Browsing by Author "Sethu Raman, Committee Member"

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Analysis of Climate Variability for Crop Management in the Southeast United States.
(2011-02-10) Dinon, Heather; Ryan Boyles, Committee Chair; Gail Wilkerson, Committee Chair; Sethu Raman, Committee Member
A Comparative Study between FLEXPART-WRF and HYSPLIT in an Operational Setting: Analysis of Fire Emissions across complex geography using WRF
(2010-04-07) Pagano, Lara Elizabeth; Ryan Boyles, Committee Chair; Sethu Raman, Committee Member; Hugh Devine, Committee Member; Helena Mitasova, Committee Member
Transport and dispersion models are frequently used by the meteorological community to understand and predict the trajectories of anthropogenic, natural and accidental chemical releases of hazardous materials. There are several reputable dispersion models that can handle a wide range of applications under the direction of global, synoptic or mesoscale forecasts. One such application is the forecast of smoke emissions from wildfires which is important to operational air quality and meteorology communities. Fire emissions have direct impacts to property and respiratory health. Operational meteorologists are responsible for providing meteorological support to emergency management agencies within their county warning area in the event of incidents involving harmful chemical releases, radiation and smoke emissions. A comparative study between two dispersion models during recent wildfire events across complex geography is presented to identify the sensitivities of each dispersion model and the operational benefits of utilizing each model for smoke emission forecasts. FLEXPART-WRF is a Lagrangian dispersion model that predicts the transport and dispersion of trace gases forward or backward from a point, line or area source. Similar to FLEXPART-WRF, the HYbrid Single Particle Integrated Trajectory (HYSPLIT) model simulates the dispersive nature of the environment. Model configuration differences include the prerequisite meteorological data, density correction, dispersion algorithms and removal calculations. Mesoscale meteorological models are needed to provide the ambient environment as well as simulate the small scale flux exchanges and boundary layer processes that can affect dispersion simulations on a local and regional scale. Therefore, both dispersion models are using meteorological input from the WRF ARW mesoscale atmospheric model using both a 12 km and 4 km grid-resolution domain. Two fire events, one along the coast of the Mid-Atlantic (Evans Road Case) and the other within the Appalachians (South Mountain Case), are investigated for this analysis. Simulations are analyzed to identify the relative performance of each dispersion model given identical meteorological input. The dispersion models are evaluated for accurate dispersive simulations and also on their ability to support operational forecast needs. Satellite observations provided by the National Environmental Satellite, Data and Information Service along with other remote sensing tools are used for evaluation of dispersion model performance. The spatial analysis, based on both case studies and resolutions, indicates that HYSPLIT disperses particles 10-20 degrees to the right of FLEXPART-WRF for at least a portion of the simulations. FLEXPART-WRF better replicates the observed plume and also yields a higher air concentration throughout most of the simulations, especially downwind. These differences in plume compositions and concentrations are likely linked to the differing diffusion equations. While the air concentration differences are small compared to the amount being released, the spatial differences are statistically significant. To account for the air concentration differences, dry deposition is analyzed. HYSPLIT sporadically deposited significantly more mass to the ground compared to FLEXPART-WRF. These deposition differences impact the diffusion process and account for only part of the concentration variations. This study suggests that FLEXPART-WRF performs better compared to HYSPLIT and may serve as an improved operational tool.
The Formation and Impact of an Incipient Cold-Air Precipitation Feature on the 24-25 January 2000 East Coast Cyclone
(2005-07-22) Brennan, Michael Joseph; Gary M. Lackmann, Committee Chair; Allen J. Riordan, Committee Member; Lian Xie, Committee Member; Sethu Raman, Committee Member
The 24–25 January 2000 East Coast cyclone was characterized by a major operational forecast failure. In an effort to understand why short-range operational numerical weather prediction (NWP) model forecasts were so poor, the impact of a cold-air incipient precipitation (IP) feature that developed prior to the rapid cyclogenesis on 24 January is investigated using potential vorticity (PV) analysis. The IP was poorly forecasted by the operational NWP models, and these models failed to produce heavy precipitation far enough inland over the Carolinas and Virginia later in the cyclone event. Here the formation of the IP is examined from an observational perspective, the impact of the IP is quantified using PV methodology, and the ability of a NWP model to simulate its formation is tested by varying model physics, initial conditions and grid spacing. It was hypothesized that latent heating associated with the IP that formed over the Gulf Coast states early on 24 January generated a lower-tropospheric PV maximum that was important to the moisture transport into the Carolinas and Virginia and the track and intensity of the surface cyclone later in the cyclone event. Calculations from a PV budget and piecewise PV inversion found that the IP was associated with the genesis of a lower-tropospheric PV maximum and that the balanced flow associated with the PV maximum contributed significantly to moisture transport into the region of heavy snowfall. Operational NWP models that failed to forecast the IP did not generate the PV maximum or the heavy precipitation over the Carolinas and Virginia. Observational analyses and radar imagery showed that the IP formed in a region of elevated convective symmetric instability (a mixture of gravitational conditional instability and conditional symmetric instability) where forcing for ascent was provided by an approaching upper-level trough/jet streak. Short-range forecasts from NWP models under-forecasted the strength of the forcing and instability, and were unable to generate the IP in the region where it was observed. An 18-member mesoscale model ensemble with 20-km horizontal grid spacing varying initial condition analyses and model physics was unable to generate the IP feature. Variance associated with the cyclone?s sea-level pressure and precipitation distributions due to initial condition variation was larger than that due to variations in model physics, although significant variation was due to poor performance by ensemble members initialized from the Global Data Assimilation System analysis. A high-resolution model simulation with 4-km grid spacing showed that the IP initially formed within a layer of elevated CSI, consistent with analyses. Buckling of absolute geostrophic momentum surfaces indicated adjustment to slantwise convection at later times. Simulations with 12-km and 20-km grid spacing degraded the representation of these features, suggesting that models run with even coarser grid spacing would be unable to capture the initial formation of the IP. Other simulations initialized only three or six hours later showed a marked improvement in the representation of the IP, the cyclone track and intensity, and the final precipitation distribution, confirming the importance of properly representing the IP feature in successful simulations of this event. The current configuration of operational models with CP schemes and grid spacing insufficient to properly resolve the effects of slantwise convection suggests that future cases may occur where NWP models fail to capture the impact of a cold-air precipitation feature (possibly associated with elevated gravitational and slantwise instability), resulting in poor forecasts of downstream moisture transport and cyclone track and intensity. Operational forecasters should be aware of this possibility and be able to anticipate the potential feedbacks from precipitation (in NWP models and in reality) onto atmospheric dynamics. Available observations and high-frequency model analyses can be used to evaluate NWP model forecasts of precipitation and the lower-tropospheric PV distribution, allowing forecasters to recognize instances when model guidance can be adjusted to improve forecasts of high-impact cyclone events.
Incorporation of the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID) into the Weather Research and Forecasting Model with Chemistry (WRF/Chem): Model Development and Retrospective Applications
(2009-07-11) Hu, Xiaoming; Pal Arya, Committee Member; Ken Schere, Committee Member; Yang Zhang, Committee Chair; Lian Xie, Committee Member; Sethu Raman, Committee Member
Gas/particle mass transfer process plays an important role in determining aerosol mass concentrations and shaping aerosol size distribution. Its treatments in three dimensional (3-D) Air Quality Models (AQMs), however, are largely uncertain. In this thesis work, the gas/particle mass transfer approaches in an aerosol module are improved and evaluated to identify an accurate yet computationally efficient approach for use in 3-D AQMs. The aerosol module with the improved gas/particle mass transfer approaches has been incorporated into a state-of-science air quality forecasting (AQF) system and evaluated with two 3-D applications. Several stand alone condensation schemes used in AQMs are first evaluated with a hypothetical condensation-only case. The original formulation of the Bott scheme as implemented in several AQMs is found to be subject to upstream diffusion thus does not warrant continuous use without modifications. The analytical predictor of condensation with a moving center approach (APC_MC) is shown to be more accurate than the Bott and Trajectory-Grid (T-G) condensation schemes, thus has been incorporated into the Model of Aerosol Dynamics, Reaction, Ionization and Dissolution (MADRID) to solve the gas/particle mass transfer process explicitly. The improved hybrid (i.e., hybrid/APC_MC) and kinetic (i.e., kinetic/APC_MC) approaches and the pre-existing bulk equilibrium approach in MADRID are tested using observational data. The hybrid/APC_MC and kinetic/APC_MC are recommended for 3-D applications due to the best compromise between accuracy and computational efficiency. The improved MADRID has been incorporated into WRF/Chem (referred to as WRF/Chem-MADRID hereafter). WRF/Chem-MADRID with three gas/particle mass transfer approaches (i.e., bulk equilibrium (EQUI), hybrid/APC_MC (HYBR), and kinetic/APC_MC (KINE)) has been tested and evaluated with a 5-day episode from the TexAQS-2000. WRF/Chem-MADRID simulates meteorological parameters fairly well. Simulated hourly O3 shows a high correlation coefficient (0.83) with observations and the overall bias is about -1.8 ppb. Some daily peak O3 mixing ratios are underpredicted, which is possibly due to uncertainties in emissions, inaccurate predictions of small scale meteorological processes, and missing of an OH source and chlorine chemistry in the gas phase mechanism. WRF/Chem-MADRID (EQUI), (HYBR), and (KINE) overpredict PM2.5 by 37.1%, 35.8%, and 36.5%, respectively. Major differences in simulation results by three gas/particle mass transfer approaches occur over coastal areas, where WRF/Chem-MADRID (EQUI) predicts higher PM2.5 concentrations than those predicted by WRF/Chem-MADRID (HYBR) and (KINE) due to improperly redistributing condensed nitrate from the chloride depletion process to fine mode. In comparison, WRF/Chem-MADRID (KINE) correctly predicts chloride depletion process. WRF/Chem-MADRID (HYBR) predicts chloride depletion process correctly for the last two sections (sections 7 and 8), which are solved by the kinetic approach, while the predictions for section 6 may be still biased due to the use of bulk equilibrium approach. In addition to its surface concentration, the column abundance of aerosol is also evaluated. WRF/Chem-MADRID captures the regional-scale AOD distribution and its day-to-day variability while biases exist over certain areas. For the application to the 2004 NEAQS episode, WRF/Chem-MADRID gives comparable overall O3 performance as other AQMs and better O3 performance than some other AQM over certain areas possibly due to the more realistic convective mixing treatment in the model. WRF/Chem-MADRID (HYBR) and WRF/Chem-MADRID (KINE) show better skill than WRF/Chem-MADRID (EQUI) in terms of nitrate predictions over coastal areas. Model simulations confirmed that NEI99 v3 overestimates the actual emissions in 2004, particularly over urban areas.
Investigation of Aerosol Single Scattering Albedo in the Ultraviolet Spectrum
(2002-07-02) Petters, Jonathan Leonard; Vinod K. Saxena, Committee Chair; Sethu Raman, Committee Member; John J. Deluisi, Committee Member
Single scattering albedo (ω), the ratio of scattering coefficient to total extinction coefficient, at UV wavelengths is an important aerosol radiative parameter in determining surface UV irradiance. Surface measurements of total and diffuse UV irradiance in the summer and fall of 1999 at the seven narrowband wavelength channels of an UV multifilter rotating shadowband radiometer (UVMFR-SR) at Black Mountain, NC were coupled with a tropospheric ultraviolet radiative transfer model to produce values of ω. Its value ranged from 0.53 – 0.94 at 300 nm to 0.55 – 1.00 at 368 nm. Error in this procedure decreases with increasing aerosol optical depth (AOD), from +/-0.19 at AOD = 0.05 to +/-0.02 at AOD=1.0. Values of ω were not found to be correlated with air mass origin. The current values of ω have a wider variation than values reported from a previous study at the same site, possibly attributable to changes in aerosol chemical composition over time. The value of ω was found to be quadratically correlated with wavelength. Little research has been conducted in the scattering and absorption properties of aerosols in the UV wavelengths, but what has been done suggests such a correlation is possible. More values of ω in the UV spectrum will allow for better estimation of this parameter for UV radiative transfer modeling and will lessen error in estimation of surface UV irradiances.
Measurements and Modeling of Emissions, Dispersion and Dry Deposition of Ammonia from Swine Facilities
(2006-08-18) Bajwa, Kanwardeep Singh; Viney P. Aneja, Committee Co-Chair; S. Pal Arya, Committee Co-Chair; Sethu Raman, Committee Member; Yang Zhang, Committee Member
Ammonia has recently gained importance for its increasing atmospheric concentrations and its role in the formation of aerosols. Studies have shown increasing atmospheric concentration levels of NH3 and NH4+, especially in the regions of concentrated animal feeding operations. Atmospheric inputs of reduced nitrogen as ammonia and ammonium by dry and wet deposition may represent a substantial contribution to the acidification of semi natural ecosystems and could also affect sensitive coastal ecosystems and estuaries. The anaerobic lagoon and spray method, commonly used for waste storage and disposal in confined animal feeding operations (CAFO), is a significant source of ammonia emissions. An accurate emission model for ammonia from aqueous surfaces can help in the development of emission factors. Study of dispersion and dry deposition patterns of ammonia downwind of a hog farm will help us to understand how much ammonia gets dry deposited near the farm, and how remaining ammonia gets transported farther away. An experimental and modeling study is conducted of emissions, dispersion and dry deposition of ammonia taking one swine farm as a unit. Measurements of ammonia flux were made at 11 swine facilities in North Carolina using dynamic flow-through chamber system over the anaerobic waste treatment lagoons. Continuous measurements of ammonia flux, meteorological and lagoon parameters were made for 8-10 days at each farm during each of the warm and cold seasons. Ammonia concentrations were continuously measured in the chamber placed over the lagoon using a Thermo Environmental Instrument Incorporated (TECO) Model 17c chemiluminescnce ammonia analyzer. A similar ammonia analyzer was used to measure ammonia concentrations at selected locations on the farm. Barn emissions were measured using open-path Fourier transform infrared (OP-FTIR) spectroscopy. A 10 m meteorological tower was erected at each site to measure wind speed and direction, temperature, relative humidity and solar radiation. Data collected from field measurements made at hog waste lagoons in south eastern North Carolina, using the flow through dynamic chamber technique, were used to evaluate the Coupled Mass Transfer and Chemical Reactions model and Equilibrium model. Sensitivity analysis shows that ammonia flux increases exponentially with lagoon temperature and pH, but a linear increase was observed with an increase in total ammoniacal nitrogen (TAN). Ammonia flux also shows a nonlinear increase with increasing wind speed. Observed ammonia fluxes were generally lower in the cold season than in the warm season when lagoon temperatures are higher. About 41% of the Equilibrium model predictions and 43% of the Coupled model predictions are found to be within a factor of two of the observed fluxes. Several model performance statistics were used to evaluate the performance of the two models against the observed flux data. These indicate that the simpler Equilibrium model does as well as the Coupled model. The possible effects of the 'artificial' environment within the chamber, which is different from that in the ambient atmospheric conditions above the open lagoon surface, on the measured fluxes are also recognized. Actual layout of barns and lagoons on the farms was used to simulate dry deposition downwind of the farm. Dry deposition velocity, dispersion and dry deposition of ammonia were studied over different seasons and under different stability conditions. Dry deposition velocities were underpredicted by AERMOD when compared with observed dry deposition velocities. Dry deposition velocities were the highest under near neutral conditions and lowest under stable conditions. The highest deposition at short range occurs under nighttime stable conditions and the lowest deposition occurs during daytime unstable conditions. Significant differences in model predicted depositions over crop and grass surfaces are found under stable conditions. Wind orientation at the farm can also affect deposition of ammonia downwind of the farm.
Modeling Tropical Cyclone Induced Inland Flooding at Tar Pamlico River Basin of North Carolina.
(2010-07-27) Tang, Qianhong; Lian Xie, Committee Chair; Fredrick Semazzi, Committee Member; Sethu Raman, Committee Member; Gary Lackmann, Committee Member
The Role of the Great Lakes in Northwest Flow Snowfall Events in the Southern Appalachian Mountains
(2007-11-06) Holloway, Blair Sterling; Yuh-Lang Lin, Committee Member; Sethu Raman, Committee Member; Gary M. Lackmann, Committee Chair
Northwest flow snowfall (NWFS) events are a regional forecasting challenge that affects much of the southern Appalachian Mountains. These events can be defined as snowfall accompanying upslope flow and low-level northwesterly winds in this region, and typically feature irregular snowfall distributions and highly variable total accumulations. Previous research done by Perry and Konrad (2004—2007) provides an excellent climatology of NWFS events, and shows that NWFS accounts for nearly 50% of mean annual snowfall along the higher elevations of the southern Appalachians. Additionally, through analysis of backward air parcel trajectories, their research shows that NWFS events that featured a Great Lakes connection exhibited increases in composite mean and maximum snowfall totals. This body of work clearly suggests that the Great Lakes can enhance snowfall in NWFS events by warming and moistening the low-level airmass upstream of the southern Appalachians. The specific objective of this study is to quantify and evaluate the role of the Great Lakes in NWFS events for select cases via model experiments using the Weather Research and Forecast (WRF) model. The selected cases occurred 5–6 March 2001, 18–20 December 2003, and 10–11 February 2005, and were investigated using a case study approach. In order to determine the effect of the Great Lakes on NWFS precipitation in these cases, two experimental runs were designed to isolate the role of the lakes. First, surface fluxes of heat and moisture were set to zero across the entire model domain (NOFLX). Second, surface fluxes of heat and moisture were set to zero across only water points (LKNOFLX). The sensitivity of the selected NWFS events to planetary boundary layer (PBL) scheme was also tested (MYJPBL). Overall, it was found that the Great Lakes play an important role in some NWFS events and can be responsible for 20–30% of the precipitation that occurs in these events. Of the selected cases, the March 2001 and February 2005 events showed large decreases in precipitation in the LKNOFLX model run compared to the control (CTRL) run. In these two events, the role of the Great lakes was to destabilize the upstream airmass and increase the Froude number. At a point roughly halfway between the Great Lakes and the southern Appalachians, the LKNOFLX model run in the February 2005 event had an average 950?850 hPa Froude number of 0.99, which was 0.40 less than the CTRL value of 1.39. Similarly in the March 2001 event, the LKNOFLX model run had an average 950–850 hPa Froude number of 1.28, which was 0.42 less than the CTRL value of 1.70. In both cases, the reduced average low-level Froude number in the LKNOFLX run compared to the CTRL shows that when the effect of warming and moistening of the low-level upstream airmass caused by the Great Lakes is removed, a more stable upstream airmass occurs which reduces the Froude number and reduces NWFS precipitation.
The Sensitivity of Tropical Cyclone Simulations in the WRF Model to Surface Layer and Planetary Boundary Layer Parameterization
(2007-03-14) Hill, Kevin Anthony; Gary M Lackmann, Committee Chair; Lian Xie, Committee Member; Sethu Raman, Committee Member
The high wind speeds found in tropical cyclones fundamentally change the physical processes by which heat, moisture and momentum are transferred between the ocean and the lower atmosphere. Despite this fact, surface and boundary layer parameterization schemes in many numerical models that are frequently used for tropical cyclone simulations are based on assumptions made in more tranquil atmospheric conditions. Limited observations in the high wind speed conditions found in strong tropical cyclones suggest that spray and foam can enhance the transfer of heat and moisture from the ocean to the atmosphere, while reducing drag. Inclusion of the effects due to sea spray in a numerical model leads to stronger tropical cyclones (Wang et al. 2001, Perrie et al. 2005). Based upon the absence of sea spray effects and the values of the exchange coefficients in the WRF model, it was anticipated that simulations using an idealized vortex and ambient environment would not reach the thermodynamically estimated theoretical maximum intensity (MPI) limit of Emanuel (1986). In addition, it was expected that simulations of Hurricane Ivan would not reach the intensity of the observed storm. The sensitivity of the model results to surface layer and PBL parameterization, and model grid spacing was tested, with the hypothesis that the simulated tropical cyclones would remain weaker than MPI theory (for the idealized simulations) or observations (for the Hurricane Ivan studies) regardless of the model physical parameterization choice. Grid spacing was also hypothesized to impact the simulated TC intensity, with the expectation that simulations with smaller grid spacing would produce more intense TCs, based on the results of previous studies. Simulated TC intensity is found to be highly sensitive to model grid spacing in experiments with Hurricane Ivan or with an idealized initial vortex. Simulations using 4-km grid spacing were able to produce TCs that exceeded the MPI of the idealized environment (determined by minimum sea level pressure), while simulations using coarser (12 or 36-km) grid spacing were not. Simulations of Hurricane Ivan using 4-km grid spacing, initialized with a vortex that was ˜60 hPa weaker than observations reached the maximum intensity of the observed system, and exceeded the observed intensity during the latter stages of the simulation. These results suggest that the WRF model, in its current configuration, overestimates TC intensity, especially with small values of horizontal grid spacing. If the exchange of moisture, heat and momentum were adjusted to more accurately portray the conditions found in high wind speed conditions, the idealized tropical cyclone would likely exceed the theoretical MPI by an even larger amount. Therefore, it is concluded that some other aspect of the model formulation may lead to an overestimation of tropical cyclone intensity, or that there are deficiencies in MPI theory. In the near future, a version of the WRF model designed for the prediction of hurricanes (HWRF) will be released, and many avenues exist for future research. The HWRF model will require extensive testing before being relied upon by the operational forecasting community. A methodology similar to the one used in this study should be employed, whereby the HWRF model is used in idealized simulations and compared to theoretical intensity limits. The idealized simulations would help to assess the ability of the new model to represent TC intensity in an accurate manner. The surface fluxes and exchange coefficients produced by the model's surface layer parameterizations at high wind speeds should be compared to observational data, to assess the schemes in their current state. Ideally, the schemes in the new model would produce values of fluxes and exchange coefficients that more closely match observations than the schemes in the current version of WRF. If this is not the case, the schemes should be modified to produce conditions that are more consistent with those found in strong TCs.
Spatiotemporal Climate Variability over Senegal and its Relationships with Global Climate
(2004-03-16) Fall, Souleymane; Hugh Devine, Committee Member; Dev Dutta S. Niyogi, Committee Co-Chair; Fredrick Semazzi, Committee Co-Chair; Sethu Raman, Committee Member
Climate variability over Senegal and its relationships with global climate is examined for the period 1971-1998. Monthly observed rainfall for 20 stations over Senegal, monthly mean temperature for 12 stations and monthly average CMAP data were averaged for the months of June July, August and September, to generate seasonal rainfall totals for the wet season and climate indices averaged over the study period. The monthly SST data is the NOAA Extended Reconstructed SST data (ERSST) provided by the NOAA-CIRES Climate Diagnostics Center in Boulder, Colorado. The monthly, seasonal and annual temperature and precipitation distributions are mapped analyzed using ArcGIS Spatial Analyst. Rainfall distribution over Senegal is dominated by a N-S gradient, and temperature distribution by an E-W gradient. The mapping of the coefficient of variation for all stations reveals that for both rainfall and the number of rainy days, June is the month that exhibits the greater variability, especially in the West and North of the country. As regard to temperatures, the months from January to April are accountable for most of the variability, especially in the sub-arid areas of the North and Northwest. Trends in precipitation and temperature are estimated using a linear regression analysis and interpolation maps for the slopes. Areas of positive slopes are limited for rainfall (Northeast and Southwest of Senegal), but important and statistically significant for temperature throughout the country. To investigate the climate variability over Senegal two EOF analyses are performed: for 1971-1998 using observations, and for 1979-7998 using additional CMAP data. The first analysis reveals a strong domination of the first EOF mode for rainfall over Senegal. The corresponding time series mostly fluctuates on a high frequency mode. Its correlations with Atlantic and Pacific SST don't show a strong relationship leading to predictability. However, the second mode for September is well correlated with North Atlantic SST. Despite modest coefficients, the best results are found with Pacific Ocean SST (lag correlation). The second EOF analysis (1979-1998) includes CMAP data and is conducted over Senegal and West Africa. The first West African mode agrees strongly with Lamb's rainfall index. One of our major findings is that EOF2 for West Africa is well correlated with EOF1 for Senegal rainfall. This relationship is supported by the projection of NCEP winds on EOF2 mode, and the grid-point correlation between the time series of EOF2 over West Africa and the Atlantic SST. Series for CMAP rainfall over Senegal show good correlation with the South Atlantic SST. Good coefficients are observed during the pre-wet season (January through May) and may offer some predictability for the rainy season. In the Pacific Ocean, the greatest coefficients (up to -0.72) are observed during the April-July period, which can provide hints for the coming rainy season. The different results obtained in the two analyses suggest an evolution in the relationship between SST fields and Senegal or West African rainfall. The correlations show that the relationships between the South Atlantic and Senegal or West Africa were stronger in the 1979-1988-period, especially for CMAP rainfall over Senegal and West Africa. The CMAP data is robust and suitable for analyses over West Africa. Based on this liability, it has been possible to use CMAP data in a second EOF analysis, as a validation for the first analysis only based on observed precipitation. Given the specificity of the coastal West Africa, traditional indices used by policy makers and end users for the whole Sahel-Sudan region will not work for Senegal.
A summertime radar climatology of convection in the coastal region of South Carolina
(2008-08-08) Booth, William Jay; Sethu Raman, Committee Member; Sandra Yuter, Committee Member; Matthew Parker, Committee Chair