Cloud Microphysics Scheme Research Articles

Sensitivity of Botswana Ex-Tropical Cyclone Dineo rainfall simulations to cloud microphysics scheme

Background: Numerical weather and climate models rely on the use of microphysics schemes to simulate clouds and produce precipitation at convective scales. It is important that we understand how different microphysics schemes perform when simulating high impact weather to inform operational forecasting. Methods: Simulations a heavy rainfall event from 17-20 February 2017 over Botswana were made with the Weather Research and Forecasting (WRF) model using four different microphysics schemes. The schemes used were the Weather Research and Forecasting Single Moment 6-class scheme (WSM6); Weather Research and Forecasting Single Moment 5-class scheme (WSM5); Stony Brook University scheme (SBU-YLIN); and Thompson scheme. WSM5 is considered as the least sophisticated of the four schemes, while Thompson is the most sophisticated. Simulations were initialized and forced by the Global Forecast System (GFS), and configured with a grid spacing of 9km over an outer domain and 3km for a nested inner domain without the convection parameterization. The simulations were produced using the University of Botswana and the Centre for High Performance Computing (CHPC) High Performance Computing (HPC) systems. Results: WSM5 and WSM6 simulations are mostly similar; the presence of graupel in WSM6 did not result in large differences in the rainfall simulations. SBU-YLIN simulated the least amount of rainfall, followed by Thompson. All the schemes captured the north-south rainfall gradient observed on 17 February, but with all simulations rainfall is simulated slightly south of where it was observed. All the schemes overestimated rainfall on 18 February over the central parts of Botswana, and underestimated rainfall on 19 February over most of Botswana. Conclusions: Simulations with different microphysics looked more similar to each other, than to observations. Future studies will test WRF configurations including a single nest over Botswana to determine the best configuration for operational forecasting by the Botswana Department of Meteorological Services.

Sensitivity of rainfall and submonthly oscillations to moist physical processes schemes on Hainan island in autumn

In the autumns of 2010 and 2011, consecutive periods of heavy precipitation occurred in Hainan Island. Two groups of monthly-scale simulation experiments were conducted using the Weather Research and Forecasting model. Sensitivity of rainfall and submonthly oscillations to cumulus parameterization and cloud microphysical schemes were evaluated. Due to that the experiment combining the Tiedtke cumulus parameterization scheme and the WDM6 cloud microphysical scheme could reproduce submonthly oscillation characteristics, which showed the higher skill scores of the precipitation simulation than other experiments. Because the Tiedtke scheme is an overall mass flux convection scheme that describes various types of convection, the Grell-Devenyi scheme does not consider the shallow convection, which affects its simulation performance on submonthly oscillations and rainfall. However, in the same cumulus parameterization scheme, different microphysics schemes also have an impact on simulation performance. Experiments that failed to simulate the characteristics of submonthly oscillations exhibited a lower skill scores of precipitation simulation.Interactions of moist physical processes and submonthly oscillations led to the occurrence and development of two consecutive heavy autumn precipitation events on Hainan Island. Submonthly oscillations resulted in a significant effect on the two strong autumn precipitation events, but dominant frequency modes of submonthly oscillations, oscillation characteristics, and propagation paths differed. The autumn 2010 rainfall was dominated by the 8–15-day quasi-biweekly oscillations. The autumn 2011 rainfall was dominated by the 3–10-day synoptic-scale oscillations.

Evaluating Warm and Cold Rain Processes in Cloud Microphysical Schemes Using OLYMPEX Field Measurements

AbstractField observations from the Olympic Mountain Experiment (OLYMPEX) around western Washington State during two atmospheric river (AR) events in November 2015 were used to evaluate several bulk microphysical parameterizations (BMPs) within the Weather Research and Forecasting (WRF) Model. These AR events were characterized by a prefrontal period of stable, terrain-blocked flow with an abundance of cold rain over the lowland region followed by less stable, unblocked flow with more warm rain, and a shift in the largest precipitation amounts to over the windward Olympic slopes. Our WRF simulations underpredicted the precipitation by 19%–36% in the Morrison (MORR) and Thompson (THOM) BMPs and 10%–23% in the predicted particle properties (P3) BMP, with the largest underpredictions over the windward slopes during the more convective, unblocked flow conditions. Several important processes related to the BMPs led to the differences in simulated precipitation. First, the prognostic single ice category parameterization in the P3 scheme promoted a more realistic evolution of rimed particles and larger cold rain production, which led to the lowest underpredictions in precipitation among the schemes. Second, efficient melting processes associated with the production of nonspherical ice and snow in the P3 and THOM BMPs, respectively, promoted a more realistic transition to rain fall speeds within the warm layer compared to the spherical snow assumption in MORR. Last, all BMPs underpredict the contribution of warm rain processes to the surface precipitation, particularly during the unblocked flow period, which may be partly explained by too weak condensational and collisional growth processes due to the neglect of turbulence parameterizations within the schemes.

On the Analysis of the Performance of WRF and NICAM in a Hyperarid Environment

Abstract The Weather Research and Forecasting (WRF) Model and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) are forced with the Global Forecast System (GFS) data and run over the United Arab Emirates (UAE) for two 4-day periods: one in the cold season (16–18 December 2017) and another in the warm season (13–15 April 2018). The models’ performance is evaluated against four observational datasets: weather station observations, eddy-covariance flux measurements at Al Ain, microwave radiometer–derived temperature profile, and twice-daily radiosonde measurements at Abu Dhabi. An overestimation of the daily mean air temperature by 1°–3°C is noticed for both models and periods. This warm bias is attributed to the reduced cloud cover and resulting increased surface downward shortwave radiation flux. A comparison with the eddy-covariance data suggested that both models also underestimate the observed albedo. However, when the models predict heavier amounts of precipitation, they tend to be colder than observations, typically by 2°–3°C. NICAM and WRF overpredict the strength of the near-surface wind speed at all weather stations by roughly 1–3 m s−1, which has been attributed to a poor representation of its subgrid-scale fluctuations and surface drag parameterization. WRF tends to be wetter and NICAM drier than the station observations, possibly because of differences in the cloud microphysics schemes. While the performance of both models for the near-surface fields is comparable, NICAM outperforms WRF in the simulation of vertical profiles of temperature, relative humidity, and wind speed, being able to partially correct some of the biases in the GFS data.

Coupling aerosols to (cirrus) clouds in the global EMAC-MADE3 aerosol–climate model

Abstract. A new cloud microphysical scheme including a detailed parameterization for aerosol-driven ice formation in cirrus clouds is implemented in the global ECHAM/MESSy Atmospheric Chemistry (EMAC) chemistry–climate model and coupled to the third generation of the Modal Aerosol Dynamics model for Europe adapted for global applications (MADE3) aerosol submodel. The new scheme is able to consistently simulate three regimes of stratiform clouds – liquid, mixed-, and ice-phase (cirrus) clouds – considering the activation of aerosol particles to form cloud droplets and the nucleation of ice crystals. In the cirrus regime, it allows for the competition between homogeneous and heterogeneous freezing for the available supersaturated water vapor, taking into account different types of ice-nucleating particles, whose specific ice-nucleating properties can be flexibly varied in the model setup. The new model configuration is tuned to find the optimal set of parameters that minimizes the model deviations with respect to observations. A detailed evaluation is also performed comparing the model results for standard cloud and radiation variables with a comprehensive set of observations from satellite retrievals and in situ measurements. The performance of EMAC-MADE3 in this new coupled configuration is in line with similar global coupled models and with other global aerosol models featuring ice cloud parameterizations. Some remaining discrepancies, namely a high positive bias in liquid water path in the Northern Hemisphere and overestimated (underestimated) cloud droplet number concentrations over the tropical oceans (in the extratropical regions), which are both a common problem in these kinds of models, need to be taken into account in future applications of the model. To further demonstrate the readiness of the new model system for application studies, an estimate of the anthropogenic aerosol effective radiative forcing (ERF) is provided, showing that EMAC-MADE3 simulates a relatively strong aerosol-induced cooling but within the range reported in the Intergovernmental Panel on Climate Change (IPCC) assessments.

Assessing the Effects of Microphysical Scheme on Convective and Stratiform Characteristics in a Mei-Yu Rainfall Combining WRF Simulation and Field Campaign Observations

Microphysics parameterization becomes increasingly important as the model grid spacing increases toward convection-resolving scales. Using observations from a field campaign for Mei-Yu rainfall in China, four bulk cloud microphysics schemes in the Weather Research and Forecasting (WRF) model were evaluated with respect to their ability to simulate precipitation, structure, and cloud microphysical properties over convective and stratiform regimes. These are the Thompson (THOM), Morrison graupel/hail (MOR_G/H), Stony Brook University (SBU_YLIN), and WRF double-moment six-class microphysics graupel/hail (WDM6_G/H). All schemes were able to predict the rain band but underestimated the total precipitation by 23%–35%. This is mainly attributed to the underestimation of stratiform precipitation and overestimation of convective rain. For the vertical distribution of radar reflectivity, many problems remain, such as lower reflectivity values aloft in both convective and stratiform regions and higher reflectivity values at middle level. Each bulk scheme has its advantages and shortcomings for different cloud regimes. Overall, the discrepancies between model output and observations mostly exist in the midlevel to upper level, which results from the inability of the model to accurately represent the particle size distribution, ice processes, and storm dynamics. Further observations from major field campaigns and more detailed evaluation are still necessary.

Evaluation of Rain Microphysics Using a Radar Simulator and Numerical Models: Comparison of Two‐Moment Bulk and Spectral Bin Cloud Microphysics Schemes

AbstractThis study extended a method to evaluate rain microphysics in a shallow cumulus regime using space‐borne radar observational data with a forward simulator of satellite measurements. We compared a two‐moment bulk scheme with a two‐moment bin scheme. A dynamic‐kinematic model was used to isolate cloud microphysics processes from their interactions with dynamics. The relationship between horizontally averaged reflectivity Zm and optical depth from the cloud top τd, that is, the Zm‐τd relationship, was similar between the bulk and the bin schemes for clouds with large updraft velocity and clean cloud condensation nuclei (CCN). However, the differences in the Zm‐τd relationship between the two schemes became more apparent for clouds with smaller updraft velocity or polluted CCN. For these clouds, the differences resulted from differences in the autoconversion rate. We increased the autoconversion rate by decreasing the shape parameter of the cloud droplet size distribution in the bulk scheme. This reduced the differences in the Zm‐τd relationship between the two schemes, indicating that for these clouds, the autoconversion rate of a cloud microphysics scheme can be evaluated by the Zm‐τd relationship derived from satellite observations. Although the clouds with the smaller updraft velocity or the polluted CCN did not contribute to the rainfall amount significantly, the lifetime of these clouds greatly affected the radiation budget. Thus, the improvement in the autoconversion rate for such weak cumulus clouds provides a better representation of precipitation efficiency and is important for global climate evaluations.

Evaluating Errors in Gamma-Function Representations of the Raindrop Size Distribution: A Method for Determining the Optimal Parameter Set for Use in Bulk Microphysics Schemes

AbstractSignificant uncertainty lies in representing the rain droplet size distribution (DSD) in bulk cloud microphysics schemes and in the derivation of parameters of the function fit to the spectrum from the varying moments of a DSD. Here we evaluate the suitability of gamma distribution functions (GDFs) for fitting rain DSDs against observed disdrometer data. Results illustrate that double-parameter GDFs with prescribed or diagnosed positive spectral shape parameters μ fit rain DSDs better than the Marshall–Palmer distribution function (with μ = 0). The relative errors of fitting the spectrum moments (especially high-order moments) decrease by an order of magnitude [from O(102) to O(101)]. Moreover, introduction of a triple-parameter GDF with mathematically solved μ decreases the relative errors to O(100). Based on further investigation of potential combinations of the three prognostic moments for triple-moment cloud microphysical schemes, it is found that the GDF with parameters determined from predictions of the zeroth, third, and fourth moments (the 034 GDF) exhibits the best fit to rain DSDs compared to other moment combinations. Therefore, we suggest that the 034 prognostic moment group should replace the widely accepted 036 group to represent rain DSDs in triple-moment cloud microphysics schemes. An evaluation of the capability of GDFs to represent rain DSDs demonstrates that 034 GDF exhibits accurate fits to all observed DSDs except for rarely occurring extremely wide spectra from heavy precipitation and extremely narrow spectra from drizzle. The knowledge gained from this assessment can also be used to improve cloud microphysics retrieval schemes and data assimilation.

Simulations of Winter Arctic Clouds and Associated Radiation Fluxes Using Different Cloud Microphysics Schemes in the Polar WRF: Comparisons With CloudSat, CALIPSO, and CERES

Key Points Winter Arctic clouds simulated by the polar version of the Weather Research and Forecasting model are compared with satellite retrievals The cloud amount and cloud top height of model simulations agree well with those of satellite retrievals The downward longwave radiation at the surface shows realistic temporal variations, but is sensitive to the cloud microphysics scheme choice

Impact of 1- and 2-moment cloud microphysics and horizontal resolution on lightning Potential Index within COSMO NWP model

Abstract Lightning is considered one of the most severe meteorological hazards. Currently, many Numerical Weather Prediction (NWP) models make operational use of the Lightning Potential Index (LPI), which enables one to determine areas prone to lightning. In this study, we investigated the ability of LPI to forecast lightning by comparing forecasts of LPI among four configurations of the COSMO NWP model. The four configurations of the COSMO NWP model differ in horizontal resolution (1.2 and 2.2 km) and cloud microphysical scheme (1- and 2-moment cloud microphysics). We evaluated binary forecasts of LPI for varying lead time (1−10h) against the observed number and peak current of lightning flashes recorded by EUCLID network. The evaluation was performed for eight area sizes (4.8 km × 4.8 km to 240 km × 240 km) around model grid points and for 8 days in 2018 when thunderstorms occurred in Central Europe. To assess the success of lightning prediction, we evaluated LPI forecasts using the Area under the Receiver Operating Characteristic (AROC). Results show that the forecasts almost always outperformed the random forecast (AROC = 0.5). As expected, the prediction was more successful for models having higher resolution and the models comprising 2-moment cloud microphysics. The results of the evaluation of LPI forecasts compared to the number of observed lightning were similar to those compared to the sum of the observed peak current. However, differences in results among the four configurations of the model were larger in the latter case. Based on the performed evaluation, we confirm that LPI is a suitable tool for implicit forecasting of lightning in the COSMO NWP model.

Impact of Different Cloud Microphysics Parameterization Schemes on Typhoon Intensity and Structure

The impact of different cloud microphysics parameterization schemes on the intensity and structure of the Super-strong Typhoon Rammasun (1409) in 2014 is investigated using the Weather Research and Forecasting model version 3.4 with eight cloud microphysics parameterization schemes. Results indicate that the uncertainty of cloud microphysics schemes results in typhoon forecast uncertainties, which increase with forecast time. Typhoon forecast uncertainty primarily affects intensity predictions, with significant differences in predicted typhoon intensity using various cloud microphysics schemes. Typhoon forecast uncertainty also affects the predicted typhoon structure. Greater typhoon intensity is accompanied by smaller vortex width, tighter vortex structure, stronger wind in the middle and lower troposphere, greater height of the strong wind region, smaller thickness of the eyewall and the outward extension of the eyewall, and a warmer warm core at upper levels of the eye. The differences among the various cloud microphysics schemes lead to different amounts and distributions of water vapor and hydrometeors in clouds. Different hydrometeors have different vertical distributions. In the radial direction, the maxima for the various hydrometeors forecast by a single cloud microphysics scheme are collocated with each other and with the center of maximum precipitation. When the hydrometeor concentration is high and hydrometeors exist at lower altitudes, more precipitation often occurs. Both the vertical and horizontal winds are the strongest at the location of maximum precipitation. Results also indicate that typhoon intensities forecast by cloud microphysics schemes containing graupel processes are noticeably greater than those forecast by schemes without graupel processes. Among the eight cloud microphysics schemes investigated, typhoon intensity forecasts using the WRF Single-Moment 6-class and Thompson schemes are the most accurate.

Hurricane Florence Makes Landfall in the Southeast USA: Sensitive Dependence on Initial Conditions, Parameterizations, and Integrated Enstrophy

The overall purpose of this paper is to post-evaluate the predictability of Hurricane Florence using the Advanced Research Weather Research Forecast (WRF) (ARW) version of a mesoscale model. This was performed over the period from 0000 UTC 13 September 2018 through 0000 UTC 18 September 2018. The WRF ARW core resolution used here was the 27-km grid spacing chosen to in order to balance finer resolution against in house processing time and storage. The large-scale analysis showed that a change in the Northern Hemisphere flow regime, especially the flow in the western part of the Northern Hemisphere may have contributed partly to the reduced forward speed of the tropical cyclone. In order to measure the predictability of a system, we will use different convective and boundary layer schemes initialized from the same conditions. The results demonstrated that the sign of the local IRE tendency was similar to that of the Northern Hemisphere Integrated Enstrophy. The results also showed that when the boundary layer, convective, and cloud microphysical schemes of the model were varied, the areal coverage of heavy precipitation of Florence was under-forecast by approximately 10% or more, and the heaviest amounts were under-forecast by an average of about 20%.

Improving the representation of supercooled liquid water in the HARMONIE-AROME weather forecast model

A realistic representation of mixed-phase clouds in weather and climate models is essential to accurately simulate the model’s radiative balance and water cycle. In addition, it is important for providing downstream applications with physically realistic model data for computation of, for instance, atmospheric icing on societal infrastructure and aircraft. An important quantity for forecasts of atmospheric icing is to model accurately supercooled liquid water (SLW). In this study, we implement elements from the Thompson cloud microphysics scheme into the numerical weather prediction model HARMONIE-AROME, with the aim to improve its ability to predict SLW. We conduct an idealised process-level evaluation of microphysical processes, and analyse the water phase budget of clouds and precipitation to compare the modified and original schemes, and also identify the processes with the most impact to form SLW. Two idealised cases representing orographic lift and freezing drizzle, both known to generate significant amounts of SLW, are setup in a 1 D column version of HARMONIE-AROME. The experiments show that the amount of SLW is largely sensitive to the ice initiation processes, snow and graupel collection of cloud water, and the rain size distribution. There is a doubling of the cloud water maximum mixing ratio, in addition to a prolonged existence of SLW, with the modified scheme compared with the original scheme. The spatial and temporal extent of cloud ice and snow are reduced, due to stricter conditions for ice nucleation. The findings are important as the HARMONIE-AROME models is used for operational forecasting in many countries in northern Europe having a colder climate, as well as for climate assessments over the Arctic region.

Topographic Rainfall of Tropical Cyclones past a Mountain Range as Categorized by Idealized Simulations

Abstract Topographic rainfall induced by westbound tropical cyclones past an island mountain is investigated using an idealized Weather Research and Forecasting (WRF) Model. Idealized simulations with varying vortex core size R (100–250 km), vortex intensity Vmax (20–35 m s−1), and steering wind speed U (4–10 m s−1) are conducted. The results show that the geometric distributions of major rainfall over the island are not greatly sensitive to cloud microphysics schemes using either single momentum or double momentum. Major rainfall is produced over northeastern and southwestern slopes of the mountain for smaller U. As U is doubled, the rainfall, however, is considerably weakened or is present only over southwestern slopes. For smaller U, a bifurcation of island rainfall with a sudden change in intensity or geometric shifting exists within a tiny range of R or Vmax. When the bifurcation occurs with small track deviations, geometric distributions of major rainfall are also more sensitive to cloud microphysics schemes. Such formation of bifurcation or double-peak rainfall, however, is significantly reduced when the terrain size is doubled. Systematic experiments are conducted to relate the topographical rainfalls over the northern half, southern half, and the whole of the mountain slopes to varying R, Vmax, and U. Larger U tends to produce much larger southern rainfall than northern rainfall. The average and maximum rainfalls generally increase with increased Vmax, except for large R. The decrease of average rainfall and maximum rainfall with increased U is more evident for smaller R, while not necessarily true for larger R.

Monsoon Mission: A Targeted Activity to Improve Monsoon Prediction across Scales

AbstractIn spite of the summer monsoon’s importance in determining the life and economy of an agriculture-dependent country like India, committed efforts toward improving its prediction and simulation have been limited. Hence, a focused mission mode program Monsoon Mission (MM) was founded in 2012 to spur progress in this direction. This article explains the efforts made by the Earth System Science Organization (ESSO), Ministry of Earth Sciences (MoES), Government of India, in implementing MM to develop a dynamical prediction framework to improve monsoon prediction. Climate Forecast System, version 2 (CFSv2), and the Met Office Unified Model (UM) were chosen as the base models. The efforts in this program have resulted in 1) unparalleled skill of 0.63 for seasonal prediction of the Indian monsoon (for the period 1981–2010) in a high-resolution (∼38 km) seasonal prediction system, relative to present-generation seasonal prediction models; 2) extended-range predictions by a CFS-based grand multimodel ensemble (MME) prediction system; and 3) a gain of 2-day lead time from very high-resolution (12.5 km) Global Forecast System (GFS)-based short-range predictions up to 10 days. These prediction skills are on par with other global leading weather and climate centers, and are better in some areas. Several developmental activities like coupled data assimilation, changes in convective parameterization, cloud microphysics schemes, and parameterization of land surface processes (including snow and sea ice) led to the improvements such as reducing the strong model biases in the Indian summer monsoon simulation and elsewhere in the tropics.

Using an Arbitrary Moment Predictor to Investigate the Optimal Choice of Prognostic Moments in Bulk Cloud Microphysics Schemes

AbstractMost bulk cloud microphysics schemes predict up to three standard properties of hydrometeor size distributions, namely, the mass mixing ratio, number concentration, and reflectivity factor in order of increasing scheme complexity. However, it is unclear whether this combination of properties is optimal for obtaining the best simulation of clouds and precipitation in models. In this study, a bin microphysics scheme has been modified to act like a bulk microphysics scheme. The new scheme can predict an arbitrary combination of two or three moments of the hydrometeor size distributions. As a first test of the arbitrary moment predictor (AMP), box model simulations of condensation, evaporation, and collision‐coalescence are conducted for a variety of initial cloud droplet distributions and for a variety of configurations of AMP. The performance of AMP is assessed relative to the bin scheme from which it was built. The results show that no double‐ or triple‐moment configuration of AMP can simultaneously minimize the error of all cloud droplet distribution moments. In general, predicting low‐order moments helps to minimize errors in the cloud droplet number concentration, but predicting high‐order moments tends to minimize errors in the cloud mass mixing ratio. The results have implications for which moments should be predicted by bulk microphysics schemes for the cloud droplet category.

Genesis of Tibetan Plateau Vortex: Roles of Surface Diabatic and Atmospheric Condensational Latent Heating

AbstractNumerical simulations of a nighttime-generated Tibetan Plateau vortex (TPV) are conducted using the advanced Weather Research and Forecasting (WRF) Model. It is found that the nighttime TPV forms as a result of the merging of convections. Although the WRF Model can reproduce the genesis of the nighttime TPV well, colder and drier biases in the lower atmosphere and drier biases in the upper atmosphere are still presented, thus degrading the simulation performance. Intercomparisons among the experiments indicate that the simulations are more sensitive to land surface schemes than to cloud microphysics schemes. The development of convection is more favorable when daytime surface diabatic heating is vigorous. Surface diabatic heating during daytime plays a dominant role in the development of daytime convection and the genesis of nighttime TPV. Further diagnosis of the PV budget reveals that the obvious increase in PV in the lower atmosphere is associated with the evidently strengthened cyclonic vorticity during TPV genesis. This could be attributed to the increased vertical component of net cross-boundary PV fluxes during the merging of convections as well as the significant positive contribution of diabatic heating effects in the lower atmosphere. Therefore, strong daytime surface diabatic heating, which is essential to convection development, could provide a favorable condition for nighttime TPV genesis. Overall results illuminate the complicated process of TPV genesis.

Passive Microwave Precipitation Retrieval Algorithm With $A~Priori$ Databases of Various Cloud Microphysics Schemes: Tropical Cyclone Applications

The accuracy of a physically based passive microwave precipitation retrieval algorithm is affected by the quality of the a priori knowledge it employs, which indicates the relationship between the precipitation information obtained from cloud-resolving models (CRMs) and the simulated brightness temperatures (TBs) from radiative transfer models. As various microphysical assumptions reflecting a wide variety of sophisticated microphysical properties are applied to the CRMs, the TBs simulated based on the model-driven 3-D precipitation fields are determined by the selected microphysical assumption. In this article, we developed a prototype precipitation retrieval algorithm that incorporates various cloud microphysics schemes in its a priori knowledge (i.e., databases). In the retrieval process, a specific a priori database is selected for every target precipitation scene by comparing the similarities of the simulated and observed microwave emission and scattering signatures. The prototype algorithm was tested through application to precipitation retrieval for tropical cyclones at various intensity stages, which occurred over the northwestern Pacific region in 2015. The a priori databases constructed using the weather research and forecasting double-moment (WDM6) and Thompson Aerosol Aware schemes are superior when used for weak-to-moderate rainfall systems, whereas the databases constructed with the other schemes are superior within strong rain rate regions. The retrieval results obtained using the best-performing database are generally superior for all rain rate regions. Furthermore, we confirm that the database quality is more important than the number of databases. In comparison with the data from the dual-precipitation radar, the retrieval’s correlations, bias, and root mean square are 0.75, 0.14, and 5.62, respectively.

Satellite-Based Assessment of Various Cloud Microphysics Schemes in Simulating Typhoon Hydrometeors

The accurate simulation of typhoon hydrometeors remains a challenge. This study attempts to evaluate the performances of five microphysics schemes (MPSs) in the Weather Research and Forecasting (WRF) model in simulating the supertyphoon Neoguri in July 2014. The observed microwave brightness temperature, as well as retrieved data from the microwave radiometer imager (MWRI) onboard Chinese FY-3B satellite, are used to test hydrometeor simulations. In particular, two MWRI radiance indices, including the emission index (EI) and scattering index (SI), are used to assess the performance of five MPSs in simulating liquid and frozen hydrometeors, respectively. Overall, the WRF model can well reproduce the overall pattern of typhoon-produced precipitation, albeit with slightly overestimated precipitation in the inner rainband and underestimated precipitation in the stratiform rainband. Moreover, ice water paths (IWPs) from all five MPS simulations are higher than those estimated from MWRI retrieval in most areas, and the spatial pattern and values of IWP for the National Severe Storms Laboratory double-moment MPS (NSSL) are much closer to those for MWRI. The NSSL scheme reproduces a more realistic joint histogram distribution of SI and EI than other MPSs do, relative to the observation. Besides, the nonlinear Lucas–Kanade optical flow approach has been used to reflect the horizontal distribution of hydrometeors in the typhoon. The results show that the simulated EI and SI from the five MPSs show a systematic southwest bias of approximately about 10∼20 km and significant intensity bias in the convection area. Further model sensitivity tests confirm that the NSSL scheme generates more realistic graupel and supercooled water close to the observations among all MPSs. The findings suggest that satellite measurements would be helpful to assess MPSs in numeric weather models, especially for hydrometeor distributions in the whole typhoon system.

Influence of cumulus convection and cloud microphysics parameterizations on the prediction of Western Disturbances

The western disturbances (WD) form over the Mediterranean region as extra-tropical low-pressure systems and lose the frontal structure while moving eastward to reach India. These systems bring cold waves, snowfalls, hailstorms and rain over north and north-west India during post monsoon and winter months. The first part (Part A) of the present paper investigates the performance of Advanced Research WRF (ARW) model with 8 combinations of cloud microphysics and cumulus convection schemes in simulating 20 WD cases. These 20 cases were simulated using a single-domain WRF model of horizontal resolution 27 km. The combination of Lin et al. cloud microphysics scheme and Betts–Miller–Janjic cumulus convection scheme (mp2cu2) performs better than other combinations in simulating temperature at 2 m height and precipitation. The performance of the combination of Ferrier (new Eta) microphysics scheme and Betts-Miller-Janjic cumulus convection scheme (mp3cu2) is very close to that of mp2cu2 combination. Analysis of box-whisker plot also shows that the combinations mp2cu2 and mp3cu2 perform better than others. In the second part (Part B) 10 cases are simulated using a double-nested WRF model with inner and outer domain resolutions 9 km and 27 km, respectively. Four cases of part B are simulated with (mp2cu2 and mp3cu2) and without (mp2cu0 and mp3cu0) cumulus convection schemes to understand the response of cloud microphysics to explicit convection and also to select the best combination of cloud microphysics and cumulus convection scheme. The combination mp2cu2 has lower RMSE of precipitation than other combinations. Remaining six cases were then simulated with the combination of mp2cu2 using the double-nested model. Spatial distribution of model simulated and TRMM estimated precipitation agree well in most of the cases. The domain-averaged RMSE of model-simulated precipitation with respect to TRMM 3B42 V7 estimated precipitation varies from 2.89 to 4.12 cm for the six WD cases. The box-whisker diagram shows that the model overestimates the maximum rainfall amount in most of the cases but it is consistent in simulating precipitation over the model domain for all the six cases.