A Statistical-Dynamical Scheme for the Extraseasonal Prediction of Summer Rainfall for 160 Observation Stations across China

The purpose of this study was to design and test a statistical-dynamical scheme for the extraseasonal（one season in advance） prediction of summer rainfall at 160 observation stations across China.The scheme combined both valuable information from the preceding observations and dynamical information from synchronous numerical predictions of atmospheric circulation factors produced by an atmospheric general circulation model.First,the key preceding climatic signals and synchronous atmospheric circulation factors that were not only closely related to summer rainfall but also numerically predictable were identified as the potential predictors.Second,the extraseasonal prediction models of summer rainfall were constructed using a multivariate linear regression analysis for 15 subregions and then 160 stations across China.Cross-validation analyses performed for the period 1983-2008 revealed that the performance of the prediction models was not only high in terms of interannual variation,trend,and sign but also was stable during the whole period.Furthermore,the performance of the scheme was confirmed by the accuracy of the real-time prediction of summer rainfall during 2009 and 2010.

An Application of the Adjoint Method to a Statistical-Dynamical Tropical-Cyclone Prediction Model （SD-90）Ⅱ：Real Tropical Cyclone Cases

In the first paper in this series, a variational data assimilation of ideal tropical cyclone （TC） tracks was performed for the statistical-dynamical prediction model SD-90 by the adjoint method, and a prediction of TC tracks was made with good accuracy for tracks containing no sharp turns. In the present paper, the cases of real TC tracks are studied. Due to the complexity of TC motion, attention is paid to the diagnostic research of TC motion. First, five TC tracks are studied. Using the data of each entire TC track, by the adjoint method, five TC tracks are fitted well, and the forces acting on the TCs are retrieved. For a given TC, the distribution of the resultant of the retrieved force and Coriolis force well matches the corresponding TC track, i.e., when a TC turns, the resultant of the retrieved force and Coriolis force acts as a centripetal force, which means that the TC indeed moves like a particle; in particular, for TC 9911, the clockwise looping motion is also fitted well. And the distribution of the resultant appears to be periodic in some cases. Then, the present method is carried out for a portion of the track data for TC 9804, which indicates that when the amount of data for a TC track is sufficient, the algorithm is stable. And finally, the same algorithm is implemented for TCs with a double-eyewall structure, namely Bilis （2000） and Winnie （1997）, and the results prove the applicability of the algorithm to TCs with complicated mesoscale structures if the TC track data are obtained every three hours.

Studies of Climate Change with Statistical-Dynamical Models: A Review

The cause-effect relationship is not always possible to trace in GCMs because of the simultaneous inclusion of several highly complex physical processes. Furthermore, the inter-GCM differences are large and there is no simple way to reconcile them. So, simple climate models, like statistical-dynamical models (SDMs), appear to be useful in this context. This kind of models is essentially mechanistic, being directed towards understanding the dependence of a particular mechanism on the other parameters of the problem. In this paper, the utility of SDMs for studies of climate change is discussed in some detail. We show that these models are an indispensable part of hierarchy of climate models.

A Hybrid Model Evaluation Based on PCA Regression Schemes Applied to Seasonal Precipitation Forecast

Possible changes in the structure and seasonal variability of the subtropical ridge may lead to changes in the rainfall’s variability modes over Caribbean region. This generates additional difficulties around water resource planning, therefore, obtaining seasonal prediction models that allow these variations to be characterized in detail, it’s a concern, specially for island states. This research proposes the construction of statistical-dynamic models based on PCA regression methods. It is used as predictand the monthly precipitation accumulated, while the predictors (6) are extracted from the ECMWF-SEAS5 ensemble mean forecasts with a lag of one month with respect to the target month. In the construction of the models, two sequential training schemes are evaluated, obtaining that only the shorter preserves the seasonal characteristics of the predictand. The evaluation metrics used, where cell-point and dichotomous methodologies are combined, suggest that the predictors related to sea surface temperatures do not adequately represent the seasonal variability of the predictand, however, others such as the temperature at 850 hPa and the Outgoing Longwave Radiation are represented with a good approximation regardless of the model chosen. In this sense, the models built with the nearest neighbor methodology were the most efficient. Using the individual models with the best results, an ensemble is built that allows improving the individual skill of the models selected as members by correcting the underestimation of precipitation in the dynamic model during the wet season, although problems of overestimation persist for thresholds lower than 50 mm.

A GCM-Based Forecasting Model for the Landfall of Tropical Cyclones in China

A statistical dynamic model for forecasting Chinese landfall of tropical cyclones （CLTCs） was developed based on the empirical relationship between the observed CLTC variability and the hindcast atmospheric circulations from the Pusan National University coupled general circulation model （PNU-CGCM）.In the last 31 years,CLTCs have shown strong year-to-year variability,with a maximum frequency in 1994 and a minimum frequency in 1987.Such features were well forecasted by the model.A cross-validation test showed that the correlation between the observed index and the forecasted CLTC index was high,with a coefficient of 0.71.The relative error percentage （16.3%） and root-mean-square error （1.07） were low.Therefore the coupled model performs well in terms of forecasting CLTCs;the model has potential for dynamic forecasting of landfall of tropical cyclones.

Technical Note on a Track-pattern-based Model for Predicting Seasonal Tropical Cyclone Activity over the Western North Pacific

Recently, the National Typhoon Center （NTC） at the Korea Meteorological Administration launched a track-pattern-based model that predicts the horizontal distribution of tropical cyclone （TC） track density from June to October. This model is the first approach to target seasonal TC track clusters covering the entire western North Pacific （WNP） basin, and may represent a milestone for seasonal TC forecasting, using a simple statistical method that can be applied at weather operation centers. In this note, we describe the procedure of the track-pattern-based model with brief technical background to provide practical information on the use and operation of the model. The model comprises three major steps. First, long-term data of WNP TC tracks reveal seven climatological track clusters. Second, the TC counts for each cluster are predicted using a hybrid statistical-dynamical method, using the seasonal prediction of large-scale environments. Third, the final forecast map of track density is constructed by merging the spatial probabilities of the seven clusters and applying necessary bias corrections. Although the model is developed to issue the seasonal forecast in mid-May, it can be applied to alternative dates and target seasons following the procedure described in this note. Work continues on establishing an automatic system for this model at the NTC.

Development of the second version of Global Prediction System for Epidemiological Pandemic

Coronavirus disease 2019(COvID-19)is a severe global public health emergency that has caused a major cri-sis in the safety of human life,health,global economy,and social order.Moreover,CovID-19 poses significant challenges to healthcare systems worldwide.The prediction and early warning of infectious diseases on a global scale are the premise and basis for countries to jointly fight epidemics.However,because of the complexity of epidemics,predicting infectious diseases on a global scale faces significant challenges.In this study,we developed the second version of Global Prediction System for Epidemiological Pandemic(GPEP-2),which combines statis-tical methods with a modified epidemiological model.The GPEP-2 introduces various parameterization schemes for both impacts of natural factors(seasonal variations in weather and environmental impacts)and human so-cial behaviors(government control and isolation,personnel gathered,indoor propagation,virus mutation,and vaccination).The GPEP-2 successfully predicted the COVID-19 pandemic in over 180 countries with an average accuracy rate of 82.7%.It also provided prediction and decision-making bases for several regional-scale CovID-19 pandemic outbreaks in China,with an average accuracy rate of 89.3%.Results showed that both anthropogenic and natural factors can affect virus spread and control measures in the early stages of an epidemic can effectively control the spread.The predicted results could serve as a reference for public health planning and policymaking.

Dynamical and Machine Learning Hybrid Seasonal Prediction of Summer Rainfall in China

Seasonal prediction of summer rainfall is crucial to reduction of regional disasters,but currently it has a low prediction skill.We developed a dynamical and machine learning hybrid(MLD)seasonal prediction method for summer rainfall in China based on circulation fields from the Chinese Academy of Sciences(CAS)Flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2)operational dynamical prediction model.Through selecting optimum hyperparameters for three machine learning methods to obtain the best fit and least overfitting,an ensemble mean of the random forest and gradient boosting regression tree methods was shown to have the highest prediction skill measured by the anomalous correlation coefficient.The skill has an average value of 0.34 in the historical cross-validation period(1981-2010)and 0.20 in the 10-yr period(2011-2020)of independent prediction,which significantly improves the dynamical prediction skill by 400%.Both reducing overfitting and using the best dynamical prediction are important in applications of the MLD method and in-depth analysis of these warrants a further investigation.

Recent progress on the seasonal tropical cyclone predictions over the western North Pacific from 2014 to 2020

This study summarized the procedure for the seasonal predictions of tropical cyclones(TCs)over the western North Pacific(WNP),which is currently operating at the Korea Meteorological Administration(KMA),Republic of Korea.The methodology was briefly described,and its prediction accuracy was verified.Seasonal predictions were produced by synthesizing spatiotemporal evolutions of various climate factors such as El Ni no–Southern Oscillation(ENSO),monsoon activity,and Madden–Julian Oscillation(MJO),using four models:a statistical,a dynamical,and two statistical–dynamical models.The KMA forecaster predicted the number of TCs over the WNP based on the results of the four models and season to season climate variations.The seasonal prediction of TCs is announced through the press twice a year,for the summer on May and fall on August.The present results showed low accuracy during the period 2014–2020.To advance forecast skill,a set of recommendations are suggested.