Abstract: A composite study is presented analyzing the influence of upper-tropospheric troughs on the evolution of precipitation in twelve Atlantic tropical cyclones (TCs) between the years 2000 � 2005. The TRMM Multi-Satellite Precipitation Analysis (TMPA) is used to examine the enhancement of precipitation within a 24 h window centered on trough interaction (TI) time in a shear-vector relative coordinate system. Eddy angular momentum flux convergence (EFC) computed from European Centre for Medium-Range Weather Forecasts (ECMWF) operational analyses is employed to objectively determine the initiation of a TI while adding insight, along with vertical wind shear, into the intensification of TC vortices. The relative roles of the dynamics (EFC and vertical wind shear) and thermodynamics (moist static energy potential) in TIs are outlined in the context of precipitation enhancement that provides quantitative insight into the “good trough”/“bad trough” paradigm. The largest precipitation rates and enhancements are found in the down-shear left quadrant of the storm, consistent with previous studies of convective asymmetries. Maximum mean enhancement values of 1.4 mm/h are found at the 200 km radius in the down-shear left quadrant. Results indicate that the largest precipitation enhancements occur with “medium” TIs; comprised of EFC values between 17 � 22 (m/s)/day and vertical wind shear Sensitivity tests on the upper vertical wind shear boundary reveal the importance of using the tropopause for wind shear computations when a TC enters mid-latitude regions. Changes in radial mean precipitation ranging from 29 � 40 % across all storm quadrants are found when using the tropopause as the upper boundary on the shear vector. Tests on the lower boundary using QuikSCAT ocean surface wind vectors expose large sensitivities on the precipitation ranging from 42 � 60 % indicating that the standard level of 850 hPa, outside of the boundary layer in most storms, is more physically reliable for computing vertical wind shear. These results should help to improve TC quantitative precipitation forecasting (QPF) as operational forecasters routinely rely on crude statistical methods and rules of thumb for forecasting TC precipitation.

Abstract: Ocean wind vectors from the SeaWinds scatterometer on QuikSCAT and GOES imagery are used to develop an objective technique that can detect and monitor tropical disturbances associated with the early stages of tropical cyclogenesis in the Atlantic basin. The technique is based on identification of surface vorticity and wind speed signatures that exceed certain threshold magnitudes, with vorticity averaged over an appropriate spatial scale. The threshold values applied herein are determined from the precursors of 15 tropical cyclones during the 1999-2004 Atlantic hurricane seasons using research-quality QuikSCAT data. Tropical disturbances are found for these cases within a range of 19 hours to 101 hours before classification as tropical cyclones by the National Hurricane Center (NHC). The 15 cases are further subdivided based upon their origination source (i.e., easterly wave, upper-level cut-off low, stagnant frontal zone, etc). Primary focus centers on the cases associated with tropical waves, since these waves account for approximately 63% of all Atlantic tropical cyclones. The detection technique illustrates the ability to track these tropical disturbances from near the coast of Africa. Analysis of the pre-tropical cyclone (TC) tracks for these cases depict stages, related to wind speed and precipitation, in the evolution of an easterly wave to tropical cyclone.

Abstract: This study focused on the effective use of a numerical climate model for agriculture in Florida, especially in the citrus farming region of the Florida peninsula, because of the impact of agriculture to Florida's economy. For the analyses of the ensemble, the climate models used in this study were the FSU/COAPS Global Spectral Model and FSU/COAPS Regional Spectral Model (FSU/COAPS RSM) coupled with a land-surface model. The multi-convective scheme method and variable initial conditions were used for the ensembles. Severe freezes impacting agriculture in Florida were associated with some major climate patterns, such as El Niño and Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). In the first part of this study, seasonal ensemble integrations of the regional model were examined for the tendencies of freezes in the Florida peninsula during each ENSO or NAO phase is examined. Mean excess values of minimum temperatures from thresholds on the basis of the Generalized Pareto Distribution (GPD), which represents the extreme data in a dataset, were used to analyze the freezes in the regional model. According to some previous studies, El Niño winters obtain fewer freezes than the other ENSO phases. Although the ensemble comprised only 19 winters, the ensemble found variability patterns in minimum temperatures in each climate phase similar to the findings in the previous studies which were based on the observed data. The FSU/COAPS RSM was coupled with Community Land Model 2.0 (CLM2), to represent the land-surface conditions. Although the coupling improved the temperature forecast of the RSM, it still has a cold bias and simulates smaller diurnal temperature changes than actually occur in southern Florida. Among the prescribed surface data, Leaf Area Index (LAI) for southern Florida in the CLM2 is lower than those observed by MODIS (Moderate Resolution Imaging Spectroradiometer). In the first experiment of this part, the sensitivity of the temperature forecast to the LAI in the climate models was investigated, by modifying the LAI data in the CLM2 based on the monthly MODIS observations. In the second experiment, newly created prescribed datasets of LAI and plant functional types for the CLM2 based on the MODIS observations were applied to the RSM. The substitution increased the diurnal temperature change in southern Florida slightly but almost consistently.

Abstract: There are two objectives of the present study. The primary objective is to undertake the following research projects involving the Arctic Oscillation (AO), the El Niño Southern Oscillation (ENSO), and the Madden Julian Oscillation (MJO): (1) an assessment of the utility of using Cyclo-stationary empirical orthogonal function (CSEOF) analysis to define the AO, (2) an empirical analysis of ENSO impacts based on varying indicator and impact regions, (3) detection and extraction of the MJO signal from QuikSCAT, and (4) the development of a general algorithm for determining optimal filter weights for time series endpoints. A secondary objective is to enumerate the statistical and analytical treatments of the AO, ENSO, and the MJO. This will include comparisons of how these three modes are defined (including their indices) and extracted from geophysical data sets. The AO is defined using empirical orthogonal function (EOF) analysis of sea level pressure north of 20'N. The resulting spatial pattern and time series captures the regional influence of its precursor, the North Atlantic Oscillation (NAO), which is a measure of mid-latitude zonal winds over the North Atlantic. ENSO was originally defined as the pressure difference between Tahiti and Darwin, Australia: the Southern Oscillation Index. Scientists now primarily use sea surface temperature (SST) anomalies averaged over one of the Ni'o regions as ENSO indices. The MJO was originally observed using spectral analysis of zonal wind time series in the Indian Ocean and Western Pacific. Present day researchers use extensions of EOF analysis to construct MJO time series. For all three climate modes, the creation of high quality space-time data sets has allowed for more sophisticated indices, supplanting the simpler point-based metrics. For the AO project, the cyclo-stationarity of Northern Hemisphere sea level pressure variability is considered. CSEOF analysis is an extension of EOF analysis that allows multiple spatial maps per mode. It accomplishes this by cyclically extending the covariance matrix based on a parameter called the nested period. By using a nested period of 12, a climate mode can be decomposed into a series of 12 monthly maps and an associated time series. Unlike EOF PC time series, which typically have larger amplitudes during winter months, CSEOF PC time series do not favor a particular season because the physical evolution of the climate mode is posited in the loading vectors (the maps) rather than the time series. This is impossible to accomplish with regular EOF analysis because it relegates each mode to one single map. A compelling case is made for a cyclo-stationary interpretation of AO variability. The leading CSEOF mode includes AO-type variability during a winter regime, as well as a summer regime characterized by pressure anomalies centered over Mongolia and associated with rainfall variability in the vicinity of the Ganges delta and eastern China. EOF modes that contribute to the resulting maps of the leading CSEOF mode are identified, including the eighth mode, which is deemed responsible for the summertime Asian pattern. CSEOF analysis of the AO mode only exemplifies the power of CSEOF analysis with regard to transferring a mode's physical evolution from a PC time series to a series of loading vectors. For the ENSO project, traditional ENSO impact analysis was recast to investigate the teleconnections between U.S. climate and varying indicator regions of SST anomalies in the tropical Pacific. This serves the dual purpose of finding a targeted indicator region for a particular impact zone (i.e. a localization of the teleconnection pattern) and indirectly assessing the viability of well-established ENSO indices (i.e. the Ni'o indices). Based on a selection of impact grid points with known ENSO responses, it appears that the most appropriate indicator region often varies from one impact grid point to another, as well as from warm SST phase to cold SST phase. In addition, air temperature composites behave differently than precipitation composites. In order to simultaneously consider the 'impact perspective' detailed above with the typical 'indicator perspective' (in which climate impacts are computed based on the well-established Ni'o indices), EOF analysis of composited climate fields, conditioned on SST phase, as functions of indicator region and impact zone was performed. The resulting modes represent indicator-impact pairs. Each mode has an impact amplitude function (a spatial temperature or precipitation anomaly signature over the impact region) and an associated indicator weighting function, which modulates the impact amplitude function based on the location of the indicator region. Based on this approach, the unusual yet well-established La Ni'a air temperature impact over the U.S. when using the Ni'o 1+2 region is accounted for as the superposition of two EOF modes. In addition, a teleconnection between tropical Pacific SST and Southeastern U.S. temperature anomalies is documented that is not related to ENSO. For the MJO project, wind data from the SeaWinds instrument on the QuikSCAT satellite are investigated to ascertain how well the surface manifestation of the MJO can be resolved. The MJO signal is detected in non-filtered gridded data using Extended EOF analysis of the zonal wind field, overshadowed by annual, semi-annual, and monsoon-related modes. After bandpass filtering with Lanczos weights, MJO signals are clearly detected in several kinematic quantities, including the zonal wind speed, the zonal pseudostress, and the velocity potential. Extraction of the MJO using QuikSCAT winds compares favorably with extraction using NCEP Reanalysis 2, except that the QuikSCAT signal appears to be more robust. For the filtering project, least squares techniques are utilized to retain endpoint intervals that are normally discarded due to filtering with convolutions in the time domain. The techniques minimize the errors between the pre-determined frequency response function (FRF) of interior points with FRF's that are to be determined for each position in the endpoint zone. The least squares techniques are differentiated by their constraints: (1) unconstrained, (2) equal-mean constraint, and (3) an equal-variance constraint. The equal-mean constraint forces the new weights to sum up to the same value as the pre-determined weights. The equal-variance constraint forces the new weights to be such that, after convolved with the input values, the expected variance is identical to the expected variance of the interior points. These 3 least squares methods are tested under three separate filtering scenarios and compared to each other as well as to the spectral filtering method, which is the standard of comparison. The results indicate that all 4 methods (including the spectral method) possess skill at determining suitable endpoints estimates. However, both the unconstrained and equal-mean schemes exhibit bias toward zero near the terminal ends due to problems with appropriating variance. The equal-variance and spectral techniques do not show evidence of this attribute and were never the worst performers. The equal-variance method showed great promise in the ENSO project involving a 5-month running mean filter, and performed at least on par with the other methods for virtually all time series positions in all three filtering scenarios.