Short-term Climate Prediction Research Articles

Inter-Model Spread in Representing the Impacts of ENSO on the South China Spring Rainfall in CMIP6 Models

A major challenge for climate system models in simulating the impacts of El Niño–Southern Oscillation (ENSO) on the interannual variations of East Asian rainfall anomalies is the wide inter-model spread of outputs, which causes considerable uncertainty in physical mechanism understanding and short-term climate prediction. This study investigates the fidelity of 40 models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) in representing the impacts of ENSO on South China Spring Rainfall (SCSR) during the ENSO decaying spring. The response of SCSR to ENSO, as well as the sea surface temperature anomalies (SSTAs) over the tropical Indian Ocean (TIO), is quite different among the models; some models even simulate opposite SCSR anomalies compared to the observations. However, the models capturing the ENSO-related warm SSTAs over TIO tend to simulate a better SCSR-ENSO relationship, which is much closer to observation. Therefore, models are grouped based on the simulated TIO SSTAs to explore the modulating processes of the TIO SSTAs in ENSO affecting SCSR anomalies. Comparing analysis suggests that the warm TIO SSTA can force the equatorial north–south antisymmetric circulation in the lower troposphere, which is conducive to the westward extension and maintenance of the western North Pacific anticyclone (WNPAC). In addition, the TIO SSTA enhances the upper tropospheric East Asian subtropical westerly jet, leading to anomalous divergence over South China. Thus, the westward extension and strengthening of WNPAC can transport sufficient water vapor for South China, which is associated with the ascending motion caused by the upper tropospheric divergence, leading to the abnormal SCSR.

A newly integrated model for synthesizing multiple external forcing signals to predict the ridge line of the western North Pacific subtropical high in summer

The western North Pacific subtropical high (WNPSH) is a critical atmospheric circulation system influencing weather and climate patterns in eastern China. Accurately predicting its activity is essential for anticipating and managing natural disasters such as droughts and floods in the region. In this study, we employ the decision tree (DT) as the fundamental model and devise a compatible framework to create an integrated model known as the Decision Tree Integration Model for Synthesizing Multiple External Forcing Signals (DTI-MS), to perform classification prediction of the WNPSH ridge line in summer. Within the construction of DTI-MS, the interpretability advantage of DT model is fully exploited. Three significant sources of external forcing signals, namely sea surface temperature in the Pacific, Tropical Indian Ocean and Atlantic Ocean, and snow depth across the Eurasian continent, are identified from distinct DT models, with clear physical meanings. Through the voting strategy, these signals are integrated, laying the groundwork for DTI-MS to synthesize multiple external forcing signals. Regarding prediction performance, DTI-MS achieves a high classification accuracy of 91% in the training set and 93% in the testing set, with recalls for the southerly and northerly positions of the WNPSH in the testing set also exceeding 80%. Compared with the commonly used machine learning models such as DT and Random Forests constructed through general modeling approaches, DTI-MS also demonstrates higher classification accuracy. Overall, DTI-MS not only shows promising potential in predicting the WNPSH ridge line, but its integration framework also provides a new paradigm for short-term climate prediction modeling.

Application of machine learning for short-term climate prediction in Indonesia

Application of machine learning for short-term climate prediction in Indonesia

Development and preliminary validation of a land surface image assimilation system based on the Common Land Model

Abstract. Data assimilation is an essential approach to improve the predictions of land surface models. Due to the characteristics of single-column models, assimilation of land surface information has mostly focused on improving the assimilation of single-point variables. However, land surface variables affect short-term climate more through large-scale anomalous forcing, so it is indispensable to pay attention to the accuracy of the anomalous spatial structure of land surface variables. In this study, a land surface image assimilation system capable of optimizing the spatial structure of the background field is constructed by introducing the curvelet analysis method and taking the similarity of image structure as a weak constraint. The fifth-generation ECMWF Reanalysis – Land (ERA5-Land) soil moisture reanalysis data are used as ideal observation for the preliminary effectiveness validation of the image assimilation system. The results show that the new image assimilation system is able to absorb the spatial-structure information of the observed data well and has a remarkable ability to adjust the spatial structure of soil moisture in the land model. The spatial correlation coefficient between the model surface soil moisture and observation increased from 0.39 to about 0.67 after assimilation. By assimilating the surface soil moisture data and combining these with the model physical processes, the image assimilation system can also gradually improve the spatial structure of soil moisture content at a depth of 7–28 cm, with the spatial correlation coefficient between the model soil moisture and observation increased from 0.35 to about 0.57. The forecast results show that the positive assimilation effect could be maintained for more than 30 d. The results of this study adequately demonstrate the application potential of image assimilation system in short-term climate prediction.

Neural network-based climate index: Advancing rainfall prediction in EI Niño contexts

Accurate short-term climate predictions, specifically the prediction of dominant rain belts during flood seasons, represent a significant challenge, particularly in the absence of notable external forcing signal anomalies in the previous winter. To address this challenge, we propose a novel climate index based on an Auto-Encoder (AE) Neural Network that exhibits superior performance in indicating several significant precipitation events in the Yangtze and Huaihe River Basins under varying El Niño conditions. Notably, the novel climate index rectifies the discrepancy between the 2019/2020 Niño index and the 2020 summer precipitation. In order to understand the information source of AE, the causality between AE and other indices was further analyzed by Granger Causality. The results show that AE index mainly contains the information of subtropical high index and ocean index, which is consistent with previous studies.

Analysis of Diurnal Sea Surface Temperature Variability in the Tropical Indian Ocean

Based on the 30-year global hourly sea surface temperature (SST) dataset (MLSST) produced by the National Marine Environmental Forecasting Center, Ministry of Natural Resources of China, we analyzed the variability of diurnal sea surface temperature amplitude (DSST) of the tropical Indian Ocean at multiple time scales, as well as its influencing factors. The results show that the DSST in the Arabian Sea, Bay of Bengal, and equatorial Indian Ocean exhibits a bimodal seasonal variation with a semi-annual cycle, while the DSST in the southern Indian Ocean shows an annual cycle. The seasonal variation of DSST is mainly influenced by factors such as sea surface wind speed, shortwave solar radiation, and precipitation. The DSST in the equatorial Indian Ocean is generally higher during El Niño years compared to La Niña years. At the intraseasonal scale, the large standard deviation of DSST in boreal winter is mainly distributed in the southern hemisphere, while the large standard deviation of DSST in boreal summer shifts northward. The intraseasonal variation amplitude of DSST in boreal winter of the tropical Indian Ocean is greater than that in boreal summer. The DSST in the tropical Indian Ocean exhibits significant variation characteristics at multi-time scales. This study provides reference for numerical simulation of air-sea interaction patterns in the tropical Indian Ocean, as well as improvement of short-term climate prediction.

Modeling of Precipitation Prediction Based on Causal Analysis and Machine Learning

The factors influencing precipitation in western China are quite complex, which increases the difficulty in determining accurate predictors. Hence, this paper models the monthly measured precipitation data from 240 meteorological stations in mainland China and the precipitation data from the European Centre for Medium-Range Weather Forecasts and the National Climate Centre and employs 88 atmospheric circulation indices to develop a precipitation prediction scheme. Specifically, a high-quality grid-point field is created by fusing and revising the precipitation data from multiple sources. This field is combined with the Empirical Orthogonal Function decomposition and the causal information flow. Next, the best predictors are screened through Empirical Orthogonal Function decomposition and causal information flow, and a data-driven precipitation prediction model is established using a Back Propagation Neural Network and a Random Forest algorithm to conduct the 1-month, 3-month, and 6-month precipitation predictions. The results show that: The machine learning-based precipitation prediction model has high accuracy and is generally able to predict the precipitation trend in the western region better. The Random Forest algorithm significantly outperforms the Back Propagation Neural Network algorithm in the prediction of the three starting times, and the prediction ability of both models gradually decreases as the starting time increases. Compared with the 2022 flood season prediction scores of the Institute of Atmospheric Sciences of the Chinese Academy of Sciences, the model improves the prediction of 1-month and 3-month precipitation in the western region and provides a new idea for the short-term climate prediction of precipitation in western China.

Assessment of the FIO-CPS v2.0 in predicting 2-meter air temperature over China

The climate prediction system is an essential tool for predicting climatological state and variability. Systematic evaluation of the output is critical for assessing the prediction performance and making improvement. In this study, we evaluate the prediction capability of the First Institute of Oceanography-Climate Prediction System version 2.0 (FIO-CPS v2.0), a short-term climate prediction system, on the 2-meter air temperature over China using five criteria, namely prediction score (PS), prediction consistency (PC), correlation coefficient (CC), root mean square error (RMSE), and distance between indices of simulation and observation (DISO). The results showed that FIO-CPS v2.0 has higher accuracy in summer, and its performance varies with different lead times depending on the evaluation criteria used. Higher overall prediction skill was mostly found in the northeastern region during July and September, and the southeastern coastal region during June–September. Our findings provide insights into the prediction ability of the FIO-CPS v2.0 on air temperature and may help to facilitate its development.

Short-Term Climate Prediction over China Mainland: An Attempt Using Machine Learning, Considering Natural and Anthropic Factors

Climate extremes pose significant natural threats to socioeconomic activities. Accurate prediction of short-term climate (STC) can provide relevant departments with warnings to effectively reduce this threat. To accurately predict STC in China, this study utilizes machine learning algorithms, particularly the random forest (RF) model, to evaluate the role of both natural and anthropogenic factors. Monthly temperature and precipitation data from 160 meteorological stations spanning China, as well as natural climate factors and an economic activity index, were obtained to perform a seasonal hindcast of air temperature and precipitation observed from 1979 to 2018. Our focus was to predict the seasonal mean temperature and precipitation, specifically the summer (June, July, and August (JJA)) and winter (December, January, and February (DJF)) air temperature and precipitation anomalies using forecast factors from the preceding season. Results show that a comprehensive consideration of both natural and anthropogenic effects provides a more accurate fit to the observed climate trends compared to using only one factor. When both factors were integrated, the model scores (coefficient of determination) exceeded 0.95, close to 1.00, which is significantly higher than those of natural (0.86 for temperature, 0.85 for precipitation) or anthropogenic (0.90 for temperature and 0.50 for precipitation) factors alone. Furthermore, we also attempted to predict similar components for 2019 and 2020. The average relative error between predictions and observations was less than 10%, indicating that this integrated model’s performance exhibited a significant improvement in predicting the STC. The findings of this study underscore the importance of accounting for both natural and anthropogenic factors in predicting climate trends to inform sustainable decision-making in China.

Evaluation on the Forecast Skills of Precipitation and Its Influencing Factors in the Flood Season in Liaoning Province of China

To clarify the precipitation forecast skills of climate forecast operations in the flood season in Liaoning Province of China, this study examines the forecast accuracies of China’s national and provincial operational climate prediction products and the self-developed objective prediction methods and climate model products by Shenyang Regional Climate Center (SRCC) in the flood season in Liaoning. Furthermore, the forecast accuracies of the main influencing factors on the precipitation in the flood season of Liaoning are assessed. The results show that the SRCC objective methods have a relatively high accuracy. The European Centre for Medium-Range Weather Forecasts (ECMWF) sub-seasonal forecast initialized at the sub-nearest time has the best performance in June. The National Climate Center (NCC) Climate System Model sub-seasonal forecast initialized at the sub-nearest time, and the ECMWF seasonal and sub-seasonal forecasts initialized at the nearest time, perform the best in July. The NCC sub-seasonal forecast initialized at the sub-nearest time has the best performance in August. For the accuracy of the SRCC objective method, the more significant the equatorial Middle East Pacific sea surface temperature (SST) anomaly is, the higher the evaluation score of the dynamic–analogue correction method is. The more significant the North Atlantic SST tripole is, the higher the score of the hybrid downscaling method is. For the forecast accuracy of the main influencing factors of precipitation, the tropical Atlantic SST and the north–south anti-phase SST in the northwest Pacific can well predict the locations of the southern vortex and the northern vortex in early summer, respectively. The warm (clod) SST in China offshore has a good forecast performance on the weak (strong) southerly wind in midsummer in Northeast China. The accuracy of using the SST in the Nino 1+2 areas to predict the north–south location of the western Pacific subtropical high is better than that of using Kuroshio SST. The accuracy of predicting northward-moving typhoons from July to September by using the SST in the west-wind-drift area is better than using the SST in the Nino 3 area. The above conclusions are of great significance for improving the short-term climate prediction in Liaoning.

A New Hybrid Machine Learning Model for Short-Term Climate Prediction by Performing Classification Prediction and Regression Prediction Simultaneously

A New Hybrid Machine Learning Model for Short-Term Climate Prediction by Performing Classification Prediction and Regression Prediction Simultaneously

Realistic ocean initial condition for stimulating the successful prediction of extreme cold events in the 2020/2021 winter

In the first half of winter 2020/2021, several unprecedented cold events occurred in most parts of China and caused record-breaking low temperatures in many cities. However, seasonal predictions related to the onset and evolution of extreme cold events are still challenging. In this study, we first evaluated the short-term climate prediction skills in winter of the CAS-ESM-c global coupled model and revealed the key dynamic processes of the onset and development of 2020/2021 extreme cold events based on the ensemble forecasts starting on October 1st, 2020. Under the background provided by the synergistic effect of the warm Arctic and the cold tropical Pacific (La Niña), the model captured the abnormal meridional atmospheric pattern well, including the intensification of the Ural Blocking High and the negative phase of the Arctic Oscillation, forecasted the negative geopotential height anomalies over the Eastern Asian region, and finally, successfully predicted the outbreak of cold waves in December 2020 two months in advance. Moreover, the dominant differences in the initial ocean fields between the best and the worst forecast members were further compared to isolate the triggering factor for predicting cold events by CAS-ESM-c. The initial warm sea surface temperature over the Barents Sea can gradually form a meridional pattern between the warm Arctic and the cold Asian continent, which could lead to a reduction in the large-scale meridional temperature gradient at mid-high latitudes and weaken the atmospheric baroclinicity with more conducive to the southward outbreak of cold waves, highlighting that realistic Arctic ocean conditions in autumn can be reasonably assimilated into coupled models to stimulate the successful prediction of extreme cold events in the 2020/2021 winter.

Responses of East Asian Climate to SST Anomalies in the Kuroshio Extension Region during Boreal Autumn

Abstract Sea surface temperature anomalies (SSTAs) in the Kuroshio Extension (KE) region play a key role in influencing midlatitude climate variations. This study investigates the impacts of SSTAs in the KE region (KE-SSTA) on East Asian climate during boreal autumn. The results reveal that positive KE-SSTA, changing the meridional temperature gradient in the lower troposphere, contributes to the formation of an anomalous quasi-barotropic anticyclonic circulation (AC) over the KE region. Such a configuration tends to be affected by wind through thermal wind adjustment. Simultaneously, it is also impacted by eddy activity related to the strengthened atmospheric baroclinicity through transient eddy feedback. By both means, obvious descending motion and warm advection are generated, which increases tropospheric temperature through adiabatic heating and further maintains the abnormal AC over the KE region. This anomalous circulation changes wind in the east of the East Asian trough, which favors the transportation of warm and wet air from the midlatitude northwest Pacific and further causes the increase of temperature in northeastern East Asia. Moreover, it also leads to the convergence and divergence of water vapor fluxes in Northeast Asia and eastern North China, favoring the increase and decrease of precipitation in the two regions, respectively. The above responsive characteristics of the midlatitude atmospheric circulation to positive KE-SSTA can be confirmed by numerical experiments. These results suggest that anomalous SST in the KE region can be used as a potentially effective predictability source for autumn climate variations in the mid- to high latitudes of East Asia. Significance Statement Anomalous variations of sea surface temperature (SST) in the Kuroshio Extension (KE) region play an essential role in influencing midlatitude climate variations. Considering the importance of autumn climate in East Asia to agricultural production, this study aims to explore the relationship between the East Asian climate and autumn SST anomaly in the KE region (KE-SSTA), and to understand how KE-SSTA effects East Asian climate during boreal autumn. Our results suggest that KE-SSTA can be used as one of the effective predictors of East Asian climate, which will contribute to the improvement of short-term climate prediction levels over East Asia.

Multi-Model Ensemble Prediction of Summer Precipitation in China Based on Machine Learning Algorithms

The development of machine learning (ML) provides new means and methods for accurate climate analysis and prediction. This study focuses on summer precipitation prediction using ML algorithms. Based on BCC CSM1.1, ECMWF SEAS5, NCEP CFSv2, and JMA CPS2 model data, we conducted a multi-model ensemble (MME) prediction experiment using three tree-based ML algorithms: the decision tree (DT), random forest (RF), and adaptive boosting (AB) algorithms. On this basis, we explored the applicability of ML algorithms for ensemble prediction of seasonal precipitation in China, as well as the impact of different hyperparameters on prediction accuracy. Then, MME predictions based on optimal hyperparameters were constructed for different regions of China. The results showed that all three ML algorithms had an optimal maximum depth less than 2, which means that, based on the current amount of data, the three algorithms could only predict positive or negative precipitation anomalies, and extreme precipitation was hard to predict. The importance of each model in the ML-based MME was quantitatively evaluated. The results showed that NCEP CFSv2 and JMA CPS2 had a higher importance in MME for the eastern part of China. Finally, summer precipitation in China was predicted and tested from 2019 to 2021. According to the results, the method provided a more accurate prediction of the main rainband of summer precipitation in China. ML-based MME had a mean ACC of 0.3, an improvement of 0.09 over the weighted average MME of 0.21 for 2019–2021, exhibiting a significant improvement over the other methods. This shows that ML methods have great potential for improving short-term climate prediction.

Developing a Climate Prediction System over Southwest China Using the 8-km Weather Research and Forecasting (WRF) Model: System Design, Model Calibration, and Performance Evaluation

Abstract A high-resolution, short-term climate prediction system for summer (June–July–August) climate over Southwest China has been developed using the Weather Research and Forecasting (WRF) Model nested with a global climate prediction system (PCCSM4). The system includes 12 ensemble members generated by PCCSM4 with different initial conditions, and the finest horizontal resolution of WRF is 8 km. This study evaluates the ability of the WRF Model to predict summer climate over Southwest China, focusing on the system design, model tuning, and evaluation of baseline model performance. Sensitivity simulations are first conducted to provide the optimal model configuration, and the model performance is evaluated against available observational data using reforecast simulations for 1981–2020. When compared to PCCSM4, the WRF Model shows major improvements in predicting the spatial distribution of major variables such as 2-m temperature, 10-m wind speed, and precipitation. WRF also shows better skill in predicting interannual temperature variability and extreme temperature events, with higher anomaly correlation coefficients. However, large model biases remain in seasonal precipitation anomaly predictions. Overall, this study highlights the potential advantages of using the high-resolution WRF Model to predict summer climate conditions over Southwest China.

Annual Frequency of Tropical Cyclones Directly Affecting Guangdong Province:Prediction Based on LSTM-FC

Tropical cyclone (TC) annual frequency forecasting is significant for disaster prevention and mitigation in Guangdong Province. Based on the NCEP-NCAR reanalysis and NOAA Extended Reconstructed global sea surface temperature (SST) V5 data in winter, the TC frequency climatic features and prediction models have been studied. During 1951-2019, 353 TCs directly affected Guangdong with an annual average of about 5.1. TCs have experienced an abrupt change from abundance to deficiency in the mid to late 1980 with a slightly decreasing trend and a normal distribution. 338 primary precursors are obtained from statistically significant correlation regions of SST, sea level pressure, 1000hPa air temperature, 850hPa specific humidity, 500hPa geopotential height and zonal wind shear in winter. Then those 338 primary factors are reduced into 19 independent predictors by principal component analysis (PCA). Furthermore, the Multiple Linear Regression (MLR), the Gaussian Process Regression (GPR) and the Long Short-term Memory Networks and Fully Connected Layers (LSTM-FC) models are constructed relying on the above 19 factors. For three different kinds of test sets from 2010 to 2019, 2011 to 2019 and 2010 to 2019, the root mean square errors (RMSEs) of MLR, GPR and LSTM-FC between prediction and observations fluctuate within the range of 1.05-2.45, 1.00-1.93 and 0.71-0.95 as well as the average absolute errors (AAEs) 0.88-1.0, 0.75-1.36 and 0.50-0.70, respectively. As for the 2010-2019 experiment, the mean deviations of the three model outputs from the observation are 0.89, 0.78 and 0.56, together with the average evaluation scores 82.22, 84.44 and 88.89, separately. The prediction skill comparisons unveil that LSTM-FC model has a better performance than MLR and GPR. In conclusion, the deep learning model of LSTM-FC may shed light on improving the accuracy of short-term climate prediction about TC frequency. The current research can provide experience on the development of deep learning in this field and help to achieve further progress of TC disaster prevention and mitigation in Guangdong Province.

Corrigendum: The Short-Term Climate Prediction System FIO-CPS v2.0 and Its Prediction Skill in ENSO

Corrigendum on: Song Y, Shu Q, Bao Y, Yang X and Song Z (2021) The Short-Term Climate Prediction System FIO-CPS v2.0 and its Prediction Skill in ENSO. Front. Earth Sci. 9:759339. doi: 10.3389/feart.2021.759339.In the original article, there was a mistake in "FIGURE 5 Figures, tables, and images will be published under a Creative Commons CC-BY licence and permission must be obtained for use of copyrighted material from other sources (including republished/adapted/modified/partial figures and images from the internet). It is the responsibility of the authors to acquire the licenses, to follow any citation instructions requested by third-party rights holders, and cover any supplementary charges.

Retraction Note to: Numerical simulation of ocean sea temperature based on feature prediction and monitoring of coastal running health data

The condition of ocean underlying surface is an important factor to determine the predictability of short-term climate. Different forms of ocean boundary conditions are often used in dynamic forecasting models for forecasting objects with different characteristic scales. Many institutions are still using a forced general circulation model (GCM) based on given SST to predict climate, but there are still differences in the degree of short-term climate prediction affected by different SST frequencies. Considering this, this paper uses the BCC model of the National Climate Center_ Agcm3 which is used to develop and conduct multiple sets of numerical simulations and experiments to predict the impact of SST at different frequencies of each month, week, and day. The results show that the frequency of SST forcing field is similar to that of Asian summer monsoon. As we all know, sprint and middle- and long-distance race belong to speed race group or endurance race group. The movement speed of two types of sports depends on their physiological mechanism, the transformation speed of cerebral cortex motor center, and the performance of nervous system. Although there are many studies on the speed of short-distance running and medium- and long-distance running, there are relatively few studies on the characteristics of middle- and long-distance runners in various sports. The system uses STM32 + MTK dual processor system and uses mpu9250 nine axis inertial sensor and max30102 heart rate sensor to develop acquisition module, positioning module, low-power consumption module, motion data detection module, and heart rate software and hardware system. Finally, this paper designs the results of SST numerical simulation and prediction experiment.

The Short-Term Climate Prediction System FIO-CPS v2.0 and its Prediction Skill in ENSO

The climate model is an important tool for simulating and predicting the mean state and variability of the climate system. The First Institute of Oceanography-Climate Prediction System (FIO-CPS), built on a climate model with the oceanic observation initialization, has been updated from version 1.0 to 2.0, with a finer resolution and more reasonable physical processes. Previous assessments show that the mean state was well simulated in version 2.0, and its influence on the prediction was further analyzed in this study. Hindcast experiments were conducted using FIO-CPS v1.0 and v2.0, and their prediction abilities based on 27 years (1993–2019) experiment data were analyzed. The results show that the sea surface temperature (SST) biases over the eastern Pacific and the Southern Ocean are improved in the initial condition of FIO-CPS v2.0. Moreover, this new system has a higher skill for predicting El Niño-Southern Oscillation (ENSO). The prediction skill represented by the anomaly correlation coefficient (ACC) of the Niño3.4 index is greater than 0.78 at the 6-month lead time, which increases by 11.09% compared to the value of 0.70 in FIO-CPS v1.0. The root mean square error (RMSE) decreases by 0.20, which accounts for 28.59% of the FIO-CPS v1.0 result. Furthermore, the improvement of the prediction skill changes seasonally, featured by the ACC significantly increasing in the boreal winter and early spring. The improvement in the annual mean SST prediction over the Equatorial Pacific mainly contributes to the enhanced ENSO prediction skill in FIO-CPS v2.0. These results indicate that a state-of-the-art climate model with a well-simulated mean state is critical in improving the prediction skill on the seasonal time scale.

RETRACTED ARTICLE: Numerical simulation of ocean sea temperature based on feature prediction and monitoring of coastal running health data

The condition of ocean underlying surface is an important factor to determine the predictability of short-term climate. Different forms of ocean boundary conditions are often used in dynamic forecasting models for forecasting objects with different characteristic scales. Many institutions are still using a forced general circulation model (GCM) based on given SST to predict climate, but there are still differences in the degree of short-term climate prediction affected by different SST frequencies. Considering this, this paper uses the BCC model of the National Climate Center_ Agcm3 which is used to develop and conduct multiple sets of numerical simulations and experiments to predict the impact of SST at different frequencies of each month, week, and day. The results show that the frequency of SST forcing field is similar to that of Asian summer monsoon. As we all know, sprint and middle- and long-distance race belong to speed race group or endurance race group. The movement speed of two types of sports depends on their physiological mechanism, the transformation speed of cerebral cortex motor center, and the performance of nervous system. Although there are many studies on the speed of short-distance running and medium- and long-distance running, there are relatively few studies on the characteristics of middle- and long-distance runners in various sports. The system uses STM32 + MTK dual processor system and uses mpu9250 nine axis inertial sensor and max30102 heart rate sensor to develop acquisition module, positioning module, low-power consumption module, motion data detection module, and heart rate software and hardware system. Finally, this paper designs the results of SST numerical simulation and prediction experiment.