Polarimetric Radar Rain Estimation through Retrieval of Drop Size Distribution Using a Bayesian Approach

2010 ◽  
Vol 49 (5) ◽  
pp. 973-990 ◽  
Author(s):  
Qing Cao ◽  
Guifu Zhang ◽  
Edward A. Brandes ◽  
Terry J. Schuur
Keyword(s):  
Bayesian Approach ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Normal Function ◽  
In Situ Measurements ◽  
Retrieval Algorithm ◽  
Convective System ◽  
Horizontal Polarization ◽  
Oklahoma Mesonet

Abstract This study proposes a Bayesian approach to retrieve raindrop size distributions (DSDs) and to estimate rainfall rates from radar reflectivity in horizontal polarization ZH and differential reflectivity ZDR. With this approach, the authors apply a constrained-gamma model with an updated constraining relation to retrieve DSD parameters. Long-term DSD measurements made in central Oklahoma by the two-dimensional video disdrometer (2DVD) are first used to construct a prior probability density function (PDF) of DSD parameters, which are estimated using truncated gamma fits to the second, fourth, and sixth moments of the distributions. The forward models of ZH and ZDR are then developed based on a T-matrix calculation of raindrop backscattering amplitude with the assumption of drop shape. The conditional PDF of ZH and ZDR is assumed to be a bivariate normal function with appropriate standard deviations. The Bayesian algorithm has a good performance according to the evaluation with simulated ZH and ZDR. The algorithm is also tested on S-band radar data for a mesoscale convective system that passed over central Oklahoma on 13 May 2005. Retrievals of rainfall rates and 1-h rain accumulations are compared with in situ measurements from one 2DVD and six Oklahoma Mesonet rain gauges, located at distances of 28–54 km from Norman, Oklahoma. Results show that the rain estimates from the retrieval agree well with the in situ measurements, demonstrating the validity of the Bayesian retrieval algorithm.

Ensemble Probabilistic Prediction of a Mesoscale Convective System and Associated Polarimetric Radar Variables Using Single-Moment and Double-Moment Microphysics Schemes and EnKF Radar Data Assimilation

2017 ◽  
Vol 145 (6) ◽  
pp. 2257-2279 ◽  
Author(s):  
Bryan J. Putnam ◽  
Ming Xue ◽  
Youngsun Jung ◽  
Nathan A. Snook ◽  
Guifu Zhang
Keyword(s):  
Mesoscale Convective System ◽  
Radar Data ◽  
Convective Precipitation ◽  
Convective System ◽  
Ensemble Forecasts ◽  
Verification Methods ◽  
Probabilistic Forecasts ◽  
Mesoscale Convective ◽  
Single Moment ◽  
Double Moment

Abstract Ensemble-based probabilistic forecasts are performed for a mesoscale convective system (MCS) that occurred over Oklahoma on 8–9 May 2007, initialized from ensemble Kalman filter analyses using multinetwork radar data and different microphysics schemes. Two experiments are conducted, using either a single-moment or double-moment microphysics scheme during the 1-h-long assimilation period and in subsequent 3-h ensemble forecasts. Qualitative and quantitative verifications are performed on the ensemble forecasts, including probabilistic skill scores. The predicted dual-polarization (dual-pol) radar variables and their probabilistic forecasts are also evaluated against available dual-pol radar observations, and discussed in relation to predicted microphysical states and structures. Evaluation of predicted reflectivity (Z) fields shows that the double-moment ensemble predicts the precipitation coverage of the leading convective line and stratiform precipitation regions of the MCS with higher probabilities throughout the forecast period compared to the single-moment ensemble. In terms of the simulated differential reflectivity (ZDR) and specific differential phase (KDP) fields, the double-moment ensemble compares more realistically to the observations and better distinguishes the stratiform and convective precipitation regions. The ZDR from individual ensemble members indicates better raindrop size sorting along the leading convective line in the double-moment ensemble. Various commonly used ensemble forecast verification methods are examined for the prediction of dual-pol variables. The results demonstrate the challenges associated with verifying predicted dual-pol fields that can vary significantly in value over small distances. Several microphysics biases are noted with the help of simulated dual-pol variables, such as substantial overprediction of KDP values in the single-moment ensemble.

The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman Filter

2014 ◽  
Vol 142 (1) ◽  
pp. 141-162 ◽  
Author(s):  
Bryan J. Putnam ◽  
Ming Xue ◽  
Youngsun Jung ◽  
Nathan Snook ◽  
Guifu Zhang
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Cold Pool ◽  
Convective System ◽  
Polarimetric Radar ◽  
Microphysics Scheme ◽  
Mesoscale Convective ◽  
Double Moment

Abstract Doppler radar data are assimilated with an ensemble Kalman Filter (EnKF) in combination with a double-moment (DM) microphysics scheme in order to improve the analysis and forecast of microphysical states and precipitation structures within a mesoscale convective system (MCS) that passed over western Oklahoma on 8–9 May 2007. Reflectivity and radial velocity data from five operational Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radars as well as four experimental Collaborative and Adaptive Sensing of the Atmosphere (CASA) X-band radars are assimilated over a 1-h period using either single-moment (SM) or DM microphysics schemes within the forecast ensemble. Three-hour deterministic forecasts are initialized from the final ensemble mean analyses using a SM or DM scheme, respectively. Polarimetric radar variables are simulated from the analyses and compared with polarimetric WSR-88D observations for verification. EnKF assimilation of radar data using a multimoment microphysics scheme for an MCS case has not previously been documented in the literature. The use of DM microphysics during data assimilation improves simulated polarimetric variables through differentiation of particle size distributions (PSDs) within the stratiform and convective regions. The DM forecast initiated from the DM analysis shows significant qualitative improvement over the assimilation and forecast using SM microphysics in terms of the location and structure of the MCS precipitation. Quantitative precipitation forecasting skills are also improved in the DM forecast. Better handling of the PSDs by the DM scheme is believed to be responsible for the improved prediction of the surface cold pool, a stronger leading convective line, and improved areal extent of stratiform precipitation.

scholarly journals Retrieval of total column and surface NO<sub>2</sub> from Pandora zenith-sky measurements

2019 ◽  
Author(s):  
Xiaoyi Zhao ◽  
Debora Griffin ◽  
Vitali Fioletov ◽  
Chris McLinden ◽  
Jonathan Davies ◽  
...  
Keyword(s):  
Transport Model ◽  
Surface Concentration ◽  
In Situ Measurements ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Chemical Transport Model ◽  
High Quality ◽  
Vertical Column

Abstract. Pandora spectrometers can retrieve nitrogen dioxide (NO2) vertical column densities (VCDs) via two viewing geometries: direct-sun and zenith-sky. The direct-sun NO2 VCD measurements have high quality (0.1 DU accuracy in clear-sky conditions) and do not rely on any radiative transfer model to calculate air mass factors (AMFs); however, they are not available when the sun is obscured by clouds. To perform NO2 measurements in cloudy conditions, a simple but robust NO2 retrieval algorithm is developed for Pandora zenith-sky measurements. This algorithm derives empirical zenith-sky NO2 AMFs from coincident high-quality direct-sun NO2 observations. Moreover, the retrieved Pandora zenith-sky NO2 VCD data are converted to surface NO2 concentrations with a scaling algorithm that uses chemical-transport-model predictions and satellite measurements as inputs. NO2 VCDs and surface concentrations are retrieved from Pandora zenith-sky measurements made in Toronto, Canada, from 2015 to 2017. The retrieved Pandora zenith-sky NO2 data (VCD and surface concentration) show good agreement with both satellite and in situ measurements. The diurnal and seasonal variations of derived Pandora zenith-sky surface NO2 data also agree well with in situ measurements (diurnal difference within ±2 ppbv). Overall, this work shows that the new Pandora zenith-sky NO2 products have the potential to be used in various applications such as future satellite validation in moderate cloudy scenes and air quality monitoring.

scholarly journals Comparison of 3DVar and EnSRF Data Assimilation Using Radar Observations for the Analysis and Prediction of an MCS

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Shibo Gao ◽  
Jinzhong Min
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Mesoscale Convective System ◽  
Final Analysis ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Convective System ◽  
Wind Fields ◽  
Radar Observations

Using radar observations, the performances of the ensemble square root filter (EnSRF) and an indirect three-dimensional variational (3DVar) data assimilation method were compared for a mesoscale convective system (MCS) that occurred in the Front Range of the Rocky Mountains, Colorado (USA). The results showed that the root mean square innovations (RMSIs) of EnSRF were lower than 3DVar for radar reflectivity and radial velocity and that the spread of EnSRF was generally consistent with its RMSIs. EnSRF substantially improved the analysis of the MCS compared with an experiment without radar data assimilation, and it produced a slight but noticeable improvement over 3DVar in terms of both coverage and intensity. Forecast results initiated from the final analysis revealed that EnSRF generally produced the best prediction of the MCS, with improved quantitative reflectivity and precipitation forecast skills. EnSRF also demonstrated better performance than 3DVar in the prediction of neighborhood probability for reflectivity at thresholds of 20 and 35 dBZ, which better matched the observed radar reflectivity in terms of both shape and extension. Additionally, the humidity, temperature, and wind fields were also improved by EnSRF; the largest error reduction was found in the water vapor field near the surface and at upper levels.

scholarly journals Comparing Airborne and Satellite Retrievals of Optical and Microphysical Properties of Cirrus and Deep Convective Clouds using a Radiance Ratio Technique

2017 ◽  
Author(s):  
Trismono C. Krisna ◽  
Manfred Wendisch ◽  
André Ehrlich ◽  
Evelyn Jäkel ◽  
Frank Werner ◽  
...  
Keyword(s):  
Vertical Profile ◽  
Effective Radius ◽  
In Situ Measurements ◽  
Spectral Radiance ◽  
Retrieval Algorithm ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Convective Clouds ◽  
Deep Convective Clouds

Abstract. Solar radiation reflected by cirrus and deep convective clouds (DCCs) was measured by the Spectral Modular Airborne Radiation Measurement System (SMART) installed on the German HALO (High Altitude and Long Range Research Aircraft) during the ML-CIRRUS and the ACRIDICON-CHUVA campaigns. In particular flights, HALO performed closely collocated measurements with overpasses of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board of Aqua satellite. Based on the nadir upward radiance, the optical thickness τ and bulk particle effective radius reff of cirrus and DCC are retrieved using a radiance ratio algorithm which considers the cloud thermodynamic phase, the cloud vertical profile, multi layer clouds, and heterogeneity of the surface albedo. For the cirrus case, the comparison of τci and reff,ci retrieved on the basis of SMART and MODIS upward radiances yields a normalized mean absolute deviation of 0.5 % for τci and 2.5 % for reff,ci. While for the DCC case, the respective deviation is 5.9 % for τdcc and 13.2 % for reff,dcc. The larger deviations in case of DCC are mainly attributed to the fast cloud evolution and three-dimensional radiative effects. Measurements of spectral radiance at near-infrared wavelengths with different absorption by cloud particles are employed to investigate the vertical profile of cirrus effective radius. The retrieved values of cirrus effective radius are further compared with corresponding in situ measurements using a vertical weighting method. Compared to the MODIS observation, spectral measurements of SMART provide an increased amount of information on the vertical distribution of particle sizes at cloud top, and therefore allow to reconstruct the profile of effective radius at cloud top. The retrieved effective radius differs to in situ measurements with a normalized mean absolute deviation between 4–19 %, depending on the wavelength chosen in the retrieval algorithm. While, the MODIS cloud product underestimates the in situ measurements by 48 %. The presence of liquid water clouds below the cirrus, the variability of particle size distributions, and the simplification in the retrieval algorithm assuming vertically homogeneous cloud are identified as the potential error contributors.

scholarly journals Identifikasi Karakteristik Quasi-Linear Convective System Pada Maret Sampai Mei 2017 Di Wilayah Pangkalan Bun Berbasis Radar Cuaca

2019 ◽  
Vol 4 ◽  
pp. 110
Author(s):  
Meldisa Putri Maulidyah ◽  
Rossian Nursiddiq Islamiardi ◽  
Rezky Fajar Maulana ◽  
Kristian Adi Putra Tamba ◽  
Imma Redha Nugraheni ◽  
...  
Keyword(s):  
Coastal Plain ◽  
Descriptive Analysis ◽  
Mesoscale Convective System ◽  
Heavy Rain ◽  
Radar Data ◽  
Horizontal Wind ◽  
Convective System ◽  
Raw Data ◽  
Mesoscale Convective ◽  
Strong Winds

<p><strong>Abstract: </strong>Quasi Linear Convective System (QLCS) is one of the phenomena of meso-scale convective weather systems (MCS), which are linear in shape with an unspecified leftime and potentially bad weather in the form of heavy rain and strong winds. This research will identify, analyze, and characterize QLCS in the Pangkalan Bun region, Central Kalimantan, as a research location with a period of March to May 2017 using raw data radar data base of Pangkalanbun type C-Band single polarization type Selex SI Gematronik. Method of research was conducted in a descriptive analysis with a description of the QLCS temporally and spatially. The results showed the most duration was 30-60 minutes. The location of the QLCS formation is dominant in the coastal plain or lowland areas. The type of formation of QLCS is dominant broken line.</p><p><strong>Abstrak: </strong>Quasi Linear Convective System (QLCS) merupakan salah satu fenomena dari sistem cuaca konvektif skala meso atau Mesoscale Convective System (MCS) yang berbentuk linear dengan masa hidup tidak ditentukan dan berpotensi cuaca buruk berupa hujan lebat dan angin kencang. Pada penelitian ini akan mengidentifikasi, menganalisis, dan mengarakteristikan QLCS di wilayah cakupan radar Pangkalan Bun, Kalimantan Tengah sebagai lokasi penelitian dengan jangka waktu bulan Maret sampai Mei tahun 2017 menggunakan raw data radar cuaca Pangkalan Bun tipe C-Band jenis polarisasi tunggal Selex SI Gematronik. Metode yang dilakukan dalam penelitian ini adalah analisis deskriptif produk Column Max (CMAX), Combined Moment (CM), Strom Structure Analysis (SSA), Severe Weather Indicator (SWI), dan Horizontal WInd (HWIND). Hasil penelitian menunjukkan durasi pembentukan QLCS terbanyak terjadi dalam rentang 30-60 menit dengan lokasi pembentukan QLCS dominan pada area coastal plain atau dataran rendah. Tipe pembentukan QLCS dominan broken line dan banyak terjadi di pagi hari.</p>

scholarly journals Retrieval of total column and surface NO<sub>2</sub> from Pandora zenith-sky measurements

2019 ◽  
Vol 19 (16) ◽  
pp. 10619-10642 ◽  
Author(s):  
Xiaoyi Zhao ◽  
Debora Griffin ◽  
Vitali Fioletov ◽  
Chris McLinden ◽  
Jonathan Davies ◽  
...  
Keyword(s):  
Transport Model ◽  
Surface Concentration ◽  
In Situ Measurements ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Chemical Transport Model ◽  
High Quality ◽  
Vertical Column

Abstract. Pandora spectrometers can retrieve nitrogen dioxide (NO2) vertical column densities (VCDs) via two viewing geometries: direct Sun and zenith sky. The direct-Sun NO2 VCD measurements have high quality (0.1 DU accuracy in clear-sky conditions) and do not rely on any radiative transfer model to calculate air mass factors (AMFs); however, they are not available when the Sun is obscured by clouds. To perform NO2 measurements in cloudy conditions, a simple but robust NO2 retrieval algorithm is developed for Pandora zenith-sky measurements. This algorithm derives empirical zenith-sky NO2 AMFs from coincident high-quality direct-Sun NO2 observations. Moreover, the retrieved Pandora zenith-sky NO2 VCD data are converted to surface NO2 concentrations with a scaling algorithm that uses chemical-transport-model predictions and satellite measurements as inputs. NO2 VCDs and surface concentrations are retrieved from Pandora zenith-sky measurements made in Toronto, Canada, from 2015 to 2017. The retrieved Pandora zenith-sky NO2 data (VCD and surface concentration) show good agreement with both satellite and in situ measurements. The diurnal and seasonal variations of derived Pandora zenith-sky surface NO2 data also agree well with in situ measurements (diurnal difference within ±2 ppbv). Overall, this work shows that the new Pandora zenith-sky NO2 products have the potential to be used in various applications such as future satellite validation in moderate cloudy scenes and air quality monitoring.

A Multi-Platform, In-Situ Kinematic and Microphysical Analysis of a Parallel Stratiform-Mesoscale-Convective System

UND Datasets ◽  
2020 ◽  
Author(s):  
Andrea Neumann ◽  
Michael Poellet ◽  
David J. Delene ◽  
Mark Askelson ◽  
Kirk North ◽  
...  
Keyword(s):  
Mesoscale Convective System ◽  
Convective System ◽  
Mesoscale Convective

Retrieval of Stable Water Vapour Isotopologues from the TROPOMI Instrument

2021 ◽  
Author(s):  
Tim Trent ◽  
Hartmut Boesch ◽  
Peter Somkuti ◽  
Mathhias Schneider ◽  
Farahnaz Khosrawi ◽  
...  
Keyword(s):  
Water Vapour ◽  
In Situ Measurements ◽  
Climate Feedback ◽  
Retrieval Algorithm ◽  
Unique Contribution ◽  
Water Vapour Content ◽  
Infrared Measurements ◽  
Water Isotopologues ◽  
The Impact

&lt;p&gt;Atmospheric moisture is a crucial factor for the redistribution of heat in the atmosphere, with a strong coupling between atmospheric circulation and moisture pathways responsible most climate feedback mechanisms. Conventional satellite and in situ measurements provide information on water vapour content and vertical distribution; however, observations of water isotopologues make a unique contribution to a better understanding of this coupling.&lt;/p&gt;&lt;p&gt;In recent years, observations of water vapour isotopologue from satellites have become available from nadir thermal infrared measurements (TES, AIRS, IASI) which are sensitive to the free troposphere and from shortwave-infrared (SWIR) sensors (GOSAT, SCIAMACHY) that provide column-averaged concentrations including sensitivity to the boundary layer. The TROPOMI instrument on-board Sentinel 5P (S5p) measures SWIR radiance spectra that allow retrieval of water isotopologue columns but with much improved spatial and temporal coverage compared to other SWIR sensors promising a step-change for scientific and operational applications.&lt;/p&gt;&lt;p&gt;Here we present the retrieval algorithm development for stable water isotopologues from TROPOMI as part of the ESA S5p Innovation programme.&amp;#160; We also discuss the validation of these types of satellite products with fiducial in situ measurements, and challenges compared with other satellite measurements. Finally, we outline the roadmap for assessing the impact of TROPOMI data against state-of-the-art isotope enabled models.&lt;/p&gt;

Evaluating Forecast Performance and Sensitivity to the GSI EnKF Data Assimilation Configuration for the 28 – 29 May 2017 Mesoscale Convective System Case

2020 ◽  
Author(s):  
Jonathan Labriola ◽  
Youngsun Jung ◽  
Chengsi Liu ◽  
Ming Xue
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Forecast Skill ◽  
Convective System ◽  
Observation Error ◽  
Forecast Performance ◽  
Radar Observations ◽  
Data Thinning

AbstractIn an effort to improve radar data assimilation configurations for potential operational implementation, GSI EnKF data assimilation experiments based on the operational system employed by the Center for Analysis and Prediction of Storms (CAPS) realtime Spring Forecast Experiments are performed. These experiments are followed by 6-hour forecasts for an MCS on 28 – 29 May 2017. Configurations examined include data thinning, covariance localization radii and inflation, observation error settings, and data assimilation frequency for radar observations.The results show experiments that assimilate radar observations more frequently (i.e., 5 – 10 minutes) are initially better at suppressing spurious convection. However, assimilating observations every 5 minutes causes spurious convection to become more widespread with time, and modestly degrades forecast skill through the remainder of the forecast window. Ensembles that assimilate more observations with less thinning of data or use a larger horizontal covariance localization radius for radar data predict fewer spurious storms and better predict the location of observed storms. Optimized data thinning and horizontal covariance localization radii have positive impacts on forecast skill during the first forecast hour that are quickly lost due to the growth of forecast error. Forecast skill is less sensitive to the ensemble spread inflation factors and observation errors tested during this study. These results provide guidance towards optimizing the configuration of the GSI EnKF system. Among DA the configurations tested, the one employed by the CAPS Spring Forecast Experiment produces the most skilled forecasts while remaining computationally efficient for realtime use.

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