Microphysical Parameterization Schemes Research Articles

Quantifying the Role of Orographic Processes in Producing Extreme Precipitation: A Case Study of an Atmospheric River Associated With Storm Bronagh

AbstractThis study explores the mechanisms contributing to heavy precipitation associated with landfalling atmospheric rivers (ARs) and orography, focusing on a high‐profile case study of an extratropical cyclone, storm Bronagh, which caused flooding and travel disruption in the UK. While prior research has established the connection between ARs, orography, and precipitation, the specific mechanisms leading to intense rainfall remain unclear. A novel methodology is introduced that quantifies the relative contribution to the total precipitation from cyclone‐induced processes and orographic processes using modifications to the Met Office Unified Model orography and microphysics parameterization scheme. Results show that the majority of storm Bronagh 's rainfall over land is attributed to cyclone‐related processes (frontal ascent and embedded convection). However, interaction of the AR associated with storm Bronagh and orography focuses more of the rain over the hills, enhancing precipitation in this region by a further . Further analysis reveals that the seeder‐feeder mechanism plays a predominant role in orographically enhanced rainfall. Accretion and riming processes occur as rain from the cyclone induced (seeder) cloud falls through the orographically induced (feeder) cloud enhancing precipitation rates. The feeder cloud formation is related to forced ascent of air within the AR over the orography. In conclusion, this study sheds light on the intricate interplay between ARs, orography, and cyclone precipitation during the landfall of extratropical cyclones. By separating the mechanisms behind heavy rainfall in the context of storm Bronagh, we quantify for the first time the effect of the seeder‐feeder effect at different heights over orography.

Comparison of Two-Moment and Three-Moment Bulk Microphysics Schemes in Thunderstorm Simulations over Indian Subcontinent

We simulated three different thunderstorm events over India using a regional model with two different multi-moments cloud microphysical schemes: (i) Morrison double moment (Morrison-2 M) and (ii) National Taiwan University (NTU) triple moment (NTU-3 M). The results show that the choice of microphysical scheme significantly impacts storm structures, cloud formations, lightning intensity, and rainfall patterns. The lightning flash counts from the in-situ lightning detection network (LDN) are also used to compare the simulated storms. The Lightning Potential Index (LPI) was computed for both Morrison-2 M and NTU-3 M microphysics schemes and compared it with the LDN observation. Differences in simulations are attributed to the simulation of ice crystals, snow, and graupel by the microphysical schemes. The effects on the size distributions of cloud hydrometeors between the two microphysical schemes are also found to be important. The inclusions of ice crystal shapes likely accounted for the key differences between the two microphysics schemes. The results demonstrate that the simulation of lightning event characteristics is sensitive to the choice of microphysical parameterization schemes in numerical weather prediction models. In comparison to both the microphysical schemes, some aspects are better in the 2 M microphysics scheme, and some are better in the 3 M microphysics scheme. Morrison-2 M simulates more mixing ratio for the majority of cloud hydrometeors across all cases than the NTU-3 M whereas the Morrison-2 M has less rainfall bias over the ocean for the majority of the cases. Overall, the present study suggests that the choice of microphysical scheme can greatly influence the accuracy of thunderstorm and lightning event predictions in operational weather forecasting models.

Rainfall Sensitivity to Microphysics and Planetary Boundary Layer Parameterizations in Convection-Permitting Simulations over Northwestern South America

Convection-permitting modeling allows us to understand mechanisms that influence rainfall in specific regions. However, microphysics parameterization (MP) and planetary boundary layer (PBL) schemes remain an important source of uncertainty, affecting rainfall intensity, occurrence, duration, and propagation. Here, we study the sensitivity of rainfall to three MP [Weather Research and Forecasting (WRF) Single-Moment 6-class (WSM6), Thompson, and Morrison] and two PBL [the Yonsei University (YSU) and Mellor–Yamada Nakanishi Niino (MYNN)] schemes with a convection-permitting resolution (4 km) over northwestern South America (NWSA). Simulations were performed by using the WRF model and the results were evaluated against soundings, rain gauges, and satellite data, considering the spatio-temporal variability of rainfall over diverse regions prone to deep convection in NWSA. MP and PBL schemes largely influenced simulated rainfall, with better results for the less computationally expensive WSM6 MP and YSU PBL schemes. Regarding rain gauges and satellite estimates, simulations with Morrison MP overestimated rainfall, especially westward of the Andes, whereas the MYNN PBL underestimated precipitation in the Amazon–Savannas flatlands. We found that the uncertainty in the rainfall representation is highly dependent on the region, with a higher influence of MP in the Colombian Pacific and PBL in the Amazon–Savannas flatlands. When analyzing rainfall-related processes, the selection of both MP and PBL parameterizations exerted a large influence on the simulated lower tropospheric moisture flux and moisture convergence. PBL schemes significantly influenced the downward shortwave radiation, with MYNN simulating a greater amount of low clouds, which decreased the radiation income. Furthermore, latent heat fluxes were greater for YSU, favoring moist convection and rainfall. MP schemes had a marked impact on vertical velocity. Specifically, Morrison MP showed stronger convection and higher precipitation rates, which is associated with a greater latent heat release due to solid-phase hydrometeor formation. This study provides insights into assessing physical parameterizations in numerical models and suggests key processes for rainfall representation in NWSA.

Assimilation of Radar Reflectivity via a Full-Hydrometeor Assimilation Scheme Based on the WSM6 Microphysics Scheme in WRF 4D-Var

Abstract The microphysical parameterization scheme employed in four-dimensional variational data assimilation (4D-Var) plays an important role in the assimilation of humidity and cloud-sensitive observations. In this study, a newly developed full-hydrometeor assimilation scheme, integrating warm-rain and cold-cloud processes, has been implemented in the Weather Research and Forecasting (WRF) 4D-Var system. This scheme is based on the WRF single-moment 6-class microphysics scheme (WSM6). Its primary objective is to directly assimilate radar reflectivity observations, with the goal of evaluating its effects on model initialization and subsequent forecasting performance. Four assimilation experiments were conducted to assess the performance of the full-hydrometeor assimilation scheme against the warm-rain assimilation scheme. These experiments also investigated reflectivity assimilation using both indirect and direct methods. We found that the nonlinearity of the radar operator in the two direct reflectivity assimilation experiments requires more iterations for cost function reduction than in the indirect assimilation method. The hydrometeor fields were reasonably analyzed using the full-hydrometeor assimilation scheme, particularly improving the simulation of ice-phase hydrometeors and reflectivity above the melting layer. The assimilation of radar reflectivity led to improvements in short-term (0–3 h) precipitation forecasting with the full-hydrometeor assimilation scheme. Assimilation experiments across multiple case studies reaffirmed that assimilating radar reflectivity observations with the full-hydrometeor assimilation scheme accelerated model spinup and yielded enhancements in 0–3-h total accumulate precipitation forecasts for a range of precipitation thresholds.

Impact of Microphysics Parameterization Schemes on the Assimilation of GOES‐16 ABI All‐Sky Infrared Radiances for a Bow Echo Analysis and Prediction

AbstractThis study aims to investigate how assimilating GOES‐16 ABI infrared brightness temperature (BT) with different microphysics schemes affects the convection‐allowing analysis and prediction of the 3 May 2020 bow echo case. The Gridpoint Statistical Interpolation‐based Ensemble Kalman Filter (GSI‐EnKF) system and Weather Research and Forecasting (WRF) model are utilized to conduct data assimilation (DA) experiments using Thompson, WDM6, NSSL, and Morrison microphysics schemes. Correlation structures between BT and model state variables indicate that assimilating infrared BT can adjust bowing MCS dynamics via latent cooling and the rear inflow jet. Such corrections during DA cycling enhance the rear inflow jet and bow echo size, primarily for microphysics schemes featuring faster hydrometeor fall velocity and stronger latent cooling. The improved analyses lead to better forecasts of the bow echo's shape, size, timing of the bowing process, and wind speeds. Substituting a larger microphysics‐dependent effective radius for a constant default value increases prior BT, the magnitude of BT innovations, and accumulated impact on the rear inflow jet, especially for the WDM6 and Morrison schemes. In the subsequent forecasts, incorporating microphysics‐dependent effective radius further improves the experiment using the Morrison scheme but degrades it when using the WDM6 scheme.

Seasonal Variation in Vertical Structure for Stratiform Rain at Mêdog Site in Southeastern Tibetan Plateau

Mêdog is located at the entrance of the water vapor channel of the Yarlung Tsangpo Great Canyon on the southeastern Tibetan Plateau (TP). In this study, the seasonal variation in the microphysical vertical structure of stratiform precipitation at the Mêdog site in 2022 was investigated using micro rain radar (MRR) observations, as there is a lack of similar studies in this region. The average melting layer height is the lowest in February, after which it gradually increases, reaches its peak in August, and then gradually decreases. For lower rain categories, the vertical distribution of small drops remains uniform in winter below the melting layer. The medium-sized drops show slight increases, leading to negative gradients in the microphysical profiles. Slight or evident decreases in concentrations of small drops are observed with decreasing height in the premonsoon, monsoon, and postmonsoon seasons, likely due to significant evaporation. The radar reflectivity, rain rate, and liquid water content profiles decrease with decreasing height according to the decrease in concentrations of small drops. With increasing rain rate, the drop size distribution (DSD) displays significant variations in winter, and the fall velocity decreases rapidly with decreasing height. In the premonsoon, monsoon, and postmonsoon seasons, the concentrations of large drops significantly decrease below the melting layer because of the breakup mechanism, leading to the decreases in the fall velocity profiles with decreasing height during these seasons. Raindrops with sizes ranging from 0.3–0.5 mm are predominant in terms of the total drop number concentration in all seasons. Precipitation in winter and postmonsoon seasons is mainly characterized by small raindrops, while that in premonsoon and monsoon seasons mainly comprises medium-sized raindrops. Understanding the seasonal variation in the vertical structure of precipitation in Mêdog will improve the radar quantitative estimation and the use of microphysical parameterization schemes in numerical weather forecast models over the TP.

Microphysical Parameterization Scheme with Satellite Data Verification and Observation Data for Analysis of Heavy Rain in Yogyakarta City

The numerical weather prediction model Weather Research and Forecasting - Advanced Research WRF (WRF-ARW) was used to simulate the atmospheric conditions during heavy rainfall in the Yogyakarta area on February 3, 2022. Several parameterization schemes were compared with observational data to obtain the best choice of microphysical parameterization scheme in describing the atmospheric conditions at the time of the event. The microphysical parameterization schemes used are: Kessler, Purdue Lin, and WRF Single-Moment. Class (WSM3). The verification results show that the Kessler microphysical parameterization scheme has the best performance in representing heavy rain events with a fairly high low cloud fraction data accuracy value ranging from 0.3 - 0.4 and the highest rainfall modeling value is 8.3 mm. It can be concluded that the Kessler scheme is sufficient to model the occurrence of heavy rain in the city of Yogyakarta.

The impacts of hail microphysics on maximum potential intensity of idealized tropical cyclone

Maximum potential intensity (MPI), which a TC may reach in certain environment conditions, can be affected by microphysical processes. Latent heat released in the process of TC development plays a significant role in it. However, the impacts of hail added both to single-moment and double-moment microphysics parameterization scheme on the MPI remain unclear. In this study, high-resolution sensitivity experiments are conducted in the Weather Research and Forecasting (WRF) model by using four bulk microphysics schemes belonging to a family, namely, WRF Single-Moment 6-Class (WSM6) scheme, WRF Double-Moment 6-Class (WDM6) scheme, WRF Single-Moment 7-Class (WSM7) scheme, WRF Double-Moment 7-Class (WDM7) scheme. Results show that SM schemes simulate the greater MPI than DM schemes. Adding hail in SM scheme increases the MPI while in DM scheme makes less difference. There is a close relationship between the MPI and the radial peak location and intensity of latent heat. The closer the latent heat peak is to the TC center and the greater the peak intensity is, the greater the MPI can be achieved. Though the presence of hail plays a cooling effect thermally, it may affect the TC structures due to the larger sedimentation speed. WSM7 scheme including hail microphysics simulates the TC with smaller size and eye wall inclination, and thus the latent heating efficiency in the eye wall is higher, which is more conducive to TC intensification. However, the larger content of hail resulting from the accretion of liquid water in WDM7 scheme brings a stronger cooling effect and probably offsets the dynamic advantage.

Combining effects of cloud microphysical and planetary boundary layer parameterization schemes on the prediction of tropical cyclone intensity

The numerical simulation of tropical cyclone (TC) can be affected by the choices in physics parameterization schemes. These effects can be from the single scheme or the combination of multiple ones due to closely linked physical processes. Previous studies have discussed the roles of different schemes in TC simulation, but the roles of scheme combinations still remain to be explored. To investigate the combining effects of cloud microphysical (MP) and planetary boundary layer (PBL) parameterization schemes on the prediction of TC intensity, two Single-Moment MP schemes, WRF 6-class (WSM6) and Goddard 4ice, and two PBL schemes, non-local closure Yonsei University (YSU) and local closure Quasi-normal Scale Elimination (QNSE), were selected for the cloud-resolving simulations of both real and idealized cases using the Weather Research and Forecasting (WRF) model.Results showed that the experiments combining the PBL scheme with different MP schemes had different performances in reproducing TC intensity. The local QNSE PBL scheme, compared with non-local YSU, simulated larger surface sensible, latent, and moisture fluxes, while not necessarily leading to a stronger TC. When used in conjunction with the Goddard MP scheme, the QNSE scheme can simulate a stronger TC than the YSU scheme, but when used with the WSM6 MP scheme, there was little difference in their intensity. Goddard 4ice scheme adding hail class, compared with WSM6, simulated a better-organized eyewall, more latent heat release and convective bursts, leading to the formation of a clear warm core in the upper troposphere and thus contributing to the intensification of TC. Only with the coupling of MP and PBL schemes, did the simulation generate a compact and erect eyewall, which further enhanced TC intensity via stronger convergence airflow and more water vapor supply at the lower layer. Idealized experiments were consistent with those in real cases, demonstrating the TC sensitivity responding to different combinations while the uncertainties remained in the selection of these schemes.

Evaluation of Cumulus and Microphysical Parameterization Schemes of the WRF Model for Precipitation Prediction in the Paraíba do Sul River Basin, Southeastern Brazil

Evaluation of Cumulus and Microphysical Parameterization Schemes of the WRF Model for Precipitation Prediction in the Paraíba do Sul River Basin, Southeastern Brazil

Modification of Microphysical Parameterization Schemes and Their Application in the Simulation of an Extremely Heavy Rainfall Event in Zhengzhou

AbstractIn the present study, the Morrison and Thompson microphysics schemes in the Weather Research and Forecasting model were improved and used to simulate a record‐breaking heavy rainfall event occurred in Henan Province over central China during 19–21 July 2021. In the modified schemes, the terminal velocity of graupel and initial cloud droplet concentration were adjusted based on sensitivity tests. Results showed that the modified Morrison scheme (MORR_MOD) better captured the spatial distribution and temporal evolution of precipitation than the original scheme (MORR). Specifically, it produced a 40‐hr cumulative rainfall amount of 963 mm and an extreme hourly rainfall rate of 157.7 mm, which were closer to observations than MORR. Analysis of the dynamic and microphysical structures of the extreme‐rain‐producing convective clusters revealed that MORR_MOD predicted stronger updrafts, higher rain mass content and larger mean mass diameter of raindrops than MORR. By analyzing the tendencies of rain mass and number concentration, it was found that MORR_MOD predicted stronger accretion rates of cloud droplets by rain and weaker auto‐conversion rates of cloud droplets than MORR, which contributed to the high rain mixing ratio and low number concentration, respectively. In addition, MORR_MOD predicted stronger diabatic heating rates than MORR, which were primarily attributed to stronger condensation of water vapor, and may have been the reason why MORR_MOD simulated stronger updrafts.

Improving Simulations of Warm Rain in a Bulk Microphysics Scheme

Abstract Current bulk microphysical parameterization schemes underpredict precipitation intensities and drop size distributions (DSDs) during warm rain periods, particularly upwind of coastal terrain. To help address this deficiency, this study introduces a set of modifications, called RCON, to the liquid-phase (warm rain) parameterization currently used in the Thompson–Eidhammer microphysical parameterization scheme. RCON introduces several model modifications, motivated by evaluating simulations from a bin scheme, which together result in more accurate precipitation simulations during periods of warm rain. Among the most significant changes are 1) the use of a wider cloud water DSD of lognormal shape instead of the gamma DSD used by the Thompson–Eidhammer parameterization and 2) enhancement of the cloud-to-rain autoconversion parameterization. Evaluation of RCON is performed for two warm rain events and an extended period during the Olympic Mountains Experiment (OLYMPEX) field campaign of winter 2015/16. We show that RCON modifications produce more realistic precipitation distributions and rain DSDs than the default Thompson–Eidhammer configuration. For the multimonth OLYMPEX period, we show that rain rates, rainwater mixing ratios, and raindrop number concentrations were increased relative to the Thompson–Eidhammer microphysical parameterization, while concurrently decreasing raindrop diameters in liquid-phase clouds. These changes are consistent with an increase in simulated warm rain. Finally, real-time evaluation of the scheme from August 2021 to August 2022 demonstrated improved precipitation prediction over coastal areas of the Pacific Northwest. Significance Statement Although the accurate simulation of warm rain is critical to forecasting the hydrology of coastal areas and windward slopes, many warm rain parameterizations underpredict precipitation in these locations. This study introduces and evaluates modifications to the Thompson–Eidhammer microphysics parameterization scheme that significantly improve the accuracy of rainfall prediction in those regions.

Sensitivity of summer precipitation simulation to the physical parameterizations in WRF over the Tibetan Plateau: A case study of 2018

The Tibetan Plateau (TP) holds a pivotal position in regional and global climate, whereas the atmospheric characteristics vary greatly in the simulation with different physical parameterization schemes at the gray-zone scale (around 9 km). In this study, 24 sets of experiments were set up with different cumulus parameterization schemes (CPSs), microphysics parameterization schemes (MPSs) and planetary boundary layer parameterization schemes (PBLSs) using the WRF model driven by the ERA5 reanalysis to explore the sensitivity and try to summarize a combination of parameterization schemes suitable for the simulation over TP. Based on comparisons against in-situ observations, most CPS experiments showed large wet bias and overestimated occurrence of precipitation events, and CTL without CPS and CU6 with Tiedtke CPS show better performance at gray-zone scale. The influence of MPS and PBLS is less than CPS on the simulation, and Thompson MPS and UW PBLS showed a better performance. Most of the CPS experiments reach an earlier and more intensive peak compared to the Integrated Multi-satellite Retrievals for GPM (IMERG) version 6 satellite precipitation product, except CTL, CU11, and CU6, which show closer but weaker peaks. CPS experiments simulate more precipitation associated with water vapor transporting into TP and anomalous cyclonic circulation against CTL. The less water vapor transport from the south edge of TP in the CTL against CU14, CU16, and CU2, leads to more local precipitation. Graupel particles and the warm-rain process play a crucial role in the precipitation simulation with different MPSs. Although with less water vapor in the low atmosphere, MP2 simulates more precipitation due to more graupel particles and warm-rain processes against CTL. PBLSs have a substantial impact on latent heat, then influence evaporation and precipitation. The lower PBL height usually leads to more latent heat and more precipitation.

Simulating Heavy Rainfall Associated with Tropical Cyclones and Atmospheric Disturbances in Thailand Using the Coupled WRF-ROMS Model—Sensitivity Analysis of Microphysics and Cumulus Parameterization Schemes

Predicting heavy rainfall events associated with Tropical Cyclones (TCs) and atmospheric disturbances in Thailand remains challenging. This study introduces a novel approach to enhance forecasting precision by utilizing the coupled Weather Research and Forecasting (WRF) and Regional Oceanic Model (ROMS), known as WRF-ROMS. We aim to identify the optimal combination of microphysics (MP) and cumulus (CU) parameterization schemes. Three CU schemes, namely, Betts-Miller-Janjic (BMJ), Grell 3D Ensemble (G3), and Kain-Fritsch (KF), along with three MP schemes, namely, Eta (ETA), Purdue Lin (LIN), and WRF Single-moment 3-class (WSM3), are selected for the sensitivity analysis. Seven instances of heavy (35.1–90.0 mm) to violent (>90.1 mm) rainfall in Thailand, occurring in 2020 and associated with tropical storms and atmospheric disturbances, are simulated using all possible combinations of the chosen physics schemes. The simulated rain intensities are compared against observations from the National Hydroinformatics Data Center. Performance was assessed using the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) metrics. While the models performed well for light (0.1–10.0 mm) to moderate (10.1–35.0 mm) rainfall, forecasting heavy rainfall remained challenging. Certain parameter combinations showed promise, like BMJ and KF with LIN microphysics, but challenges persisted. Analyzing density distribution of daily rainfall, we found effective parameterizations for different sub-regions. Our findings emphasize the importance of tailored parameterizations for accurate rainfall prediction in Thailand. This customization can benefit water resource management, flood control, and disaster preparedness. Further research should expand datasets, focusing on significant heavy rainfall events and considering climate factors, for example, the Madden-Julian Oscillation (MJO) for extended-range forecasts, potentially contributing to sub-seasonal and seasonal (S2S) predictions.

Simulations of a Heavy Snowfall Event in Xinjiang via the WRF Model Coupled with Different Land Surface Parameterization Schemes

Frequent heavy snowfall in Xinjiang plays an important role in the land water cycle. In this study, 18 groups of simulation experiments are conducted on the heavy snowfall event in Xinjiang during 9–13 December of 2015 using the Weather Research and Forecasting (WRF) model. In these experiments, the combination of six land surface parameterization schemes (the Noah scheme, Noah-MP scheme, RUC scheme, CLM4 scheme, PX scheme, and TD scheme) with three microphysical parameterization schemes (the WSM6 scheme, Thompson scheme, and Lin scheme) are adopted, where the observed snowfall data are used for performance evaluation. Results show that the simulated snowfall intensity and snowfall range in different areas are very sensitive to the selection of the land surface scheme. The snowfall in southern Xinjiang is overestimated by almost all six schemes, where the Noah-MP scheme performs more reasonably than the others. The Noah scheme shows its advantage in northwestern Xinjiang. The three different microphysical schemes vary significantly in producing snowfall amount. The WSM6 scheme produced the largest snowfall amount, and the Lin scheme resulted in the smallest snowfall amount. In addition, the accumulated snowfall amounts above 10 mm are generally underestimated by all six land surface schemes, while the accumulated snowfall amounts below 10 mm are overestimated by most of the schemes. The Noah-MP scheme performs the best in the simulation of the snowfall amount in the whole region. However, the Noah scheme shows an advantage in areas with a large snowfall amount.

Study on the Vertical Structure and the Evolution of Precipitation Particle Spectrum Parameters of Stratocumulus Clouds over North China Based on Aircraft Observation

The understanding of the macro- and micro-structure, particle spectrum parameters, and their evolutions in different parts of stratocumulus clouds based on aircraft observation data, is important basic data for the development of cloud microphysical parameterization schemes and the quantitative retrieval of cloud-precipitation by radar and satellite detections. In this study, a total of ten vertical measurements during three aircraft observations were selected to analyze the vertical distribution of cloud microphysical properties in different parts of stratocumulus clouds in Hebei, North China. It was found that the downdraft in the cumulus cloud area was stronger than that in the stratiform cloud area, with the temperature at the same height higher than that in the stratiform cloud area, and the height of the 0 °C layers was correspondingly higher. In terms of particle spectrum parameters, the intercept and slope parameters of particle spectrum below melting levels in the cumulus part were higher than those in stratiform clouds area in the same weather process. In different vertical detection, it was found that the ice particles have begun to melt in the negative temperature layer near 0 °C level, and there might be sublimation, fragmentation, and aggregation in the melting process of ice phase particles. In addition, the melting process changed the spectral parameters greatly and also changed the correlation between the intercept and slope of the particle spectrum. The slope below the 0 °C level increased with the increase of intercept, which was greater than that above the 0 °C level. The relationship obtained between the intercept parameter of the particle’s spectrum and temperature, and the correlation between the maximum diameter and slope parameter of the particle spectrum, have certain reference significance for cloud physical parameterization and the quantitative retrieval of cloud precipitation by radar and satellite in North China and similar climate background areas.

Sensitivity of Real‐Time Forecast for Typhoons Around Korea to Cumulus and Cloud Microphysics Schemes

AbstractIn numerical weather prediction models, the typhoon track and intensity forecast performances are highly sensitive to physics parameterization schemes. To investigate the impact of physics parameterization schemes on the real‐time short‐term forecasts, we simulated six typhoons which directly/indirectly affected the South Korea region in recent years using the Weather Research and Forecasting (WRF) model. Three cumulus parameterization schemes (CPSs) of Kain‐Fritsch (KF), Betts‐Miller‐Janjić (BMJ), modified Tiedtke (TDK), and two cloud microphysics parameterization schemes (MPSs) of WRF‐single‐moment‐microphysics class 6 (WSM6), Predicted Particle Properties (P3) 1‐category were selected for the sensitivity experiment. The results showed that there was a significant difference in simulated typhoon track and intensity performances depending on the physics schemes. On average, the typhoon forecast performances were improved when applying the KF scheme for CPS and WSM6 scheme for MPS in our experimental setup. The BMJ‐applied runs showed the worst performances, which simulated westward shifted typhoon tracks compared to other runs. Overall, the typhoon track and intensity spreads tended to be more sensitive to CPSs and MPSs, respectively. We conducted additional sensitivity experiments using the BMJ scheme with modified reference and temperature profiles. The result showed that the overactivity of the BMJ scheme at low latitudes was reasonably reduced, leading to the improved simulation of typhoons and synoptic fields.

Characteristic Differences of CrIS All-Sky Simulations of Brightness Temperature with Different Microphysics Parameterization Schemes

Abstract Influences of cloud liquid water, cloud ice, rain, snow, and graupel on all-sky simulations of the Cross-track Infrared Sounder (CrIS) brightness temperature (TB) are assessed for the 399 data assimilation (DA) channels. The analyses generated by the Gridpoint Statistical Interpolation (GSI) 3D-Var system assimilating conventional and clear-sky satellite radiance observations are used as initial conditions for the Weather Research and Forecasting Model to generate 6-h forecasts with three different microphysics schemes (WSM6, Thompson, and Morrison), which are then used as input to the Community Radiative Transfer Model for all-sky TB simulations. Under all-sky conditions, biases with the WSM6 scheme are negative for all channels and greater in magnitude than −3.5 K. The biases with the Thompson and Morrison schemes vary between −1 and 1 K for all channels. Bias differences among three MP schemes are small in stratus, altocumulus, and cumulus clouds, but large in cirrus and cirrocumulus clouds. The TB simulations in stratus, altocumulus, and cumulus clouds are mostly influenced by the cloud top pressure, while that in cirrus and cirrocumulus clouds depends strongly on cloud optical depth. All-sky TB simulations in cirrus conditions are more positively biased than those under cirrocumulus conditions, probably due to the microphysics schemes producing too thick cirrus clouds. Sensitivity experiments suggest that the TB discrepancies among DA experiments with three MP schemes are mostly caused by the ice or snow type rather than the effective radius of hydrometeor in the upper troposphere. Finally, we propose to combine a cloud-effect parameter with cloud types for modeling observation error characteristics in all-sky DA.

Preliminary Application of a Multi-Physical Ensemble Transform Kalman Filter in Cloud and Precipitation Forecasts

In this study, based on the retrieval data from the Fengyun geostationary meteorological satellite and the Tropical Rainfall Measuring Mission satellite, a large-scale precipitation case in eastern China is selected to address the systematic deviations of deterministic forecasts for clouds and precipitation. A multi-physical ensemble transform Kalman filter (ETKF) is constructed in this research based on the Weather Research and Forecast model version 3.6, and its forecasting ability in terms of cloud-top height and temperature, hydrometeors, and precipitation is evaluated by quantitatively comparing three microphysical parameterization schemes (Lin, Morrison, and CAM5.1 schemes) and their corresponding multi-physical ensemble mean. The results show that the Lin, Morrison, and CAM5.1 schemes all underestimate the range of cloud systems and have different advantages and disadvantages in forecasting different elements, while the forecasting improvement of the multi-physical ensemble mean is limited. However, the multi-physical ETKF can effectively improve the forecast accuracy of the cloud system range. In addition, the multi-physical ETKF has the advantages of different physical parameterization schemes, which can dramatically improve the forecast accuracy of cloud hydrometeors, reduce precipitation forecast errors, and improve threat scores.

Direct Assimilation of Radar Reflectivity Data Using Ensemble Kalman Filter Based on a Two-Moment Microphysics Scheme for the Analysis and Forecast of Typhoon Lekima (2019)

We investigate the impact of directly assimilating radar reflectivity data using an ensemble Kalman filter (EnKF) based on a double-moment (DM) microphysics parameterization (MP) scheme in GSI-EnKF data assimilation (DA) framework and WRF model for a landfall typhoon Lekima (2019). Observations from a single operational coastal Doppler are quality-controlled and assimilated. Compared with the baseline experiment initialized by GFS analysis, the reflectivity data assimilation experiment (Z-DA) resulted in an obvious improvement in both structural analysis and typhoon forecast skills in terms of intensity, precipitation, and track. Sensitivity experiments were conducted to evaluate the ability of EnKF to update certain state variables considering that the degree of freedom of analytical variables increased with a DM MP scheme. When either the total number concentration or other large-scale state variables that are not directly linked to reflectivity observations via the observation operator are not updated, the tendency of RMSIs and PS to be imbalanced is significantly increased during DA cycles compared to those of Z-DA with updating a full set of state variables, resulting in increased intensity and track forecast errors. These results indicate that the reliable ensemble covariance could handle the underconstraint issue associated with the DM scheme, and helps in obtaining more physically balanced analytical fields.