Abstract

The existence of an annual variation in height and temperature of the tropopause over tropical regions has long been recognized, but has not been fully explained. In this paper it is proposed that the variation is a fairly direct response to the annual variation in average tropical surface insolation. The variation in insolation causes a corresponding annual cycle in average tropical sea surface temperature with a total range of order 1 K. The consequent variation in absolute humidity in turn produces an annual variation in upper tropospheric potential temperatures, and hence in the height and temperature of the tropopause.

The physical link between the surface and the tropopause is provided by convection in the cores of the giant cumulonimbus clouds (hot towers) of the tropical oceanic regions, in which air parcels can achieve the maximum possible heating by release of latent heat. The process is modeled quantitatively in a simplified way, and excellent agreement is found between the predicted and observed phase and amplitude of the annual variation in tropopause potential temperature.

Since the regular seasonal variation in insolation is relatively small in the tropics, the annual variation in sun-earth distance is an important factor in the variation of surface insolation. The annual cycle in the properties of the tropical tropopause thus provides the first identifiable effect of the earth's orbital eccentricity on climate parameters.

Abstract

An algorithm has been developed that classifies precipitating clouds into either stratiform, mixed stratiform/convective, deep convective, or shallow convective clouds by analyzing the vertical structure of reflectivity, velocity, and spectral width derived from measurements made with the vertical beam of a 915-MHz Doppler wind profiler. The precipitating clouds classified as stratiform and convective clouds match the physical and radar properties deduced by Doppler weather radars in the GATE and EMEX programs. The mixed stratiform/convective cloud category is a hybrid regime containing a melting-layer signature associated with stratiform clouds yet is turbulent above the melting level similar to convective clouds. Shallow convective clouds have hydrometeors confined entirely below the melting level implying that warm rain processes are occurring exclusively. The algorithm is illustrated by classifying precipitating clouds from 10 months of observations at Manus Island (2°S, 147°E) in the western Pacific. The sensitivity of the algorithm to threshold criteria is investigated using the Manus Island data.

Abstract

This paper describes a method of absolutely calibrating and routinely monitoring the reflectivity calibration from a scanning weather radar using a vertically profiling radar that has been absolutely calibrated using a collocated surface disdrometer. The three instruments have different temporal and spatial resolutions, and the concept of upscaling is used to relate the small resolution volume disdrometer observations with the large resolution volume scanning radar observations. This study uses observations collected from a surface disdrometer, two profiling radars, and the National Weather Service (NWS) Weather Surveillance Radar-1988 Doppler (WSR-88D) scanning weather radar during the Texas–Florida Underflight-phase B (TEFLUN-B) ground validation field campaign held in central Florida during August and September 1998.

The statistics from the 2062 matched profiling and scanning radar observations during this 2-month period indicate that the WSR-88D radar had a reflectivity 0.7 dBZ higher than the disdrometer-calibrated profiler, the standard deviation was 2.4 dBZ, and the 95% confidence interval was 0.1 dBZ. This study implies that although there is large variability between individual matched observations, the precision of a series of observations is good, allowing meaningful comparisons useful for calibration and monitoring.

Abstract

VHF wind profiler measurements of zonal and meridional winds are compared with the NCEP–NCAR reanalysis at sites in the tropical Pacific. By December 1999 the profilers at Darwin, Australia, and Biak, Indonesia, in the western Pacific; Christmas Island, Kiribati, in the central Pacific; and Piura Peru, in the eastern Pacific had collected between 8 and 13 yr of nearly continuous data. While these profilers routinely observe winds up to about 20 km, only winds at Christmas Island are assimilated into the reanalysis. The long period of profiler operation provides an opportunity to study differences between the profiler and reanalysis winds in the equatorial Pacific, a region with geographically sparse observations. Mean and seasonal mean zonal and meridional winds are used to identify differences in the profiler and reanalysis winds. Two potential causes for the discrepancy between profiler and reanalysis winds are identified. The first of these is related to different spatial and temporal characteristics of the reanalysis and profiler data. The second cause is the geographical sparseness of rawinsonde data, and not assimilating wind profiler observations. The closest agreement between the mean and seasonal mean zonal winds was found at Christmas Island, a site at which profiler winds are assimilated. A good agreement between reanalysis and profiler meridional and zonal winds is also shown at Darwin, where nearby rawinsonde observations are available. The poorest agreement was found at Piura (where profiler winds are not assimilated), the closest rawinsonde is almost 2000 km from the profiler site, and topography is not adequately resolved in the reanalysis.

Abstract

The motivation for this research is to move in the direction of improved algorithms for the remote sensing of rainfall, which are crucial for meso- and large-scale circulation studies and climate applications through better determinations of precipitation type and latent heating profiles. Toward this end a comparison between two independent techniques, designed to classify precipitation type from 1) a disdrometer and 2) a 915-MHz wind profiler, is presented, based on simultaneous measurements collected at the same site during the Intensive Observing Period of the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. Disdrometer-derived quantities such as differences in drop size distribution parameters, particularly the intercept parameter N ₀ and rainfall rate, were used to classify rainfall as stratiform or convective. At the same time, profiler-derived quantities, namely, Doppler velocity, equivalent reflectivity, and spectral width, from Doppler spectra were used to classify precipitation type in four categories: shallow convective, deep convective, mixed convective–stratiform, and stratiform.

Overall agreement between the two algorithms is found to be reasonable. Given the disdrometer stratiform classification, the mean profile of reflectivity shows a distinct bright band and associated large vertical gradient in Doppler velocity, both indicators of stratiform rain. For the disdrometer convective classification the mean profile of reflectivity lacks a bright band, while the vertical gradient in Doppler velocity below the melting level is opposite to the stratiform case. Given the profiler classifications, in the order shallow–deep–mixed–stratiform, the composite raindrop spectra for a rainfall rate of 5 mm h⁻¹ show an increase in D ₀, the median volume diameter, consistent with the dominant microphysical processes responsible for drop formation. Nevertheless, the intercomparison does reveal some limitations in the classification methodology utilizing the disdrometer or profiler algorithms in isolation. In particular, 1) the disdrometer stratiform classification includes individual cases in which the vertical profiles appear convective, but these usually occur at times when the disdrometer classification is highly variable; 2) the profiler classification scheme also appears to classify precipitation too frequently as stratiform by including cases that have small vertical Doppler velocity gradients at the melting level but no bright band; and 3) the profiler classification scheme includes a category of mixed (stratiform–convective) precipitation that has some features in common with deep convection (e.g., enhanced spectral width above the melting level) but other features in common with stratiform precipitation (e.g., well-developed melting layer signature). Comparison of the profiler-derived vertical structure with disdrometer-determined rain rates reveals that almost all cases of rain rates greater than 10 mm h⁻¹ are convective. For rain rates less than 5 mm h⁻¹ all four profiler-determined precipitation classes are well represented.

Abstract

Multifrequency radar measurements collected at 2.8 (S band), 33.12 (Ka band), and 94.92 GHz (W band) are processed using a neural network to estimate median particle size and peak number concentration in ice-phase clouds composed of dry crystals or aggregates. The model data used to train the neural network assume a gamma particle size distribution function and a size–density relationship having decreasing density with size. Results for the available frequency combinations show sensitivity to particle size for distributions with median volume diameters greater than approximately 0.2 mm.

Measurements are presented from the Maritime Continent Thunderstorm Experiment, which was held near Darwin, Australia, during November and December 1995. The University of Massachusetts—Amherst 33.12/94.92-GHz Cloud Profiling Radar System, the NOAA 2.8-GHz profiler, and other sensors were clustered near the village of Garden Point, Melville Island, where numerous convective storms were observed. Attenuation losses by the NOAA radar signal are small over the pathlengths considered so the cloud-top reflectivity values at 2.8 GHz are used to remove propagation path losses from the higher-frequency measurements. The 2.8-GHz measurements also permit estimation of larger particle diameters than is possible using only 33.12 and 94.92 GHz. The results suggest that the median particle size tends to decrease with height for stratiform cloud cases. However, this trend is not observed for convective cloud cases where measurements indicate that large particles can exist even near the cloud top.

Abstract

A 2835-MHz (10.6-cm wavelength) profiler and a 920-MHz (32.6-cm wavelength) profiler were collocated by the NOAA Aeronomy Laboratory at Garden Point, Australia, in the Tiwi Islands during the Maritime Continent Thunderstorm Experiment (MCTEX) field campaign in November and December 1995. The two profilers were directed vertically and observed vertical velocities in the clear atmosphere and hydrometeor fall velocities in deep precipitating cloud systems. In the absence of Rayleigh scatterers, the profilers obtain backscattering from the refractive index irregularities created from atmospheric turbulence acting upon refractive index gradients. This kind of scattering is commonly referred to as Bragg scattering and is only weakly dependent on the radar wavelength provided the radar half-wavelength lies within the inertial subrange of homogeneous, isotropic turbulence. In the presence of hydrometeors the profilers observe Rayleigh backscattering from hydrometeors much as weather radars do and this backscatter is very dependent upon radar wavelength, strongly favoring the shorter wavelength profiler resulting in a 20-dB enhancement of the ability of the 2835-MHz profiler to observe hydrometeors. This paper presents observations of equivalent reflectivity, Doppler velocity, and spectral width made by the collocated profilers during MCTEX. Differential reflectivity is used to diagnose the type of echo observed by the profilers in the spectral moment data. When precipitation or other particulate backscatter is dominant, the equivalent reflectivities are essentially the same for both profilers. When Bragg scattering is the dominant process, equivalent reflectivity observed by the 1-GHz profiler exceeds the equivalent reflectivity observed by the 3-GHz profiler by approximately 18 dBZe. However, when the 3-GHz profiler half-wavelength is smaller than the inner scale of turbulence, the equivalent reflectivity difference exceeds 18 dBZe, and when both Rayleigh scattering and Bragg scattering are observed simultaneously, the equivalent reflectivity difference is less than 18 dBZe. The results obtained confirm the capability of two collocated profilers to unambiguously identify the type of echo being observed and hence enable the segregation of “clear air” and precipitation echoes for studies of atmospheric dynamics and precipitating cloud systems.

Abstract

A 3-GHz profiler has been developed by the National Oceanic and Atmospheric Administration’s Aeronomy Laboratory to observe the evolution and vertical structure of precipitating cloud systems. The profiler is very portable, robust, and relatively inexpensive, so that continuous, unattended observations of overhead precipitation can be obtained, even at remote locations. The new profiler is a vertically looking Doppler radar that operates at S band, a commonly used band for scanning weather radars (e.g., WSR-88D). The profiler has many features in common with the 915-MHz profiler developed at the Aeronomy Laboratory during the past decade primarily for measurement of lower-tropospheric winds in the Tropics. This paper presents a description of the new profiler and evaluates it in the field in Illinois and Australia in comparison with UHF lower-tropospheric profilers. In Illinois, the new profiler was evaluated alongside a collocated 915-MHz profiler at the Flatland Atmospheric Observatory. In Australia it was evaluated alongside a 920-MHz profiler during the Maritime Continent Thunderstorm Experiment. The results from these campaigns confirm the approximate 20-dB improvement in sensitivity, as expected for Rayleigh scatter. The results show that the new profiler provides a substantial improvement in the ability to observe deep cloud systems in comparison with the 915-MHz profilers.

Abstract

Doppler radar profilers are widely used for routine measurement of wind, especially in the lower troposphere. The same profilers with minor modifications are useful tools for precipitation research. Specifically, the profilers are now increasingly being used to explore the structure of precipitating cloud systems and to provide calibration and validation of other instruments used in precipitation research, including scanning radars and active and passive satellite-borne sensors. A vertically directed profiler is capable of resolving the vertical structure of precipitating cloud systems that pass overhead. Standard profiler measurements include reflectivity, reflectivity-weighted Doppler velocity, and spectral width. This paper presents profiler observations of precipitating cloud systems observed during Tropical Rainfall Measuring Mission (TRMM) Ground Validation field campaigns. The observations show similarities and differences between convective systems observed in Florida; Brazil; and Kwajalein, Republic of the Marshall Islands. In addition, it is shown how a profiler can be calibrated using a collocated Joss–Waldvogel disdrometer, how the profiler can then be used to calibrate a scanning radar, and how the profiler may be used to retrieve drop size distributions.

Abstract

Comparisons of data taken by collocated Doppler wind profilers using 100-, 500-, and 1000-m pulse lengths show that the velocity profiles obtained with the longer pulses are displaced in height from contemporaneous profiles measured with the shorter pulses. These differences are larger than can be expected from random measurement errors. In addition, there is evidence that the 500-m pulse may underestimate the wind speed when compared with the 100-m pulse.

The standard radar equation does not adequately account for the conditions under which observations are made. In particular, it assumes that atmospheric reflectivity is constant throughout the pulse volume and that observations can be assigned to the peak of the range-weighting function. However, observations from several tropical profilers show that reflectivity gradients with magnitudes greater than 10 dB km⁻¹ are common. Here, a more general radar equation is used to simulate the radar response to the atmosphere. The simulation shows that atmospheric reflectivity gradients cause errors in the range placement. Observed reflectivity gradients can be used to calculate a correction to the range location of the observations that helps to reduce these errors.

Examples of these errors and the application of the correction to selected cases are shown. The evidence presented shows that reflectivity gradients are the main cause of the pervasive differences observed between the different radar observations.