scholarly journals Evaluation of the COSMO model (v5.1) in polarimetric radar space – impact of uncertainties in model microphysics, retrievals and forward operators

2022 ◽  
Vol 15 (1) ◽  
pp. 291-313
Author(s):  
Prabhakar Shrestha ◽  
Jana Mendrok ◽  
Velibor Pejcic ◽  
Silke Trömel ◽  
Ulrich Blahak ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Critical Diameter ◽  
Model Space ◽  
Threshold Temperature ◽  
Polarimetric Radar ◽  
Surface Precipitation ◽  
The Difference ◽  
Microphysical Processes ◽  
Nwp Model ◽  
Lower Magnitude

Abstract. Sensitivity experiments with a numerical weather prediction (NWP) model and polarimetric radar forward operator (FO) are conducted for a long-duration stratiform event over northwestern Germany to evaluate uncertainties in the partitioning of the ice water content and assumptions of hydrometeor scattering properties in the NWP model and FO, respectively. Polarimetric observations from X-band radar and retrievals of hydrometeor classifications are used for comparison with the multiple experiments in radar and model space. Modifying the critical diameter of particles for ice-to-snow conversion by aggregation (Dice) and the threshold temperature responsible for graupel production by riming (Tgr), was found to improve the synthetic polarimetric moments and simulated hydrometeor population, while keeping the difference in surface precipitation statistically insignificant at model resolvable grid scales. However, the model still exhibited a low bias (lower magnitude than observation) in simulated polarimetric moments at lower levels above the melting layer (−3 to −13 ∘C) where snow was found to dominate. This necessitates further research into the missing microphysical processes in these lower levels (e.g. fragmentation due to ice–ice collisions) and use of more reliable snow-scattering models to draw valid conclusions.

scholarly journals Evaluation of the COSMO model (v5.1) in polarimetric radar space – Impact of uncertainties in model microphysics, retrievals, and forward operator

2021 ◽  
Author(s):  
Prabhakar Shrestha ◽  
Jana Mendrok ◽  
Velibor Pejcic ◽  
Silke Trömel ◽  
Ulrich Blahak ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Model Space ◽  
Polarimetric Radar ◽  
Surface Precipitation ◽  
The Difference ◽  
Microphysical Processes ◽  
Nwp Model ◽  
Two Parameters ◽  
Ice Water ◽  
Forward Operator

Abstract. Sensitivity experiments with a numerical weather prediction (NWP) model and polarimetric radar forward operator (FO) are conducted for a long-duration stratiform event over northwestern Germany, to evaluate uncertainties in the partitioning of the ice water content and assumptions of hydrometeor scattering properties in the NWP model and FO, respectively. Polarimetric observations from X-band radar and retrievals of hydrometeor classifications are used for comparison with the multiple experiments in radar and model space. Modifying two parameters (Dice and Tgr) responsible for the production of snow and graupel, respectively, was found to improve the synthetic polarimetric moments and simulated hydrometeor population, while keeping the difference in surface precipitation statistically insignificant at model resolvable grid scales. However, the model still exhibited a low bias in simulated polarimetric moments at lower levels above the melting layer (−3 to −13 °C) where snow was found to dominate. This necessitates further research into the missing microphysical processes in these lower levels (e.g., fragmentation due to ice-ice collisions), and use of more reliable snow scattering models to draw valid conclusions.

scholarly journals Toward a variational assimilation of polarimetric radar observations in a convective-scale numerical weather prediction (NWP) model

2020 ◽  
Vol 13 (5) ◽  
pp. 2279-2298
Author(s):  
Guillaume Thomas ◽  
Jean-François Mahfouf ◽  
Thibaut Montmerle
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Observation Operator ◽  
Weather Radars ◽  
Convective Scale ◽  
The Difference ◽  
Input Variables ◽  
Nwp Model

Abstract. This paper presents the potential of nonlinear and linear versions of an observation operator for simulating polarimetric variables observed by weather radars. These variables, deduced from the horizontally and vertically polarized backscattered radiations, give information about the shape, the phase and the distributions of hydrometeors. Different studies in observation space are presented as a first step toward their inclusion in a variational data assimilation context, which is not treated here. Input variables are prognostic variables forecasted by the AROME-France numerical weather prediction (NWP) model at convective scale, including liquid and solid hydrometeor contents. A nonlinear observation operator, based on the T-matrix method, allows us to simulate the horizontal and the vertical reflectivities (ZHH and ZVV), the differential reflectivity ZDR, the specific differential phase KDP and the co-polar correlation coefficient ρHV. To assess the uncertainty of such simulations, perturbations have been applied to input parameters of the operator, such as dielectric constant, shape and orientation of the scatterers. Statistics of innovations, defined by the difference between simulated and observed values, are then performed. After some specific filtering procedures, shapes close to a Gaussian distribution have been found for both reflectivities and for ZDR, contrary to KDP and ρHV. A linearized version of this observation operator has been obtained by its Jacobian matrix estimated with the finite difference method. This step allows us to study the sensitivity of polarimetric variables to hydrometeor content perturbations, in the model geometry as well as in the radar one. The polarimetric variables ZHH and ZDR appear to be good candidates for hydrometeor initialization, while KDP seems to be useful only for rain contents. Due to the weak sensitivity of ρHV, its use in data assimilation is expected to be very challenging.

scholarly journals Predicting the Snow-to-Liquid Ratio of Surface Precipitation Using a Bulk Microphysics Scheme

2012 ◽  
Vol 140 (8) ◽  
pp. 2461-2476 ◽  
Author(s):  
J. A. Milbrandt ◽  
A. Glazer ◽  
D. Jacob
Keyword(s):  
Subjective Evaluation ◽  
Weather Prediction ◽  
Total Solid ◽  
Liquid Ratio ◽  
Microphysics Scheme ◽  
New Approach ◽  
Surface Precipitation ◽  
Precipitation Forecasting ◽  
Nwp Model ◽  
Crystal Aggregate

Abstract Bulk microphysics parameterizations play an increasingly important role for quantitative precipitation forecasting (QPF) in operational numerical weather prediction (NWP). For wintertime, numerical prediction of snowfall amounts is done by applying an estimated snow-to-liquid ratio to the liquid-equivalent QPF from the NWP model. A method has been developed to use prognostic fields from a detailed bulk scheme to predict the instantaneous snow-to-liquid ratio of precipitating snow. By exploiting aspects of the parameterization of the large crystal/aggregate (snow) category, which allow for a prediction of the mean particle size and a corresponding realistic bulk density, combined with pristine ice and graupel fields, the total volume flux of ice-phase precipitation (excluding hail) is computed, independently from the computation of the total solid mass flux. Ultimately, the accumulated unmelted solid precipitation quantity is thus predicted without having to estimate the average snow-to-liquid ratio for a given event, as is typically done for wintertime QPF. The new technique has been implemented into the two-moment version of the Milbrandt–Yau microphysics scheme, which was used in a high-resolution (2.5 and 1 km) NWP modeling system over the Vancouver–Whistler region of Canada in support of forecasting for the Vancouver 2010 Olympic and Paralympic Games. Experimental fields were produced including the instantaneous snow-to-liquid ratio and the snowfall accumulation predicted directly from the scheme using the new approach. Subjective evaluation indicates that the model can discriminate between low-density and high-density snow for instantaneous precipitation. Comparison of the predicted snow-to-liquid ratio to observed climatologies indicates that the scheme produces a realistic probability distribution.

scholarly journals GEOVISUAL ANALYTICS ON THE VERIFICATION OF THE PAGASA OPERATIONAL NUMERICAL WEATHER PREDICTION MODEL RAINFALL FORECAST

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 215-222
Author(s):  
R. A. A. Flores
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Model Performance ◽  
Time Frame ◽  
Model Verification ◽  
Climate Type ◽  
Surface Precipitation ◽  
Numerical Weather ◽  
Geovisual Analytics ◽  
Nwp Model

Abstract. Assessment of NWP model performance is an integral part of operational forecasting as well as in research and development. Understanding the bias propagation of an NWP model and how it propagates across space can provide more insight in determining underlying causes and weaknesses not easily determined in traditional methods. The study aims to introduce the integration of the spatial distribution of error in interpreting model verification results by assessing how well the operational numerical weather prediction system of PAGASA captures the country’s weather pattern in each of its climate type. It also discusses improvements in model performance throughout the time-frame of analysis. Error propagation patterns were identified using Geovisual Analytics to allow comparison of verification scores among individual stations. The study concluded that a major update in the physics parameterization of the model in 2016 and continued minor updates in the following years, surface precipitation forecasts greatly improved from an average RMSE of 9.3, MAE of 3.2 and Bias of 1.36 in 2015 to an RMSE of 7.9, MAE of 2.5 and bias of −0.63 in 2018.

From InSAR derived relative tropospheric Slant Total Delay maps to absolute Zenith Total Delay maps: comparisons between tropospheric delay products to define a strategy for meteorological applications.

2020 ◽  
Author(s):  
Stefano Barindelli ◽  
Andrea Gatti ◽  
Martina Lagasio ◽  
Marco Manzoni ◽  
Alessandra Mascitelli ◽  
...  
Keyword(s):  
Positive Impact ◽  
Weather Prediction ◽  
Circulation Model ◽  
Tropospheric Delay ◽  
Total Delay ◽  
Zenith Total Delay ◽  
Iterative Interpolation ◽  
Physically Based ◽  
The Difference ◽  
Nwp Model

<p>InSAR derived Atmospheric Phase Screens (APSs) contain the difference between the atmospheric delay along the SAR sensor line-of-sight of two acquisition epochs: the slave and the master epochs. Using estimates of the atmospheric state at the master epoch, coming from independent sources, the APSs can be transformed into maps of tropospheric Zenith Total Delay (ZTD), that is related to the columnar atmospheric water vapor content. Assimilation experiments of such products into numerical weather prediction (NWP) models have shown a positive impact in the prediction of convective storms.</p><p>In this work, a systematical comparison between various APS and ZTD products aims at determining the optimal procedure to go from APSs to InSAR-derived absolute ZTD maps, i.e. to estimate the master delay map. Two different approaches are compared.</p><p>The first is based on a stack of ZTD maps produced with the assimilation of GNSS ZTD observations into an NWP model. This acts as a physically based interpolator of the GNSS values, which have a spatial resolution much coarser than the InSAR APS one.</p><p>The second is based on a stack of ZTD maps derived by an Iterative Tropospheric Decomposition (ITD) model, as implemented in the GACOS service. In this case, the high-resolution ZTD maps are obtained by an iterative interpolation of a global atmospheric circulation model values and GNSS values where available.</p><p>The results of the comparisons and sensitivity tests on the number of ZTD maps needed to derive the unknown master delay map are shown.</p><p> </p><p> </p><p> </p><p><strong> </strong></p><p><strong> </strong></p>

scholarly journals Toward a variational assimilation of polarimetric radar observation in a convective scale NWP model

2019 ◽  
Author(s):  
Guillaume Thomas ◽  
Jean-François Mahfouf ◽  
Thibaut Montmerle
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Observation Operator ◽  
Weather Radars ◽  
Non Linear ◽  
Convective Scale ◽  
The Difference ◽  
Input Variables ◽  
Nwp Model ◽  
Differential Reflectivity

Abstract. This paper presents the potential of non-linear and linear versions of an observation operator for simulating polarimetric variables observed by weather radars. These variables, deduced from the horizontally and vertically polarised backscattered radiations, give information about the shape, the phase and the distributions of hydrometeors. Different studies in observation space are presented, as a first step toward their inclusion in a variational data assimilation context, which is not treated here. Input variables are prognostic variables forecasted by the AROME-France Numerical Weather Prediction (NWP) model at convective scale, including liquid and solid hydrometeor contents. A non-linear observation operator, based on the T-matrix method, allows to simulate the horizontal and the vertical reflectivities (ZHH and ZVV), the differential reflectivity ZDR, the specific differential phase KDP and the copolar correlation coefficient ρHV. To assess the uncertainty of such simulations, perturbations have been applied on input parameters of the operator, such as dielectric constant, shape and orientation of the scatterers. Statistics of innovations, defined by the difference between simulated and observed values, are then performed. After some specific filtering procedures, shapes close to Gaussian have been found for both reflectivities and for ZDR, contrarily to KDP and ρHV. A linearised version of this observation operator has been obtained by its Jacobian matrix estimated with the finite difference method. This step allows to study the sensitivity of polarimetric variables to hydrometeor content perturbations, in the model geometry as well as in the radar one. The polarimetric variables ZHH and ZDR appear to be good candidates for hydrometeor initialisation, while KDP seems to be useful only for rain contents. Due to the weak sensitivity of ρHV, its use in data assimilation is expected to be very challenging.

scholarly journals A Polarimetric Radar Forward Operator for Model Evaluation

2008 ◽  
Vol 47 (12) ◽  
pp. 3202-3220 ◽  
Author(s):  
M. Pfeifer ◽  
G. C. Craig ◽  
M. Hagen ◽  
C. Keil
Keyword(s):  
Weather Prediction ◽  
Systematic Evaluation ◽  
Polarimetric Radar ◽  
Model Errors ◽  
Direct Evaluation ◽  
Improve Model ◽  
Sensitivity Studies ◽  
Nwp Model ◽  
Microphysical Parameterization ◽  
Forward Operator

Abstract A polarimetric radar forward operator has been developed as a tool for the systematic evaluation of microphysical parameterization schemes in high-resolution numerical weather prediction (NWP) models. The application of such a forward operator allows a direct comparison of the model simulations to polarimetric radar observations. While the comparison of observed and synthetic reflectivity gives information on the quality of quantitative precipitation forecasts, the information from the polarimetric quantities allows for a direct evaluation of the capacity of the NWP model to realistically describe the processes involved in the formation and interactions of the hydrometeors and, hence, the performance of the microphysical parameterization scheme. This information is expected to be valuable for detecting systematic model errors and hence improve model physics. This paper summarizes the technical characteristics of the synthetic polarimetric radar (SynPolRad). Different polarimetric radar quantities are computed from model forecasts using a T-matrix scattering code and ice phase hydrometeors are explicitly considered. To do so, the sensitivities of the scattering processes to the microphysical characteristics of different ice hydrometeors are investigated using sensitivity studies. Furthermore, beam propagation effects are considered, including attenuation and beam bending. The performance of SynPolRad and the consistence of the assumptions made in the derivation of the input parameters are illustrated in a case study. The resulting synthetic quantities as well as hydrometeor classification are compared with observations and are shown to be consistent with the model assumptions.

scholarly journals A Real-Time Evaporation Correction Scheme for Radar-Derived Mosaicked Precipitation Estimations

2018 ◽  
Vol 19 (1) ◽  
pp. 87-111 ◽  
Author(s):  
Steven M. Martinaitis ◽  
Heather M. Grams ◽  
Carrie Langston ◽  
Jian Zhang ◽  
Kenneth Howard
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Surface Precipitation ◽  
Correction Scheme ◽  
Evaporation Technique ◽  
Microphysical Processes ◽  
Rain Events ◽  
2D And 3D

Abstract Precipitation values estimated by radar are assumed to be the amount of precipitation that occurred at the surface, yet this notion is inaccurate. Numerous atmospheric and microphysical processes can alter the precipitation rate between the radar beam elevation and the surface. One such process is evaporation. This study determines the applicability of integrating an evaporation correction scheme for real-time radar-derived mosaicked precipitation rates to reduce quantitative precipitation estimate (QPE) overestimation and to reduce the coverage of false surface precipitation. An evaporation technique previously developed for large-scale numerical modeling is applied to Multi-Radar Multi-Sensor (MRMS) precipitation rates through the use of 2D and 3D numerical weather prediction (NWP) atmospheric parameters as well as basic radar properties. Hourly accumulated QPE with evaporation adjustment compared against gauge observations saw an average reduction of the overestimation bias by 57%–76% for rain events and 42%–49% for primarily snow events. The removal of false surface precipitation also reduced the number of hourly gauge observations that were considered as “false zero” observations by 52.1% for rain and 38.2% for snow. Optimum computational efficiency was achieved through the use of simplified equations and hourly 10-km horizontal resolution NWP data. The run time for the evaporation correction algorithm is 6–7 s.

scholarly journals Automatic Model Calibration: A New Way to Improve Numerical Weather Forecasting

2017 ◽  
Vol 98 (5) ◽  
pp. 959-970 ◽  
Author(s):  
Q. Duan ◽  
Z. Di ◽  
J. Quan ◽  
C. Wang ◽  
W. Gong ◽  
...  
Keyword(s):  
Model Calibration ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Model Performance ◽  
Model Parameters ◽  
Mathematical Methods ◽  
Numerical Weather ◽  
Calibration Methods ◽  
The Difference ◽  
Nwp Model

Abstract Weather forecasting skill has been improved over recent years owing to advances in the representation of physical processes by numerical weather prediction (NWP) models, observational systems, data assimilation and postprocessing, new computational capability, and effective communications and training. There is an area that has received less attention so far but can bring significant improvement to weather forecasting—the calibration of NWP models, a process in which model parameters are tuned using certain mathematical methods to minimize the difference between predictions and observations. Model calibration of the NWP models is difficult because 1) there are a formidable number of model parameters and meteorological variables to tune, and 2) a typical NWP model is very expensive to run, and conventional model calibration methods require many model runs (up to tens of thousands) or cannot handle the high dimensionality of NWP models. This study demonstrates that a newly developed automatic model calibration platform can overcome these difficulties and improve weather forecasting through parameter optimization. We illustrate how this is done with a case study involving 5-day weather forecasting during the summer monsoon in the greater Beijing region using the Weather Research and Forecasting Model. The keys to automatic model calibration are to use global sensitivity analysis to screen out the most important parameters influencing model performance and to employ surrogate models to reduce the need for a large number of model runs. Through several optimization and validation studies, we have shown that automatic model calibration can improve precipitation and temperature forecasting significantly according to a number of performance measures.

scholarly journals Parameterized Forward Operators for Simulation and Assimilation of Polarimetrie Radar Data with Numerical Weather Predictions

2021 ◽  
Vol 38 (5) ◽  
pp. 737-754
Author(s):  
Guifu Zhang ◽  
Jidong Gao ◽  
Muyun Du
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Weather Prediction ◽  
Computing Time ◽  
Radar Data ◽  
Polarimetric Radar ◽  
State Variables ◽  
Numerical Weather ◽  
Nwp Model

AbstractMany weather radar networks in the world have now provided polarimetric radar data (PRD) that have the potential to improve our understanding of cloud and precipitation microphysics, and numerical weather prediction (NWP). To realize this potential, an accurate and efficient set of polarimetric observation operators are needed to simulate and assimilate the PRD with an NWP model for an accurate analysis of the model state variables. For this purpose, a set of parameterized observation operators are developed to simulate and assimilate polarimetric radar data from NWP model-predicted hydrometeor mixing ratios and number concentrations of rain, snow, hail, and graupel. The polarimetric radar variables are calculated based on the T-matrix calculation of wave scattering and integrations of the scattering weighted by the particle size distribution. The calculated polarimetric variables are then fitted to simple functions of water content and volume-weighted mean diameter of the hydrometeor particle size distribution. The parameterized PRD operators are applied to an ideal case and a real case predicted by the Weather Research and Forecasting (WRF) model to have simulated PRD, which are compared with existing operators and real observations to show their validity and applicability. The new PRD operators use less than one percent of the computing time of the old operators to complete the same simulations, making it efficient in PRD simulation and assimilation usage.

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