoma Mesonet, an automated network of 116 remote meteorological stations across Oklahoma, installed Campbell Scientific 229-L devices to measure soil moisture conditions. Herein, background information on the soil moisture measurements, the technical design of the soil moisture network embedded within the Oklahoma Mesonet, and the quality assurance (QA) techniques applied to the observations are provided. This project also demonstrated the importance of operational QA regarding the data collected, whereby the percentage of observations that passed the QA procedures increased significantly once daily QA was applied. [ABSTRACT FROM AUTHOR]

Severe hail is a common event in the United States and few studies have been conducted using high-resolution data to determine the spatial and temporal variability of hail occurrence. Given the coverage of Weather Surveillance Radar-88 Doppler radars (WSR-88D; Crum and Alberty 1993 ) over the Southern Plains of the United States, recent technological advancements including hail detection algorithms and powerful new geographic information systems (GIS) tools, a new methodology was developed to quantify the occurrence of severe hail. The methodology was tested using a dataset spanning 2001–2003 from 15 radar sites across eight states. The Hailswath algorithm developed at Weather Decision Technologies, Inc. was used to estimate severe and significant hail for each individual storm during the study period. Due to the discrete nature of the Hailswath algorithm output, it was manually contoured to create a coherent swath of the most likely area of severe (19 mm in diameter; 0.75 inches) and significant hail (51 mm in diameter; 2 inches). Finally, the results of these analyses were compiled monthly, annually, and for the total duration of the study period using a variety of GIS tools. A thorough demonstration of the data analysis process and results is presented, and the benefits and limitations of the method used to investigate the occurrence of severe hail are explained. [ABSTRACT FROM AUTHOR]

The El Reno Oklahoma Mesonet (ELRE) site is one of a few Oklahoma Mesonet sites that has measured inversions of 10°C or greater between 1.5 and 9 m. Historical analyses revealed that strong inversions at ELRE have occurred because of rapid cooling near the surface shortly after sunset when conditions are calm, clear, and dry. In addition, ELRE is a very well sited station and is located on very slightly sloped terrain with no obstructions nearby. Four Portable Automated Research Micrometeorological Stations (PARMS) were deployed along a transect orthogonal to ELRE for 3 months in the spring of 2005 to quantify the micrometeorological processes that caused rapid cooling and subsequent strong inversions to form. One-minute data collected from the PARMS and ELRE during the study verified the variability and duration of strong inversion events. Analyses from the field study also revealed that significant horizontal and vertical differences in air temperature and wind speed existed during periods of differential wind speeds between the PARMS and ELRE. [ABSTRACT FROM AUTHOR]

Investigates how land–atmosphere interactions played an important role in modifying the local environment. Synoptic conditions; Role of evaporation and transpiration; Impact of moisture in the low-level atmosphere; Potential for severe weather.

The Oklahoma Mesonet, an automated network of 115 meteorological observing stations, includes soil moisture monitoring devices at 60 locations. The Campbell Scientific model 229-L matric potential (water potential) sensor was chosen for operational use based on its capability to perform as a fully automated soil water measuring device. Extensive laboratory calibrations were performed on each sensor to ensure the quality of the matric potential measurements. Examination of the data from the Norman site during July 1997 revealed significant inconsistencies between near-surface (5 and 25 cm) measurements of soil moisture and deep-layer (60 and 75 cm) measurements of soil moisture. In particular, a heavy precipitation event was followed by only a small increase in near-surface soil water potential values, while a much larger increase occurred in the deep-layer values. It is theorized that an installation flaw is the cause for these inconsistencies. A solution is proposed in the hope that future efforts to measure soil moisture will not be hindered by similar problems. [ABSTRACT FROM AUTHOR]

Wintertime cold air outbreaks (CAOs) in the Great Plains of the United States have significant socioeconomic, environmental, and infrastructural impacts; the events of December 1983 and February 2021 are key examples of this. Previous studies have investigated CAOs in other parts of North America, particularly the eastern United States, but the development of CAOs in the Great Plains and their potential subseasonal-to-seasonal (S2S) predictability have yet to be assessed. This study first identifies 37 large-scale CAOs in the Great Plains between 1950 and 2021, before examining their characteristics, evolution, and driving mechanisms. These events occur under two dominant weather regimes at event onset: one set associated with anomalous ridging over Alaska and the other set associated with anomalous pan-Arctic ridging. Alaskan ridge CAOs evolve quickly (i.e., on synoptic time scales) and involve stratospheric wave reflection. Conversely, Arctic high CAOs are preceded by weak stratospheric polar vortex conditions several weeks prior to the event. Both categories of CAOs feature anomalous upward wave activity flux from Siberia, with downward wave activity flux over Canada seen only in the Alaskan ridge CAOs. The rapid development of the Alaskan ridge CAOs, also linked with a North Pacific wave train and anomalous wave activity flux from the central Pacific, suggests that these events could be forced by tropical modes of variability. These findings present evidence that different forcing mechanisms, with contrasting time scales, may produce distinct sources of predictability for these CAOs on the S2S time scale. [ABSTRACT FROM AUTHOR]

Although significant improvements have been made to the prediction and understanding of extreme precipitation events in recent decades, there is still much to learn about these impactful events on the subseasonal time scale. This study focuses on identifying synoptic patterns and precursors ahead of an extreme precipitation event over the contiguous United States (CONUS). First, we provide a robust definition for 14-day ‘‘extreme precipitation events’’ and partition the CONUS into six different geographic regions to compare and contrast the synoptic patterns associated with events in those regions. Then, several atmospheric variables from ERA-Interim (e.g., geopotential height and zonal winds) are composited to understand the evolution of the atmospheric state before and during a 14-day extreme precipitation event. Common synoptic signals seen during events include significant zonally oriented trough–ridge patterns, an energized subtropical jet stream, and enhanced moisture transport into the affected area. Also, atmospheric-river activity increases in the specific region during these events. Modes of climate variability and lagged composites are then investigated for their potential use in lead-time prediction. Key findings include synoptic-scale anomalies in the North Pacific Ocean and regional connections to modes such as the Pacific–North American pattern and the North Pacific Oscillation. Taken together, our results represent a significant step forward in understanding the evolution of 14-day extreme precipitation events for potential damage and casualty mitigation. [ABSTRACT FROM AUTHOR]

Backward trajectories were derived from North American Regional Reanalysis data for 19 253 flash flood reports published by the National Weather Service to determine the along-path contribution of the land surface to the moisture budget for flash flood events in the conterminous United States. The impact of land surface interactions was evaluated seasonally and for six regions: the West Coast, Arizona, the Front Range, Flash Flood Alley, the Missouri Valley, and the Appalachians. Parcels were released from locations that were impacted by flash floods and traced backward in time for 120 h. The boundary layer height was used to determine whether moisture increases occurred within the boundary layer or above it. Moisture increases occurring within the boundary layer were attributed to evapotranspiration from the land surface, and surface properties were recorded from an offline run of the Noah land surface model. In general, moisture increases attributed to the land surface were associated with anomalously high surface latent heat fluxes and anomalously low sensible heat fluxes (resulting in a positive anomaly of evaporative fraction) as well as positive anomalies in top-layer soil moisture. Over the ocean, uptakes were associated with positive anomalies in sea surface temperatures, the magnitude of which varies both regionally and seasonally. Major oceanic surface-based source regions of moisture for flash floods in the United States include the Gulf of Mexico and the Gulf of California, while boundary layer moisture increases in the southern plains are attributable in part to interactions between the land surface and the atmosphere. [ABSTRACT FROM AUTHOR]

This study uses backward trajectories derived from North American Regional Reanalysis data for 19 253 flash flood reports during the period 2007–13 published by the National Weather Service to assess the origins of air parcels for flash floods in the conterminous United States. The preferred flow paths for parcels were evaluated seasonally and for six regions of interest: the West Coast, Arizona, the Front Range of the Rocky Mountains, Flash Flood Alley in south-central Texas, the Missouri Valley, and the Appalachians. Parcels were released from vertical columns in the atmosphere at times and locations where there were reported flash floods; these were traced backward in time for 5 days. The temporal and seasonal cycles of flood events in these regions are also explored. The results show the importance of trajectories residing for long periods over oceanic regions such as the Gulf of Mexico and the Caribbean Sea. The flow is generally unidirectional with height in the lower layers of the atmosphere. The trajectory paths from oceanic genesis regions to inland hotspots and their orientation with height provide clues that can assist in the diagnosis of impending flash floods. Part II of this manuscript details the land–atmosphere interactions along the trajectory paths. [ABSTRACT FROM AUTHOR]

Abstract Winter wheat (Triticum aestivum L.) and tallgrass prairie are common land cover types in the Southern Plains of the United States. During the last century, agricultural expansion into native grasslands was extensive, particularly managed pasture or winter wheat. In this study, we measured carbon dioxide (CO 2) and water vapor (H 2 O) fluxes from winter wheat and tallgrass prairie sites in Central Oklahoma using the eddy covariance in 2015 and 2016. The objective of this study was to contrast CO 2 and H 2 O fluxes between these two ecosystems to provide insights on the impacts of conversion of tallgrass prairie to winter wheat on carbon and water budgets. Daily net ecosystem CO 2 exchange (NEE) reached seasonal peaks of −9.4 and −8.8 g C m−2 in 2015 and −6.2 and −7.5 g C m−2 in 2016 at winter wheat and tall grass prairie sites, respectively. Both sites were net sink of carbon during their growing seasons. At the annual scale, the winter wheat site was a net source of carbon (56 ± 13 and 33 ± 9 g C m−2 year−1 in 2015 and 2016, respectively). In contrast, the tallgrass prairie site was a net sink of carbon (−128 ± 69 and −119 ± 53 g C m−2 year−1 in 2015 and 2016, respectively). Daily ET reached seasonal maximums of 6.0 and 5.3 mm day−1 in 2015, and 7.2 and 8.2 mm day−1 in 2016 at the winter wheat and tallgrass prairie sites, respectively. Although ecosystem water use efficiency (EWUE) was higher in winter wheat than in tallgrass prairie at the seasonal scale, summer fallow contributed higher water loss from the wheat site per unit of carbon fixed, resulting into lower EWUE at the annual scale. Results indicate that the differences in magnitudes and patterns of fluxes between the two ecosystems can influence carbon and water budgets. Graphical abstract Unlabelled Image Highlights • Carbon and water fluxes measured in two ecosystems located at 2.7 km apart are compared. • Winter wheat and tallgrass prairie are both carbon sinks during the growing season. • Summer fallow caused the wheat ecosystem to be a source of carbon at annual scale. • Management practices caused changes in the fluxes of prairie and wheat ecosystems. [ABSTRACT FROM AUTHOR]

As NWP and climate models continue to evolve toward finer grid spacing, efforts have been undertaken to better represent urban effects. For this study, the single-layer urban canopy model (SLUCM) of the High-Resolution Land Data Assimilation System (HRLDAS) and WRF Model was used to investigate the sensitivity of near-surface air temperatures and energy fluxes to SLUCM parameters in uncoupled (land) and coupled (land-atmosphere) predictions. Output from HRLDAS and WRF was compared with observations from the Oklahoma Mesonet and Joint Urban 2003 experiment. Variations in roof albedo (0.04-0.4) produced 40-135 W m22 changes in net radiation and sensible heat fluxes. Sensible and ground heat fluxes varied by 40-100 W m22 with changes in roof thermal conductivity (0.05-1.4). The urban fraction was found to be the only SLUCM parameter to significantly impact latent heat fluxes. Near-surface air temperatures, particularly during the daytime, did not show significant variations with SLUCM parameters (remaining within the 0.5-K range). Differences in urban air temperatures due to the change in boundary layer scheme were greater than the temperature changes due to SLUCM parameter variations. The sensitivity of near-surface air temperatures to SLUCM parameters depended on the method used to calculate the skin temperature of the impervious surface. For all simulations, predicted 2-m urban air temperatures were consistently higher than observations, with deviations approaching 8K during the day and below 3K at night. These large errors affected the model's skill in reproducing the diurnal cycle of UHI intensity. [ABSTRACT FROM AUTHOR]

Agricultural drought, a common phenomenon in most parts of the world, is one of the most challenging natural hazards to monitor effectively. Land surface water index (LSWI), calculated as a normalized ratio between near infrared (NIR) and short-wave infrared (SWIR), is sensitive to vegetation and soil water content. This study examined the potential of a LSWI-based, drought-monitoring algorithm to assess summer drought over 113 Oklahoma Mesonet stations comprising various land cover and soil types in Oklahoma. Drought duration in a year was determined by the number of days with LSWI <0 (DNLSWI) during summer months (June-August). Summer rainfall anomalies and LSWI anomalies followed a similar seasonal dynamics and showed strong correlations ( r = 0.62-0.73) during drought years (2001, 2006, 2011, and 2012). The DNLSWI tracked the east-west gradient of summer rainfall in Oklahoma. Drought intensity increased with increasing duration of DNLSWI, and the intensity increased rapidly when DNLSWI was more than 48 days. The comparison between LSWI and the US Drought Monitor (USDM) showed a strong linear negative relationship; i.e., higher drought intensity tends to have lower LSWI values and vice versa. However, the agreement between LSWI-based algorithm and USDM indicators varied substantially from 32 % ( D class, moderate drought) to 77 % (0 and D class, no drought) for different drought intensity classes and varied from ∼30 % (western Oklahoma) to >80 % (eastern Oklahoma) across regions. Our results illustrated that drought intensity thresholds can be established by counting DNLSWI (in days) and used as a simple complementary tool in several drought applications for semi-arid and semi-humid regions of Oklahoma. However, larger discrepancies between USDM and the LSWI-based algorithm in arid regions of western Oklahoma suggest the requirement of further adjustment in the algorithm for its application in arid regions. [ABSTRACT FROM AUTHOR]

ABSTRACT Ice storms, defined by the US National Weather Service as freezing rain accumulations over 0.635 cm (0.25 inch), are often costly and destructive. Formation processes include the classic 'melting' process and supercooled warm rain process. Freezing rain is most commonly found ahead of a warm front or occlusion, where warm air is lifted over a cold shallow layer near the surface. Other synoptic patterns conducive to freezing rain include arctic fronts, isentropic lift over an arctic air mass, and cold air damming. Causes of spatial and temporal variations in freezing rain include, but are not limited to, terrain and proximity to water. Areas with the most occurrences of freezing rain in the United States include the Pacific Northwest, Upper Midwest, and Northeast/Appalachian regions. Empirical forecasting methods and numerical weather prediction are currently used to predict freezing rain. Successful forecasting of ice storm events requires evaluation of the thermodynamic profile of the atmosphere. Local effects such as proximity to water and topography must be taken into account, and non-linear processes such as latent heating and cooling must not be ignored. Ice accumulation can cause tree damage, which, in addition to breakage of electrical cables, can lead to power outages. Deposition of ice also impacts road, rail, and air travel, with associated economic costs due to lost hours as workers are unable to travel. Ice storms also provide a significant risk to human health and life, with falling debris and slippery surfaces being primary threats. [ABSTRACT FROM AUTHOR]

The purpose of this study was to quantify dipole events (a drought year followed by a pluvial year) for various spatial scales including the nine Oklahoma climate divisions and the author-defined regions of the U.S. Southern Great Plains (SGP), High Plains (HP), and Northern Great Plains (NGP). Analyses revealed that, on average, over twice as many standard deviation (STDEV) dipoles existed in the latter half of the dataset (1955-2013) relative to the first half (1896-1954), suggesting that dramatic increases in precipitation from one year to the next within the Oklahoma climate divisions are increasing with time. For the larger regions within the Great Plains of the United States, the percent chance of a significant pluvial year following a significant drought year was approximately 25% of the time for the SGP and NGP and approximately 16% of the time for the HP. The STDEV dipole analyses further revealed that the frequency of dipoles was consistent between the first and second half of the dataset for the NGP and HP but was increasing with time in the SGP. The temporal periods of anomalous precipitation during relative pluvial years within the STDEV dipole events were unique for each region whereby October occurred most frequently (70%) within the SGP, September occurred most frequently (60%) within the HP, and May occurred most frequently (62%) within the NGP. [ABSTRACT FROM AUTHOR]

• Ground-radar precip product upscaled spatiotemporally to coarser satellite resolution. • Upscaled products used to simulate discrete flood characteristics. • Satellite uncertainty shown to overpower weaker errors from upscaled products. • Upscaled products suffer from negative water balance errors after modeling. • Spatial upscaling adds uncertainty to simulations while temporal upscaling adds bias. This research focuses on the effects of spatio-temporal resolutions of global satellite precipitation on simulated flood events characteristics. The analysis is carried out by spatially, temporally, and spatiotemporally upscaling fine-scale precipitation forcings from the ground-radar-based Multi-Radar Multi-Sensor system (MRMS) to the resolution of the Integrated Multi-satellitE Retrievals for GPM (IMERG) satellite precipitation product. These upscaled products were then run through the Ensemble Framework for Flash Flood Forecasting hydrologic modeling framework over the U.S. to assess how the different spatial and temporal resolutions impact the simulated outputs of flood event magnitude, duration, and timing. It was found that the quantitative uncertainties generated by the IMERG Early (IMERG-E) simulations overpowered those associated with resolution across all flood characteristics. When compared to native MRMS simulations, however, the upscaled precipitation estimates tended to underestimate peak discharges and event durations, associated with distinct negative water balance errors. It was also found that the upscaled precipitation simulations exhibited a scaling effect with regards to error contribution of peak discharge, with higher contributions at smaller basin sizes and decreasing contributions as basin sizes increased. Looking at how upscaling can affect the modeled output desired allows for improved understanding of which resolutions lead to the greatest changes in simulation accuracy, increasing the potential global use and utility of current or future satellite products, especially in regions where high-resolution ground radars are sparse. [ABSTRACT FROM AUTHOR]

Monitoring plants' responses to water deficit using remote sensing still faces large uncertainty, mostly due to the inaccurate characterization of plants' physiological responses. Solar induced chlorophyll fluorescence (SIF) contains information on plants' physiological processes which regulates the energy partitioning after solar radiation is absorbed by chlorophyll, providing new opportunities to monitor plant response to drought stress. However, the drought-induced physiological, biochemical, and structural changes are strongly coupled, hindering the mechanistic understanding of drought impacts on plants. Here, using tower-based observations of SIF together with high spectral resolution reflectance measurements, we derived the time series of the fraction of absorbed photosynthetically active radiation by canopy, chlorophyll content, and fluorescence efficiency using two radiative transfer model-based decomposition methods, and evaluated their responses to two consecutive dry spells at a tall-grass prairie site in the USA (34°59′05.0″ N, 97°31′20.6″ W). We observed a robust signal of afternoon depression based on the fluorescence efficiency estimates during the second dry spell, which had much lower soil moisture than the first one. The strong decline in fluorescence efficiency in the afternoon was likely caused by the high temperature and atmospheric dryness when the soil was dry. Such a direct physiological response contributed to 14.4% to 36.0% of seasonal variation of afternoon SIF, depending on the decomposition method used. Sustained water stress also caused lagged responses. Despite the subsequent rainfall after the dry spell, we observed a continued decline of SIF due to the lagged decline of chlorophyll content and green canopy coverage. Our study demonstrates the use of continuous SIF measurements to understand the development of drought effects on plants, and highlights the importance of afternoon SIF measurements for physiological stress detection. • SIF were decomposed into the structural, biochemical, and physiological components. • SIF captured the immediate drought stress through a decline in fluorescence efficiency. • SIF captured the lagged effect of a dry spell through a decrease in canopy greenness. [ABSTRACT FROM AUTHOR]

• First study to evaluate the impacts of MODIS vs. LS2 on GPP VPM and T VTM estimates. • Combined Landsat/Sentinel-2 (LS2) tracked phenology of prairie, alfalfa, and wheat. • LS2-enhanced vegetation index accurately identified site level optimal temperature. • LS2 data produced better gross primary production (GPP) estimates than MODIS. • The vegetation transpiration model is a simpler approach to estimating transpiration (T). Although agroecosystems have a significant potential to offset carbon dioxide (CO 2), the amount of CO 2 captured can vary significantly depending on management practices. Accurate estimation of gross primary production (GPP) and transpiration (T) of agroecosystems at the field scale are essential for the study of food security and water resource management. To date, the carbon and water fluxes data products for commercial agroecosystems are limited, mostly at the moderate spatial resolution (MSR, hundreds of meters), which cannot be used to assess the temporal dynamics of GPP and T at the field scale. This study used the vegetation photosynthesis model (VPM) and vegetation transpiration model (VTM) to estimate field-level daily GPP (GPP VPM) and T (T VTM), respectively, in native prairie, alfalfa (Medicago sativa L.), and winter wheat (Triticum aestivum L.) in central Oklahoma, USA. We evaluated the reliability and advantages of vegetation indices (enhanced vegetation index, EVI and land surface water index, LSWI) in monitoring the land surface phenology using moderate spatial resolution data from Moderate Resolution Imaging Spectroradiometer (MODIS) and high spatial resolution (HSR, tens of meters) data from Landsat and Sentinel-2. The accuracy of GPP VPM and T VTM estimates at different spatial scales was evaluated using GPP (GPP EC) and evapotranspiration (ET EC) from the eddy flux tower sites, respectively. Results demonstrate the capacity of VPM and VTM to estimate the field-level carbon and water flux dynamics and their responses to weather conditions. The use of HSR vegetation indices helped to address certain challenges faced by MSR indices, especially in capturing the crop phenology in smaller areas with conservation measures or disturbances. The findings highlight the importance of using HSR GPP estimates to reduce uncertainty in quantifying CO 2 fluxes for croplands and grasslands. The findings also demonstrate the ability of the models to track field-level vegetation phenology, carbon uptake, and water use in agroecosystems under different management practices. [ABSTRACT FROM AUTHOR]

Soil microbes drive decomposition of soil organic matter (SOM) and regulate soil carbon (C) dynamics. Process‐based models have been developed to quantify changes in soil organic carbon (SOC) and carbon dioxide (CO2) fluxes in agricultural ecosystems. However, microbial processes related to SOM decomposition have not been, or are inadequately, represented in these models, limiting predictions of SOC responses to changes in microbial activities. In this study, we developed a microbial‐mediated decomposition model based on a widely used biogeochemical model, DeNitrification‐DeComposition (DNDC), to simulate C dynamics in agricultural ecosystems. The model simulates organic matter decomposition, soil respiration, and SOC formation by simulating microbial and enzyme dynamics and their controls on decomposition, and considering impacts of climate, soil, crop, and farming management practices (FMPs) on C dynamics. When evaluated against field observations of net ecosystem CO2 exchange (NEE) and SOC change in two winter wheat systems, the model successfully captured both NEE and SOC changes under different FMPs. Inclusion of microbial processes improved the model's performance in simulating peak CO2 fluxes induced by residue return, primarily by capturing priming effects of residue inputs. We also investigated impacts of microbial physiology, SOM, and FMPs on soil C dynamics. Our results demonstrated that residue or manure input drove microbial activity and predominantly regulated the CO2 fluxes, and manure amendment largely regulated long‐term SOC change. The microbial physiology had considerable impacts on the microbial activities and soil C dynamics, emphasizing the necessity of considering microbial physiology and activities when assessing soil C dynamics in agricultural ecosystems. Plain Language Summary: Soil microbes drive decomposition of soil organic matter (SOM) and regulate soil carbon (C) dynamics. Process‐based models are useful tools for quantifying changes in soil organic carbon (SOC) and carbon dioxide (CO2) fluxes in agricultural ecosystems. However, microbial processes related to SOM decomposition have not been, or are inadequately, represented in these models, limiting predictions of SOC responses to changes in microbial activities. We developed a microbial‐mediated decomposition model based on a widely used biogeochemical model, DeNitrification‐DeComposition (DNDC), to simulate C dynamics in agricultural ecosystems. The model simulates organic matter decomposition, soil respiration, and SOC formation by simulating microbial dynamics and controls on decomposition, and considering impacts of climate, soil, crop, and farming management practices (FMPs) on C dynamics. We also investigated impacts of microbial physiology, SOM, and FMPs on soil C dynamics. Our results demonstrated that residue or manure input drove microbial activity and predominantly regulated CO2 fluxes, and manure amendment largely regulated long‐term SOC change. The microbial physiology had considerable impacts on microbial activities and soil C dynamics, emphasizing the necessity of considering microbial physiology and activities when assessing soil C dynamics in agricultural ecosystems. These results provide insights in simulating microbial‐mediated soil C dynamics in agricultural ecosystems. Key Points: A biogeochemical model was improved to simulate microbe‐driven soil organic matter (SOM) decomposition and carbon dynamics in agricultural ecosystemsThe model simulates carbon dynamics by simulating both microbial controls and impacts of soil, crop, and farming management practicesCarbon input drives microbial activity and exerts large impacts on carbon dynamics while microbes play a central role in decomposing SOM [ABSTRACT FROM AUTHOR]

Oklahoma Mesonet surface data and North American Regional Reanalysis data were integrated with the tracks of over 900 tornadic and nontornadic supercell thunderstorms in Oklahoma from 1994 to 2003 to observe the evolution of near-storm environments with data currently available to operational forecasters. These data are used to train a complex data-mining algorithm that can analyze the variability of meteorological data in both space and time and produce a probabilistic prediction of tornadogenesis given variables describing the near-storm environment. The algorithm was assessed for utility in four ways. First, its probability forecasts were scored. The algorithm did produce some useful skill in discriminating between tornadic and nontornadic supercells as well as in producing reliable probabilities. Second, its selection of relevant attributes was assessed for physical significance. Surface thermodynamic parameters, instability, and bulk wind shear were among the most significant attributes. Third, the algorithm's skill was compared with the skill of single variables commonly used for tornado prediction. The algorithm did noticeably outperform all of the single variables, including composite parameters. Fourth, the situational variations of the predictions from the algorithm were shown in case studies. They revealed instances both in which the algorithm excelled and in which the algorithm was limited. [ABSTRACT FROM AUTHOR]

• Percentage maps of juniper forest encroachment at 1 km are generated during 2000–2015. • Climate variable changes are examined at regional scale by pairwise analysis models. • We quantified climate variable changes with each percentage increase of woody cover. • Roles of hydroclimates are assessed on climate impacts of juniper woody encroachment. Woody plant encroachment (WPE) into grasslands has been exacerbated by climate change and human activities. WPE may affect local climate by altering the exchange of mass and energy between the land surface and the atmosphere. The lack of studies on the effects of WPE on local climate hinders our understanding of the interactions between changes in regional vegetation cover and climate. Here, we analyzed the differences of land surface temperature (ΔLST), albedo (ΔAlbedo) and evapotranspiration (ΔET) between juniper-woody-encroached grasslands and adjacent pure grasslands using 16-years of remote sensing data. Our results showed that juniper woody plant encroachment (JWPE) into the semi-arid and sub-humid grasslands reduced daytime LST and albedo, but increased nighttime LST and ET from an annual scale analysis. With each one percent increase in juniper cover, annual mean daytime ΔLST decreased ~0.026 °C, nighttime ΔLST increased ~0.01 °C, daily ΔLST decreased ~0.008 °C, Δalbedo decreased ~0.053%, and ΔET increased ~1.31 mm/year. Furthermore, it is suggested that the impacts of JWPE on LST and ET were stronger in dry years than in normal and pluvial years, and that no significant variations in albedo were found among the different hydrological conditions. These results provide insights into applying satellite-based techniques to understand the feedbacks between woody vegetation dynamics and local climate change. [ABSTRACT FROM AUTHOR]

One of the benefits of training a process-based, land surface model is the capacity to use it in ungauged sites as a complement to standard weather stations for predicting energy fluxes, evapotranspiration, and surface and root-zone soil temperature and moisture. In this study, dynamic (i.e., time-evolving) vegetation parameters were derived from remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and coupled with a physics-based land surface model (tin-based Real-time Integrated Basin Simulator (tRIBS)) at four eddy covariance (EC) sites in south-central U.S. to test the predictability of micro-meteorological, soil-related, and energy flux-related variables. One cropland and one grassland EC site in northern Oklahoma, USA, were used to tune the model with respect to energy fluxes, soil temperature, and moisture. Calibrated model parameters, mostly related to the soil, were then transferred to two other EC sites in Oklahoma with similar soil and vegetation types. New dynamic vegetation parameter time series were updated according to MODIS imagery at each site. Overall, the tRIBS model captured both seasonal and diurnal cycles of the energy partitioning and soil temperatures across all four stations, as indicated by the model assessment metrics, although large uncertainties appeared in the prediction of ground heat flux, surface, and root-zone soil moisture at some stations. The transferability of previously calibrated model parameters and the use of MODIS to derive dynamic vegetation parameters enabled rapid yet reasonable predictions. The model was proven to be a convenient complement to standard weather stations particularly for sites where eddy covariance or similar equipment is not available. [ABSTRACT FROM AUTHOR]

Polarimetric radar observations from the NEXRAD WSR-88D operational radar network in the contiguous United States, routinely available since 2013, are used to reveal three prominent microphysical signatures in landfalling tropical cyclones: 1) hydrometeor size sorting within the eyewall convection, 2) vertical displacement of the melting layer within the inner core, and 3) dendritic growth layers within stratiform regions of the inner core. Size sorting signatures within eyewall convection are observed with greater frequency and prominence in more intense hurricanes, and are observed predominantly within the deep-layer environmental wind shear vector-relative quadrants that harbor the greatest frequency of deep convection (i.e., downshear and left-of-shear). Melting-layer displacements are shown that exceed 1 km in altitude compared to melting-layer altitudes in outer rainbands and are complemented by analyses of archived dropsonde data. Dendritic growth and attendant snow aggregation signatures in the inner core are found to occur more often when echo-top altitudes are low (≤10 km MSL), nearer the −15°C isotherm commonly associated with dendritic growth. These signatures, uniquely observed by polarimetric radar, provide greater insight into the physical structure and thermodynamic characteristics of tropical cyclones, which are important for improving rainfall estimation and the representation of tropical cyclones in numerical models. [ABSTRACT FROM AUTHOR]

• IMERG satellite and MRMS radar precipitation applied for integrated hydrologic validation. • Discrete flood characteristics assessed with respect to USGS stream gauges. • Hydrologic model impacts on flood discharge, duration, and timing are investigated. • IMERG-E discharge errors are higher than MRMS, but IMERG-E duration errors are lower. • Flood timings are better simulated at smaller basin sizes with low systematic error. This study investigates the hydrologic utility of satellite precipitation estimates from the Global Precipitation Measurement mission by comparing flood signals produced across the Continental United States by a ten-year span of in-situ, ground-based radar and satellite-based precipitation data. The flood characteristics generated with radar and satellite precipitation through a distributed hydrologic model are contrasted against reference stream gauge data as a method of integrated validation to assess and quantify error budgets between precipitation products by highlighting precipitation products' accuracy, hydrologic scaling effects, and the impact of the hydrologic model. It is found that systematic and random errors associated with flood characteristics behave similarly to trends previously seen in precipitation rate errors between precipitation products, establishing a clear link through propagation of errors into the water cycle. Additionally, behaviors associated with both water balance and routing schemes within the hydrologic model were shown to affect outputs. Errors generated by water balance tend to cause overestimation of peak discharge values, while errors associated with routing tend to cause underestimation of flood durations and push flood timings earlier than the stream gauge reference. [ABSTRACT FROM AUTHOR]

• Managed pasture showed higher sensitivity to drought than native pasture. • Spring temperature and fall rainfall were critical for managed pasture. • Management activities modulated the effects of environmental variables on productivity. Future weather and climates, especially rainfall, are expected to have larger variability in the Southern Plains of the United States. However, the degree and timing of environmental variability that affect productivity of pastures managed differently have not been well studied. We examined the impacts of environmental variability on grassland productivity using 17 years of gross primary productivity (GPP) data for co-located native and managed prairie pastures in Oklahoma. We also considered the interactive effects of management factors and environmental variability into the regression models and identified the critical temporal windows of environmental variables (CWE) that influence annual variability in GPP. Managed pasture (MP) showed greater variability of GPP than did native pasture (NP), particularly with reduced GPP in drought years. The resilience of native prairies under unfavorable climate extremes was evident by lower GPP anomalies in NP than MP during the 2011–2012 drought. Although both pastures experienced the same degree of environmental variability, the CWE affecting GPP was significantly different between NP and MP due to the modulating impact of management practices on the responses of GPP. Not only the range but also the timings of the CWE were different between NP and MP as MP was more responsive to the spring temperature and fall rainfall. Our findings warrant the incorporation of MP as a different commodity from NP when accounting for the ecosystem responses to environmental variability in global climate models. [ABSTRACT FROM AUTHOR]

Given the increasing use of the term “flash drought” by the media and scientific community, it is prudent to develop a consistent definition that can be used to identify these events and to understand their salient characteristics. It is generally accepted that flash droughts occur more often during the summer owing to increased evaporative demand; however, two distinct approaches have been used to identify them. The first approach focuses on their rate of intensification, whereas the second approach implicitly focuses on their duration. These conflicting notions for what constitutes a flash drought (i.e., unusually fast intensification vs short duration) introduce ambiguity that affects our ability to detect their onset, monitor their development, and understand the mechanisms that control their evolution. Here, we propose that the definition for “flash drought” should explicitly focus on its rate of intensification rather than its duration, with droughts that develop much more rapidly than normal identified as flash droughts. There are two primary reasons for favoring the intensification approach over the duration approach. First, longevity and impact are fundamental characteristics of drought. Thus, short-term events lasting only a few days and having minimal impacts are inconsistent with the general understanding of drought and therefore should not be considered flash droughts. Second, by focusing on their rapid rate of intensification, the proposed “flash drought” definition highlights the unique challenges faced by vulnerable stakeholders who have less time to prepare for its adverse effects. [ABSTRACT FROM AUTHOR]

Severe droughts in the Southern Great Plains (SGP: Kansas, Oklahoma, and Texas) in recent years have reduced the productivity of tallgrass prairie and resulted in substantial economic losses to the beef cattle industry in this region. Understanding spatial and temporal patterns of agricultural drought in the SGP can help ranchers to develop and implement drought mitigation strategies. In this study, the Land Surface Water Index (LSWI), calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS) near infrared and shortwave infrared bands, was used to assess agricultural drought in the tallgrass prairie region of the SGP during 2000–2013. The number of consecutive days with LSWI < 0 (DNLSWI) during the growing season was defined as the drought duration, which, was then used to identify and analyze frequency of summer drought and whole growing season drought (WGSD). The spatial pattern of DNLSWI was consistent with the east-to-west decreasing precipitation gradient across the SGP region. Summer drought duration as depicted by the DNLSWI in the western portion of the study area was around one and a half month. The occurrence of WGSD increased from one year in the east to up to six years in the west, demonstrating the susceptibility of the tallgrass prairie region to drought. In addition to the total amount of precipitation, its intra-annual distribution also played an important role in drought development. A comparison with other widely used national drought products, namely the Evaporative Stress Index (ESI), the Vegetation Drought Response Index (VegDRI), and the United States Drought Monitor (USDM), shows that LSWI-based drought has good agreement with ESI and USDM. Quantitative analyses indicate that LSWI-based drought agreed better with ESI in severe drought conditions than in moderate or pre-drought conditions. Severe drought periods characterized by the USDM also had low LSWI values. The areas affected by drought derived from the LSWI-based drought index were significantly correlated with hay production. As an indicator of vegetation water stress at moderate spatial resolution (∼500 m), the LSWI has the potential to show drought conditions for an individual ranch and offer guidance for drought mitigation activities and livestock production. [ABSTRACT FROM AUTHOR]

• A novel framework is developed for integrated hydrologic validation of satellites. • Assessment focuses on flood characteristics in favor of bulk metrics. • The effects of IMERG resolution are seen on flood peak discharge. • Basin size is also found to play a role in over or underestimation of peak discharge. • IMERG simulated floods events start earlier and end later than the MRMS benchmark. In recent years, a great amount of research has been done towards evaluating precipitation data generated by satellites, but less has focused on how these estimates and their uncertainties manifest further into the water cycle. In this study, ten years of satellite-based and ground-based radar data are used as forcings for a distributed hydrologic model across the Continental United States. They are compared using a methodology designed to assess the flood signals and characteristics generated by the model. By focusing on how well the model reproduces flood characteristics rather than fits traditional bulk statistics, this research provides robust insights into satellite precipitation deficiencies. It is found that satellite data has greater success at resolving lower magnitude flood events while tending to generate floods of longer durations. Additionally, flood managers should note that satellites tend to generate floods that characteristically both begin earlier and end later than the ground radar reference. Subsequent research is recommended into other satellite data products in order to better understand these discrepancies and mitigate or plan for them in the future. In recent years, satellites have been increasingly used to provide valuable insights on rainfall across the globe, especially in locations where radars are unable to be installed on the ground. By nature, however, the rainfall data provided by satellites has uncertainties. Large amounts of research has gone into the difference in accuracy between satellite radar and ground references, but less has focused on how these differences impact flood simulations, respectively. In this study it was found that the rainfall data from satellites tends to predict floods that are more severe (have higher magnitudes), last longer (have longer durations), and have different timings (start earlier and end later) than the reference. It is also found that satellites struggle with predictions involving extreme rainfall values. Further research is suggested to better understand these issues, as well as compare against other forms of satellite rainfall data. [ABSTRACT FROM AUTHOR]

Burning, grazing, and baling (hay harvesting) are common management practices in grassland. To develop and adopt sustainable management practices, it is essential to better understand and quantify the impacts of management practices on plant phenology and carbon fluxes. In this study, we combined multiple data sources, including in-situ PhenoCam digital images, eddy covariance data, and satellite data (Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS)) to examine the impacts of burning, baling, and grazing on canopy dynamics, plant phenology, and carbon fluxes in a pasture in El Reno, Oklahoma in 2014. Landsat images were used to assess the baling area and the trajectory of vegetation recovery. MODIS vegetation indices (VIs) were used in the Vegetation Photosynthesis Model (VPM) to estimate gross primary production (GPP VPM ) at a MODIS pixel for the flux tower (baled) site. For comparison between baled and unbaled conditions, we used MODIS VIs for a neighbor MODIS pixel (unbaled) and ran VPM. Daily PhenoCam images and green chromatic coordinate (GCC) tracked canopy dynamics and plant phenology well. The grassland greened up immediately after burning in April. GCC values showed two peaks with the similar magnitude because of quick recovery of grassland after baling. Satellite-derived VIs and GPP VPM showed that the pasture recovered in one month after baling. The GPP VPM matched well (R 2 = 0.89) with the eddy covariance-derived GPP (GPP EC ). Grazing in the late growing season did not influence plant phenology (VIs and GCC) and carbon uptake (GPP) as plants were in the late growing stage. Neither did it affect GPP differently in those two conditions because of even grazing intensity. The reduction in GPP after baling was compensated by higher GPP after large rain events in late July and early September, causing little seasonal differences in GPP (-0.002 g C m −2 day −1 ) between the baled and unbaled conditions. Interactions of different management practices with climate make it complicated to understand the impacts of different management practices on carbon dynamics and plant phenology. Thus, it is necessary to further investigate the responses of pastures to different management practices under different climate regimes at multiple temporal and spatial scales. [ABSTRACT FROM AUTHOR]

Understanding the factors controlling evapotranspiration (ET) of spatially distributed tallgrass prairie is crucial to accurately upscale ET and to predict the response of tallgrass prairie ecosystems to current and future climate. The Moderate Resolution Imaging Spectroradiometer (MODIS)-derived enhanced vegetation index (EVI) and ground-based climate variables were integrated with eddy covariance tower-based ET (ET EC ) at six AmeriFlux tallgrass prairie sites in the central United States to determine major climatic factors that control ET over multiple timescales and to develop a simple and robust statistical model for predicting ET. Variability in ET was nearly identical across sites over a range of timescales, and it was most strongly driven by photosynthetically active radiation (PAR) at hourly-to-weekly timescales, by vapor pressure deficit (VPD) at weekly-to-monthly timescales, and by temperature at seasonal-to-interannual timescales at all sites. Thus, the climatic drivers of ET change over multiple timescales. The EVI tracked the seasonal variation of ET EC well at both individual sites (R 2 > 0.70) and across six sites (R 2 = 0.76). The inclusion of PAR further improved the ET-EVI relationship (R 2 = 0.86). Based on this result, we used ET EC , EVI, and PAR (MJ m −2 d −1 ) data from four sites (15 site-years) to develop a statistical model (ET = 0.11 PAR + 5.49 EVI − 1.43, adj. R 2 = 0.86, P < 0.0001) for predicting daily ET at 8-day intervals. This predictive model was evaluated against additional two years of ET EC data from one of the four model development sites and two independent sites. The predicted ET (ET EVI+PAR ) captured the seasonal patterns and magnitudes of ET EC , and correlated well with ET EC , with R 2 of 0.87-0.96 and RMSE of 0.35-0.49 mm d −1 , and it was significantly improved compared to the standard MODIS ET product. This study demonstrated that tallgrass prairie ET can be accurately predicted using a multiple regression model that uses EVI and PAR which can be readily derived from remote sensing data. [ABSTRACT FROM AUTHOR]

The article discusses the changes made to the format of the international contest sponsored by the AMS Committee on Artificial Intelligence and Its Applications to Environmental Science for 2013-2014 to determine which approach produces the best total daily solar energy forecast. These changes include the use of the year prior to the contest to gather a more complex dataset for predictions, the hosting of the contest Website on Kaggle and the extension of the contest's time window.