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1
Bae, Jeong-Ho, and Ki-Hong Min. "Forecast Characteristics of Radar Data Assimilation Based on the Scales of Precipitation Systems." Remote Sensing 14, no. 3 (January 27, 2022): 605. http://dx.doi.org/10.3390/rs14030605.
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Radar data with high spatiotemporal resolution and automatic weather station (AWS) data are used in the data assimilation experiment to improve the precipitation forecast of a numerical model. The numerical model considered in this study is the Weather Research and Forecasting (WRF) model with double-moment 6-class microphysics scheme (WDM6). We calculated the radar equivalent reflectivity factor using high resolution WRF and compared it with radar observations in South Korea. To compare the precipitation forecast characteristics of the three-dimensional variational (3D-Var) assimilation of radar data, four experiments were performed based on the scales of precipitation systems. Comparison of the 24 h accumulated rainfall with surface observation data, contoured frequency by altitude diagram (CFAD), time–height cross sections (THCS), and vertical hydrometeor profiles was used to evaluate the accuracy of the simulation of precipitation. The model simulations were performed with and without 3D-VAR radar reflectivity, radial velocity and AWS assimilation for two mesoscale convective cases and two synoptic scale cases. The combined effect of the radar and AWS data assimilation experiment improved the location of the precipitation area and rainfall intensity compared to the control run. There is a noticeable scale dependence in the improvement of precipitation systems. Improvements in simulating mesoscale convective systems were larger compared to synoptically driven precipitation systems.
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Min, Ki-Hong, Sunhee Choo, Daehyung Lee, and Gyuwon Lee. "Evaluation of WRF Cloud Microphysics Schemes Using Radar Observations." Weather and Forecasting 30, no. 6 (November 19, 2015): 1571–89. http://dx.doi.org/10.1175/waf-d-14-00095.1.
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Abstract The Korea Meteorological Administration (KMA) implemented a 10-yr project to develop its own global model (GM) by 2020. To reflect the complex topography and unique weather characteristics of the Korean Peninsula, a high-resolution model with accurate physics and input data is required. The WRF single-moment 6-class microphysics scheme (WSM6) and WRF double-moment 6-class microphysics scheme (WDM6) that will be implemented in the Korea GM (KGM) are evaluated. Comparisons of the contoured frequency by altitude diagram (CFAD), time–height cross sections, and vertical profiles of hydrometeors are utilized to assess the two schemes in simulating summer monsoon and convective precipitation cases over the Korean Peninsula during 2011. The results show that WSM6 and WDM6 overestimate the height of the melting level and bright band as compared to radar observations. However, the accuracy of WDM6 is in better agreement with radar observations. This is attributed to the difference in the sedimentation process simulated by the additional second-moment total number concentrations of liquid-phase particles in WDM6. WDM6 creates larger raindrops and higher relative humidity beneath the melting layer, allowing the scheme to simulate a more realistic reflectivity profile than WSM6 for the summer monsoon case. However, for the convective case, both schemes underestimate the precipitation and there is resolution dependence in the WRF Model’s ability to simulate convective precipitation.
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Guy, Nick, Xiping Zeng, Steven A. Rutledge, and Wei-Kuo Tao. "Comparing the Convective Structure and Microphysics in Two Sahelian Mesoscale Convective Systems: Radar Observations and CRM Simulations." Monthly Weather Review 141, no. 2 (February 1, 2013): 582–601. http://dx.doi.org/10.1175/mwr-d-12-00053.1.
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Abstract Two mesoscale convective systems (MCSs) observed during the African Monsoon Multidisciplinary Analyses (AMMA) experiment are simulated using the three-dimensional (3D) Goddard Cumulus Ensemble model. This study was undertaken to determine the performance of the cloud-resolving model in representing distinct convective and microphysical differences between the two MCSs over a tropical continental location. Simulations are performed using 1-km horizontal grid spacing, a lower limit on current embedded cloud-resolving models within a global multiscale modeling framework. Simulated system convective structure and microphysics are compared to radar observations using contoured frequency-by-altitude diagrams (CFADs), calculated ice and water mass, and identified hydrometeor variables. Vertical distributions of ice hydrometeors indicate underestimation at the mid- and upper levels, partially due to the inability of the model to produce adequate system heights. The abundance of high-reflectivity values below and near the melting level in the simulation led to a broadening of the CFAD distributions. Observed vertical reflectivity profiles show that high reflectivity is present at greater heights than the simulations produced, thought to be a result of using a single-moment microphysics scheme. Relative trends in the population of simulated hydrometeors are in agreement with observations, though a secondary convective burst is not well represented. Despite these biases, the radar-observed differences between the two cases are noticeable in the simulations as well, suggesting that the model has some skill in capturing observed differences between the two MCSs.
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Lee, Ji-Won, Ki-Hong Min, Young-Hee Lee, and GyuWon Lee. "X-Net-Based Radar Data Assimilation Study over the Seoul Metropolitan Area." Remote Sensing 12, no. 5 (March 10, 2020): 893. http://dx.doi.org/10.3390/rs12050893.
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This study investigates the ability of the high-resolution Weather Research and Forecasting (WRF) model to simulate summer precipitation with assimilation of X-band radar network data (X-Net) over the Seoul metropolitan area. Numerical data assimilation (DA) experiments with X-Net (S- and X-band Doppler radar) radial velocity and reflectivity data for three events of convective systems along the Changma front are conducted. In addition to the conventional assimilation of radar data, which focuses on assimilating the radial velocity and reflectivity of precipitation echoes, this study assimilates null-echoes and analyzes the effect of null-echo data assimilation on short-term quantitative precipitation forecasting (QPF). A null-echo is defined as a region with non-precipitation echoes within the radar observation range. The model removes excessive humidity and four types of hydrometeors (wet and dry snow, graupel, and rain) based on the radar reflectivity by using a three-dimensional variational (3D-Var) data assimilation technique within the WRFDA system. Some procedures for preprocessing radar reflectivity data and using null-echoes in this assimilation are discussed. Numerical experiments with conventional radar DA over-predicted the precipitation. However, experiments with additional null-echo information removed excessive water vapor and hydrometeors and suppressed erroneous model precipitation. The results of statistical model verification showed improvements in the analysis and objective forecast scores, reducing the amount of over-predicted precipitation. An analysis of a contoured frequency by altitude diagram (CFAD) and time–height cross-sections showed that increased hydrometeors throughout the data assimilation period enhanced precipitation formation, and reflectivity under the melting layer was simulated similarly to the observations during the peak precipitation times. In addition, overestimated hydrometeors were reduced through null-echo data assimilation.
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Aprilia, Bunga, Marzuki Marzuki, Imam Taufiq, and Findy Renggono. "Development of a Method for Classifying Convective and Stratiform Rains from Micro Rain Radar (MRR) Observation Data Using Artificial Neural Network." International Journal of Data Science 3, no. 2 (September 25, 2022): 71–79. http://dx.doi.org/10.18517/ijods.3.2.71-79.2022.
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This study examined the performance of Artificial Neural Network (ANN)-backpropagation to classify rain types from observations of Micro Rain Radar (MRR) in Serpong (6.359oSL; 106.673oEL). The inputs of ANN are radar reflectivity, Doppler velocity, and Liquid Water Content (LWC). Rain events on January 5, 2017; at 16.28 – 21.21 local time were used as training data. The ANN results were validated with rain classified by the Bright Band (BB) and Countour Frequency by Altitude Diagram (CFAD) methods. The most appropriate ANN-backpropagation architecture is the 3-6-1 architecture (input layer-hidden layer-output layer), with an activation-transfer function being competitive and a learning rate of 0.9. The Mean Square Error (MSE) of the training step was 0.0098735, and the average percentage of accuracy for the test step was 94%. A rain event with a single type of rain can be classified accurately by ANN and gives the same results as the CFAD method. Thus, the ANN can be a solution to the shortcomings of the BB method, which sometimes classification results of a single type of rain events is interspersed with another type, which is physically impossible.
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Rudolph, James V., and Katja Friedrich. "Dynamic and Thermodynamic Predictors of Vertical Structure in Radar-Observed Regional Precipitation." Journal of Climate 27, no. 5 (February 24, 2014): 2143–58. http://dx.doi.org/10.1175/jcli-d-13-00239.1.
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Abstract Radar-observed vertical structure of precipitation as defined by contoured frequency by altitude diagrams (CFADs) is related to dynamic and thermodynamic environmental parameters. CFADs from 559 storms occurring over the years 2004–11 in the vicinity of Locarno, Switzerland, combined with Interim ECMWF Re-Analysis (ERA-Interim) data show that the radar-observed vertical structure of precipitation correlates with synoptic pattern (as defined by 1000- and 500-hPa geopotential heights), integrated water vapor flux, atmospheric stability, and vertical profiles of temperature, moisture, and wind. Following the analysis of vertical structure and environmental parameters, a generalized linear model (GLM) is developed for radar-observed vertical structure as a function of data from ERA-Interim. The GLM provides expected values for the vertical extent and magnitude of radar reflectivity and predicts storm vertical structure type with 79% overall accuracy. The relationships found between environmental parameters and storm vertical structure underscore the importance of including both dynamic and thermodynamic variables when evaluating climate change effects on precipitation. In addition, the ability of the GLM to reproduce storm types shows the potential for using GLMs as a link between lower-resolution global model data and high-resolution precipitation observations.
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Huang, Yi, Alain Protat, Steven T. Siems, and Michael J. Manton. "A-Train Observations of Maritime Midlatitude Storm-Track Cloud Systems: Comparing the Southern Ocean against the North Atlantic." Journal of Climate 28, no. 5 (February 26, 2015): 1920–39. http://dx.doi.org/10.1175/jcli-d-14-00169.1.
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Abstract Cloud and precipitation properties of the midlatitude storm-track regions over the Southern Ocean (SO) and North Atlantic (NA) are explored using reanalysis datasets and A-Train observations from 2007 to 2011. In addition to the high-level retrieval products, lower-level observed variables—CloudSat radar reflectivity and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar attenuated backscatter—are directly examined using both contoured frequency by altitude diagrams (CFADs) and contoured frequency by temperature diagrams (CFTDs) to provide direct insight into thermodynamic phase properties. While the wintertime temperature profiles are similar over the two regions, the summertime environment is warmer over the NA. The NA atmosphere is generally moister than the SO, while the SO boundary layer is moister during winter. The results herein suggest that although the two regions exhibit many similarities in the prevalence of boundary layer clouds (BLCs) and frontal systems, notable differences exist. The NA environment exhibits stronger seasonality in thermodynamic structure, cloud, and precipitation properties than the SO. The regional differences of cloud properties are dominated by microphysics in winter and thermodynamics in summer. Glaciated clouds with higher reflectivities are found at warmer temperatures over the NA. BLCs (primarily below 1.5 km) are a predominant component over the SO. The wintertime boundary layer is shallower over the SO. Midlevel clouds consisting of smaller hydrometeors in higher concentration (potentially supercooled liquid water) are more frequently observed over the SO. Cirrus clouds are more prevalent over the NA. Notable differences exist in both the frequencies of thermodynamic phases of precipitation and intensity of warm rain over the two regions.
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Shrestha, Prabhakar, Silke Trömel, Raquel Evaristo, and Clemens Simmer. "Evaluation of modelled summertime convective storms using polarimetric radar observations." Atmospheric Chemistry and Physics 22, no. 11 (June 13, 2022): 7593–618. http://dx.doi.org/10.5194/acp-22-7593-2022.
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Abstract. Ensemble simulations with the Terrestrial Systems Modelling Platform (TSMP) covering northwestern Germany are evaluated for three summertime convective storms using polarimetric X-band radar measurements. Using a forward operator, the simulated microphysical processes have been evaluated in radar observation space. Observed differential reflectivity (ZDR) columns, which are proxies for updrafts, and multi-variate fingerprints for size sorting and aggregation processes are captured by the model, but co-located specific differential phase (KDP) columns in observations are not reproduced in the simulations. Also, the simulated ZDR columns, generated by only small-sized supercooled drops, show smaller absolute ZDR values and a reduced width compared to their observational counterparts, which points to deficiencies in the cloud microphysics scheme as well as the forward operator, which does not have explicit information of water content of ice hydrometeors. Above the melting layer, the simulated polarimetric variables also show weak variability, which can be at least partly explained by the reduced particle diversity in the model and the inability of the T-matrix method to reproduce the polarimetric signatures of snow and graupel; i.e. current forward operators need to be further developed to fully exploit radar data for model evaluation and improvement. Below the melting level, the model captures the observed increase in reflectivity, ZDR and specific differential phase (KDP) towards the ground. The contoured frequency altitude diagrams (CFADs) of the synthetic and observed polarimetric variables were also used to evaluate the model microphysical processes statistically. In general, CFADs of the cross-correlation coefficient (ρhv) were poorly simulated. CFADs of ZDR and KDP were similar but the model exhibits a relatively narrow distribution above the melting layer for both, and a bimodal distribution for ZDR below the melting layer, indicating either differences in the mechanism of precipitation formation or errors in forward operator which uses a functional form of drop size distribution. In general, the model was found to underestimate the convective area fraction, high reflectivities, and the width/magnitude of ZDR columns, all leading to an underestimation of the frequency distribution for high precipitation values.
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Mecikalski, John R., Xuanli Li, Lawrence D. Carey, Eugene W. McCaul, and Timothy A. Coleman. "Regional Comparison of GOES Cloud-Top Properties and Radar Characteristics in Advance of First-Flash Lightning Initiation." Monthly Weather Review 141, no. 1 (January 1, 2013): 55–74. http://dx.doi.org/10.1175/mwr-d-12-00120.1.
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Abstract Lightning initiation (LI) events over Florida and Oklahoma are examined and statistically compared to understand the behavior of observed radar and infrared satellite interest fields (IFs) in the 75-min time frame surrounding LI. Lightning initiation is defined as the time of the first lightning, of any kind, generated in a cumulonimbus cloud. Geostationary Operational Environmental Satellite (GOES) infrared IFs, contoured frequency by altitude diagrams (CFADs) of radar reflectivity, and model sounding data, analyzed in concert, show the mean characteristics over time for 36 and 23 LI events over Florida and Oklahoma, respectively. CFADs indicate that radar echoes formed 60 min before Florida LI, yet Oklahoma storms exhibited a ~30-min delayed development. Large ice volumes in Florida developed from the freezing of lofted liquid hydrometeors formed by long-lived (~45 min) warm rain processes, which are mostly absent in Oklahoma. However, ice volumes developed abruptly in Oklahoma storms despite missing a significant warm rain component. GOES fields were significantly different before 30 min prior to LI between the two locations. Compared to Florida storms, lower precipitable water (PW), higher convective available potential energy, and higher 3.9-μm reflectance in Oklahoma, suggest stronger and drier updrafts producing a greater abundance of small ice particles. Somewhat larger 15-min 10.7-μm cooling rates in Oklahoma confirm stronger updrafts, while clouds in the 60–30-min pre-LI period show more IF variability (e.g., in the 6.5–10.7-μm difference). Florida storms (high PW, slower growth) offer more lead time for LI predictability, compared to Oklahoma storms (low PW, explosive growth), with defined anvils being obvious at the time of LI.
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Nicholls, Stephen D., Steven G. Decker, Wei-Kuo Tao, Stephen E. Lang, Jainn J. Shi, and Karen I. Mohr. "Influence of bulk microphysics schemes upon Weather Research and Forecasting (WRF) version 3.6.1 nor'easter simulations." Geoscientific Model Development 10, no. 2 (March 3, 2017): 1033–49. http://dx.doi.org/10.5194/gmd-10-1033-2017.
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Abstract. This study evaluated the impact of five single- or double-moment bulk microphysics schemes (BMPSs) on Weather Research and Forecasting model (WRF) simulations of seven intense wintertime cyclones impacting the mid-Atlantic United States; 5-day long WRF simulations were initialized roughly 24 h prior to the onset of coastal cyclogenesis off the North Carolina coastline. In all, 35 model simulations (five BMPSs and seven cases) were run and their associated microphysics-related storm properties (hydrometer mixing ratios, precipitation, and radar reflectivity) were evaluated against model analysis and available gridded radar and ground-based precipitation products. Inter-BMPS comparisons of column-integrated mixing ratios and mixing ratio profiles reveal little variability in non-frozen hydrometeor species due to their shared programming heritage, yet their assumptions concerning snow and graupel intercepts, ice supersaturation, snow and graupel density maps, and terminal velocities led to considerable variability in both simulated frozen hydrometeor species and radar reflectivity. WRF-simulated precipitation fields exhibit minor spatiotemporal variability amongst BMPSs, yet their spatial extent is largely conserved. Compared to ground-based precipitation data, WRF simulations demonstrate low-to-moderate (0.217–0.414) threat scores and a rainfall distribution shifted toward higher values. Finally, an analysis of WRF and gridded radar reflectivity data via contoured frequency with altitude diagrams (CFADs) reveals notable variability amongst BMPSs, where better performing schemes favored lower graupel mixing ratios and better underlying aggregation assumptions.
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Onderlinde, Matthew J., and David S. Nolan. "Tropical Cyclone–Relative Environmental Helicity and the Pathways to Intensification in Shear." Journal of the Atmospheric Sciences 73, no. 2 (February 1, 2016): 869–90. http://dx.doi.org/10.1175/jas-d-15-0261.1.
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Abstract Tropical cyclone–relative environmental helicity (TCREH) is a measure of how the wind vector changes direction with height, and it has been shown to modulate the rate at which tropical cyclones (TCs) develop both in idealized simulations and in reanalysis data. The channels through which this modulation occurs remain less clear. This study aims to identify the mechanisms that lead to the observed variations in intensification rate. Results suggest that the difference in intensification rate between TCs embedded in positive versus negative TCREH primarily results from the position of convection and associated latent heat fluxes relative to the wind shear vector. When TCREH is positive, convection is more readily advected upshear and air parcels that experience larger fluxes are more frequently ingested into the TC core. Trajectories computed from high-resolution simulations demonstrate the recovery of equivalent potential temperature downwind of convection, latent heat flux near the TC core, and parcel routes through updrafts in convection. Differences in trajectory characteristics between TCs embedded in positive versus negative TCREH are presented. Contoured frequency-by-altitude diagrams (CFADs) show that convection is distributed differently around TCs embedded in environments characterized by positive versus negative TCREH. They also show that the nature of the most intense convection differs only slightly between cases of positive and negative TCREH. The results of this study emphasize the fact that significant variability in TC-intensification rate results from vertical variations in the environmental wind direction, even when the 850–200-hPa wind shear vector remains unchanged.
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You, Cheng-Rong, Kao-Shen Chung, and Chih-Chien Tsai. "Evaluating the Performance of a Convection-Permitting Model by Using Dual-Polarimetric Radar Parameters: Case Study of SoWMEX IOP8." Remote Sensing 12, no. 18 (September 15, 2020): 3004. http://dx.doi.org/10.3390/rs12183004.
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In this study, a dual-polarimetric radar observation operator is established and modified for the Taiwan area for the purpose of model verification. A severe squall line case during the Southwest Monsoon Experiment Intensive Observing Period 8 (SoWMEX IOP#8) on 14 June 2008, is selected and examined. Because the operator is adopted from the use of the midlatitude region, sensitivity tests are performed to obtain the optimal setting of the operator in the subtropical region. To accurately capture the dynamic structure of the squall lines, the ensemble-based data assimilation system, which assimilates both radial wind and reflectivity data, is used to obtain the optimal analysis field on the mesoscale for evaluating the performance of model simulation. The characteristics of two microphysics schemes are investigated, and the results obtained using the schemes are compared with the S-band dual-polarimetric radar observations. The horizontal and vertical cross-sections show that the analyses resemble the observations. Both schemes can replicate the polarimetric parameter signature such as ZDR and KDP columns. When comparing model simulation with polarimetric parameters through the drawing of contour frequency by altitude diagrams (CFADs), the results reveal that the single moment microphysics scheme performs better than the double moment scheme in this case. However, the reflectivity field in the stratiform area is more accurately captured when using the double moment scheme. Furthermore, validation with polarimetric variables (ZH, ZDR and KDP) histograms shows underestimation of the KDP field in both schemes. Overall, this study indicates the benefit of assimilating radial wind and reflectivity data for the analyses of severe precipitation systems and the necessity of assimilating polarimetric parameters for the accuracy of microphysical processes, especially complex microphysics schemes in subtropical region.
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Lang, S., W.-K. Tao, J. Simpson, R. Cifelli, S. Rutledge, W. Olson, and J. Halverson. "Improving Simulations of Convective Systems from TRMM LBA: Easterly and Westerly Regimes." Journal of the Atmospheric Sciences 64, no. 4 (April 1, 2007): 1141–64. http://dx.doi.org/10.1175/jas3879.1.
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Abstract The 3D Goddard Cumulus Ensemble model is used to simulate two convective events observed during the Tropical Rainfall Measuring Mission Large-Scale Biosphere–Atmosphere (TRMM LBA) experiment in Brazil. These two events epitomized the type of convective systems that formed in two distinctly different environments observed during TRMM LBA. The 26 January 1999 squall line formed within a sheared low-level easterly wind flow. On 23 February 1999, convection developed in weak low-level westerly flow, resulting in weakly organized, less intense convection. Initial simulations captured the basic organization and intensity of each event. However, improvements to the model resolution and microphysics produced better simulations as compared to observations. More realistic diurnal convective growth was achieved by lowering the horizontal grid spacing from 1000 to 250 m. This produced a gradual transition from shallow to deep convection that occurred over a span of hours as opposed to an abrupt appearance of deep convection. Eliminating the dry growth of graupel in the bulk microphysics scheme effectively removed the unrealistic presence of high-density ice in the simulated anvil. However, comparisons with radar reflectivity data using contoured-frequency-with-altitude diagrams (CFADs) revealed that the resulting snow contents were too large. The excessive snow was reduced primarily by lowering the collection efficiency of cloud water by snow and resulted in further agreement with the radar observations. The transfer of cloud-sized particles to precipitation-sized ice appears to be too efficient in the original scheme. Overall, these changes to the microphysics lead to more realistic precipitation ice contents in the model. However, artifacts due to the inability of the one-moment scheme to allow for size sorting, such as excessive low-level rain evaporation, were also found but could not be resolved without moving to a two-moment or bin scheme. As a result, model rainfall histograms underestimated the occurrence of high rain rates compared to radar-based histograms. Nevertheless, the improved precipitation-sized ice signature in the model simulations should lead to better latent heating retrievals as a result of both better convective–stratiform separation within the model as well as more physically realistic hydrometeor structures for radiance calculations.
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Guy, Nick, and David P. Jorgensen. "Kinematic and Precipitation Characteristics of Convective Systems Observed by Airborne Doppler Radar during the Life Cycle of a Madden–Julian Oscillation in the Indian Ocean." Monthly Weather Review 142, no. 4 (March 27, 2014): 1385–402. http://dx.doi.org/10.1175/mwr-d-13-00252.1.
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Abstract This study presents characteristics of convective systems observed during the Dynamics of the Madden–Julian oscillation (DYNAMO) experiment by the instrumented NOAA WP-3D aircraft. Nine separate missions, with a focus on observing mesoscale convective systems (MCSs), were executed to obtain data in the active and inactive phase of a Madden–Julian oscillation (MJO) in the Indian Ocean. Doppler radar and in situ thermodynamic data are used to contrast the convective system characteristics during the evolution of the MJO. Isolated convection was prominent during the inactive phases of the MJO, with deepening convection during the onset of the MJO. During the MJO peak, convection and stratiform precipitation became more widespread. A larger population of deep convective elements led to a larger area of stratiform precipitation. As the MJO decayed, convective system top heights increased, though the number of convective systems decreased, eventually transitioning back to isolated convection. A distinct shift of echo top heights and contoured frequency-by-altitude diagram distributions of radar reflectivity and vertical wind speed indicated that some mesoscale characteristics were coupled to the MJO phase. Convective characteristics in the climatological initiation region (Indian Ocean) were also apparent. Comparison to results from the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) in the western Pacific indicated that DYNAMO MCSs were linearly organized more parallel to the low-level shear and without strong cold pools than in TOGA COARE. Three-dimensional MCS airflow also showed a different dynamical structure, with a lack of the descending rear inflow present in shear perpendicularly organized TOGA COARE MCSs. Weaker, but deeper updrafts were observed in DYNAMO.
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Rogers, Robert F., Michael L. Black, Shuyi S. Chen, and Robert A. Black. "An Evaluation of Microphysics Fields from Mesoscale Model Simulations of Tropical Cyclones. Part I: Comparisons with Observations." Journal of the Atmospheric Sciences 64, no. 6 (June 2007): 1811–34. http://dx.doi.org/10.1175/jas3932.1.
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This study presents a framework for comparing hydrometeor and vertical velocity fields from mesoscale model simulations of tropical cyclones with observations of these fields from a variety of platforms. The framework is based on the Yuter and Houze constant frequency by altitude diagram (CFAD) technique, along with a new hurricane partitioning technique, to compare the statistics of vertical motion and reflectivity fields and hydrometeor concentrations from two datasets: one consisting of airborne radar retrievals and microphysical probe measurements collected from tropical cyclone aircraft flights over many years, and another consisting of cloud-scale (1.67-km grid length) tropical cyclone simulations using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). Such comparisons of the microphysics fields can identify biases in the simulations that may lead to an identification of deficiencies in the modeling system, such as the formulation of various physical parameterization schemes used in the model. Improvements in these schemes may potentially lead to better forecasts of tropical cyclone intensity and rainfall. In Part I of this study, the evaluation framework is demonstrated by comparing the radar retrievals and probe measurements to MM5 simulations of Hurricanes Bonnie (1998) and Floyd (1999). Comparisons of the statistics from the two datasets show that the model reproduces many of the gross features seen in the observations, though notable differences are evident. The general distribution of vertical motion is similar between the observations and simulations, with the strongest up- and downdrafts making up a small percentage of the overall population in both datasets, but the magnitudes of vertical motion are weaker in the simulations. The model-derived reflectivities are much higher than observed, and correlations between vertical motion and hydrometeor concentration and reflectivity show a much stronger relationship in the model than what is observed. Possible errors in the data processing are discussed as potential sources of differences between the observed and simulated datasets in Part I. In Part II, attention will be focused on using the evaluation framework to investigate the role that different model configurations (i.e., different resolutions and physical parameterizations) play in producing different microphysics fields in the simulation of Hurricane Bonnie. The microphysical and planetary boundary layer parameterization schemes, as well as higher horizontal and vertical resolutions, will be tested in the simulation to identify the extent to which changes in these schemes are reflected in improvements of the statistical comparisons with the observations.
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Janapati, Jayalakshmi, Balaji Kumar Seela, and Pay-Liam Lin. "Regional discrepancies in the microphysical attributes of summer season rainfall over Taiwan using GPM DPR." Scientific Reports 13, no. 1 (July 26, 2023). http://dx.doi.org/10.1038/s41598-023-38245-z.
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AbstractTaiwan, an island located in the northwest Pacific region, is influenced by heavy rainfall events during warm seasons, particularly from June to August. Interaction of precipitating clouds with the complex topography results in inhomogeneous and intense rainfall over Taiwan. Hence, the present study investigates the regional discrepancies in the microphysical characteristics of summer season rainfall over (north, south, east, and central) Taiwan using 9 years (2014–2022) of GPM DPR measurements. The results showed clear distinctions in the precipitation and raindrop size distributions over the north, south, east, and central Taiwan. The contoured frequency by altitude diagrams (CFADs) of radar reflectivity, rainfall rate, drop diameter, and concentration clearly infer the dominance of large-size super cooled liquid and ice particles above the melting layer and rain particles below the melting layers in the south and central Taiwan. Central (north) Taiwan is dominated by large-size (small) drops among four regions. Higher concentrations of large drops over central Taiwan (principally from convective precipitation) and south Taiwan (primarily from stratiform precipitation) is attributed to higher rainfall amounts over these two regions than the north and east Taiwan. Furthermore, irrespective of precipitation type and geographic region, summer monsoon rainfall over Taiwan is dominated by coalescence and breakup processes. The microphysical characteristics of summer season rainfall addressed in this study could assist in refining the cloud modeling simulations over complex topography in Taiwan.
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Cheng, Yung-Yun, Chia-Tung Chang, Buo-Fu Chen, Hung-Chi Kuo, and Cheng-Shang Lee. "Extracting 3-D Radar Features to Improve Quantitative Precipitation Estimation in Complex Terrain based on Deep Learning Neural Networks." Weather and Forecasting, November 30, 2022. http://dx.doi.org/10.1175/waf-d-22-0034.1.
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Abstract This paper proposes a new quantitative precipitation estimation (QPE) technique to provide accurate rainfall estimates in complex terrain, where conventional QPE has limitations. The operational radar QPE in Taiwan is mainly based on the simplified relationship between radar reflectivity Z and rain rate R [R(Z) relation] and only utilizes the single-point lowest available echo to estimate rain rates, leading to low accuracy in complex terrain. Here, we conduct QPE using deep learning that extracts features from 3-D radar reflectivities to address the above issues. Convolutional neural networks (CNN) are used to analyze contoured frequency by altitude diagrams (CFADs) to generate the QPE. CNN models are trained on existing rain gauges in northern and eastern Taiwan with the three-year data during 2015–17 and validated and tested using 2018 data. The weights of heavy rains (≧10 mm h-1) are increased in the model loss calculation to handle the unbalanced rainfall data and improve accuracy. Results show that the CNN outperforms the R(Z) relation based on the 2018 rain-gauge data. Furthermore, this research proposes methods to conduct 2-D gridded QPE at every pixel by blending estimates from various trained CNN models. Verification based on independent rain gauges shows that the CNN QPE solves the underestimation of the R(Z) relation in mountainous areas. Case studies are presented to visualize the results, showing that the CNN QPE generates better small-scale rainfall features and more accurate precipitation information. This deep learning QPE technique may be helpful for the disaster prevention of small-scale flash floods in complex terrain.