High-resolution NU-WRF simulations of a deep convective-precipitation system during MC3E: Further improvements and comparisons between Goddard microphysics schemes and observations

Regional background ozone estimation for China through data fusion of observation and simulation

Regional background ozone (O3_RBG) is an important component of surface ozone (O3). However, due to the uncertainties in commonly used Chemical Transport Models (CTMs) and statistical models, accurately assessing O3_RBG in China is challenging. In this study, we calculated the O3_RBG concentrations with the CTM - Brute Force Method (BFM) and constrained the results with site observations of O3 with the multiple linear regression (MLR) model. The annual average O3_RBG concentration in China region in 2020 is 35 ± 4 ppb, accounting for 81 ± 5 % of the maximum 8-h average O3 (MDA8 O3). We applied the random forest and Shapley additive explanations based on meteorological standardization techniques to separate the contributions of meteorology and natural emissions to O3_RBG. Natural emissions contribute more significantly to O3_RBG than meteorology in various Chineses regions (30-40 ppb), with higher contributions during the warm season. Meteorological factors show higher contributions in the spring and summer seasons (2-3 ppb) than the other seasons. Temperature and humidity are the primary contributors to O3_RBG in regions with severe O3 pollution in China, with their individual impacts ranging from 30 % to 62 % of the total impacts of all meteorological factors in different seasons. For policy implications, we tracked the contributions of O3_RBG and local photochemical reaction contributions (O3_LC) to total O3 concentration at different O3 levels. We found that O3_LC contribute over 45 % to MDA8 O3 on polluted days, supporting the current Chinese policy of reducing O3 peak concentrations by cutting down precursor emissions. However, as the contribution of O3_RBG is not considered in the policy, additional efforts are needed to achieve the control groal of O3 concentration. As the implementation of stringent O3 control measurements in China, the contribution of O3_RBG become increasingly significant, suggesting the need for attention to O3_RBG and regional joint prevention and control.

Data Assimilation of Doppler Wind Lidar for the Extreme Rainfall Event Prediction over Northern Taiwan: A Case Study

On 4 June 2021, short-duration extreme precipitation occurred in Taipei. Within 2 h, over 200 mm of rainfall accumulated in the Xinyi district. In this study, advanced data assimilation technology (e.g., hybrid data and 3D variations) was incorporated to develop a high-resolution, small-scale (e.g., northern Taiwan) data assimilation forecast system, namely the weather research and forecast-grid statistical interpolation (WRF-GSI) model. The 3D wind field data recorded by the Doppler wind lidar system of Taipei Songshan Airport were assimilated for effective simulation of the extreme precipitation. The results revealed that the extreme rainfall was caused by the interaction between the northeast wind incurred by a front to the north of Taiwan, a humid southerly wind generated by Typhoon Choi-wan, and the regional sea–land breeze circulation. For the Xinyi district, the WRF-GSI_lidar model reported accumulated rainfall 30 mm higher than that in the non-assimilated experiment (WRF-GSI_noDA), indicating that the WRF-GSI model with lidar observation was improved 15% more than the nonassimilated run.

A Comparison of Spectral Bin Microphysics versus Bulk Parameterization in Forecasting Typhoon In-Fa (2021) before, during, and after Its Landfall

Typhoon In-Fa hit continental China in July 2021 and caused an unprecedented rainfall amount, making it a typical case to examine the ability of numerical models in forecasting landfalling typhoons. The record-breaking storm was simulated using a 3-km-resolution weather research and forecast (WRF) model with spectral bin microphysics scheme (BIN) and two-moment seven-class bulk parameterization scheme (BULK). The simulations were then separated into three different typhoon landfall periods (i.e., pre-landfall, landfall, and post-landfall). It was found that typhoon intensity prediction is sensitive to microphysical schemes regardless of landfall periods, while typhoon track prediction tends to be more (less) sensitive to microphysical schemes after (before) typhoon landfall. Moreover, significant differences exist between BIN and BULK schemes in simulating the storm intensity, track, and rainfall distribution. BIN scheme simulates stronger (weaker) typhoon intensity than BULK scheme after (before) landfall, while BULK scheme simulates typhoon moving faster (slower) than BIN scheme before (after) landfall. BIN scheme produces much more extensive and homogeneous typhoon rainbands than BULK scheme, whereas BULK scheme produces stronger (weaker) rainfall in the typhoon inner (outer) rainbands. The possible reasons for such differences are discussed. At present, the ability of WRF and other mesoscale models to accurately simulate the typhoon precipitation hydrometeors is still limited. To evaluate the performances of BIN and BULK schemes of WRF model in simulating the condensed water in Typhoon In-Fa, the observed microwave brightness temperature and radar reflectivity from the core observatory of Global Precipitation Mission (GPM) satellite are directly used for validation with the help of a satellite simulator. It is suggested that BIN scheme has better performance in estimating the spatial structure, overall amplitude, and precise location of the condensed water in typhoons before landfall. During typhoon landfall, the performance of BIN scheme in simulating the structure and location of the condensate is close to that of BULK scheme, but the condensate intensity prediction by BIN scheme is still better; BULK scheme performs even better than BIN scheme in the prediction of condensate structure and location after typhoon landfall. Both schemes seem to have poorer performances in simulating the spatial structure of precipitation hydrometeors during typhoon landfall than before/after typhoon landfall. Moreover, BIN scheme simulates more (less) realistic warm (cold) rain processes than BULK scheme, especially after typhoon landfall. BULK scheme simulates more cloud water and larger convective updraft than BIN scheme, and this is also reported in many model studies comparing BIN and BULK schemes

Error Features in Predicting Typhoon Winds: A Case Study Comparing Simulated and Measured Data

Simulating a typhoon’s wind field via mesoscale models is important in terms of providing not only the guidelines for urban planning and onshore/offshore constructions, but also the provision of insight into the dynamics and thermodynamics of tropical cyclone systems. Therefore, the errors that are contained in simulation results were investigated in the present study, in association with large-scale meteorological patterns and localized wind conditions in the typhoon boundary layer. In detail, the full-set three-dimensional simulations of three typhoon cases were carried out in order to provide the typhoon wind fields that were required to compare with the observations that were obtained through land weather stations and offshore buoys. Although the reliability of typhoon simulations has been thoroughly investigated, the previous works mostly concentrated on the configurations and dynamic core of the model. The present study reveals, however, the influences of the characteristics of the specific weather system on the simulation’s results, which provides the foundation for the proposition of empirical corrections to improve the mesoscale simulation results of typhoon wind fields without updating the model’s algorithm.

Revision of WDM7 Microphysics Scheme and Evaluation for Precipitating Convection over the Korean Peninsula

The Weather Research and Forecasting (WRF) Double-Moment 7-Class (WDM7) cloud microphysics scheme was developed to parameterize cloud and precipitation processes explicitly for mesoscale phenomena in the Korean Integrated Model system. However, the WDM7 scheme has not been evaluated for any precipitating convection system over the Korean peninsula. This study modified WDM7 and evaluated simulated convection during summer and winter. The suggested modifications included the integration of the new fall velocity–diameter relationship of raindrops and mass-weighted terminal velocity of solid-phase precipitable hydrometeors (the latter is for representing mixed-phase particles). The mass-weighted terminal velocity for snow and graupel has been suggested by Dudhia et al. (2008) to allow for a more realistic representation of partially rimed particles. The WDM7 scheme having an additional hail category does not apply this terminal velocity only for hail. Additionally, the impact of enhanced collision-coalescence (C-C) efficiency was investigated. An experiment with enhanced C-C efficiency overall improved the precipitation skill scores, such as probability of detection, equitable threat score, and spatial pattern correlation, compared with those of the control experiment for the summer and winter cases. With application of the new mass-weighted terminal velocity of solid-phase hydrometeors, the hail mixing ratio at the surface was considerably reduced, and rain shafts slowed down low-level winds for the winter convective system. Consequently, the simulated hydrometeors were consistent with observations retrieved via remote sensing. The fall velocity–diameter relationship of raindrops further reduced the cloud ice amount. The proposed modifications in our study improved the simulated precipitation and hydrometeor profiles, especially for the selected winter convection case.

On the Assessment GPS-Based WRFDA for InSAR Atmospheric Correction: A Case Study in Pearl River Delta Region of China

The accuracy and applications of synthetic aperture radar interferometry (InSAR) are severely suppressed by tropospheric error. Numerical Weather Models (NWMs) and GPS-derived tropospheric delays have been widely used to correct the tropospheric error considering their complete spatial coverage or high accuracy. However, few studies focus on the fusion of both NWMs and GPS for the tropospheric error correction. In this study, we used the Weather Research and Forecasting (WRF) to obtain NWMs with a higher spatial-temporal resolution of 3 km and 20 s from both ERAI (79 km and 6 h) and ERA5 (0.25° and 1 h). After that, we utilized the WRF Data Assimilation (WRFDA) system to assimilate the GPS ZTD into these enhanced NWMs and generate merged NWMs products. The tropospheric correction effectiveness from different NWMs products was evaluated in a case in the Pearl River Delta region of China. The results showed that all the NWMs products could correct the stratified component in the interferogram but could not mitigate the turbulence well, even after improving the spatial-temporal resolution. As for the trend component, the merged NWMs products showed obvious superiority over other products. From the statistics perspective, the stdev of the interferogram decreased further over 20% by the merged NWMs products than other products when using both ERAI and ERA5, indicating the significant effectiveness of GPS ZTD assimilation.

Impact of Microphysical Parameterizations on Simulated Hurricanes—Using Multi-Parameter Satellite Data to Determine the Particle Size Distributions that Produce Most Realistic Storms

Understanding and forecasting hurricanes remains a challenge for the operational and research communities. To accurately predict the Tropical Cyclone (TC) evolution requires properly reflecting the storm’s inner core dynamics by using: (i) high-resolution models; (ii) realistic physical parameterizations. The microphysical processes and their representation in cloud-permitting models are of crucial importance. In particular, the assumed Particle Size Distribution (PSD) functions affect nearly all formulated microphysical processes and are among the most fundamental assumptions in the bulk microphysics schemes. This paper analyzes the impact of the PSD assumptions on simulated hurricanes and their synthetic radiometric signatures. It determines the most realistic, among the available set of assumptions, based on comparison to multi-parameter satellite observations. Here we simulated 2005′s category-5 Hurricane Rita using the cloud-permitting community Weather Research and Forecasting model (WRF) with two different microphysical schemes and with seven different modifications of the parametrized hydrometeor properties within one of the two schemes. We then used instrument simulators to produce satellite-like observations. The study consisted in evaluating the structure of the different simulated storms by comparing, for each storm, the calculated microwave signatures with actual satellite observations made by (a) the passive microwave radiometer that was carried by the Tropical Rainfall Measuring Mission (TRMM) satellite—the TRMM microwave imager TMI, (b) TRMM’s precipitation radar (PR) and (c) the ocean-wind-vector scatterometer carried by the QuikSCAT satellite. The analysis reveals that the different choices of microphysical parameters do produce significantly different microwave signatures, allowing an objective determination of a “best” parameter combination whose resulting signatures are collectively most consistent with the wind and precipitation observations obtained from the satellites. In particular, we find that assuming PSDs with larger number of smaller hydrometeors produces storms that compare best to observations.

Evaluating Precipitation Features and Rainfall Characteristics in a Multi-Scale Modeling Framework

Cloud and precipitation systems are simulated with a multi-scale modeling framework (MMF) and compared over the Tropics and Subtropics against the Tropical Rainfall Measuring Mission (TRMM) Radar-defined Precipitation Features (RPFs) product. A methodology, in close analogy to the TRMM RPFs, is developed to produce simulated precipitation features (PFs) from the output of the embedded two-dimensional (2D) cloud-resolving models (CRMs) within an MMF. Despite the limitations of 2D CRMs, the simulated population distribution, horizontal and vertical structure of PFs, and the geographical location and local rainfall contribution of mesoscale convective systems (MCSs) are in good agreement with the TRMM observations. However, some model discrepancies are found and can be identified and quantified within the PF distributions. Using model biases in relative population and rainfall contributions, PFs can be characterized into four size categories: small, medium to large, very large, and extremely large. Four different major mechanisms might account for the model biases in each different category: (1) the two-dimensionality of the CRMs, (2) a positive convection-wind-evaporation feedback loop, (3) an artificial dynamic constraint in a bounded CRM domain with cyclic boundaries, and (4) the limited CRM domain size. The second and fourth mechanisms tend to contribute to the excessive tropical precipitation biases commonly found in most MMFs, whereas the other mechanisms reduce rainfall contributions from small and very large PFs. MMF sensitivity experiments with various CRM domain sizes and grid spacings showed that larger domains (higher resolutions) tend to shift PF populations toward larger (smaller) sizes.

Numerical Rainfall Simulation of Different WRF Parameterization Schemes with Different Spatiotemporal Rainfall Evenness Levels in the Ili Region

To obtain high-precision precipitation simulation results, different types of rainfall events in the Ili Region are simulated by using the Weather Research and Forecasting (WRF) model with different physical parameterization schemes. According to the spatiotemporal distribution of rainfall evenness, six rainfall events in the Ili Region are divided into four types. Six microphysical parameterization (MP) schemes, five planetary boundary layer (PBL) schemes, and five cumulus (CU) schemes are combined into 14 parameterization members to simulate the rainfall events. It is worth noting that the simulation result sequence of the WRF model (from best to worst) is as follows: type I (events 3 and 5) > type II (events 1 and 6) > type III (event 2) > type IV (event 4). This finding would imply that the WRF model has the best performance for rainfall events with even spatiotemporal distributions, while it is hard to achieve good simulation results for rainfall events with highly uneven spatial and temporal distributions. The results suggest that no single combination of parameterization members provides the best performance for all rainfall events. According to the overall scheme rankings, d, n, and j are the optimal parameterization combination members that accurately describe the spatiotemporal characteristics of the six rainfall events. The study provides guidance for the selection of the physical parameters for the accurate simulation of different types of rainfall events in the arid region of northwestern China.

Expanding the Goddard CSH Algorithm for GPM: New Extratropical Retrievals

The Goddard Convective-Stratiform Heating (CSH) algorithm has been used to retrieve latent heating (LH) associated with clouds and cloud systems in support of the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) mission. The CSH algorithm required the use of a cloud-resolving model (CRM) to simulate LH profiles to build look-up tables (LUTs). However, the current LUTs in the CSH algorithm are not suitable for retrieving LH profiles at high latitudes or winter conditions that are needed for GPM. The NASA Unified-Weather Research and Forecasting (NU-WRF) model is used to simulate three eastern continental US (CONUS) synoptic winter and three western coastal/offshore events. The relationship between LH structures (or profiles) and other precipitation properties (radar reflectivity, freezing level height, echo-top height, maximum radar reflectivity height and surface precipitation rate) is examined, and a new classification system is adopted with varying ranges for each of these precipitation properties to create LUTs representing high latitude/winter conditions. The performance of the new LUTs is examined using a self-consistency check for one CONUS and one West Coast offshore event by comparing LH profiles retrieved from the LUTs using model-simulated precipitation properties with those originally simulated by the model. The results of the self-consistency check validate the new classification and LUTs. High latitude retrievals from the new LUTs are merged with those from the CSH algorithm to retrieve LH profiles over the GPM domain using precipitation properties retrieved from the GPM combined algorithm.

The impact of simulated mesoscale convective systems on global precipitation: A multiscale modeling study

The importance of precipitating mesoscale convective systems (MCSs) has been quantified from TRMM precipitation radar and microwave imager retrievals. MCSs generate more than 50% of the rainfall in most tropical regions. MCSs usually have horizontal scales of a few hundred kilometers (km); therefore, a large domain with several hundred km is required for realistic simulations of MCSs in cloud-resolving models (CRMs). Almost all traditional global and climate models do not have adequate parameterizations to represent MCSs. Typical multiscale modeling frameworks (MMFs) may also lack the resolution (4 km grid spacing) and domain size (128 km) to realistically simulate MCSs. The impact of MCSs on precipitation is examined by conducting model simulations using the Goddard Cumulus Ensemble (GCE, a CRM) model and Goddard MMF that uses the GCEs as its embedded CRMs. Both models can realistically simulate MCSs with more grid points (i.e., 128 and 256) and higher resolutions (1 or 2 km) compared to those simulations with fewer grid points (i.e., 32 and 64) and low resolution (4 km). The modeling results also show the strengths of the Hadley circulations, mean zonal and regional vertical velocities, surface evaporation, and amount of surface rainfall are weaker or reduced in the Goddard MMF when using more CRM grid points and higher CRM resolution. In addition, the results indicate that large-scale surface evaporation and wind feedback are key processes for determining the surface rainfall amount in the GMMF. A sensitivity test with reduced sea surface temperatures shows both reduced surface rainfall and evaporation.

Influence of Bulk Microphysics Schemes upon Weather Research and Forecasting (WRF) Version 3.6.1 Nor'easter Simulations

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 winter time cyclones impacting the Mid-Atlantic United States. Five-day long WRF simulations were initialized roughly 24 hours prior to the onset of coastal cyclogenesis off the North Carolina coastline. In all, 35 model simulations (5 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 lead to considerable variability in both simulated frozen hydrometeor species and radar reflectivity. WRF-simulated precipitation fields exhibit minor spatio-temporal 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 (CFAD) diagrams reveals notable variability amongst BMPSs, where better performing schemes favored lower graupel mixing ratios and better underlying aggregation assumptions.

Evaluation of NU-WRF rainfall forecasts for IFloodS

The Iowa Flood Studies (IFloodS) campaign was conducted in eastern Iowa as a pre-GPM-launch campaign from 1 May to 15 June 2013. During the campaign period, real time forecasts are conducted utilizing NASA-Unified Weather Research and Forecasting (NU-WRF) model to support the everyday weather briefing. In this study, two sets of the NU-WRF rainfall forecasts are evaluated with Stage IV and Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE), with the objective to understand the impact of Land Surface initialization on the predicted precipitation. NU-WRF is also compared with North American Mesoscale Forecast System (NAM) 12 km forecast. In general, NU-WRF did a good job at capturing individual precipitation events. NU-WRF is also able to replicate a better rainfall spatial distribution compare with NAM. Further sensitivity tests show that the high-resolution makes a positive impact on rainfall forecast. The two sets of NU-WRF simulations produce very close rainfall characteristics. The Land surface initialization do not show significant impact on short-term rainfall forecast, and it is largely due to the soil conditions during the field campaign period.

"On the Land-Ocean Contrast of Tropical Convection and Microphysics Statistics Derived from TRMM Satellite Signals and Global Storm-Resolving Models"

A 14-year climatology of Tropical Rainfall Measuring Mission (TRMM) collocated multi-sensor signal statistics reveal a distinct land-ocean contrast as well as geographical variability of precipitation type, intensity, and microphysics. Microphysics information inferred from the TRMM precipitation radar and Microwave Imager (TMI) show a large land-ocean contrast for the deep category, suggesting continental convective vigor. Over land, TRMM shows higher echo-top heights and larger maximum echoes, suggesting taller storms and more intense precipitation, as well as larger microwave scattering, suggesting the presence of more/larger frozen convective hydrometeors. This strong land-ocean contrast in deep convection is invariant over seasonal and multi-year time-scales. Consequently, relatively short-term simulations from two global storm-resolving models can be evaluated in terms of their land-ocean statistics using the TRMM Triple-sensor Three-step Evaluation via a satellite simulator. The models evaluated are the NASA Multi-scale Modeling Framework (MMF) and the Non-hydrostatic Icosahedral Cloud Atmospheric Model (NICAM). While both simulations can represent convective land-ocean contrasts in warm precipitation to some extent, near-surface conditions over land are relatively moisture in NICAM than MMF, which appears to be the key driver in the divergent warm precipitation results between the two models. Both the MMF and NICAM produced similar frequencies of large CAPE between land and ocean. The dry MMF boundary layer enhanced microwave scattering signals over land, but only NICAM had an enhanced deep convection frequency over land. Neither model could reproduce a realistic land-ocean contrast in in deep convective precipitation microphysics. A realistic contrast between land and ocean remains an issue in global storm-resolving modeling.