Engineering topics

Bell, Thomas L.

Publications and source records attributed to Bell, Thomas L..

At least 19 records

NASA NTRS2013

Reply to Comment by S. E. Yuter et al. on 'Why do Tornados and Hailstorms Rest on Weekends?'

We show here that none of the concerns of Yuter et al. [2013, hereinafter Y2013] have any validity. We use this opportunity to clarify issues that may have been misunderstood by some readers (Y2013 among them) of Rosenfeld and Bell [2011, hereinafter RB2011], elaborate on our explanations there and further substantiate the evidence showing the impacts of aerosols on severe convective storms and the ways by which they are manifested in the weekly cycle. [2] Y2013 raise two general questions concerning the evidence for a weekly cycle of tornadoes given by RB2011: (1) whether the statistical analysis is valid, and (2) whether the discussion of physical mechanisms that explain the weekly cycle is correct. [3] In section 2, we show that there is no basis for the criticisms of Y2013 concerning the physical mechanisms proposed in RB2011 behind the observed changes in tornado activity and show further the mechanism by which aerosols can modulate tornadoes. Most of the comments by Y2013 appear to result from a misunderstanding and misinterpretation of RB2011. In section 3, we show that the comments by Y2013 concerning the statistical analysis are either incorrect or irrelevant. Some of the questions they raised were already answered previously by RB2011, and references therein. We also show that the complaint of Y2013, that the spatial averaging used in RB2011 inappropriately blends tornado behavior of different kinds in different regions, is unfounded.

severe storms↗

NASA NTRS2010

Why do Tornados and Hail Storms Rest on Weekends?

When anthropogenic aerosols over the eastern USA during summertime are at their weekly mid-week peak, tornado and hail storm activity there is also near its weekly maximum. The weekly cycle in storm activity is statistically significant and unlikely to be due to natural variability. The pattern of variability supports the hypothesis that air pollution aerosols invigorate deep convective clouds in a moist, unstable atmosphere, to the extent of inducing production of large hailstones and tornados. This is caused by the effect of aerosols on cloud-drop nucleation, making cloud drops smaller, delaying precipitation-forming processes and their evaporation, and hence affecting cloud dynamics.

Rosenfeld, Daniel↗

NASA NTRS2009

Weekly Cycle of Lightning: Evidence of Storm Invigoration by Pollution

We have examined summertime 1998 2009 U.S. lightning data from the National Lightning Detection Network (NLDN) to look for weekly cycles in lightning activity. As was found by Bell et al. (2008) for rain over the southeast U.S., there is a significant weekly cycle in afternoon lightning activity that peaks in the middle of the week there. The weekly cycle appears to be reduced over population centers. Lightning activity peaks on weekends over waters near the SE U.S. The statistical significance of weekly cycles over the western half of the country is generally small. We found no evidence of a weekly cycle of synoptic-scale forcing that might explain these patterns. The lightning behavior is entirely consistent with the explanation suggested by Bell et al. (2008) for the cycles in rainfall and other atmospheric data from the SE U.S., that aerosols can cause storms to intensify in humid, convectively unstable environments.

Bell, Thomas L.↗

NASA NTRS2008

Comment on "Weekly Precipitation Cycles? Lack of Evidence from United States Surface Stations"

There is a good deal of interest lately in whether or not rainfall varies with the day of the week in response to the weekly variations in human activity. The most likely cause of such changes in the U.S. would be from the weekly variations in pollution levels that are known to occur throughout the country. A paper on this topic will soon be published by the Journal of Geophysical Research entitled, "Midweek Increase in U.S. Summer Rain and Storm Heights Suggests Air Pollution Invigorates Rainstorms, by T. L. Bell, D. Rosenfeld, K.-M. Kim, J.-M. Yoo, M.-I. Lee, and M. Hahnenberger (referred to here as "Bell et al."). A paper by D. M. Schultz and co-authors was recently published in Geophysical Research Letters that claimed to contradict some of the results in Bell et al. The paper can be found here: . Our Comment points out that Schultz and co-authors ignored the fact that the results from satellite data obtained by Bell et al. were for a later time period than Schultz et al. examined, and that Bell et al. in fact also analyzed rainfall data for the same time period as Schultz et al. and, like them, also failed to find signs of a weekly cycle in rainfall during this time period. The contradictions claimed by Schultz et al. are non-existent. We point out some other problems with the methods and presentation by Schultz et al.

Bell, Thomas L.↗

NASA NTRS2007

Midweek Increase in U.S. Summer Rain and Storm Heights, Suggests Air Pollution Invigorates Rainstorms

Tropical Rainfall Measuring Mission (TRMM) satellite data show a significant midweek increase in summertime rainfall over the southeast U.S., due to afternoon intensification. TRMM radar data show a significant midweek increase in rain area and in the heights reached by afternoon storms. Weekly variations in model-reanalysis wind patterns over the region and in rain-gauge data are consistent with the satellite data. A midweek decrease of rainfall over the nearby Atlantic is also seen. EPA measurements of particulate concentrations show a midweek peak over much of the U.S. These observations are consistent with the theory that anthropogenic air pollution suppresses cloud-drop coalescence and early rainout during the growth of thunderstorms over land, allowing more water to be carried above the 0 C isotherm, where freezing yields additional latent heat, invigorating the storms--most dramatically evidenced by the shift in the midweek distribution of afternoon-storm heights--and producing large ice hydrometeors. The enhanced convection induces regional convergence, uplifting and an overall increase of rainfall. Compensating downward air motion suppresses convection over the adjacent ocean areas. Pre-TRMM-era data suggest that the weekly cycle only became strong enough to be detectable beginning in the 1980's. Rain-gauge data also suggest that a weekly cycle may have been detectable in the 1940's, but with peak rainfall on Sunday or Monday, possibly explained by the difference in composition of aerosol pollution at that time. This "weekend effect" may thus offer climate researchers an opportunity to study the regional climate-scale impact of aerosols on storm development and monsoon-like circulation.

Bell, Thomas L.↗

NASA NTRS2006

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part II: Evaluation of Estimates Using Independent Data

Rainfall rate estimates from spaceborne microwave radiometers are generally accepted as reliable by a majority of the atmospheric science community. One of the Tropical Rainfall Measuring Mission (TRMM) facility rain-rate algorithms is based upon passive microwave observations from the TRMM Microwave Imager (TMI). In Part I of this series, improvements of the TMI algorithm that are required to introduce latent heating as an additional algorithm product are described. Here, estimates of surface rain rate, convective proportion, and latent heating are evaluated using independent ground-based estimates and satellite products. Instantaneous, 0.5 deg. -resolution estimates of surface rain rate over ocean from the improved TMI algorithm are well correlated with independent radar estimates (r approx. 0.88 over the Tropics), but bias reduction is the most significant improvement over earlier algorithms. The bias reduction is attributed to the greater breadth of cloud-resolving model simulations that support the improved algorithm and the more consistent and specific convective/stratiform rain separation method utilized. The bias of monthly 2.5 -resolution estimates is similarly reduced, with comparable correlations to radar estimates. Although the amount of independent latent heating data is limited, TMI-estimated latent heating profiles compare favorably with instantaneous estimates based upon dual-Doppler radar observations, and time series of surface rain-rate and heating profiles are generally consistent with those derived from rawinsonde analyses. Still, some biases in profile shape are evident, and these may be resolved with (a) additional contextual information brought to the estimation problem and/or (b) physically consistent and representative databases supporting the algorithm. A model of the random error in instantaneous 0.5 deg. -resolution rain-rate estimates appears to be consistent with the levels of error determined from TMI comparisons with collocated radar. Error model modifications for nonraining situations will be required, however. Sampling error represents only a portion of the total error in monthly 2.5 -resolution TMI estimates; the remaining error is attributed to random and systematic algorithm errors arising from the physical inconsistency and/or nonrepresentativeness of cloud-resolving-model-simulated profiles that support the algorithm.

Yang, Song↗

NASA NTRS2006

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry: Improved Method and Uncertainties - Part 1

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5 -resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%-80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5deg resolution is relatively small (less than 6% at 5 mm day.1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%-35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%-15% at 5 mm day.1, with proportionate reductions in latent heating sampling errors.

Olson, William S.↗

NASA NTRS2006

Weekend Effect" in Summertime U.S. Rainfall: Evidence for Midweek Intensification of Storms by Pollution

Persistent and strong dependence of rain rate on the day of the week has been found in Tropical Rainfall Measuring Mission (TRMM) satellite estimates of summer afternoon rainfall over the southeast U.S. and the nearby Atlantic from 1998 to 2005. Midweek (Tue-Thu) rain rates and rain area appear to increase over land, and this increase is accompanied by a corresponding diminution of rainfall over nearby waters. Reanalysis data from atmospheric models, suggest that there is a corresponding weekly variation in atmospheric winds consistent with the changes in rainfall. These variations are almost certainly caused by weekly variations in human activity. The most likely cause of the observed changes in rainfall is the well documented weekly variation in atmospheric pollution. Particulate pollution is highest in the middle of the week. Considerable observational and modeling evidence has accumulated concerning the effects of aerosols on precipitation. Most of this evidence relates to the suppression of precipitation by aerosols, but it has been argued that storms in highly unstable moist environments can be invigorated by aerosols, and some modeling studies seem to confirm this. The strong weekly cycle in rainfall observed over the southeast U.S. along with what appears to be dynamical suppression of rainfall over the nearby Atlantic, and the lack of an observable cycle over the southwest U.S., are consistent with this theory.

Bell, Thomas L.↗

NASA NTRS2004

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry: Evaluation of Estimates Using Independent Data - Part 2

Rainfall rate estimates from space-borne k&ents are generally accepted as reliable by a majority of the atmospheric science commu&y. One-of the Tropical Rainfall Measuring Mission (TRh4M) facility rain rate algorithms is based upon passive microwave observations fiom the TRMM Microwave Imager (TMI). Part I of this study describes improvements in the TMI algorithm that are required to introduce cloud latent heating and drying as additional algorithm products. Here, estimates of surface rain rate, convective proportion, and latent heating are evaluated using independent ground-based estimates and satellite products. Instantaneous, OP5resolution estimates of surface rain rate over ocean fiom the improved TMI algorithm are well correlated with independent radar estimates (r approx. 0.88 over the Tropics), but bias reduction is the most significant improvement over forerunning algorithms. The bias reduction is attributed to the greater breadth of cloud-resolving model simulations that support the improved algorithm, and the more consistent and specific convective/stratiform rain separation method utilized. The bias of monthly, 2.5 deg. -resolution estimates is similarly reduced, with comparable correlations to radar estimates. Although the amount of independent latent heating data are limited, TMI estimated latent heating profiles compare favorably with instantaneous estimates based upon dual-Doppler radar observations, and time series of surface rain rate and heating profiles are generally consistent with those derived from rawinsonde analyses. Still, some biases in profile shape are evident, and these may be resolved with: (a) additional contextual information brought to the estimation problem, and/or; (b) physically-consistent and representative databases supporting the algorithm. A model of the random error in instantaneous, 0.5 deg-resolution rain rate estimates appears to be consistent with the levels of error determined from TMI comparisons to collocated radar. Error model modifications for non-raining situations will be required, however. Sampling error appears to represent only a fraction of the total error in monthly, 2S0-resolution TMI estimates; the remaining error is attributed to physical inconsistency or non-representativeness of cloud-resolving model simulated profiles supporting the algorithm.

Yang, Song↗

NASA NTRS2004

Renormalization Group Tutorial

Complex physical systems sometimes have statistical behavior characterized by power- law dependence on the parameters of the system and spatial variability with no particular characteristic scale as the parameters approach critical values. The renormalization group (RG) approach was developed in the fields of statistical mechanics and quantum field theory to derive quantitative predictions of such behavior in cases where conventional methods of analysis fail. Techniques based on these ideas have since been extended to treat problems in many different fields, and in particular, the behavior of turbulent fluids. This lecture will describe a relatively simple but nontrivial example of the RG approach applied to the diffusion of photons out of a stellar medium when the photons have wavelengths near that of an emission line of atoms in the medium.

Bell, Thomas L.↗

NASA NTRS2004

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry: Method and Uncertainties - Part 1

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating/drying profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and non-convective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud resolving model simulations, and from the Bayesian formulation itself. Synthetic rain rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in instantaneous rain rate estimates at 0.5 deg resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. These errors represent about 70-90% of the mean random deviation between collocated passive microwave and spaceborne radar rain rate estimates. The cumulative algorithm error in TMI estimates at monthly, 2.5 deg resolution is relatively small (less than 6% at 5 mm/day) compared to the random error due to infrequent satellite temporal sampling (8-35% at the same rain rate).

Olson, William S.↗

NASA NTRS2003

Estimates of Random Error in Satellite Rainfall Averages

Satellite rain estimates are most accurate when obtained with microwave instruments on low earth-orbiting satellites. Estimation of daily or monthly total areal rainfall, typically of interest to hydrologists and climate researchers, is made difficult, however, by the relatively poor coverage generally available from such satellites. Intermittent coverage by the satellites leads to random "sampling error" in the satellite products. The inexact information about hydrometeors inferred from microwave data also leads to random "retrieval errors" in the rain estimates. In this talk we will review approaches to quantitative estimation of the sampling error in area/time averages of satellite rain retrievals using ground-based observations, and methods of estimating rms random error, both sampling and retrieval, in averages using satellite measurements themselves.

Bell, Thomas L.↗

NASA NTRS2003

Stochastic Models of Tropical Rain-Rate Fields

Because of the extreme variability of rain rate in space and time and the difficulties with remote sensing methods of measuring rain rates, accurate determination of rainfall over large areas and time periods has long been a problem for hydrologists, meteorogists, and climatologists. A number of statistical models of rain have been developed in order to investigate the impact of rain variability on satellite remote sensing methods, validation of satellite rain products, and generation of rain maps with accompanying error estimates. These models may be useful in examining 'sub-grid scale' issues in representing precipitation in numerical mdoels. A stochastic model will first be described which can generate time-dependent high-resolution spatial rain fields with space and time correlations similar to those seen in rain data, as well as representing the presence of areas with zero rain rate and log-normally distributed rain rates where there is rain. A simpler model derived from this, formulated in the spectral domain, seems to imply fractal-like rain statistics at small scales when fit to rain data.

Bell, Thomas L.↗

NASA NTRS2003

A Stochastic Model of Space-Time Variability of Mesoscale Rainfall: Statistics of Spatial Averages

A characteristic feature of rainfall statistics is that they depend on the space and time scales over which rain data are averaged. A previously developed spectral model of rain statistics that is designed to capture this property, predicts power law scaling behavior for the second moment statistics of area-averaged rain rate on the averaging length scale L as L right arrow 0. In the present work a more efficient method of estimating the model parameters is presented, and used to fit the model to the statistics of area-averaged rain rate derived from gridded radar precipitation data from TOGA COARE. Statistical properties of the data and the model predictions are compared over a wide range of averaging scales. An extension of the spectral model scaling relations to describe the dependence of the average fraction of grid boxes within an area containing nonzero rain (the "rainy area fraction") on the grid scale L is also explored.

Kundu, Prasun K.↗

NASA NTRS2002

Comparison of Two Methods for Estimating the Sampling-Related Uncertainty of Satellite Rainfall Averages Based on a Large Radar Data Set

The uncertainty of rainfall estimated from averages of discrete samples collected by a satellite is assessed using a multi-year radar data set covering a large portion of the United States. The sampling-related uncertainty of rainfall estimates is evaluated for all combinations of 100 km, 200 km, and 500 km space domains, 1 day, 5 day, and 30 day rainfall accumulations, and regular sampling time intervals of 1 h, 3 h, 6 h, 8 h, and 12 h. These extensive analyses are combined to characterize the sampling uncertainty as a function of space and time domain, sampling frequency, and rainfall characteristics by means of a simple scaling law. Moreover, it is shown that both parametric and non-parametric statistical techniques of estimating the sampling uncertainty produce comparable results. Sampling uncertainty estimates, however, do depend on the choice of technique for obtaining them. They can also vary considerably from case to case, reflecting the great variability of natural rainfall, and should therefore be expressed in probabilistic terms. Rainfall calibration errors are shown to affect comparison of results obtained by studies based on data from different climate regions and/or observation platforms.

Lau, William K. M.↗

NASA NTRS2002

Optimal Strategies for Validating TRMM Rainfall Estimates with Rain-Gauge Data

In spite of all their problems, rain gauges measure rainfall in such a direct way when compared with other methods of estimating rainfall that comparing their totals to satellite estimates remains an essential tool in the validation of satellite products. Some disagreement between averages of satellite data and rain-gauge data is expected because of the very different sampling patterns of the two systems--the satellite provides only occasional snapshots of large areas, whereas rain gauges provide continuous measurements over very small areas. A quantitative estimate of the expected level of disagreement due to the differences in sampling is needed in order for comparisons of the two kinds of averages to be informative. As part of an earlier effort to determine the sampling error of satellite averages, a space-time model for rainfall statistics was developed. Sets of parameter values for the model have been obtained for which the model statistics are found to agree quite well with those of radar data taken over the Atlantic during GATE and the western Pacific during TOGA COARE. Although the model was intended to represent the statistics of relatively large scale fluctuations of rain, it is surprisingly consistent with the very different scales on which rain gauges observe. Implications for maximizing the benefit in comparisons of rain gauge averages and TRMM estimates are derived using the model predictions.

Bell, Thomas L.↗

NASA NTRS2002

SPECIAL SESSION: (H21) on Global Precipitation Mission for Hydrology and Hydrometeorology. Sampling-Error Considerations for GPM-Era Rainfall Products

The proposed Global Precipitation Mission (GPM) builds on the success of the Tropical Rainfall Measuring Mission (TRMM), offering a constellation of microwave-sensor-equipped smaller satellites in addition to a larger, multiply-instrumented "mother" satellite that will include an improved precipitation radar system to which the precipitation estimates of the smaller satellites can be tuned. Coverage by the satellites will be nearly global rather than being confined as TRMM was to lower latitudes. It is hoped that the satellite constellation can provide observations at most places on the earth at least once every three hours, though practical considerations may force some compromises. The GPM system offers the possibility of providing precipitation maps with much better time resolution than the monthly averages around which TRMM was planned, and therefore opens up new possibilities for hydrology and data assimilation into models. In this talk, methods that were developed for estimating sampling error in the rainfall averages that TRMM is providing will be used to estimate sampling error levels for GPM-era configurations. Possible impacts on GPM products of compromises in the sampling frequency will be discussed.

Bell, Thomas L.↗

NASA NTRS2002

Estimation of Rainfall Sampling Uncertainty: A Comparison of Two Diverse Approaches

The spatial and temporal intermittence of rainfall causes the averages of satellite observations of rain rate to differ from the "true" average rain rate over any given area and time period, even if the satellite observations are perfectly accurate. The difference of satellite averages based on occasional observation by satellite systems and the continuous-time average of rain rate is referred to as sampling error. In this study, rms sampling error estimates are obtained for average rain rates over boxes 100 km, 200 km, and 500 km on a side, for averaging periods of 1 day, 5 days, and 30 days. The study uses a multi-year, merged radar data product provided by Weather Services International Corp. at a resolution of 2 km in space and 15 min in time, over an area of the central U.S. extending from 35N to 45N in latitude and 100W to 80W in longitude. The intervals between satellite observations are assumed to be equal, and similar In size to what present and future satellite systems are able to provide (from 1 h to 12 h). The sampling error estimates are obtained using a resampling method called "resampling by shifts," and are compared to sampling error estimates proposed by Bell based on earlier work by Laughlin. The resampling estimates are found to scale with areal size and time period as the theory predicts. The dependence on average rain rate and time interval between observations is also similar to what the simple theory suggests.

Steiner, Matthias↗