1,721,182 research outputs found
A land data assimilation system (LDAS) based dataset for regional agro-climatic assessments
Full text linkThis study is part of a USDA sponsored project ----Useful to Usable (U2U): Transforming Climate Variability and Change Information for Cereal Crop Producers . The broader objective includes improving farm resilience and profitability in the U.S. Corn Belt region by transforming existing climate/weather data into usable knowledge and tools for the agricultural community. The specific tasks of this research are: (1) Build a high-resolution (4 km, daily) agro-climatic dataset using a Land Data Assimilation System (LDAS). (2) Estimate regional corn yield across the Corn Belt with crop models and the agro-climatic dataset. (3) Evaluate the impacts of climate variability due to El Niño-Southern Oscillation (ENSO) on corn yield in the Corn Belt. Accordingly, a high-resolution (4 km, 1979-2012, daily) agro-climatic dataset across the U.S. Corn Belt has been built using the North America Land Data Assimilation System version 2 (NLDAS2) product. This newly developed dataset includes daily maximum/minimum temperature, precipitation, solar radiation, soil moisture, and soil temperature at four soil depths (0-10 cm, 10-40 cm, 40-100 cm, and 100-200 cm). Validations indicate strong agreement between this dataset and field measurements. The agro-climatic dataset was then used with a Hybrid-Maize crop model to estimate regional corn yield at grid scale. The crop model was first validated at the field and county scale and found to consistently overestimate yields at the county scale. This was attributed to the optimum field conditions considered in the model and the overall uncertainties. Comparison with NASS yield survey data indicates a 0.6 multiplicative factor provides good agreement with actual yields, and is recommended for county-scale simulations. Following the field/county scale model tests, a modeling framework was developed to simulate gridded crop yields. Results indicate that integrating spatial climatic information improved the regional performance of the Hybrid Maize model and this agro-climatic dataset shows good potential for developing agro-meteorological related applications. Finally, the impacts of the El Nino-Southern Oscillation (ENSO) on observed and simulated corn yields were examined. As a result, La Niña shows a significant negative impact on corn yield in the Corn Belt while the impact from El Niño is insignificant. It also has been found that La Niña correlates with relatively late planting dates in the Corn Belt. Based on a crop model study, the results indicate that for some counties, under optimal conditions, late planting dates can mitigate the negative impacts from the La Niña phase. Based on the studies above, reliable performance of the Hybrid Maize crop model and superior data ability of the new agro-climatic dataset have good potential to simulate regional corn yield with climate projections. The significant impacts of ENSO on corn yield indicate that advance ENSO warning may benefit field management in the Corn Belt
The role of anomalous soil moisture on the inland reintensification of Tropical Storm Erin (2007)
No full textPrior research on tropical storm systems that have made landfall and undergone a period of sustainability or reintensification have been linked to the synoptic environment at the time the storm restrengthened. Tropical Storm (TS) Erin is an interesting case study in that it did not take on hurricane-like structure nor reach hurricane intensity until it moved through west-central Oklahoma on 19 August 2007. This study seeks to examine the possible impact of anomalously wet soils across much of Oklahoma on the reintensification of TS Erin during the early morning hours of 19 August 2007. To determine the degree to which the antecedent soil state impacted TS Erin.s inland evolution and reintensification, analyses of the synoptic environment and the mesoscale environment/boundary layer environment is undertaken using operational and research datasets such as upper-air soundings, surface soil moisture and temperature data, and multiple products from the Storm Prediction Center (SPC) mesoanalysis archive. This observational assessment is complemented with numerical experiments using the Weather Research and Forecast Model, Advanced Research Version 3.2 (WRF-ARW) to further study the role of soil moisture availability and surface fluxes that may have led to boundary layer feedback and inland reintensification. Observational analysis and model results indicate that anomalously wet conditions over the central Oklahoma region may have helped develop a regional boundary layer feedback that appears to have contributed positively to the inland reintensification of TS Erin
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Impacts of land surface properties on temperature trends over the United States: Assessment using the US historical climate network and North American regional reanalysis datasets
No full textTemperature trends result from natural and anthropogenic factors. The latter was first seen as the result of radiative forcings, mainly the increasing concentrations of greenhouse gases. However, the increasing evidence that some non-radiative forcings such as land use/land cover (LULC) change may also be major factors contributing to climate change has prompted the National Research Council (NRC, 2005) to recommend the broadening of the climate change issue to include LULC processes as an important climate forcing. In addition, at the station level, increasing attention has been given to non-climatic biases that affect temperature records due to changes of the local environment at the vicinity of the station, changes in instrumentation and/or observations practices. This study (i) uses comparisons between in-situ observations and reanalysis datasets as an independent method to estimate temperature trends and variability and evaluate adjustments made to temperature records to correct non-climatic biases, (ii) uses the Observation Minus Reanalysis (OMR) method to investigate the impacts of sensitivity of surface temperature trends to LULC change over the conterminous United States and (iii) compares temperature and equivalent temperature (which is a variable that combines both temperature and moisture) and analyzes their respective correlation to vegetation properties. The comparison between the reanalysis and in-situ temperature observations shows that the reanalysis faithfully captures the intraseasonal and interannual variability of the station observations and also provides valuable information about the effects of individual station location (well or poorly sited) on temperature observations. Moreover, the comparison between surface observations and the North American Regional Reanalysis (NARR) using the Mean Square Difference (MSD) method is efficient in detecting LULC changes that took place at the vicinity of stations or changes related to observation practices, and in evaluating the impacts of adjustments performed on raw observations. OMR trends were found to be sensitive to land cover types and results indicate that land use conversion often results in more warming than cooling. Overall, our results confirm the robustness of the OMR method for capturing patterns of LULC changes at local and regional scales. The comparison between temperature and equivalent temperature demonstrates that atmospheric heat content may help to quantify the differences between surface and tropospheric trends, and hence the impact of land cover types on the surface temperature changes. Moreover, equivalent temperature is more correlated to biomass increase, vegetation transpiration and other surface moisture characteristics. Overall, this study suggests that in addition to considering the greenhouse gases-driven radiative forcings, multi-decadal and longer climate models simulations must further include LULC changes
The impacts of land use / land cover changes on the tropical maritime climate of Puerto Rico
No full textPrevious studies of the influences of Land Use / Land Cover Changes (LULCC) on the climate of continental areas have provided a basis for our current understanding of LULCC impacts. However continental climates may not provide complete explanations or answer specific scientific questions for other regions, such as small tropical-maritime dominated islands. Here we provide a detailed analysis of century-scale climate change for Puerto Rico, and assess the degree to which some of this change might be related to LULCC. We used long-term data, Geographic Information Systems (GIS), statistical analysis and Regional Atmospheric Modeling Systems (RAMS) to detect and assess the impact of local urban development on temperature and precipitation. We found strong evidence of a relationship linking temperature and precipitation magnitudes to local urban development. Findings for maximum, average and minimum temperature are robust showing that urbanization has increased local temperatures and levels of impact found here represent minimum estimates since they were based on data that had some prior adjustment intended to control for urban signals. Strong evidence of this relationship was also found in the precipitation data analysis, but no clear correlation was found in the direction, magnitude, period and location of rain with urban development implying that other factors dominate or are playing some role in this relationship. RAMS numerical modeling results were inconclusive suggesting that further tuning of settings and parameters are needed before model results can be used to guide decision-making
A hydroclimatic assessment of the U.S. corn belt across spatial and temporal scales
Full text linkThe term hydroclimate is used to describe the climate of a given location as determined by the incident radiant energy (temperature) and the existence of water in its various forms on Earth. Two types of climate comprise the science of hydroclimatology: the climate as established by general global circulation patterns at specific locations on Earth (large-scale climate) and the climate established at Earth\u27s surface resulting from the daily fluxes of radiant energy and water in its various forms between the atmosphere, Earth\u27s surface, and the subsurface (local-scale climate) (Shelton 2009). This dissertation investigates different spatial and temporal scales of the U.S. Corn Belt hydroclimate and includes analysis of large- and local-scale hydroclimatic feedbacks. Large-scale hydroclimate research in this assessment investigates how general circulation patterns and teleconnections, specifically the El Niño-Southern Oscillation and the Arctic Oscillation, influence climate variability in the form of temperature and precipitation patterns across the U.S. Corn Belt with findings applicable to agricultural decision making. A large- and local-scale hydroclimatic assessment examines the rainfall contribution of land-falling tropical cyclones to the Eastern U.S. Corn Belt. Locale-scale hydroclimate research considers the role of land-surface feedbacks in the life cycle of land-falling tropical cyclones. Results from the assessments that comprise this dissertation show that the spatial and temporal scales at which hydroclimatic feedbacks are examined are important to the understanding of hydroclimate system interactions. It is suggested from the results of this comprehensive assessment that the newly identified, large- and local-scale hydroclimatic feedbacks be given stronger consideration in forecasts and climate projection models. Additionally, it is suggested that more hydroclimate assessments across spatial and temporal scales be completed to better prepare for and mitigate the effects of projected climate variability and climate change. A framework for climatological applications to agronomy is discussed in the first chapter, with the findings of the hydroclimatological assessments in subsequent chapters primarily applied to agronomic decision making
Impact of improved land surface representation on modeling land surface atmosphere interactions under heterogeneous soil moisture conditions
No full textThe purpose of this study was to focus on how soil moisture and vegetation heterogeneity can play an important role on surface processes such as atmospheric feedback under drought and non-drought conditions. The study also aims to improve land surface representation climatology in terms of single and coupled modes of land surface and weather forecasting models. The general methodology was applied to evaluate the performance of the offline high resolution Noah land surface model (version 3.1) versus the Noah land surface model with the photosynthesis-based Gas exchange Evapotranspiration Model (Noah GEM) using different land types such as forest and agricultural areas (i.e., the Niwot Ridge Ameriflux site, USA and the Avignon CarboEurope site, France) The coupled model Weather Research - Advanced Research Version (ARW ver. 3) was also employed to help understand the coupled processes between biochemical plant physiology, soil moisture, and atmosphere. Three cases were conducted: 1) an LLJ (low level jet) event observed on 3 June 2002 over the IHOP (International H20 Project) field experiment, 2) a severe drought from 11-19 June 2006 over the Southern Great Plains region (SGP); and 3) deep and shallow convection from 10-13 June 2007 over the CLASIC (Cloud and Land Surface Interaction Campaign) SGP region. Field experiment and aircraft data from the Ameriflux and CarboEurope site, IHOP, and the CLASIC campaign were used to calibrate and validate the models. All three hypotheses have been answered showing 1) The improved land surface initial conditions (soil moisture and temperature) using a high resolution land data assimilation system (HRLDAS) will lead to enhanced predictions of summer daytime and nighttime mesoscale forcing under both weak and intense synoptically-driven cumulus convection conditions. 2) Vegetation transpiration is more efficient than soil evaporation in transporting moisture from the land surface to the atmosphere during convection simulations. 3) Interactions between local and large-scale land surface heterogeneity can affect regional convection in the Southern Great Plains for both IHOP and the CLASIC field phase (June 2002 and June 2007). This study provides some of the first results highlighting land surface-vegetation-soil moisture-atmospheric feedback as an important factor not only for daytime processes but also for improved simulation of early morning and nighttime convection. Also the improved Noah land surface model predicted more accurate energy flux, cloud radiation, rainfall, soil moisture, and soil temperature during extreme drought conditions and shallow cumulus convection. Future works include the use of finer-scale data assimilation and long-term soil moisture climatology to improve model performance. Additional plant physiological biochemistry formulations need further evaluation. The impact of convection triggers and vegetation transpiration on deep convection also need further investigation
Modeling the impact of land surface feedbacks on post landfall tropical cyclones
Full text linkThe land surface is an important component of numerical models. The land surface models are modules that control energy partitioning, compute surface exchange coefficients and form the only physical boundary in a regional scale numerical model. Thus, an accurate representation of land surface is critical to compute surface fluxes, represent the boundary layer evolution and affect changes in weather systems. Land surface can affect landfalling tropical cyclones in two ways: (i) when the cyclone is offshore and land can influence cyclones by introducing dry (or moist) air that can weaken (or strengthen) the organized convective structure of cyclones, and (ii) land can affect the evolution of cyclones post landfall by modifying the surface heat fluxes and introducing additional surface drag. In this dissertation, the hypothesis that improved representation of land surface conditions will improve the prediction of landfalling tropical cyclones is tested. To that effect, a comprehensive review of land surface effects on tropical cyclones was undertaken and an idealized study was conducted to study the impact of antecedent soil temperature on the sustenance/reintensification of tropical cyclones over land. Rainfall verification for cyclone events over the Atlantic Ocean was conducted and a comparison study between land models—GFDL Slab and Noah, also considers the sensitivity of tropical cyclone models to land surface parameterizations. The recent adoption of Noah land model with hydrology products in HWRF offers a unique opportunity to couple a river routing model to HWRF to provide streamflow estimations from the HWRF model and this dissertation has outlined techniques to real time predict streamflow for United States with HWRF forcing. Results from this dissertation research indicate antecedent land surface conditions can affect tropical cyclone evolution post landfall and high soil temperature and thermally diffusive soil texture of land surface are critical factors contributing to re-intensification/ sustenance of tropical cyclones. This idealized study, in addition to enabling improved understanding of the land surface effects on cyclones, has also led to a developmental effort to incorporate landfalling capability in the idealized framework of HWRF model and is available for use for the wider tropical cyclone community. The development of river routing coupled HWRF model could also be used in the operational mode to improve flooding and streamflow predictions and efforts are underway to integrate this new capability in HWRF. Study findings contribute to the understanding regarding the effects of land surface on landfalling cyclones and helps translate research products into HWRF’s operational framework for predicting tropical cyclones
Observing and Modeling Urban Thunderstorm Modification Due to Land Surface and Aerosol Effects
No full textUrban meteorology has developed in parallel to other sub-fields in the science, but in many ways remains poorly described. In particular, the study of urban rainfall modification remains behind compared to other comparable features. Urban rainfall modification refers to the change of a precipitation feature as it crosses an urban area. Typically, this manifests as rainfall initiation, local suppression, local invigoration, and/or storm morphology changes. Research in the prior decades have shown urban rainfall modification to arise from a combination of land-atmosphere and aerosol-cloud interaction. Urban areas create a greater surface roughness, which produces local convergence and divergence, modifying local thunderstorm inflow and morphology. The land surface also generates vertical velocity perturbations which can act to initiate or modify existing convection. Urban aerosols act as CCN to perturb existing cloud and precipitation characteristics. Higher CCN narrows the cloud droplet distribution, creating more smaller cloud droplets, and initially reducing precipitation efficiency by keeping more liquid water in the cloud than what would form into rain. The CCN-cloud interaction eventually increasing heavy rainfall production as graupel riming is enhanced by the narrower cloud droplet distribution, leading to more larger raindrops and higher rain in areas.This dissertation addresses the observation and modeling of urban thunderstorm interaction from both the land surface and aerosol perspective. It reassesses the original urban rainfall anomaly: The La Porte Anomaly. First analyzed in the late 1960s, the La Porte Anomaly was ultimately dismissed by 1980 as either a temporary, biased, or otherwise unexplainable observation, as the process level understanding had yet to be explained. The contemporary analysis utilizes all existing data and objective optimal interpolation to show that a rainfall anomaly downwind of Chicago has indeed existed at least since the 1930s. The current rainfall anomaly exists as a broad region of warm season rainfall downwind of Chicago that is 20-30% greater than the regional average. Using synoptic parameters, the rainfall anomaly is shown to be independent of wind direction and most closely associated with local land surface forcing. Weekdays, where local aerosol loading has been measured at 40% or more greater than weekends, have up to 50% more warm season rainfall than weekends. The analysis is able to show that there is a land surface and aerosol contribution to the rainfall anomaly, but cannot unambiguously separate them.In order to separate the land surface and aerosol effects on urban rainfall distribution, a numerical model was improved to better handle urban weather interaction. The Regional Atmospheric Modeling System (RAMS 6.0) was chosen for its base land surface and cloud physics parameterization. The Town Energy Budget (TEB) urban canopy model was coupled to RAMS to handle the urban land surface. The Simple Photochemical Module (SPM) was coupled with the cloud physics to handle conversion of surface emissions to CCN. The model utilized an external traffic simulation to create a realistic diurnal and weekly cycle of surface emissions, based on human behavior. The new Urban RAMS was used to study the land surface sensitivity of city size and of aerosol loading in two studies using the Real Atmosphere Idealized Land surface (RAIL) method, by which all non-urban features of the land surface are removed to isolate the urban effects
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