A Short-Range Quantitative Precipitation Forecast Algorithm Using Back-Propagation Neural Network Approach

A back-propagation neural network （BPNN） was used to establish relationships between the shortrange （0-3-h） rainfall and the predictors ranging from extrapolative forecasts of radar reflectivity, satelliteestimated cloud-top temperature, lightning strike rates, and Nested Grid Model （NGM） outputs. Quan- titative precipitation forecasts （QPF） and the probabilities of categorical precipitation were obtained. Results of the BPNN algorithm were compared to the results obtained from the multiple linear regression algorithm for an independent dataset from the 1999 warm season over the continental United States. A sample forecast was made over the southeastern United States. Results showed that the BPNN categorical rainfall forecasts agreed well with Stage Ⅲ observations in terms of the size and shape of the area of rainfall. The BPNN tended to over-forecast the spatial extent of heavier rainfall amounts, but the positioning of the areas with rainfall ≥25.4 mm was still generally accurate. It appeared that the BPNN and linear regression approaches produce forecasts of very similar quality, although in some respects BPNN slightly outperformed the regression.

Quantitative Precipitation Forecast Experiment Based on Basic NWP Variables Using Deep Learning

The quantitative precipitation forecast(QPF)performance by numerical weather prediction(NWP)methods depends fundamentally on the adopted physical parameterization schemes(PS).However,due to the complexity of the physical mechanisms of precipitation processes,the uncertainties of PSs result in a lower QPF performance than their prediction of the basic meteorological variables such as air temperature,wind,geopotential height,and humidity.This study proposes a deep learning model named QPFNet,which uses basic meteorological variables in the ERA5 dataset by fitting a non-linear mapping relationship between the basic variables and precipitation.Basic variables forecasted by the highest-resolution model(HRES)of the European Centre for Medium-Range Weather Forecasts(ECMWF)were fed into QPFNet to forecast precipitation.Evaluation results show that QPFNet achieved better QPF performance than ECMWF HRES itself.The threat score for 3-h accumulated precipitation with depths of 0.1,3,10,and 20 mm increased by 19.7%,15.2%,43.2%,and 87.1%,respectively,indicating the proposed performance QPFNet improved with increasing levels of precipitation.The sensitivities of these meteorological variables for QPF in different pressure layers were analyzed based on the output of the QPFNet,and its performance limitations are also discussed.Using DL to extract features from basic meteorological variables can provide an important reference for QPF,and avoid some uncertainties of PSs.

Improving the Short-Range Precipitation Forecast of Numerical Weather Prediction through a Deep Learning-Based Mask Approach

Due to various technical issues,existing numerical weather prediction(NWP)models often perform poorly at forecasting rainfall in the first several hours.To correct the bias of an NWP model and improve the accuracy of short-range precipitation forecasting,we propose a deep learning-based approach called UNet Mask,which combines NWP forecasts with the output of a convolutional neural network called UNet.The UNet Mask involves training the UNet on historical data from the NWP model and gridded rainfall observations for 6-hour precipitation forecasting.The overlap of the UNet output and the NWP forecasts at the same rainfall threshold yields a mask.The UNet Mask blends the UNet output and the NWP forecasts by taking the maximum between them and passing through the mask,which provides the corrected 6-hour rainfall forecasts.We evaluated UNet Mask on a test set and in real-time verification.The results showed that UNet Mask outperforms the NWP model in 6-hour precipitation prediction by reducing the FAR and improving CSI scores.Sensitivity tests also showed that different small rainfall thresholds applied to the UNet and the NWP model have different effects on UNet Mask's forecast performance.This study shows that UNet Mask is a promising approach for improving rainfall forecasting of NWP models.

BMA probability quantitative precipitation forecasting of land-falling typhoons in south-east China

The probability of quantitative precipitation forecast(PQPF)of three Bayesian Model Averaging(BMA)models based on three raw super ensemble prediction schemes(i.e.,A,B,and C)are established,which through calibration of their parameters using 1-3 day precipitation ensemble prediction systems(EPSs)from the China Meteorological Administration(CMA),the European Centre for Medium-Range Weather Forecasts(ECMWF)and the National Centers for Environmental Prediction(NCEP)and observation during land-falling of three typhoons in south-east China in 2013.The comparison of PQPF shows that the performance is better in the BMA than that in raw ensemble forecasts.On average,the mean absolute error(MAE)of 1 day lead time forecast is reduced by 12.4%,and its continuous ranked probability score(CRPS)of 1-3 day lead time forecast is reduced by 26.2%,respectively.Although the amount of precipitation prediction by the BMA tends to be underestimated,but in view of the perspective of probability prediction,the probability of covering the observed precipitation by the effective forecast ranges of the BMA are increased,which is of great significance for the early warning of torrential rain and secondary disasters induced by it.

The Impact of Assimilating Radar-estimated Rain Rates on Simulation of Precipitation in the 17-18 July 1996 Chicago Floods

Rainfall prediction remains one of the most challenging problems in weather forecasting. In order to improve high-resolution quantitative precipitation forecasts （QPF）, a new procedure for assimilating rainfall rate derived from radar composite reflectivity has been proposed and tested in a numerical simulation of the Chicago floods of 17–18 July 1996. The methodology is based on the one-dimensional variation scheme （1DVAR） assimilation approach introduced by Fillion and Errico but applied here using the Kain-Fritsch convective parameterization scheme （KF CPS）. The novel feature of this work is the continuous assimilation of radar estimated rain rate over a three hour period, rather than a single assimilation at the initial （analysis） time. Most of the characteristics of this precipitation event, including the propagation, regeneration of mesoscale convective systems, the frontal boundary across the Midwest and the evolution of the low-level jet are better captured in the simulation as the radar-estimated precipitation rate is assimilated. The results indicate that precipitation assimilation during the early stage can improve the simulated mesoscale feature of the convection system and shorten the spin-up time significantly. Comparison of precipitation forecasts between the experiments with and without the 1DVAR indicates that the 1DVAR scheme has a positive impact on the QPF up to 36 hours in terms of the bias and bias equalized threat scores.

Impact of Soil Moisture Uncertainty on Summertime Short-range Ensemble Forecasts

To investigate the impact of soil moisture uncertainty on summertime short-range ensemble forecasts（SREFs）, a fivemember SREF experiment with perturbed initial soil moisture（ISM） was performed over a northern China domain in summertime from July to August 2014. Five soil moisture analyses from three different operational/research centers were used as the ISM for the ensemble. The ISM perturbation produced notable ensemble spread in near-surface variables and atmospheric variables below 800 h Pa, and produced skillful ensemble-mean 24-h accumulated precipitation（APCP24） forecasts that outperformed any single ensemble member. Compared with a second SREF experiment with mixed microphysics parameterization options, the ISM-perturbed ensemble produced comparable ensemble spread in APCP24 forecasts, and had better Brier scores and resolution in probabilistic APCP24 forecasts for 10-mm, 25-mm and 50-mm thresholds. The ISM-perturbed ensemble produced obviously larger ensemble spread in near-surface variables. It was, however, still under-dispersed, indicating that perturbing ISM alone may not be adequate in representing all the uncertainty at the near-surface level, indicating further SREF studies are needed to better represent the uncertainties in land surface processes and their coupling with the atmosphere.

Evaluation of Radar and Automatic Weather Station Data Assimilation for a Heavy Rainfall Event in Southern China

To improve the accuracy of short-term （0-12 h） forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System （HAPS）, has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting （WRF-ARW） model and the Advanced Regional Prediction System （ARPS） three-dimensional variational data assimilation （3DVAR） pack- age. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station （AWS） data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting （QPF） by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to 9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6-9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score （FSS） and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.

ON THE SENSITIVITY OF PRECIPITATION FORECASTS TO THE MOIST PHYSICS AND THE HORIZONTAL RESOLUTION OF NUMERICAL MODEL

The impacts of the enhanced model's moist physics and horizontal resolution upon the QPFs (quantitative precipitation forecasts)are investigated by applying the HIRLAM(high resolution limited area model)to the summer heavy-rain cases in China.The performance of the control run, for which a 0.5°×0.5°grid spacing and a traditional“grid-box supersaturation removal+Kuo type convective paramerization”are used as the moist physics,is compared with that of the sensitivity runs with an enhanced model's moist physics(Sundqvist scheme)and an increased horizontal resolution(0.25°×0.25°),respectively.The results show: (1)The enhanced moist physics scheme(Sundqvist scheme),by introducing the cloud water content as an additional prognostic variable and taking into account briefly of the microphysics involved in the cloud-rain conversion,does bring improvements in the model's QPFs.Although the deteriorated QPFs also occur occasionally,the improvements are found in the majority of the cases,indicating the great potential for the improvement of QPFs by enhancing the model's moist physics. (2)By increasing the model's horizontal resolution from 0.5°×0.5°,which is already quite high compared with that of the conventional atmospheric soundings,to 0.25°×0.25°without the simultaneous enhancement in model physics and objective analysis,the improvements in QPFs are very limited.With higher resolution,although slight amelioration in locating the rainfall centers and in resolving some finer structures of precipitation pattern are made,the number of the mis- predicted fine structures in rainfall field increases with the enhanced model resolution as well.

Improvement of typhoon rainfall prediction based on optimization of the Kain-Fritsch convection parameterization scheme using a micro-genetic algorithm

Inclusion of cloud processes is essential for precipitation prediction with a numerical weather prediction model.However,convective parameterization contains numerous parameters whose values are in large uncertainties.In particular,it is still not clear how the parameters of a sub-grid-scale convection scheme can be modified to improve high-resolution precipitation prediction.To address these issues,a micro-genetic(micro-GA)algorithm is coupled to the Kain-Fritsch(KF)convective parameterization scheme(CPS)in the WRF to improve the quantitative precipitation forecast(QPF).The optimization focuses on two parameters in the KF scheme:the convective time scale and the conversion rate.The optimizing process is controlled by the micro-GA using a QPF skill score as the fitness function.Two heavy rainfall events related to typhoons that made landfall over the south-east coastal region of China are selected,and for each case the parameter values are adjusted to achieve the best QPF skill.Significant improvements in QPF are evident with an increase in the average equitable threat score(ETS)by 5.8%for the first case,and by 18.4%for the second case.The results demonstrate that the micro-GAKF coupling system is effective in optimizing the parameter values,which affect the applicability of CPS in a high-resolution model,and therefore improves the rainfall prediction in both ETS and spatial distribution.