Areas Of Heavy Precipitation Research Articles

FY‐4A/AGRI Infrared Brightness Temperature Estimation of Precipitation Based on Multi‐Model Ensemble Learning

AbstractSatellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data.

Contribution of Shallow Convection to the Localization of a Band-Shaped Area of Heavy Precipitation on 4 July 2020

Contribution of Shallow Convection to the Localization of a Band-Shaped Area of Heavy Precipitation on 4 July 2020

Estimation of satellite link’s fade margin using non-meteorological technique and worst month analysis

<span lang="EN-US">Satellite technology is shifting to higher frequencies such as Q or V-band to cater to greater bandwidth and higher data rates applications such as videoconferencing, internet of things (IoT) and telemedicine. The main challenge in deploying high-frequency bands in heavy precipitation areas is severe rain attenuation. In this paper, a frequency scaling technique was developed to estimate the fade margin at a higher frequency. The worst month analysis was also conducted since the analysis is also important in determining dependable fade margin. The result was evaluated and analyzed using root mean square error (RMSE) and percentage error. The proposed model offers the smallest RMSE and lowest percentage error when compared to all existing prediction models. A dependable fade margin acquired from high-accuracy rain attenuation estimation is very important. This is to apply the best mitigation technique in overcoming rain attenuation in the satellite-Earth link so that, the best system performance can be delivered.</span>

Study on the Quantitative Precipitation Estimation of X-Band Dual-Polarization Phased Array Radar from Specific Differential Phase

In this study, the quantitative precipitation estimation (QPE) capability of three X-band dual-polarization phased array radars (PAR) in Guangzhou, South China, was demonstrated, with an S-band operational dual-polarization radar as the benchmark. Rainfall rate (R) estimators based on the specific differential phase (KDP) for summer precipitation for both X-band and S-band radars were derived from the raindrop size distributions (DSDs) observed by a 2-dimensional video disdrometer (2DVD) in South China. Rainfall estimates from the radars were evaluated with gauge observations in three events, including pre-summer rainfall, typhoon precipitation, and local severe convective precipitation. Observational results showed that radar echoes from the X-band PARs suffered much more severely from attenuation than those from the S-band radar. Compared to S-band observations, the X-band echoes can disappear when the signal-to-noise ratio drops to a certain level due to severe attenuation, resulting in different estimated rainfall areas for X- and S-band radars. The attenuation corrected by KDP had good consistency with S-band observations, but the accuracy of attenuation correction was affected by DSD uncertainty and may vary in different types of precipitation. The QPE results demonstrated that the R(KDP) estimator produced better rainfall accumulations from the X-band PAR observations compared to the S-band observations. For both the X-band and S-band radars, the estimates of hourly accumulated rainfall became more accurate in heavier rainfall, due to the decreases of both the DSD uncertainty and the impact of measurement errors. In the heavy precipitation area, the estimation accuracy of the X-band radar was high, and the overestimation of the S-band radar was obvious. Through the analysis of the ZH-ZDR distribution in the three weather events, it was found that the X-band PAR with the capability of high spatiotemporal observations can capture minute-level changes in the microphysical characteristics, which help improve the estimation accuracy of ground rainfall.

Effects of Stratospheric Gravity Waves on Convection in the Troposphere During Typhoon Lekima 2019

AbstractThe influence of stratospheric gravity waves (SGWs) on the intensity of convection (CI) in the area of heavy precipitation around Typhoon Lekima (2019) is studied. Using theory and simulations, the energy and phase of the SGWs are shown to propagate upward and downward. The SGWs can propagate downward into the areas of convective instability in the upper troposphere in 35–85 min. Downward‐propagating SGWs can promote the release of convective instability energy, so the CI is enhanced. The relationship between SGWs and CI after 85 min is studied using a two‐layer model hypothesis for both a linear regression model and a Long Short‐Term Memory neural network model (nonlinear). The relationship is shown to be primarily linear (nonlinear) when the SGWs are weak (intensive). Therefore, the SGWs may be used as a proxy for the CI.

Analysis on the Mechanism of the Heavy Snow Process in the Northeast of Tarim Basin, Xinjiang, in 2018

An analysis was made on the routine data of meteorological stations, high-resolution satellite cloud images and the NCEP/NCA R 1°×1° reanalysis data during one instance of heavy snow precipitation that took place in northern Bazhou, Xinjiang on October 18, 2018, followed by a diagnostic analysis of physical quantities, including the large-scale circulation anomalies, upper and lower-level circulation conditions and water vapor transport. The following are the results: (1) the heavy snow precipitation process in northern Bazhou, Xinjiang is a heavy precipitation process largely caused by a convergence of severely cold air carried from Siberia by an upper vortex, and a strong, warm and humid air current from southwest China. During this weather process, the upper layer was dry and cold, while the lower layer was warm and humid; an obvious wind shear and convergence center appeared at 700hPa, 850hPa in the lower layer over northern Bazhou; the heavy precipitation area was located in the front of the trough bottom at 500hPa, on the left side of the lower-level shear line. 2) The secondary circulation formed on the right side of the upper-level jet stream entrance area at 200hpa and 300hpa enlarged the ascending motion of the heavy snowfall area, while the low-level easterly jet stream played a critical role in transporting and accumulating water vapor at 850hpa. 3) The variations of temperature at 850hpa and on the ground layer were of certain indicative significance to the forecast of the precipitation phase state, and the temperature field at 850hpa well-indicated the change from rain to snow. When the air temperature fell to 0.5°C or so, and the ground temperature fell below 0°C given T700hpa<-10°C and T850hpa<-2°C, it could be used as a criterion for the judgment of rain changing to snow. 4) The vapor condition, lifting force condition and unstable stratification condition in the numerically-forecasted physical quantity field, all had a good guiding effect on the heavy precipitation process that took place in the Yanqi Reclamation Area.

Idealized High-Resolution Simulations of a Back-Building Convective System that Causes Torrential Rain

AbstractRecord-breaking precipitation due to a back-building convective system occurred in Kyushu Island, Japan, on 5 July 2017. In this paper, a quasi-stationary back-building convective system is reproduced using a regional weather prediction model initialized with a single representative sounding in which the land–sea distribution around the observed convective system is crudely simplified. The simulated convective system and heavy precipitation are reasonably similar to observations. Horizontal resolution finer than 1 km is found to be necessary for reproducing the convective system. The area of heavy precipitation tends to shift downstream with finer horizontal resolution. The surface temperature contrasts at the northern and southern coastlines cause sea breezes and a stationary convergence line between them continuously triggers cumulus clouds. The horizontal convergence near the surface is further enhanced by preceding cumulus clouds that cause the latent heating aloft and generate a mesoscale surface pressure depression. Vertical shear of the environmental wind is also found to be important for organizing the convective system but veering of its wind direction and a cold pool are not essential.

Quantifying the link between heavy precipitation and Northern Hemisphere blocking—A Lagrangian analysis

AbstractAtmospheric blocks strongly influence surface weather, including extremes such as heat waves and cold spells. Recently, diabatic heating and associated upper‐tropospheric potential vorticity (PV) modification have been identified as important modulators of atmospheric blocking dynamics. Also, robust links between atmospheric blocks and proximate heavy precipitation events have been established. This leads to the question of the extent to which diabatic heating associated with heavy precipitation events influences Northern Hemisphere blocking. This study uses 5 years of 3‐day back trajectories started from objectively identified blocks in the ERA‐Interim dataset to investigate this relationship. A substantial fraction of air parcels in blocks pass through heavy precipitation areas. The exact fraction depends on the choice of heavy precipitation threshold. Roughly 19% of all the trajectories in a block pass a heavy precipitation area (&gt;95th percentile) area while being saturated. Of the air parcels in a block that are heated at least 5 K, 60% pass a heavy precipitation area while saturated. This fraction varies with the season and geographical area. The overall fraction is lowest in summer and highest in winter, higher over oceans than over land, and higher over the Pacific than over the Atlantic. In summer, heating is relevant over the continents and heating over North America influences blocks over the eastern Atlantic. For summer blocks in the North Atlantic and over Scandinavia, heating happens partly over the European continent.

Generation of Ensemble Mean Precipitation Forecasts from Convection-Allowing Ensembles

Abstract Methods for generating ensemble mean precipitation forecasts from convection-allowing model (CAM) ensembles based on a simple average of all members at each grid point can have limited utility because of amplitude reduction and overprediction of light precipitation areas caused by averaging complex spatial fields with strong gradients and high-amplitude features. To combat these issues with the simple ensemble mean, a method known as probability matching is commonly used to replace the ensemble mean amounts with amounts sampled from the distribution of ensemble member forecasts, which results in a field that has a bias approximately equal to the average bias of the ensemble members. Thus, the probability matched mean (PM mean hereafter) is viewed as a better representation of the ensemble members compared to the mean, and previous studies find that it is more skillful than any of the individual members. Herein, using nearly a year’s worth of data from a CAM-based ensemble running in real time at the National Severe Storms Laboratory, evidence is provided that the superior performance of the PM mean is at least partially an artifact of the spatial redistribution of precipitation amounts that occur when the PM mean is computed over a large domain. Specifically, the PM mean enlarges big areas of heavy precipitation and shrinks or even eliminates smaller ones. An alternative approach for the PM mean is developed that restricts the grid points used to those within a specified radius of influence. The new approach has an improved spatial representation of precipitation and is found to perform more skillfully than the PM mean at large scales when using neighborhood-based verification metrics.

Evaluation of Precipitation Estimates by at-Launch Codes of GPM/DPR Algorithms Using Synthetic Data from TRMM/PR Observations

The Global Precipitation Measurement (GPM) Core Observatory will carry a Dual-frequency Precipitation Radar (DPR) consisting of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). In this study, “at-launch” codes of DPR precipitation algorithms, which will be used in GPM ground systems at launch, were evaluated using synthetic data based upon the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data. Results from the codes (Version 4.20131010) of the KuPR-only, KaPR-only, and DPR algorithms were compared with “true values” calculated based upon drop size distributions assumed in the synthetic data and standard results from the TRMM algorithms at an altitude of 2 km over the ocean. The results indicate that the total precipitation amounts during April 2011 from the KuPR and DPR algorithms are similar to the true values, whereas the estimates from the KaPR data are underestimated. Moreover, the DPR estimates yielded smaller precipitation rates for rates less than about 10 mm/h and greater precipitation rates above 10 mm/h. Underestimation of the KaPR estimates was analyzed in terms of measured radar reflectivity (Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> ) of the KaPR at an altitude of 2 km. The underestimation of the KaPR data was most pronounced during strong precipitation events of Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> <; 18 dBZ (high attenuation cases) over heavy precipitation areas in the Tropics, whereas the underestimation was less pronounced when the Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> > 26 (moderate attenuation cases). The results suggest that the underestimation is caused by a problem in the attenuation correction method, which was verified by the improved codes.

The Influence of an SST Front on a Heavy Rainfall Event over Coastal Taiwan during TiMREX

Abstract A long-lived heavy precipitation area was observed along the southwest coast of Taiwan from 13 to 18 June 2008 during the Terrain-Influenced Monsoon Rainfall Experiment (TiMREX). Rainfall amounts exceeded 500 mm along portions of the coast, and the coastal plains experienced severe flooding. The precipitation systems were influenced by blocking effects, as the southerly moist monsoon flow impinged on the island. A relatively strong gradient in the sea surface temperature (SST) off the southwest coast of Taiwan existed during the rainfall event. Mesoscale SST fronts are known to influence the planetary boundary layer (PBL) such that low-level convergence and precipitation are enhanced under certain circumstances. In this study, the authors investigate the role of the SST front in enhancing the 13–18 June 2008 precipitation event over Taiwan using the Weather Research and Forecasting (WRF) Model. In control simulations with the observed SST, there is a transition from a well-mixed to a stable PBL across the front, causing the low-level flow to decelerate, resulting in an enhancement of horizontal convergence. Such a transition in the PBL and the associated convergence is greatly reduced in smoothed SST gradient model simulations, which produce over 20% less precipitation over southwest Taiwan. Sensitivity tests show that, qualitatively, the results are independent of the existence of the island of Taiwan. These findings indicate that the SST gradient over the northern South China Sea during the early summer monsoon can have a significant impact on the intensity of rainfall over Taiwan.

Object-based evaluation of precipitation forecast quality

The method of object-based diagnostic evaluation (MODE) is used to estimate the quality of forecasts of intensive precipitation zones using the WRF-ARW mesoscale nonhydrostatic model. The model is integrated on the horizontal 3-km grid without the cumulus parameterization. The prognostic fields of the reflectivity at the level of 700 hPa are compared with the observed radar reflectivity at the level of 3 km. Objects in the simulated and observed fields obtained by means of the convolution (smoothing) and thresholding (>20 dBZ) were matched according to the distance separating their centers of gravity (centroids). This distance was 50 km or lower in 12% of the total number of forecasts and 100 km or lower, in 30% of forecasts. It is revealed that the model overestimates the heavy precipitation areas.

Improving Geostationary Satellite Rainfall Estimates Using Lightning Observations: Underlying Lightning–Rainfall–Cloud Relationships

AbstractThis study quantifies the relationships among lightning activity, convective rainfall, and associated cloud properties on both convective-system scale (or storm scale) and satellite-pixel scale (~5 km) on the basis of 13 yr of Tropical Rainfall Measuring Mission measurements of rainfall, lightning, and clouds. Results show that lightning frequency is a good proxy to separate storms of different intensity, identify convective cores, and screen out false convective-core signatures in areas of thick anvil debris. Significant correlations are found between storm-scale lightning parameters and convective rainfall for systems over the southern United States, the focus area of the study. Storm-scale convective rainfall or heavy-precipitation area has the best correlation (coefficient r = 0.75–0.85) with lightning-flash area. It also increases linearly with increasing lightning-flash rate, although correlations between convective/heavy rainfall and lightning-flash rate are somewhat weaker (r = 0.55–0.75). Statistics further show that active lightning and intense precipitation are not well collocated on the pixel scale (5 km); for example, only 50% of the lightning flashes are coincident with heavy-rain cores, and more than 20% are distributed in light-rain areas. Simple positive correlations between lightning-flash rate and precipitation intensity are weak on the pixel scale. Lightning frequency and rain intensity have positive probabilistic relationships, however: the probability of heavy precipitation, especially on a coarser pixel scale (~20 km), increases with increasing lightning-flash density. Therefore, discrete thresholds of lightning density could be applied in a rainfall estimation scheme to assign precipitation in specific rate categories.

Drip‐tips are Associated with Intensity of Precipitation in the Amazon Rain Forest

AbstractDrip‐tips are a common feature of the leaves of rain forest trees, but their functional significance remains contested. The most widely accepted hypothesis is that drip‐tips assist drainage of the lamina thereby aiding drying of the leaf surface and reducing the rate of colonization and abundance of epiphyllic organisms. The drying action of drip‐tips may also enhance transpiration and reduce the need for investment in support structures. Furthermore, drip‐tips may help prevent splash erosion around the base of the tree. Data from 130 forest Amazonian plots are used to investigate the abundance and distribution of drip‐tips and, through regression methods that incorporate spatial autocorrelation, seek to identify associations between the frequency of drip‐tips and a range of climatic variables. The average frequency of species and trees with drip‐tips across all plots was 32 and 33 percent, respectively. Trees and species with drip‐tips were significantly more prevalent in the Central‐East Amazon than the other regions. Drip‐tips were also associated with tree species that have smaller maximum heights and with trees with smaller trunk diameters. The proportion of species and individuals with drip‐tips was more strongly correlated with precipitation of the wettest trimester than with total annual precipitation or length of the dry season. Our results extend and provide support for both existing hypotheses for the functional benefit of possessing a drip‐tip. Moreover, the currently unrecognized macrogeographic association between the frequency of drip‐tips in trees of the tropical forest understory and areas of heavy precipitation suggests a new function for this trait.

Assessing possible dynamical effects of condensate in high resolution climate simulations

In areas of heavy precipitation, condensed water species can add significant mass to an atmospheric column. This mass can create positive pressure anomalies of up to several hPa at the surface. This pressure is expected to force a divergent component in the low‐level flow that may have an impact on the evolution of the precipitating system. In this study we examine results from a cloud resolving model simulation of tropical convection to estimate the pressure induced by condensates. A simple parameterization of this condensate loading as a function of surface rain rate is derived and implemented in the National Center for Atmospheric Research's Community Atmosphere Model version 5 (CAM5). Our results suggest that at horizontal resolutions of 25 km condensate loading is an important factor in controlling the frequency of intense rain rates in the model.

Bark Beetles and their Natural Enemies on Oriental Spruce from the Black Sea Region of Turkey

Oriental spruce, Picea orientalis (L.) Link. (Pinaceae), is one of the main tree species that naturally grows in the Caucasus Mountains and the northeastern part of Turkey, known as the Black Sea Region. This tree species reaches optimal growth in the mountainous areas of heavy precipitation in Ordu, Giresun, Rize, Trabzon, and Artvin Provinces of the Eastern Black Sea Region of Turkey, but it can tolerate a wide range of climatic zones. The bark beetle species (Coleoptera: Curculionidae: Scolytinae) that attack oriental spruce, and their predators and parasitoids, were studied in Turkey in 2005–2006. Twenty-three species of bark beetles belonging to six subtribes and two tribes of Scolytinae were identified. In addition, 84 predatory species from 21 families and 23 parasitoid species from five families were detected. Identification of these predatory and parasitic species is an important starting point for the biological control of bark beetles attacking oriental spruce in Turkey.

Observing Climate at High Elevations Using United States Climate Reference Network Approaches

AbstractThe U.S. Climate Reference Network (USCRN) was deployed between 2001 and 2008 for the purpose of yielding high-quality and temporally stable in situ climate observations in pristine environments over the twenty-first century. Given this mission, USCRN stations are engineered to operate largely autonomously with great reliability and accuracy. A triplicate approach is used to provide redundant measurements of temperature and precipitation at each location, allowing for observations at a specific time to be compared for quality control. This approach has proven to be robust in the most extreme environments, from extreme cold (−49°C) to extreme heat (+52°C), in areas of heavy precipitation (4700 mm yr−1), and in locations impacted by strong winds, freezing rain, and other hazards. In addition to a number of stations enduring extreme winter environments in Alaska and the northern United States, seven of the USCRN stations are located at elevations over 2000 m, including stations on Mauna Loa, Hawaii (3407 m) and on Niwot Ridge above Boulder, Colorado (2996 m). The USCRN temperature instruments and radiation shield have also been installed and run successfully at a station on the Quelccaya Ice Cap in Peru (5670 m). This paper reviews the performance of the USCRN station network during its brief lifetime and the potential utility of its triplicate temperature instrument configuration for measuring climate change at elevation.

Statistical analysis of the relationship between summer monsoon precipitation extremes and foodgrain yield over India

AbstractThe Indian economy largely depends on agriculture which is highly influenced by the spatio‐temporal variability of precipitation. Kharif and rabi are the two main crop‐growing seasons which require major proportion of rainfall. Increase in heavy precipitation events, however, can have adverse effects on the crops. This study, therefore, is mainly focused on understanding the variation of extremes in precipitation during summer monsoon season and its impact on kharif foodgrain yield over India. For this, several objectively defined indices of observed precipitation extremes, in terms of frequencies and intensities, have been computed for the period 1951–2003 using daily gridded data of 1° latitude × 1° longitude resolution. Efforts have also been made to prepare the climatology of extremes in precipitation (1961–1990) to determine their basic characteristics over the Indian region.Correlation analysis reveals that a large part of the country exhibits positive relationship between kharif foodgrain yield and the indices of extreme precipitation, significant at 5% level. Correlations with the indices of frequencies defined as seasonal count of days when rainfall exceeds 85th, 75th, and 65th percentiles (and 30, 20, and 10 mm) show that spatial extent and strength of the positive relationship decreases with increase in threshold values. This indicates that very heavy daily rainfall is less useful than the moderate daily rainfall. Strong negative relationship between the yield and indices of extreme precipitation observed over heavy precipitation areas of northeastern parts of the country indicate that very heavy daily rainfall have an adverse effect on crop. The study also points out that increase in heavy rainfall activities during July are favourable for the yield, but correlations become weak and statistically insignificant during the month of August. Individual crops show similar results as that obtained from aggregate kharif foodgrain, however, magnitude of the correlations varies from crop to crop. Copyright © 2011 Royal Meteorological Society

Short-range forecast of heavy precipitation and strong wind using the convection-allowing WRF models

The results are stated of estimation of short-range forecasting of heavy precipitation (P ≥ 10 mm/12 hours) and strong wind at the height of 10 m (V ≥ 10 m/s) using three nonhydrostatic models from the WRF family: ARW, ARW Glob, and NMM. The forecasts on the basis of all three models were carried out using two grids. The horizontal resolution of external grids varied from 9 to 16.5 km and that of the nested grids, from 3 to 5.5 km. For the ARW and NMM models, the values at side boundaries of external nested grids were taken from the forecasts on the basis of the global GFS NCEP model and for the ARW Glob model, from the global forecasts based on this model. The convection parameterization was turned off at nested grids for all models. The forecasts of heavy precipitation and strong wind at nested grids over the European territory of Russia were estimated from the radar and station measurements in summer 2008. It is obtained that all three models reproduce well enough the mesoscale convective systems and associated areas of heavy precipitation and strong wind but they have common shortcomings: they overestimate the amount and area of heavy precipitation and underestimate the strong wind speed. To a lesser degree, these shortcomings are typical of the ARW model. The possible reasons for systematic errors in the forecasts are discussed.

Sensitivity of Mesoscale Model Forecast During a Satellite Launch to Different Cumulus Parameterization Schemes in MM5

The identification of the model discrepancy and skill is crucial when a forecast is issued. The characterization of the model errors for different cumulus parameterization schemes (CPSs) provides more confidence on the model outputs and qualifies which CPSs are to be used for better forecasts. Cases of good/bad skill scores can be isolated and clustered into weather systems to identify the atmospheric structures that cause difficulties to the forecasts. The objective of this work is to study the sensitivity of weather forecast, produced using the PSU-NCAR Mesoscale Model version 5 (MM5) during the launch of an Indian satellite on 5th May, 2005, to the way in which convective processes are parameterized in the model. The real-time MM5 simulations were made for providing the weather conditions near the launch station Sriharikota (SHAR). A total of 10 simulations (each of 48 h) for the period 25th April to 04th May, 2005 over the Indian region and surrounding oceans were made using different CPSs. The 24 h and 48 h model predicted wind, temperature and moisture fields for different CPSs, namely the Kuo, Grell, Kain-Fritsch and Betts-Miller, are statistically evaluated by calculating parameters such as mean bias, root-mean-squares error (RMSE), and correlation coefficients by comparison with radiosonde observation. The performance of the different CPSs, in simulating the area of rainfall is evaluated by calculating bias scores (BSs) and equitable threat scores (ETSs). In order to compute BSs and ETSs the model predicted rainfall is compared with Tropical Rainfall Measuring Mission (TRMM) observed rainfall. It was observed that model simulated wind and temperature fields by all the CPSs are in reasonable agreement with that of radiosonde observation. The RMSE of wind speed, temperature and relative humidity do not show significant differences among the four CPSs. Temperature and relative humidity were overestimated by all the CPSs, while wind speed is underestimated, except in the upper levels. The model predicted moisture fields by all CPSs show substantial disagreement when compared with observation. Grell scheme outperforms the other CPSs in simulating wind speed, temperature and relative humidity, particularly in the upper levels, which implies that representing entrainment/detrainment in the cloud column may not necessarily be a beneficial assumption in tropical atmospheres. It is observed that MM5 overestimates the area of light precipitation, while the area of heavy precipitation is underestimated. The least predictive skill shown by Kuo for light and moderate precipitation asserts that this scheme is more suitable for larger grid scale (>30 km). In the predictive skill for the area of light precipitation the Betts-Miller scheme has a clear edge over the other CPSs. The evaluation of the MM5 model for different CPSs conducted during this study is only for a particular synoptic situation. More detailed studies however, are required to assess the forecast skill of the CPSs for different synoptic situations.