Betts Miller Janjic Research Articles

The performance of different cumulus parameterization schemes in simulating the 2006/2007 southern peninsular Malaysia heavy rainfall episodes

The performance of four different cumulus parameterization schemes (CPS) in the Weather Research and Forecasting (WRF) model for simulating three heavy rainfall episodes over the southern peninsular Malaysia during the winter monsoon of 2006/2007 were examined. The modelled rainfall was compared with the 3-hourly satellite observation and objectively scored using a verification technique called the acuity–fidelity. The technique is based on minimization of a cost function that is calculated from four parameters taking into account errors in distance, time, intensity, and missed events. All simulations were made for 72 hours for the three episodes starting at 1200 UTC 17 December 2006, 1200 UTC 24 December 2006 and 1200 UTC 11 January 2007, respectively. The four different CPSs used are the new Kain–Fritsch scheme (KF2), the Betts–Miller–Janjic scheme (BMJ), the Grell–Devenyi ensemble scheme (GD) and the older Kain–Fritsch scheme (KF1). While the BMJ scheme shows some success in the second and third episodes, it shows high location errors in the first episode, leading to high acuity errors. The GD, KF2 and KF1 schemes performed poorly, although both the BMJ and GD schemes simulated the observed drastic increase of rainfall at 2100 UTC 18 December 2006 during the first episode. Overall, the KF1 and KF2 schemes produced positive biases in terms of coverage, while the GD scheme showed persistent location bias, producing a scattered line of precipitation over the eastern coastline of peninsular Malaysia. Although the BMJ scheme has better results, its poor performance for the first episode suggests that suitability of CPS may be case dependent.

Simulation of Heavy Rainfall Events during Retreat Phase of Summer Monsoon Season over Parts of Andhra Pradesh

The main aim of this paper is to simulate monsoon heavy rainfall episodes that caused floods across some parts of Andhra Pradesh (AP) state, India during 29th September through 2nd October, 2009. A heavy rainfall quantity of 21 cm was observed near Amaravathi station (16.15°N; 80.5°E) in Guntur district due to a meso-α low pressure system extended from the Bay of Bengal and widespread rainfall episodes were also appeared to many adjoining places in other three districts namely Mahaboob Nagar, Kurnool and Krishna in AP state simultaneously on 29th September. The rainy situation continued till 2nd October and caused floods over above districts of AP state which lead to a death toll of 33 people and heavy crop loss. To quantify the above catastrophic monsoon heavy precipitation events a high resolution (9 km) Weather Research and Forecast (WRF-ARW) model is centered at Amaravathi station to simulate rainfall episodes over the study region. In the present case study the simulated sensitive experiment highlights the dynamical characteristics of the meso-α system interms of circulation changes at different levels. Secondly, the thermodynamical characteristics for the generation of convective activity of this meso-α event in terms of Convective Available Potential Energy (CAPE) and Convective Inhibition Energy (CINE) are also simulated. Dynamical and thermodynamical simulated results support heavy rainfall episodes due to a low pressure system around Amaravathi station. Thus circulation changes, high CAPE and low CINE magnitudes have well defined not only the strength of meso-α system, but also quantum of rain-fall to a tune of 19 cm near Amaravathi station on 29th September. The observed rainfall was 21 cm on 29th September and thus this model underestimates rainfall about 9.5% not only at Amaravathi station, but also at other stations as well. Similar results are noticed over the study region on other three days. In this numerical study heavy rainfall events are better represented by Kain-Fritsch (KF) scheme than Betts-Miller-Janjic (BMJ) and Grell-Deveneyi (GD) schemes. Finally simulated circulation features and rainfall quantities are validated with observed rainfall of the India Meteorological Department (IMD) and satellite derived datasets of KALPANA-1, while CAPE and CINE quantities are checked against available Wyoming University observations. The results are promising.

Evaluation of the weather research and forecasting (WRF) model over Guyana

In the present study, diagnostic studies were undertaken using station-based rainfall data sets of selected stations of Guyana to understand the variability of rainfall. The multidecadal variation in rainfall of coastal station Georgetown and inland station Timehri has shown that the rainfall variability was less during the May–July (20–30%) of primary wet season compared to the December--January (60–70%) of second wet season. The rainfall analysis of Georgetown based on data series from 1916 to 2007 shows that El Nino/La Nina has direct relation with monthly mean rainfall of Guyana. The impact is more predominant during the second wet season December--January. A high-resolution Weather Research and Forecasting model was made operational to generate real-time forecasts up to 84 h based on 00 UTC global forecast system (GFS), NCEP initial condition. The model real-time rainfall forecast during July 2010 evaluation has shown a reasonable skill of the forecast model in predicting the heavy rainfall events and major circulation features for day-to-day operational forecast guidance. In addition to the operational experimental forecast, as part of model validation, a few sensitivity experiments are also conducted with the combination of two cloud cumulus (Kain--Fritsch (KF) and Betts–Miller–Janjic (BMJ)) and three microphysical schemes (Ferrier et al. WSM-3 simple ice scheme and Lin et al.) for heavy rainfall event occurred during 28–30 May 2010 over coastal Guyana and tropical Hurricane ‘EARL’ formed during 25 August–04 September 2010 over east Caribbean Sea. It was observed that there are major differences in the simulations of heavy rainfall event among the cumulus schemes, in spite of using the same initial and boundary conditions and model configuration. Overall, it was observed that the combination of BMJ and WSM-3 has shown qualitatively close to the observed heavy rainfall event even though the predicted amounts are less. In the case of tropical Hurricane ‘EARL’, the forecast track in all the six experiments based on 00 UTC of 28 August 2010 initial conditions for the forecast up to 84 h has shown that the combination of KF cumulus and Ferrier microphysics scheme has shown less track errors compared to other combinations. The overall average position errors for all the six experiments taken together work out to 103 km in 24, 199 km in 48, 197 km in 72 and 174 km in 84 h.

Simulation of monsoon depression over India using high resolution WRF Model – Sensitivity to convective parameterization schemes

The monsoon depression of September 2008, which crossed Orissa coast near Chandbali on 16th had contributed heavy rainfall over Orissa, Chhattisgarh and northern India along the track of the system. The sensitivity of three cumulus parameterization schemes viz., Kain-Fritch (KF) scheme, Grell-Devenyi (GD) scheme and Betts-Miller-Janjic (BMJ) Scheme are tested using high resolution advanced version (3.0) Weather Research Forecasting (WRF) model in forecasting the monsoon depression.
 The results of the present study shows that the genesis of the system was almost well captured in the model as indicated in 48hr forecast with all three convective parameterization schemes. It is seen that the track of monsoon depression is quite sensitive to the cumulus parameterization schemes used in the model and is found that the track forecast using three different cumulus schemes are improved when the model was started from the initial condition of a depression stage compared to that when it started from the initial condition of low pressure area. It is also seen that when the system was over land all the schemes performed reasonably well with KF and GD schemes closely followed the observed track compared to that of BMJ track. The performance of KF and GD schemes are almost similar till 72 hrs with lowest landfall error in KF scheme compared to other two schemes, whereas the BMJ scheme gives lowest mean forecast error upto 48 hr and largest mean forecast error at 72 hr. The overall rainfall forecast associated with the monsoon depression is also well captured in WRF model with KF scheme compared to that of GD scheme and BMJ scheme with observed heavy rainfall over Orissa, Chhattisgarh and western Himalayas is well captured in the model with KF scheme compared to that with GD scheme and BMJ scheme.

Case Studies of Easterly Wave Disturbances over Northeast Brazil Using the Eta Model

Abstract This research aims to analyze two easterly wave disturbance events that reached the east coast of Northeast Brazil (NEB) on 15 May 2005 and 20 June 2006. The performance of the regional Eta Model was evaluated through four sets of experiments using different horizontal resolutions (10 and 40 km) and convective parameterization schemes [Betts–Miller–Janjić (BMJ) and Kain–Fritsch (KF)]. Through analysis of 700-hPa wind fields, all experiments, with 40- and 10-km horizontal resolutions and using the BMJ and KF schemes, were shown to well represent a trough propagating westward from about 20°W to NEB, with a 5-day forecast range. When evaluating the model sensitivity to different convective parameterizations, all the experiments showed similar results and underestimated rainfall in most of the domain. The obtained results suggest the need to extend this analysis to a larger number of cases and to test other cumulus parameterizations in order to improve the rainfall forecast in eastern NEB.

Impact of Cumulus Parameterization Schemes with Different Horizontal Grid Sizes on Prediction of Heavy Rainfall

This study investigates the impact of cumulus parameterization scheme (CPS) with different horizontal grid sizes on the simulation of the local heavy rainfall case over the Korean Peninsula. The Weather Research and Forecasting (WRF)-based real-time forecast system of the Joint Center for High-impact Weather and Climate Research (JHWC) is used. Three CPSs are used for sensitivity experiments: the BMJ (Betts-Miller-Janjic), GD (Grell-Devenyi ensemble), and KF (Kain-Fritsch) CPSs. The heavy rainfall case selected in this study is characterized by low-level jet and low-level transport of warm and moist air. In 27-km simulations (DM1), simulated precipitation is overestimated in the experiment with BMJ scheme, and it is underestimated with GD scheme. The experiment with KF scheme shows well-developed precipitation cells in the southern and the central region of the Korean Peninsula, which are similar to the observations. All schemes show wet bias and cold bias in the lower troposphere. The simulated rainfall in 27-km horizontal resolution has influence on rainfall forecast in 9-km horizontal resolution, so the statements on 27-km horizontal resolution can be applied to 9-km horizontal resolution. In the sensitivity experiments of CPS for DM3 (3-km resolution), the experiment with BMJ scheme shows better heavy rainfall forecast than the other experiments. The experiments with CPS in 3-km horizontal resolution improve rainfall forecasts compared to the experiments without CPS, especially in rainfall distribution. The experiments with CPS show lower LCL(Lifted Condensation Level) than those without CPS at the maximum rainfall point, and weaker vertical velocity is simulated in the experiments with CPS compared to the experiments without CPS. It means that CPS suppresses convective instability and influences mainly convective rainfall. Consequently, heavy rainfall simulation with BMJ CPS is better than the other CPSs, and even in 3-km horizontal resolution, CPS should be applied to control convective instability. This conclusion can be generalized by conducting more experiments for a variety of cases over the Korean Peninsula.

Indian Summer Monsoon Precipitation Climatology in a High-Resolution Regional Climate Model: Impacts of Convective Parameterization on Systematic Biases

Abstract In an attempt to develop a better simulation of the climatology of monsoon precipitation in climate models, this paper investigates the impacts of different convective closures on systematic biases of an Indian monsoon precipitation climatology in a high-resolution regional climate model. For this purpose, the Weather Research Forecast (WRF) model is run at 45- and 15-km (two-way nested) resolution with three convective parameterization schemes, namely the Grell–Devenyi (GD), the Betts–Miller–Janjić (BMJ), and the Kain–Fritsch (KF), for the period 1 May–31 October 2001–07. The model is forced with the NCEP–NCAR reanalysis data as the initial and boundary conditions. The simulated June–September (JJAS) mean monsoon rainfall with the three convective schemes is compared with the observations. KF is found to have a high moist bias over the central and western coastal Indian region while GD shows the opposite. Among the three, BMJ is able to produce a reasonable mean monsoon pattern. In an attempt to get further insight into the seasonal bias and its evolution, the probability distribution function (PDF) of different rain-rate categories and their percentage contribution to the seasonal total are computed. BMJ and KF underestimate the observations for lighter rain rates and overestimate for rain-rate categories of more than 10 mm day−1. GD shows an overestimation for lighter rain and an underestimation of PDF for moderate categories. The seasonal patterns of evolution of PDF plots of three rain-rate categories are analyzed to determine whether the convective schemes show any systematic bias throughout the season or if they have problems during certain phases of the monsoon. This shows that the GD systematically overestimates the lighter rain rate and underestimates the moderate rain rate throughout the season, whereas BMJ and KF have problems in the initial stages. The heavy rain category is systematically overestimated by the KF compared to the other two. To further evaluate the proportionate contribution of each rain-rate bin to the total rain, the percentage contribution of each rain rate to the seasonal total is computed. Analyzing all the rain-rate simulations produced by the three schemes, it is found that KF has a moist bias and GD has a dry bias in the spatiotemporal distribution of the monsoon precipitation. Further, this paper investigates the causes behind the mean monsoon precipitation bias. It is shown that GD produces a model climate where the vertical velocity is less than that of the observations up to 500 hPa and the vertically integrated moist instability is also weaker. KF, on the other hand, shows a higher than the observed vertical velocity and a stronger moist instability. Along with this, the vertical profile of heating suggests a warmer middle level in the KF case and significantly reduced midlevel heating for GD. Thus, KF (GD) has produced a model atmosphere that has a stronger (weaker) convective instability to produce the observed bias in the model precipitation. BMJ is found to simulate a reasonable heating profile, along with the realistic moist instability and seasonal cycle of evaporation and condensation. Insight derived from the analysis is expected to help improve the convective parameterizations.

The sensitivity of numerical forecasts to convective parameterization during the warm period and the use of lightning data as an indicator for convective occurrence

The sensitivity of numerical model quantitative precipitation forecasts to the choice of the convective parameterization scheme (CPS) is examined for twenty selected cases characterized by intense convective activity and widespread precipitation over Greece, during the warm period of 2005–2007. Namely, the study is conducted using MM5 model and the following three different CPSs: Kain–Fritsch, Grell and Betts–Miller–Janjic. Sixty numerical simulations were carried out on two nested domains, with horizontal grid increments of 24 and 8 km respectively. The simulated precipitation from the 8-km grid was verified against raingauge measurements and lightning data provided by the ZEUS long-range lightning detection system, operated by the National Observatory of Athens. Verification results showed that for all three schemes the model presented a tendency to overestimate light to moderate rain while in general it underestimated the high precipitation amounts. The validation against both sources of data showed that among the three CPSs, the more consistent behavior in quantitative precipitation forecasting was obtained by the Kain–Fritsch scheme that provided the best statistical scores. However, the differences of the results of statistical analysis between the Kain–Fritsch and Grell schemes were not large.

The simulation of heavy rainfall episode over Mumbai: impact of horizontal resolutions and cumulus parameterization schemes

In this study, the simulation of an extreme weather event like heavy rainfall over Mumbai (India) on July 26, 2005 has been attempted with different horizontal resolutions using the Advanced Research Weather Research Forecast model version 2.0.1 developed at the National Center for Atmospheric Research (NCAR), USA. The study uses the Betts–Miller–Janjic (BMJ) and the Grell–Devenyi ensemble (GDE) cumulus parameterization schemes in single and nested domain configurations. The model performance was evaluated by examining the different predicted parameters like upper and lower level circulations, moisture, temperature, and rainfall. The large-scale circulation features, moisture, and temperature were compared with the National Centers for Environmental Prediction analyses. The rainfall prediction was assessed quantitatively by comparing rainfall from the Tropical Rainfall Measuring Mission products and the observed station values reported in the Indian Daily Weather Reports from India Meteorological Department (IMD). The quantitative validation of the simulated rainfall was done by calculating the categorical skill scores like frequency bias, threat scores (TS), and equitable threat scores (ETS). It is found that in all simulations, both in single and nested domains, the GDE scheme has outperformed the BMJ scheme for the simulation of rainfall for this specific event.

Using Betts-Miller-Janjic convective parameterization scheme in H14-31 model to forecast heavy rainfall in Vietnam

According to Krishnamurti, improvements of physical parameterizations will mainly affect simulations for the tropics [10]. The study of William A. Gallus Jr. showed that the higher the model resolution and more detailed convective parameterizations, the better the skill in quantitative precipitation forecast (QPF) in general [16]. The quality of precipitation forecast is so sensitive to convective parameterization scheme (CPS) used in the model as well as model resolution. The fact shows that for high resolution regional model like H14-31 CPS based on low-level moisture convergence as Tiedtke did not give good heavy rainfall forecast in Vietnam. In this paper we used the scheme of Betts-Miller-Janjic (BMJ) based on the convective adjustment toward tropical observationally structures in reality instead of Tiedtke in Hl4-31. Statistical verification results and verification using CRA method of Hl4-31 of two CPSs for seperated cases and for three rain seasons (2003-2005) shows that heavy rainfall forecast of Hl4-31/BMJ is better than one of H14-31/TK for Vietnam-South China Sea. CRA verification also shows that it is possible to say that heavy rainfall forecast skill of l-I14-31/BMJ in tropics is nearly similar to the skill of LAPS of Australia.

Comparison of Impacts of WRF Dynamic Core, Physics Package, and Initial Conditions on Warm Season Rainfall Forecasts

Abstract A series of simulations for 15 events occurring during August 2002 were performed using the Weather Research and Forecasting (WRF) model over a domain encompassing most of the central United States to compare the sensitivity of warm season rainfall forecasts with changes in model physics, dynamics, and initial conditions. Most simulations were run with 8-km grid spacing. The Advanced Research WRF (ARW) and the nonhydrostatic mesoscale model (NMM) dynamic cores were used. One physics package (denoted NCEP) used the Betts–Miller–Janjic convective scheme with the Mellor–Yamada–Janjic planetary boundary layer (PBL) scheme and GFDL radiation package; the other package (denoted NCAR) used the Kain–Fritsch convective scheme with the Yonsei University PBL scheme and the Dudhia rapid radiative transfer model radiation. Other physical schemes were the same (e.g., the Noah land surface model, Ferrier et al. microphysics) in all runs. Simulations suggest that the sensitivity of the model to changes in physics is a function of which the dynamic core is used, and the sensitivity to the dynamic core is a function of the physics used. The greatest sensitivity in general is associated with a change in physics packages when the NMM core is used. Sensitivity to a change in physics when the ARW core is used is noticeably less. For light rainfall, the spread in the rainfall forecasts when physics are changed under the ARW core is actually less at most times than when the dynamic core is changed while NCAR physics are used. For light rainfall, the WRF model using NCAR physics is much more sensitive to a change in dynamic core than the WRF model using NCEP physics. For heavier rainfall, the opposite is true with a greater sensitivity occurring when NCEP physics is used. Sensitivity to initial conditions (Eta versus the Rapid Update Cycle with an accompanying small change in grid spacing) is generally less substantial than the sensitivity to changes in dynamic core or physics, except in the first 6–12 h of the forecast when it is comparable. As might be expected for warm season rainfall, the finescale structure of rainfall forecasts is more affected by the physics used than the dynamic core used. Surprisingly, however, the overall areal coverage and rain volume within the domain may be more influenced by the dynamic core choice than the physics used.

The Sensitivity of Numerical Forecasts to Convective Parameterization: A Case Study of the 17 February 2004 East Coast Cyclone

Abstract The sensitivity of numerical model forecasts of coastal cyclogenesis and frontogenesis to the choice of model cumulus parameterization (CP) scheme is examined for the 17 February 2004 southeastern U.S. winter weather event. This event featured a complex synoptic and mesoscale environment, as the presence of cold-air damming, a developing coastal surface cyclone, and an upper-level trough combined to present a daunting winter weather forecast scenario. The operational forecast challenge was further complicated by erratic numerical model predictions. The most poignant area of disagreement between model runs was the treatment of a coastal cyclone and an associated coastal front, features that would affect the location and timing of precipitation and influence the precipitation type. At the time of the event, it was hypothesized that the Betts–Miller–Janjić (BMJ) CP scheme was dictating the location and intensity of the initial coastal cyclone center in operational Eta Model forecasts. For this reason, forecasts for this case were rerun with the workstation Eta Model using the Kain–Fritsch (KF) CP scheme to further examine the sensitivity to this parameterization choice. Results confirm that the model CP scheme played a major role in the forecast for this case, affecting the quantitative precipitation forecast as well as the strength, location, and structure of coastal cyclogenesis and coastal frontogenesis. The Eta Model forecast using the KF CP scheme produced a relatively uniform distribution of convective precipitation oriented along the axis of an inverted trough and strong coastal front. In contrast, the BMJ forecasts resulted in a weaker coastal front and the development of multiple distinct closed cyclonic circulations in association with more localized convective precipitation centers. An additional BMJ forecast in which the shallow mixing component of the scheme was disabled bore a closer semblance to the KF forecasts relative to the original BMJ forecast. Suggestions are provided to facilitate the identification of CP-driven cyclones using standard operational model output parameters.

Representing Convective Organization in Prediction Models by a Hybrid Strategy

The mesoscale organization of precipitating convection is highly relevant to next-generation global numerical weather prediction models, which will have an intermediate horizontal resolution (grid spacing about 10 km). A primary issue is how to represent dynamical mechanisms that are conspicuously absent from contemporary convective parameterizations. A hybrid parameterization of mesoscale convection is developed, consisting of convective parameterization and explicit convectively driven circulations. This kind of problem is addressed for warm-season convection over the continental United States, although it is argued to have more general application. A hierarchical strategy is adopted: cloud-system-resolving model simulations represent the mesoscale dynamics of convective organization explicitly and intermediate resolution simulations involve the hybrid approach. Numerically simulated systems are physically interpreted by a mechanistic dynamical model of organized propagating convection. This model is a formal basis for approximating mesoscale convective organization (stratiform heating and mesoscale downdraft) by a first-baroclinic heating couplet. The hybrid strategy is implemented using a predictor–corrector strategy. Explicit dynamics is the predictor and the first-baroclinic heating couplet the corrector. The corrector strengthens the systematically weak mesoscale downdrafts that occur at intermediate resolution. When introduced to the Betts–Miller–Janjic convective parameterization, this new hybrid approach represents the propagation and dynamical structure of organized precipitating systems. Therefore, the predictor–corrector hybrid approach is an elementary practical framework for representing organized convection in models of intermediate resolution.

Sensitivity of the Simulated Monsoons of 1987 and 1988 to Convective Parameterization Schemes in MM5

AbstractIn this study the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) version 3.5.2 was used to simulate the Indian summer monsoon during the two contrasting years of 1987 and 1988, a dry year and a wet year, respectively. Three different convection parameterization schemes of Betts–Miller–Janjic, Kain–Fritsch, and Grell were used to study the sensitivity of monsoon to cumulus effects. The model was integrated for a period of 6 months, starting from three different initial conditions of 0000 UTC on 1, 2, and 3 May of each year using the NCEP–NCAR reanalysis data as input. The 6-hourly reanalysis data were used to provide the lateral boundary conditions, and the observed weekly Reynolds sea surface temperature, linearly interpolated to 6 h, was used as the lower boundary forcing. The results show that all three cumulus schemes were able to simulate the interannual and intraseasonal variabilities in the monsoon with reasonable accuracy. However, the spatial distribution of the rainfall and its quantity were different in all the schemes. The Grell scheme underestimated the rainfall in both the years. The Kain–Fritsch scheme simulated the observed rainfall well during July and August, the peak monsoon months, of the year 1988 but overestimated the rainfall in June and September of 1988 and throughout the monsoon season of 1987. The Betts–Miller–Janjic scheme simulated less rainfall in the drought year of 1987 and overestimated the rainfall in June and July of 1988. The circulation patterns simulated by the Betts–Miller–Janjic and Kain–Fritsch schemes are comparable to the observed patterns.

Comparison of Cumulus Parameterizations and Entrainment Using Domain-Mean Wind Divergence in a Regional Model

Several different cumulus parameterizations are compared in a 10-day regional model simulation over the tropical Americas in northern summer. A simple bulk diagnostic test is devised, comparing the model's preferred domain-mean wind divergence profile with “observed” drivergence. The latter is obtained by a line integral of the normal wind component at the model's outer boundary, from the ECMWF reanalysis data used as lateral boundary conditions. The former is obtained from a line integral one grid point in from the boundary, a perimeter that encloses almost exactly the same region. Even though the model fields near the boundary are strongly nudged toward the ECMWF values, the difference is distinct, and indicative of systematic errors in the model's heating field throughout the interior of the domain. Heating reflects the effects of the convection scheme, both direct and indirect (e.g., through its impact on resolved condensation). A useful axis along which to characterize schemes appears to be overactive versus underactive. Underactive convective schemes tend to produce too little low-level convergence and upper-level divergence, while overactive schemes produce too much. This categorization is also reflected in rainfall fields, as overactive schemes produce widespread light convective rain while underactive schemes produce sparse occasional storms. For example, the Kain–Fritsch scheme is overactive with its default entraining-plume radius of 1500 m, a value optimized for midlatitudes over land. A value of 750 m makes the regional divergence magnitude about right, but makes the upper-tropospheric outflow altitude too low, illustrating a classic dilemma of entraining-plume models of convection. Schemes with other conceptual structures give widely varying divergence errors. The largest errors are found with the Anthes–Kuo scheme, while the smallest errors are found with the Betts–Miller–Janjic scheme, which has no consistent divergence bias over time. Diagnosis of other North American monsoon simulations supports the general underactive/ overactive characterization, but shows that the best scheme and parameters may depend on weather regime.

MCS Rainfall Forecast Accuracy as a Function of Large-Scale Forcing

The large-scale forcing associated with 20 mesoscale convective system (MCS) events has been evaluated to determine how the magnitude of that forcing influences the rainfall forecasts made with a 10-km grid spacing version of the Eta Model. Different convective parameterizations and initialization modifications were used to simulate these Upper Midwest events. Cases were simulated using both the Betts–Miller–Janjić (BMJ) and the Kain–Fritsch (KF) convective parameterizations, and three different techniques were used to improve the initialization of mesoscale features important to later MCS evolution. These techniques included a cold pool initialization, vertical assimilation of surface mesoscale observations, and an adjustment to initialized relative humidity based on radar echo coverage. As an additional aspect in this work, a morphology analysis of the 20 MCSs was included. Results suggest that the model using both schemes performs better when net large-scale forcing is strong, which typically is the case when a cold front moves across the domain. When net forcing is weak, which is often the case in midsummer situations north of a warm or stationary front, both versions of the model perform poorly. Runs with the BMJ scheme seem to be more affected by the magnitude of surface frontogenesis than the KF runs. Runs with the KF scheme are more sensitive to the CAPE amount than the BMJ runs. A fairly well-defined split in morphology was observed, with squall lines having trailing stratiform regions likely in scenarios associated with higher equitable threat scores (ETSs) and nonlinear convective clusters strongly dominating the more poorly forecast weakly forced events.

Some contrasts between good and bad forecasts of warm season MCS rainfall

Large variations in the accuracy of warm season MCS rainfall predictions are investigated by examining several well-forecasted and poorly forecasted cases from a sample of 20 events simulated using a 10 km grid spacing version of the Eta model. Two different convective schemes, the Betts–Miller–Janjic and Kain–Fritsch, have been used in the simulations to investigate in detail differences in simulated rainfall fields. The accuracy of the forecasts is evaluated using both subjective analysis and objective skill measures. The fact that subjective analysis does not necessarily agree with an objective analysis further argues for the development of new objective verification methods for quantitative precipitation forecasting. To gain some insight into possible causes of errors in the most poorly forecasted events, tests are performed where initialized relative humidity is adjusted to either encourage or discourage rainfall formation in specific regions.

Hydrometeorological Response of the Modeled North American Monsoon to Convective Parameterization

This paper describes the second part of a study to document the sensitivity of the modeled regional moisture flux patterns and hydrometeorological response of the North American monsoon system (NAMS) to convective parameterization. Use of the convective parameterization schemes of Betts–Miller–Janjic, Kain–Fritsch, and Grell was investigated during the initial phase of the 1999 NAMS using version 3.4 of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) running in a pseudoclimate mode. Substantial differences in both the stationary and transient components of the moisture flux fields were found between the simulations, resulting in differences in moisture convergence patterns, precipitation, and surface evapotranspiration. Basin-average calculations of hydrologic variables indicate that, in most of the basins for which calculations were made, the magnitude of the evaporation-minus-precipitation moisture source/sink differs substantially between simulations and, in some cases, even the sign of the source/sink changed. There are substantial differences in rainfall–runoff processes because the basin-average rainfall intensities, proportion of rainfall from convective origin, and the runoff coefficients differ between simulations. The results indicate that, in regions of sustained, deep convection, the selection of the subgrid convective parameterization in a high-resolution atmospheric model can potentially have a hydrometeorological impact in regional analyses, which is at least as important as the effect of land surface forcing.

Impact of Improved Initialization of Mesoscale Features on Convective System Rainfall in 10-km Eta Simulations

A 10-km version of the NCEP Eta Model has been run over a roughly 1000 km × 1000 km domain centered over the upper Midwest for 20 cases where heavy warm season rainfall occurred from mesoscale convective systems to investigate the response of the precipitation forecasts to improvements in the depiction of mesoscale features at initialization time. Modifications to the initial conditions included (i) use of a cold pool initialization scheme, (ii) inclusion of mesonetwork surface observations using the model's own vertical diffusion formulation to allow the surface data to be assimilated into a deeper layer through a simulated initialization period, and (iii) addition of water vapor at points covered by radar echo to ensure relative humidities greater than 80%. All of these modifications were implemented in runs using both the operational Betts–Miller–Janjic (BMJ) and Kain–Fritsch (KF) convective parameterizations. In addition, simulations were also run with a doubling of the convective time step, alternation of the two convective schemes within one run, and exclusion of a convective scheme in another run. For all 20 cases, 14 variants in the model initilization/moist physics were used, creating a high grid resolution (10-km grid spacing) ensemble. Although techniques (i) and (ii) both resulted in initial surface fields agreeing better with available observations, average skill scores for precipitation forecasts did not change appreciably when (i) was used, with (ii) resulting in a modest improvement in equitable threat score (ETS), with an increase in the bias that already exceeded 1.0 for most precipitation thresholds in the BMJ runs. Skill scores among the cases varied widely; no single adjustment consistently improved the scores. Interestingly, the simplest modification, the addition of water vapor in relatively dry atmospheric regions at points where radar echo was present, had the greatest positive impact on ETSs for most precipitation thresholds. Although the impacts were greatest in the first 6 h of the forecasts, some improvements occurred through the full 24-h integration period. Variations among the runs for a given case were far greater when different convective schemes were used than when initialization modifications were made, further supporting other recent research suggesting that high grid resolution short-range ensembles may benefit from the use of a variety of models or physical parameterizations.

Applying the Betts–Miller–Janjic Scheme of Convection in Prediction of the Indian Monsoon

The performance of the Betts–Miller–Janjic scheme of convection has been investigated for prediction of the Indian monsoons. For this purpose a limited area numerical weather prediction model with two schemes of convection, one with the Betts–Miller scheme and other with the Betts–Miller–Janjic scheme, is run for five cases of monsoon depression that made landfall over the Indian coast. The results from the two schemes are compared. Detailed analyses of mean sea level pressure, wind, and rainfall have shown that the Betts–Miller–Janjic scheme has considerably improved the rainfall prediction over the Indian landmass and improvement is also seen in the mean sea level pressure fields and cyclonic circulation associated with the depression at the 850-hPa level. The forecast results are further verified by computing the root-mean-square errors, and the difference in the skill scores between the two model runs are tested for their statistical significance. It is found that the Betts–Miller–Janjic scheme has a statistically significant effect on the model skill beyond 24 h, with maximum impact on mean sea level pressure and geopotential height.