Comparisons of precipitation measurements by the Advanced Microwave Precipitation Radiometer and multiparameter radar

Includes bibliographical references.Multiparameter microwave radar measurements are based on dual-polarization and dual-frequency techniques and are well suited for microphysical inferences of complex precipitating clouds, since they depend upon the size, shape, composition, and orientation of a collection of discrete random scatterers. Passive microwave radiometer observations represent path integrated scattering and absorption phenomena of the same scatterers. The response of the upwelling brightness temperatures TB to the precipitation structure depends on the vertical distribution of the various hydrometeors and gases, and the surface features. As a result, combinations of both active and passive techniques contain great potential to markedly improve the longstanding issue of precipitation measurement from space. The NASA airborne Advanced Microwave Precipitation Radiometer (AMPR) and the National Center for Atmospheric Research (NCAR) CP-2 multiparameter radar were jointly operated during the 1991 Convection and Precipitation/Electrification experiment (CaPE) in central Florida. The AMPR is a four channel, high resolution, across-track scanning total power radiometer system using the identical multifrequency feedhorn as the widely utilized Special Sensor Microwave/Imager (SSM/I) satellite system. Surface and precipitation features are separable based on the TB behavior as a function of the AMPR channels. The radar observations are presented in a remapped format suitable for comparison with the multifrequency AMPR imagery. Striking resemblances are noted between the AMPR imagery and the radar reflectivity at successive heights, while vertical profiles of the CP-2 products along the nadir trace suggest a storm structure consistent with the viewed AMPR TB. Directly over the storm cores, the difference between the 37 and 85 GHz TB was noted to approach (and in some cases fall below) zero. Microwave radiative transfer computations show that this is theoretically possible for hail regions suspended aloft in the core of strong convective storms.This work was supported by the NASA Earth Science and Applications Division under Grant NAG8-890. The National Center for Atmospheric Research is sponsored by the National Science Foundation

Stratiform and convective rain classification using machine learning models and micro rain radar

Rain type classification into convective and stratiform is an essential step required to improve quantitative precipitation estimations by remote sensing instruments. Previous studies with Micro Rain Radar (MRR) measurements and subjective rules have been performed to classify rain events. However, automating this process by using machine learning (ML) models provides the advantages of fast and reliable classification with the possibility to classify rain minute by minute. A total of 20,979 min of rain data measured by an MRR at Das in northeast Spain were used to build seven types of ML models for stratiform and convective rain type classification. The proposed classification models use a set of 22 parameters that summarize the reflectivity, the Doppler velocity, and the spectral width (SW) above and below the so-called separation level (SL). This level is defined as the level with the highest increase in Doppler velocity and corresponds with the bright band in stratiform rain. A pre-classification of the rain type for each minute based on the rain microstructure provided by the collocated disdrometer was performed. Our results indicate that complex ML models, particularly tree-based ensembles such as xgboost and random forest which capture the interactions of different features, perform better than simpler models. Applying methods from the field of interpretable ML, we identified reflectivity at the lowest layer and the average spectral width in the layers below SL as the most important features. High reflectivity and low SW values indicate a higher probability of convective rainPostprint (published version

Modeled And Observed Dynamical Characteristics Of Convective Mass Transport

Convection can rapidly and efficiently transport polluted boundary layer air to the upper troposphere and lower stratosphere, thereby influencing the chemical composition and distribution of greenhouse gases in the atmosphere. Whether mass detrains into the upper troposphere or lower stratosphere has differing impacts on the radiative budget and hence, on climate. Currently, there have been only a few observing platforms capable of studying convective mass transport, which have significant limitations and are frequently restricted to field campaigns resulting in a small number of case studies. Outside of these case studies, little is known about the actual heights that convection detrains mass to or how much dilution a parcel rising in the updraft experiences due to processes such as entrainment. Entrainment not only reduces updraft buoyancy resulting in lower mass detrainment altitudes, but also dilutes updrafts that may be vertically transporting polluted boundary layer air, changing the chemistry of the detrained air aloft. To account for many of the limitations in observations, model simulations are commonly utilized; however, these models are unconstrained and need to correctly depict both the chemistry and dynamics. To improve our understanding of convective mass transport and help constrain model simulations, this study focuses on 1) identifying whether convection-allowing models can accurately depict the dynamics of mass transport, 2) building a large database of observed convective detrainment heights to determine the heights that convection detrains mass to, and 3) developing a methodology to retrieve observed fractional entrainment rates for deep convection that can be used to determine how much dilution is experienced by rising parcels. These three objectives were researched as follows. First, biases within high-resolution convection-allowing model forecasts were identified with focus on the vertical structure and depth of deep moist convection. The object-based validation revealed that while the models performed well near the surface, there were large biases aloft. Overall, model forecasts generated too many convective elements that were individually too large and contained convection that reached the mid-troposphere twice as often as observations, leading to an over-estimation of the amount of mass being transported. Second, to determine the heights that convection actually detrains mass to, a large observational database of convective detrainment heights for the midlatitudes was built using ground-based radar observations. A newly developed radar echo stratification scheme was combined with high-resolution radar composites and an anvil-proxy methodology to retrieve the level of maximum detrainment (LMD) for convection across seven years for the months of May and July. Results showed that on average the LMD height was around 4.3 km below the tropopause, but can be as high as 2 km above the tropopause, with at least some mass transport occurring up to 6 km above the tropopause. May storms had a slightly higher mean tropopause-relative LMD height but July contained storms with the deepest transport. An analysis focusing on morphology found that quasi-isolated strong convection had higher LMD heights than mesoscale convective systems, with the highest LMD heights belonging to supercells. When subset by region, the southern regions were found to have lower mean LMD heights due to a large amount of diurnally-driven convection. Third and finally, to better understand why storms detrain mass to certain altitudes and to investigate the dilution of parcels with updrafts, a buoyancy-based methodology was developed that builds upon and constrains plume theory with observations. The methodology works on the principles of comparing the buoyancy of an ideal parcel to that of a mixed parcel with attributes derived from observations of vertical velocity and environmental temperature and moisture. The method was applied to a case of weaker, mid-level convection and a case of a deep convective cluster. The deep convective cluster was found to have mean fractional entrainment rates of around 0.26 km-1, which was about half of the mean rate found for the weaker, mid-level convective cell. The entrainment results also illustrated the importance of accounting for processes such as hydrometeor drag and the ice phase within the rising plume. Overall, this study demonstrates the importance of including vertical information in analysis of both models and observations. The identified model biases in convective structure showcase where the convection-allowing models still need improvement and can be used to investigate where biases in precipitation fields originate. The LMD height retrievals depict the heights of mass detrainment and can be used to constrain chemical transport models in order to get more accurate approximations of transport heights for radiative and climate models. The statistical distribution of detrainment heights can also be used to estimate the amount of mass transport that occurs into the troposphere and stratosphere. The entrainment retrieval methodology can be applied to several observational datasets to retrieve fractional entrainment rates for convection of various morphologies and depths as long as vertical velocity and environmental temperature and moisture information is present. By incorporating observations, the entrainment rate retrievals can be used to constrain cumulus parameterization and idealized parcel models. Furthermore, the LMD and detrainment envelope retrievals can be merged with the entrainment retrieval methodology to determine how much dilution parcels experience before being detrained. Lastly, further study is required to investigate why supercells detrain mass to higher altitudes than other forms of convection

Development of a polarimetric radar based hydrometeor classification algorithm for winter precipitation

2012 Fall.Includes bibliographical references.The nation-wide WSR-88D radar network is currently being upgraded for dual-polarized technology. While many convective, warm-season fuzzy-logic hydrometeor classification algorithms based on this new suite of radar variables and temperature have been refined, less progress has been made thus far in developing hydrometeor classification algorithms for winter precipitation. Unlike previous studies, the focus of this work is to exploit the discriminatory power of polarimetric variables to distinguish the most common precipitation types found in winter storms without the use of temperature as an additional variable. For the first time, detailed electromagnetic scattering of plates, dendrites, dry aggregated snowflakes, rain, freezing rain, and sleet are conducted at X-, C-, and S-band wavelengths. These physics-based results are used to determine the characteristic radar variable ranges associated with each precipitation type. A variable weighting system was also implemented in the algorithm's decision process to capitalize on the strengths of specific dual-polarimetric variables to discriminate between certain classes of hydrometeors, such as wet snow to indicate the melting layer. This algorithm was tested on observations during three different winter storms in Colorado and Oklahoma with the dual-wavelength X- and S-band CSU-CHILL, C-band OU-PRIME, and X-band CASA IP1 polarimetric radars. The algorithm showed success at all three frequencies, but was slightly more reliable at X-band because of the algorithm's strong dependence on specific differential phase. While plates were rarely distinguished from dendrites, the latter were satisfactorily differentiated from dry aggregated snowflakes and wet snow. Sleet and freezing rain could not be distinguished from rain or light rain based on polarimetric variables alone. However, high-resolution radar observations illustrated the refreezing process of raindrops into ice pellets, which has been documented before but not yet explained. Persistent, robust patterns of decreased correlation coefficient, enhanced differential reflectivity, and an inflection point around enhanced reflectivity occurred over the exact depth of the surface cold layer indicated by atmospheric soundings during times when sleet was reported at the surface. It is hypothesized that this refreezing signature is produced by a modulation of the drop size distribution such that smaller drops preferentially freeze into ice pellets first. The melting layer detection algorithm and fall speed spectra from vertically pointing radar also captured meaningful trends in the melting layer depth, height, and mean correlation coefficient during this transition from freezing rain to sleet at the surface. These findings demonstrate that this new radar-based winter hydrometeor classification algorithm is applicable for both research and operational sectors

First observations of polarized scattering over ice clouds at close-to-millimeter wavelengths (157 GHz) with MADRAS on board the Megha-Tropiques mission

Polarized scattering by frozen hydrometeors is investigated for the first time up to 157 GHz, based on the passive microwave observations of the Microwave Analysis and Detection of Rain and Atmospheric Structures (MADRAS) instrument on board the Indo-French Megha-Tropiques satellite mission. A comparison with time-coincident Tropical Rainfall Measurement Mission Microwave Imager records confirms the consistency of the coincident observations collected independently by the two instruments up to 89 GHz. The MADRAS noise levels of 1.2 K at 89 GHz and of 2.5 K at 157 GHz are in agreement with the required specifications of the mission. Compared to the 89 GHz polarized channels that mainly sense large ice particles (snow and graupel), the 157 GHz polarized channel is sensitive to smaller particles and provides additional information on the cloud systems. The analysis of the radiometric signal at 157 GHz reveals that the ice scattering can induce a polarization difference of the order of 10 K at that frequency. Based on radiative transfer modeling the specific signature is interpreted as the effect of mainly horizontally oriented ice cloud particles. This suggests that the effects of the cloud particle orientation should be considered in rain and cloud retrievals using passive radiometry at microwave and millimeter wavelengths.Fil: Defer, Eric. Centre National de la Recherche Scientifique. Observatoire de Paris; FranciaFil: Galligani, Victoria Sol. Centre National de la Recherche Scientifique. Observatoire de Paris; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Prigent, Catherine. Centre National de la Recherche Scientifique. Observatoire de Paris; FranciaFil: Jimenez, Carlos. Centre National de la Recherche Scientifique. Observatoire de Paris; Franci

Quantitative precipitation estimates from dual-polarization weather radar in lazio region

Many phenomena (such as attenuation and range degradation) can influence the accuracy of rainfall radar estimates. They introduce errors that increase as the distance from the radar increases, thereby decreasing the reliability of radar estimates for applications that require quantitative precipitation estimation. The aim of the present work is to develop a range dependent error model called adjustment factor, that can be used as a range error pattern for allowing to correct the mean error which affects long-term quantitative precipitation estimates. A range dependent gauge adjustment technique was applied in combination with other processing of radar data in order to correct the range dependent error affecting radar measurements. Issues like beam blocking, path attenuation, vertical structure of precipitation related error, bright band, and incorrect Z-R relationship are implicitly treated with this type of method. In order to develop the adjustment factor, radar error was determined with respect to rain gauges measurements through a comparison between the two devices, based on the assumption that gauge rain was real. Therefore, the G/R ratio between the yearly rainfall amount measured in each rain gauge position during 2008 and the corresponding radar rainfall amount was calculated against the distance from radar. Trend of the G/R ratio shows two behaviors: a concave part due to the melting layer effect close to the radar location, and an almost linear increasing trend at greater distance. Then, a linear best fitting was used to find an adjustment factor, which estimates the radar error at a given range. The effectiveness of the methodology was verified by comparing pairs of rainfall time series that were observed simultaneously by collocated rain gauges and radar. Furthermore, the variability of the adjustment factor was investigated at the scale of event, both for convective and stratiform events. The main result is that there is not an univocal range error pattern, as it is also a function of the event characteristics. On the other hand, the adjustment factor tends to stabilize over long periods of observation as in the case of a whole year of measures

X-band dual-polarization radar-based hydrometeor classification for Brazilian tropical precipitation systems

The dominant hydrometeor types associated with Brazilian tropical precipitation systems are identified via research X-band dual-polarization radar deployed in the vicinity of the Manaus region (Amazonas) during both the GoAmazon2014/5 and ACRIDICON-CHUVA field experiments. The present study is based on an agglomerative hierarchical clustering (AHC) approach that makes use of dual polarimetric radar observables (reflectivity at horizontal polarization ZH, differential reflectivity ZDR, specific differential-phase KDP, and correlation coefficient ρHV) and temperature data inferred from sounding balloons. The sensitivity of the agglomerative clustering scheme for measuring the intercluster dissimilarities (linkage criterion) is evaluated through the wet-season dataset. Both the weighted and Ward linkages exhibit better abilities to retrieve cloud microphysical species, whereas clustering outputs associated with the centroid linkage are poorly defined. The AHC method is then applied to investigate the microphysical structure of both the wet and dry seasons. The stratiform regions are composed of five hydrometeor classes: drizzle, rain, wet snow, aggregates, and ice crystals, whereas convective echoes are generally associated with light rain, moderate rain, heavy rain, graupel, aggregates, and ice crystals. The main discrepancy between the wet and dry seasons is the presence of both low- and high-density graupel within convective regions, whereas the rainy period exhibits only one type of graupel. Finally, aggregate and ice crystal hydrometeors in the tropics are found to exhibit higher polarimetric values compared to those at midlatitudes.</p

Precipitation observations from high frequency spaceborne polarimetric synthetic aperture radar and ground-based radar: theory and model validation

2010 Fall.Includes bibliographical references.Global weather monitoring is a very useful tool to better understand the Earth's hydrological cycle and provide critical information for emergency and warning systems in severe cases. Developed countries have installed numerous ground-based radars for this purpose, but they obviously are not global in extent. To address this issue, the Tropical Rainfall Measurement Mission (TRMM) was launched in 1997 and has been quite successful. The follow-on Global Precipitation Measurement (GPM) mission will replace TRMM once it is launched. However, a single precipitation radar satellite is still limited, so it would be beneficial if additional existing satellite platforms can be used for meteorological purposes. Within the past few years, several X-band Synthetic Aperture Radar (SAR) satellites have been launched and more are planned. While the primary SAR application is surface monitoring, and they are heralded as "all weather'' systems, strong precipitation induces propagation and backscatter effects in the data. Thus, there exists a potential for weather monitoring using this technology. The process of extracting meteorological parameters from radar measurements is essentially an inversion problem that has been extensively studied for radars designed to estimate these parameters. Before attempting to solve the inverse problem for SAR data, however, the forward problem must be addressed to gain knowledge on exactly how precipitation impacts SAR imagery. This is accomplished by simulating storms in SAR data starting from real measurements of a storm by ground-based polarimetric radar. In addition, real storm observations by current SAR platforms are also quantitatively analyzed by comparison to theoretical results using simultaneous acquisitions by ground radars even in single polarization. For storm simulation, a novel approach is presented here using neural networks to accommodate the oscillations present when the particle scattering requires the Mie solution, i.e., particle diameter is close to the radar wavelength. The process of transforming the real ground measurements to spaceborne SAR is also described, and results are presented in detail. These results are then compared to real observations of storms acquired by the German TerraSAR-X satellite and by one of the Italian COSMO-SkyMed satellites both operating in co-polar mode (i.e., HH and VV). In the TerraSAR-X case, two horizontal polarization ground radars provided simultaneous observations, from which theoretical attenuation is derived assuming all rain hydrometeors. A C-band fully polarimetric ground radar simultaneously observed the storm captured by the COSMO-SkyMed SAR, providing a case to begin validating the simulation model. While previous research has identified the backscatter and attenuation effects of precipitation on X-band SAR imagery, and some have noted an impact on polarimetric observations, the research presented here is the first to quantify it in a holistic sense and demonstrate it using a detailed model of actual storms observed by ground radars. In addition to volumetric effects from precipitation, the land backscatter is altered when water is on or near the surface. This is explored using TRMM, Canada's RADARSAT-1 C-band SAR and Level 3 NEXRAD ground radar data. A weak correlation is determined, and further investigation is warranted. Options for future research are then proposed

Development and validation of an X-band dual polarization Doppler weather radar test node for a tropical network, The

2012 Fall.Includes bibliographical references.An automated network of three X-band dual polarization Doppler weather radars is in process of being deployed and operational on the western coast of Puerto Rico. Colorado State University and the University of Puerto Rico at Mayaguez have collaborated to install the first polarimetric weather radar network in a tropical environment, known as TropiNet, to observe the lowest 2 km of the troposphere where the National Weather Service NEXRAD radar in Cayey, PR (TJUA) has obstructed views of the west coast, below 1.5 km due to terrain blockage and the Earth curvature problem. The CSU-X25P radar test node was developed, validated, and deployed to Mayaguez, PR in early 2011 to make first observations of this tropical region, and served as a pilot project to verify the infrastructure of the TropiNet network. This research describes the CSU-X25P radar test node, presenting the radar system specifications and an overview of the data acquisition and signal processing sub-systems, and the antenna positioner and control sub-system. The development and validation process included integration, sub-system calibration and test, and a final evaluation by conducting end-to-end calibration of the radar system. Validation of the calculated data moments, include Doppler velocity, reflectivity, differential reflectivity, differential propagation phase, and specific differential phase. The validation was accomplished by comparative analysis of data from coordinated scans between CSU-X25P and the well-established CSU-CHILL S-band polarimetric Doppler weather radar, in Greeley, CO. Upon validation, CSU-X25P was disassembled, packaged, and shipped to Puerto Rico to be fully deployed for operation in a tropical seaside environment. This research presents select observations of severe weather events, such as tropical storms and hurricanes, which attest to the robustness of the radar test node, and the TropiNet network infrastructure