Search Results
You are looking at 1 - 10 of 21 items for
- Author or Editor: Mark L. Morrissey x
- Refine by Access: All Content x
Abstract
Theoretical and observational studies suggest that the equatorial western Pacific plays an important role in the origin and maintenance of the El Niño-Southern Oscillation phenomenon. Historical data within this critical region are sparse except for a scattering of island stations and a merchant shipping lane along 155°E. The usefulness of ship data along this track is assessed utilizing exploratory data analysis and analysis of variance. Systematic biases are revealed in the surface wind, pressure and sea surface temperature measurements. The noise level in the data is quantified using standard error estimates and signal-to-noise ratios associated with various time averages. It is shown that zonal wind is capable of detailing synoptic-scale variations. However, meridional wind, surface pressure and sea surface temperature are better suited for estimating lower frequency variations.
Abstract
Theoretical and observational studies suggest that the equatorial western Pacific plays an important role in the origin and maintenance of the El Niño-Southern Oscillation phenomenon. Historical data within this critical region are sparse except for a scattering of island stations and a merchant shipping lane along 155°E. The usefulness of ship data along this track is assessed utilizing exploratory data analysis and analysis of variance. Systematic biases are revealed in the surface wind, pressure and sea surface temperature measurements. The noise level in the data is quantified using standard error estimates and signal-to-noise ratios associated with various time averages. It is shown that zonal wind is capable of detailing synoptic-scale variations. However, meridional wind, surface pressure and sea surface temperature are better suited for estimating lower frequency variations.
Abstract
The effect of data resolution on the area threshold method is studied using a stochastic model of radar snapshots of tropical rainfall taken during the Global Atmospheric Research Program Atlantic Tropical Experiment. The results indicate that significant biases and random error can arise when using radar or satellite data having spatial resolutions different from those used to calibrate the area threshold method. Although the use of a threshold decreases the bias and random error in the fractional coverage of rain rates, the larger calibration coefficients associated with larger thresholds tend to increase the bias and random error in the resulting areal mean rain-rate estimates.
Abstract
The effect of data resolution on the area threshold method is studied using a stochastic model of radar snapshots of tropical rainfall taken during the Global Atmospheric Research Program Atlantic Tropical Experiment. The results indicate that significant biases and random error can arise when using radar or satellite data having spatial resolutions different from those used to calibrate the area threshold method. Although the use of a threshold decreases the bias and random error in the fractional coverage of rain rates, the larger calibration coefficients associated with larger thresholds tend to increase the bias and random error in the resulting areal mean rain-rate estimates.
Abstract
A point process model for tropical rain rates is developed through the derivation of the third moment expression for a combined point process model. The model is a superposition of a Neyman–Scott rectangular pulse model and a Poisson white noise process model. The model is scalable in the temporal dimension. The derivation of the third moment for this model allows for the inclusion of the skewness parameter, which is necessary to adequately represent rainfall intensity. Analysis of the model fit to tropical tipping-bucket rain gauge data ranging in temporal scale from five min to one day indicates that it can adequately produce synthesized rainfall having the statistical characteristics of rain rate over the range of scales tested. Of special interest is the model’s capability to accurately preserve the probability of extreme tropical rain rates at different scales. In addition to various hydrological applications, the model also has many potential uses in the field of meteorology, such as the study and development of radar rain rate algorithms for the tropics, which need to parameterize attenuation due to heavy rain.
Abstract
A point process model for tropical rain rates is developed through the derivation of the third moment expression for a combined point process model. The model is a superposition of a Neyman–Scott rectangular pulse model and a Poisson white noise process model. The model is scalable in the temporal dimension. The derivation of the third moment for this model allows for the inclusion of the skewness parameter, which is necessary to adequately represent rainfall intensity. Analysis of the model fit to tropical tipping-bucket rain gauge data ranging in temporal scale from five min to one day indicates that it can adequately produce synthesized rainfall having the statistical characteristics of rain rate over the range of scales tested. Of special interest is the model’s capability to accurately preserve the probability of extreme tropical rain rates at different scales. In addition to various hydrological applications, the model also has many potential uses in the field of meteorology, such as the study and development of radar rain rate algorithms for the tropics, which need to parameterize attenuation due to heavy rain.
Abstract
An analysis of the statistical relationships among observed daily rainfall, outgoing longwave radiation (OLR) and the moisture budget (precipitation minus evaporation or P − E), obtained from three independent data sources during January through March, 1979, indicates that on a daily basis P − E and OLR correlate significantly better with each other than they do with observed rainfall over open-ocean regions where the spatial density of rainfall observing stations is low. A spatial correlation over the Pacific Ocean indicates that P − E and OLR correlate well in most—but not all—highly convective regions where both variables have moderate to high variances, and are uncorrelated in dry regions. Low correlations are obtained in regions of shallow convection and in areas of weak moisture convergence with cirrus at upper levels.
It is demonstrated that OLR, P − E, or observed rainfall alone cannot properly define the areal extent of large scale convective activity. A technique is developed in which P − E is used in conjunction with OLR to better establish the intensity and spatial bounds of large-scale convective activity.
Abstract
An analysis of the statistical relationships among observed daily rainfall, outgoing longwave radiation (OLR) and the moisture budget (precipitation minus evaporation or P − E), obtained from three independent data sources during January through March, 1979, indicates that on a daily basis P − E and OLR correlate significantly better with each other than they do with observed rainfall over open-ocean regions where the spatial density of rainfall observing stations is low. A spatial correlation over the Pacific Ocean indicates that P − E and OLR correlate well in most—but not all—highly convective regions where both variables have moderate to high variances, and are uncorrelated in dry regions. Low correlations are obtained in regions of shallow convection and in areas of weak moisture convergence with cirrus at upper levels.
It is demonstrated that OLR, P − E, or observed rainfall alone cannot properly define the areal extent of large scale convective activity. A technique is developed in which P − E is used in conjunction with OLR to better establish the intensity and spatial bounds of large-scale convective activity.
Abstract
Satellite rainfall estimates from a microwave emission-based algorithm by Wilheit et al. are verified using the noncontiguous rain gauge method incorporating monthly Pacific atoll rain gauge data. The results are compared with those obtained using an infrared-based satellite algorithm, the GOES precipitation index. Comparisons between satellite estimates with simple Spatial averages of point rain gauge data are shown to be ineffective at identifying statistically significant differences between the two algorithms due to substantial amounts of spatial sampling error in the rain gauge spatial averages. By effectively reducing this error, the noncontiguous rain gauge method reveals distinctive differences in the ability of each of the algorithms to accurately estimate monthly rainfall over the open ocean. The results indicate that the microwave algorithm, while slightly biased, is significantly less biased than the infrared, which tends to overestimate high rainfall values and underestimate low rainfall values. However, the random error associated with both algorithms is essentially the same.
Abstract
Satellite rainfall estimates from a microwave emission-based algorithm by Wilheit et al. are verified using the noncontiguous rain gauge method incorporating monthly Pacific atoll rain gauge data. The results are compared with those obtained using an infrared-based satellite algorithm, the GOES precipitation index. Comparisons between satellite estimates with simple Spatial averages of point rain gauge data are shown to be ineffective at identifying statistically significant differences between the two algorithms due to substantial amounts of spatial sampling error in the rain gauge spatial averages. By effectively reducing this error, the noncontiguous rain gauge method reveals distinctive differences in the ability of each of the algorithms to accurately estimate monthly rainfall over the open ocean. The results indicate that the microwave algorithm, while slightly biased, is significantly less biased than the infrared, which tends to overestimate high rainfall values and underestimate low rainfall values. However, the random error associated with both algorithms is essentially the same.
Abstract
The effect of temporal sampling error in satellite estimates of climate-scale rainfall is to produce a “conditional” bias where the algorithm overestimates high rainfall and underestimates low rainfall. Thus, the bias is conditional on the value of the estimate. This paper illustrates the problem using satellite infrared rainfall estimates together with a well-known satellite algorithm and shows it to be a function of the averaging scale, the sampling rate, and the temporal autocorrelation structure of the satellite estimates. Using realistic sampling rates, it is shown that significant biases exist in satellite rainfall estimates if polar-orbiting data are used in their construction. A simple correction for this bias based upon the estimated autocorrelation structure is given.
Abstract
The effect of temporal sampling error in satellite estimates of climate-scale rainfall is to produce a “conditional” bias where the algorithm overestimates high rainfall and underestimates low rainfall. Thus, the bias is conditional on the value of the estimate. This paper illustrates the problem using satellite infrared rainfall estimates together with a well-known satellite algorithm and shows it to be a function of the averaging scale, the sampling rate, and the temporal autocorrelation structure of the satellite estimates. Using realistic sampling rates, it is shown that significant biases exist in satellite rainfall estimates if polar-orbiting data are used in their construction. A simple correction for this bias based upon the estimated autocorrelation structure is given.
Abstract
The use of the two-parameter Weibull function as an estimator of the wind speed probability density function (PDF) is known to be problematic when a high accuracy of fit is required, such as in the computation of the wind power density function. Various types of nonparametric kernels can provide excellent fits to wind speed histograms but cannot provide tractable analytical expressions. Analytic expressions for the wind speed PDF are needed for many applications, particularly in the downscaling of model or satellite wind speed estimates to the regional or point scale. It is demonstrated that the judicious use of an expansion of orthogonal polynomials can produce more accurate estimates of the wind speed PDF than relatively simply parametric functions, such as the commonly used Weibull function. This study examines four such expansions applied to two different surface wind speed datasets in Oklahoma. The results indicate that the accuracy of fit of a given expansion is strongly related to how close the basis weight function in an expansion resembles the wind speed histogram. It is shown that this basis function, which is the first term in the expansion, acts as a first “best guess” to the true wind speed PDF and that the additional terms act to “adjust” the fit to converge on the true density function. The results indicate that appropriately chosen orthogonal polynomials can provide an excellent fit and are quite tractable.
Abstract
The use of the two-parameter Weibull function as an estimator of the wind speed probability density function (PDF) is known to be problematic when a high accuracy of fit is required, such as in the computation of the wind power density function. Various types of nonparametric kernels can provide excellent fits to wind speed histograms but cannot provide tractable analytical expressions. Analytic expressions for the wind speed PDF are needed for many applications, particularly in the downscaling of model or satellite wind speed estimates to the regional or point scale. It is demonstrated that the judicious use of an expansion of orthogonal polynomials can produce more accurate estimates of the wind speed PDF than relatively simply parametric functions, such as the commonly used Weibull function. This study examines four such expansions applied to two different surface wind speed datasets in Oklahoma. The results indicate that the accuracy of fit of a given expansion is strongly related to how close the basis weight function in an expansion resembles the wind speed histogram. It is shown that this basis function, which is the first term in the expansion, acts as a first “best guess” to the true wind speed PDF and that the additional terms act to “adjust” the fit to converge on the true density function. The results indicate that appropriately chosen orthogonal polynomials can provide an excellent fit and are quite tractable.
Analysis of recently compiled tropical Pacific rain gauge measurements shows a trend toward increased precipitation in the central tropical Pacific during the period 1971–90. Previous studies of precipitation trends in this region have used satellite data and shipboard measurements, which have been demonstrated to contain a variety of known and unknown biases that could artificially produce a trend. Using rain gauge data, an independent and direct measure of the precipitation trends in the Pacific corroborates previous results based on satellite measurements, estimates of oceanic evaporation from shipboard meteorological observations, and results from numerical models. Furthermore, the result is consistent with suggestions that an enhancement of the tropical hydrologic cycle has been responsible for the increases in globally averaged tropospheric temperatures during the past two decades.
Analysis of recently compiled tropical Pacific rain gauge measurements shows a trend toward increased precipitation in the central tropical Pacific during the period 1971–90. Previous studies of precipitation trends in this region have used satellite data and shipboard measurements, which have been demonstrated to contain a variety of known and unknown biases that could artificially produce a trend. Using rain gauge data, an independent and direct measure of the precipitation trends in the Pacific corroborates previous results based on satellite measurements, estimates of oceanic evaporation from shipboard meteorological observations, and results from numerical models. Furthermore, the result is consistent with suggestions that an enhancement of the tropical hydrologic cycle has been responsible for the increases in globally averaged tropospheric temperatures during the past two decades.
Abstract
Rainfall estimates for two simple satellite-based rainfall algorithms are verified over the tropical Pacific using a new method that incorporates sparsely distributed raingages. The resulting linear regression relationship between monthly areal rainfall and the highly reflective cloud index agrees with earlier results. However, the GOES precipitation index (GPI), which was calibrated using radar rainfall data obtained from the eastern tropical Atlantic, produces biased areas rainfall estimates over most of the tropical Pacific. However, its precision is greater than the highly reflective cloud index, perhaps due to the GPI's larger spatial dimensions. With the incorporation of calibration coefficients determined in this study, the GPI will produce unbiased estimates of areal rainfall for the tropical Pacific region.
Abstract
Rainfall estimates for two simple satellite-based rainfall algorithms are verified over the tropical Pacific using a new method that incorporates sparsely distributed raingages. The resulting linear regression relationship between monthly areal rainfall and the highly reflective cloud index agrees with earlier results. However, the GOES precipitation index (GPI), which was calibrated using radar rainfall data obtained from the eastern tropical Atlantic, produces biased areas rainfall estimates over most of the tropical Pacific. However, its precision is greater than the highly reflective cloud index, perhaps due to the GPI's larger spatial dimensions. With the incorporation of calibration coefficients determined in this study, the GPI will produce unbiased estimates of areal rainfall for the tropical Pacific region.
Abstract
The quality of ship data within the equatorial western Pacific is investigated using statistical analyses, and by comparison with data from neighboring island stations extracted from National Weather Service analyses. Results indicate that ship-measured sea surface temperature has an inherently small spatial scale. Surface pressure, on the other hand, has an inherently large spatial scale, which allows sparse measurements to record large-scale variations precisely. On the average, ship-measured wind, spatially averaged within a lane located near 150°E, is as good a measure of the large-scale wind flow as are the winds recorded at the sparse island stations within the western Pacific. Inaccuracies in the spatially averaged ship elements indicate that further smoothing of the data is required.
Abstract
The quality of ship data within the equatorial western Pacific is investigated using statistical analyses, and by comparison with data from neighboring island stations extracted from National Weather Service analyses. Results indicate that ship-measured sea surface temperature has an inherently small spatial scale. Surface pressure, on the other hand, has an inherently large spatial scale, which allows sparse measurements to record large-scale variations precisely. On the average, ship-measured wind, spatially averaged within a lane located near 150°E, is as good a measure of the large-scale wind flow as are the winds recorded at the sparse island stations within the western Pacific. Inaccuracies in the spatially averaged ship elements indicate that further smoothing of the data is required.
