Estimation of liquid water path below the melting layer in stratiform precipitation systems using radar measurements during MC3E

In this study, the liquid water path (LWP) below the melting layer in stratiform precipitation systems is retrieved, which is a combination of rain liquid water path (RLWP) and cloud liquid water path (CLWP). The retrieval algorithm uses measurements from the vertically pointing radars (VPRs) at 35 and 3 GHz operated by the US Department of Energy Atmospheric Radiation Measurement (ARM) and National Oceanic and Atmospheric Administration (NOAA) during the field campaign Midlatitude Continental Convective Clouds Experiment (MC3E). The measured radar reflectivity and mean Doppler velocity from both VPRs and spectrum width from the 35 GHz radar are utilized. With the aid of the cloud base detected by a ceilometer, the LWP in the liquid layer is retrieved under two different situations: (I) no cloud exists below the melting base, and (II) cloud exists below the melting base. In (I), LWP is primarily contributed from raindrops only, i.e., RLWP, which is estimated by analyzing the Doppler velocity differences between two VPRs. In (II), cloud particles and raindrops coexist below the melting base. The CLWP is estimated using a modified attenuation-based algorithm. Two stratiform precipitation cases (20 and 11 May 2011) during MC3E are illustrated for two situations, respectively. With a total of 13 h of samples during MC3E, statistical results show that the occurrence of cloud particles below the melting base is low (9 %); however, the mean CLWP value can be up to 0.56 kgm(2), which is much larger than the RLWP (0.10 kgm(2)). When only raindrops exist below the melting base, the average RLWP value is larger (0.32 kgm(2)) than the with-cloud situation. The overall mean LWP below the melting base is 0.34 kgm(2) for stratiform systems during MC3E.DOE CMDV project [DE-SC0017015]; NASA CERES project [NNX17AC52G]; DOE ASR project [DE-SC0014294]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Retrieval of vertical air motion in precipitating clouds using Mie scattering and comparison with in situ measurements

The article of record as published may be located at http://dx.doi.org/10.1175/JAMC-D-16-0158.1For the first time, the Mie notch retrieval technique is applied to airborne cloud Doppler radar observations in warm precipitating clouds to retrieve the vertical air velocity profile above the aircraft. The retrieval algorithm prescribed here accounts for two major sources of bias: aircraft motion and horizontal wind. The retrieval methodology is evaluated using the aircraft in situ vertical air velocity measurements. The standard deviations of the residuals for the retrieved and in situ measured data for an 18-s time segment are 0.21 and 0.24 m s−1, respectively; the mean difference between the two is 0.01 m s−1. For the studied cases, the total theoretical uncertainty is less than 0.19 m s−1 and the actual retrieval uncertainty is about 0.1 m s−1. These results demonstrate that the Mie notch technique combined with the bias removal procedure described in this paper can successfully retrieve vertical air velocity from airborne radar observations with low spectral broadening due to Doppler fading, which enables new opportunities in cloud and precipitation research. A separate spectral peak due to returns from the cloud droplets is also observed in the same radar Doppler spectra and is also used to retrieve vertical air motion. The vertical air velocities retrieved using the two different methods agree well with each other, and the correlation coefficient is as high as 0.996, which indicates that the spectral peak due to cloud droplets might provide another way to retrieve vertical air velocity in clouds when the Mie notch is not detected but the cloud droplets’ spectral peak is discernable.ONR N000140810465

Precipitation type classification of micro rain radar data using an improved doppler spectral processing methodology

This research was funded by the Spanish Government through projects CGL2015-65627-C3-1-R, CGL2015-65627-C3-2-R (MINECO/FEDER), CGL2016-81828-REDT and RTI2018-098693-B-C32 (AEI/FEDER).This paper describes a methodology for processing spectral raw data from Micro Rain Radar (MRR), a K-band vertically pointing Doppler radar designed to observe precipitation profiles. The objective is to provide a set of radar integral parameters and derived variables, including a precipitation type classification. The methodology first includes an improved noise level determination, peak signal detection and Doppler dealiasing, allowing us to consider the upward movements of precipitation particles. A second step computes for each of the height bin radar moments, such as equivalent reflectivity (Ze), average Doppler vertical speed (W), spectral width (σ), the skewness and kurtosis. A third step performs a precipitation type classification for each bin height, considering snow, drizzle, rain, hail, and mixed (rain and snow or graupel). For liquid precipitation types, additional variables are computed, such as liquid water content (LWC), rain rate (RR), or gamma distribution parameters, such as the liquid water content normalized intercept (Nw) or the mean mass-weighted raindrop diameter (Dm) to classify stratiform or convective rainfall regimes. The methodology is applied to data recorded at the Eastern Pyrenees mountains (NE Spain), first with a detailed case study where results are compared with different instruments and, finally, with a 32-day analysis where the hydrometeor classification is compared with co-located Parsivel disdrometer precipitation-type present weather observations. The hydrometeor classification is evaluated with contingency table scores, including Probability of Detection (POD), False Alarm Rate (FAR), and Odds Ratio Skill Score (ORSS). The results indicate a very good capacity of Method3 to distinguish rainfall and snow (PODs equal or greater than 0.97), satisfactory results for mixed and drizzle (PODs of 0.79 and 0.69) and acceptable for a reduced number of hail cases (0.55), with relatively low rate of false alarms and good skill compared to random chance in all cases (FAR 0.70). The methodology is available as a Python language program called RaProM at the public github repository

Cloud and Precipitation Observed with Radar

Meteorological radar is an essential tool for research, diagnosis, and nowcasting of clouds and precipitation. Cloud radars use short wavelengths to enable detection of small ice particles or cloud droplets. The cloud radar at UFS Schneefernerhaus is operated since end of 2011. It has been used for a number of studies related to clouds and precipitation. In a synergistic combination with additional remote sensing instruments, a large variety of cloud and precipitation properties can be retrieved. The measurements at UFS Schneefernerhaus can be used for the evaluation of numerical weather prediction models and satellite measurements. The long-term observations allow assessing the seasonal and long-term evolution of cloud properties above the UFS in a warming climate

Vertical structure and kinematics of tropical monsoon precipitation observed from a 2875-MHz profiler during NAME

Fall 2006.Includes bibliographical references (pages 100-105).Deep cloud systems in the Tropics play a significant role in the global heat budget. This is due to the fact that atmospheric circulations, such as the Hadley and Walker cells, are sensitive to the shape of the diabatic heating profile, which in turn depends on the vertical structure of tropical convective systems. The goal of this project is to create a climatology of the vertical structure of precipitating cloud systems that characterized the 2004 North American monsoon. The study utilized data from the 2875-MHz profiler stationed near Sinaloa, Mexico from early July through mid-September of 2004 for the North American Monsoon Experiment (NAME). The profiler observed 23 rain events. Climatologic frequency distributions of reflectivity, Doppler velocity, and spectral width were created for various precipitation regimes. The NAME distributions compared favorably with results from previous studies. Stratiform precipitation exhibited a radar bright band and a strong Doppler velocity gradient in the melting layer, and weak spectral width above the melting layer. Mixed stratiform/convective regions contained low reflectivity and a weak bright band. Convective profiles contained high reflectivity, large Doppler velocities, and high spectral width. Vertical air motions derived from the 2875-MHz profiler were compared with EVAD and 449-MHz profiler retrievals. The 2875-MHz pro filer vertical air motion estimates contained a negative bias to both methods of approximately 0.5 m s-1. Though the errors in the stratiform vertical air motion estimates were of the same order as the stratiform air motions, the NAME vertical air motion composites for stratiform and mixed stratiform/convective precipitation exhibited similar features to composites from previous studies. However, convective composites from past studies showed ascent throughout the troposphere while the NAME composite showed a significant region of descent between 4 and 6 km. This discrepancy cannot be fully explained by the negative bias of 0.5 m s-1 in the NAME estimates. Climatologic vertical profiles of precipitating clouds were successfully created from the 2875-MHz pro filer NAME dataset for various precipitation regimes. While the vertical air motion estimates yielded unexpected values in the melting layer of convective precipitation, they proved useful in analyzing the vertical structure of vertical air motion for various precipitation regimes in a mean sense as well as assessing general updraft and downdraft intensity in individual convective cells

Investigating aggregation in ice and snow clouds using novel combination of triple-frequency cloud radars and radar Doppler spectra

Clouds are essential component of the hydrological cycle for transporting of water and distributing precipitation at different parts of the planet. On a global scale, around 63% of the precipitation originates via ice phase. Different ice microphysical processes can lead to growth (e.g. deposition, aggregation, riming) or reduction (e.g. sublimation, breakup) of ice particle sizes. Aggregation, in particular, rapidly increases ice particles sizes and continuously changes the particle size distribution. However, aggregation and other ice microphysical processes are not fully understood. In order to improve the current knowledge about aggregation and the other microphysical processes, microwave radars are used to observe clouds due to their capability of retrieving information through the different parts of the clouds. Additionally, if Doppler radars operating at different frequencies (multi-frequency setup) are used to observe the same region of clouds, the multi-frequency observations can be used to retrieve information of particles sizes and velocities. This thesis uses multi-frequency Doppler observations (6 months) to investigate scenarios that intensify aggregation and the impact of increasing aggregate sizes on raindrop sizes. To this end, a multi-frequency data processing framework is introduced to minimize the effect of attenuation (e.g. atmospheric gases, snow, wet radome) and radar miscalibration; it also assigns a set of quality flags to the different correction steps. The statistical analysis from the observations of the ice part of the clouds indicates that aggregation is intensified in two temperature regions. The first region is between -20 and -10 °C and coincides with the dendritic growth zone (DGZ). Dendritic crystals can favour aggregation due to their branched structure. In addition to the growth of dendrites, the statistical results suggest that an intensification of aggregation in this temperature region correlates with an intensification of updrafts (up to 0.3 m/s). The statistics also show that approximately 25% of the cases where aggregation intensifies an additional mode of small and slow falling particles are present. The temperature region between -10 and 0 °C coincides with the region where the stickiness of ice surfaces increases due to the effect of a quasi liquid layer on the ice surface. Due to this increased stickiness, aggregation intensifies towards the 0 °C isotherm. The statistical results indicate that the growth of large aggregates in the DGZ favour, but it is not sufficient to guarantee the presence of even larger aggregates close to 0 °C. The results also indicate that an increase in aggregate sizes close to the 0 °C correlates with an increase in raindrop sizes. The multi-frequency processing framework and the other filtering processes introduced in this thesis can be used as the foundations for future multi-frequency experiments. The highly qualified multi-frequency dataset and the statistical results from this thesis can be used to evaluate the ice microphysical processes implemented in numerical models

The multi-parameter remote measurement of rainfall

The measurement of rainfall by remote sensors is investigated. One parameter radar rainfall measurement is limited because both reflectivity and rain rate are dependent on at least two parameters of the drop size distribution (DSD), i.e., representative raindrop size and number concentration. A generalized rain parameter diagram is developed which includes a third distribution parameter, the breadth of the DSD, to better specify rain rate and all possible remote variables. Simulations show the improvement in accuracy attainable through the use of combinations of two and three remote measurables. The spectrum of remote measurables is reviewed. These include path integrated techniques of radiometry and of microwave and optical attenuation