Cumulus Parameterization Schemes Research Articles

Forecasts of MJO Events during DYNAMO with a Coupled Atmosphere-Ocean Model: Sensitivity to Cumulus Parameterization Scheme

An operational weather forecast model, coupled to an oceanic model, was used to predict the initiation and propagation of two major Madden-Julian Oscillation (MJO) events during the dynamics of the MJO (DYNAMO) campaign period. Two convective parameterization schemes were used to understand the sensitivity of the forecast to the model cumulus scheme. The first is the Tiedtke (TDK) scheme, and the second is the Simplified Arakawa-Schubert (SAS) scheme. The TDK scheme was able to forecast the MJO-1 and MJO-2 initiation at 15- and 45-day lead, respectively, while the SAS scheme failed to predict the convection onset in the western equatorial Indian Ocean (WEIO). The diagnosis of the forecast results indicates that the successful prediction with the TDK scheme is attributed to the model capability to reproduce the observed intraseasonal outgoing longwave radiation-sea surface temperature (OLR-SST) relationship. On one hand, the SST anomaly (SSTA) over the WEIO was induced by surface heat flux anomalies associated with the preceding suppressed-phase MJO. The change of SSTA, in turn, caused boundary layer convergence and ascending motion, which further induced a positive column-integrated moist static energy (MSE) tendency, setting up a convectively unstable stratification for MJO initiation. The forecast with the SAS scheme failed to reproduce the observed OLR-SST-MSE relation. The propagation characteristics differed markedly between the two forecasts. Pronounced eastward phase propagation in the TDK scheme is attributed to a positive zonal gradient of the MSE tendency relative to the MJO center, similar to the observed, whereas a reversed gradient appeared in the forecast with the SAS scheme with dominant westward propagation. The difference is primarily attributed to anomalous vertical and horizontal MSE advection.

Bayesian network model for flood forecasting based on atmospheric ensemble forecasts

Abstract. The purpose of this study is to propose the Bayesian network (BN) model to estimate flood peaks from atmospheric ensemble forecasts (AEFs). The Weather Research and Forecasting (WRF) model was used to simulate historic storms using five cumulus parameterization schemes. The BN model was trained to compute flood peak forecasts from AEFs and hydrological pre-conditions. The mean absolute relative error was calculated as 0.076 for validation data. An artificial neural network (ANN) was applied for the same problem but showed inferior performance with a mean absolute relative error of 0.39. It seems that BN is less sensitive to small data sets, thus it is more suited for flood peak forecasting than ANN.

The Sensitivity of Rainfall Characteristics to Cumulus Parameterization Schemes from a WRF Model. Part I: A Case Study Over East Africa During Wet Years

The suitability of a weather research and forecasting model in simulating mean rainfall, number of rainy days (NRDs), intensity of rainy days and their frequencies is investigated over East Africa for selected wet years. Physical cumulus parameterizations using Kain–Fritsch (KF), Kain–Fritsch with a moisture advection-based trigger function (KFT), Grell–Devenyi (GRELL) and Betts–Miller–Janjic (BML) schemes were investigated. Results show that the schemes reproduce the general pattern of observed mean rainfall with reasonable accuracy; KF and GRELL schemes were more sensitive to simulations of rainfall characteristics. The KF (GRELL) simulations characteristically were associated with excess (deficient) rainfall. The BML scheme simulated a double signal of wet and dry rainfall biases. The KF and GRELL schemes simulated too many (fewer) NRDs under light (heavy) rainfall categories. Using a moisture advection function in the KFT scheme potentially lowered the excess rainfall simulated in the KF scheme. The reduced wet bias in simulated rainfall is due to better explicit physical mechanism realism of the trigger function in the KF scheme. A coherent statistical difference between KF and GRELL schemes was noted. The findings provide a basis upon which researchers and forecasters can improve numerical weather systems for better forecasting and simulations of East Africa extreme rainfall characteristics.

Toward a Stochastic Relaxation for the Quasi‐Equilibrium Theory of Cumulus Parameterization: Multicloud Instability, Multiple Equilibria, and Chaotic Dynamics

AbstractThe representation of clouds and organized tropical convection remains one of the biggest sources of uncertainties in climate and long‐term weather prediction models. Some of the most common cumulus parameterization schemes, namely, mass‐flux schemes, rely on the quasi‐equilibrium (QE) closure, which assumes that convection consumes the large‐scale instability and restores large‐scale equilibrium instantaneously. However, the QE hypothesis has been challenged both conceptually and in practice. Subsequently, the QE assumption was relaxed, and instead, prognostic equations for the cloud work function (CWF) and the cumulus kinetic energy (CKE) were derived and used. It was shown that even if the CWF kernel serves to damp the CWF, the prognostic system exhibits damped oscillations on a timescale of a few hours, giving parameterized‐cumulus‐clouds enough memory to interact with each other, with the environment, and with stratiform anvils in particular. Herein, we show that when cloud‐cloud interactions are reintroduced into the CWF‐CKE equations, the coupled system becomes unstable. Moreover, we couple the CWF‐CKE prognostic equations to dynamical equations for the cloud area fractions, based on the mean field limit of a stochastic multicloud model. Qualitative analysis and numerical simulations show that the CKE‐CWF‐cloud area fraction equations exhibit interesting dynamics including multiple equilibria, limit cycles, and chaotic behavior both when the large‐scale forcing is held fixed and when it oscillates with various frequencies. This is representative of cumulus convection variability, and its capability to transition between various regimes of organization at multiple scales and regimes of scattered convection, in an intermittent and chaotic fashion.

Sensitivity Study of North Atlantic Summer Cyclone Activity in Dynamical Downscaled Simulations

AbstractWe conduct a sensitivity study of the North Atlantic cyclone climate, with respect to the cumulus parameterization (CP) and planetary boundary layer (PBL) schemes, applied in the dynamical downscaling of HadGEM2‐ES simulations using Polar Weather Research and Forecasting. A set of seven sensitivity experiments is conducted for 1979–2004 using these CP and PBL schemes. CP schemes are Kain‐Fritsch (KF), Zhang‐McFarlane (ZM), and the modified Tiedtke (TZ). PBL schemes are the local Mellor‐Yamada‐Janjić (MYJ), Mellor‐Yamada‐Nakanishi‐Niino Level 2.5 (MYNN2) schemes, and nonlocal Yonsei University (YSU) scheme. In terms of cyclone intensities, our results show that the fine‐resolution Weather Research and Forecasting simulations capture higher intensities of summer cyclones than the HadGEM2‐ES results, in better agreement with the ERA‐Interim reanalysis, and exhibit a weak sensitivity to CP and PBL schemes. However, the frequency of summer cyclones is quite sensitive to CP and PBL schemes. Four KF experiments suggest comparable numbers of cyclone tracks as derived from ERA‐Interim reanalysis data, which is about 21% more than the ZM‐MYJ or TZ experiments and 11% more than HadGEM2‐ES results. Larger tropospheric potential vorticity and more cyclogenesis in the KF simulation than in TZ or ZM simulations are associated with different cyclone triggering functions and different vertical distributions of heating. We also find increased cyclone activities over the North Atlantic in the YSU or MYNN2 schemes compared to MYJ, but results also depend on the KF CP scheme. This can be attributed to stronger vertical mixing in YSU and MYNN schemes, resulting in smaller static stability and enhanced atmospheric moisture.

Aerosol Indirect Effects on the Predicted Precipitation in a Global Weather Forecasting Model

Aerosol indirect effects on precipitation were investigated in this study using a Global/Regional Integrated Model system (GRIMs) linked with a chemistry package devised for reducing the heavy computational burden occurring in common atmosphere–chemistry coupling models. The chemistry package was based on the Goddard Chemistry Aerosol Radiation and Transport scheme of Weather Research and Forecasting with Chemistry (WRF-Chem), and five tracers that are relatively important for cloud condensation nuclei (CCN) formation were treated as prognostic variables. For coupling with the cloud physics processes in the GRIMs, the CCN number concentrations derived from the simplified chemistry package were utilized in the cumulus parameterization scheme (CPS) and the microphysics scheme (MPS). The simulated CCN number concentrations were higher than those used in original cloud physics schemes and, overall, the amount of incoming shortwave radiation reaching the ground was indirectly reduced by an increase in clouds owing to a high CCN. The amount of heavier precipitation increased over the tropics owing to the inclusion of enhanced riming effects under deep precipitating convection. The trend regarding the changes in non-convective precipitation was mixed depending on the atmospheric conditions. The increase in small-size cloud water owing to a suppressed autoconversion led to a reduction in precipitation. More precipitation can occur when ice particles fall under high CCN conditions owing to the accretion of cloud water by snow and graupel, along with their melting.

Performance of a high resolution regional ocean–atmosphere coupled model over western North Pacific region: sensitivity to cumulus parameterizations

In this study, we developed a high-resolution regional ocean–atmosphere coupled model, based on RegCM4 and a North Pacific Ocean model, through the OASIS3 coupler. The horizontal resolution of the atmospheric component (oceanic component) was set to 15 km (0.1°). With the motivation to customize the model over western North Pacific (WNP) region, the sensitivity to three cumulus parameterization schemes (MIT-Emanuel, Tiedtke, Kain–Fritsch) was investigated with the focus on the WNP summer monsoon of 2005. The results indicated that a simulation with the Tiedtke scheme exhibited the relatively best performance over this region in terms of sea surface temperature (SST), rainfall, and circulation. Fifty percent of the model grids had biases of SST (rainfall) within ± 0.5 °C (± 2 mm/day) over WNP. In addition, the simulation with the Tiedtke scheme reasonably captured the shape and magnitude of the monsoon trough rainfall, the northward movement of the rainband and the associated circulation changes over WNP. However, the apparent heat source (Q1) and moisture sink (Q2) in the simulation with the MIT-Emanuel (Kain–Fritsch) scheme were nearly half (double) those of the observed measurements, especially in the lower troposphere over the monsoon trough region. The too weak (too strong) diabatic heating favored a weaker (stronger) ascending motion, which led to the underestimation (overestimation) of vertical moisture advection over the monsoon trough, thus resulting in evident dry (wet) biases. Our analysis suggested that the improved performance using the Tiedtke scheme could be attributed to the improved simulated vertical profile of diabatic heating, the inclusion of the effects of large-scale forcing in the cumulus parameterization, and the reasonable simulated relationship of precipitation with low-level vertical velocity.

Study on Different Parameterization Schemes to Simulate the Physical Process of a Heavy Rainfall on Qinghai-Tibet Plateau

In this paper, we conducted the mesoscale numerical simulation to study a rainfall on Qinghai-Tibet Plateau in 2016 based on WRF. Eight microphysical processes parameterization schemes and three cumulus parameterization schemes are combined together to find the most suitable in the area of physical parameterization schemes. The results showed that for small to heavy rain level, the microphysics scheme can achieve higher sensitivity than the cumulus convection scheme. In cumulus convection scheme, BMJ is better to simulate small rain, and KF is better than BMJ GF to simulate moderate to heavy rain. In microphysics scheme, Kessler is better compared to other programs on the simulated small rain, while Lin is better for moderate to heavy rain. In the case of other parameterization schemes fixed, BMJ cumulus convection scheme with Kessler microphysics scheme is best for small rain, while the KF cumulus convection scheme with Lin microphysics scheme is best for moderate to heavy rain.

Sensitivity of diurnal cycle of simulated rainfall to cumulus parameterization during Indian summer monsoon seasons

The sensitivity of simulation of the diurnal cycle (DC) to cumulus parameterisation is studied by using nine combinations (3 over Ocean × 3 over land) of cumulus schemes viz. Tiedtke, Emanuel, and Grell schemes for years 2008–2012 in the regional climate model RegCM4.4. The harmonic analysis is used to analyze the DC of rainfall simulated by the nine combinations of CPSs. It is seen that Emanual scheme predicts the spatial distribution of amplitude of the first harmonic and the variance of the diurnal cycle more appropriately compared to other CPSs. However, all schemes advanced the phase of the DC of rainfall by 3–6 h which is the result of the development of early convection in the model. The analysis of the development of rapid convection in the model suggests that sensible heat flux and vertically integrated moisture flux convergence peaks 3 to 6 h earlier compared to the phase of DC of rainfall estimated from TRMM rainfall. The early development of convection in the model may result in the inappropriate representation of condensation and evaporation cycle and hence DC of rainfall. The study emphasize that improvement in the phase of triggering of convection by cumulus parameterization schemes is essential to improve DC rainfall simulated by regional climate models.

Impact of Assimilating Himawari‐8‐Derived Layered Precipitable Water With Varying Cumulus and Microphysics Parameterization Schemes on the Simulation of Typhoon Hato

AbstractUnderstanding moisture information ahead of tropical cyclone (TC) convection is very important for predicting TC track, intensity, and precipitation. The advanced Himawari imager onboard the Japanese Himawari‐8/‐9 satellite can provide high spatial and temporal resolution moisture information. Three‐layered precipitable water (LPW) with its three water vapor absorption infrared bands can be assimilated to generate better understanding and prediction of TC evolution. The impacts of LPW assimilation in the Weather Research and Forecasting model with nine combinations of physical parameterization schemes, including three cumulus parameterization (CP) and three microphysics parameterization (MP) schemes on TC prediction, have been comprehensively analyzed using Typhoon Hato as a case study. The results indicate that LPW assimilation reduces the average track error and speed up TC movement by better adjustment of the atmospheric circulation fields via changing the vertical structure of moisture and thermal profile. The track forecasts retain sensitivity to CP schemes after LPW assimilation. Also, LPW assimilation improves TC intensity prediction because the latent heat release process is accurately adjusted. It has been revealed that LPW assimilation can weaken the intensity sensitivity to MP schemes more than to CP schemes. Skill scores were used to evaluate precipitation forecasts after Hato's landfall. The results indicate that heavy precipitation forecasts are more sensitive to the choice of MP schemes. After LPW assimilation, the equitable threat scores among different results become similar and all forecast skills are increased. In addition, group statistic results with different initial time show the same conclusions.

Performance evaluation of WRF for extreme flood forecasts in a coastal urban environment

Increasing incidences of flash floods highlight the need for a reliable flood forecasting system to minimize the losses of lives and property. The most formidable challenge of flood forecasting is the availability of high resolution and accurate precipitation forecast despite having a sophisticated 3-ways flood hydrodynamic model. Global rainfall forecasting products are of coarse resolution, which makes them less reliable for urban flood forecasting. Therefore, high-resolution regional weather forecasting models such as the Weather Research and Forecasting (WRF) model are used to generate fine-scale rainfall estimates. Precipitation forecasting from the WRF model is highly dependent on model configuration, especially cumulus (CU) parameterization and microphysics (MP) schemes. In the present study, three physics schemes that include two urban, four CU and three MP schemes of WRF model are investigated for extreme precipitation estimates. The six events comprised of two of the highest rainfall events of the years 2012, 2013 and 2014 have been selected for investigation over Mumbai, India. The events are simulated using initial and boundary conditions from the ERA-Interim Reanalysis dataset. The simulated rainfall events are evaluated against the observations from 28 automatic weather stations over Mumbai. The analysis suggests that building environment parameterization (BEP) scheme influences the spatial pattern of the rainfall along with the reduction in rainfall bias. Further, CU schemes affect the magnitude of the rainfall while MP schemes have a lesser impact than the former. WRF simulations with BEP urban scheme, Grell-Devenyi 3D CU, and Lin MP scheme performs best (out of selected combinations). Besides, the best performing scheme has been tested with initial and boundary conditions from the global forecasting system (GFS) for the same events; the results have shown improved rainfall estimates than the GFS forecasts.

Impact of Cumulus Parameterization on Model Convergence of Tropical Cyclone Destructive Potential Simulation at Grey-Zone Resolutions: A Numerical Investigation

The Weather Research Forecast model (WRF) is used to examine the destructive potential of tropical cyclone (TC) Shanshan (2006) at various horizontal resolutions (7.5 km–1 km) with different cumulus parameterization (CP) schemes. It is found that the calculated Power Dissipation Index (PDI) increases while the size-dependent destructive potential (PDS) decreases as the grid spacing decreases for all CP-scheme simulations, which indicates a weak model convergence in both PDI and PDS calculations. Moreover, it is change of the storm intensity and inner-core size that lead to the non-convergence of PDI and PDS respectively. At a higher resolution, convection becomes more explicitly resolved, which leads to larger diabatic heating. As a result, the radial pressure gradient force (PGF) increases, and the radius of maximum wind (RMW) decreases. The area of strong diabatic heating subsequently becomes closer to the TC center, which further increases the TC intensity and the PGF near the eyewall. With such a positive feedback loop, the PGF increases and the RMW decreases as the resolution increases. Note that a perfect model should converge well in the simulation of both TC intensity and size, and thus converge in the PDS. For some CP experiments, the calculated PDS convergence is relatively strong, but it is a result of offset between the non-convergent simulations of TC intensity and size. In contrast, the Grell–Freitas scheme exhibits a stronger convergence in the simulations of TC intensity and size, although the convergence in PDS is relatively weak, but is closer to the truth.

Effects of the Convective Triggering Process in a Cumulus Parameterization Scheme on the Diurnal Variation of Precipitation over East Asia

Effects of the convective triggering process in a cumulus parameterization scheme on the diurnal variation of precipitation over East Asia are examined using a regional climate model. Based on a cloud-resolving simulation showing the irrelevance of convective inhibition once convection is initiated and the sensitivity experiments to trigger conditions, the triggering process in the simplified Arakawa-Schubert (SAS) convection scheme is modified to use different convective initiation and termination conditions. The diurnal variation of precipitation frequency with the modified triggering process becomes in phase with the observed one, leading to a delayed afternoon peak in precipitation rate that is in better agreement with the observation. However, the bias in the phase of precipitation intensity is not resolved and the bias of excessive precipitation increases, indicating that adequate representation of not only the triggering process but also other moist convective processes that determine the strength of convection is required for further improvement in the simulation of the diurnal variation of precipitation.

Sensitivity of Numerical Simulations of Hurricane Joaquin (2015) to Cumulus Parameterization Schemes: Implications for Processes Controlling a Hairpin Turn in the Track

Sensitivity of Numerical Simulations of Hurricane Joaquin (2015) to Cumulus Parameterization Schemes: Implications for Processes Controlling a Hairpin Turn in the Track

Assessment of various cumulus parameterization schemes for the simulation of very heavy rainfall event based on optimal ensemble approach

In this study, an attempt has been made to investigate one of the recently occurred extreme rainfall events (EREs) viz.; cloudburst over Pithoragarh district (July 1, 2016) of Uttrakhand in the North-West Himalayan (NWH) region using Weather Research and Forecast model (WRF 3.8.1) based on optimal ensemble approach. Four ensemble members, initialized with perturbed initial conditions corresponding to various cumulus parameterization (CP) schemes, have been generated. Accumulated rainfall for 24 h based on 4 ensemble members have been validated against the remotely sensed HE-rain derived from INSAT-3D measurements. For the simulation purpose, WRF model is tuned with variations in various scale not-aware and scale aware CP schemes viz.; Kain-Fritsch (KF), Bettes’ Miller Janjic (BMJ), Arakawa Schubert (AS), Tiedtke, Grell-Freitas (GF), Multi-Scale Kain-Fritsch (MSKF) and new Tiedtke. It is noted from the present analysis that two out of four ensemble members corresponding to KF scheme simulate rainfall close to the observation. In addition, it is also noted that the spread amongst the ensemble members is very large in case of BMJ, AS, MSKF, Tiedtke, GF and new Tiedtke schemes. Considering large bias amongst various ensemble members for each of the CP schemes, it is suggested that the results based on one ensemble member may be regarded spurious. Further, based on the closest match of ensemble mean with the observation and comparison amongst the simulated grid scale and sub grid scale rainfall corresponding to various CP schemes, it is suggested that scale aware MSKF scheme may be utilized for the simulations of EREs over the NWH region.

Seasonal Forecasting of Onset of Summer Rains over South Africa

AbstractIn this study, we attempted to forecast the onset of summer rains over South Africa using seasonal precipitation forecasts generated by the Scale Interaction Experiment–Frontier Research Center for Global Change, version 2 (SINTEX-F2), seasonal forecasting system. The precipitation forecasts of the 12-member SINTEX-F2 system, initialized on 1 August and covering the period 1998–2015, were used for the study. The SINTEX-F2 forecast precipitation was also downscaled using dynamical and statistical techniques to improve the spatial and temporal representation of the forecasts. The Weather Research and Forecasting (WRF) Model with two cumulus parameterization schemes was used to dynamically downscale the SINTEX-F2 forecasts. The WRF and SINTEX-F2 precipitation forecasts were corrected for biases using a linear scaling method with a 31-day moving window. The results indicate the onset dates derived from the raw and bias-corrected model precipitation forecasts to have realistic spatial distribution over South Africa. However, the forecast onset dates have root-mean-square errors of more than 30 days over most parts of South Africa except over the northeastern province of Limpopo and over the Highveld region of Mpumalanga province, where the root-mean-square errors are about 10–15 days. The WRF Model with Kain–Fritsch cumulus scheme (bias-corrected SINTEX-F2) has better performance in forecasting the onset dates over Limpopo (the Highveld region) compared to other models, thereby indicating the forecast of onset dates over different regions of South Africa to be model dependent. The results of this study are important for improving the forecast of onset dates over South Africa.

A comparison of the rainfall forecasting skills of the WRF ensemble forecasting system using SPCPT and other cumulus parameterization error representation schemes

The scientific community mainly uses ensemble systems to represent various sources of uncertainties and to produce better forecasts. The inability to choose accurate initial conditions leads to failed forecasts due to the well-known butterfly effect; however, in modern weather models, researchers have paid more attention to inadequacies in the sense of physical parameterization schemes and other dynamic processes. In this study, the uncertainties caused by cumulus parameterization are represented by the stochastic perturbed parameterization tendency (SPPT) scheme, and the results are compared with those of classic schemes, including the multi cumulus parameterization scheme, the parameter-perturbed scheme, and the Gaussian-noise-perturbed tendency scheme. The impacts of these various schemes on the precipitation predictions of the Weather Research and Forecasting (WRF) model are compared for Southeast Asia. The results using the stochastic perturbed cumulus parameterization tendency (SPCPT) perturbation scheme are contrasted with those of the total parameterization tendency perturbation scheme. Compared to the other schemes, the multi cumulus parameterization scheme has better Brier skill scores (BSS) and greater spreads. However, the SPCPT scheme shows significant improvements in the root mean square error (RMSE) and the Gerrity skill score (GSS) for rainfall prediction. This result also implies that the noise pattern is critical to the ensemble system; thus, using a single error representation scheme may be insufficient to estimate the error in the cumulus parameterization process.

The Impact of Hybrid Usage of a Cumulus Parameterization Scheme on Tropical Convection and Large‐Scale Circulations in a Global Cloud‐System Resolving Model

AbstractThe impact of activating a cumulus parameterization scheme in the global nonhydrostatic icosahedral atmospheric model (NICAM) coupled with a one‐dimensional (1‐D) mixed‐layer ocean model is assessed using a cloud‐system‐resolving, 14‐km mesh. The Chikira‐Sugiyama (CS) scheme, which employs an entrainment rate sensitive to the humidity of the environment, can consider congestus clouds in the tropics when used in conventional global climate models. Congestus clouds are underresolved in the default 14‐km mesh NICAM. In the present study, boreal summer NICAM simulations are performed with and without the CS scheme, and several different scheme parameters are evaluated. The results show that the horizontal scale of convection and precipitable water increased in the tropics when using the CS scheme. Model adjustments were apparent at two different timescales: a rapid adjustment within the first week and a slower adjustment at 1 to 2 months. Both effects were magnified in the simulations that employed smaller values for the parameter that defines the fractional of loss of buoyancy‐generated energy in parameterized convection. The upward branch of the Hadley circulation shifted northward, and the Walker circulation was enhanced when the CS scheme was activated. These large‐scale adjustments suggested that increased midtropospheric moisture in the tropics tends to favor larger organized convective activities, which require an abundant supply of moisture, which, in this case, is available to the north of the equatorial West Pacific Ocean.

Effects of Resolution, Cumulus Parameterization Scheme, and Probability Forecasting on Precipitation Forecasts in a High-Resolution Limited-Area Ensemble Prediction System

This study investigates the effects of horizontal resolution, cumulus parameterization scheme (CPS), and probability forecasting on precipitation forecasts over the Korean Peninsula from 00 UTC 15 August to 12 UTC 14 September 2013, using the limited-area ensemble prediction system (LEPS) of the Korea Meteorological Administration. To investigate the effect of resolution, the control members of the LEPS with 1.5- and 3-km resolution were compared. Two 3-km experiments with and without the CPS were conducted for the control member, because a 3-km resolution lies within the gray zone. For probability forecasting, 12 ensemble members with 3-km resolution were run using the LEPS. The forecast performance was evaluated for both the whole study period and precipitation cases categorized by synoptic forcing. The performance of precipitation forecasts using the 1.5-km resolution was better than that using the 3-km resolution for both the total period and individual cases. The result of the 3-km resolution experiment with the CPS did not differ significantly from that without it. The 3-km ensemble mean and probability matching (PM) performed better than the 3-km control member, regardless of the use of the CPS. The PM complemented the defect of the ensemble mean, which better predicts precipitation regions but underestimates precipitation amount by averaging ensembles, compared to the control member. Further, both the 3-km ensemble mean and PM outperformed the 1.5-km control member, which implies that the lower performance of the 3-km control member compared to the 1.5-km control member was complemented by probability forecasting.

Objective Quantification of Convective Clustering Observed During the AMIE/DYNAMO Two‐Day Rain Episodes

AbstractOne critical bottleneck in developing and evaluating ways to represent the mesoscale organization of convection in cumulus parameterization schemes is that there is no single accepted method of objectively quantifying the degree of convective organization or clustering from observations. This study addresses this need using high‐quality S‐PolKa radar data from the Atmospheric Radiation Measurement Madden‐Julian Oscillation Investigation Experiment/Dynamics of the Madden‐Julian Oscillation (AMIE/DYNAMO) field campaign. We first identify convective elements (contiguous convective echoes [CCEs]) from radar reflectivity observations using the rain type classification algorithm of Powell et al. (2016, https://doi.org/10.1175/JTECH‐D‐15‐0135.1). Then we apply scalar clustering metrics, including the organization index (Iorg) of Tompkins and Semie, to the radar CCEs to test their ability of quantifying convective clustering during the observed two‐day rain episodes. Our results show two distinct phases of convective clustering during the two‐day rain episodes, with each phase covering about 10 hr before (Phase 1) and after (Phase 2) the time of peak rain rate. In Phase 1 clustering, the number of CCEs increases and convective cells cluster as new cells form preferentially near existing convective entities. The number of CCEs decreases as the environment stabilizes in Phase 2 clustering, during which already clustered cells with associated stratiform clouds are preferred over the isolated ones. Iorg is capable of capturing convective clustering in both phases. The possible mechanisms for convective clustering are discussed, including cold pool‐updraft feedback, moisture‐convection interaction, and mesoscale circulations. Our results suggest that parameterizations of convective organization should represent the feedback processes that are responsible for the convective clustering during both phases.