Numerical Simulation of Torrential Rainfall and Vortical Hot Towers in a Midlatitude Mesoscale Convective System

A cloud-resolving model simulation of a mesoscale convective system （MCS） producing torrential rainfall is performed with the finest horizontal resolution of 444 m. It is shown that the model reproduces the observed MCS, including its rainfall distribution and amounts, as well as the timing and location of leading rainbands and trailing stratiform clouds. Results show that discrete convective hot towers, shown in Vis5D at a scale of 2-5 kin, are triggered by evaporatively driven cold outflows converging with the high-θe air ahead. Then, they move rearward, with respect to the leading rainbands, to form stratiform clouds. These convective towers generate vortical tubes of opposite signs, with more intense cyclonic vorticity occurring in the leading convergence zone. The results appear to have important implications for the improvement of summertime quantitative precipitation forecasts and the understanding of vortical hot towers, as well midlevel mesoscale convective vortices.     

Statistical Characteristics of Raindrop Size Distribution in the Tibetan Plateau and Southern China

The characteristics of raindrop size distribution(DSD) over the Tibetan Plateau and southern China are studied in this paper, using the DSD data from April to August 2014 collected by HSC-PS32 disdrometers in Nagqu and Yangjiang, comprising a total of 9430 and 6366 1-min raindrop spectra, respectively. The raindrop spectra, characteristics of parameter variations with rainfall rate, and the relationships between reflectivity factor(Z) and rainfall rate(R) are analyzed, as well as their DSD changes with precipitation type and rainfall rate. The results show that the average raindrop spectra appear to be one-peak curves, the number concentration for larger drops increase significantly with rainfall rate, and its value over southern China is much higher, especially in convective rain. Standardized Gamma distributions better describe DSD for larger drops, especially for convective rain in southern China. All three Gamma parameters for stratiform precipitation over the Tibetan Plateau are much higher, while its shape parameter(μ) and mass-weighted mean diameter(D_m), for convective precipitation, are less. In terms of parameter variation with rainfall rate, the normalized intercept parameter(N_w) over the Tibetan Plateau for stratiform rain increases with rainfall rate, which is opposite to the situation in convective rain. The μover the Tibetan Plateau for stratiform and convective precipitation types decreases with an increase in rainfall rate, which is opposite to the case for D m variation. In Z–R relationships, like "Z = AR~b", the coefficient A over the Tibetan Plateau is smaller, while its b is higher, when the rain type transfers from stratiform to convective ones. Furthermore, with an increase in rainfall rate, parameters A and b over southern China increase gradually, while A over the Tibetan Plateau decreases substantially, which differs from the findings of previous studies. In terms of geographic location and climate over the Tibetan Plateau and southern China, the precipitation in the pre-flood seasons is dominated by strong convective rain, while weak convective rain occurs frequently in northern Tibet with lower humidity and higher altitude.     

Heavy Rainfall Ensemble Prediction： Initial Condition Perturbation vs Multi-Physics Perturbation

Mesoscale ensemble is an encouraging technology for improving the accuracy of heavy rainfall predictions. Occurrences of heavy rainfall are closely related to convective instability and topography. In mid-latitudes, perturbed initial fields for medium-range weather forecasts are often configured to focus on the baroclinic instability rather than the convective instability. Thus, alternative approaches to generate initial perturba- tions need to be developed to accommodate the uncertainty of the convective instability. In this paper, an initial condition perturbation approach to mesoscale heavy rainfall ensemble prediction, named as Different Physics Mode Method （DPMM）, is presented in detail. Based on the PSU/NCAR mesoscale model MM5, an ensemble prediction experiment on a typical heavy rainfall event in South China is carried out by using the DPMM, and the structure of the initial condition perturbation is analyzed. Further, the DPMM ensem- ble prediction is compared with a multi-physics ensemble prediction, and the results show that the initial perturbation fields from the DPMM have a reasonable mesoscale circulation structure and could reflect the prediction uncertainty in the sensitive regions of convective instability. An evaluation of the DPMM ini- tial condition perturbation indicates that the DPMM method produces better ensemble members than the multi-physics perturbation method, and can significantly improve the precipitation forecast than the control non-ensemble run.     

VERIFICATION OF TROPICAL CYCLONE RAINFALL PREDICTIONS FROM CMA AND JMA GLOBAL MODELS

The number of tropical cyclone (TC) genesis over the South China Sea and the Northwest Pacific Ocean in 2009 is significantly less than the average (27.4). However, the number of landfall TC over mainland China and its associated rainfall is more than the average. This paper focuses on the performance of numerical weather prediction (NWP) of landfall TC precipitation over China in 2009. The China Meteorological Administration (CMA) and Japan Meteorological Agency (JMA) models are compared. Although the schemes of physical processes, the data assimilation system and the dynamic frame are entirely different for the two models, the results of forecast verification are similar to each other for TC rainfall and track except for TC Goni. In this paper, a day with daily rainfall amount greater than 50 mm was selected as a storm rain day when there was a TC affecting the mainland. There are 32 storm rain days related to the landing of typhoons and tropical depressions. The rainfall forecast verification methods of National Meteorological Centre (NMC) of CMA are selected to verify the models’ rainfall forecast. Observational precipitation analyses related to TCs in 2009 indicate a U-shape spatial distribution in China. It is found that the rain belt forecasted by the two models within 60 hours shows good agreement with observations, both in the location and the maximum rainfall center. Beyond 3 days, the forecasted rainfall belt shifts northward on average, and the rainfall amount of the model forecasts becomes under-predicted. The rainfall intensity of CMA model forecast is more reasonable than that of JMA model. For heavy rain, the JMA model made more missing forecasts. The TC rainfall is verified in Guangdong, Guangxi, Fujian and Hainan where rainfall amount related to TCs is relatively larger than in other regions. The results indicate that the model forecast for Guangdong and Guangxi is more skillful than that for Hainan. The rainfall forecast for Hainan remains difficult for the models because of insufficient observation data and special tropical ocean climate.     

Statistics of the Z–R Relationship for Strong Convective Weather over the Yangtze–Huaihe River Basin and Its Application to Radar Reflectivity Data Assimilation for a Heavy Rain Event

The relationship between the radar reflectivity factor(Z) and the rainfall rate(R) is recalculated based on radar observations from 10 Doppler radars and hourly rainfall measurements at 6529 automatic weather stations over the Yangtze–Huaihe River basin. The data were collected by the National 973 Project from June to July 2013 for severe convective weather events. The Z–R relationship is combined with an empirical qr–R relationship to obtain a new Z–qr relationship, which is then used to correct the observational operator for radar reflectivity in the three-dimensional variational(3 DVar) data assimilation system of the Weather Research and Forecasting(WRF) model to improve the analysis and prediction of severe convective weather over the Yangtze–Huaihe River basin. The performance of the corrected reflectivity operator used in the WRF 3 DVar data assimilation system is tested with a heavy rain event that occurred over Jiangsu and Anhui provinces and the surrounding regions on 23 June 2013. It is noted that the observations for this event are not included in the calculation of the Z–R relationship. Three experiments are conducted with the WRF model and its 3 DVar system, including a control run without the assimilation of reflectivity data and two assimilation experiments with the original and corrected reflectivity operators. The experimental results show that the assimilation of radar reflectivity data has a positive impact on the rainfall forecast within a few hours with either the original or corrected reflectivity operators, but the corrected reflectivity operator achieves a better performance on the rainfall forecast than the original operator. The corrected reflectivity operator extends the effective time of radar data assimilation for the prediction of strong reflectivity. The physical variables analyzed with the corrected reflectivity operator present more reasonable mesoscale structures than those obtained with the original reflectivity operator. This suggests that the new statistical Z–R relationship is more suitable for predicting severe convective weather over the Yangtze–Huaihe River basin than the Z–R relationships currently in use.     

DIAGNOSTIC ANALYSIS ON THE DISTRIBUTION OF RAINFALL ASSOCIATED WITH TYPHOON “MOLAVE” (0906)

This work examines the mechanism of rainfall associated with typhoon Molave(0906)in Guangdong province and Guangxi Zhuang Autonamous Region with rainfall observations,radar mosaics from China National Meteorological Center and the final analysis data of National Center of Environmental Prediction(FNL/NCEP,USA).The result shows that the mechanism is different for the rainfall in the these areas.The rainfall in eastern Guangdong is mainly associated with a convective line to the front-right of the typhoon.The convective line is about 200 km away from the typhoon center.The rainfall in western Guangdong and Guangxi appear ahead of or to the left of the typhoon and is very close to the typhoon center.Both rainfall moves forward with the typhoon anticlockwise.It was also found that the rainfall occurred in the boundary between unstable and low-level convergent areas and closer to the convergent area.The unstable area is located in the downstream of rainfall and ahead of the convective line.It is an important factor to the development and convection.Strong frontogenesis is observed in the backward or upstream convective area of rainfall and is thus an important lifting condition for the formation of rainfall.When the low-level convergent area moves to the unstable area ahead of it,the unstable energy is left behind and as a result the convection is strengthened.     

Seasonal Variations of Observed Raindrop Size Distribution in East China

Seasonal variations of rainfall microphysics in East China are investigated using data from the observations of a twodimensional video disdrometer and a vertically pointing micro rain radar. The precipitation and rain drop size distribution(DSD) characteristics are revealed for different rain types and seasons. Summer rainfall is dominated by convective rain,while during the other seasons the contribution of stratiform rain to rainfall amount is equal to or even larger than that of convective rain. The mean mass-weighted diameter versus the generalized intercept parameter pairs of convective rain are plotted roughly around the "maritime" cluster, indicating a maritime nature of convective precipitation throughout the year in East China. The localized rainfall estimators, i.e., rainfall kinetic energy–rain rate, shape–slope, and radar reflectivity–rain rate relations are further derived. DSD variability is believed to be a major source of diversity of the aforementioned derived estimators. These newly derived relations would certainly improve the accuracy of rainfall kinetic energy estimation, DSD retrieval, and quantitative precipitation estimation in this specific region.     

用一种新的同化方法同化降水量资料

Observations of accumulated precipitation are extremely valuable for effectively improving rainfall analysis and forecast. It is, however, difficult to use such observations directly through sequential assimilation methods, such as three-dimensional variational data assimilation or an Ensemble Kalman Filter. In this study, the authors illustrate a new approach that makes effective use of precipitation data to improve rainfall forecast. The new method directly obtains an optimal solution in a reduced space by fitting observations with historical time series generated by the model; it also avoids the implementation of tangent linear model and its adjoint. A lot of historical samples are produced as the ensemble of precipitation observations with the fully nonlinear forecast model. The results show that the new approach is capable of extracting information from precipitation observations to improve the analysis and forecast. This method provides comparable performance with the standard four- dimensional variational data assimilation at a much lower computational cost.     

Explicit and parameterized episodes of warm-season precipitation over the continental United States

This paper describes explicit and parameterized simulations of midsummer precipitation over the continental United States for two distinct episodes： moderate large-scale forcing and weak forcing. The objective is to demonstrate the capability of explicit convection at currently affordable grid-resolution and compare it with parameterized realizations. Under moderate forcing, 3-kin grid-resolution explicit simulations represent rainfall coherence remarkably well. The observed daily convective generation near the Continental Divide and the subsequent organization and propagation are reproduced qualitatively. The propagation speed, zonal extent and duration of the rainfall streaks compare favorably with their observed counterparts, although the streak frequency is underestimated. The simulations at -10-km grid-resolution applying conventional convective parameterization schemes also replicate reasonably well the diurnal convective regeneration in moderate forcing. The performance of the 3-km grid-resolution model demonstrates the potential of -1-km-resolution explicit cloud-resolving models for the prediction of warm season precipitation for moderately forced environments. In weak forcing conditions, however, predictions of precipitation coherence and diurnal variability are much poorer. This suggests that an even finer resolution explicit model is required to adequately treat convective initiation and upscale organization typical of the warm season over the continental U.S.     

Effect of Length Scale Tuning of Background Error in WRF-3DVAR System on Assimilation of High-Resolution Surface Data for Heavy Rainfall Simulation

We investigated the impact of tuning the length scale of the background error covariance in the Weather Research and Forecasting(WRF) three-dimensional variational assimilation(3DVAR) system.In particular,we studied the effect of this parameter on the assimilation of high-resolution surface data for heavy rainfall forecasts associated with mesoscale convective systems over the Korean Peninsula.In the assimilation of high-resolution surface data,the National Meteorological Center method tended to exaggerate the length scale that determined the shape and extent to which observed information spreads out.In this study,we used the difference between observation and background data to tune the length scale in the assimilation of high-resolution surface data.The resulting assimilation clearly showed that the analysis with the tuned length scale was able to reproduce the small-scale features of the ideal field effectively.We also investigated the effect of a double-iteration method with two different length scales,representing large and small-length scales in the WRF-3DVAR.This method reflected the large and small-scale features of observed information in the model fields.The quantitative accuracy of the precipitation forecast using this double iteration with two different length scales for heavy rainfall was high;results were in good agreement with observations in terms of the maximum rainfall amount and equitable threat scores.The improved forecast in the experiment resulted from the development of well-identified mesoscale convective systems by intensified low-level winds and their consequent convergence near the rainfall area.     

REMOTE SENSING OF RAINFALL PARAMETERS BY LASER SCINTILLATION CORRELATION METHOD-COMPLETE EQUATION AND NUMERICAL SIMULATION

Remote sensing of rainfall parameters by using laser scintillation effect, originally proposed by Wang et al. is a unique approach for getting rainfall rate and raindrop size distribution with excellent temporal and spatial representativeness. In this paper, we review Wang's work, point out the weakness of their basic equations, then establish a complete remote sensing equation in which the observable quantity, the scintillation of light intensity is used. The relationships between the rainfall parameters and the spatial-temporal correlation function of light scintilla tion are systematically discussed. Numerical investigations show that this equation gives at least four different ways to obtain the rainfall rate, and the kernel functions for raindrop size distribution are of excellent resolution. At last, the retrieval scheme of the drop size distribution is discussed.     

Using Satellite Data to Analyze the Initiation and Evolution of Deep Convective Clouds

In this study, two deep convective cloud cases were analyzed in detail to study their initiation and evolution. In both cases, all deep convective clouds were positioned at the rear of the cold front cloud bands and propagated backward. Satellite data showed that prior to initiation of the deep convective clouds, thermodynamic and moist conditions were favorable for their formation. In the morning, a deep convective cloud at the rear of cold front cloud band propagated backward, the outflow boundary of which created favorable conditions for initiation. An additional deep convective cloud cluster moved in from the west and interacted with the outflow boundary to develop a mesoscale convective system（MCS） with large, ellipse-shaped deep convective clouds that brought strong rainfall. The initiation and evolution of these clouds are shown clearly in satellite data and provide significant information for nowcasting and short-term forecasting.     

Seasonal Prediction for May Rainfall over Southern China Based on the NCEP CFSv2

In this study, we assess the prediction for May rainfall over southern China (SC) by using the NCEP CFSv2 outputs. Results show that the CFSv2 is able to depict the climatology of May rainfall and associated circulations. However, the model has a poor skill in predicting interannual variation due to its poor performance in capturing related anomalous circulations. In observation, the above-normal SC rainfall is associated with two anomalous anticyclones over the western tropical Pacific and northeastern China, respectively, with a low-pressure convergence in between. In the CFSv2, however, the anomalous circulations exhibit the patterns in response to the El Ni?o-Southern Oscillation (ENSO), demonstrating that the model overestimates the relationship between May SC rainfall and the ENSO. Because of the onset of the South China Sea monsoon, the atmospheric circulation in May over SC is more complex, so the prediction for May SC rainfall is more challenging. In this study, we establish a dynamic-statistical forecast model for May SC rainfall based on the relationship between the interannual variation of rainfall and large-scale ocean-atmosphere variables in the CFSv2. The sea surface temperature anomalies (SSTAs) in the northeastern Pacific and the central-eastern equatorial Pacific, and the 500-hPa geopotential height anomalies over western Siberia in previous April, which exert great influence on the SC rainfall in May, are chosen as predictors. Furthermore, multiple linear regression is employed between the predictors obtained from the CFSv2 and observed May SC rainfall. Both cross validation and independent test show that the hybrid model significantly improve the model''s skill in predicting the interannual variation of May SC rainfall by two months in advance.     

An Analysis of Thermally-Related Surface Rainfall Budgets Associated with Convective and Stratiform Rainfall

Both water vapor and heat processes play key roles in producing surface rainfall.While the water vapor effects of sea surface temperature and cloud radiative and microphysical processes on surface rainfall have been investigated in previous studies,the thermal effects on rainfall are analyzed in this study using a series of two-dimensional equilibrium cloud-resolving model experiments forced by zonally-uniform,constant,large-scale zonal wind and zero large-scale vertical velocity.The analysis of thermally-related surface rainfall budget reveals that the model domain mean surface rain rate is primarily associated with the mean infrared cooling rate.Convective rainfall and transport of hydrometeor concentration from convective regions to raining stratiform regions corresponds to the heat divergence over convective regions,whereas stratiform rainfall corresponds to the transport of hydrometeor concentration from convective regions and heat divergence over raining stratiform regions.The heat divergence over convective regions is mainly balanced by the heat convergence over rainfall-free regions,which is,in turn,offset by the radiative cooling over rainfall-free regions.The sensitivity experiments of rainfall to the effects of sea surface temperature and cloud radiative and microphysical processes show that the sea surface temperature and cloud processes affect convective rainfall through the changes in infrared cooling rate over rainfall-free regions and transport rate of heat from convective regions to rainfall-free regions.     

Seasonal Prediction of Boreal Winter Rainfall over the Western Maritime Continent during ENSO

Since the beginning of the Association of Southeast Asian Nations Climate Outlook Forum(ASEANCOF)in 2013,the most difficult challenge has been the rainfall forecast in boreal winter.This is the Maritime Continent monsoon season during which rainfall reaches maximum in the annual cycle.This forecast difficulty arises in spite of the general notion that seasonal predictability of the Maritime Continent rainfall may be higher than most places because of the strong and robust influences of ENSO.The lower predictability is consistent with the lower correlation between ENSO and western Maritime Continent rainfall that reaches minimum during the boreal winter monsoon.Various theories have been proposed to explain this low correlation.In this paper,we review the research on ENSO–Maritime Continent rainfall relationship and show that the main cause of the forecast difficulty is the wind–terrain interaction involving the Sumatran and Malay Peninsula mountains,rather than the effect of sea surface temperature(SST).The wind–terrain interaction due to the low-level regional scale anomalous horizontal circulation offsets the anomalous Walker circulation during both El Ni?o and La Ni?a.The net result of these two opposing responses to ENSO is a lower local predictability.We propose to call this low-predictability region the WIMP(Western Indonesia–Malay Peninsula)region both for its geographical location and its special characteristic of causing difficulties for forecasters to make a confident forecast for the boreal winter.Our result suggests that climate models lack skills in forecasting rainfall in this region because their predictability depends strongly on SST.     

The Application of a Meteo-hydrological Forecasting System with Rainfall Bias Correction in a Small and Medium-sized Catchment

Meteo-hydrological forecasting models are an effective way to generate high-resolution gridded rainfall data for water source research and flood forecast. The quality of rainfall data in terms of both intensity and distribution is very important for establishing a reliable meteo-hydrological forecasting model. To improve the accuracy of rainfall data, the successive correction method is introduced to correct the bias of rainfall, and a meteo-hydrological forecasting model based on WRF and WRF-Hydro is applied for streamflow forecast over the Zhanghe River catchment in China. The performance of WRF rainfall is compared with the China Meteorological Administration Multi-source Precipitation Analysis System (CMPAS), and the simulated streamflow from the model is further studied. It shows that the corrected WRF rainfall is more similar to the CMPAS in both temporal and spatial distribution than the original WRF rainfall. By contrast, the statistical metrics of the corrected WRF rainfall are better. When the corrected WRF rainfall is used to drive the WRF-Hydro model, the simulated streamflow of most events is significantly improved in both hydrographs and volume than that of using the original WRF rainfall. Among the studied events, the largest improvement of the NSE is from -0.68 to 0.67. It proves that correcting the bias of WRF rainfall with the successive correction method can greatly improve the performance of streamflow forecast. In general, the WRF / WRF-Hydro meteo-hydrological forecasting model based on the successive correction method has the potential to provide better streamflow forecast in the Zhanghe River catchment.     

Diurnal cycles of precipitation over subtropical China in IPCC AR5 AMIP simulations

Atmospheric Intercomparison Project simulations of the summertime diurnal cycle of precipitation and low-level winds over subtropical China by Intergovernmental Panel on Climate Change Fifth Assessment Report models were evaluated. By analyzing the diurnal variation of convective and stratiform components, results confirmed that major biases in rainfall diurnal cycles over subtropical China are due to convection parameterization and further pointed to the diurnal variation of convective rainfall being closely related to the closure of the convective scheme. All models captured the early-morning peak of total rainfall over the East China Sea, but most models had problems in simulating diurnal rainfall variations over land areas of subtropical China. When total rainfall was divided into stratiform and convective rainfall, all models successfully simulated the diurnal variation of stratiform rainfall with a maximum in the early morning. The models, overestimating noon-time （nocturnal） total rainfall over land, generally simulated too much convective rainfall, which peaked close to noon （midnight）, sharing some similarities in the closures of their deep convection schemes. The better performance of the Meteorological Research Institute atmospherer. ocean coupled global climate model version 3 （MRI-CGCM3） is attributed to the well captured ratio of the two kinds of rainfall, but not diurnal variations of the two components. Therefore, a proper ratio of convective and stratiform rainfall to total rainfall is also important to improve simulated diurnal rainfall variation.     

Numerical Simulation of a Torrential Rain Event in the Northeast of Huaihe Basin. Part I: Model Verification and Analysis of MCSs

A torrential rain event accompanying Typhoon Prapiroon occurred in 2000, with 24-h rainfall amount reaching 800mm near the typhoon center. This event is simulated by the nonhydrostatic mesoscale model ARPS (V5.2), with thriple one-way nested-grids. Grid spacings of 45, 15, and 5 km are chosen for the three nested domains. The corresponding grid sizes are 75×75, 140×140, and 180×180, respectively. The NCEP/NCAR reanalysis data, radar echoes, and GMS-5 satellite images are assimilated with the ARPS model initially using a 3-D data assimilation system--ADAS. The simple ice phase scheme and the Kain-Fritsch cumulus parameterization scheme are used. There are 35 layers in the vertical, with a vertical grid spacing of about 625 m. The integration is performed up to 48 h from 0800 BT 29 to 0800 BT 31 August 2000. Compared with radar echoes, GMS-5 satellite images, and intensive surface observations, the results show that the heavy rain area down between the 500-hPa trough and the subtropical high in the left-front of Prapiroon is well simulated by the model ARPS, and the simulated rainfall centers are consistent with observations. A comparison of the radar echoes with these retrieved from the simulated hydrometeors reveals that there are meso-β scale convective systems that exhibit distinctive characteristics, and there are four convective belts converging in the vicinity of Xiangshui, where the maximum rainfall is observed. A further comparision of skew T-lgp diagrams from simulated and observed data demonstrates significant instability in this torrential rain process. The persistent vertical wind shear provides kinetic energy for the development of the MCSs, hence promoting the baroclinic development of convective cells, and the concentration of heavy rain at the specific location. The consistency between model results and observations encourages a further study of the torrential rain event using the simulation data.     

DIAGNOSTIC INVESTIGATION OF SIMULATION BIAS WITH THE GRAPES-MESO MODEL FOR A TORRENTIAL RAIN CASE

In this paper, the numerical simulation bias of the non-hydrostatic version GRAPES-Meso (Mesoscale of the Global and Regional Assimilation and Prediction System) at the resolution of 0.18o for a torrential rain case, which happened in May 31st to June 1st 2005 over Hunan province, are diagnosed and investigated by using the radiosondes, intensive surface observation, and the operational global analysis data, and the sensitivity experimental results as well. It is shown in the result that the GRAPES-Meso could reproduce quite well the main features of large-cale circulation and the distribution of the accumulated 24h precipitation and the key locations of the torrential rainfall are captured reasonably well by the model. However, bias exist in the simulation of the mesoscale features of the torrential rain and details of the relevant systems, for example, the simulated rainfall that is too earlier in model integration and remarkable underprediction of the peak value of rainfall rates over the heaviest rainfall region, the weakness of the upper jet simulation and the overprediction of the south-west wind in the lower troposphere etc. The investigation reveals that the sources of the simulation bias are different. The erroneous model rainfall in the earlier integration stage over the heaviest rainfall region is induced by the model initial condition bias of the wind field at about 925hPa over the torrential rainfall region, where the bias grow rapidly and spread upward to about 600hPa level within the few hours into the integration and result in abnormal convergence of the wind and moisture, and thus the unreal rainfall over that region. The large bias on the simulated rainfall intensity over the heaviest rainfall region might be imputed to the following combined factors of (1) the simulation bias on the strength and detailed structures of the upper-level jet core which bring about significant underpredictions of the dynamic conditions (including upper-level divergence and the upward motion) for heavy rainfall due to unfavorable mesoscale vertical coupling between the strong upper-level divergence and lower-level convergence; and (2) the inefficient coupling of the cumulous parameterization scheme and the explicit moisture in the integration, which causes the failure of the explicit moisture scheme in generating grid-scale rainfall in a certain extent through inadequate convective adjustment and feedback to the grid-scale. In addition, the interaction of the combined two factors could form a negative feedback to the rainfall intensity simulation, and eventually lead to the obvious underprediction of the rainfall rate.     

OBSERVATION RESEARCH FOR THE MEASURING RAINFALL CAPACITY OF TRMM/TMI-85.5G BASED ON PRECIPITATION DATA DURING THE HEAVY RAIN EXPERIMENT IN SOUTHERN CHINA

The capacity of Tropical Rainfall Measuring Mission (TRMM) Satellite for measuring rainfall was examined by using TMI-85.5 GHz microwave image data and precipitation data during a heavy rainfall experiment in southern China. From comparisons with the distribution of rain amount in an hour with BB T of 85.5 GHz microwave, it is clear that the center of heavy rain corresponds with an area of low BB T value. The location and shape of BB T distribution is similar to that of precipitation, and the larger the rainfall rates, the lower the BB T . A statistic analysis shows that the correlation coefficients between BB T and rain rates is negative and significant. Especially, when the rain rate is over 7 mm/h, the correlation degree between BB T and rain rates is more significant. The results shows that TRMM/TMI-85.5 G has great ability to measure convective heavy rain.