Neuroid BP-type Model Applied to the Study of Monthly Rainfall Forecasting

A neuroid BP-type three-layer mapping model is used for monthly rainfall forecasting in terms of 1946-1985Naming monthly precipitation records as basic sequences and the model has the form i×j=8×3, K=1; by steadilymodifying the weighing coefficient, long-range monthly forecasts for January to December, 1986 are constructed and1986 month-to-month predictions are made based on, say, the January measurement for February rainfall and soon, with mean absolute error reaching 6,07 and 5,73 mm, respectively. Also, with a different monthly initial value forJune through September, 1994, neuroid forecasting is done,indicating the same result of the drought in Naming during the summer, an outcome that is in sharp agreement with the observation.

A Numerical Study on Forecasting the Henan Extraordinarily Heavy Rainfall Event in August 1975

This study is essentially an experiment on the control experiment in the August 1975 catastrophe which was the heaviest rainfall in China's Mainland with a maximum 24-h rainfall of 1060.3 mm, and it significantly demonstrates that the limited area model can still skillfully give reasonable results even only the conventional data are available. For such a heavy rainfall event, a grid length of 90 km is too large while 45 km seems acceptable. Under these two grid sizes, the cumulus parameterization scheme is evidently superior to the explicit scheme since it restricts instabilities such as CISK to limited extent. The high resolution scheme for the boundary treatment does not improve forecasts significantly.The experiments also revealed some interesting phenomena such as the forecast rainfall being too small while affecting synoptic system so deep as compared with observations. Another example is the severe deformation of synoptic systems both in initial conditions and forecast fields in the presence of complicated topography. Besides, the fixed boundary condition utilized in the experiments along with current domain coverage set some limitations to the model performances.

Time-series analysis of monthly rainfall data for the Mahanadi River Basin, India

Time series analysis has two goals, modeling random mechanisms and predicting future series using historical data. In the present work, a uni-variate time series autoregressive integrated moving average （ARIMA） model has been developed for （a） simulating and forecasting mean rainfall, obtained using Theissen weights; over the Mahanadi River Basin in India, and （b） simula^ag and forecasting mean rainfall at 38 rain-gauge stations in district towns across the basin. For the analysis, monthly rainfall data of each district town for the years 1901-2002 （102 years） were used. Theissen weights were obtained over the basin and mean monthly rainfall was estimated. The trend and seasonality observed in ACF and PACF plots of rainfall data were removed using power transformation （a=0.5） and first order seasonal differencing prior to the development of the ARIMA model. Interestingly, the AR1MA model （1,0,0）（0,1,1）12 developed here was found to be most suitable for simulating and forecasting mean rainfall over the Mahanadi River Basin and for all 38 district town rain-gauge stations, separately. The Akaike Information Criterion （AIC）, good- ness of fit （Chi-square）, R2 （coefficient of determination）, MSE （mean square error） and MAE （mea absolute error） were used to test the validity and applicability of the developed ARIMA model at different stages. This model is considered appropriate to forecast the monthly rainfall for the upcoming 12 years in each district town to assist decision makers and policy makers establish priorities for water demand, storage, distribution, and disaster management.

Forecasting Short Time Series with Missing Data by Means of Energy Associated to Series

In this work an algorithm to predict short times series with missing data by means energy associated of series using artificial neural networks (ANN) is presented. In order to give the prediction one step ahead, a comparison between this and previous work that involves a similar approach to test short time series with uncertainties on their data, indicates that a linear smoothing is a well approximation in order to employ a method for uncompleted datasets. Moreover, in function of the long- or short-term stochastic dependence of the short time series considered, the training process modifies the number of patterns and iterations in the topology according to a heuristic law, where the Hurst parameter H is related with the short times series, of which they are considered as a path of the fractional Brownian motion. The results are evaluated on high roughness time series from solutions of the Mackey-Glass Equation (MG) and cumulative monthly historical rainfall data from San Agustin, Cordoba. A comparison with ANN nonlinear filters is shown in order to see a better performance of the outcomes when the information is taken from geographical point observation.

AN OPERATIONAL STATISTICAL SCHEME FOR TROPICAL CYCLONE INDUCED RAINFALL FORECAST

A non-parametric method is used in this study to analyze and predict short-term rainfall due to tropical cyclones(TCs) in a coastal meteorological station. All 427 TCs during 1953-2011 which made landfall along the Southeast China coast with a distance less than 700 km to a certain meteorological station- Shenzhen are analyzed and grouped according to their landfalling direction, distance and intensity. The corresponding daily rainfall records at Shenzhen Meteorological Station(SMS) during TCs landfalling period(a couple of days before and after TC landfall) are collected. The maximum daily rainfall(R-24) and maximum 3-day accumulative rainfall(R-72) records at SMS for each TC category are analyzed by a non-parametric statistical method, percentile estimation. The results are plotted by statistical boxplots, expressing in probability of precipitation. The performance of the statistical boxplots is evaluated to forecast the short-term rainfall at SMS during the TC seasons in 2012 and 2013. Results show that the boxplot scheme can be used as a valuable reference to predict the short-term rainfall at SMS due to TCs landfalling along the Southeast China coast.

Evaluation of WRF-based Convection-Permitting Multi-Physics Ensemble Forecasts over China for an Extreme Rainfall Event on 21 July 2012 in Beijing

On 21 July 2012,an extreme rainfall event that recorded a maximum rainfall amount over 24 hours of 460 mm,occurred in Beijing,China. Most operational models failed to predict such an extreme amount. In this study,a convective-permitting ensemble forecast system（CEFS）,at 4-km grid spacing,covering the entire mainland of China,is applied to this extreme rainfall case. CEFS consists of 22 members and uses multiple physics parameterizations. For the event,the predicted maximum is 415 mm d^-1 in the probability-matched ensemble mean. The predicted high-probability heavy rain region is located in southwest Beijing,as was observed. Ensemble-based verification scores are then investigated. For a small verification domain covering Beijing and its surrounding areas,the precipitation rank histogram of CEFS is much flatter than that of a reference global ensemble. CEFS has a lower（higher） Brier score and a higher resolution than the global ensemble for precipitation,indicating more reliable probabilistic forecasting by CEFS. Additionally,forecasts of different ensemble members are compared and discussed. Most of the extreme rainfall comes from convection in the warm sector east of an approaching cold front. A few members of CEFS successfully reproduce such precipitation,and orographic lift of highly moist low-level flows with a significantly southeasterly component is suggested to have played important roles in producing the initial convection. Comparisons between good and bad forecast members indicate a strong sensitivity of the extreme rainfall to the mesoscale environmental conditions,and,to less of an extent,the model physics.

An Assessment of the Seasonal Rainfall and Its Societal Implications in Zanzibar Islands during the Season of October to December, 2019

The assessment of the performance of the October to December (OND), 2019 rainfall season in Zanzibar (Unguja and Pemba) with reference to local downscaled Tanzania Meteorological Authority (TMA) forecast, and regional (Intergovernmental Authority on Development Climate Prediction and Application Center (IGAD-ICPAC) weather forecasts were assessed by comparing the long term average of OND rainfall data and previous OND rainfall seasons of 2016, 2017 and 2018 as well as extreme positive Indian Ocean Dipole (IOD) during OND seasons of 1961, 1994, 1997, 2006 and 2019 for Zanzibar. The study assessed zonal (u) and meridional (v) winds at 850 and 200 mb, monthly and dekadal sea surface temperature (SST);the Madden Julien Oscillations (MJO) forecast reports and the ocean heat content data. Both gridded and observed datasets were processed into dekadal, monthly and seasonal averages and then analysed. The results revealed that, based on the observations, above normal rainfall of 936 and 908 mm were reported at stations of Kisauni (Unguja) and Karume airport (Pemba) during 2019 OND season. This amount was the first and second ever recorded for the extreme positive IOD during OND seasons of 1961, 1994, 1997, 2006 and 2019, and also the first for the previous higher OND rainfall seasons of 2016, 2017 and 2018 which was highly variable. Moreover, these values were second ever-recorded highest OND rainfall since 1916 to 2019 where the first one was observed in 1961. Furthermore, the results revealed that 2019 OND seasonal rainfall had the highest amount of contribution based on historical climatology. For instance, the 2019 OND rainfall for Kisauni, Pemba airport and Matangatuani contributed to 198%, 303% and 231% of the long term (1987-2016) mean OND rainfall in Zanzibar. Indeed, the results show that the presence of the MJO during OND and the enhanced positive IOD was among the causes for the observed wetness of the 2019 OND in Zanzibar and most parts of the country. Moreover, the dominant easterly, southeasterly and northwesterly onshore winds during 2019 OND also contributed to heavy rainfall. The monthly rainfall variability among stations had the highest amount of rainfall which ranges from 400 to 500 mm which was observed during October in Kisauni and Karume airport, while the lowest amount ranging from 150 to 180 mm was observed during November in Matangatuani and the surrounding stations. Based on the comparison of the forecasted reports issued by ICPAC and TMA, the results revealed that irrespective of not considering the likelihood of occurrence of MJO and strong positive IOD both forecasts has performed well with that of ICPAC being leading. Conclusively, since the 2019 OND season has been uniquely characterized by the presence of MJO and IOD polarities it would be worthful to consider the two as input parameters during the OND rainfall forecast over the region.

OPERATIONAL FORECAST OF RAINFALL INDUCED BY LANDFALLING TROPICAL CYCLONES ALONG GUANGDONG COAST

Following previous studies of the rainfall forecast in Shenzhen owing to landfalling tropical cyclones(TCs),a nonparametric statistical scheme based on the classification of the landfalling TCs is applied to analyze and forecast the rainfall induced by landfalling TCs in the coastal area of Guangdong province,China.All the TCs landfalling with the distance less than 700 kilometers to the 8 coastal stations in Guangdong province during 1950—2013 are categorized according to their landfalling position and intensity.The daily rainfall records of all the 8 meteorological stations are obtained and analyzed.The maximum daily rainfall and the maximum 3 days’accumulated rainfall at the 8 coastal stations induced by each category of TCs during the TC landfall period(a couple of days before and after TC landfalling time)from 1950 to 2013 are computed by the percentile estimation and illustrated by boxplots.These boxplots can be used to estimate the rainfall induced by landfalling TC of the same category in the future.The statistical boxplot scheme is further coupled with the model outputs from the European Centre for Medium-Range Weather Forecasts(ECMWF)to predict the rainfall induced by landfalling TCs along the coastal area.The TCs landfalling in south China from 2014 to 2017 and the corresponding rainfall at the 8 stations area are used to evaluate the performance of these boxplots and coupled boxplots schemes.Results show that the statistical boxplots scheme and coupled boxplots scheme can perform better than ECMWF model in the operational rainfall forecast along the coastal area in south China.

Application of Wavelet Random Coupling Model in Monthly Rainfall Prediction

A Trous algorithm of wavelet transform was used to decompose wavelet signal, and the cross-correlation analysis was used to analyze the sequence of each wavelet transform, and then the mathematical model correspond with wavelet transform sequence was established, finally wavelet random coupling model was obtained by wavelet reconstruction algorithm. Then, according to the rainfall data in crop growth period of Farm Chahayang from 1956 to 2008, the wavelet random coupling model was established to fit the model prediction test. The results showed that the prediction and fitting accuracy of the model was high, the model could reflect the rainfall variation regulation in the region, and it was a practical prediction model. It was very important for us to determine reasonably irrigation schedule and to use efficiency coefficient of precipitation resource.

Effects of Initial and Boundary Conditions on Heavy Rainfall Simulation over the Yellow Sea and the Korean Peninsula:Comparison of ECMWF and NCEP Analysis Data Effects and Verification with Dropsonde Observation

This study evaluated the simulation performance of mesoscale convective system(MCS)-induced precipitation,focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula.The evaluation was conducted for the European Centre for Medium-Range Weather Forecasts(ECMWF)and National Centers for Environmental Prediction(NCEP)analysis data,as well as the simulation result using them as initial and lateral boundary conditions for the Weather Research and Forecasting model.Particularly,temperature and humidity profiles from 3D dropsonde observations from the National Center for Meteorological Science of the Korea Meteorological Administration served as validation data.Results showed that the ECMWF analysis consistently had smaller errors compared to the NCEP analysis,which exhibited a cold and dry bias in the lower levels below 850 hPa.The model,in terms of the precipitation simulations,particularly for high-intensity precipitation over the Yellow Sea,demonstrated higher accuracy when applying ECMWF analysis data as the initial condition.This advantage also positively influenced the simulation of rainfall events on the Korean Peninsula by reasonably inducing convective-favorable thermodynamic features(i.e.,warm and humid lower-level atmosphere)over the Yellow Sea.In conclusion,this study provides specific information about two global analysis datasets and their impacts on MCS-induced heavy rainfall simulation by employing dropsonde observation data.Furthermore,it suggests the need to enhance the initial field for MCS-induced heavy rainfall simulation and the applicability of assimilating dropsonde data for this purpose in the future.

Experiments with the Improved Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation over South China

In recent work,three physical factors of the Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation(DSAEF_LTP model)have been introduced,namely,tropical cyclone(TC)track,TC landfall season,and TC intensity.In the present study,we set out to test the forecasting performance of the improved model with new similarity regions and ensemble forecast schemes added.Four experiments associated with the prediction of accumulated precipitation were conducted based on 47 landfalling TCs that occurred over South China during 2004-2018.The first experiment was designed as the DSAEF_LTP model with TC track,TC landfall season,and intensity(DSAEF_LTP-1).The other three experiments were based on the first experiment,but with new ensemble forecast schemes added(DSAEF_LTP-2),new similarity regions added(DSAEF_LTP-3),and both added(DSAEF_LTP-4),respectively.Results showed that,after new similarity regions added into the model(DSAEF_LTP-3),the forecasting performance of the DSAEF_LTP model for heavy rainfall(accumulated precipitation≥250 mm and≥100 mm)improved,and the sum of the threat score(TS250+TS100)increased by 4.44%.Although the forecasting performance of DSAEF_LTP-2 was the same as that of DSAEF_LTP-1,the forecasting performance was significantly improved and better than that of DSAEF_LTP-3 when the new ensemble schemes and similarity regions were added simultaneously(DSAEF_LTP-4),with the TS increasing by 25.36%.Moreover,the forecasting performance of the four experiments was compared with four operational numerical weather prediction models,and the comparison indicated that the DSAEF_LTP model showed advantages in predicting heavy rainfall.Finally,some issues associated with the experimental results and future improvements of the DSAEF_LTP model were discussed.

Test Study on Flood Forecast by Merging Multi Precipitation Data

Shuibuya control basin in upper reaches of Qingjiang River,Hubei Province was taken as the case. By combining grouping Z-I relation with ground meteorological rainfall station,rainfall estimation by radar was calibrated,and actual average surface rainfall in the basin was calculated.By combining genetic algorithm with neural network,the corrected AREM rainfall forecast model was established,to improve rainfall forecast accuracy by AREM. Finally,AREM rainfall forecast models before and after correction were input in Xin＇an River hydrologic model for flood forecast test. The results showed that the corrected AREM rainfall forecast model could significantly improve forecast accuracy of accumulative rainfall,and decrease range of average relative error was more than 60%. Hourly rainfall forecast accuracy was improved somewhat,but there was certain difference from actual situation. Average deterministic coefficient of AREM flood forest test before and after correction was improved from -32. 60% to 64. 38%,and relative error of flood peak decreased from 39. 00% to 25. 04%. The improved effect of deterministic coefficient was better than relative error of flood peak,and whole flood forecast accuracy was improved somewhat.

Seasonal Rainfall Forecasts for the Yangtze River Basin of China in Summer 2019 from an Improved Climate Service

Rainfall forecasts for the summer monsoon season in the Yangtze River basin(YRB) allow decision-makers to plan for possible flooding, which can affect the lives and livelihoods of millions of people. A trial climate service was developed in 2016, producing a prototype seasonal forecast product for use by stakeholders in the region, based on rainfall forecasts directly from a dynamical model. Here, we describe an improved service based on a simple statistical downscaling approach. Through using dynamical forecast of an East Asian summer monsoon(EASM) index, seasonal mean rainfall for the upper and middle/lower reaches of YRB can be forecast separately by use of the statistical downscaling, with significant skills for lead times of up to at least three months. The skill in different sub-basin regions of YRB varies with the target season. The rainfall forecast skill in the middle/lower reaches of YRB is significant in May–June–July(MJJ), and the forecast skill for rainfall in the upper reaches of YRB is significant in June–July–August(JJA). The mean rainfall for the basin as a whole can be skillfully forecast in both MJJ and JJA. The forecasts issued in 2019 gave good guidance for the enhanced rainfall in the MJJ period and the near-average conditions in JJA. Initial feedback from users in the basin suggests that the improved forecasts better meet their needs and will enable more robust decision-making.

Rainfall forecast errors in different landfall stages of Super Typhoon Lekima (2019)

The rainfall forecast performance of the Tropical Cyclone(TC)version Model of Global and Regional Assimilation PrEdiction System(GRAPESTCM)of the China Meteorological Administration for landfalling Super Typhoon Lekima(2019)is studied by using the object-oriented verification method of contiguous rain area(CRA).The major error sources and possible reasons for the rainfall forecast uncertainties in different landfall stages(including near landfall and moving further inland)are compared.Results show that different performance and errors of rainfall forecast exist in the different TC stages.In the near landfall stage the asymmetric rainfall distribution is hard to be simulated,which might be related to the too strong forecasted TC intensity and too weak vertical wind shear accompanied.As Lekima moves further inland,the rain pattern and volume errors gradually increase.The Equitable Threat Score of the 24 h forecasted rainfall over 100 mm declines quickly with the time-length over land.The diagnostic analysis shows that there exists an interaction between the TC and the mid-latitude westerlies,but too weak frontogenesis is simulated.The results of this research indicate that for the current numerical model,the forecast ability of persistent heavy rainfall is very limited,especially when the weakened landing TC moves further inland.

Analysis of a Heavy Rainfall Event over Beijing During 21-22 July2012 Based on High Resolution Model Analyses and Forecasts

The heaviest rainfall over 61 yr hit Beijing during 21-22 July 2012.Characterized by great rainfall amount and intensity,wide range,and high impact,this record-breaking heavy rainfall caused dozens of deaths and extensive damage.Despite favorable synoptic conditions,operational forecasts underestimated the precipitation amount and were late at predicting the rainfall start time.To gain a better understanding of the performance of mesoscale models,verification of high-resolution forecasts and analyses from the WRFbased BJ-RUCv2.0 model with a horizontal grid spacing of 3 km is carried out.The results show that water vapor is very rich and a quasi-linear precipitation system produces a rather concentrated rain area.Moreover,model forecasts are first verified statistically using equitable threat score and BIAS score.The BJ-RUCv2.0forecasts under-predict the rainfall with southwestward displacement error and time delay of the extreme precipitation.Further quantitative analysis based on the contiguous rain area method indicates that major errors for total precipitation（〉 5 mm h^（-1）） are due to inaccurate precipitation location and pattern,while forecast errors for heavy rainfall（〉 20 mm h^（-1）） mainly come from precipitation intensity.Finally,the possible causes for the poor model performance are discussed through diagnosing large-scale circulation and physical parameters（water vapor flux and instability conditions） of the BJ-RUCv2.0 model output.

Improvement of displacement error of rainfall and wind field forecast associated with landfalling tropical cyclone AMPHAN

Spatial distribution of rainfall and wind speed forecast errors associated with landfalling tropical cyclones(TC)occur significantly due to incorrect location forecast by numerical models.Two major areas of errors are:(i)over-estimation over the model forecast locations and(ii)underestimation over the observed locations of the TCs.A modification method is proposed for real-time improvement of rainfall and wind field forecasts and demonstrated for the typical TC AMPHAN over the Bay of Bengal in 2020.The proposed method to improve the model forecasts is a relocation method through shifting of model forecast locations of TC to the real-time official forecast locations of India Meteorological Department(IMD).The modification is applied to the forecasts obtained from the operational numerical model,the Global Forecast System(GFS)of IMD.Application of the proposed method shows considerable improvement of both the parameters over both the locations.The rainfall forecast errors due to displacement are found to have improved by 44.1%–69.8%and 72.1%–85.2%over the GFS forecast locations and over the observed locations respectively for the respective forecast lead times 48 h,72 h,and 96 h.Similarly,the wind speed forecasts have improved by 27.6%–56.0%and 63.7%–84.6%over the GFS forecast locations and over the observed locations respectively for the respective forecast lead times 60 h,72 h,and 84 h.The results show that the proposed technique has capacity to provide improved spatial distributions of rainfall and wind speed forecasts associated with landfalling TCs and useful guidance to operational forecasters.

Numerical Research on Effects Upon Precipitation Forecast of Doppler-Radar Estimated Precipitation and Retrieved Wind Field Under Different Model Initial Schemes

On the basis of the joint estimated 1-h precipitation from Changde, Jingzhou, and Yichang Doppler radars as well as Wuhan digital radar, and the retrieved wind fields from Yichang and Jingzhou Doppler radars, a series of numerical experiments with an advanced regional η-coordinate model （AREM） under different model initial schemes, i.e., Grapes-3DVAR, Barnes objective analysis, and Barnes-3DVAR, are carried out for a torrential rain process occurring along the Yangtze River in the 24-h period from 2000 BT 22 July 2002 to investigate the effects of the Doppler-radar estimated rainfall and retrieved winds on the rainfall forecast. The main results are as follows： （1） The simulations are obviously different under three initial schemes with the same data source （the radiosounding and T213L31 analysis）. On the whole, Barnes-3DVAR, which combines the advantages of the Barnes objective analysis and the Grapes-3DVAR method, gives the best simulations： well-simulated rain band and clear mesoscale structures, as well as their location and intensity close to observations. （2） Both Barnes-3DVAR and Grapes-3DVAR schemes are able to assimilate the Doppler-radar estimated rainfall and retrieved winds, but differences in simulation results are very large, with Barnes-3DVAR＇s simulation much better than Grapes-3DVAR＇s. （3） Under Grapes- 3DVAR scheme, the simulation of 24-h rainfall is improved obviously when assimilating the Doppler-radar estimated precipitation into the model in compared with the control experiment; but it becomes a little worse when assimilating the Doppler-radar retrieved winds into the model, and it becomes worse obviously when assimilating the Doppler-radar estimated precipitation as well as retrieved winds into the model. However, the simulation is different under Barnes-3DVAR scheme. The simulation is improved to a certain degree no matter assimilating the estimated precipitation or retrieved winds, or both of them. The result is the best when assimilating both of them into the model. And （4） Barnes-3DVAR is a new and efficient initial scheme for assimilating the radar estimated rainfall and retrieved winds.

12 HOUR PRECIPITATION FORECASTS FOR NAIROBI,KENYA

Radiosonde profiles of temperature and dewpoint temperature from one station are used to forecast 12-h precipita- tion over Nairobi,Kenya.The forecast scheme is based on statistical regression modelling.Four predictors are derived from data to use in a prognostic equation to get 12-h precipitation forecast.Observed and predicted rainfall values are plotted on a graph against time.Forecast verification shows that the forecasts are positively correlated with observations.