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Paul Schmid and Dev Niyogi

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A new objective method to determine the height of the planetary boundary layer (PBL) is presented here. PBL heights are computed using the statistical variance and kurtosis of dewpoint and virtual potential temperature differences measured from radio soundings at the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) program at the Southern Great Plains (SGP) site. These heights are compared with those derived from lidar, also on the site, and with gridded model data from the North American Regional Reanalysis (NARR). A climatology of mean heights in the early (1800 UTC) and late (0000 UTC) afternoon from 2002 to 2010 is presented to show the effectiveness of the method. Future work using the new method include producing an observational climatology of PBL heights and understanding the aerosol loading within the PBL as well as a better understanding of the coupling between the surface and free atmosphere.

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Umarporn Charusombat and Dev Niyogi

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Characterizing and developing drought climatology continues to be a challenging problem. As decision makers seek guidance on water management strategies, there is a need for assessing the performance of drought indices. This requires the adaptation of appropriate drought indices that aid in monitoring droughts and hydrological vulnerability on a regional scale. This study aims to assist the process of developing a statewide water shortage and assessment plan (WSP) for the state of Indiana by conducting a focused hydroclimatological assessment of drought variability. Drought climatology was assessed using in situ observations and regional reanalysis data. A summary of precipitation and evaporation trends, estimated drought variability, worst-case drought scenarios, drought return period, and frequency and duration was undertaken, using multiple drought indices and streamflow analysis. Results indicated a regional and local variability in drought susceptibility. The worst-case (200-yr return period) prediction showed that Indiana has a 0.5% probability of receiving 45% of normal precipitation over a 12-month drought in any year. Consistent with other studies, the standardized precipitation index (SPI) was found to be appropriate for detecting short-term drought conditions over Indiana. This recommendation has now been incorporated in the 2009 Indiana water shortage plan. This study also highlights the difficulties in identifying past droughts from available climatic data, and the authors’ results suggest a persistent, high degree of uncertainty in making drought predictions using future climatic projections.

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Olivia Kellner and Dev Niyogi

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Land surface heterogeneity affects mesoscale interactions, including the evolution of severe convection. However, its contribution to tornadogenesis is not well known. Indiana is selected as an example to present an assessment of documented tornadoes and land surface heterogeneity to better understand the spatial distribution of tornadoes. This assessment is developed using a GIS framework taking data from 1950 to 2012 and investigates the following topics: temporal analysis, effect of ENSO, antecedent rainfall linkages, population density, land use/land cover, and topography, placing them in the context of land surface heterogeneity.

Spatial analysis of tornado touchdown locations reveals several spatial relationships with regard to cities, population density, land-use classification, and topography. A total of 61% of F0–F5 tornadoes and 43% of F0–F5 tornadoes in Indiana have touched down within 1 km of urban land use and land area classified as forest, respectively, suggesting the possible role of land-use surface roughness on tornado occurrences. The correlation of tornado touchdown points to population density suggests a moderate to strong relationship. A temporal analysis of tornado days shows favored time of day, months, seasons, and active tornado years. Tornado days for 1950–2012 are compared to antecedent rainfall and ENSO phases, which both show no discernible relationship with the average number of annual tornado days. Analysis of tornado touchdowns and topography does not indicate any strong relationship between tornado touchdowns and elevation. Results suggest a possible signature of land surface heterogeneity—particularly that around urban and forested land cover—in tornado climatology.

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Olivia Kellner and Dev Niyogi

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El Niño–Southern Oscillation (ENSO) and Arctic Oscillation (AO) climatology (1980–2010) is developed and analyzed across the U.S. Corn Belt using state climate division weather and historic corn yield data using analysis of variance (ANOVA) and correlation analysis. Findings provide insight to agroclimatic conditions under different ENSO and AO episodes and are analyzed with a perspective for potential impacts to agricultural production and planning, with findings being developed into a web-based tool for the U.S. Corn Belt.

This study is unique in that it utilizes the oceanic Niño index and explores two teleconnection patterns that influence weather across different spatiotemporal scales. It is found that the AO has a more frequent weak to moderate correlation to historic yields than ENSO when correlated by average subgrowing season index values. Yield anomaly and ENSO and AO episode analysis affirms the overall positive impact of El Niño events on yields compared to La Niña events, with neutral ENSO events in between as found in previous studies. Yields when binned by the AO episode present more uncertainty. While significant temperature and precipitation impacts from ENSO and AO are felt outside of the primary growing season, correlation between threshold variables of episode-specific temperature and precipitation and historic yields suggests that relationships between ENSO and AO and yield are present during specific months of the growing season, particularly August. Overall, spatial climatic variability resulting from ENSO and AO episodes contributes to yield potential at regional to subregional scales, making generalization of impacts difficult and highlighting a continued need for finescale resolution analysis of ENSO and AO signal impacts on corn production.

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Adam L. Houston and Dev Niyogi

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Numerical experiments are conducted using an idealized cloud-resolving model to explore the sensitivity of deep convective initiation (DCI) to the lapse rate of the active cloud-bearing layer [ACBL; the atmospheric layer above the level of free convection (LFC)]. Clouds are initiated using a new technique that involves a preexisting airmass boundary initialized such that the (unrealistic) adjustment of the model state variables to the imposed boundary is disassociated from the simulation of convection. Reference state environments used in the experiment suite have identical mixed layer values of convective inhibition, CAPE, and LFC as well as identical profiles of relative humidity and wind. Of the six simulations conducted for the experiment set, only the three environments with the largest ACBL lapse rates support DCI. The simulated deep convection is initiated from elevated sources (parcels in the convective clouds originate near 1300 m) despite the presence of a surface-based boundary. Thermal instability release is found to be more likely in the experiments with larger ACBL lapse rates because the forced ascent at the preexisting boundary is stronger (despite nearly identical boundary depths) and because the parcels’ LFCs are lower, irrespective of parcel dilution. In one experiment without deep convection, DCI failure occurs even though thermal instability is released. Results from this experiment along with the results from a heuristic Lagrangian model reveal the existence of two convective regimes dependent on the environmental lapse rate: a supercritical state capable of supporting DCI and a subcritical state that is unlikely to support DCI. Under supercritical conditions the rate of increase in buoyancy due to parcel ascent exceeds the reduction in buoyancy due to dilution. Under subcritical conditions, the rate of increase in buoyancy due to parcel ascent is outpaced by the rate of reduction in buoyancy from dilution. Overall, results demonstrate that the lapse rate of the ACBL is useful in diagnosing and/or predicting DCI.

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Paul E. Schmid and Dev Niyogi

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This study introduces a methodology to simulate how spatially heterogeneous urban aerosols modify a precipitating thunderstorm in a numerical weather model. An air quality model (simple photochemical model) was coupled with a high-resolution mesoscale weather model (the Regional Atmospheric Modeling System) and generated variable urban cloud condensation nuclei values consistent with those measured in previous field studies. The coupled emission model was used to simulate the passage of a synoptic low pressure system with embedded thunderstorms over an idealized city using the real-atmosphere idealized land surface (RAIL) method. Experiments were conducted to calibrate the surface formation of cloud-nucleating aerosols in an urban environment and then to assess the specific response of different aerosol loads on simulated precipitation. The model response to aerosol heterogeneity reduced the total precipitation but significantly increased simulated rain rates. High-aerosol-loading scenarios produced a peak city-edge precipitation rate of over 100 mm h−1 greater than a control containing only a city land surface with no emissions. In comparing the control with a scenario with no city, it was seen that the land surface effect produced a rain rate increase of up to 20 mm h−1. Results indicate, within the limits of the model framework, that the urban rainfall modification is a combination of land heterogeneity causing the dynamical lifting of the air mass and aerosols, with rainfall enhancement being maintained and synergistically increased because of the aerosol indirect effects on cloud properties.

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Vinodkumar, A. Chandrasekar, K. Alapaty, and Dev Niyogi

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This study investigates the impact of the Flux-Adjusting Surface Data Assimilation System (FASDAS) and the four-dimensional data assimilation (FDDA) using analysis nudging on the simulation of a monsoon depression that formed over India during the 1999 Bay of Bengal Monsoon Experiment (BOBMEX) field campaign. FASDAS allows for the indirect assimilation/adjustment of soil moisture and soil temperature together with continuous direct surface data assimilation of surface temperature and surface humidity. Two additional numerical experiments [control (CTRL) and FDDA] were conducted to assess the relative improvements to the simulation by FASDAS. To improve the initial analysis for the FDDA and the surface data assimilation (SDA) runs, the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) simulation utilized the humidity and temperature profiles from the NOAA Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS), surface winds from the Quick Scatterometer (QuikSCAT), and the conventional meteorological upper-air (radiosonde/rawinsonde, pilot balloon) and surface data. The results from the three simulations are compared with each other as well as with NCEP–NCAR reanalysis, the Tropical Rainfall Measuring Mission (TRMM), and the special buoy, ship, and radiosonde observations available during BOBMEX. As compared with the CTRL, the FASDAS and the FDDA runs resulted in (i) a relatively better-developed cyclonic circulation and (ii) a larger spatial area as well as increased rainfall amounts over the coastal regions after landfall. The FASDAS run showed a consistently improved model simulation performance in terms of reduced rms errors of surface humidity and surface temperature as compared with the CTRL and the FDDA runs.

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Souleymane Fall, Dev Niyogi, and Fredrick H. M. Semazzi

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This paper presents a GIS-based analysis of climate variability over Senegal, West Africa. It responds to the need for developing a climate atlas that uses local observations instead of gridded global analyses. Monthly readings of observed rainfall (20 stations) and mean temperature (12 stations) were compiled, digitized, and quality assured for a period from 1971 to 1998. The monthly, seasonal, and annual temperature and precipitation distributions were mapped and analyzed using ArcGIS Spatial Analyst. A north–south gradient in rainfall and an east–west gradient in temperature variations were observed. June exhibits the greatest variability for both quantity of rainfall and number of rainy days, especially in the western and northern parts of the country. Trends in precipitation and temperature were studied using a linear regression analysis and interpolation maps. Air temperature showed a positive and significant warming trend throughout the country, except in the southeast. A significant correlation is found between the temperature index for Senegal and the Pacific sea surface temperatures during the January–April period, especially in the El Niño zone. In contrast to earlier regional-scale studies, precipitation does not show a negative trend and has remained largely unchanged, with a few locations showing a positive trend, particularly in the northeastern and southwestern regions. This study reveals a need for more localized climate analyses of the West Africa region because local climate variations are not always captured by large-scale analysis, and such variations can alter conclusions related to regional climate change.

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Dev Niyogi, Ming Lei, Chandra Kishtawal, Paul Schmid, and Marshall Shepherd

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The relationship between rainfall characteristics and urbanization over the eastern United States was examined by analyzing four datasets: daily rainfall in 4593 surface stations over the last 50 years (1958–2008), a high-resolution gridded rainfall product, reanalysis wind data, and a proxy for urban land use (gridded human population data). Results indicate that summer monthly rainfall amounts show an increasing trend in urbanized regions. The frequency of heavy rainfall events has a potential positive bias toward urbanized regions. Most notably, consistent with case studies for individual cities, the climatology of rainfall amounts downwind of urban–rural boundaries shows a significant increasing trend. Analysis of heavy (90th percentile) and extreme (99.5th percentile) rainfall events indicated decreasing trends of heavy rainfall events and a possible increasing trend for extreme rainfall event frequency over urban areas. Results indicate that the urbanization impact was more pronounced in the northeastern and midwestern United States with an increase in rainfall amounts. In contrast, the southeastern United States showed a slight decrease in rainfall amounts and heavy rainfall event frequencies. Results suggest that the urbanization signature is becoming detectable in rainfall climatology as an anthropogenic influence affecting regional precipitation; however, extracting this signature is not straightforward and requires eliminating other dynamical confounding feedbacks.

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Christopher Holder, Ryan Boyles, Ameenulla Syed, Dev Niyogi, and Sethu Raman

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The National Weather Service's Cooperative Observer Program (COOP) is a valuable climate data resource that provides manually observed information on temperature and precipitation across the nation. These data are part of the climate dataset and continue to be used in evaluating weather and climate models. Increasingly, weather and climate information is also available from automated weather stations. A comparison between these two observing methods is performed in North Carolina, where 13 of these stations are collocated. Results indicate that, without correcting the data for differing observation times, daily temperature observations are generally in good agreement (0.96 Pearson product–moment correlation for minimum temperature, 0.89 for maximum temperature). Daily rainfall values recorded by the two different systems correlate poorly (0.44), but the correlations are improved (to 0.91) when corrections are made for the differences in observation times between the COOP and automated stations. Daily rainfall correlations especially improve with rainfall amounts less than 50 mm day−1. Temperature and rainfall have high correlation (nearly 1.00 for maximum and minimum temperatures, 0.97 for rainfall) when monthly averages are used. Differences of the data between the two platforms consistently indicate that COOP instruments may be recording warmer maximum temperatures, cooler minimum temperatures, and larger amounts of rainfall, especially with higher rainfall rates. Root-mean-square errors are reduced by up to 71% with the day-shift and hourly corrections.

This study shows that COOP and automated data [such as from the North Carolina Environment and Climate Observing Network (NCECONet)] can, with simple corrections, be used in conjunction for various climate analysis applications such as climate change and site-to-site comparisons. This allows a higher spatial density of data and a larger density of environmental parameters, thus potentially improving the accuracy of the data that are relayed to the public and used in climate studies.

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