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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]

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]

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]

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]

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]

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]

Understanding of the underlying causes of spatial variation in exchange of carbon and water vapor fluxes between grasslands and the atmosphere is crucial for accurate estimates of regional and global carbon and water budgets, and for predicting the impact of climate change on biosphere–atmosphere feedbacks of grasslands. We used ground-based eddy flux and meteorological data, and the Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) from 12 grasslands across the United States to examine the spatial variability in carbon and water vapor fluxes and to evaluate the biophysical controls on the spatial patterns of fluxes. Precipitation was strongly associated with spatial and temporal variability in carbon and water vapor fluxes and vegetation productivity. Grasslands with annual average precipitation <600 mm generally had neutral annual carbon balance or emitted small amount of carbon to the atmosphere. Despite strong coupling between gross primary production (GPP) and evapotranspiration (ET) across study sites, GPP showed larger spatial variation than ET, and EVI had a greater effect on GPP than on ET. Consequently, large spatial variation in ecosystem water use efficiency (EWUE = annual GPP/ET; varying from 0.67 ± 0.55 to 2.52 ± 0.52 g C mm −1 ET) was observed. Greater reduction in GPP than ET at high air temperature and vapor pressure deficit caused a reduction in EWUE in dry years, indicating a response which is opposite than what has been reported for forests. Our results show that spatial and temporal variations in ecosystem carbon uptake, ET, and water use efficiency of grasslands were strongly associated with canopy greenness and coverage, as indicated by EVI. [ABSTRACT FROM AUTHOR]

Vegetation growth is one of the important indicators of drought events. Greenness-related vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) are often used for the assessment of agricultural drought. There is a need to evaluate the sensitivity of water-related vegetation indices such as Land Surface Water Index (LSWI) to assess drought and associated impacts. Moderate-Resolution Imaging Spectroradiometer (MODIS) derived time series NDVI, EVI and LSWI data during 2000–2013 were compared for their sensitivity to drought at two tallgrass prairie sites in the Oklahoma Mesonet (Marena and El Reno). Each site has continuous soil moisture measurements at three different depths (5, 25 and 60 cm) and precipitation data for the study period (2000–2013) at 5-min intervals. As expected, averaged values of vegetation indices consistently lower under drought conditions than normal conditions. LSWI decreased the most in drought years (2006, 2011 and 2012) when compared to its magnitudes in pluvial years (2007, 2013), followed by EVI and NDVI, respectively. Because green vegetation has positive LSWI values (>0) and dry vegetation has negative LSWI values (<0), much longer durations of LSWI < 0 were found in the summer periods of drought years rather than in pluvial years. A LSWI-based drought severity scheme (LSWI > 0.1; 0 < LSWI ⩽ 0.1; −0.1 < LSWI ⩽ 0; LSWI ⩽ −0.1) corresponded well with the drought severity categories (0; D0; D1: D2; D3 and D4) defined by the United States Drought Monitor (USDM) at these two study sites. Our results indicate that the number of days with LSWI < 0 during the summer and LSWI-based drought severity scheme can be simple, effective and complementary indicator for assessing drought in tallgrass prairie grasslands at a 500-m spatial resolution. [ABSTRACT FROM AUTHOR]

Reliable indicators of rapid drought onset can help to improve the effectiveness of drought early warning systems. In this study, the evaporative stress index (ESI), which uses remotely sensed thermal infrared imagery to estimate évapotranspiration (ET), is compared to drought classifications in the U.S. Drought Monitor (USDM) and standard precipitation-based drought indicators for several cases of rapid drought development that have occurred across the United States in recent years. Analysis of meteorological time series from the North American Regional Reanalysis indicates that these events are typically characterized by warm air temperature and low cloud cover anomalies, often with high winds and dewpoint depressions that serve to hasten evaporative depletion of soil moisture reserves. Stan-dardized change anomalies depicting the rate at which various multiweek ESI composites changed over different time intervals are computed to more easily identify areas experiencing rapid changes in ET. Overall, the results demonstrate that ESI change anomalies can provide early warning of incipient drought impacts on agricultural systems, as indicated in crop condition reports collected by the National Agricultural Statistics Service. In each case examined, large negative change anomalies indicative of rapidly drying conditions were either coincident with the introduction of drought in the USDM or lead the USDM drought depiction by several weeks, depending on which ESI composite and time-differencing interval was used. Incorporation of the ESI as a data layer used in the construction of the USDM may improve timely depictions of moisture conditions and vegetation stress associated with flash drought events. [ABSTRACT FROM AUTHOR]

A comprehensive evaluation of split-window and triple-window algorithms to estimate land surface temperature (LST) from Geostationary Operational Environmental Satellites (GOES) that were previously described by Sun and Pinker is presented. The evaluation of the split-window algorithm is done against ground observations and against independently developed algorithms. The triple-window algorithm is evaluated only for nighttime against ground observations and against the Sun and Pinker split-window (SP-SW) algorithm. The ground observations used are from the Atmospheric Radiation Measurement Program (ARM) Central Facility, Southern Great Plains site (April 1997–March 1998); from five Surface Radiation Budget Network (SURFRAD) stations (1996–2000); and from the Oklahoma Mesonet. The independent algorithms used for comparison include the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data and Information Service operational method and the following split-window algorithms: that of Price, that of Prata and Platt, two versions of that of Ulivieri, that of Vidal, two versions of that of Sobrino, that of Coll and others, the generalized split-window algorithm as described by Becker and Li and by Wan and Dozier, and the Becker and Li algorithm with water vapor correction. The evaluation against the ARM and SURFRAD observations indicates that the LST retrievals from the SP-SW algorithm are in closer agreement with the ground observations than are the other algorithms tested. When evaluated against observations from the Oklahoma Mesonet, the triple-window algorithm is found to perform better than the split-window algorithm during nighttime. [ABSTRACT FROM AUTHOR]

This paper describes important characteristics of an uncoupled high-resolution land data assimilation system (HRLDAS) and presents a systematic evaluation of 18-month-long HRLDAS numerical experiments, conducted in two nested domains (with 12- and 4-km grid spacing) for the period from 1 January 2001 to 30 June 2002, in the context of the International H2O Project (IHOP_2002). HRLDAS was developed at the National Center for Atmospheric Research (NCAR) to initialize land-state variables of the coupled Weather Research and Forecasting (WRF)–land surface model (LSM) for high-resolution applications. Both uncoupled HRDLAS and coupled WRF are executed on the same grid, sharing the same LSM, land use, soil texture, terrain height, time-varying vegetation fields, and LSM parameters to ensure the same soil moisture climatological description between the two modeling systems so that HRLDAS soil state variables can be used to initialize WRF–LSM without conversion and interpolation. If HRLDAS is initialized with soil conditions previously spun up from other models, it requires roughly 8–10 months for HRLDAS to reach quasi equilibrium and is highly dependent on soil texture. However, the HRLDAS surface heat fluxes can reach quasi-equilibrium state within 3 months for most soil texture categories. Atmospheric forcing conditions used to drive HRLDAS were evaluated against Oklahoma Mesonet data, and the response of HRLDAS to typical errors in each atmospheric forcing variable was examined. HRLDAS-simulated finescale (4 km) soil moisture, temperature, and surface heat fluxes agreed well with the Oklahoma Mesonet and IHOP_2002 field data. One case study shows high correlation between HRLDAS evaporation and the low-level water vapor field derived from radar analysis. [ABSTRACT FROM AUTHOR]

Disaggregation of regional-scale (103 m) flux estimates to micrometeorological scales (101–102 m) facilitates direct comparison between land surface models and ground-based observations. Inversely, it also provides a means for upscaling flux-tower information into a regional context. The utility of the Atmosphere–Land Exchange Inverse (ALEXI) model and associated disaggregation technique (DisALEXI) in effecting regional to local downscaling is demonstrated in an application to thermal imagery collected with the Geostationary Operational Environmental Satellite (GOES) (5-km resolution) and Landsat (60-m resolution) over the state of Oklahoma on 4 days during 2000–01. A related algorithm (DisTrad) sharpens thermal imagery to resolutions associated with visible–near-infrared bands (30 m on Landsat), extending the range in scales achievable through disaggregation. The accuracy and utility of this combined multiscale modeling system is evaluated quantitatively in comparison with measurements made with flux towers in the Oklahoma Mesonet and qualitatively in terms of enhanced information content that emerges at high resolution where flux patterns can be identified with recognizable surface phenomena. Disaggregated flux fields at 30-m resolution were reaggregated over an area approximating the tower flux footprint and agreed with observed fluxes to within 10%. In contrast, 5-km flux predictions from ALEXI showed a higher relative error of 17% because of the gross mismatch in scale between model and measurement, highlighting the efficacy of disaggregation as a means for validating regional-scale flux predictions over heterogeneous landscapes. Sharpening the thermal inputs to DisALEXI with DisTrad did not improve agreement with observations in comparison with a simple bilinear interpolation technique because the sharpening interval associated with Landsat (60–30 m) was much smaller than the dominant scale of heterogeneity (200–500 m) in the scenes studied. Greater benefit is expected in application to Moderate Resolution Imaging Spectroradiometer (MODIS) data, where the potential sharpening interval (1 km to 250 m) brackets the typical agricultural field scale. Thermal sharpening did, however, significantly improve output in terms of visual information content and model convergence rate. [ABSTRACT FROM AUTHOR]

Flash droughts are characterized by a period of rapid intensification over sub-seasonal time scales that culminates in the rapid emergence of new or worsening drought impacts. This study presents a new flash drought intensity index (FDII) that accounts for both the unusually rapid rate of drought intensification and its resultant severity. The FDII framework advances our ability to characterize flash drought because it provides a more complete measure of flash drought intensity than existing classification methods that only consider the rate of intensification. The FDII is computed using two terms measuring the maximum rate of intensification (FD_INT) and average drought severity (DRO_SEV). A climatological analysis using soil moisture data from the Noah land surface model from 1979–2017 revealed large regional and interannual variability in the spatial extent and intensity of soil moisture flash drought across the US. Overall, DRO_SEV is slightly larger over the western and central US where droughts tend to last longer and FD_INT is ~75% larger across the eastern US where soil moisture variability is greater. Comparison of the FD_INT and DRO_SEV terms showed that they are strongly correlated (r = 0.82 to 0.90) at regional scales, which indicates that the subsequent drought severity is closely related to the magnitude of the rapid intensification preceding it. Analysis of the 2012 US flash drought showed that the FDII depiction of severe drought conditions aligned more closely with regions containing poor crop conditions and large yield losses than that captured by the intensification rate component (FD_INT) alone. [ABSTRACT FROM AUTHOR]