Investigation of Maximum Hail-Size Forecasting Using Bulk Microphysics Schemes

The Southern Great Plains (SGP) region exhibits a relatively high frequency of periods with extremely high rainfall rates (RR) and hail. Seven months of 2017 are simulated using the Weather Research and Forecasting (WRF) Model applied at convection-permitting resolution with the Milbrandt–Yau microphysics scheme. Simulation fidelity is evaluated, particularly during intense convective events, using data from ASOS stations, dual-polarization radar, and gridded datasets and observations at the DOE Atmospheric Radiation Measurement site. The spatial gradients and temporal variability of precipitation and the cumulative density functions for both RR and wind speeds exhibit fidelity. Odds ratios > 1 indicate that WRF is also skillful in simulating high composite reflectivity (cREF, used as a measure of widespread convection) and RR > 5 mm h−1 over the domain. Detailed analyses of the 10 days with highest spatial coverage of cREF > 30 dBZ show spatially similar reflectivity fields and high RR in both radar data and WRF simulations. However, during periods of high reflectivity, WRF exhibits a positive bias in terms of very high RR (>25 mm h−1) and hail occurrence, and during the summer and transition months, maximum hail size is underestimated. For some renewable energy applications, fidelity is required with respect to the joint probabilities of wind speed and RR and/or hail. While partial fidelity is achieved for the marginal probabilities, performance during events of critical importance to these energy applications is currently not sufficient. Further research into optimal WRF configurations in support of potential damage quantification for these applications is warranted. Significance Statement Heavy rainfall and hail during convective events are challenging for numerical models to simulate in both space and time. For some applications, such as to estimate damage to wind turbine blades and solar panels, fidelity is also required with respect to hail size and joint probabilities of wind speed and hydrometeor type and rainfall rates (RR). This demands fidelity that is seldom evaluated. We show that, although this simulation exhibits fidelity for the marginal probabilities of wind speed, RR, and hail occurrence, the joint probabilities of these properties and the simulation of maximum size of hail are, as yet, not sufficient to characterize potential damage to these renewable energy industries.

The global navigation satellite system (GNSS) can provide centimeter positioning accuracy at low costs. However, in order to obtain the desired high accuracy, it is necessary to use high-quality atmospheric models. We focus on the troposphere, which is an important topic of research in Brazil where the tropospheric characteristics are unique, both spatially and temporally. There are dry regions, which lie mainly in the central part of the country. However, the most interesting area for the investigation of tropospheric models is the wet region which is located in the Amazon forest. This region substantially affects the variability of humidity over other regions of Brazil. It provides a large quantity of water vapor through the humidity convergence zone, especially for the southeast region. The interconnection and large fluxes of water vapor can generate serious deficiencies in tropospheric modeling. The CPTEC/INPE (Center for Weather Forecasting and Climate Studies/Brazilian Institute for Space Research) has been providing since July 2012 a numerical weather prediction (NWP) model for South America, known as Eta. It has yield excellent results in weather prediction but has not been used in GNSS positioning. This NWP model was evaluated in precise point positioning (PPP) and network-based positioning. Concerning PPP, the best positioning results were obtained for the station SAGA, located in Amazon region. Using the NWP model, the 3D RMS are less than 10 cm for all 24 h of data, whereas the values reach approximately 60 cm for the Hopfield model. For network-based positioning, the best results were obtained mainly when the tropospheric characteristics are critical, in which case an improvement of up to 7.2 % was obtained in 3D RMS using NWP models.

There are a growing number of advanced imagers for geostationary meteorological satellites, which can provide water vapor radiance observations with high temporal and spatial resolutions. To assess the impact of those imagers, radiance assimilation experiments were conducted with the Advanced Baseline imager (ABI) on board the Geostationary Operational Environmental Satellite‐16. The radiances from the three water vapor absorption bands of Geostationary Operational Environmental Satellite‐16 ABI were assimilated through the National Oceanic and Atmospheric Administration Gridpoint Statistical Interpolation data assimilation system in a regional numerical weather prediction (NWP) model. The forecast impacts for Hurricane Irma (2017) and Hurricane Harvey (2017) have been studied and analyzed in this work. Due to complicated surface situations (emissivity, terrain height, etc.) over land, the infrared (IR) radiance assimilation is still limited; thus, handling surface effects in radiance assimilation needs to be considered. By analyzing the Jacobian function of skin temperature in the ABI radiance assimilation process, it is shown that assimilating water vapor IR radiances over high elevation surfaces or in dry regions is problematic even where the bands are mostly sensitive to the upper level of the atmosphere such as Band 8 (6.19 μm). Additional quality control steps using skin temperature Jacobians to eliminate the contamination from the surface impact are developed and added for ABI radiance assimilation. The results show that ABI radiance assimilation with quality controls is able to improve tropical cyclone forecasts. The methodology used in this study can be applied to the assimilation of IR radiances from other geostationary satellites or polar‐orbiting satellites.

Abstract. Meteorological observations over the Tibetan Plateau (TiP) are scarce, and precipitation estimations over this remote region are difficult. The constantly improving capabilities of numerical weather prediction (NWP) models offer the opportunity to reduce this problem by providing precipitation fields and other meteorological variables of high spatial and temporal resolution. Longer time periods of years to decades can be simulated by NWP models by successive model runs of shorter periods, which can be described by the term "regional atmospheric reanalysis". In this paper, we assess the Weather Research and Forecasting (WRF) models capacity in retrieving rain- and snowfall on the TiP in such a configuration using a nested approach: the simulations are conducted with three nested domains at spatial resolutions of 30, 10, and 2 km. A validation study is carried out for a one-month period with a special focus on one-week (22–28 October 2008), during which strong rain- and snowfall was observed on the TiP. The output of the model in each resolution is compared to the Tropical Rainfall Measuring Mission (TRMM) data set for precipitation and to the Moderate Resolution Imaging Spectroradiometer (MODIS) data set for snow extent. TRMM and WRF data are then compared to weather-station measurements. Our results suggest an overall improvement from WRF over TRMM with respect to weather-station measurements. Various configurations of the model with different nesting and forcing strategies, as well as physical parameterisation schemes are compared to propose a suitable design for a regional atmospheric reanalysis over the TiP. The WRF model showed good accuracy in simulating snow- and rainfall on the TiP for a one-month simulation period. Our study reveals that there is nothing like an optimal model strategy applicable for the high-altitude TiP, its fringing high-mountain areas of extremely complex topography and the low-altitude land and sea regions from which much of the precipitation on the TiP is originating. The choice of the physical parameterisation scheme will thus be always a compromise depending on the specific purpose of a model simulation. Our study demonstrates the high importance of orographic precipitation, but the problem of the orographic bias remains unsolved since reliable observational data are still missing. The results are relevant for anyone interested in carrying out a regional atmospheric reanalysis. Many hydrological analyses and applications like rainfall-runoff modelling or the analysis of flood events require precipitation rates at daily or even hourly intervals. Thus, our study offers a process-oriented alternative for retrieving precipitation fields of high spatio-temporal resolution in regions like the TiP, where other data sources are limited.

A two‐moment bulk microphysical scheme has been implemented into the Weather Research and Forecasting (WRF) model to investigate the aerosol‐cloud interaction. The microphysical scheme calculates the mass mixing ratios and number concentrations of aerosols and five types of hydrometeors and accounts for various cloud processes including warm and mixed phase microphysics. The representation of the aerosol size distribution is evaluated, showing that the three‐moment modal method produces results better in agreement with the sectional approach than the two‐moment modal method for variable supersaturation conditions in clouds. The effects of aerosols on cloud processes are investigated using the two‐moment bulk microphysical scheme in a convective cumulus cloud event occurring on 24 August 2000 in Houston, Texas. The modeled evolution of the distribution of radar reflectivity in the y‐z section, the cell lifetime, and averaged accumulated precipitation with the aerosol concentration under the polluted urban condition are qualitatively consistent with the measurements. Sensitivity simulations are initialized using a set of aerosol profiles with the number concentrations ranging from 200 to 50,000 cm−3 and mass ranging from 1 to 10 μg m−3 at the surface level. The response of precipitation to the increase of aerosol concentrations is nonmonotonic, because of the complicated interaction between cloud microphysics and dynamics. The precipitation increases with aerosol concentrations from clean maritime to continental background conditions, but is considerably reduced and completely suppressed under highly polluted conditions, indicating that the aerosol concentration exhibits distinct effects on the precipitation efficiency under different aerosol conditions. The maximal cloud cover, core updraft, and maximal vertical velocity exhibit similar responses as precipitation. Comparison is made to evaluate the effects of different autoconversion parameterizations and bulk microphysical schemes on cloud properties. Because of its broad application in numerical weather prediction, implementation of the two‐moment microphysical scheme to the WRF model will greatly facilitate assessment of aerosol‐cloud interaction from individual cumulus to mesoscale convective systems.

Abstract. Broadband short-wave (SW) surface direct and diffuse irradiances are not typically within the set of output variables produced by numerical weather prediction (NWP) models. However, they are frequently requested for solar energy applications. In order to compute them, a detailed representation of the aerosol optical properties is important. Nonetheless, NWP models typically oversimplify aerosol representation or even neglect their effect. In this work, a flexible method to account for the SW aerosol optical properties in the computation of broadband SW surface direct and diffuse irradiances is presented. It only requires aerosol optical depth at 0.55 μm and knowledge of the type of predominant aerosol. Other parameters needed to consider spectral aerosol extinction, namely, Angström exponent, aerosol single-scattering albedo and aerosol asymmetry factor, are parameterized. The parameterization has been tested using the Rapid Radiative Transfer Model for climate and weather models (RRTMG) SW scheme of the Weather Research and Forecasting (WRF) NWP model for data over the continental US. In principle, it can be adapted to any other SW radiative transfer band model. It has been verified against a control experiment and using data from five radiometric stations in the contiguous US. The control experiment consisted of a clear-sky evaluation of the RRTMG solar radiation estimates obtained in WRF when RRTMG is driven with ground-observed aerosol optical properties. Overall, the verification has shown satisfactory results for both broadband SW surface direct and diffuse irradiances. The parameterization has proven effective in significantly reducing the prediction error and constraining the seasonal bias in clear-sky conditions to within the typical observational error expected in well maintained radiometers.

Abstract. Regions with high ice water content (HIWC), composed of mainly small ice crystals, frequently occur over convective clouds in the tropics. Such regions can have median mass diameters (MMDs) <300 µm and equivalent radar reflectivities <20 dBZ. To explore formation mechanisms for these HIWCs, high-resolution simulations of tropical convective clouds observed on 26 May 2015 during the High Altitude Ice Crystals – High Ice Water Content (HAIC-HIWC) international field campaign based out of Cayenne, French Guiana, are conducted using the Weather Research and Forecasting (WRF) model with four different bulk microphysics schemes: the WRF single‐moment 6‐class microphysics scheme (WSM6), the Morrison scheme, and the Predicted Particle Properties (P3) scheme with one- and two-ice options. The simulations are evaluated against data from airborne radar and multiple cloud microphysics probes installed on the French Falcon 20 and Canadian National Research Council (NRC) Convair 580 sampling clouds at different heights. WRF simulations with different microphysics schemes generally reproduce the vertical profiles of temperature, dew-point temperature, and winds during this event compared with radiosonde data, and the coverage and evolution of this tropical convective system compared to satellite retrievals. All of the simulations overestimate the intensity and spatial extent of radar reflectivity by over 30 % above the melting layer compared to the airborne X-band radar reflectivity data. They also miss the peak of the observed ice number distribution function for 0.1<Dmax<1 mm. Even though the P3 scheme has a very different approach representing ice, it does not produce greatly different total condensed water content or better comparison to other observations in this tropical convective system. Mixed-phase microphysical processes at −10 ∘C are associated with the overprediction of liquid water content in the simulations with the Morrison and P3 schemes. The ice water content at −10 ∘C increases mainly due to the collection of liquid water by ice particles, which does not increase ice particle number but increases the mass/size of ice particles and contributes to greater simulated radar reflectivity.

A new approach to forecast solar irradiances using WRF and libRadtran models, validated with MERRA-2 reanalysis data and pyranometer measures

Deep learning approaches for bias correction in WRF model outputs for enhanced solar and wind energy estimation: A case study in East and West Malaysia

In this study, the Weather Research and Forecasting (WRF) model version 3.2 is used to examine the impact of precipitating ice and especially snow-graupel partitioning in the simulation of a heavy rainfall event over Chalkidiki peninsula in Northern Greece. This major precipitation event, associated with a case of cyclogenesis over the Aegean Sea, occurred on the 8th of October 2006 causing severe flooding and damage. Two widely used microphysical parameterizations, the Purdue Lin (PLIN) and WRF Single-Moment 6-class scheme (WSM6) are compared with available raingauge measurements over the complex topography of Chalkidiki. To further investigate the importance of snow and graupel relative mass content and the treatment of precipitating ice sedimentation velocity, two older versions of the WSM6 scheme were compiled and run with the current model. The verification results indicate that all simulations were found to match raingauge data more closely over the eastern mountainous Chalkidiki peninsula where maximum accumulations were observed. In other stations all schemes overestimate 24h accumulated rainfall except a station situated at the western part of the peninsula, where none of the simulations was able to reproduce observed rainfall. Graupel dominance in PLIN generates rapid precipitation fallout at the point of maximum predicted 24h accumulation. Similar behavior is shown in WSM6 from WRF version 2, but with significant less rainfall. Increasing snow amounts aloft, due to the unified treatment of precipitating ice in WSM6 from WRF version 3, modifies rain dynamics which decrease rainfall rates, but increases 24h accumulations. A sensitivity experiment where PLIN is used with snow accretion by graupel turned off, indicated that this process seems to be the most important factor controlling the differences in surface precipitation between PLIN and WSM6 from WRF version 3, determining the spatial and temporal distribution of this heavy precipitation event. The results also revealed that snow overestimation can lead to high rainfall accumulations, even though rain is more evenly distributed over the 24h period, deteriorating precipitation forecast.

This study examines how cloud microphysical parameterizations in the Weather Research and Forecasting (WRF) model influence the simulation of rainfall over Kuwait during November 2018, with particular focus on the extreme event of 14 November. The WRF model was configured at 4-km resolution and dynamically downscaled from the Community Climate System Model version 4 (CCSM4) to evaluate the performance of four bulk microphysics schemes: WSM6, Lin, Thompson, and Morrison. Model output was evaluated against observations from the Kuwait Automatic Weather Station (29.22°N, 47.96°E) using cumulative and distributional rainfall characteristics, event-scale analysis, and standard statistical metrics (RMSE, MAE, bias, and correlation). All schemes reproduced the timing of rainfall events, while notable differences were found in simulated rainfall intensity. WSM6 produced rainfall amounts closest to observations, with the lowest error values and a small positive bias. Lin showed moderate overestimation, Thompson produced larger overestimation during heavy-rain periods, and Morrison consistently underestimated rainfall totals. For the 14 November event, which recorded 79.0 mm at the station, simulated totals ranged from 55.6 to 91.6 mm across the schemes, indicating that inter-scheme differences were dominated by rainfall magnitude rather than timing. These results highlight the sensitivity of convection-permitting rainfall simulations in arid regions to microphysical formulation. Evaluation using additional events and seasons is needed to assess whether these results extend beyond the period examined.

Numerical weather prediction (NWP) models such as the Weather Research and Forecasting (WRF) model are increasingly used over the Indian region to forecast extreme rainfall events. However, studies which explore the application of high-resolution rainfall simulations obtained from the WRF model in urban hydrology are limited. In this paper, the utility of a model coupling framework to predict urban floods is explored through the case study of Bangalore city in India. This framework is used to simulate multiple extreme events that occurred over the city for the monsoons of years 2020 and 2021. To address the uncertainty from the WRF model, a 12-member convection permitting ensemble is used. Model configurations using Kain Fritsch and WSM6 parameterization schemes could simulate the spatial and temporal pattern of the selected event. The city is easily flooded with rainfall events above a threshold of 60 mm/day and to capture the response of the urban catchment, the Personal Computer Storm Water Management Model (PCSWMM) is used in this study. Flood forecasts are created using the outputs from the WRF ensemble and the Global Forecasting System (GFS). The high temporal and spatial resolution of the rainfall forecasts (<4 km at 15-min intervals), has proved critical in reproducing the urban flood event. The flood forecasts created using the WRF ensemble indicate that flooding and water levels are comparable to the observed whereas the GFS underestimates these to a large extent. Thus, the coupled WRF–PCSWMM modelling framework is found effective in forecasting flood events over an Indian city.

Recent studies have shown the unique value of satellite-observed land surface thermal infrared (TIR) information (e.g., skin temperature) and the feasibility of assimilating land surface temperature (LST) into land surface models (LSMs) to improve the simulation of land-atmosphere water and energy exchanges. In this study, two different types of LST assimilation techniques are implemented and the benefits from the techniques are compared. One of the techniques is to directly assimilate LST using ensemble Kalman filter (EnKF) data assimilation (DA) utilities. The other is to use the Atmosphere-Land Exchange Inversion model (ALEXI) as an “observation operator” that converts LST retrievals into the soil moisture (SM) proxy based on the ratio of actual to potential evapotranspiration (fPET), which is then assimilated into an LSM. While most current studies have shown some success in both directly the assimilating LST and assimilating ALEXI SM proxy into offline LSMs, the potential impact of the assimilation of TIR information through coupled numerical weather prediction (NWP) models is unclear. In this study, a semi-coupled Land Information System (LIS) and Weather Research and Forecast (WRF) system is employed to assess the impact of the two different techniques for assimilating the TIR observations from NOAA GOES satellites on WRF model forecasts. The NASA LIS, equipped with a variety of LSMs and advanced data assimilation tools (e.g., the ensemble Kalman Filter (EnKF)), takes atmospheric forcing data from the WRF model run, generates updated initial land surface conditions with the assimilation of either LST- or TIR-based SM and returns them to WRF for initializing the forecasts. The WRF forecasts using the daily updated initializations with the TIR data assimilation are evaluated against ground weather observations and re-analysis products. It is found that WRF forecasts with the LST-based SM assimilation have better agreement with the ground weather observations than those with the direct LST assimilation or without the land TIR data assimilation.

Assimilation of accurate external satellite-based water vapor data has been demonstrated to be able to improve the Numerical Weather Prediction (NWP) model’s forecasting performance. In this study, two types of near-infrared Precipitable Water Vapor (PWV) data from the Ocean and Land Colour Instrument (OLCI) of the Sentinel-3A and -3B satellites are assimilated into the Weather Research and Forecasting (WRF) model to enhance the weather forecasting performance over the South China area. One type is the raw PWV products obtained directly from the Sentinle-3A and -3B satellites; the other type is the Sentinel-3A and -3B PWV calibrated by PWV from Global Navigation Satellite System (GNSS). The Back Propagation Neural Network (BPNN) model is used in the PWV calibration. Correspondingly, two WRF assimilation schemes are adopted to assimilate the Sentinel-3 raw PWV and Sentinel-3 calibrated PWV. Additionally, the WRF without data assimilation scheme is used as the background run to investigate the impact of data assimilation. Three WRF experimental schemes are performed for two periods, i.e., February 2020 (dry period) and July 2020 (wet period). The forecasting performance of these three WRF schemes are evaluated by observations from GNSS, radiosonde, and surface meteorological stations. The comparison results with GNSS PWV show that assimilation of Sentienl-3 calibrated PWV can improve PWV forecasting accuracy by 5.6% for February period for the first 6 h after data assimilation. Evaluated by radiosonde profiles, the assimilation of Sentinel-3 both raw and calibrated PWV can improve WRF humidity and temperature forecasting accuracy over the July period. The accuracy improvement can be up to 9.2% (at 2 km to 3 km of altitude) for water vapor mixing ratio and 6.0% for temperature (at 4 km to 5 km of altitude). The WRF rainfall evaluation result has been validated by rainfall observations from meteorological stations and it shows that the assimilation of Sentinel-3 PWV can improve the rainfall forecasting performance over the July period. The rainfall forecasting success rates of three schemes of assimilating no data, Sentinel-3 raw PWV, and Sentinel-3 calibrated PWV are 69.4%, 69.6%, and 71.1%, respectively, over the July 2020 in the South China region.

Performance evaluation of the Weather Research and Forecasting (WRF) model for assessing wind resource in Greece